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🔼 SeedVR2 Batch Upscaler — Sleep On It, Wake Up 4K

Drop a folder, come back to 4K

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ComfyUI batch image upscaler powered by SeedVR2, the SOTA diffusion-based restoration model from ByteDance. Load an entire folder of QC-approved images, queue once (one image per batch), and watch them upscale to 4K in a single session. No single-image re-queuing, no repetition—feed sequential images through the same upscaler instance to your output folder, all with automatic date-stamped organization. Built for production stock photography, concept art detail recovery, and QC'd generation batches.

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✨ Features

✅ Batch Folder Iterator — Place 10–100 images in input folder, queue once-per-image, all process sequentially without re-loading model

✅ Whole-Folder Workflow — ImageIterator node handles file discovery, sorting, and index auto-increment

✅ SeedVR2 7B fp16 (default) — Diffusion-based upscaler, SOTA quality; fp8 and 3B variants available for speed/VRAM trade-off

✅ 4× Upscale Target — 1024×1024 input → 4096×4096 output (fixed mode); scales smaller images proportionally, caps at 4096 max

✅ Block-Swap Ready — Pre-configured with blocks_to_swap=36 + CPU offload + SDPA attention for 16GB VRAM fit (requires ~33GB system RAM)

✅ Automated Output Organization — Results saved to output/ folder with automatic date stamp [YYYY-MM-DD]

✅ Torch.compile Optional — Node included but disabled by default; enable if you have triton-windows for inference speedup

✅ Metadata-Safe Export — PNG output; embedded workflow metadata has no machine paths or language-specific notes

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📦 Required Models (3 files, ~7-16 GB depending on variant)

Primary (default — recommended)

• seedvr2_ema_7b_fp16.safetensors — Main diffusion upscaler (highest quality, fits 16GB with block-swap)

• ema_vae_fp16.safetensors — VAE codec for image reconstruction

Alternative Variants (choose one — do NOT mix in same batch)

• seedvr2_ema_7b_fp8_e4m3fn.safetensors + ema_vae_fp16.safetensors — Faster, near-identical quality, lower VRAM

• seedvr2_ema_3b_fp16.safetensors + ema_vae_fp16.safetensors — Fastest option, fits 16GB without block-swap

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⬇️ Download Links (verified HuggingFace repositories)

7B fp16 (default — recommended for quality)

• seedvr2_ema_7b_fp16.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp16.safetensors

• ema_vae_fp16.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors

7B fp8 (faster, lower VRAM)

• seedvr2_ema_7b_fp8_e4m3fn.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp8_e4m3fn.safetensors

• ema_vae_fp16.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors

3B (fastest, native 16GB fit)

• seedvr2_ema_3b_fp16.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp16.safetensors

• ema_vae_fp16.safetensors — https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors

Installation Path:

• Diffusion models: ComfyUI/models/diffusion_models/

• VAE files: ComfyUI/models/vae/

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🧩 Required Custom Nodes

⚠️ Manual Install Step (do this first — it's the only one not in ComfyUI Manager):

Image_Anything (Batch Folder Iterator) — NOT in ComfyUI Manager registry

1. Open terminal/PowerShell in your ComfyUI root directory

2. Navigate to custom_nodes: cd custom_nodes

3. Clone the repo: git clone https://github.com/ComfyUI-Kelin/ComfyUI_Image_Anything.git

4. Restart ComfyUI

Then install these via ComfyUI Manager (search → install):

• ComfyUI-SeedVR2_VideoUpscaler (numz) — canonical SeedVR2 node pack, in Manager registry, searchable by name

- Install via Manager: Manager → Install Custom Nodes → search "SeedVR2" → select numz's ComfyUI-SeedVR2_VideoUpscaler → Install

• was-node-suite-comfyui (ltdrdata) — Image Save node (PNG/WebP export); MIT license, in Manager registry

- Install via Manager: search "was-node-suite" → install

Verify Installation: After restart, load this workflow in ComfyUI. If nodes resolve (no red outlines), you're good to go.

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🚀 How to Use

1. Prepare Images — Place your QC-approved images (PNG/JPG, any resolution 256–4096px) in ComfyUI's ./input folder

2. Load This Workflow — Open SeedVR2_Batch_Upscale_v1.json in ComfyUI

3. Set Queue Count — In the ComfyUI UI:

- For N images, click Queue (Instant) ×N (if your version supports instant queue)

- OR click Queue once per image in the batch (simpler, same result)

4. Monitor Progress — Watch the ImageIterator node report "current_index / total_count" as it walks the folder

5. Collect Output — Check ComfyUI's ./output folder for upscaled images, organized by date [YYYY-MM-DD]

Example Workflow:

- Input folder: ComfyUI/input/ (contains 5 PNG files)

- Click "Queue (Instant) ×5" (or Queue 5 times)

- Wait ~2–5 minutes per image (depending on model + VRAM config)

- Output appears in ComfyUI/output/[2026-07-03]/ with your original filenames (SeedVR2 preserves source names by default)

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⚙️ Settings & Parameters

Model Loading

• Model Path: Automatically detects from ComfyUI/models/diffusion_models/ (VAE loads from ComfyUI/models/vae/)

• Device: CUDA (GPU); falls back to CPU if CUDA unavailable

• Offload Device: CPU (system RAM) — keeps VRAM lean

Upscale Output

• Resolution Mode: "fixed" (always 4× upscale, 1024→4096, smaller images scale proportionally)

