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--- |
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license: cc-by-nc-4.0 |
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library_name: diffusers |
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base_model: runwayml/stable-diffusion-v1-5 |
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tags: |
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- lora |
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- text-to-image |
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inference: False |
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--- |
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# ⚡ FlashDiffusion: FlashSD ⚡ |
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Flash Diffusion is a diffusion distillation method proposed in [ADD ARXIV]() *by Clément Chadebec, Onur Tasar and Benjamin Aubin.* |
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This model is a **26.4M** LoRA distilled version of SD1.5 model that is able to generate images in **2-4 steps**. The main purpose of this model is to reproduce the main results of the paper. |
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<p align="center"> |
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<img style="width:700px;" src="images/hf_grid.png"> |
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</p> |
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# How to use? |
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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**. |
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```python |
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from diffusers import StableDiffusionPipeline, LCMScheduler |
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adapter_id = "jasperai/flash-sd" |
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pipe = StableDiffusionPipeline.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", |
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use_safetensors=True, |
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) |
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pipe.scheduler = LCMScheduler.from_pretrained( |
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"runwayml/stable-diffusion-v1-5", |
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subfolder="scheduler", |
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timestep_spacing="trailing", |
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) |
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pipe.to("cuda") |
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# Fuse and load LoRA weights |
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pipe.load_lora_weights(adapter_id) |
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pipe.fuse_lora() |
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prompt = "A raccoon reading a book in a lush forest." |
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image = pipe(prompt, num_inference_steps=4, guidance_scale=0).images[0] |
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``` |
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<p align="center"> |
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<img style="width:400px;" src="images/raccoon.png"> |
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</p> |
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# Training Details |
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The model was trained for 20k iterations on 2 H100 GPUs (representing approx. **13 hours** of training). Please refer to the [paper]() for further parameters details. |
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**Metrics on COCO 2017 validation set** |
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- FID-5k: 22.6 (2 NFE) / 22.5 (4 NFE) |
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- CLIP Score (ViT-g/14): 0.306 (2 NFE) / 0.311 (4 NFE) |
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**Metrics on COCO 2014 validation** |
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- FID-30k: 12.07 (2 NFE) |
