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README.md
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---
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base_model:
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- Wan-AI/Wan2.2-T2V-A14B
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base_model_relation: quantized
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pipeline_tag: text-to-video
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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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# DFloat11 Compressed Model: `Wan-AI/Wan2.2-T2V-A14B`
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This is a **DFloat11 losslessly compressed** version of the original `Wan-AI/Wan2.2-T2V-A14B` 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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🔥🔥🔥 Thanks to DFloat11 compression, `Wan-AI/Wan2.2-T2V-A14B` can now generate a 5-second 720P video on a single 24GB GPU, while maintaining full model quality. 🔥🔥🔥
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### 📊 Performance Comparison
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| Model | Model Size | Peak GPU Memory (5-second 720P generation) | Generation Time (A100 GPU) |
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|----------------------------------------------------|--------------------|----------------------------------------------|----------------------------|
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| Wan-AI/Wan2.2-T2V-A14B (BFloat16) | ~56 GB | O.O.M. | - |
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| Wan-AI/Wan2.2-T2V-A14B (DFloat11) | 19.46 + 19.39 GB | 41.06 GB | 42 minutes |
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| Wan-AI/Wan2.2-T2V-A14B (DFloat11 + CPU Offloading) | 19.46 + 19.39 GB | 22.49 GB | 44 minutes |
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### 🔍 How It Works
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We apply Huffman coding to the exponent bits of BFloat16 model weights, which are highly compressible. We leverage hardware-aware algorithmic designs to enable highly efficient, on-the-fly weight decompression directly on the GPU. Find out more in our [research paper](https://arxiv.org/abs/2504.11651).
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### 🔧 How to Use
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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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```bash
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pip install -U dfloat11[cuda12]
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```
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2. Install the latest `diffusers` package from source:
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```bash
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pip install git+https://github.com/huggingface/diffusers
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```
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3. Save the following code to a Python file `t2v.py`:
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```python
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import time
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import torch
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import argparse
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from diffusers import WanPipeline, AutoencoderKLWan
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from diffusers.utils import export_to_video
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from dfloat11 import DFloat11Model
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# Set up argument parser
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parser = argparse.ArgumentParser(description='Run Wan2.2 T2V model with custom parameters')
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parser.add_argument('--prompt', type=str, default="A serene koi pond at night, with glowing lanterns reflecting on the rippling water. Ethereal fireflies dance above as cherry blossoms gently fall, creating a dreamlike atmosphere.",
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help='Text prompt for video generation')
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parser.add_argument('--negative_prompt', type=str, default="色调艳丽,过曝,静态,细节模糊不清,字幕,风格,作品,画作,画面,静止,整体发灰,最差质量,低质量,JPEG压缩残留,丑陋的,残缺的,多余的手指,画得不好的手部,画得不好的脸部,畸形的,毁容的,形态畸形的肢体,手指融合,静止不动的画面,杂乱的背景,三条腿,背景人很多,倒着走",
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help='Negative prompt for video generation')
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parser.add_argument('--width', type=int, default=1280, help='Width of output video')
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parser.add_argument('--height', type=int, default=720, help='Height of output video')
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parser.add_argument('--num_frames', type=int, default=81, help='Number of frames to generate')
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parser.add_argument('--guidance_scale', type=float, default=4.0, help='Guidance scale for first stage')
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parser.add_argument('--guidance_scale_2', type=float, default=3.0, help='Guidance scale for second stage')
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parser.add_argument('--num_inference_steps', type=int, default=40, help='Number of inference steps')
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parser.add_argument('--cpu_offload', action='store_true', help='Enable CPU offloading')
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parser.add_argument('--output', type=str, default='t2v_out.mp4', help='Output video file path')
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parser.add_argument('--fps', type=int, default=16, help='FPS of output video')
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args = parser.parse_args()
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# Initialize models
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vae = AutoencoderKLWan.from_pretrained("Wan-AI/Wan2.2-T2V-A14B-Diffusers", subfolder="vae", torch_dtype=torch.float32)
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pipe = WanPipeline.from_pretrained("Wan-AI/Wan2.2-T2V-A14B-Diffusers", vae=vae, torch_dtype=torch.bfloat16)
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# Load DFloat11 models
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DFloat11Model.from_pretrained(
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"DFloat11/Wan2.2-T2V-A14B-DF11",
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device="cpu",
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cpu_offload=args.cpu_offload,
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bfloat16_model=pipe.transformer,
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)
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DFloat11Model.from_pretrained(
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"DFloat11/Wan2.2-T2V-A14B-2-DF11",
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device="cpu",
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cpu_offload=args.cpu_offload,
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bfloat16_model=pipe.transformer_2,
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)
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pipe.enable_model_cpu_offload()
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start_time = time.time()
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# Generate video
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output = pipe(
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prompt=args.prompt,
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negative_prompt=args.negative_prompt,
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height=args.height,
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width=args.width,
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num_frames=args.num_frames,
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guidance_scale=args.guidance_scale,
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guidance_scale_2=args.guidance_scale_2,
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num_inference_steps=args.num_inference_steps,
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).frames[0]
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print(f"Time taken: {time.time() - start_time:.2f} seconds")
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export_to_video(output, args.output, fps=args.fps)
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# Print memory usage
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max_memory = torch.cuda.max_memory_allocated()
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print(f"Max memory: {max_memory / (1000 ** 3):.2f} GB")
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```
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4. To run without CPU offloading (40GB VRAM required):
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```bash
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python t2v.py
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```
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To run with CPU offloading (22.5GB VRAM required):
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```bash
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python t2v.py --cpu_offload
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```
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### 📄 Learn More
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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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