Extensions/ComfyUI Qwen-Image DFloat11 Nodes
ComfyUI Extension

ComfyUI Qwen-Image DFloat11 Nodes

ComfyUI custom nodes for the DFloat11 compressed Qwen-Image model. This package provides efficient image generation with reduced memory usage through DFloat11…

By verIdyia·Created about a year ago·Updated 5 months ago· 11
verIdyia/ComfyUI-Qwen-Image-DF11
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ComfyUI Qwen-Image DFloat11 Nodes

ComfyUI custom nodes for the DFloat11 compressed Qwen-Image model. Generate high-quality images with 32% less memory through lossless DFloat11 compression — bit-identical outputs to the original model.

Nodes

| Node | Description | |------|-------------| | DFloat11 Qwen-Image Loader | Load the compressed model with optional CPU offloading | | Qwen-Image Text Encode | Process text prompts with optional quality-boosting magic prompt | | Qwen-Image Sampler | Generate images with full parameter control (steps, CFG, size, batch) | | Qwen-Image Aspect Ratio | Quick preset aspect ratios (1:1, 16:9, 9:16, 4:3, 3:4, 3:2, 2:3, 21:9) | | Qwen-Image Preset (All-in-One) | Single-node generation with quality presets (draft/fast/balanced/quality/max) |

Installation

1. Install Custom Nodes

Via ComfyUI Manager (recommended): Search for "Qwen-Image-DF11" in ComfyUI Manager and install.

Manual:

cd ComfyUI/custom_nodes
git clone https://github.com/verIdyia/ComfyUI-Qwen-Image-DF11.git
cd ComfyUI-Qwen-Image-DF11
pip install -r requirements.txt

2. Download Models

Set up the model directory structure in ComfyUI/models/diffusion_models/:

Qwen-Image/
├── model_index.json              # From Qwen/Qwen-Image
├── transformer/
│   ├── config.json               # From Qwen/Qwen-Image
│   └── diffusion_pytorch_model.safetensors  # From DFloat11/Qwen-Image-DF11
├── text_encoder/                 # From Qwen/Qwen-Image
├── vae/                          # From Qwen/Qwen-Image
├── tokenizer/                    # From Qwen/Qwen-Image
└── scheduler/                    # From Qwen/Qwen-Image

Step 1 — Download the base model:

cd ComfyUI/models/diffusion_models
git lfs clone https://huggingface.co/Qwen/Qwen-Image

Step 2 — Replace the transformer with the DFloat11 version:

cd Qwen-Image/transformer
mv diffusion_pytorch_model.safetensors diffusion_pytorch_model.safetensors.backup
wget https://huggingface.co/DFloat11/Qwen-Image-DF11/resolve/main/diffusion_pytorch_model.safetensors

Step 3 — Merge config files (required for DFloat11 loading):

import json
from huggingface_hub import hf_hub_download

# Download DFloat11 config
df11_config_path = hf_hub_download("DFloat11/Qwen-Image-DF11", "config.json")
with open(df11_config_path) as f:
    df11_config = json.load(f)

# Merge with existing Qwen transformer config
config_path = "ComfyUI/models/diffusion_models/Qwen-Image/transformer/config.json"
with open(config_path) as f:
    qwen_config = json.load(f)

merged = {**qwen_config, **df11_config}
with open(config_path, "w") as f:
    json.dump(merged, f, indent=2)

3. Restart ComfyUI

Memory Requirements

VRAM (GPU)

| Mode | VRAM Required | Peak VRAM | |------|--------------|-----------| | CPU Offload ON | 16 GB+ | ~16.7 GB | | CPU Offload OFF | 32 GB+ | ~29.7 GB |

System RAM

| | Minimum | Recommended | |---|---------|-------------| | RAM | 96 GB | 128 GB |

Important: During model loading, the system needs ~68 GB of RAM simultaneously (41 GB model shell + 27 GB compressed weights). 64 GB is insufficient and will crash on Windows. 96 GB works with headroom; 128 GB is ideal. Linux with 64 GB may work due to mmap overcommit, but is not guaranteed.

  • Qwen-Image: 20.4B parameter model (bf16 = 41 GB)
  • DFloat11 compressed transformer: 28.42 GB (32% smaller than original)
  • Text encoder (Qwen2.5-VL-7B): ~16 GB additional
  • Lossless compression — produces bit-identical outputs

Preset Quality Modes

| Preset | Steps | CFG | Use Case | |--------|-------|-----|----------| | draft | 12 | 3.0 | Quick previews | | fast | 20 | 3.5 | Rapid iteration | | balanced | 50 | 4.0 | General use | | quality | 80 | 4.5 | High quality | | max_quality | 100 | 5.0 | Best possible output |

Requirements

  • System RAM: 64 GB minimum, 128 GB recommended
  • GPU: NVIDIA with 16+ GB VRAM (32+ GB without CPU offload)
  • CUDA: 12.1+
  • Python: 3.10+
  • Disk: ~43 GB for model files
  • diffusers >= 0.35.0, transformers >= 4.51.3, dfloat11[cuda12]

License

Apache License 2.0 — see LICENSE.