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PaperBanana-CN scientific figure workbench with a generated multimodal fault-diagnosis figure

PaperBanana-CN generates methodology diagrams from research descriptions and statistical plots from CSV or JSON data. It uses the PaperBanana scientific workflow and adds separate VLM and image-service connections, a Chinese Studio interface, and explicit aspect-ratio and resolution controls.

Launch Studio Install from PyPI

CI status Package version 2.0.1 MIT License

## One run from input to figure

A PaperBanana-CN Studio run progressing from configured inputs to a completed scientific figure

The recording follows a methodology-diagram run from submitted inputs to the completed result. ## Before you start You need Python 3.10-3.12, [uv](https://docs.astral.sh/uv/), and a desktop browser. | Task | Required model connections | |---|---| | Methodology diagram | VLM and image generation | | Statistical plot | VLM only | | Multi-panel composition and run browsing | None | Each connection specifies its own protocol, Base URL, API key, model name, and timeout. The VLM and image roles may use the same service or two different services. ## Quick start ### 1. Launch Studio ```bash uvx paperbanana-cn studio ``` Open . `uvx` runs the package in an isolated environment and does not modify Debian or Ubuntu's system Python. ### 2. Add the model connections Open **Settings → VLM connection**, fill in the service fields, and select **Save and use**. Repeat under **Image connection** before generating a methodology diagram. Editing a saved connection does not activate it. An empty API-key field keeps the stored key. Studio does not fill stored keys back into the browser.
Connection manager screenshot and protocol notes

PaperBanana-CN connection manager with separate fields for a VLM service

The [connection guide](https://github.com/mituan-ai/PaperBanana-CN/blob/main/docs/CONNECTIONS.md) lists the supported protocols, credential storage rules, connection tests, and legacy mode.
### 3. Generate a figure Open **Methodology diagram**, provide the method content and figure caption, then choose an aspect ratio, resolution, and output format. The same task from the CLI: ```bash paperbanana-cn generate \ --input method.txt \ --caption "Overview of the proposed architecture" \ --aspect-ratio 16:9 \ --resolution 2K \ --format png ``` ## What PaperBanana-CN adds ### Separate VLM and image connections The two model roles have independent protocol, Base URL, API key, model, and timeout settings. Studio, CLI, and MCP resolve the same saved connections. Saved profiles contain credential references; API keys remain outside the repository and run metadata. Official APIs, OpenAI-compatible services, and Gemini-compatible services are supported. Provider specifics stay in the adapters rather than the scientific workflow. ### Chinese and English Studio The Studio interface, help text, validation, progress messages, and errors are available in Chinese and English. Changing the interface language does not rewrite prompts, paper text, or labels inside the generated figure. ### Aspect ratios and resolution Supported aspect ratios: `1:1` · `4:3` · `3:2` · `5:4` · `16:9` · `21:9` · `4:5` · `3:4` · `2:3` · `9:16` Resolution tiers: `1K` · `2K` · `4K` Studio shows the request size or provider-native tier before generation. If an adapter cannot produce the selected combination, validation stops the request and reports the unsupported option. ## Studio workflows | Area | Workflow | Model connections | |---|---|---| | Create | Methodology diagram | VLM and image | | Create | Statistical plot | VLM | | Improve | Continue a saved run | Depends on the saved run | | Improve | Quality evaluation | VLM | | Automate | Full-paper orchestration | VLM and image | | Automate | Batch generation | Depends on the task type | | Automate | Parameter sweep | VLM and image | | Tools | Multi-panel composition | None | | Tools | Run browser | None |

PaperBanana-CN methodology-diagram workspace with task inputs and a completed result

Methodology-diagram workspace

PaperBanana-CN statistical-plot workspace with CSV input and a completed line chart

