https://github.com/supSugam/ComfyUI-FlowMatch-Advanced
A custom node that replicates the exact resolution-aware Flow Matching shifts for Flux, Z-Image, and Qwen for "better" results.
https://github.com/supSugam/ComfyUI-FlowMatch-Advanced
ai-toolkit comfyui comfyui-custom-node comfyui-nodes flowmatch flux sampler scheduler z-image-turbo
Last synced: 3 months ago
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A custom node that replicates the exact resolution-aware Flow Matching shifts for Flux, Z-Image, and Qwen for "better" results.
- Host: GitHub
- URL: https://github.com/supSugam/ComfyUI-FlowMatch-Advanced
- Owner: supSugam
- Created: 2026-02-15T10:00:16.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2026-02-25T06:55:20.000Z (4 months ago)
- Last Synced: 2026-03-05T04:29:08.733Z (3 months ago)
- Topics: ai-toolkit, comfyui, comfyui-custom-node, comfyui-nodes, flowmatch, flux, sampler, scheduler, z-image-turbo
- Language: Python
- Homepage:
- Size: 11.7 KB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 3
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-comfyui - **ComfyUI-FlowMatch-Advanced** - matching behavior for Flux, Qwen-Image, and Z-Image. (Workflows pushed in 7 days)
README
# ComfyUI-FlowMatch-Advanced
Custom nodes to make ComfyUI sampling closer to `ai-toolkit` flow-matching behavior for **Flux**, **Qwen-Image**, and **Z-Image**.
## Dependencies
- This custom node has a `requirements.txt` and needs:
- ai-toolkit-matched `diffusers` pinned to commit `8600b4c10d67b0ce200f664204358747bd53c775`
- If you install manually, run in your ComfyUI Python environment:
- `pip install -r custom_nodes/ComfyUI-FlowMatch-Advanced/requirements.txt`
## Node
- `FlowMatch Sampler (ai-toolkit exact)`
Single all-in-one sampler node: patches model sampling, builds ai-toolkit flowmatch sigmas, and runs sampling directly.
## Model Presets
- `flux`: dynamic shift (`base_shift=0.5`, `max_shift=1.15`, `max_seq_len=4096`)
- `qwen`: dynamic shift (`base_shift=0.5`, `max_shift=0.9`, `max_seq_len=8192`)
- `z-image`: static shift (`shift=3.0`)
## Workflow
1. Load your model and LoRA.
2. Use `FlowMatch Sampler (ai-toolkit exact)`:
- `sampler_name=euler` (or `res_multistep` if your training setup uses it)
- defaults match this repo's `config.yaml` sample block (`model_type=z-image`, `seed=42`, `steps=8`, `guidance_scale=1`, `width=768`, `height=1024`)
- switch `model_type` only when sampling non Z-Image models
- `width/height` must match your generation resolution
3. Decode the returned latent with VAE.
## Notes
- This node is the only supported path in this repo.
- `force_aitk_timesteps=true` uses `1.0 -> 1.0/steps` timesteps before shift math, matching ai-toolkit behavior more closely.
- When available, the node attempts to use ai-toolkit's own scheduler backend first and falls back to local formulas automatically.