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generator (torch.Generator, optional) —
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One or a list of torch generator(s)
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to make generation deterministic.
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num_inference_steps (int, optional, defaults to 50) —
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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output_type (str, optional, defaults to "pil") —
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The output format of the generate image. Choose between
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PIL: PIL.Image.Image or np.array.
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return_dict (bool, optional, defaults to True) —
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Whether or not to return a ImagePipelineOutput instead of a plain tuple.
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Returns
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ImagePipelineOutput or tuple
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~pipelines.utils.ImagePipelineOutput if return_dict is
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True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
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Custom Pipelines
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For more information about community pipelines, please have a look at this issue.
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Community examples consist of both inference and training examples that have been added by the community.
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Please have a look at the following table to get an overview of all community examples. Click on the Code Example to get a copy-and-paste ready code example that you can try out.
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If a community doesn’t work as expected, please open an issue and ping the author on it.
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Example
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Description
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Code Example
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Colab
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Author
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CLIP Guided Stable Diffusion
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Doing CLIP guidance for text to image generation with Stable Diffusion
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CLIP Guided Stable Diffusion
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Suraj Patil
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One Step U-Net (Dummy)
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Example showcasing of how to use Community Pipelines (see https://github.com/huggingface/diffusers/issues/841)
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One Step U-Net
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-
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Patrick von Platen
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Stable Diffusion Interpolation
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Interpolate the latent space of Stable Diffusion between different prompts/seeds
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Stable Diffusion Interpolation
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-
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Nate Raw
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Stable Diffusion Mega
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One Stable Diffusion Pipeline with all functionalities of Text2Image, Image2Image and Inpainting
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Stable Diffusion Mega
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-
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Patrick von Platen
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Long Prompt Weighting Stable Diffusion
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One Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt.
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Long Prompt Weighting Stable Diffusion
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-
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SkyTNT
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Speech to Image
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Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images
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Speech to Image
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-
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Mikail Duzenli
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To load a custom pipeline you just need to pass the custom_pipeline argument to DiffusionPipeline, as one of the files in diffusers/examples/community. Feel free to send a PR with your own pipelines, we will merge them quickly.
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Copied
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pipe = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4", custom_pipeline="filename_in_the_community_folder"
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)
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Example usages
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CLIP Guided Stable Diffusion
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CLIP guided stable diffusion can help to generate more realistic images
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by guiding stable diffusion at every denoising step with an additional CLIP model.
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The following code requires roughly 12GB of GPU RAM.
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Copied
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from diffusers import DiffusionPipeline
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from transformers import CLIPFeatureExtractor, CLIPModel
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import torch
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feature_extractor = CLIPFeatureExtractor.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K")
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clip_model = CLIPModel.from_pretrained("laion/CLIP-ViT-B-32-laion2B-s34B-b79K", torch_dtype=torch.float16)
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guided_pipeline = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="clip_guided_stable_diffusion",
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clip_model=clip_model,
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