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DFloat11
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FLUX.1-Kontext-dev-DF11

Text-to-Image
Diffusers
Safetensors
llama
dfloat11
df11
lossless compression
70% size, 100% accuracy
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  1. README.md +83 -0
  2. config.json +36 -0
  3. diffusion_pytorch_model.safetensors +3 -0
  4. single_transformer_blocks_0.safetensors +3 -0
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README.md ADDED
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+ ---
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+ base_model:
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+ - black-forest-labs/FLUX.1-Kontext-dev
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+ base_model_relation: quantized
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+ pipeline_tag: text-to-image
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+ tags:
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+ - dfloat11
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+ - df11
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+ - lossless compression
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+ - 70% size, 100% accuracy
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+ ---
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+
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+ # DFloat11 Compressed Model: `black-forest-labs/FLUX.1-Kontext-dev`
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+
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+ This is a **DFloat11 losslessly compressed** version of the original `black-forest-labs/FLUX.1-Kontext-dev` model. It reduces model size by **32%** compared to the original BFloat16 model, while maintaining **bit-identical outputs** and supporting **efficient GPU inference**.
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+
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+ 🔥🔥🔥 Thanks to DFloat11 compression, FLUX.1-Kontext-dev can now run smoothly on a single 24GB GPU without any quality loss. 🔥🔥🔥
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+
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+ ### 📊 Performance Comparison
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+
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+ | Metric | FLUX.1-Kontext-dev (BFloat16) | FLUX.1-Kontext-dev (DFloat11) |
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+ | ----------------------------------------------- | ------------------- | ------------------- |
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+ | Model Size | 23.80 GB | 16.33 GB |
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+ | Peak GPU Memory<br>(1024×1024 image generation) | 24.86 GB | 18.12 GB |
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+ | Generation Time<br>(A100 GPU) | 72 seconds | 83 seconds |
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+
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+ ### 🔧 How to Use
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+
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+ 1. Install or upgrade the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*:
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+
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+ ```bash
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+ pip install -U dfloat11[cuda12]
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+ # or if you have CUDA version 11:
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+ # pip install -U dfloat11[cuda11]
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+ ```
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+
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+ 2. Install diffusers from the main branch until future stable release.
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+
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+ ```bash
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+ pip install git+https://github.com/huggingface/diffusers.git
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+ ```
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+
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+ 3. To use the DFloat11 model, run the following example code in Python:
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+
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+ ```python
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+ import torch
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+ from diffusers import FluxKontextPipeline
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+ from diffusers.utils import load_image
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+ from dfloat11 import DFloat11Model
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+
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+ pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16)
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+ DFloat11Model.from_pretrained(
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+ "DFloat11/FLUX.1-Kontext-dev-DF11",
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+ device="cpu",
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+ bfloat16_model=pipe.transformer,
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+ )
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+ pipe.enable_model_cpu_offload()
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+
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+ input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png")
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+
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+ image = pipe(
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+ image=input_image,
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+ prompt="Add a hat to the cat",
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+ guidance_scale=2.5,
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+ ).images[0]
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+
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+ image.save("kontext.png")
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+ ```
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+
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+
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+ ### 🔍 How It Works
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+
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+ We apply **Huffman coding** to losslessly compress the exponent bits of BFloat16 model weights, which are highly compressible (their 8 bits carry only ~2.6 bits of actual information). To enable fast inference, we implement a highly efficient CUDA kernel that performs on-the-fly weight decompression directly on the GPU.
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+
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+ The result is a model that is **~32% smaller**, delivers **bit-identical outputs**, and achieves performance **comparable to the original** BFloat16 model.
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+
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+ Learn more in our [research paper](https://arxiv.org/abs/2504.11651).
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+
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+ ### 📄 Learn More
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+
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+ * **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651)
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+ * **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11)
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+ * **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11)
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+ "model_type": "llama"
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