Features Added:
- Model type filters: --t2i-only, --v2v-only, --v2i-only, --3d-only, --tts-only, --audio-only
- Enhanced model list table with new capability columns (V2V, V2I, 3D, TTS)
- Updated detect_model_type() to detect all model capabilities

MCP Server Updates:
- Added videogen_video_to_video tool for V2V style transfer
- Added videogen_apply_video_filter tool for video filters
- Added videogen_extract_frames tool for frame extraction
- Added videogen_create_collage tool for thumbnail grids
- Added videogen_upscale_video tool for AI upscaling
- Added videogen_convert_3d tool for 2D-to-3D conversion
- Added videogen_concat_videos tool for video concatenation
- Updated model list filter to support all new types

SKILL.md Updates:
- Added V2V, V2I, 3D to generation types table
- Added model filter examples
- Added 8 new use cases for V2V, filters, frames, collage, upscale, 3D, concat

Features Added:
- Video-to-Video (V2V): Style transfer, filters, concatenation
- Video-to-Image (V2I): Frame extraction, keyframes, collages
- 2D-to-3D Conversion: SBS, anaglyph, VR 360 formats
- Video upscaling with AI (ESRGAN, Real-ESRGAN, SwinIR)
- Video filters (grayscale, sepia, blur, speed, slow-mo, etc.)

Command-line Arguments:
- --video: Input video file for V2V/V2I operations
- --video-to-video: Enable V2V style transfer
- --video-filter: Apply video filters
- --extract-frame, --extract-keyframes, --extract-frames
- --convert-3d-sbs, --convert-3d-anaglyph, --convert-vr
- --upscale-video, --upscale-method

Model Discovery:
- Added depth estimation models to --update-models
- Added 2D-to-3D model searches
- Added V2V style transfer models

Documentation:
- Updated README.md with new features
- Added comprehensive V2V/V2I/2D-to-3D examples
- Added multi-node cluster setup guide
- Added NFS shared storage configuration

- Add video frame extraction (extract_video_frames, extract_keyframes)
- Add video info retrieval (get_video_info)
- Add frames to video conversion (frames_to_video)
- Add video upscaling with AI support (upscale_video)
- Add video-to-video style transfer (video_to_video_style_transfer)
- Add video-to-image extraction (video_to_image)
- Add video collage creation (create_video_collage)
- Add video filters (apply_video_filter - grayscale, sepia, blur, etc.)
- Add video concatenation (concat_videos)
- Add image upscaling (upscale_image)

Features:
- Extract frames at specific FPS or timestamps
- AI upscaling with ESRGAN/SwinIR support
- Scene detection for keyframe extraction
- Multiple video filters and effects
- Video concatenation with re-encoding or stream copy

- 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>