TiledWan ComfyUI Node Set

A custom node set for ComfyUI that provides tiled processing capabilities.

Installation

1. Clone this repository into your ComfyUI custom_nodes directory:

cd ComfyUI/custom_nodes
git clone <your-repo-url> comfyUI-TiledWan

2. Restart ComfyUI

Nodes

TiledWan Image To Mask

Converts an image to a mask by extracting a specific color channel (red, green, blue, or alpha) with optional clamping and normalization.

Inputs:

• image: IMAGE - Input image to convert to mask
• channel: COMBO - Channel to extract ("red", "green", "blue", "alpha")
• clamp_output: BOOLEAN - Whether to clamp output values between 0 and 1 (default: True)
• normalize_output: BOOLEAN - Whether to normalize output to 0-1 range (default: True)

Outputs:

• mask: MASK - The extracted channel as a mask

Features:

• Supports RGBA images (if alpha channel is requested but doesn't exist, returns a fully opaque mask)
• Optional normalization: scales the mask values to use the full 0-1 range
• Optional clamping: ensures all values are between 0 and 1
• Robust handling of different image formats

TiledWan Image Statistics

Analyze images and display comprehensive statistics including min, max, mean, variance, median and distribution analysis.

Inputs:

• image: IMAGE - Input image to analyze
• show_per_channel: BOOLEAN - Whether to show per-channel statistics for RGB/RGBA images (default: True)

Outputs:

• image: IMAGE - The original image (pass-through)
• min_value: FLOAT - Minimum pixel value in the image
• max_value: FLOAT - Maximum pixel value in the image
• mean_value: FLOAT - Average pixel value
• variance: FLOAT - Variance of pixel values
• median_value: FLOAT - Median pixel value

Features:

• Comprehensive statistics: min, max, mean, median, variance, standard deviation
• Per-channel analysis for multi-channel images
• Value distribution histogram (10 bins)
• Percentile analysis (1st, 5th, 10th, 25th, 50th, 75th, 90th, 95th, 99th)
• Memory usage and image format information
• Automatic issue detection (negative values, out-of-range values, constant images)
• Console output with detailed formatting for easy reading

TiledWan Mask Statistics

Analyze masks and display comprehensive statistics including coverage, density, connected components and distribution analysis.

Inputs:

• mask: MASK - Input mask to analyze
• analyze_connected_components: BOOLEAN - Whether to perform connected components analysis (default: True)

Outputs:

• mask: MASK - The original mask (pass-through)
• min_value: FLOAT - Minimum pixel value in the mask
• max_value: FLOAT - Maximum pixel value in the mask
• mean_value: FLOAT - Average pixel value
• variance: FLOAT - Variance of pixel values
• median_value: FLOAT - Median pixel value
• white_pixel_count: INT - Number of pixels considered "white" (>0.5)

Features:

• Standard statistics: min, max, mean, median, variance, standard deviation
• Mask-specific analysis: white/black pixel counts, coverage percentage, density classification
• Value distribution: exact counts for few unique values, histogram for complex masks
• Connected components analysis: estimated number and size of separate mask regions
• Mask quality assessment: binary mask detection, uniformity analysis
• Automatic issue detection: negative values, out-of-range values, sparse/dense masks

TiledWan Video Sampler Test Concat

A comprehensive pipeline node that chains multiple WanVideo operations into a single node. This is a test/development node that combines WanVideoSampler, WanVideoTeaCache, WanVideoVACEEncode, WanVideoSLG, WanVideoExperimentalArgs, and WanVideoDecode for streamlined video generation.

Inputs (Required):

• model: WANVIDEOMODEL - The WanVideo model to use for generation
• vae: WANVAE - The VAE for encoding/decoding video latents
• image_embeds: WANVIDIMAGE_EMBEDS - Image embeddings for the video
• steps: INT - Number of sampling steps (default: 30)
• cfg: FLOAT - Classifier-free guidance scale (default: 6.0)
• shift: FLOAT - Shift parameter for scheduler (default: 5.0)
• seed: INT - Random seed for generation
• scheduler: COMBO - Sampling scheduler type (unipc, euler, dpm++, etc.)

