nodes/fblissjr-ComfyUI-QwenImageWanBridge

Files

T

Fred Bliss 4a091e9298 Fix coordinate handling and make object_ref optional

- Fixed generateTokens to properly handle different region types (bounding_box, polygon, object_reference)
- Added checkbox UI to control includeObjectRef for boxes/polygons
- Object references now correctly handle 2 coordinates instead of expecting 4
- Updated Python code to respect includeObjectRef flag from JavaScript
- Fixed coordinate format issues (x1,y1,x2,y2 for boxes, x,y pairs for polygons)
- Restored updateBasePromptWithTokens function for editable prompts
- Default behavior keeps object_ref (checkbox checked) for backward compatibility
- Without object_ref, generates pure spatial tokens like <|box_start|>0.1,0.2,0.3,0.4<|box_end|>

2025-09-06 09:33:32 -05:00

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Qwen-Image-Edit Workflow Examples

This directory contains example workflows demonstrating the Qwen-Image-Edit nodes.

🔴 CRITICAL: Understanding Latent Input Choice

The Most Important Decision in Image Editing

When doing image editing, you have TWO fundamentally different approaches based on what you connect to KSampler's latent_image input:

Option 1: VAE Encode Input Image → KSampler (Structure Preservation)

LoadImage → VAEEncode → KSampler.latent_image

Purpose: Keep composition, change details
Denoise: 0.3-0.7 (lower = more preservation)
Template: Use minimal_edit
Example: "Change car color to blue" - keeps exact car shape/position
When to use: Small edits, color changes, style tweaks

Option 2: Empty Latent → KSampler (Vision-Guided Generation)

EmptyLatentImage → KSampler.latent_image

Purpose: Complete transformation using vision understanding
Denoise: 0.9-1.0 (must be high - generating from scratch!)
Template: Use creative or default
Example: "Transform into cyberpunk style" - full reimagining
When to use: Major transformations, style changes, creative edits

KEY INSIGHT: The reference latents (passed via VAE in QwenVLTextEncoder) provide vision guidance through conditioning REGARDLESS of which method you choose. They don't provide structure - the latent input does!

Workflow Categories

0. Enhanced Workflow (NEW!)

qwen_enhanced_workflow.json - RECOMMENDED STARTING POINT
- All fixes from DiffSynth-Studio/Engine
- RoPE batch fix applied
- Template styles (creative/minimal_edit/photorealistic)
- Optimal resolution support
- Lightning LoRA 4-step generation
- Clear notes on VAE Encode vs Empty Latent choice

1. Basic Workflows

qwen_t2i_basic.json - Simple text-to-image generation
qwen_i2i_edit_basic.json - Basic image editing with vision tokens
qwen_simple_node.json - Using the simplified all-in-one node

2. Advanced Editing

qwen_autoregressive_edit.json - Sequential editing with context
qwen_entity_control.json - Regional generation with EliGen
qwen_prompt_interpolation.json - Semantic blending between prompts

3. Sampler Integration

qwen_sampler_wrapper.json - Using proper reference latents
qwen_sampler_advanced.json - Advanced sampling with entity masks
qwen_iterative_sampler.json - Progressive refinement

4. Helper Workflows

qwen_resolution_helper.json - Finding optimal dimensions
qwen_vae_normalizer.json - Applying proper VAE normalization
qwen_model_validator.json - Validating model configuration

5. Experimental

qwen_wan_bridge.json - Attempting Qwen→WAN (experimental)
qwen_controlnet.json - BlockWise ControlNet integration

Node Descriptions

Core Nodes

QwenImageEditTextEncoder

Replaces ComfyUI's broken Qwen text encoder
Handles vision tokens properly
Supports T2I, Edit, and Autoregressive modes
Template dropping for better quality

QwenImageEditUnified

All-in-one workflow node
Combines all features in single interface
Auto-detects mode based on inputs
Includes ControlNet support

QwenImageEditSimple

Simplified version for basic use
Auto mode detection
Minimal inputs required

Feature Nodes

QwenEliGenController

Entity-level generation control
Up to 4 entities with masks
Regional attention control

QwenPromptInterpolator

Learned semantic blending
Smooth transitions between prompts
Training capability for custom interpolations

QwenAutoregressiveEditor

Sequential editing chains
Context-aware generation
Edit history tracking

Helper Nodes

QwenImageResolutionHelper

Calculates optimal dimensions
Based on target pixel count
Ensures 32-pixel alignment

QwenVAENormalizer

Applies official normalization
16-channel mean/std values
Required for proper generation

QwenModelValidator

Verifies token IDs
Checks VAE configuration
Validates model dimensions

Quick Start

Load the Qwen-Image-Edit model
Use QwenImageEditSimple for basic generation
For editing: provide an image and use edit mode
For advanced features: use the unified node

Important Notes

Always use 16-channel VAE (not SD VAE)
Vision tokens only work in edit mode
Autoregressive editing builds on previous edits
Entity masks should match entity prompt count
Low denoise (0.3-0.5) preserves structure better

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