Update README.md

README.md

@@ -1,3 +1,68 @@
 ---
-license: openrail
+license: openrail++
+tags:
+- text-to-image
+- stable-diffusion
+library_name: diffusers
+inference: false
 ---
+
+# SDXS-512-DreamShaper
+
+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.
+
+SDXS-512-DreamShaper is the version we trained specifically for share. 
+The model is trained without focusing on FID, and sacrifices diversity for consistent image generation quality. 
+In order to avoid some possible risks, the SDXS-512-1.0 and SDXS-1024-1.0 will not be available shortly. 
+Watch [our repo](https://github.com/IDKiro/sdxs) for any updates.
+
+Model Information:
+- Teacher DM: [dreamshaper-8-lcm](https://huggingface.co/Lykon/dreamshaper-8-lcm)
+- Offline DM: [dreamshaper-8](https://huggingface.co/Lykon/dreamshaper-8)
+- VAE: [TAESD](https://huggingface.co/madebyollin/taesd)
+
+Similar to SDXS-512-0.9, since our image decoder is not compatible with diffusers, we use TAESD. 
+Currently, our pull request has been merged in to reduce the gap between TAESD and our proprietary image decoder. 
+In the next diffusers release update, we may replace the image decoder.
+
+## Diffusers Usage
+
+![](output.png)
+
+```python
+import torch
+from diffusers import StableDiffusionPipeline, AutoencoderKL
+
+repo = "IDKiro/sdxs-512-dreamshaper"
+seed = 42
+weight_type = torch.float16     # or float32
+
+# Load model.
+pipe = StableDiffusionPipeline.from_pretrained(repo, torch_dtype=weight_type)
+pipe.to("cuda")
+
+prompt = "a close-up picture of an old man standing in the rain"
+
+# Ensure using 1 inference step and CFG set to 0.
+image = pipe(
+    prompt, 
+    num_inference_steps=1, 
+    guidance_scale=0,
+    generator=torch.Generator(device="cuda").manual_seed(seed)
+).images[0]
+
+image.save("output.png")
+```
+
+## Cite Our Work
+
+```
+@article{song2024sdxs,
+  author    = {Yuda Song, Zehao Sun, Xuanwu Yin},
+  title     = {SDXS: Real-Time One-Step Latent Diffusion Models with Image Conditions},
+  journal   = {arxiv},
+  year      = {2024},
+}
+```