ComfyUI-FlowMatching-Inverter

ComfyUI-FlowMatching-Inverter
★ 7

反向-重采样Qwen-VL兼容潜变量反演可控图像变体
基于Rectified Flow（如Qwen-VL）的ComfyUI节点，通过引导反演把图像转换为带噪潜变量，再以该潜变量为起点生成高质量、可控的重采样变体，支持与Skoogeer-Noise联用。
💡 将已有图像反演为噪声潜变量并以其为起点生成高质量变体。
🍴 3 Forks💻 Python🔄 2026-01-01
📦
网盘下载
复制链接后前往夸克网盘下载
https://pan.quark.cn/s/2df45d172dc1
📦 requirements.txt
#
Dependencies
needed
to
run
the
test
suite;
these
dependencies
#
are
provided
by
ComfyUI
natively
when
the
node
is
used
within
#
ComfyUI's
environment.
pytest>=8.0.0,<9.0.0
torch>=2.1.0
📄 README

ComfyUI Qwen Rectified Flow Inverter

Custom nodes for ComfyUI that target rectified-flow models (for example, Qwen). This pack focuses on inversion and diagnostics; the latent/image/conditioning noise nodes live in Skoogeer-Noise (see ../Skoogeer-Noise).

Installation

  • Clone this repository into ComfyUI/custom_nodes/ComfyUI-QwenRectifiedFlowInverter.
  • (Optional but recommended) Also install Skoogeer-Noise into ComfyUI/custom_nodes/Skoogeer-Noise for the forward-diffusion/noise/conditioning helpers.
  • Restart ComfyUI.

    • Data types

    LATENT

    ComfyUI latents are dictionaries containing a "samples" tensor.

  • Common SD-style latents: samples has shape (B, C, H, W).
  • Video / flow-matching latents may be 5D: (B, C, T, H, W).

    • CONDITIONING

    ComfyUI represents prompt conditioning as a list of [embedding, metadata] entries. This pack reads the embedding tensor from positive[0][0] and uses it as the model context during inversion.

    Nodes

    Qwen Rectified Flow Inverter

    Runs the rectified-flow integration to produce a partially inverted (re-noised) latent. Supports deterministic velocity amplification, seeded stochastic perturbation, and optional output normalization.

  • Menu category: Qwen/Sampling
  • Returns: LATENT

    • Inputs

    | Field | Type | Default | Notes |

    |——|——|———|——|

    | model | MODEL | – | Diffusion model / UNet used to predict velocity. |

    | latent_image | LATENT | – | Source latent to invert. |

    | positive | CONDITIONING | – | Positive prompt conditioning used as context. |

    | seed | INT | 0 | Seed for the velocity perturbation noise. |

    | steps | INT | 20 | Integration budget; effective steps = int(steps * inversion_strength). |

    | inversion_strength | FLOAT | 0.5 | Fraction of the schedule to traverse (0 = no-op). |

    | velocity_amplification | FLOAT | 0.0 | Multiplies predicted velocity by (1 + velocity_amplification) each step. |

    | velocity_perturb_strength | FLOAT | 0.0 | Adds seeded noise to velocity (scaled by velocity std). |

    | internal_precision | enum | bfloat16 | Autocast dtype while running the UNet (force_float32 is most stable). |

    | normalize_output | enum | enable | When enabled, normalizes each batch item to mean 0 / std 1 at the end. |

    Notes

  • If you see NaN detected at step ..., increase precision (internal_precision = force_float32) and/or reduce velocity_amplification / velocity_perturb_strength.
  • normalize_output = enable is strongly recommended at higher inversion_strength.

    • Latent Hybrid Inverter (Qwen)

    Runs the inverter and an internal forward-diffusion anchor pass, then blends both latents via spherical interpolation (SLERP).

  • Menu category: Qwen/Sampling
  • Returns: LATENT

    • Inputs

    | Field | Type | Default | Notes |

    |——|——|———|——|

    | model | MODEL | – | Model used for both passes. |

    | latent_image | LATENT | – | Source latent. |

    | positive | CONDITIONING | – | Context used by the inverter pass. |

    | steps | INT | 20 | Schedule/integration steps. |

    | strength | FLOAT | 0.5 | Target noise level (also used as the forward-diffusion strength). |

    | blend_factor | FLOAT | 0.5 | 0 = 100% forward diffusion (stable), 1 = 100% inverter (creative). |

    | inverter_seed | INT | 0 | Seed for the inverter’s velocity perturbation. |

    | forward_diffusion_seed | INT | 1 | Seed for the forward-diffusion anchor noise. |

    | velocity_amplification | FLOAT | 1.0 | Passed through to the inverter node. |

    | velocity_perturb_strength | FLOAT | 0.2 | Passed through to the inverter node. |

    | internal_precision | enum | bfloat16 | Passed through to the inverter node. |

    | normalize_output | enum | enable | Passed through to the inverter node. |

    Memory Diagnostics (Pass-Through)

    Passes any input through unchanged while printing basic GPU memory statistics to the console.

  • Menu category: Qwen/Diagnostics
  • Returns: same as input

    • Inputs

    | Field | Type | Default | Notes |

    |——|——|———|——|

    | data | any | – | Input payload (any ComfyUI type). |

    | label | STRING | "" | Optional label included in the printed line. |

    | synchronize | enum | enable | When enabled, synchronizes GPU kernels before sampling memory. |

    Example workflows

    Manual composition (recommended with Skoogeer-Noise)

  • Encode an image to a latent.
  • Run Qwen Rectified Flow Inverter.
  • In Skoogeer-Noise, run Forward Diffusion (Add Scheduled Noise) with the same steps and noise_strength but a different seed (this gives an anchor latent samplers expect).
  • Blend the two latents (e.g. Latent Slerp) and sample with denoise matching the strength you used.

    • Hybrid node quickstart

  • Encode an image to a latent.
  • Run Latent Hybrid Inverter (Qwen).
  • Sample the output latent with denoise = strength.

    • Running the tests

    Run:

    ./run_tests.sh

    The script creates (or reuses) a .venv folder, installs the dependencies from requirements.txt, and launches pytest.