When selecting a Wan 2.1 I2V model that is a LoRA or tensor weight,
the base model now uses Wan-AI/Wan2.1-I2V-14B-720P-Diffusers instead
of the generic Wan-AI/Wan2.1-I2V-14B-Diffusers.

Fixed ValueError: too many values to unpack (expected 5)
- Line 3929: Added missing 6th element to unpacking
- Line 3936: Added missing 6th element to unpacking

The results tuple has 6 elements:
(name, info, caps, is_disabled, fail_count, orig_idx)

Fix I2V pipeline auto-detection

See merge request !1

- Add detect_model_family() to identify model family (wan, sdxl, sd, ltx, etc.)
- Add get_pipeline_for_model_family() for proper pipeline selection based on family + task
- Enhance detect_generation_type() to check --image FIRST for I2V detection
- Add support for --image_model, --prompt_image, --prompt_animation as I2V indicators
- Add support for audio/subtitle options as T2V+V2V chaining indicators

This fixes the issue where SDXL models were incorrectly using WanPipeline
for I2V tasks, causing type mismatch errors (expected UMT5EncoderModel,
got CLIPTextModel). Now SDXL models correctly use DiffusionPipeline
or StableDiffusionXLPipeline.

- Add --model-list-batch to EXAMPLES.md for batch model listing
- Add new CLI options to MCP server (--output-dir, --yes, --audio-chunk)
- Add new CLI options to webapp build_command function
- Update README.md with Output Options section

Added --audio-chunk argument with 3 modes:
- overlap (default): overlapping chunks like [0-60], [58-118]
- word-boundary: uses Whisper timestamps to split at word boundaries
- vad: uses Voice Activity Detection to skip silence

Also added --audio-chunk-overlap to control overlap duration.

New functions added:
- process_video_with_vad(): VAD-based chunking
- process_video_word_boundary(): Word-boundary chunking using Whisper

Modified:
- transcribe_video_audio(): accepts audio_chunk_type and audio_chunk_overlap params
- _transcribe_chunked(): accepts chunk_type and overlap params

Models like Faber8/AbyssOrangeMix2 are SD1.5 models, not Flux.
Now detected as StableDiffusionPipeline instead of FluxPipeline.

When a model has a VAE configured but the VAE files don't exist in
the repository, try loading with the default VAE instead.

Allow using full HuggingFace model ID (e.g., Faber8/AbyssOrangeMix2_nsfw)
as --model argument by looking up both short name and full ID in MODELS.

The repos attribute from scan_cache_dir() is a frozenset, not a list,
so .pop() doesn't work. Fixed by using next(iter()) instead.

The message is now suppressed when using --model-list-batch to make
the output cleaner for scripts.

Added --yes / -y argument that automatically answers 'yes' to confirmation
prompts when deleting cached models or clearing the entire cache.

Usage:
  videogen --remove-cached-model MODEL_ID --yes
  videogen --clear-cache --yes

The --model-list-batch option was added but wasn't being handled properly.
Now it correctly exits after printing the batch output, and is also
added to the list of options that don't require --prompt.

- Fixed model ID consistency: numeric IDs now remain the same when using
  filters like --nsfw-friendly, --t2v-only, --i2v-only, etc.
  Previously, filtered lists would renumber models making --show-model
  by numeric ID unreliable.

- Added --model-list-batch option for script-friendly output:
  Outputs 'NUMERIC_ID:FULL_MODEL_NAME' format for easy parsing

- Added --output-dir option to specify output directory:
  Sets the directory where output files will be saved

- Fixed syntax error in argparse epilog string that was causing
  'SyntaxError: invalid decimal literal'

- Fixed model ID normalization to handle hyphens (in addition to underscores)
- Fixed dictionary key ordering in base_model_fallbacks so more specific keys (wan2.2.i2v) are checked before generic keys (wan2.2)
- Fixed Wan 2.1 I2V base model mapping (was incorrectly pointing to T2V)
- Fixed base model detection in earlier code sections to check model ID directly instead of relying on m_info.get('supports_i2v')
- Fixed typo: Wan 2.2 generic fallback now correctly uses Wan2.2-T2V

Now Wan 2.2 I2V models like Wan-AI/Wan2.2-I2V-A14B will correctly use Wan-AI/Wan2.2-I2V-14B-Diffusers as the base model instead of the incorrect Wan-AI/Wan2.2-T2V-14B-Diffusers.

