# What do I need to run StoryDiffusion's consistent-character comic generation for the first time (prompts, GPU, defaults), what does it keep consistent, and what is and is not released or licensed?

- id: art_01M5MCBTS1RKAVG24T7T5H8536
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T03, T05 · tags: none
- updated: 2026-10-23T12:39:23.674Z
- url: https://veyrul.com/articles/art_01M5MCBTS1RKAVG24T7T5H8536

StoryDiffusion's image part (Consistent Self-Attention) is training-free: it lets the images of one batch share sampled tokens so faces and attire converge. Per the README the user must give at least 3 prompts and 5-6 are recommended; the low-VRAM Gradio script was tested on a 24 GB A10 with 30 GB RAM. In that script the first 4 prompt rows are identity prompts, the sampling rate is 0.5 and output is 768x768; the reference-image mode is implemented with PhotoMaker; cartoon reference images are marked unsupported. The paper's own author-run test shows slightly higher CLIP-based text and character similarity than IP-Adapter and PhotoMaker, and its stated limitation is minor clothing details. The video model's code and weights are unchecked TODO items in the README. Code is Apache-2.0; the third-party ViStoryBench run scores it below OmniGen2 and UNO on cross-CIDS and states that the method cannot take several character references in one shot.

StoryDiffusion's image part (Consistent Self-Attention) is training-free: it lets the images of one batch share sampled tokens so faces and attire converge. Per the README the user must give at least 3 prompts and 5-6 are recommended; the low-VRAM Gradio script was tested on a 24 GB A10 with 30 GB RAM. In that script the first 4 prompt rows are identity prompts, the sampling rate is 0.5 and output is 768x768; the reference-image mode is implemented with PhotoMaker; cartoon reference images are marked unsupported. The paper's own author-run test shows slightly higher CLIP-based text and character similarity than IP-Adapter and PhotoMaker, and its stated limitation is minor clothing details. The video model's code and weights are unchecked TODO items in the README. Code is Apache-2.0; the third-party ViStoryBench run scores it below OmniGen2 and UNO on cross-CIDS and states that the method cannot take several character references in one shot.

Curator qualifications: StoryDiffusion consistent self-attention is training-free across a batch; README asks for at least 3 prompts (5-6 recommended); low-VRAM script tested on 24 GB.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://github.com/HVision-NKU/StoryDiffusion/tree/8de45e424887766fdd84dc917436ff8605f00149",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2405.01434v1",
      "note": "candidate evidence excerpts"
    },
    {
      "url": "https://arxiv.org/html/2505.24862v5",
      "note": "candidate evidence excerpts"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] StoryDiffusion's image part (Consistent Self-Attention) is training-free: it lets the images of one batch share sampled tokens so faces and attire converge. Per the README the user must give at least 3 prompts and 5-6 are recommended; the low-VRAM Gradio script was tested on a 24 GB A10 with 30 GB RAM. In that script the first 4 prompt rows are identity prompts, the sampling rate is 0.5 and output is 768x768; the reference-image mode is implemented with PhotoMaker; cartoon reference images are marked unsupported. The paper's own author-run test shows slightly higher CLIP-based text and character similarity than IP-Adapter and PhotoMaker, and its stated limitation is minor clothing details. The video model's code and weights are unchecked TODO items in the README. Code is Apache-2.0; the third-party ViStoryBench run scores it below OmniGen2 and UNO on cross-CIDS and states that the method cannot take several character references in one shot. · locator: candidate evidence excerpts · source: https://github.com/HVision-NKU/StoryDiffusion/tree/8de45e424887766fdd84dc917436ff8605f00149

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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