━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━🔼 SeedVR2 Batch Upscaler — Sleep On It, Wake Up 4KDrop a folder, come back to 4K━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━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.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━✨ 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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━📦 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 reconstructionAlternative 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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⬇️ Download Links (verified HuggingFace repositories)7B fp16 (default — recommended for quality)• seedvr2_ema_7b_fp16.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp16.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp16.safetensors</a>• ema_vae_fp16.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors</a>7B fp8 (faster, lower VRAM)• seedvr2_ema_7b_fp8_e4m3fn.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp8_e4m3fn.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_7b_fp8_e4m3fn.safetensors</a>• ema_vae_fp16.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors</a>3B (fastest, native 16GB fit)• seedvr2_ema_3b_fp16.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp16.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/seedvr2_ema_3b_fp16.safetensors</a>• ema_vae_fp16.safetensors — <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors">https://huggingface.co/numz/SeedVR2_comfyUI/blob/main/ema_vae_fp16.safetensors</a>Installation Path:• Diffusion models: ComfyUI/models/diffusion_models/• VAE files: ComfyUI/models/vae/━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━🧩 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 registry1. Open terminal/PowerShell in your ComfyUI root directory2. Navigate to custom_nodes: cd custom_nodes3. Clone the repo: git clone <a target="_blank" rel="ugc" href="https://github.com/ComfyUI-Kelin/ComfyUI_Image_Anything.git">https://github.com/ComfyUI-Kelin/ComfyUI_Image_Anything.git</a>4. Restart ComfyUIThen 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" → installVerify Installation: After restart, load this workflow in ComfyUI. If nodes resolve (no red outlines), you're good to go.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━🚀 How to Use1. Prepare Images — Place your QC-approved images (PNG/JPG, any resolution 256–4096px) in ComfyUI's ./input folder2. Load This Workflow — Open SeedVR2_Batch_Upscale_v1.json in ComfyUI3. 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 folder5. 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)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⚙️ Settings &amp; ParametersModel 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 leanUpscale 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 variantTorch.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 hallucinationVAE Tiling (if image &gt;2048px)• Encode Tiled: Enabled (1024px tiles, 128px overlap)• Decode Tiled: Enabled (same tiling)→ Prevents OOM on large inputs━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━💡 Performance TipsVRAM &amp; 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)━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━🔗 My Workflow SuiteI 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: <a target="_blank" rel="ugc" href="https://github.com/Thinni63/comfyui-workflows/tree/main/seedvr2-batch-upscale">https://github.com/Thinni63/comfyui-workflows/tree/main/seedvr2-batch-upscale</a>━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━📝 Notes &amp; 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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⭐ 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━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Roadmap TeaserA video-batch variant of this upscaler is coming soon. Stay tuned!━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━⚖️ Model Attribution &amp; LicensingSeedVR2 (ByteDance Seed Team)• License: Apache License 2.0 — <a target="_blank" rel="ugc" href="https://huggingface.co/ByteDance-Seed/SeedVR2-7B">https://huggingface.co/ByteDance-Seed/SeedVR2-7B</a>• Attribution Required: "SeedVR2 by ByteDance Seed Team, licensed under Apache License 2.0."• Safetensors Conversion: Hosted at <a target="_blank" rel="ugc" href="https://huggingface.co/numz/SeedVR2_comfyUI">https://huggingface.co/numz/SeedVR2_comfyUI</a> 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 LicenseThis 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.

Files with SHA256: 304a25fcc8373ddbbdc31dd8ca6dad038723ba22a245f37006fddf080b5497d5