954 lines
47 KiB
Markdown
954 lines
47 KiB
Markdown
# Community Examples
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> **For more information about community pipelines, please have a look at [this issue](https://github.com/huggingface/diffusers/issues/841).**
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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 | Description | Code Example | Colab | Author |
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|:---------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|-----------------------------------------------------------:|
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| CLIP Guided Stable Diffusion | Doing CLIP guidance for text to image generation with Stable Diffusion | [CLIP Guided Stable Diffusion](#clip-guided-stable-diffusion) | [](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/CLIP_Guided_Stable_diffusion_with_diffusers.ipynb) | [Suraj Patil](https://github.com/patil-suraj/) |
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| One Step U-Net (Dummy) | Example showcasing of how to use Community Pipelines (see https://github.com/huggingface/diffusers/issues/841) | [One Step U-Net](#one-step-unet) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
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| Stable Diffusion Interpolation | Interpolate the latent space of Stable Diffusion between different prompts/seeds | [Stable Diffusion Interpolation](#stable-diffusion-interpolation) | - | [Nate Raw](https://github.com/nateraw/) |
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| Stable Diffusion Mega | **One** Stable Diffusion Pipeline with all functionalities of [Text2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py), [Image2Image](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py) and [Inpainting](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py) | [Stable Diffusion Mega](#stable-diffusion-mega) | - | [Patrick von Platen](https://github.com/patrickvonplaten/) |
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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) | - | [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) | - | [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) | - | [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) | - | [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) | - | [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) | - | [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) | - | [Juan Carlos Piñeros](https://github.com/juancopi81) |
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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) | - | [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](#image-to-image-inpainting-stable-diffusion) | - | [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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| Checkpoint Merger Pipeline | Diffusion Pipeline that enables merging of saved model checkpoints | [Checkpoint Merger Pipeline](#checkpoint-merger-pipeline) | - | [Naga Sai Abhinay Devarinti](https://github.com/Abhinay1997/) |
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Stable Diffusion v1.1-1.4 Comparison | Run all 4 model checkpoints for Stable Diffusion and compare their results together | [Stable Diffusion Comparison](#stable-diffusion-comparisons) | - | [Suvaditya Mukherjee](https://github.com/suvadityamuk) |
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MagicMix | Diffusion Pipeline for semantic mixing of an image and a text prompt | [MagicMix](#magic-mix) | - | [Partho Das](https://github.com/daspartho) |
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| Stable UnCLIP | Diffusion Pipeline for combining prior model (generate clip image embedding from text, UnCLIPPipeline `"kakaobrain/karlo-v1-alpha"`) and decoder pipeline (decode clip image embedding to image, StableDiffusionImageVariationPipeline `"lambdalabs/sd-image-variations-diffusers"` ). | [Stable UnCLIP](#stable-unclip) | - |[Ray Wang](https://wrong.wang) |
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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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```py
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", 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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```python
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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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"runwayml/stable-diffusion-v1-5",
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custom_pipeline="clip_guided_stable_diffusion",
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clip_model=clip_model,
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feature_extractor=feature_extractor,
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torch_dtype=torch.float16,
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)
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guided_pipeline.enable_attention_slicing()
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guided_pipeline = guided_pipeline.to("cuda")
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prompt = "fantasy book cover, full moon, fantasy forest landscape, golden vector elements, fantasy magic, dark light night, intricate, elegant, sharp focus, illustration, highly detailed, digital painting, concept art, matte, art by WLOP and Artgerm and Albert Bierstadt, masterpiece"
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generator = torch.Generator(device="cuda").manual_seed(0)
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images = []
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for i in range(4):
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image = guided_pipeline(
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prompt,
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num_inference_steps=50,
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guidance_scale=7.5,
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clip_guidance_scale=100,
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num_cutouts=4,
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use_cutouts=False,
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generator=generator,
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).images[0]
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images.append(image)
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# save images locally
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for i, img in enumerate(images):
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img.save(f"./clip_guided_sd/image_{i}.png")
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```
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The `images` list contains a list of PIL images that can be saved locally or displayed directly in a google colab.
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Generated images tend to be of higher qualtiy than natively using stable diffusion. E.g. the above script generates the following images:
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.
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### One Step Unet
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The dummy "one-step-unet" can be run as follows:
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```python
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from diffusers import DiffusionPipeline
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pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="one_step_unet")
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pipe()
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```
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**Note**: This community pipeline is not useful as a feature, but rather just serves as an example of how community pipelines can be added (see https://github.com/huggingface/diffusers/issues/841).
