Custom nodes to make ComfyUI sampling closer to ai-toolkit flow-matching behavior for Flux, Qwen-Image, and Z-Image.

• 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

• FlowMatch Sampler (ai-toolkit exact)
  Single all-in-one sampler node: patches model sampling, builds ai-toolkit flowmatch sigmas, and runs sampling directly.

• 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)

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.

• 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.