taef1

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license:mit
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silveroxides
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Quick Summary

TAEF1 is very tiny autoencoder which uses the same "latent API" as FLUX.

Code Examples

Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")
Using in 🧨 diffuserspythonpytorch
import torch
from diffusers import FluxPipeline, AutoencoderTiny

pipe = FluxPipeline.from_pretrained(
    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16
)
pipe.vae = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=torch.bfloat16)
pipe.enable_sequential_cpu_offload()

prompt = "slice of delicious New York-style berry cheesecake"
image = pipe(
    prompt,
    guidance_scale=0.0,
    num_inference_steps=4,
    max_sequence_length=256,
).images[0]
image.save("cheesecake.png")

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