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### Stable Diffusion Interpolation
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The following code can be run on a GPU of at least 8GB VRAM and should take approximately 5 minutes.
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipe = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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revision='fp16',
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torch_dtype=torch.float16,
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safety_checker=None, # Very important for videos...lots of false positives while interpolating
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custom_pipeline="interpolate_stable_diffusion",
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).to('cuda')
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pipe.enable_attention_slicing()
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frame_filepaths = pipe.walk(
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prompts=['a dog', 'a cat', 'a horse'],
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seeds=[42, 1337, 1234],
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num_interpolation_steps=16,
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output_dir='./dreams',
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batch_size=4,
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height=512,
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width=512,
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guidance_scale=8.5,
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num_inference_steps=50,
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)
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```
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The output of the `walk(...)` function returns a list of images saved under the folder as defined in `output_dir`. You can use these images to create videos of stable diffusion.
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> **Please have a look at https://github.com/nateraw/stable-diffusion-videos for more in-detail information on how to create videos using stable diffusion as well as more feature-complete functionality.**
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### Stable Diffusion Mega
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The Stable Diffusion Mega Pipeline lets you use the main use cases of the stable diffusion pipeline in a single class.
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```python
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#!/usr/bin/env python3
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from diffusers import DiffusionPipeline
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import PIL
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import requests
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from io import BytesIO
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import torch
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def download_image(url):
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response = requests.get(url)
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return PIL.Image.open(BytesIO(response.content)).convert("RGB")
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pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", custom_pipeline="stable_diffusion_mega", torch_dtype=torch.float16, revision="fp16")
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pipe.to("cuda")
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pipe.enable_attention_slicing()
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### Text-to-Image
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images = pipe.text2img("An astronaut riding a horse").images
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### Image-to-Image
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init_image = download_image("https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg")
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prompt = "A fantasy landscape, trending on artstation"
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images = pipe.img2img(prompt=prompt, image=init_image, strength=0.75, guidance_scale=7.5).images
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### Inpainting
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img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.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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init_image = download_image(img_url).resize((512, 512))
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mask_image = download_image(mask_url).resize((512, 512))
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prompt = "a cat sitting on a bench"
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images = pipe.inpaint(prompt=prompt, image=init_image, mask_image=mask_image, strength=0.75).images
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```
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As shown above this one pipeline can run all both "text-to-image", "image-to-image", and "inpainting" in one pipeline.
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### Long Prompt Weighting Stable Diffusion
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Features of this custom pipeline:
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- Input a prompt without the 77 token length limit.
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- Includes tx2img, img2img. and inpainting pipelines.
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- Emphasize/weigh part of your prompt with parentheses as so: `a baby deer with (big eyes)`
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- De-emphasize part of your prompt as so: `a [baby] deer with big eyes`
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- Precisely weigh part of your prompt as so: `a baby deer with (big eyes:1.3)`
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Prompt weighting equivalents:
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- `a baby deer with` == `(a baby deer with:1.0)`
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- `(big eyes)` == `(big eyes:1.1)`
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- `((big eyes))` == `(big eyes:1.21)`
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- `[big eyes]` == `(big eyes:0.91)`
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You can run this custom pipeline as so:
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#### pytorch
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipe = DiffusionPipeline.from_pretrained(
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'hakurei/waifu-diffusion',
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custom_pipeline="lpw_stable_diffusion",
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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 = "best_quality (1girl:1.3) bow bride brown_hair closed_mouth frilled_bow frilled_hair_tubes frills (full_body:1.3) fox_ear hair_bow hair_tubes happy hood japanese_clothes kimono long_sleeves red_bow smile solo tabi uchikake white_kimono wide_sleeves cherry_blossoms"
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neg_prompt = "lowres, bad_anatomy, error_body, error_hair, error_arm, error_hands, bad_hands, error_fingers, bad_fingers, missing_fingers, error_legs, bad_legs, multiple_legs, missing_legs, error_lighting, error_shadow, error_reflection, text, error, extra_digit, fewer_digits, cropped, worst_quality, low_quality, normal_quality, jpeg_artifacts, signature, watermark, username, blurry"
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pipe.text2img(prompt, negative_prompt=neg_prompt, width=512,height=512,max_embeddings_multiples=3).images[0]
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```
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#### onnxruntime
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipe = DiffusionPipeline.from_pretrained(
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'CompVis/stable-diffusion-v1-4',
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custom_pipeline="lpw_stable_diffusion_onnx",
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revision="onnx",
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provider="CUDAExecutionProvider"
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)
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prompt = "a photo of an astronaut riding a horse on mars, best quality"
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neg_prompt = "lowres, bad anatomy, error body, error hair, error arm, error hands, bad hands, error fingers, bad fingers, missing fingers, error legs, bad legs, multiple legs, missing legs, error lighting, error shadow, error reflection, text, error, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
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pipe.text2img(prompt,negative_prompt=neg_prompt, width=512, height=512, max_embeddings_multiples=3).images[0]
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```
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if you see `Token indices sequence length is longer than the specified maximum sequence length for this model ( *** > 77 ) . Running this sequence through the model will result in indexing errors`. Do not worry, it is normal.