TeaCache Parameters:

• teacache_rel_l1_thresh: FLOAT - TeaCache threshold for caching decisions (default: 0.3)
• teacache_start_step: INT - Step to start applying TeaCache (default: 1)
• teacache_end_step: INT - Step to end applying TeaCache (default: -1)
• teacache_use_coefficients: BOOLEAN - Use calculated coefficients for accuracy (default: True)

VACE Encode Parameters:

• vace_width: INT - Video width in pixels (default: 832)
• vace_height: INT - Video height in pixels (default: 480)
• vace_num_frames: INT - Number of frames to generate (default: 81)
• vace_strength: FLOAT - VACE encoding strength (default: 1.0)
• vace_start_percent: FLOAT - Start percentage for VACE application (default: 0.0)
• vace_end_percent: FLOAT - End percentage for VACE application (default: 1.0)

SLG Parameters:

• slg_blocks: STRING - Transformer blocks to skip unconditioned guidance on (default: "10")
• slg_start_percent: FLOAT - Start percentage for SLG application (default: 0.1)
• slg_end_percent: FLOAT - End percentage for SLG application (default: 1.0)

Experimental Parameters:

• exp_video_attention_split_steps: STRING - Steps to split video attention for multiple prompts
• exp_cfg_zero_star: BOOLEAN - Enable CFG Zero Star optimization (default: False)
• exp_use_zero_init: BOOLEAN - Use zero initialization (default: False)
• exp_zero_star_steps: INT - Number of zero star steps (default: 0)
• exp_use_fresca: BOOLEAN - Enable FreSca frequency scaling (default: False)
• exp_fresca_scale_low: FLOAT - FreSca low frequency scale (default: 1.0)
• exp_fresca_scale_high: FLOAT - FreSca high frequency scale (default: 1.25)
• exp_fresca_freq_cutoff: INT - FreSca frequency cutoff (default: 20)

Decode Parameters:

• decode_enable_vae_tiling: BOOLEAN - Enable VAE tiling for memory efficiency (default: False)
• decode_tile_x: INT - VAE tile width in pixels (default: 272)
• decode_tile_y: INT - VAE tile height in pixels (default: 272)
• decode_tile_stride_x: INT - VAE tile stride X (default: 144)
• decode_tile_stride_y: INT - VAE tile stride Y (default: 128)

Optional Inputs:

• text_embeds: WANVIDEOTEXTEMBEDS - Text embeddings for guided generation
• samples: LATENT - Initial latents for video-to-video processing
• denoise_strength: FLOAT - Denoising strength for v2v (default: 1.0)
• vace_input_frames: IMAGE - Input frames for VACE conditioning
• vace_ref_images: IMAGE - Reference images for VACE
• vace_input_masks: MASK - Input masks for VACE
• And many more optional parameters for fine control...

Outputs:

• video: IMAGE - Generated video frames as image sequence
• latents: LATENT - The generated latent representation

Features:

• Complete Pipeline: Integrates the entire WanVideo generation stack in one node
• TeaCache Optimization: Automatic caching for faster inference
• VACE Encoding: Advanced video-aware conditional encoding
• SLG Guidance: Selective layer guidance for improved quality
• Experimental Features: Access to cutting-edge techniques like FreSca and CFG Zero Star
• Memory Efficient: VAE tiling options for high-resolution generation
• Progress Tracking: Detailed console output showing pipeline progress
• Error Handling: Graceful fallback with dummy outputs if errors occur
• Flexible Parameters: Comprehensive control over every aspect of generation

Usage

TiledWan Image To Mask

The TiledWan Image To Mask node can be found in the "TiledWan" category in the ComfyUI node browser.

1. Connect an image to the input
2. Select which channel to extract (red, green, blue, or alpha)
3. Configure clamping and normalization options as needed
4. The node will output a mask based on the selected channel

Tips:

• Use normalization when you want to maximize contrast in the resulting mask
• Use clamping to ensure compatibility with other ComfyUI nodes that expect 0-1 values
• The alpha channel option works even with RGB images (will create a fully opaque mask)

TiledWan Image Statistics

The TiledWan Image Statistics node can be found in the "TiledWan" category in the ComfyUI node browser.

