cfde12451c
Training an image LoRA required knowing the Studio home layout and copying files onto the server by hand, which is the most confusing step of the whole flow. Two small endpoints fix that: - GET /api/train/diffusion/info reports the datasets and outputs roots plus every dataset folder that contains images (with image/caption counts), so the UI can offer a picker instead of a blind free-text path. - POST /api/train/diffusion/dataset uploads images and optional caption .txt / metadata.jsonl files into a named folder under the datasets root, creating it on first use and accumulating on repeat uploads so large sets can arrive in batches. Names are validated to a single path component and files stream to disk under the same per-upload size cap as LLM dataset uploads. The returned name is a valid data_dir for /diffusion/start. |
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| .. | ||
| .gitkeep | ||
| __init__.py | ||
| auth.py | ||
| data_recipe.py | ||
| datasets.py | ||
| export.py | ||
| inference.py | ||
| mcp_servers.py | ||
| models.py | ||
| providers.py | ||
| responses.py | ||
| training.py | ||
| users.py | ||