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### Speech to Image
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The following code can generate an image from an audio sample using pre-trained OpenAI whisper-small and Stable Diffusion.
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```Python
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import torch
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import matplotlib.pyplot as plt
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from datasets import load_dataset
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from diffusers import DiffusionPipeline
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from transformers import (
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WhisperForConditionalGeneration,
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WhisperProcessor,
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)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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audio_sample = ds[3]
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text = audio_sample["text"].lower()
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speech_data = audio_sample["audio"]["array"]
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model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small").to(device)
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processor = WhisperProcessor.from_pretrained("openai/whisper-small")
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diffuser_pipeline = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="speech_to_image_diffusion",
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speech_model=model,
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speech_processor=processor,
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torch_dtype=torch.float16,
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)
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diffuser_pipeline.enable_attention_slicing()
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diffuser_pipeline = diffuser_pipeline.to(device)
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output = diffuser_pipeline(speech_data)
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plt.imshow(output.images[0])
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```
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This example produces the following image:
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### Wildcard Stable Diffusion
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Following the great examples from https://github.com/jtkelm2/stable-diffusion-webui-1/blob/master/scripts/wildcards.py and https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Custom-Scripts#wildcards, here's a minimal implementation that allows for users to add "wildcards", denoted by `__wildcard__` to prompts that are used as placeholders for randomly sampled values given by either a dictionary or a `.txt` file. For example:
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Say we have a prompt:
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```
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prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
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```
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We can then define possible values to be sampled for `animal`, `object`, and `clothing`. These can either be from a `.txt` with the same name as the category.
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The possible values can also be defined / combined by using a dictionary like: `{"animal":["dog", "cat", mouse"]}`.
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The actual pipeline works just like `StableDiffusionPipeline`, except the `__call__` method takes in:
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`wildcard_files`: list of file paths for wild card replacement
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`wildcard_option_dict`: dict with key as `wildcard` and values as a list of possible replacements
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`num_prompt_samples`: number of prompts to sample, uniformly sampling wildcards
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A full example:
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create `animal.txt`, with contents like:
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```
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dog
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cat
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mouse
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```
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create `object.txt`, with contents like:
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```
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chair
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sofa
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bench
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```
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```python
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from diffusers import DiffusionPipeline
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import torch
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pipe = DiffusionPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4",
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custom_pipeline="wildcard_stable_diffusion",
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torch_dtype=torch.float16,
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)
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prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
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out = pipe(
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prompt,
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wildcard_option_dict={
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"clothing":["hat", "shirt", "scarf", "beret"]
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},
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wildcard_files=["object.txt", "animal.txt"],
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num_prompt_samples=1
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)
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```
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### Composable Stable diffusion
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[Composable Stable Diffusion](https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/) proposes conjunction and negation (negative prompts) operators for compositional generation with conditional diffusion models.
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```python
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import torch as th
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import numpy as np
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import torchvision.utils as tvu
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from diffusers import DiffusionPipeline
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--prompt", type=str, default="mystical trees | A magical pond | dark",
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help="use '|' as the delimiter to compose separate sentences.")
