Upload folder using huggingface_hub
Browse files- main/README.md +45 -20
- main/lpw_stable_diffusion_xl.py +17 -2
main/README.md
CHANGED
@@ -24,12 +24,12 @@ Please also check out our [Community Scripts](https://github.com/huggingface/dif
|
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| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/long_prompt_weighting_stable_diffusion.ipynb) | [SkyTNT](https://github.com/SkyTNT) |
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| Speech to Image | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images | [Speech to Image](#speech-to-image) |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/speech_to_image.ipynb) | [Mikail Duzenli](https://github.com/MikailINTech)
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| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values | [Wildcard Stable Diffusion](#wildcard-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/wildcard_stable_diffusion.ipynb) | [Shyam Sudhakaran](https://github.com/shyamsn97) |
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| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) |
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| Seed Resizing Stable Diffusion | Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/seed_resizing.ipynb) | [Mark Rich](https://github.com/MarkRich) |
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| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image | [Imagic Stable Diffusion](#imagic-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/imagic_stable_diffusion.ipynb) | [Mark Rich](https://github.com/MarkRich) |
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| Multilingual Stable Diffusion | Stable Diffusion Pipeline that supports prompts in 50 different languages. | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/multilingual_stable_diffusion.ipynb) | [Juan Carlos Piñeros](https://github.com/juancopi81) |
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| GlueGen Stable Diffusion | Stable Diffusion Pipeline that supports prompts in different languages using GlueGen adapter. | [GlueGen Stable Diffusion](#gluegen-stable-diffusion-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/gluegen_stable_diffusion.ipynb) | [Phạm Hồng Vinh](https://github.com/rootonchair) |
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| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting | [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) |
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| Text Based Inpainting Stable Diffusion | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting | [Text Based Inpainting Stable Diffusion](#text-based-inpainting-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/text_based_inpainting_stable_dffusion.ipynb) | [Dhruv Karan](https://github.com/unography) |
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| Bit Diffusion | Diffusion on discrete data | [Bit Diffusion](#bit-diffusion) | - | [Stuti R.](https://github.com/kingstut) |
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| K-Diffusion Stable Diffusion | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
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| UnCLIP Image Interpolation Pipeline | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/unclip_image_interpolation.ipynb)| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
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| DDIM Noise Comparative Analysis Pipeline | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227)) | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ddim_noise_comparative_analysis.ipynb)| [Aengus (Duc-Anh)](https://github.com/aengusng8) |
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| CLIP Guided Img2Img Stable Diffusion Pipeline | Doing CLIP guidance for image to image generation with Stable Diffusion | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/clip_guided_img2img_stable_diffusion.ipynb) | [Nipun Jindal](https://github.com/nipunjindal/) |
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| TensorRT Stable Diffusion Text to Image Pipeline | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline) |
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| EDICT Image Editing Pipeline | Diffusion pipeline for text-guided image editing | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/edict_image_pipeline.ipynb) | [Joqsan Azocar](https://github.com/Joqsan) |
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| Stable Diffusion RePaint | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.09865) for inpainting. | [Stable Diffusion RePaint](#stable-diffusion-repaint )|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_repaint.ipynb)| [Markus Pobitzer](https://github.com/Markus-Pobitzer) |
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| TensorRT Stable Diffusion Image to Image Pipeline | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) |
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| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
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| sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
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| sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
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| prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) |
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| Latent Consistency Pipeline | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) | [Latent Consistency Pipeline](#latent-consistency-pipeline) | - | [Simian Luo](https://github.com/luosiallen) |
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| Latent Consistency Img2img Pipeline | Img2img pipeline for Latent Consistency Models | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline) | - | [Logan Zoellner](https://github.com/nagolinc) |
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| Latent Consistency Interpolation Pipeline | Interpolate the latent space of Latent Consistency Models with multiple prompts | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
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images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
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grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
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tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')
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```
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### Imagic Stable Diffusion
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For example, this could be used to place a logo on a shirt and make it blend seamlessly.
