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Start with the complete error in the startup view, activity center, or dialog. Identify the failing stage before changing project files or caches.
First launch can download native packages before the project browser becomes interactive. Let active downloads finish and confirm the repositories are reachable.If initialization fails repeatedly, close every Koharu process, update the GPU driver, restart the computer, and retry on a stable network. Capture the complete error.Do not remove a runtime directory while a process may have its libraries loaded.
Check access to the model’s Hugging Face repository and available cache disk space. Proxies, regional filtering, authentication, antivirus scanning, or interrupted writes can block downloads.Retry the same model once. If the same file fails, include the repository, filename, and full error in your report.
A detected GPU still needs a compatible driver, backend, and runtime package. Check the resource monitor and startup logs for the device actually selected. CPU fallback is expected when no complete accelerator path is usable. See hardware.
Check that the page is upright and readable. Inspect detected regions before changing OCR models. Adjust thresholds conservatively across several pages, try a manga-specific recognizer for Japanese, and correct source text before translating again.
Paint a smaller Remove mask and avoid bubble borders and line art. Try a direct inpainting model before a heavier generative model. Keep manual touch-ups on a separate authored raster layer.
Verify credentials, base URL, and provider-specific fields in Settings → Providers, then reopen the model picker. For a compatible server, check the chat endpoint and enable Vision input only if the selected model accepts images.
Cancel the existing sign-in or request before retrying. Check that browser authorization completed for the intended ChatGPT account. Agent sign-in is separate from OpenAI translation-provider credentials.
Confirm the layer contains translated text, is visible, has nonzero opacity, and uses a font covering the target script. Reset automatic fitting after large text changes. Source-only text is not rendered.
Capture the project revision, affected page, format, canvas screenshot, and exported image. Canvas and exports start from the same prepared frame, so a meaningful mismatch should be reported.

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Collect detailed logs

Start Koharu from a terminal to enable logging for that process. A terminal runs commands you type or paste; leave it open while reproducing the problem.
Save your work and close every Koharu window first. Logging applies only to the newly launched process.
1

Open PowerShell

Open Start, search for PowerShell, and launch Windows PowerShell. Windows Terminal also works with a PowerShell tab. Use PowerShell rather than Command Prompt.
2

Start Koharu

Paste the command with Ctrl+V or right-click, then press Enter. Do not paste the PS C:\Users\YourName> prompt.
3

Use a full path if needed

If koharu.exe is not recognized, search for Koharu in Start, right-click Open file location, then open the shortcut’s Properties and copy Target. Replace the example below with that path, keeping & and the quotes.
The environment setting lasts only in this PowerShell window. Closing it clears the setting.
Reproduce the problem with this copy of Koharu. The terminal may remain busy until the app closes; keep the complete surrounding output, not just the last error. Close Koharu normally afterward, or press Ctrl+C once in the terminal if the launched process is frozen.

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Save a log file

In a new terminal, run the command for your platform, reproduce the problem, and close Koharu. The log is written to your home directory. Substitute the full executable path if the short command was unavailable.
Before sharing, replace credentials, private filenames, and private page text with [removed], keeping surrounding diagnostics. Include your OS, Koharu version, intended action, reproduction steps, and approximate error time. Attach the text log to a GitHub issue or ask on Discord.