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parser.add_argument("--steps", type=int, default=50)
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parser.add_argument("--scale", type=float, default=7.5)
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parser.add_argument("--weights", type=str, default="7.5 | 7.5 | -7.5")
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parser.add_argument("--seed", type=int, default=2)
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parser.add_argument("--model_path", type=str, default="CompVis/stable-diffusion-v1-4")
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parser.add_argument("--num_images", type=int, default=1)
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args = parser.parse_args()
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has_cuda = th.cuda.is_available()
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device = th.device('cpu' if not has_cuda else 'cuda')
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prompt = args.prompt
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scale = args.scale
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steps = args.steps
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pipe = DiffusionPipeline.from_pretrained(
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args.model_path,
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custom_pipeline="composable_stable_diffusion",
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).to(device)
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pipe.safety_checker = None
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images = []
|
|
generator = th.Generator("cuda").manual_seed(args.seed)
|
|
for i in range(args.num_images):
|
|
image = pipe(prompt, guidance_scale=scale, num_inference_steps=steps,
|
|
weights=args.weights, generator=generator).images[0]
|
|
images.append(th.from_numpy(np.array(image)).permute(2, 0, 1) / 255.)
|
|
grid = tvu.make_grid(th.stack(images, dim=0), nrow=4, padding=0)
|
|
tvu.save_image(grid, f'{prompt}_{args.weights}' + '.png')
|
|
|
|
```
|
|
|
|
### Imagic Stable Diffusion
|
|
Allows you to edit an image using stable diffusion.
|
|
|
|
```python
|
|
import requests
|
|
from PIL import Image
|
|
from io import BytesIO
|
|
import torch
|
|
import os
|
|
from diffusers import DiffusionPipeline, DDIMScheduler
|
|
has_cuda = torch.cuda.is_available()
|
|
device = torch.device('cpu' if not has_cuda else 'cuda')
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
safety_checker=None,
|
|
use_auth_token=True,
|
|
custom_pipeline="imagic_stable_diffusion",
|
|
scheduler = DDIMScheduler(beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False)
|
|
).to(device)
|
|
generator = torch.Generator("cuda").manual_seed(0)
|
|
seed = 0
|
|
prompt = "A photo of Barack Obama smiling with a big grin"
|
|
url = 'https://www.dropbox.com/s/6tlwzr73jd1r9yk/obama.png?dl=1'
|
|
response = requests.get(url)
|
|
init_image = Image.open(BytesIO(response.content)).convert("RGB")
|
|
init_image = init_image.resize((512, 512))
|
|
res = pipe.train(
|
|
prompt,
|
|
image=init_image,
|
|
generator=generator)
|
|
res = pipe(alpha=1, guidance_scale=7.5, num_inference_steps=50)
|
|
os.makedirs("imagic", exist_ok=True)
|
|
image = res.images[0]
|
|
image.save('./imagic/imagic_image_alpha_1.png')
|
|
res = pipe(alpha=1.5, guidance_scale=7.5, num_inference_steps=50)
|
|
image = res.images[0]
|
|
image.save('./imagic/imagic_image_alpha_1_5.png')
|
|
res = pipe(alpha=2, guidance_scale=7.5, num_inference_steps=50)
|
|
image = res.images[0]
|
|
image.save('./imagic/imagic_image_alpha_2.png')
|
|
```
|
|
|
|
### Seed Resizing
|
|
Test seed resizing. Originally generate an image in 512 by 512, then generate image with same seed at 512 by 592 using seed resizing. Finally, generate 512 by 592 using original stable diffusion pipeline.
|
|
|
|
```python
|
|
import torch as th
|
|
import numpy as np
|
|
from diffusers import DiffusionPipeline
|
|
|
|
has_cuda = th.cuda.is_available()
|
|
device = th.device('cpu' if not has_cuda else 'cuda')
|
|
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
use_auth_token=True,
|
|
custom_pipeline="seed_resize_stable_diffusion"
|
|
).to(device)
|
|
|
|
def dummy(images, **kwargs):
|
|
return images, False
|
|
|
|
pipe.safety_checker = dummy
|
|
|
|
|
|
images = []
|
|
th.manual_seed(0)
|
|
generator = th.Generator("cuda").manual_seed(0)
|
|
|
|
seed = 0
|
|
prompt = "A painting of a futuristic cop"
|
|
|
|
width = 512
|
|
height = 512
|
|
|
|
res = pipe(
|
|
prompt,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=50,
|
|
height=height,
|
|
width=width,
|
|
generator=generator)
|
|
image = res.images[0]
|
|
image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
|
|
|
|
|
|
th.manual_seed(0)
|
|
generator = th.Generator("cuda").manual_seed(0)
|
|
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
use_auth_token=True,
|
|
custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
|
|
).to(device)
|
|
|
|
width = 512
|
|
height = 592
|
|
|
|
res = pipe(
|
|
prompt,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=50,
|
|
height=height,
|
|
width=width,
|
|
generator=generator)
|
|
image = res.images[0]
|
|
image.save('./seed_resize/seed_resize_{w}_{h}_image.png'.format(w=width, h=height))
|
|
|
|
pipe_compare = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
use_auth_token=True,
|
|
custom_pipeline="/home/mark/open_source/diffusers/examples/community/"
|
|
).to(device)
|
|
|
|
res = pipe_compare(
|
|
prompt,
|
|
guidance_scale=7.5,
|
|
num_inference_steps=50,
|
|
height=height,
|
|
width=width,
|
|
generator=generator
|
|
)
|
|
|
|
image = res.images[0]
|
|
image.save('./seed_resize/seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height))
|
|
```
|
|
|
|
### Multilingual Stable Diffusion Pipeline
|
|
|
|
The following code can generate an images from texts in different languages using the pre-trained [mBART-50 many-to-one multilingual machine translation model](https://huggingface.co/facebook/mbart-large-50-many-to-one-mmt) and Stable Diffusion.
