Update README.md

README.md

@@ -1,27 +1,81 @@
 ---
-library_name: peft
+library_name: diffusers
+base_model: runwayml/stable-diffusion-v1-5
 tags:
 - lora
+- text-to-image
+license: openrail++
+inference: false
 ---
 
-```py
-from diffusers import LCMScheduler, DiffusionPipeline
+# Latent Consistency Model (LCM) LoRA: SDv1-5
+
+Latent Consistency Model (LCM) LoRA was proposed in [LCM-LoRA: A universal Stable-Diffusion Acceleration Module](TODO:) 
+by *Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu et al.*
+
+It is a distilled consistency adapter for [`runwayml/stable-diffusion-v1-5`](https://huggingface.co/runwayml/stable-diffusion-v1-5) that allows
+to reduce the number of inference steps to only between **2 - 8 steps**.
+
+| Model                                                                      | Params / M | 
+|----------------------------------------------------------------------------|------------|
+| [lcm-lora-sdv1-5](https://huggingface.co/latent-consistency/lcm-lora-sdv1-5)   | 67.5       |
+| [**lcm-lora-ssd-1b**](https://huggingface.co/latent-consistency/lcm-lora-ssd-1b)   | **105**        |
+| [lcm-lora-sdxl](https://huggingface.co/latent-consistency/lcm-lora-sdxl) | 197M  |
+
+## Usage
+
+LCM-LoRA 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 SDv1-5 or deviratives. Here we use [`Lykon/dreamshaper-7`](https://huggingface.co/Lykon/dreamshaper-7). 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
 import torch
-import PIL.Image
-import requests
+from diffusers import LCMScheduler, AutoPipelineForText2Image
+
+model_id = "Lykon/dreamshaper-7"
+adapter_id = "latent-consistency/lcm-lora-sdv1-5"
 
-pipe = DiffusionPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16, variant="fp16")
+pipe = AutoPipelineForText2Image.from_pretrained(model_id, torch_dtype=torch.float16, variant="fp16")
 pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
+pipe.to("cuda")
 
 # load and fuse lcm lora
-pipe.load_lora_weights("latent-consistency/lcm-lora-ssd-1b")
+pipe.load_lora_weights(adapter_id)
 pipe.fuse_lora()
 
-pipe.to(device="cuda")
 
-prompt = "a red Porsche"
+prompt = "Self-portrait oil painting, a beautiful cyborg with golden hair, 8k"
+
+# disable guidance_scale by passing 0
+image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```
+
+### 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
+
+## Training
 
-torch.manual_seed(0)
-# guidance_scale=1.0 to disable CFG
-image = pipe(prompt=prompt, num_inference_steps=4, guidance_scale=1.0).images[0]
-```
+TODO