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--- |
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license: openrail++ |
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tags: |
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- text-to-image |
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- stable-diffusion |
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library_name: diffusers |
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inference: false |
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--- |
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# SDXS-512-DreamShaper |
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SDXS is a model that can generate high-resolution images in real-time based on prompt texts, trained using score distillation and feature matching. |
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For more information, please refer to our research paper: [SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions](https://arxiv.org/abs/2403.16627). |
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We open-source the model as part of the research. |
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SDXS-512-DreamShaper is the version we trained specifically for community. |
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The model is trained without focusing on FID, and sacrifices diversity for better image generation quality. |
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In order to avoid some possible risks, the SDXS-512-1.0 and SDXS-1024-1.0 will not be available shortly. |
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Watch [our repo](https://github.com/IDKiro/sdxs) for any updates. |
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Model Information: |
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- Teacher DM: [dreamshaper-8-lcm](https://huggingface.co./Lykon/dreamshaper-8-lcm) |
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- Offline DM: [dreamshaper-8](https://huggingface.co./Lykon/dreamshaper-8) |
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- VAE: [TAESD](https://huggingface.co./madebyollin/taesd) |
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Similar to SDXS-512-0.9, since our image decoder is not compatible with diffusers, we use TAESD. |
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Currently, our pull request has been merged in to reduce the gap between TAESD and our image decoder. |
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In the next diffusers release update, we may replace the image decoder. |
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## Diffusers Usage |
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```python |
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import torch |
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from diffusers import StableDiffusionPipeline, AutoencoderKL |
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repo = "IDKiro/sdxs-512-dreamshaper" |
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seed = 42 |
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weight_type = torch.float16 # or float32 |
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# Load model. |
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pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type) |
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pipe.to("cuda") |
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prompt = "a close-up picture of an old man standing in the rain" |
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# Ensure using 1 inference step and CFG set to 0. |
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image = pipe( |
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prompt, |
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num_inference_steps=1, |
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guidance_scale=0, |
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generator=torch.Generator(device="cuda").manual_seed(seed) |
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).images[0] |
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image.save("output.png") |
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``` |
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## Cite Our Work |
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``` |
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@article{song2024sdxs, |
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author = {Yuda Song, Zehao Sun, Xuanwu Yin}, |
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title = {SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions}, |
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journal = {arxiv}, |
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year = {2024}, |
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} |
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``` |
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