|
|
|
|
```python
|
|
from PIL import Image
|
|
|
|
import torch
|
|
|
|
from diffusers import DiffusionPipeline
|
|
from transformers import (
|
|
pipeline,
|
|
MBart50TokenizerFast,
|
|
MBartForConditionalGeneration,
|
|
)
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
device_dict = {"cuda": 0, "cpu": -1}
|
|
|
|
# helper function taken from: https://huggingface.co/blog/stable_diffusion
|
|
def image_grid(imgs, rows, cols):
|
|
assert len(imgs) == rows*cols
|
|
|
|
w, h = imgs[0].size
|
|
grid = Image.new('RGB', size=(cols*w, rows*h))
|
|
grid_w, grid_h = grid.size
|
|
|
|
for i, img in enumerate(imgs):
|
|
grid.paste(img, box=(i%cols*w, i//cols*h))
|
|
return grid
|
|
|
|
# Add language detection pipeline
|
|
language_detection_model_ckpt = "papluca/xlm-roberta-base-language-detection"
|
|
language_detection_pipeline = pipeline("text-classification",
|
|
model=language_detection_model_ckpt,
|
|
device=device_dict[device])
|
|
|
|
# Add model for language translation
|
|
trans_tokenizer = MBart50TokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-one-mmt")
|
|
trans_model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-one-mmt").to(device)
|
|
|
|
diffuser_pipeline = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
custom_pipeline="multilingual_stable_diffusion",
|
|
detection_pipeline=language_detection_pipeline,
|
|
translation_model=trans_model,
|
|
translation_tokenizer=trans_tokenizer,
|
|
|
|
torch_dtype=torch.float16,
|
|
)
|
|
|
|
diffuser_pipeline.enable_attention_slicing()
|
|
diffuser_pipeline = diffuser_pipeline.to(device)
|
|
|
|
prompt = ["a photograph of an astronaut riding a horse",
|
|
"Una casa en la playa",
|
|
"Ein Hund, der Orange isst",
|
|
"Un restaurant parisien"]
|
|
|
|
output = diffuser_pipeline(prompt)
|
|
|
|
images = output.images
|
|
|
|
grid = image_grid(images, rows=2, cols=2)
|
|
```
|
|
|
|
This example produces the following images:
|
|
|
|
|
|
### Image to Image Inpainting Stable Diffusion
|
|
|
|
Similar to the standard stable diffusion inpainting example, except with the addition of an `inner_image` argument.
|
|
|
|
`image`, `inner_image`, and `mask` should have the same dimensions. `inner_image` should have an alpha (transparency) channel.
|
|
|
|
The aim is to overlay two images, then mask out the boundary between `image` and `inner_image` to allow stable diffusion to make the connection more seamless.
|
|
For example, this could be used to place a logo on a shirt and make it blend seamlessly.
|
|
|
|
```python
|
|
import PIL
|
|
import torch
|
|
|
|
from diffusers import DiffusionPipeline
|
|
|
|
image_path = "./path-to-image.png"
|
|
inner_image_path = "./path-to-inner-image.png"
|
|
mask_path = "./path-to-mask.png"
|
|
|
|
init_image = PIL.Image.open(image_path).convert("RGB").resize((512, 512))
|
|
inner_image = PIL.Image.open(inner_image_path).convert("RGBA").resize((512, 512))
|
|
mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))
|
|
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"runwayml/stable-diffusion-inpainting",
|
|
custom_pipeline="img2img_inpainting",
|
|
|
|
torch_dtype=torch.float16
|
|
)
|
|
pipe = pipe.to("cuda")
|
|
|
|
prompt = "Your prompt here!"