Statistical-plot workspace using synthetic demonstration data

## CLI, MCP, Docker, and Colab | Entry point | Command or link | |---|---| | Studio | `paperbanana-cn studio` | | CLI | `paperbanana-cn generate --help` | | MCP server | `paperbanana-cn mcp` | | GitHub Action | [Action reference](https://github.com/mituan-ai/PaperBanana-CN/blob/main/integrations/github-action/README.md) | | Docker | `ghcr.io/mituan-ai/paperbanana-cn:2.0.1` | | Colab | [Quickstart notebook](https://colab.research.google.com/github/mituan-ai/PaperBanana-CN/blob/main/notebooks/PaperBanana_CN_Quickstart.ipynb) |
MCP client configuration ```json { "mcpServers": { "paperbanana-cn": { "command": "uvx", "args": ["paperbanana-cn", "mcp"] } } } ``` The server provides 11 tools and reads the same active connections as Studio and CLI. See the [MCP guide](https://github.com/mituan-ai/PaperBanana-CN/blob/main/mcp_server/README.md) for the tool list and arguments.
Docker ```bash docker run --rm -p 7860:7860 \ -v paperbanana-cn-config:/home/paperbanana/.config/paperbanana-cn \ -v paperbanana-cn-data:/home/paperbanana/.local/share/paperbanana-cn \ -v paperbanana-cn-outputs:/work/outputs \ ghcr.io/mituan-ai/paperbanana-cn:2.0.1 \ studio --host 0.0.0.0 ```
Permanent install, source setup, and optional providers Install the command in a uv-managed environment: ```bash uv tool install paperbanana-cn paperbanana-cn studio ``` Run the current source checkout: ```bash git clone https://github.com/mituan-ai/PaperBanana-CN.git cd PaperBanana-CN uv sync uv run paperbanana-cn studio ``` The default package includes Studio, MCP, PDF input, OpenAI-compatible services, and Gemini. | Optional adapter | Install | |---|---| | AWS Bedrock | `uv tool install "paperbanana-cn[bedrock]"` | | Anthropic | `uv tool install "paperbanana-cn[anthropic]"` | | LiteLLM | `uv tool install "paperbanana-cn[litellm]"` | | All optional providers | `uv tool install "paperbanana-cn[all-providers]"` | For CI, use `paperbanana-cn connections add --api-key-env ENV_VAR` so the key is read from an environment variable instead of a command-line value.
## V1 and upstream V2 is maintained on `main` as the `paperbanana-cn` distribution, the `paperbanana_cn` Python module, and the `paperbanana-cn` command. - [Browse the frozen `v1` branch](https://github.com/mituan-ai/PaperBanana-CN/tree/v1) - [Download the `v1.0.0` release](https://github.com/mituan-ai/PaperBanana-CN/releases/tag/v1.0.0) The scientific figure-generation core is based on [`llmsresearch/paperbanana`](https://github.com/llmsresearch/paperbanana). PaperBanana-CN is an unofficial community implementation and is not affiliated with or endorsed by the upstream authors. ## Community PaperBanana-CN is maintained by [mituan](https://github.com/mituan-ai) under the [MIT License](https://github.com/mituan-ai/PaperBanana-CN/blob/main/LICENSE). - Ask usage questions in [Discussions](https://github.com/mituan-ai/PaperBanana-CN/discussions). - Report reproducible bugs in [Issues](https://github.com/mituan-ai/PaperBanana-CN/issues). - Report vulnerabilities through [Private Vulnerability Reporting](https://github.com/mituan-ai/PaperBanana-CN/security/advisories/new). - Read [CONTRIBUTING.md](https://github.com/mituan-ai/PaperBanana-CN/blob/main/CONTRIBUTING.md) before opening a pull request.
Development checks ```bash git clone https://github.com/mituan-ai/PaperBanana-CN.git cd PaperBanana-CN uv sync --extra dev uv run pytest tests/ -q uv run ruff check paperbanana_cn/ mcp_server/ tests/ scripts/ ``` Do not upload API keys, private relay URLs, unpublished papers, private datasets, local connection stores, or generated run directories.
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