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```python
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import PIL
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import torch
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from diffusers import DiffusionPipeline
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pipe = DiffusionPipeline.from_pretrained(
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custom_pipeline="img2img_inpainting",
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torch_dtype=torch.float16
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)
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pipe = pipe.to("cuda")
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prompt = "
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image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
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```
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```python
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import torch
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import numpy as np
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import matplotlib.pyplot as plt
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from diffusers import DiffusionPipeline
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pipe = DiffusionPipeline.from_pretrained(
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prompts = [
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cross_attention_kwargs = {
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"edit_type": "replace",
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"self_replace_steps": 0.4
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}
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outputs = pipe(
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```
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And abbreviated examples for the other edits:
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| Long Prompt Weighting Stable Diffusion | **One** Stable Diffusion Pipeline without tokens length limit, and support parsing weighting in prompt. | [Long Prompt Weighting Stable Diffusion](#long-prompt-weighting-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/long_prompt_weighting_stable_diffusion.ipynb) | [SkyTNT](https://github.com/SkyTNT) |
|
25 |
| Speech to Image | Using automatic-speech-recognition to transcribe text and Stable Diffusion to generate images | [Speech to Image](#speech-to-image) |[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/speech_to_image.ipynb) | [Mikail Duzenli](https://github.com/MikailINTech)
|
26 |
| Wild Card Stable Diffusion | Stable Diffusion Pipeline that supports prompts that contain wildcard terms (indicated by surrounding double underscores), with values instantiated randomly from a corresponding txt file or a dictionary of possible values | [Wildcard Stable Diffusion](#wildcard-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/wildcard_stable_diffusion.ipynb) | [Shyam Sudhakaran](https://github.com/shyamsn97) |
|
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+
| [Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) | Stable Diffusion Pipeline that supports prompts that contain "|" in prompts (as an AND condition) and weights (separated by "|" as well) to positively / negatively weight prompts. | [Composable Stable Diffusion](#composable-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/composable_stable_diffusion.ipynb) | [Mark Rich](https://github.com/MarkRich) |
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| Seed Resizing Stable Diffusion | Stable Diffusion Pipeline that supports resizing an image and retaining the concepts of the 512 by 512 generation. | [Seed Resizing](#seed-resizing) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/seed_resizing.ipynb) | [Mark Rich](https://github.com/MarkRich) |
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| Imagic Stable Diffusion | Stable Diffusion Pipeline that enables writing a text prompt to edit an existing image | [Imagic Stable Diffusion](#imagic-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/imagic_stable_diffusion.ipynb) | [Mark Rich](https://github.com/MarkRich) |
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| Multilingual Stable Diffusion | Stable Diffusion Pipeline that supports prompts in 50 different languages. | [Multilingual Stable Diffusion](#multilingual-stable-diffusion-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/multilingual_stable_diffusion.ipynb) | [Juan Carlos Piñeros](https://github.com/juancopi81) |
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| GlueGen Stable Diffusion | Stable Diffusion Pipeline that supports prompts in different languages using GlueGen adapter. | [GlueGen Stable Diffusion](#gluegen-stable-diffusion-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/gluegen_stable_diffusion.ipynb) | [Phạm Hồng Vinh](https://github.com/rootonchair) |
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+
| Image to Image Inpainting Stable Diffusion | Stable Diffusion Pipeline that enables the overlaying of two images and subsequent inpainting | [Image to Image Inpainting Stable Diffusion](#image-to-image-inpainting-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/image_to_image_inpainting_stable_diffusion.ipynb) | [Alex McKinney](https://github.com/vvvm23) |
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| Text Based Inpainting Stable Diffusion | Stable Diffusion Inpainting Pipeline that enables passing a text prompt to generate the mask for inpainting | [Text Based Inpainting Stable Diffusion](#text-based-inpainting-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/text_based_inpainting_stable_dffusion.ipynb) | [Dhruv Karan](https://github.com/unography) |