1. Connect any image to the input
2. Enable or disable per-channel analysis based on your needs
3. Run the workflow - statistics will be displayed in the ComfyUI console
4. The node also outputs individual statistic values that can be used by other nodes

Console Output includes:

• Basic Info: Image dimensions, total pixels, memory usage, data type
• Global Statistics: Min, max, mean, median, variance, standard deviation
• Per-Channel Analysis: Individual statistics for R, G, B, A channels
• Distribution Analysis: 10-bin histogram showing value distribution
• Percentiles: Key percentile values for detailed distribution analysis
• Issue Detection: Automatic warnings for potential problems

Use Cases:

• Quality Control: Detect images with unusual value ranges or distributions
• Debugging: Understand what's happening to your images in complex workflows
• Optimization: Identify images that might need preprocessing or normalization
• Analysis: Extract numerical statistics for further processing or decision making

Tips:

• Enable per-channel analysis for color images to understand channel balance
• Use the output values in mathematical nodes for dynamic workflow control
• Check the console output for warnings about potential image issues
• The pass-through design allows you to insert this node anywhere without breaking your workflow

TiledWan Mask Statistics

The TiledWan Mask Statistics node can be found in the "TiledWan" category in the ComfyUI node browser.

1. Connect any mask to the input
2. Enable or disable connected components analysis based on your needs
3. Run the workflow - statistics will be displayed in the ComfyUI console
4. The node also outputs individual statistic values and white pixel count for further processing

Console Output includes:

• Basic Info: Mask dimensions, total pixels, memory usage, data type
• Global Statistics: Min, max, mean, median, variance, standard deviation
• Mask-Specific Analysis: White/black pixel counts, coverage percentage, density classification
• Value Distribution: Exact counts for simple masks or histogram for complex ones
• Connected Components: Estimated number and size of separate mask regions
• Quality Assessment: Binary mask detection, uniformity warnings

Use Cases:

• Mask Quality Control: Verify mask coverage and density
• Segmentation Analysis: Understand mask complexity and component distribution
• Workflow Optimization: Use coverage data to skip processing on empty masks
• Debugging: Identify issues with mask generation or processing
• Data Analysis: Extract numerical mask properties for decision making

Tips:

• Enable connected components analysis for segmentation masks to understand object count
• Use the white_pixel_count output to conditionally process based on mask coverage
• Perfect binary masks (0 and 1 only) will be automatically detected and reported
• The pass-through design allows insertion anywhere in mask processing workflows
• Check console warnings for potential mask quality issues

TiledWan Video Sampler Test Concat

The TiledWan Video Sampler Test Concat node can be found in the "TiledWan" category in the ComfyUI node browser.

1. Connect Required Inputs: Connect your WanVideo model, VAE, and image embeddings
2. Configure Basic Parameters: Set steps, CFG, shift, seed, and scheduler
3. Adjust Advanced Settings: Fine-tune TeaCache, VACE, SLG, and experimental parameters as needed
4. Optional Inputs: Connect text embeddings, input frames, or masks if using advanced features
5. Run the Workflow: The node will execute the complete pipeline and output video frames

Console Output includes:

• Pipeline Progress: Step-by-step execution progress with emojis for visual tracking
• Parameter Setup: Confirmation of TeaCache, SLG, and experimental argument preparation
• VACE Processing: VACE encoding progress and configuration
• Sampling Progress: WanVideo sampling execution with all optimizations
• Decoding Progress: VAE decoding with optional tiling information
• Error Handling: Detailed error messages if any step fails

Use Cases:

• Rapid Prototyping: Test complete WanVideo pipelines without connecting multiple nodes
• Parameter Testing: Experiment with different WanVideo settings in a single node
• Production Workflows: Use as a complete video generation solution
• Educational: Learn how WanVideo components work together
• Development: Test new WanVideo features and optimizations

Tips:

• Start with default parameters and adjust gradually based on results
• Enable TeaCache for faster inference but watch for quality trade-offs
• Use VAE tiling if running out of VRAM during decoding
• Check console output for detailed progress and error information
• The node includes error handling but check inputs carefully for best results
• Consider memory usage when setting high frame counts or resolutions