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--- |
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base_model: |
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- CompVis/stable-diffusion-v1-4 |
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pipeline_tag: text-to-image |
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tags: |
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- art |
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--- |
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<h1 align="center">The Superposition of Diffusion Models Using the It么 Density Estimator: <em>Pipeline</em></h1> |
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<p align="center"> |
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<a href="https://arxiv.org/abs/2412.17762"><img src="https://img.shields.io/badge/Arxiv-2412.17762-red?style=for-the-badge&logo=Arxiv" alt="arXiv"/></a> |
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</p> |
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This pipeline shows how to superimpose different text prompts from [Stable Diffusion v1-4](https://huggingface.co./CompVis/stable-diffusion-v1-4) based the paper [The Superposition of Diffusion Models Using the It么 Density Estimator](https://www.arxiv.org/abs/2412.17762). |
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<p align="center"> |
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<img src="https://huggingface.co./superdiff/superdiff-sd-v1-4/resolve/main/superdiff_small.gif" alt="drawing" style="width:500px;"> |
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</p> |
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## Requirements |
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This pipeline can be run with the following packages & versions: |
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- `PyTorch 2.5.1` |
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- `Diffusers 0.32.1` |
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- `Accelerate 1.2.1` |
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- `Transformers 4.47.1` |
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You can install these with: |
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``` |
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pip install torch |
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pip install diffusers accelerate transformers |
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``` |
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## Example usage |
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``` |
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from PIL import Image |
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from diffusers import DiffusionPipeline |
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pipeline = DiffusionPipeline.from_pretrained("superdiff/superdiff-sd-v1-4", custom_pipeline='pipeline', trust_remote_code=True) |
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output = pipeline("a flamingo", "a candy cane", seed=1, num_inference_steps=1000, batch_size=1) |
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image = Image.fromarray(output[0].cpu().numpy()) |
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image.save("superdiff_output.png") |
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``` |
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Arguments that can be set by user in `pipeline()`: |
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- `prompt_1` [required]: text prompt describing first concept to superimpose (e.g. "a flamingo") |
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- `prompt_2`[required]: text prompt describing second concept to superimpose (e.g. "a candy cane") |
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- `seed`[optional: default=None]: seed for random noise generator for reproducibility; for non-deterministic outputs, set to `None` |
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- `num_inference_steps`[optional: default=1000]: number of denoising steps (we recommend 1000!) |
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- `batch_size` [optional: default=1]: batch size |
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- `lift` [optional: default=0.0]: bias value that favours generation towards one prompt over the other |
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- `guidance_scale` [optional: default=7.5]: scale for classifier-free guidance |
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- `height`, `width` [optional: default=512]: height and width of generated images |
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To replicate images from Section 4.2 of the paper, you can use the following: |
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``` |
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image = pipeline(prompt_1, prompt_2, seed=1, num_inference_steps=1000, batch_size=20, lift=0.0, guidance_scale=7.5) |
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``` |
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(Note: the runtime for a batch size of 1 on an NVIDIA A40 GPU is around 3 mins 30 sec.) |
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## Citation |
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**BibTeX:** |
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``` |
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@article{skreta2024superposition, |
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title={The Superposition of Diffusion Models Using the It$\backslash$\^{} o Density Estimator}, |
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author={Skreta, Marta and Atanackovic, Lazar and Bose, Avishek Joey and Tong, Alexander and Neklyudov, Kirill}, |
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journal={arXiv preprint arXiv:2412.17762}, |
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year={2024} |
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} |
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``` |
