DFloat11 / HiDream-I1-Full-DF11

DFloat11 Compressed Model: HiDream-ai/HiDream-I1-Full

This is a DFloat11 losslessly compressed version of the original HiDream-ai/HiDream-I1-Full model. It reduces model size by 30% compared to the original BFloat16 model, while maintaining bit-identical outputs and supporting efficient GPU inference .

🔥🔥🔥 Thanks to DFloat11 compression, HiDream-I1-Full can now run smoothly on a single 32GB GPU without any quality loss. 🔥🔥🔥

📊 Performance Comparison

🔧 How to Use

1. Install or upgrade the DFloat11 pip package (installs the CUDA kernel automatically; requires a CUDA-compatible GPU and PyTorch installed) :

   pip install -U dfloat11[cuda12]
   # or if you have CUDA version 11:
   # pip install -U dfloat11[cuda11]
   

2. Install or upgrade the diffusers library.

   pip install -U diffusers
   

3. To use the DFloat11 model, run the following example code in Python:

   import torch
   from transformers import AutoTokenizer
   from diffusers import HiDreamImagePipeline
   from dfloat11 import DFloat11Model
   
   tokenizer_4 = AutoTokenizer.from_pretrained("DFloat11/Llama-3.1-8B-Instruct-DF11")
   text_encoder_4 = DFloat11Model.from_pretrained("DFloat11/Llama-3.1-8B-Instruct-DF11", device="cpu")
   text_encoder_4.config.output_hidden_states = True
   text_encoder_4.config.output_attentions = True
   
   pipe = HiDreamImagePipeline.from_pretrained(
       "HiDream-ai/HiDream-I1-Full",
       tokenizer_4=tokenizer_4,
       text_encoder_4=text_encoder_4,
       torch_dtype=torch.bfloat16,
   )
   DFloat11Model.from_pretrained(
       "DFloat11/HiDream-I1-Full-DF11",
       device="cpu",
       bfloat16_model=pipe.transformer,
   )
   pipe.enable_model_cpu_offload()
   
   image = pipe(
       'A cat wearing a vintage astronaut suit, floating inside a spaceship and gazing out the window at Earth.',
       height=1024,
       width=1024,
       guidance_scale=5.0,
       num_inference_steps=50,
       generator=torch.Generator("cuda").manual_seed(0),
   ).images[0]
   image.save("output.png")
   

🔍 How It Works

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.

The result is a model that is ~30% smaller , delivers bit-identical outputs , and achieves performance comparable to the original BFloat16 model.

Learn more in our research paper .

📄 Learn More

• Paper : 70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float
• GitHub : https://github.com/LeanModels/DFloat11
• HuggingFace : https://huggingface.co/DFloat11