sdxl-vae

Name: sdxl-vae
Author: wangkanai

—

wangkanai

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Quick Summary

AI model with specialized capabilities.

Code Examples

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Load directly from Hugging Facepythonpytorch

from diffusers import StableDiffusionXLPipeline, AutoencoderKL
import torch

# Load your existing SDXL pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
    "your-sdxl-model-path",
    torch_dtype=torch.float16
)

# Replace with improved VAE
improved_vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
)

pipe.vae = improved_vae
pipe = pipe.to("cuda")

# Generate with improved quality
image = pipe("detailed portrait photograph").images[0]

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

Generate with improved qualitypythonpytorch

from diffusers import AutoencoderKL
from PIL import Image
import torch
from torchvision import transforms

# Load VAE
vae = AutoencoderKL.from_pretrained(
    "E:/huggingface/sdxl-vae/vae/sdxl",
    torch_dtype=torch.float16
).to("cuda")

# Load and preprocess image
image = Image.open("input.png").convert("RGB")
transform = transforms.Compose([
    transforms.Resize((1024, 1024)),
    transforms.ToTensor(),
    transforms.Normalize([0.5], [0.5])
])
image_tensor = transform(image).unsqueeze(0).to("cuda", dtype=torch.float16)

# Encode to latent space
with torch.no_grad():
    latents = vae.encode(image_tensor).latent_dist.sample()
    latents = latents * vae.config.scaling_factor

# Decode back to image space
with torch.no_grad():
    latents = latents / vae.config.scaling_factor
    reconstructed = vae.decode(latents).sample

# Convert to PIL image
reconstructed = (reconstructed / 2 + 0.5).clamp(0, 1)
reconstructed = reconstructed.cpu().permute(0, 2, 3, 1).numpy()[0]
output_image = Image.fromarray((reconstructed * 255).astype("uint8"))
output_image.save("reconstructed.png")

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