Update README.md

README.md

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 # ⚡ 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.
 
+
+<p align="center">
+   <img style="width:400px;" src="images/hf_grid.png">
+</p>
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 # 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**.
@@ -47,3 +49,22 @@ image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0]
 <p align="center">
    <img style="width:400px;" src="images/raccoon.png">
 </p>
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+# Training Details
+The model was trained for 20k iterations on 2 H100 GPUs (representing approx. **13 hours** of training).
+
+**Metrics on COCO 2017 validation set**
+
+- 2 steps:
+  - FID-5k: 22.6
+  - CLIP Score (ViT-g/14): 0.306
+  
+- 4 steps:
+  - FID-5k: 22.5
+  - CLIP Score (ViT-g/14):
+
+**Metrics on COCO 2014 validation**
+- 2 steps:
+  - FID-30k:
+- 4 steps:
+  - FID-30k: