- Add IP-Adapter integration for character consistency using reference images
- Add InstantID support for superior face identity preservation
- Add Character Profile System to store reference images and face embeddings
- Add LoRA Training Workflow for perfect character consistency
- Add command-line arguments for all character consistency features
- Update EXAMPLES.md with comprehensive character consistency documentation
- Update requirements.txt with optional dependencies (insightface, onnxruntime)

New commands:
- --character: Use saved character profile
- --create-character: Create new character profile from reference images
- --list-characters: List all saved profiles
- --show-character: Show profile details
- --ipadapter: Enable IP-Adapter for consistency
- --instantid: Enable InstantID for face identity
- --train-lora: Train custom LoRA for character

- When --update-models detects a LoRA adapter, validate that the base
  model exists on HuggingFace before adding it to the model list
- Skip LoRAs whose base models are not found on HuggingFace
- Added support for flux and sdxl base model detection
- Print informative messages when skipping LoRAs with missing base models

PEFT (Parameter-Efficient Fine-Tuning) is required for loading LoRA
adapters with pipe.load_lora_weights(). Without it, LoRA loading fails
with: 'PEFT backend is required for this method.'

- Add update_model_pipeline_class() function to update model config
- Call function when main model pipeline mismatch is corrected
- Call function when image model pipeline mismatch is corrected
- Ensures future runs use the correct pipeline class automatically

When defer_i2v_loading=True (I2V mode without provided image), the code
sets pipe=None but then tried to call pipe.load_lora_weights() and
pipe.enable_model_cpu_offload() on None, causing AttributeError.

This fix wraps the LoRA loading and offloading configuration blocks
inside an 'if not defer_i2v_loading:' condition so they are skipped
when the I2V model loading is deferred until after image generation.

- Defer I2V model loading when in I2V mode without provided image
- Generate image first with T2I model
- Unload T2I model completely (del, empty_cache, gc.collect)
- Then load I2V model and generate video
- This ensures only one model is in memory at a time
- Fixes Linux OOM killer issue when loading multiple models

- Add auto_disable.json to track failure counts and disabled status
- Models that fail 3 times in auto mode are automatically disabled
- Disabled models are skipped during auto model selection
- Manual selection of a disabled model re-enables it for auto mode
- Model list now shows 'Auto' column with status (Yes, OFF, or X/3)
- Disabled models shown with 🚫 indicator in model list
- New functions: load_auto_disable_data(), save_auto_disable_data(),
  record_model_failure(), is_model_disabled(), re_enable_model(),
  get_model_fail_count()

- Detect LoRA adapters from tags (lora, LoRA) and files (*.safetensors)
- Extract base model from tags (format: base_model:org/model-name)
- Skip model_index.json fetch for LoRA-only repos
- Determine pipeline class from base model for LoRA adapters
- Improves handling of models like enhanceaiteam/Flux-Uncensored-V2

- Replace locals().get('goto_after_loading', False) with properly initialized boolean flag
- The locals() approach failed because locals() returns a copy, not a reference
- Now the fallback correctly skips error handling when pipeline loads successfully via detected class

- Add fallback mechanism for models with incorrect model_index.json
- Detect pipeline class from model ID patterns when component mismatch occurs
- Fix indentation error in auto mode retry logic block
- Properly handle Wan2.2-I2V models with misconfigured pipeline class

- Track if user explicitly specified --model before auto mode runs
- Skip retry with alternative models when user's model fails
- Show clear error message explaining user's choice is preserved
- Only auto-selected models can be retried with alternatives

- Track failed base models in _failed_base_models set
- Skip LoRA adapters that depend on failed base models during retry
- Try non-LoRA alternatives when all LoRAs with same base fail
- Improve error detection for 'Repository Not Found' errors
- Show skipped LoRA count during retry process

- Skip models not found on HuggingFace instead of adding with defaults
- Add deep search for model variants from known organizations
- Search organizations: Alpha-VLLM, stepvideo, hpcai-tech, tencent,
  rhymes-ai, THUDM, genmo, Wan-AI, stabilityai, black-forest-labs
- Remove non-existent models from known_large_models list
- Better error handling for model validation

- Add HF_TOKEN support to main pipeline loading (pipe_kwargs)
- Add HF_TOKEN support to VAE loading for Wan models
- Add HF_TOKEN support to image model loading for I2V mode
- Enhanced pipeline detection with multiple strategies
- Improved error messages for authentication errors (401, gated models)
- Added debug output for HF token status

- Fix retry logic bug: only run auto mode once (check for _auto_mode flag)
- Prevent infinite retry loops by preserving retry count across recursive calls
- Add better error handling for pipeline compatibility issues (FrozenDict, scale_factor errors)
- Add helpful troubleshooting messages for diffusers version incompatibilities
- Show retry exhaustion message when all alternative models fail

- Add DiffusionPipeline to PIPELINE_CLASS_MAP for generic model loading
- Add fallback to DiffusionPipeline for unknown pipeline classes
- Add return_all parameter to select_best_model() for getting all candidates
- Store alternative models in auto mode for retry support
- Implement retry logic when model loading fails in auto mode
- Retry up to 3 times with alternative models before failing
- Add debug output for model loading troubleshooting
- Improve error messages with troubleshooting hints

Features:
- Audio generation: TTS via Bark/Edge-TTS, music via MusicGen
- Audio sync: stretch, trim, pad, loop modes
- Lip sync: Wav2Lip and SadTalker integration
- Auto mode: automatic model selection with NSFW detection
- MCP server: AI agent integration via Model Context Protocol
- Model management: external config, search, validation
- T2I/I2I support: static image and image-to-image generation
- Time estimation: detailed timing breakdown for each step

Documentation:
- README.md: comprehensive installation and usage guide
- EXAMPLES.md: 100+ command-line examples
- SKILL.md: AI agent integration guide
- LICENSE.md: GPLv3 license

Copyleft © 2026 Stefy <stefy@nexlab.net>