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README.md

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+---
+base_model:
+  - black-forest-labs/FLUX.1-schnell
+base_model_relation: quantized
+pipeline_tag: text-to-image
+tags:
+- dfloat11
+- df11
+- lossless compression
+- 70% size, 100% accuracy
+---
+
+## DFloat11 Compressed Model: `black-forest-labs/FLUX.1-schnell`
+
+This is a **losslessly compressed** version of [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) using our custom **DFloat11** format. The outputs of this compressed model are **bit-for-bit identical** to the original BFloat16 model, while reducing GPU memory consumption by approximately **30%**.
+
+### 🔍 How It Works
+
+DFloat11 compresses model weights using **Huffman coding** of BFloat16 exponent bits, combined with **hardware-aware algorithmic designs** that enable efficient on-the-fly decompression directly on the GPU. During inference, the weights remain compressed in GPU memory and are **decompressed just before matrix multiplications**, then **immediately discarded after use** to minimize memory footprint.
+
+Key benefits:
+
+* **No CPU decompression or host-device data transfer** --- all operations are handled entirely on the GPU.
+* DFloat11 is **much faster than CPU-offloading approaches**, enabling practical deployment in memory-constrained environments.
+* The compression is **fully lossless**, guaranteeing that the model’s outputs are **bit-for-bit identical** to those of the original model.
+
+### 🔧 How to Use
+
+1. Install the DFloat11 pip package *(installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed)*:
+
+    ```bash
+    pip install dfloat11[cuda12]
+    # or if you have CUDA version 11:
+    # pip install dfloat11[cuda11]
+    ```
+
+2. To use the DFloat11 model, run the following example code in Python:
+    ```python
+    import torch
+    from diffusers import FluxPipeline
+    from dfloat11 import DFloat11Model
+
+    pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+    pipe.enable_model_cpu_offload()
+
+    DFloat11Model.from_pretrained('DFloat11/FLUX.1-schnell-DF11', device='cpu', bfloat16_model=pipe.transformer)
+
+    prompt = "A futuristic cityscape at sunset, with flying cars, neon lights, and reflective water canals"
+    image = pipe(
+        prompt,
+        guidance_scale=0.0,
+        num_inference_steps=4,
+        max_sequence_length=256,
+        generator=torch.Generator("cpu").manual_seed(0)
+    ).images[0]
+    image.save("flux-schnell.png")
+    ```
+
+### 📄 Learn More
+
+* **Paper**: [70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float](https://arxiv.org/abs/2504.11651)
+* **GitHub**: [https://github.com/LeanModels/DFloat11](https://github.com/LeanModels/DFloat11)
+* **HuggingFace**: [https://huggingface.co/DFloat11](https://huggingface.co/DFloat11)

config.json

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+{
+    "dfloat11_config": {
+      "bytes_per_thread": 8,
+      "pattern_dict": {
+        "transformer_blocks.\\d+": [
+          "norm1.linear",
+          "norm1_context.linear",
+          "attn.to_q",
+          "attn.to_k",
+          "attn.to_v",
+          "attn.add_k_proj",
+          "attn.add_v_proj",
+          "attn.add_q_proj",
+          "attn.to_out.0",
+          "attn.to_add_out",
+          "ff.net.0.proj",
+          "ff.net.2",
+          "ff_context.net.0.proj",
+          "ff_context.net.2"
+        ],
+        "single_transformer_blocks.\\d+": [
+            "norm.linear",
+            "proj_mlp",
+            "proj_out",
+            "attn.to_q",
+            "attn.to_k",
+            "attn.to_v"
+        ]
+      },
+      "threads_per_block": [
+        512
+      ],
+      "version": "0.2.0"
+    },
+    "model_type": "llama"
+  }
+  

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