Update README.md

README.md

@@ -1,4 +1,40 @@
-```py
+---
+library_name: diffusers
+base_model: stabilityai/stable-diffusion-xl-base-1.0
+tags:
+- lora
+- text-to-image
+license: openrail++
+inference: false
+---
+
+# Latent Consistency Model (LCM): SDXL
+
+Latent Consistency Model (LCM) was proposed in [Latent Consistency Models: Synthesizing High-Resolution Images with Few-Step Inference](https://arxiv.org/abs/2310.04378) 
+by *Simian Luo, Yiqin Tan et al.* and [Simian Luo](https://huggingface.co/SimianLuo), [Suraj Patil](https://huggingface.co/valhalla), and [Daniel Gu](https://huggingface.co/dg845)
+succesfully applied the same approach to create LCM for SDXL.
+
+This checkpoint is a LCM distilled version of [`stable-diffusion-xl-base-1.0`](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) that allows
+to reduce the number of inference steps to only between **2 - 8 steps**.
+
+
+## Usage
+
+LCM SDXL is supported in 🤗 Hugging Face Diffusers library from version v0.23.0 onwards. To run the model, first 
+install the latest version of the Diffusers library as well as `peft`, `accelerate` and `transformers`.
+audio dataset from the Hugging Face Hub:
+
+```bash
+pip install --upgrade pip
+pip install --upgrade diffusers transformers accelerate peft
+```
+
+### Text-to-Image
+
+The adapter can be loaded with it's base model `stabilityai/stable-diffusion-xl-base-1.0`. Next, the scheduler needs to be changed to [`LCMScheduler`](https://huggingface.co/docs/diffusers/v0.22.3/en/api/schedulers/lcm#diffusers.LCMScheduler) and we can reduce the number of inference steps to just 2 to 8 steps.
+Please make sure to either disable `guidance_scale` or use values between 1.0 and 2.0.
+
+```python
 from diffusers import UNet2DConditionModel, DiffusionPipeline, LCMScheduler
 
 unet = UNet2DConditionModel.from_pretrained("latent-consistency/lcm-sdxl", torch_dtype=torch.float16, variant="fp16")
@@ -10,4 +46,30 @@ pipe.set_progress_bar_config(disable=None)
 prompt = "a red car standing on the side of the street"
 
 image = pipe(prompt, num_inference_steps=4, guidance_scale=8.0).images[0]
-```
+```
+
+![](./image.png)
+
+### Image-to-Image
+
+Works as well! TODO docs
+
+### Inpainting
+
+Works as well! TODO docs
+
+### ControlNet
+
+Works as well! TODO docs
+
+### T2I Adapter
+
+Works as well! TODO docs
+
+## Speed Benchmark
+
+TODO
+
+## Training
+
+TODO