|
|
image = pipe(prompt=prompt, image=init_image, inner_image=inner_image, mask_image=mask_image).images[0]
|
|
```
|
|
|
|
|
|
|
|
### Text Based Inpainting Stable Diffusion
|
|
|
|
Use a text prompt to generate the mask for the area to be inpainted.
|
|
Currently uses the CLIPSeg model for mask generation, then calls the standard Stable Diffusion Inpainting pipeline to perform the inpainting.
|
|
|
|
```python
|
|
from transformers import CLIPSegProcessor, CLIPSegForImageSegmentation
|
|
from diffusers import DiffusionPipeline
|
|
|
|
from PIL import Image
|
|
import requests
|
|
|
|
processor = CLIPSegProcessor.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
model = CLIPSegForImageSegmentation.from_pretrained("CIDAS/clipseg-rd64-refined")
|
|
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"runwayml/stable-diffusion-inpainting",
|
|
custom_pipeline="text_inpainting",
|
|
segmentation_model=model,
|
|
segmentation_processor=processor
|
|
)
|
|
pipe = pipe.to("cuda")
|
|
|
|
|
|
url = "https://github.com/timojl/clipseg/blob/master/example_image.jpg?raw=true"
|
|
image = Image.open(requests.get(url, stream=True).raw).resize((512, 512))
|
|
text = "a glass" # will mask out this text
|
|
prompt = "a cup" # the masked out region will be replaced with this
|
|
|
|
image = pipe(image=image, text=text, prompt=prompt).images[0]
|
|
```
|
|
|
|
### Bit Diffusion
|
|
Based https://arxiv.org/abs/2208.04202, this is used for diffusion on discrete data - eg, discreate image data, DNA sequence data. An unconditional discreate image can be generated like this:
|
|
|
|
```python
|
|
from diffusers import DiffusionPipeline
|
|
pipe = DiffusionPipeline.from_pretrained("google/ddpm-cifar10-32", custom_pipeline="bit_diffusion")
|
|
image = pipe().images[0]
|
|
|
|
```
|
|
|
|
### Stable Diffusion with K Diffusion
|
|
|
|
Make sure you have @crowsonkb's https://github.com/crowsonkb/k-diffusion installed:
|
|
|
|
```
|
|
pip install k-diffusion
|
|
```
|
|
|
|
You can use the community pipeline as follows:
|
|
|
|
```python
|
|
from diffusers import DiffusionPipeline
|
|
|
|
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
|
|
pipe = pipe.to("cuda")
|
|
|
|
prompt = "an astronaut riding a horse on mars"
|
|
pipe.set_scheduler("sample_heun")
|
|
generator = torch.Generator(device="cuda").manual_seed(seed)
|
|
image = pipe(prompt, generator=generator, num_inference_steps=20).images[0]
|
|
|
|
image.save("./astronaut_heun_k_diffusion.png")
|
|
```
|
|
|
|
To make sure that K Diffusion and `diffusers` yield the same results:
|
|
|
|
**Diffusers**:
|
|
```python
|
|
from diffusers import DiffusionPipeline, EulerDiscreteScheduler
|
|
|
|
seed = 33
|
|
|
|
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4")
|
|
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe = pipe.to("cuda")
|
|
|
|
generator = torch.Generator(device="cuda").manual_seed(seed)
|
|
image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
|
|
```
|
|
|
|
|
|
|
|
**K Diffusion**:
|
|
```python
|
|
from diffusers import DiffusionPipeline, EulerDiscreteScheduler
|
|
|
|
seed = 33
|
|
|
|
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="sd_text2img_k_diffusion")
|
|
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe = pipe.to("cuda")
|
|
|
|
pipe.set_scheduler("sample_euler")
|
|
generator = torch.Generator(device="cuda").manual_seed(seed)
|
|
image = pipe(prompt, generator=generator, num_inference_steps=50).images[0]
|
|
```
|
|
|
|
|
|
|
|
### Checkpoint Merger Pipeline
|
|
Based on the AUTOMATIC1111/webui for checkpoint merging. This is a custom pipeline that merges upto 3 pretrained model checkpoints as long as they are in the HuggingFace model_index.json format.
|
|
|
|
The checkpoint merging is currently memory intensive as it modifies the weights of a DiffusionPipeline object in place. Expect atleast 13GB RAM Usage on Kaggle GPU kernels and
|
|
on colab you might run out of the 12GB memory even while merging two checkpoints.
|
|
|
|
Usage:-
|
|
```python
|
|
from diffusers import DiffusionPipeline
|
|
|
|
#Return a CheckpointMergerPipeline class that allows you to merge checkpoints.