- Fixed mapping table to use correct I2V base model (Wan-AI/Wan2.2-I2V-14B-Diffusers)
- Fixed Diffuser -> Diffusers typo in model IDs
- Updated all Wan 2.2 I2V references to use correct model ID

- Add correct base model for Wan 2.2 I2V: Wan-AI/Wan2.2-I2V-A14B-Diffuser
- Add specific VRAM estimate for Wan 2.2 I2V MoE models (~14GB)
- Apply more conservative VRAM calculation for models with weights/LoRAs
- Fix indentation error in add_model_from_hf function

Calculate: base_vram + 2GB + 50%
This ensures a 14B model estimated at 18GB will require ~29GB instead of 22.5GB.

Instead of adding a fixed 2GB overhead, now calculates 25% more
VRAM for base models that will have fine-tuned weights/tensors
or LoRA adapters loaded on top.

The user confirmed that Wan2.2-I2V models should use Wan2.2-T2V
as the base model, not the I2V variant.

The issue was that model IDs from HuggingFace use dots (wan2.2-i2v-a14b)
while user config names use underscores (wan2_2_i2v_a14b).

Now we normalize the model ID by replacing underscores with dots before
matching against the base_model_fallbacks dictionary.

The issue was that model IDs like 'wan2_2_i2v_a14b' would match
'wan2_2' (T2V) before 'wan2_2_i2v' (I2V) because 'wan2_2' comes
first in the dictionary.

Now the dictionary is ordered with more specific keys first:
- wan2_2_i2v_a14b (most specific)
- wan2.2_i2v_a14b
- wan2_2_i2v
- wan2.2_i2v
- wan2_2
- wan2.2
etc.

This ensures longer/more specific keys are checked before shorter ones.

Before this fix, using Wan 2.2 I2V models like 'wan2_2_i2v_a14b' would
incorrectly try to load 'Wan-AI/Wan2.1-T2V-14B-Diffusers' as the base
model because the fallback logic only matched 'wan' generically.

Now the base_model_fallbacks dictionary includes specific entries for:
- Wan 2.2 I2V models: wan2_2_i2v, wan2.2_i2v
- Wan 2.2 T2V models: wan2_2, wan2.2
- Wan 2.1 I2V models: wan2_1_i2v, wan2.1_i2v
- Wan 2.1 T2V models: wan2_1, wan2.1
- Generic Wan fallback: wan

The more specific keys are checked first, so model IDs like
'wan2_2_i2v_a14b' will correctly match 'wan2_2_i2v' and use
'Wan-AI/Wan2.2-I2V-14B-Diffusers' as the base model.

- Add get_pipeline_for_task() function that determines pipeline
  based on model ID AND task type (t2v, i2v, t2i, i2i, v2v)
- Pipeline class is now ALWAYS detected at runtime, not from config
- Remove old dynamic switching code that's now redundant
- Update check_model.py to show runtime detection instead of fixing config
- Update check_pipelines.py to show V2V pipelines

- Add WanImageToVideoPipeline and WanVideoToVideoPipeline to PIPELINE_CLASS_MAP
- Add CogVideoXImageToVideoPipeline and CogVideoXVideoToVideoPipeline
- Add AnimateDiffVideoToVideoPipeline
- Add StableDiffusionImg2ImgPipeline for SD 1.5
- Add dynamic pipeline switching logic for Wan, LTX, CogVideoX, AnimateDiff
- The pipeline class is now selected at runtime based on task mode
- Fix detect_pipeline_class to correctly identify Wan models
- Remove duplicate LTX handling code