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https://github.com/invoke-ai/InvokeAI
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b0f7b555b7
* feat(z-image): add Z-Image Base (undistilled) model variant support - Add ZImageVariantType enum with 'turbo' and 'zbase' variants - Auto-detect variant on import via scheduler_config.json shift value (3.0=turbo, 6.0=zbase) - Add database migration to populate variant field for existing Z-Image models - Re-add LCM scheduler with variant-aware filtering (LCM hidden for zbase) - Auto-reset scheduler to Euler when switching to zbase model if LCM selected - Update frontend to show/hide LCM option based on model variant - Add toast notification when scheduler is auto-reset Z-Image Base models are undistilled and require more steps (28-50) with higher guidance (3.0-5.0), while Z-Image Turbo is distilled for ~8 steps with CFG 1.0. LCM scheduler only works with distilled (Turbo) models. * Chore ruff format * Chore fix windows path * feat(z-image): filter LoRAs by variant compatibility and warn on mismatch LoRA picker now hides Z-Image LoRAs with incompatible variants (e.g. ZBase LoRAs when using Turbo model). LoRAs without a variant are always shown. Backend loaders warn at runtime if a LoRA variant doesn't match the transformer variant. * Chore typegen --------- Co-authored-by: Lincoln Stein <lincoln.stein@gmail.com> |
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| .. | ||
| controlnet | ||
| dype | ||
| extensions | ||
| ip_adapter | ||
| modules | ||
| redux | ||
| custom_block_processor.py | ||
| denoise.py | ||
| flux_state_dict_utils.py | ||
| math.py | ||
| model.py | ||
| sampling_utils.py | ||
| schedulers.py | ||
| text_conditioning.py | ||
| util.py | ||