|
|
#The checkpoint passed here is ignored. But still pass one of the checkpoints you plan to
|
|
#merge for convenience
|
|
pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger")
|
|
|
|
#There are multiple possible scenarios:
|
|
#The pipeline with the merged checkpoints is returned in all the scenarios
|
|
|
|
#Compatible checkpoints a.k.a matched model_index.json files. Ignores the meta attributes in model_index.json during comparision.( attrs with _ as prefix )
|
|
merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","CompVis/stable-diffusion-v1-2"], interp = "sigmoid", alpha = 0.4)
|
|
|
|
#Incompatible checkpoints in model_index.json but merge might be possible. Use force = True to ignore model_index.json compatibility
|
|
merged_pipe_1 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion"], force = True, interp = "sigmoid", alpha = 0.4)
|
|
|
|
#Three checkpoint merging. Only "add_difference" method actually works on all three checkpoints. Using any other options will ignore the 3rd checkpoint.
|
|
merged_pipe_2 = pipe.merge(["CompVis/stable-diffusion-v1-4","hakurei/waifu-diffusion","prompthero/openjourney"], force = True, interp = "add_difference", alpha = 0.4)
|
|
|
|
prompt = "An astronaut riding a horse on Mars"
|
|
|
|
image = merged_pipe(prompt).images[0]
|
|
|
|
```
|
|
Some examples along with the merge details:
|
|
|
|
1. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" ; Sigmoid interpolation; alpha = 0.8
|
|
|
|
|
|
|
|
2. "hakurei/waifu-diffusion" + "prompthero/openjourney" ; Inverse Sigmoid interpolation; alpha = 0.8
|
|
|
|
|
|
|
|
|
|
3. "CompVis/stable-diffusion-v1-4" + "hakurei/waifu-diffusion" + "prompthero/openjourney"; Add Difference interpolation; alpha = 0.5
|
|
|
|
|
|
|
|
|
|
### Stable Diffusion Comparisons
|
|
|
|
This Community Pipeline enables the comparison between the 4 checkpoints that exist for Stable Diffusion. They can be found through the following links:
|
|
1. [Stable Diffusion v1.1](https://huggingface.co/CompVis/stable-diffusion-v1-1)
|
|
2. [Stable Diffusion v1.2](https://huggingface.co/CompVis/stable-diffusion-v1-2)
|
|
3. [Stable Diffusion v1.3](https://huggingface.co/CompVis/stable-diffusion-v1-3)
|
|
4. [Stable Diffusion v1.4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
|
|
|
|
```python
|
|
from diffusers import DiffusionPipeline
|
|
import matplotlib.pyplot as plt
|
|
|
|
pipe = DiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4', custom_pipeline='suvadityamuk/StableDiffusionComparison')
|
|
pipe.enable_attention_slicing()
|
|
pipe = pipe.to('cuda')
|
|
prompt = "an astronaut riding a horse on mars"
|
|
output = pipe(prompt)
|
|
|
|
plt.subplots(2,2,1)
|
|
plt.imshow(output.images[0])
|
|
plt.title('Stable Diffusion v1.1')
|
|
plt.axis('off')
|
|
plt.subplots(2,2,2)
|
|
plt.imshow(output.images[1])
|
|
plt.title('Stable Diffusion v1.2')
|
|
plt.axis('off')
|
|
plt.subplots(2,2,3)
|
|
plt.imshow(output.images[2])
|
|
plt.title('Stable Diffusion v1.3')
|
|
plt.axis('off')
|
|
plt.subplots(2,2,4)
|
|
plt.imshow(output.images[3])
|
|
plt.title('Stable Diffusion v1.4')
|
|
plt.axis('off')
|
|
|
|
plt.show()
|
|
```
|
|
|
|
As a result, you can look at a grid of all 4 generated images being shown together, that captures a difference the advancement of the training between the 4 checkpoints.
|
|
|
|
### Magic Mix
|
|
|
|
Implementation of the [MagicMix: Semantic Mixing with Diffusion Models](https://arxiv.org/abs/2210.16056) paper. This is a Diffusion Pipeline for semantic mixing of an image and a text prompt to create a new concept while preserving the spatial layout and geometry of the subject in the image. The pipeline takes an image that provides the layout semantics and a prompt that provides the content semantics for the mixing process.
