# Fanghua-Yu/SUPIR

- id: src_01M569CC8ZA92K16ZH6X9TFJ1R
- type: source · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=metadata_checked, execution=not_run, review=unreviewed
- categories: T05 · tags: supir, diffusion-upscaling, restoration, sdxl, llava, cvpr-2024, non-commercial, level:advanced
- updated: 2026-10-18T01:17:53.812Z
- url: https://veyrul.com/sources/src_01M569CC8ZA92K16ZH6X9TFJ1R

Source: https://github.com/fanghua-yu/supir

SeedVR/SeedVR2 (https://github.com/ByteDance-Seed/SeedVR, Apache 2.0 per README, one 80GB H100 for roughly 100 frames at 720x1280) was fetched as a possible alternative but not recorded in this lane.

```json
{
  "archived": false,
  "checked_at": "2026-10-17T00:00:00.000Z",
  "parent_url": null,
  "usefulness": "CVPR 2024 diffusion-based photo-realistic restoration/upscaling built on SDXL with LLaVA captioning for guidance. High-fidelity but heavy; its custom 'SUPIR Software License Agreement' with a non-commercial declaration is the clearest example in this lane of a code release that cannot be assumed usable in commercial pipelines.",
  "source_type": "repository",
  "availability": "available",
  "dependencies": [
    "SDXL base 1.0",
    "SDXL CLIP encoders",
    "LLaVA v1.5 13B + LLaVA CLIP",
    "Python 3.8 conda env"
  ],
  "source_owner": "Fanghua Yu / SupPixel Pty Ltd",
  "next_check_at": "2026-10-31T00:00:00.000Z",
  "successor_url": null,
  "hardware_notes": "Sourced: low-VRAM option about 12G (diffusion) + 16G (LLaVA); default uses two GPUs.",
  "known_limitations": [
    "Non-commercial restriction.",
    "Multiple large downloads, legacy Python 3.8 env, heavy VRAM.",
    "Diffusion upscaling can alter faces and fine details; the quality/fidelity trade-off is documented by the authors."
  ],
  "upstream_updated_at": "2025-05-12T09:49:11.000Z"
}
```

Evidence claims:
- [sourced_fact] README: restoration/upscaling code for a CVPR 2024 paper; low-VRAM mode (half params, tiled VAE, 8-bit LLaVA) described as needing about 12G for the diffusion model and 16G for LLaVA; default examples use two GPUs. · source: https://github.com/Fanghua-Yu/SUPIR
- [sourced_fact] A 'Non-Commercial Use Only Declaration' limits use, reproduction and distribution to non-commercial purposes, with commercial use needing written permission; no separate licence stated for checkpoints SUPIR-v0Q/v0F or dependencies (SDXL, LLaVA v1.5 13B, optional Juggernaut-XL). · source: https://github.com/Fanghua-Yu/SUPIR
- [sourced_fact] LICENSE file is titled 'SUPIR Software License Agreement', copyright 2024 SupPixel Pty Ltd, work jointly owned by SupPixel Pty Ltd. · source: https://raw.githubusercontent.com/Fanghua-Yu/SUPIR/master/LICENSE
- [sourced_fact] README notes a quality-vs-fidelity trade-off: settings that raise visual quality can lower fidelity to the input. · source: https://github.com/Fanghua-Yu/SUPIR
- [upstream_claim] Research review verdict at export: reviewed (as of 2026-10-17). Recorded by the research pipeline, not a hub review outcome; evidence.review stays unreviewed until an independent reviewer records one.

Rights:
- code: license=SUPIR-Software-License-Agreement hosting=restricted redistribution=restricted modification=restricted training=unknown · Custom agreement plus Non-Commercial Use Only Declaration; commercial use needs permission from the named author. Only the first lines of the LICENSE were read. · evidence: https://raw.githubusercontent.com/Fanghua-Yu/SUPIR/master/LICENSE, https://github.com/Fanghua-Yu/SUPIR
- model_weights: license=unknown hosting=unknown redistribution=unknown modification=unknown training=unknown · Checkpoint licence not stated on README; dependent SDXL (OpenRAIL++-M), LLaVA and Juggernaut models carry separate terms not evaluated here. · evidence: https://github.com/Fanghua-Yu/SUPIR

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