🔁 ComfyUI Sequential Batcher & Video Loop Master (Beta v0.9.3)

Important

This version is currently in BETA. We have fully transitioned the terminology from "Job" to Batch to align with ComfyUI standards and the project name.

The ultimate tool for creating complex iterative workflows and frame-by-frame video processing in ComfyUI. Designed to handle huge tasks (like high-res video generation with Wan2.2 or LTX Video) without crashing your GPU by using intelligent sequential looping instead of VRAM-heavy batching.

🚀 Why use this?

Standard ComfyUI batching processes everything at once (4D tensors). For video or large batches, this leads to Out of Memory (OOM) errors. Sequential Batcher allows you to "split" these tasks and process them one-by-one (sequentially) within a single "Queue Prompt" run, then "gather" the results back into a single batch or video file.

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🛠️ Installation

1. Clone this repo into custom_nodes/comfyui-sequential-batcher.
2. Restart ComfyUI.
3. Dependencies (torch, numpy) will be handled automatically if using ComfyUI Manager.

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📖 Key Concepts

• SEQUENCE: A simple list of values (numbers, strings, etc.).
• BATCH: A structured collection of "steps". Each step has named Attributes.
• Iteration: The magic happens in nodes like Batch To List, Image Batch To List, or Latent Batch To List. When ComfyUI sees a "List" output from these nodes, it executes all downstream nodes once for each item in that list.

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🎞️ Video Workflow (Wan2.2 / LTX-Video / Future Models)

Video models produce many frames that can easily exceed 24GB VRAM.

1. Split: Use Latent Batch To List to turn your video latent into a list of single frames.
2. Process: Connect to your KSampler/VAE Decoder. ComfyUI will process Frame 1, then Frame 2, then Frame 3...
3. Gather: Use Latent List To Batch (or Image List To Batch if you decoded first) to reconstruct the full video batch for saving.
4. Stitch (VHS Combine): To stitch heavy video chunks sequentially:
   • Option A (Math): Range -> MakeBatch (attribute: "skip_frames") -> BatchToList -> GetAttributeInt (name: "skip_frames") connected to VHS_LoadVideo.
   • Option B (CSV): LoadCSV (with a skip_frames column) -> BatchToList -> GetAttributeInt.
   • Connect the output of VHS_VideoCombine (specifically the VHS_FILENAMES output) to the FFmpeg Video Stitcher node (video_paths). The Stitcher will wait for all chunks to finish and combine them automatically!

Warning

OOM Prevention (Memory Warning): While BatchToList solves the data passing issue, you must still manage PyTorch VRAM. It is highly recommended to use the easy cleanGpuUsed node (or similar VRAM clearing nodes) after the VAE Decoder in each cycle of the Batch. Without manual VRAM clearing in each iteration, the sequential loop will accumulate garbage in your graphics card and eventually cause an Out of Memory error.

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🔢 Detailed Node Reference

🔄 Loop Category (🔁 Sequential Batcher/Loop)

• 🔁 Sequential Loop Index: The simplest way to start a loop.
  • Input: count (How many times to run).
  • Output: index (0, 1, 2...). Useful for seeding or selecting specific items.
• 🔁 Repeat: Takes any input and repeats it N times.
  • Input: input (Any), count (INT).
  • Output: output (List of the same input).

🛠️ Batch Category (🔁 Sequential Batcher/Batch)

• 📂 Load CSV: Loads a CSV file as a Batch. Now includes a table preview in the workflow.
  • Input: path (File location), delimiter, quotechar.
  • Optional Input: index (To pick a specific row).
  • Output: batch (The full list), current_attributes (Dict of the selected row), count (Total rows).
• 📊 Preview Batch: Displays a table of the batch content in the workflow.
  • Input: batch, index (Highlight specific row), max_rows.
• 🛠️ Make Batch: Turns a sequence into a "Batch" object.
  • Input: sequence (The data), name (The attribute name, e.g., "cfg_scale").
• 🖇️ Combine Batches: Merges multiple batches.
  • Modes: zip (paired) or product (every combination).
• 🔄 Batch To List: CRITICAL. Converts a Batch into a stream of attributes that triggers the sequential loop.
• 📥 Get Attribute: Extracts a specific value from the current batch step by its name.

🖼️ Image & Latent Category (🔁 Sequential Batcher/Image & /Latent)

• 🖼️ Image Batch To List: Splits a [N,H,W,C] tensor into N separate images.
• 🖼️ Image List To Batch: Reconstructs a batch from iterated images.
• 🎞️ Latent Batch To List: Splits video latents frame-by-frame for VRAM-safe processing.
• 🎞️ Latent List To Batch: Merges individual frames back into a video latent batch.
• ⏳ Progress Bar: Generates a visual progress indicator.

🎞️ Video Category (🔁 Sequential Batcher/Video)

• 🎞️ FFmpeg Video Stitcher: Final node in a video loop. Waits for the entire sequential batch to finish and stitches the video chunks together using FFmpeg without re-encoding.
  • Input: video_paths (List of VHS_FILENAMES), output_filename (String).
  • Output: final_video_path (Path to the stitched video).
• 🎞️ Incremental Auto-Stitcher: Incremental stitching node designed specifically for Auto Queue cycles. It runs immediately after each chunk is saved (triggered by VHS_FILENAMES) and continuously merges all available chunks matching a given prefix.
  • Input: trigger (VHS_FILENAMES, acts merely as a signal), video_prefix (String), output_filename (String).
  • Output: final_video_path (Path to the incrementally stitched backup video).

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💡 Pro Tips & Use Cases

📝 Using CSV for Prompts and Scenes

You can create a CSV with columns like prompt, negative_prompt, and seed.

1. Use 📂 Load CSV to load your file.
2. Connect batch to 🔄 Batch To List.
3. Use 📥 Get Attribute to pull the prompt into your CLIP Text Encode.
4. Each row in your CSV will be processed as one "frame" or "job" in the sequence.

🎬 Scene Timings for Video

If you have a CSV with frame_start and prompt, you can use it to change prompts at specific points in a video generation loop.

🧪 XY Plots

Use 🖇️ Combine Batches in product mode to create XY Plots (e.g., test every Prompt against every CFG scale).

🔍 Automatic Model Iteration

Use 🔍 Model Finder to automatically iterate through a folder of LoRAs or Checkpoints.