|
|
|
|
There are 3 parameters for the method-
|
|
- `mix_factor`: It is the interpolation constant used in the layout generation phase. The greater the value of `mix_factor`, the greater the influence of the prompt on the layout generation process.
|
|
- `kmax` and `kmin`: These determine the range for the layout and content generation process. A higher value of kmax results in loss of more information about the layout of the original image and a higher value of kmin results in more steps for content generation process.
|
|
|
|
Here is an example usage-
|
|
|
|
```python
|
|
from diffusers import DiffusionPipeline, DDIMScheduler
|
|
from PIL import Image
|
|
|
|
pipe = DiffusionPipeline.from_pretrained(
|
|
"CompVis/stable-diffusion-v1-4",
|
|
custom_pipeline="magic_mix",
|
|
scheduler = DDIMScheduler.from_pretrained("CompVis/stable-diffusion-v1-4", subfolder="scheduler"),
|
|
).to('cuda')
|
|
|
|
img = Image.open('phone.jpg')
|
|
mix_img = pipe(
|
|
img,
|
|
prompt = 'bed',
|
|
kmin = 0.3,
|
|
kmax = 0.5,
|
|
mix_factor = 0.5,
|
|
)
|
|
mix_img.save('phone_bed_mix.jpg')
|
|
```
|
|
The `mix_img` is a PIL image that can be saved locally or displayed directly in a google colab. Generated image is a mix of the layout semantics of the given image and the content semantics of the prompt.
|
|
|
|
E.g. the above script generates the following image:
|
|
|
|
`phone.jpg`
|
|
|
|
|
|
|
|
`phone_bed_mix.jpg`
|
|
|
|
|
|
|
|
For more example generations check out this [demo notebook](https://github.com/daspartho/MagicMix/blob/main/demo.ipynb).
|
|
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### Stable UnCLIP
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UnCLIPPipeline("kakaobrain/karlo-v1-alpha") provide a prior model that can generate clip image embedding from text.
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StableDiffusionImageVariationPipeline("lambdalabs/sd-image-variations-diffusers") provide a decoder model than can generate images from clip image embedding.
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```python
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import torch
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from diffusers import DiffusionPipeline
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device = torch.device("cpu" if not torch.cuda.is_available() else "cuda")
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pipeline = DiffusionPipeline.from_pretrained(
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"kakaobrain/karlo-v1-alpha",
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torch_dtype=torch.float16,
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custom_pipeline="stable_unclip",
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decoder_pipe_kwargs=dict(
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image_encoder=None,
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),
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)
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pipeline.to(device)
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prompt = "a shiba inu wearing a beret and black turtleneck"
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random_generator = torch.Generator(device=device).manual_seed(1000)
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output = pipeline(
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prompt=prompt,
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width=512,
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height=512,
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generator=random_generator,
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prior_guidance_scale=4,
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prior_num_inference_steps=25,
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decoder_guidance_scale=8,
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decoder_num_inference_steps=50,
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)
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image = output.images[0]
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image.save("./shiba-inu.jpg")
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# debug
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# `pipeline.decoder_pipe` is a regular StableDiffusionImageVariationPipeline instance.
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# It is used to convert clip image embedding to latents, then fed into VAE decoder.
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print(pipeline.decoder_pipe.__class__)
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# <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_image_variation.StableDiffusionImageVariationPipeline'>
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# this pipeline only use prior module in "kakaobrain/karlo-v1-alpha"
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# It is used to convert clip text embedding to clip image embedding.
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print(pipeline)
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# StableUnCLIPPipeline {
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# "_class_name": "StableUnCLIPPipeline",
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# "_diffusers_version": "0.12.0.dev0",
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# "prior": [
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# "diffusers",
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# "PriorTransformer"
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# ],
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# "prior_scheduler": [
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# "diffusers",
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# "UnCLIPScheduler"
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# ],
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# "text_encoder": [
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# "transformers",
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# "CLIPTextModelWithProjection"
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# ],
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# "tokenizer": [
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# "transformers",
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# "CLIPTokenizer"
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# ]
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# }
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# pipeline.prior_scheduler is the scheduler used for prior in UnCLIP.
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print(pipeline.prior_scheduler)
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# UnCLIPScheduler {
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# "_class_name": "UnCLIPScheduler",
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# "_diffusers_version": "0.12.0.dev0",
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# "clip_sample": true,
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# "clip_sample_range": 5.0,
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# "num_train_timesteps": 1000,
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# "prediction_type": "sample",
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# "variance_type": "fixed_small_log"
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# }
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```
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`shiba-inu.jpg`
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