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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-512-0.9 |
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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. 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). We open-source the model as part of the research. |
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SDXS-512-0.9 is a **old version** of SDXS-512. For some reasons, we are only releasing this version for the time being, and will gradually release other versions. |
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**In order to avoid some possible risks (especially commercial and copyright risks), the SDXS-512-1.0 and SDXS-1024-1.0 will not be available shortly, and as an alternative we will provide new versions with the same generation quality. We expect to have a new version of SDXS-512 available within a week, with almost the same generation quality as SDXS-512-1.0.** |
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Model Information: |
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- Teacher DM: [SD Turbo](https://huggingface.co/stabilityai/sd-turbo) |
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- Offline DM: [SD v2.1 base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base) |
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- VAE: [TAESD](https://huggingface.co/madebyollin/taesd) |
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The main differences between this model and version 1.0 are in three aspects: |
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1. This version employs TAESD, which may produce low-quality images when weight_type is float16. Our image decoder is not compatible with the current version of diffusers, so it will not be provided now. |
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2. This version did not perform the LoRA-GAN finetune mentioned in the implementation details section, which may result in slightly inferior image details. |
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3. This version replaces self-attention with cross-attention in the highest resolution stages, which introduces minimal overhead compared to directly removing them. |
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There is a third-party [Demo](https://huggingface.co/spaces/ameerazam08/SDXS-GPU-Demo) from @ameerazam08. We'll provide an official demo when 1.0 is officially released, which hopefully won't be long. |
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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-512-0.9" |
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seed = 42 |
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weight_type = torch.float32 # or float16 |
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# Load model. |
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pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type) |
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# use original VAE |
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# pipe.vae = AutoencoderKL.from_pretrained("IDKiro/sdxs-512-0.9/vae_large") |
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pipe.to("cuda") |
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prompt = "portrait photo of a girl, photograph, highly detailed face, depth of field, moody light, golden hour" |
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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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