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sayakpaul
/
diffusers-qa-chatbot-artifacts

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sample (torch.FloatTensor) —
current instance of sample being created by diffusion process.
generator (torch.Generator, optional) — Random number generator.
return_dict (bool) — option for returning tuple rather than EulerAncestralDiscreteSchedulerOutput class
Returns
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput or tuple
~schedulers.scheduling_utils.EulerAncestralDiscreteSchedulerOutput if return_dict is True, otherwise
a tuple. When returning a tuple, the first element is the sample tensor.
Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion
process from the learned model outputs (most often the predicted noise).
Adapt a model to a new task Many diffusion systems share the same components, allowing you to adapt a pretrained model for one task to an entirely different task. This guide will show you how to adapt a pretrained text-to-image model for inpainting by initializing and modifying the architecture of a pretrained UNet2DCo...
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", use_safetensors=True)
pipeline.unet.config["in_channels"]
4 Inpainting requires 9 channels in the input sample. You can check this value in a pretrained inpainting model like runwayml/stable-diffusion-inpainting: Copied from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", use_safetensors=True)
pipeline.unet.config["in_channels"]
9 To adapt your text-to-image model for inpainting, you’ll need to change the number of in_channels from 4 to 9. Initialize a UNet2DConditionModel with the pretrained text-to-image model weights, and change in_channels to 9. Changing the number of in_channels means you need to set ignore_mismatched_sizes=True and low_c...
model_id = "runwayml/stable-diffusion-v1-5"
unet = UNet2DConditionModel.from_pretrained(
model_id,
subfolder="unet",
in_channels=9,
low_cpu_mem_usage=False,
ignore_mismatched_sizes=True,
use_safetensors=True,
) The pretrained weights of the other components from the text-to-image model are initialized from their checkpoints, but the input channel weights (conv_in.weight) of the unet are randomly initialized. It is important to finetune the model for inpainting because otherwise the model returns noise.
Text-guided image-inpainting
The StableDiffusionInpaintPipeline allows you to edit specific parts of an image by providing a mask and a text prompt. It uses a version of Stable Diffusion, like runwayml/stable-diffusion-inpainting specifically trained for inpainting tasks.
Get started by loading an instance of the StableDiffusionInpaintPipeline:
Copied
import PIL
import requests
import torch
from io import BytesIO
from diffusers import StableDiffusionInpaintPipeline
pipeline = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting",
torch_dtype=torch.float16,
)
pipeline = pipeline.to("cuda")
Download an image and a mask of a dog which you’ll eventually replace:
Copied
def download_image(url):
response = requests.get(url)
return PIL.Image.open(BytesIO(response.content)).convert("RGB")
img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
init_image = download_image(img_url).resize((512, 512))
mask_image = download_image(mask_url).resize((512, 512))
Now you can create a prompt to replace the mask with something else:
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prompt = "Face of a yellow cat, high resolution, sitting on a park bench"
image = pipe(prompt=prompt, image=init_image, mask_image=mask_image).images[0]
image
mask_image
prompt
output
Face of a yellow cat, high resolution, sitting on a park bench
A previous experimental implementation of inpainting used a different, lower-quality process. To ensure backwards compatibility, loading a pretrained pipeline that doesn’t contain the new model will still apply the old inpainting method.