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--- |
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license: other |
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license_name: flux-1-dev-non-commercial-license |
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license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/resolve/main/LICENSE.md |
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base_model: |
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- black-forest-labs/FLUX.1-dev |
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pipeline_tag: text-to-image |
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library_name: diffusers |
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tags: |
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- flux |
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- text-to-image |
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--- |
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# Flux.1 Lite |
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We are thrilled to announce the alpha release of Flux.1 Lite, an 8B parameter transformer model distilled from the FLUX.1-dev model. This version uses 7 GB less RAM and runs 23% faster while maintaining the same precision (bfloat16) as the original model. |
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## Text-to-Image |
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Flux.1 Lite is ready to unleash your creativity! For the best results, we strongly **recommend using a `guidance_scale` of 3.5 and setting `n_steps` between 22 and 30**. |
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```python |
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import torch |
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from diffusers import FluxPipeline |
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base_model_id = "Freepik/flux.1-lite-8B-alpha" |
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torch_dtype = torch.bfloat16 |
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device = "cuda" |
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# Load the pipe |
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model_id = "Freepik/flux.1-lite-8B-alpha" |
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pipe = FluxPipeline.from_pretrained( |
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model_id, torch_dtype=torch_dtype |
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).to(device) |
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# Inference |
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prompt = "A close-up image of a green alien with fluorescent skin in the middle of a dark purple forest" |
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guidance_scale = 3.5 # Keep guidance_scale at 3.5 |
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n_steps = 28 |
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seed = 11 |
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with torch.inference_mode(): |
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image = pipe( |
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prompt=prompt, |
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generator=torch.Generator(device="cpu").manual_seed(seed), |
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num_inference_steps=n_steps, |
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guidance_scale=guidance_scale, |
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height=1024, |
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width=1024, |
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).images[0] |
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image.save("output.png") |
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``` |
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## Motivation |
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Inspired by [Ostris](https://ostris.com/2024/09/07/skipping-flux-1-dev-blocks/) findings, we analyzed the mean squared error (MSE) between the input and output of each block to quantify their contribution to the final result, revealing significant variability. |
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As Ostris pointed out, not all blocks contribute equally. While skipping just one of the early MMDIT blocks can significantly impact model performance, skipping the rest of the blocks does not have a significant impact over the final image quality. |
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Furthermore, as displayed in the following image, only when you skip one of the first MMDIT blocks, the performance of the model severely impacts the model's performance. |
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## Future work |
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Stay tuned! Our goal is to distill FLUX.1-dev further until it can run smoothly on 24 GB consumer-grade GPU cards, maintaining its original precision (bfloat16), and running even faster, making high-quality AI models accessible to everyone. |
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## ComfyUI |
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We've also crafted a ComfyUI workflow to make using Flux.1 Lite even more seamless! Find it in `comfy/flux.1-lite_workflow.json`. |
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The safetensors checkpoint is available here: [flux.1-lite-8B-alpha.safetensors](flux.1-lite-8B-alpha.safetensors) |
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## Try it out at Freepik! |
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Our [AI generator](https://www.freepik.com/pikaso/ai-image-generator) is now powered by Flux.1 Lite! |
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## 🔥 News 🔥 |
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* Oct 23, 2024. Alpha 8B checkpoint is publicly available on [HuggingFace Repo](https://huggingface.co/Freepik/flux.1-lite-8B-alpha). |
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## Citation |
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If you find our work helpful, please cite it! |
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```bibtex |
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@article{flux1-lite, |
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title={Flux.1 Lite: Distilling Flux1.dev for Efficient Text-to-Image Generation}, |
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author={Daniel Verdú, Javier Martín}, |
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email={[email protected], [email protected]}, |
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year={2024}, |
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} |
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``` |
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