• Max Resolution: 4096 (hard cap; never exceeds this)

• Batch Size: 1 per queue (sequential processing)

• Color Correction: "lab" (perceptually-aware color preservation)

• Attention Mode: "sdpa" (scaled-dot-product attention — memory-efficient)

Block-Swap (VRAM Management)

• blocks_to_swap: 36 (default, pre-configured for 16GB VRAM)

- Swaps 36 UNet blocks to system RAM to fit model on GPU

- Requires ~33GB system RAM; 64GB recommended for smooth performance

- If you have less RAM, switch to 7B fp8 or 3B variant

Torch.compile (Optional Speedup)

• Status: DISABLED by default (node present, not connected)

• Reason: Requires triton-windows, which most Windows users lack → would error on first run

• How to Enable (if you have triton): Reconnect the SeedVR2TorchCompileSettings node output → SeedVR2LoadDiTModel input. Can add ~20–30% speed boost, but only if dependencies are met.

Random Seed

• Seed: randomized per batch item for variation (or set fixed value for reproducibility)

• Latent/Input Noise: Low (0.05 / 0.0) — preserves detail, prevents hallucination

VAE Tiling (if image >2048px)

• Encode Tiled: Enabled (1024px tiles, 128px overlap)

• Decode Tiled: Enabled (same tiling)

→ Prevents OOM on large inputs

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💡 Performance Tips

VRAM & System RAM Honesty

| Variant | VRAM Required | System RAM Offloaded | Notes |

|---------|---|---|---|

| 7B fp16 + swap36 (default, shipped) | ~16GB | ~33GB to CPU | Highest quality. Requires 64GB system RAM for smooth batch processing; 32GB will thrash/be very slow. Measured: ~62s/image (steady-state, 17-image production batch, 1280×1600→3276×4096)* |

| 7B fp8_e4m3fn | ~9GB | ~18GB to CPU | Faster inference (~30% speedup estimated, not yet benchmarked). Quality near-identical to fp16. Fits 32GB system RAM comfortably. |

| 3B fp16 | ~16GB native | None (no swap needed) | Fastest option (not yet benchmarked). No block-swap overhead. Fits 16GB without offload. |

*Measured on RTX 5080, 7B fp16 variant only, back-to-back queue (model already loaded). fp8/3B timings not yet benchmarked — estimates only.

Practical Guidance

• For highest quality + 64GB system RAM: Use 7B fp16 (default config)

• For speed + 32GB RAM: Switch model to 7B fp8 (same setup, different checkpoint file)

• For minimal VRAM/RAM: Use 3B variant (nearly as good, no block-swap delays)

• Batch 10+ images together to amortize model load time (~30sec per session)

• Avoid running alongside heavy generation (e.g., Krea2 gen) — too much total VRAM pressure

• Input image quality: Sharp, well-lit images upscale better than low-contrast originals (expected for all upscalers)

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🔗 My Workflow Suite

I maintain a growing library of ComfyUI workflows. Check them out:

• LTX-2.3 Image-to-Video — Lock-camera i2v with auto motion prompt (QwenVL)

• Krea2 Turbo Dual-Mode — Fast diffusion gen (text or image-to-image)

• Z-Image-Turbo — Another fast gen option with auto-prompt

• SeedVR2 Batch Upscaler — This workflow (batch folder upscale to 4K)

Find LTX-2.3 and Krea2 Turbo on my Civitai profile page. More coming soon — follow for updates!

GitHub Mirror: https://github.com/Thinni63/comfyui-workflows/tree/main/seedvr2-batch-upscale

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📝 Notes & AI Disclosure

• AI-Generated Example Images — All provided examples are AI-generated via Krea2 + upscaled with this workflow

• Hardware Tested — Verified on RTX 5080 16GB VRAM, Windows 11, CUDA 12.1+

• Update Requirement — Requires recent/latest ComfyUI (subgraph support needed)

• Model Weights License — SeedVR2 weights are NOT distributed with the workflow; you download them separately from HuggingFace (see Download Links above)

• Metadata Safety — This workflow JSON has no machine paths or language-specific notes; it's safe to share and distribute

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⭐ Found this useful?

• Like if it saved you time

• Comment your results — I read every one

• Follow for new ComfyUI workflows, all tested on 16 GB VRAM

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Roadmap Teaser

A video-batch variant of this upscaler is coming soon. Stay tuned!

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⚖️ Model Attribution & Licensing

SeedVR2 (ByteDance Seed Team)

• License: Apache License 2.0 — https://huggingface.co/ByteDance-Seed/SeedVR2-7B

• Attribution Required: "SeedVR2 by ByteDance Seed Team, licensed under Apache License 2.0."

• Safetensors Conversion: Hosted at https://huggingface.co/numz/SeedVR2_comfyUI for ComfyUI-compatible format

• Free for commercial and non-commercial use (see license for full terms)

Custom Nodes

• ComfyUI-SeedVR2_VideoUpscaler (numz) — Check repository for license

• was-node-suite-comfyui (ltdrdata) — MIT License

• ComfyUI_Image_Anything (ComfyUI-Kelin) — MIT License

This Workflow (JSON Configuration)

• Original work by TP_AI_63 (Civitai) / Thinni63 (GitHub)

• Shared under MIT License; credit appreciated

• Model weights must be downloaded separately (not included)

All example outputs are AI-generated via Krea2 generation and SeedVR2 upscaling.