Update README.md

README.md

@@ -1,3 +1,67 @@
 ---
 license: mit
+language:
+- en
+pipeline_tag: text-to-image
+tags:
+- text-to-image
 ---
+
+# Latent Consistency Models
+
+Official Repository of the paper: *[Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378)*.
+
+Project Page: https://latent-consistency-models.github.io
+
+
+<p align="center">
+    <img src="teaser.png">
+</p>
+
+By distilling classifier-free guidance into the model's input, LCM can generate high-quality images in very short inference time. We compare the inference time at the setting of 768 x 768 resolution, CFG scale w=8, batchsize=4, using a A800 GPU. 
+
+<p align="center">
+    <img src="speed_fid.png">
+</p>
+
+## Usage
+
+You can try out Latency Consistency Models directly on:
+[![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/SimianLuo/Latent_Consistency_Model)
+
+To run the model yourself, you can leverage the 🧨 Diffusers library:
+1. Install the library:
+```
+pip install diffusers transformers accelerate
+```
+
+2. Run the model:
+```py
+from diffusers import DiffusionPipeline
+import torch
+
+pipe = DiffusionPipeline.from_pretrained("SimianLuo/LCM_Dreamshaper_v7", custom_pipeline="latent_consistency_txt2img")
+
+# To save GPU memory, torch.float16 can be used, but it may compromise image quality.
+pipe.to(torch_device="cuda", torch_dtype=torch.float32)
+
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# Can be set to 1~50 steps. LCM support fast inference even <= 4 steps. Recommend: 1~8 steps.
+num_inference_steps = 4 
+
+images = pipe(prompt=prompt, num_inference_steps=num_inference_steps, guidance_scale=8.0, lcm_origin_steps=50, output_type="pil", custom_revision=main).images
+```
+
+## BibTeX
+
+```bibtex
+@misc{luo2023latent,
+      title={Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference}, 
+      author={Simian Luo and Yiqin Tan and Longbo Huang and Jian Li and Hang Zhao},
+      year={2023},
+      eprint={2310.04378},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```