Update README.md

README.md

@@ -1,3 +1,49 @@
----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+library_name: diffusers
+base_model: runwayml/stable-diffusion-v1-5
+tags:
+- lora
+- text-to-image
+---
+# ⚡ FlashDiffusion: FlashSD ⚡
+
+<p align="center">
+   <img style="width:400px;" src="images/hf_grid.png">
+</p>
+
+Flash Diffusion is a diffusion distillation method proposed in [ADD ARXIV]() *by Clément Chadebec, Onur Tasar and Benjamin Aubin.*
+This model is a 26.4M LoRA distilled version of SD1.5 model. The main purpose of this model is to reproduce the main results of the paper.
+
+# How to use?
+
+The model can be used using the `StableDiffusionPipeline` from `diffusers` library directly. It can allow reducing the number of required sampling steps to **2-4 steps**.
+
+```python
+from diffusers import StableDiffusionPipeline, LCMScheduler
+
+adapter_id = "jasperai/flash-sd"
+
+pipe = StableDiffusionPipeline.from_pretrained(
+  "runwayml/stable-diffusion-v1-5",
+  use_safetensors=True,
+)
+
+pipe.scheduler = LCMScheduler.from_pretrained(
+  "runwayml/stable-diffusion-v1-5",
+  subfolder="scheduler",
+  timestep_spacing="trailing",
+)
+pipe.to("cuda")
+
+# Fuse and load LoRA weights
+pipe.load_lora_weights(adapter_id)
+pipe.fuse_lora()
+
+prompt = "A raccoon reading a book in a lush forest."
+
+image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
+```
+<p align="center">
+   <img style="width:400px;" src="images/raccoon.png">
+</p>