ldm-super-resolution-4x-openimages

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license:apache-2.0
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Quick Summary

Paper: High-Resolution Image Synthesis with Latent Diffusion Models By decomposing the image formation process into a sequential application of denoising autoe...

Code Examples

Usagepythonpytorch
!pip install git+https://github.com/huggingface/diffusers.git

import requests
from PIL import Image
from io import BytesIO
from diffusers import LDMSuperResolutionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "CompVis/ldm-super-resolution-4x-openimages"

# load model and scheduler
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)

# let's download an  image
url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))

# run pipeline in inference (sample random noise and denoise)
upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
# save image
upscaled_image.save("ldm_generated_image.png")
Usagepythonpytorch
!pip install git+https://github.com/huggingface/diffusers.git

import requests
from PIL import Image
from io import BytesIO
from diffusers import LDMSuperResolutionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "CompVis/ldm-super-resolution-4x-openimages"

# load model and scheduler
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)

# let's download an  image
url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))

# run pipeline in inference (sample random noise and denoise)
upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
# save image
upscaled_image.save("ldm_generated_image.png")
Usagepythonpytorch
!pip install git+https://github.com/huggingface/diffusers.git

import requests
from PIL import Image
from io import BytesIO
from diffusers import LDMSuperResolutionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "CompVis/ldm-super-resolution-4x-openimages"

# load model and scheduler
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)

# let's download an  image
url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))

# run pipeline in inference (sample random noise and denoise)
upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
# save image
upscaled_image.save("ldm_generated_image.png")
Usagepythonpytorch
!pip install git+https://github.com/huggingface/diffusers.git

import requests
from PIL import Image
from io import BytesIO
from diffusers import LDMSuperResolutionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "CompVis/ldm-super-resolution-4x-openimages"

# load model and scheduler
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)

# let's download an  image
url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))

# run pipeline in inference (sample random noise and denoise)
upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
# save image
upscaled_image.save("ldm_generated_image.png")
Usagepythonpytorch
!pip install git+https://github.com/huggingface/diffusers.git

import requests
from PIL import Image
from io import BytesIO
from diffusers import LDMSuperResolutionPipeline
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"
model_id = "CompVis/ldm-super-resolution-4x-openimages"

# load model and scheduler
pipeline = LDMSuperResolutionPipeline.from_pretrained(model_id)
pipeline = pipeline.to(device)

# let's download an  image
url = "https://user-images.githubusercontent.com/38061659/199705896-b48e17b8-b231-47cd-a270-4ffa5a93fa3e.png"
response = requests.get(url)
low_res_img = Image.open(BytesIO(response.content)).convert("RGB")
low_res_img = low_res_img.resize((128, 128))

# run pipeline in inference (sample random noise and denoise)
upscaled_image = pipeline(low_res_img, num_inference_steps=100, eta=1).images[0]
# save image
upscaled_image.save("ldm_generated_image.png")

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