ComfyUI Qwen Rectified Flow Tools

This custom node pack for ComfyUI provides a suite of advanced tools for performing "invert-and-resample" workflows with Rectified Flow models like Qwen-VL. It allows you to take an existing image, convert it to a noisy latent using a guided inversion process, and then use that latent as a starting point to generate creative, high-quality variations.

The pack provides both the individual building blocks for maximum flexibility and a final "convenience node" that encapsulates the optimal workflow for ease of use.

Features

• Guided Latent Inversion: Go beyond simple noise addition. Use the model's own understanding of the image (its velocity field) to intelligently re-noise a latent.
• Creative Controls: Deterministically amplify the inversion process or stochastically perturb it with seeded noise to create unique variations.
• Stability Tools: Includes specialized nodes to solve common artifacts in advanced workflows, such as VAE "waffle patterns" and sampler "brown-out" collapse.
• Modular and Combined Nodes: Use the individual components to experiment, or use the final Latent Hybrid Inverter for a powerful, one-node solution.

Nodes Included

1. Qwen Rectified Flow Inverter

This is the core node of the pack. It takes a clean latent and "re-noises" it by running the Rectified Flow ODE integration in reverse.

• Key Inputs:
  • inversion_strength: How far to re-noise the image (0.0 = no change, 1.0 = full noise).
  • velocity_amplification: Deterministically speeds up or slows down the inversion.
  • velocity_perturb_strength: Adds seeded random chaos to the inversion path for creative variations.
  • normalize_output: Crucial for stability. Fixes the statistics of the output latent to prevent sampler errors.

2. Latent Gaussian Blur

A utility node to fix high-frequency artifacts in latents. Its primary purpose is to eliminate the "waffle pattern" that can appear in img2img workflows.

• Key Inputs:
  • sigma: The strength of the blur. A small value (0.5-1.5) is usually enough.
  • blur_mode: Choose between Spatial Only (safe, recommended) or the experimental Spatial and Channel for artistic effects.

3. Add Latent Noise (Seeded)

A simple but powerful utility to add a configurable amount of seeded Gaussian noise to any latent. Useful for re-introducing "perfect" noise to a latent that has an unnatural distribution.

• Key Inputs:
  • strength: How much noise to add, relative to the latent's existing signal strength.
  • seed: Makes the random noise pattern repeatable.

4. Forward Diffusion (Add Scheduled Noise)

This node performs the "natural" noising process that diffusion models are trained on. It takes a clean latent and adds the mathematically "perfect" amount of noise for a given step in the schedule. This creates a stable "anchor" latent.

• Key Inputs:
  • noise_strength: The target noise level (0.0 to 1.0). Should match your KSampler's denoise value.

5. Latent Hybrid Inverter (Qwen)

The final convenience node that combines the best practices discovered into a single, powerful tool. It internally performs the creative inversion, creates a stable anchor latent via forward diffusion, and blends them together to produce an ideal starting latent for resampling.

• Key Inputs:
  • strength: The target noise level for both passes.
  • blend_factor: Controls the mix between the creative inverter latent (1.0) and the stable anchor latent (0.0). A 50/50 blend (0.5) is recommended.
  • Contains all the creative controls from the inverter and separate seeds for each internal process.

Workflows

Basic Invert and Resample (Img2Img)

This workflow demonstrates the basic principle but may be prone to artifacts at high denoise strengths.

1. VAE Encode your source image.
2. Connect the latent to the Qwen Rectified Flow Inverter.
   • Set inversion_strength to 0.8.
3. Connect the output to a KSampler.
   • Crucially, set denoise to match the inversion_strength (0.8).
4. VAE Decode the result.

Advanced: The "Anchor" Workflow (Recommended for High Quality)

This is the optimal workflow that solves both the "waffle pattern" and "brown-out" artifacts, allowing for high-strength, high-quality variations.

1. VAE Encode the source image.
2. Create the "Creative" Latent:
   • Feed the clean latent into the Qwen Rectified Flow Inverter. Set your desired inversion_strength, amplification, perturbation, etc.
   • (Optional but Recommended) Feed the result into a Latent Gaussian Blur with a low sigma (e.g., 1.0) to remove VAE artifacts.
3. Create the "Anchor" Latent:
   • Feed the original clean latent from the VAE Encode into the Forward Diffusion (Add Scheduled Noise) node.
   • Set its noise_strength to exactly match the inversion_strength from the inverter. Use a different seed.
4. Blend the Latents:
   • Use a Latent Blend or Latent Slerp node to mix the "Creative" and "Anchor" latents. A blend factor of 0.5 is a great starting point.
5. Sample from the Blend:
   • Feed the blended latent into a KSampler (or KSampler Advanced with add_noise disabled).
   • Set the KSampler's denoise value to exactly match the strength used in the previous steps.
6. VAE Decode the final image.

(Note: A diagram of the advanced workflow would be ideal here)

Ultimate Convenience: The Hybrid Inverter Workflow

This workflow uses the single convenience node to achieve the same result as the advanced workflow with fewer nodes.

1. VAE Encode the source image.
2. Connect the latent to the Latent Hybrid Inverter.
   • Set the desired strength (e.g., 0.75).
   • Set the blend_factor (e.g., 0.5).
   • Configure seeds and creative parameters.
3. Connect the output to a KSampler.
   • Set the denoise to match the strength from the hybrid inverter.
4. VAE Decode the result.

Installation

1. Clone or download this repository.
2. Place the entire folder (e.g., ComfyUI-Qwen-Inverter-Pack) into the ComfyUI/custom_nodes/ directory.
3. Restart ComfyUI. The nodes will be available under the Qwen/Sampling and Latent categories.