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| Bit Diffusion | Diffusion on discrete data | [Bit Diffusion](#bit-diffusion) | - | [Stuti R.](https://github.com/kingstut) |
|
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| K-Diffusion Stable Diffusion | Run Stable Diffusion with any of [K-Diffusion's samplers](https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/sampling.py) | [Stable Diffusion with K Diffusion](#stable-diffusion-with-k-diffusion) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
|
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| UnCLIP Image Interpolation Pipeline | Diffusion Pipeline that allows passing two images/image_embeddings and produces images while interpolating between their image-embeddings | [UnCLIP Image Interpolation Pipeline](#unclip-image-interpolation-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/unclip_image_interpolation.ipynb)| [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
|
42 |
| DDIM Noise Comparative Analysis Pipeline | Investigating how the diffusion models learn visual concepts from each noise level (which is a contribution of [P2 weighting (CVPR 2022)](https://arxiv.org/abs/2204.00227)) | [DDIM Noise Comparative Analysis Pipeline](#ddim-noise-comparative-analysis-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/ddim_noise_comparative_analysis.ipynb)| [Aengus (Duc-Anh)](https://github.com/aengusng8) |
|
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| CLIP Guided Img2Img Stable Diffusion Pipeline | Doing CLIP guidance for image to image generation with Stable Diffusion | [CLIP Guided Img2Img Stable Diffusion](#clip-guided-img2img-stable-diffusion) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/clip_guided_img2img_stable_diffusion.ipynb) | [Nipun Jindal](https://github.com/nipunjindal/) |
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+
| TensorRT Stable Diffusion Text to Image Pipeline | Accelerates the Stable Diffusion Text2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Text to Image Pipeline](#tensorrt-text2image-stable-diffusion-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/tensorrt_text2image_stable_diffusion_pipeline.ipynb) | [Asfiya Baig](https://github.com/asfiyab-nvidia) |
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| EDICT Image Editing Pipeline | Diffusion pipeline for text-guided image editing | [EDICT Image Editing Pipeline](#edict-image-editing-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/edict_image_pipeline.ipynb) | [Joqsan Azocar](https://github.com/Joqsan) |
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| Stable Diffusion RePaint | Stable Diffusion pipeline using [RePaint](https://arxiv.org/abs/2201.09865) for inpainting. | [Stable Diffusion RePaint](#stable-diffusion-repaint )|[Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_repaint.ipynb)| [Markus Pobitzer](https://github.com/Markus-Pobitzer) |
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| TensorRT Stable Diffusion Image to Image Pipeline | Accelerates the Stable Diffusion Image2Image Pipeline using TensorRT | [TensorRT Stable Diffusion Image to Image Pipeline](#tensorrt-image2image-stable-diffusion-pipeline) | - | [Asfiya Baig](https://github.com/asfiyab-nvidia) |
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| FABRIC - Stable Diffusion with feedback Pipeline | pipeline supports feedback from liked and disliked images | [Stable Diffusion Fabric Pipeline](#stable-diffusion-fabric-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/stable_diffusion_fabric.ipynb)| [Shauray Singh](https://shauray8.github.io/about_shauray/) |
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| sketch inpaint - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion Pipeline](#stable-diffusion-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
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| sketch inpaint xl - Inpainting with non-inpaint Stable Diffusion | sketch inpaint much like in automatic1111 | [Masked Im2Im Stable Diffusion XL Pipeline](#stable-diffusion-xl-masked-im2im) | - | [Anatoly Belikov](https://github.com/noskill) |
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| prompt-to-prompt | change parts of a prompt and retain image structure (see [paper page](https://prompt-to-prompt.github.io/)) | [Prompt2Prompt Pipeline](#prompt2prompt-pipeline) | [Notebook](https://github.com/huggingface/notebooks/blob/main/diffusers/prompt_2_prompt_pipeline.ipynb) | [Umer H. Adil](https://twitter.com/UmerHAdil) |
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| Latent Consistency Pipeline | Implementation of [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) | [Latent Consistency Pipeline](#latent-consistency-pipeline) | - | [Simian Luo](https://github.com/luosiallen) |
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| Latent Consistency Img2img Pipeline | Img2img pipeline for Latent Consistency Models | [Latent Consistency Img2Img Pipeline](#latent-consistency-img2img-pipeline) | - | [Logan Zoellner](https://github.com/nagolinc) |
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| Latent Consistency Interpolation Pipeline | Interpolate the latent space of Latent Consistency Models with multiple prompts | [Latent Consistency Interpolation Pipeline](#latent-consistency-interpolation-pipeline) | [](https://colab.research.google.com/drive/1pK3NrLWJSiJsBynLns1K1-IDTW9zbPvl?usp=sharing) | [Aryan V S](https://github.com/a-r-r-o-w) |
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images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
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grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
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tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')
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print("Image saved successfully!")
