6bd3e87c6f
- normalized() + family_train_infos() mirror the inference fp8 deny for Qwen-Image (activation outliers exceed fp8's range and corrupt the trained result); int8 stays allowed and the UI no longer advertises fp8 for it. - _resolve_base_precision() gates an explicit int8 on a FUNCTIONAL torchao, the same gate auto and /info already apply, so a missing/stub torchao fails fast instead of silently loading dense with compile disabled. - train_precision_modes() gates the dense modes (bf16/int8/fp8/auto) on torch.cuda.is_bf16_supported(), so a non-bf16 CUDA GPU (T4/V100/RTX 20xx) is offered only nf4 instead of a start that evicts resident models and then fails. - start_diffusion_training preflights bf16 support for the DiT families BEFORE _free_gpu_for_diffusion_training(), so any DiT start (nf4 included, since the trainer requires bf16 unconditionally on CUDA) fails fast without eviction. |
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|---|---|---|
| .. | ||
| data_recipe | ||
| .gitkeep | ||
| __init__.py | ||
| auth.py | ||
| chat_history.py | ||
| datasets.py | ||
| export.py | ||
| inference.py | ||
| llama.py | ||
| mcp_servers.py | ||
| models.py | ||
| preview.py | ||
| prompts.py | ||
| providers.py | ||
| rag.py | ||
| settings.py | ||
| training.py | ||
| training_history.py | ||
| training_vram.py | ||