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```
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### Imagic Stable Diffusion
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```python
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import torch
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import requests
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from diffusers import DiffusionPipeline
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image_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
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inner_image_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
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mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"
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def load_image(url, mode="RGB"):
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init_image = load_image(image_url, mode="RGB")
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inner_image = load_image(inner_image_url, mode="RGBA")
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mask_image = load_image(mask_url, mode="RGB")
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pipe = DiffusionPipeline.from_pretrained(
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"stable-diffusion-v1-5/stable-diffusion-inpainting",
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image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
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image.save("output.png")
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```
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```python
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import torch
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|
|
|
|
3267 |
from diffusers import DiffusionPipeline
|
3268 |
+
import numpy as np
|
3269 |
+
from PIL import Image
|
3270 |
|
3271 |
+
pipe = DiffusionPipeline.from_pretrained(
|
3272 |
+
"CompVis/stable-diffusion-v1-4",
|
3273 |
+
custom_pipeline="pipeline_prompt2prompt"
|
3274 |
+
).to("cuda")
|
3275 |
|
3276 |
+
prompts = [
|
3277 |
+
"A turtle playing with a ball",
|
3278 |
+
"A monkey playing with a ball"
|
3279 |
+
]
|
3280 |
|
3281 |
cross_attention_kwargs = {
|
3282 |
"edit_type": "replace",
|
|
|
3284 |
"self_replace_steps": 0.4
|
3285 |
}
|
3286 |
|
3287 |
+
outputs = pipe(
|
3288 |
+
prompt=prompts,
|
3289 |
+
height=512,
|
3290 |
+
width=512,
|
3291 |
+
num_inference_steps=50,
|
3292 |
+
cross_attention_kwargs=cross_attention_kwargs
|
3293 |
+
)
|
3294 |
+
|
3295 |
+
outputs.images[0].save("output_image_0.png")
|
3296 |
```
|
3297 |
|
3298 |
And abbreviated examples for the other edits:
|
main/lpw_stable_diffusion_xl.py
CHANGED
@@ -1773,7 +1773,7 @@ class SDXLLongPromptWeightingPipeline(
|
|
1773 |
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
1774 |
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
1775 |
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
1776 |
-
f" = {num_channels_latents+num_channels_masked_image+num_channels_mask}. Please verify the config of"
|
1777 |
" `pipeline.unet` or your `mask_image` or `image` input."
|
1778 |
)
|
1779 |
elif num_channels_unet != 4:
|
@@ -1924,7 +1924,22 @@ class SDXLLongPromptWeightingPipeline(
|
|
1924 |
self.upcast_vae()
|
1925 |
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
1926 |
|
1927 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1928 |
|
1929 |
# cast back to fp16 if needed
|
1930 |
if needs_upcasting:
|
|
|
1773 |
f"Incorrect configuration settings! The config of `pipeline.unet`: {self.unet.config} expects"
|
1774 |
f" {self.unet.config.in_channels} but received `num_channels_latents`: {num_channels_latents} +"
|
1775 |
f" `num_channels_mask`: {num_channels_mask} + `num_channels_masked_image`: {num_channels_masked_image}"
|
1776 |
+
f" = {num_channels_latents + num_channels_masked_image + num_channels_mask}. Please verify the config of"
|
1777 |
" `pipeline.unet` or your `mask_image` or `image` input."
|
1778 |
)
|
1779 |
elif num_channels_unet != 4:
|
|
|
1924 |
self.upcast_vae()
|
1925 |
latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
|
1926 |
|
1927 |
+
# unscale/denormalize the latents
|
1928 |
+
# denormalize with the mean and std if available and not None
|
1929 |
+
has_latents_mean = hasattr(self.vae.config, "latents_mean") and self.vae.config.latents_mean is not None
|
1930 |
+
has_latents_std = hasattr(self.vae.config, "latents_std") and self.vae.config.latents_std is not None
|
1931 |
+
if has_latents_mean and has_latents_std:
|
1932 |
+
latents_mean = (
|
1933 |
+
torch.tensor(self.vae.config.latents_mean).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
1934 |
+
)
|
1935 |
+
latents_std = (
|
1936 |
+
torch.tensor(self.vae.config.latents_std).view(1, 4, 1, 1).to(latents.device, latents.dtype)
|
1937 |
+
)
|
1938 |
+
latents = latents * latents_std / self.vae.config.scaling_factor + latents_mean
|
1939 |
+
else:
|
1940 |
+
latents = latents / self.vae.config.scaling_factor
|
1941 |
+
|
1942 |
+
image = self.vae.decode(latents, return_dict=False)[0]
|
1943 |
|
1944 |
# cast back to fp16 if needed
|
1945 |
if needs_upcasting:
|