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https://github.com/vladmandic/automatic
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CalamitousFelicitousness
10647c7b67
Wan 2.2 A14B ships a per-model boundary_ratio (0.9 I2V, 0.875 T2V) that selects the high- or low-noise expert per step. The video and base-model image loaders both load the shipped value; the slider override is applied at generation time in set_pipeline_args, the one point both paths pass through before invoking the pipeline. The denoising loop reads config.boundary_ratio each call, so tuning takes effect with no reload for video and base-model images alike. The slider defaults to -1, meaning use the model's value; 0 to 1 set the boundary explicitly. Single-expert stages stay load-time because they drop a transformer to free VRAM.
107 lines
5.2 KiB
Python
107 lines
5.2 KiB
Python
import os
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import torch
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import diffusers
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from modules import shared, processing, devices
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from modules.logger import log
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from modules.video_models.models_def import Model
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debug = log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
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def load_override(selected: Model, **load_args):
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kwargs = {}
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# Allegro
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if 'Allegro T2V' in selected.name:
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kwargs['vae'] = diffusers.AutoencoderKLAllegro.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir, **load_args)
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# LTX
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if 'LTXVideo 0.9.5 I2V' in selected.name:
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kwargs['vae'] = diffusers.AutoencoderKLLTXVideo.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir, **load_args)
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# OzzyGT LTX-2.3 mirrors pack connectors/ twice by design: sharded (*-00001-of-0000N +
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# .index.json) and unsharded diffusion_pytorch_model.safetensors of the byte-identical
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# weights. snapshot_download faithfully fetches both; diffusers' component loader picks
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# sharded when the index is present. ignore_patterns skips the ~6.3 GB unsharded copy
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# without reaching for a cleaner upstream mirror.
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ltx2_redundant_connector_repos = {
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'OzzyGT/LTX-2.3',
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'OzzyGT/LTX-2.3-sdnq-dynamic-int4',
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}
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if selected.repo in ltx2_redundant_connector_repos:
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kwargs['ignore_patterns'] = ['connectors/diffusion_pytorch_model.safetensors']
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# LTX2TextConnectors weights are byte-identical across all 2.3 variants (verified by blob
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# hash). Pre-load from a canonical repo so per-variant fetches skip connectors/ entirely.
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# FP16 variants share OzzyGT/LTX-2.3; SDNQ variants share the pre-quantized mirror.
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ltx2_connectors_cls = None
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try:
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from diffusers.pipelines.ltx2 import LTX2TextConnectors
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ltx2_connectors_cls = LTX2TextConnectors
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except ImportError as e:
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log.warning(f'Video load: LTX2TextConnectors unavailable ({e}); dedup of LTX-2.3 connectors disabled')
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if ('LTXVideo 2.3' in selected.name and shared.opts.te_shared_te and ltx2_connectors_cls is not None):
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conn_repo = 'OzzyGT/LTX-2.3-sdnq-dynamic-int4' if 'SDNQ' in selected.name else 'OzzyGT/LTX-2.3'
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log.debug(f'Video load: module=connectors repo="{conn_repo}" cls={ltx2_connectors_cls.__name__} shared={shared.opts.te_shared_te}')
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kwargs['connectors'] = ltx2_connectors_cls.from_pretrained(
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conn_repo,
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subfolder='connectors',
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torch_dtype=devices.dtype,
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cache_dir=shared.opts.hfcache_dir,
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ignore_patterns=['connectors/diffusion_pytorch_model.safetensors'],
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**load_args,
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)
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# WAN
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if 'WAN 2.1 14B' in selected.name:
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kwargs['vae'] = diffusers.AutoencoderKLWan.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir, **load_args)
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if ('A14B' in selected.name) or ('14B VACE' in selected.name):
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# combined keeps both experts loaded and tunes boundary_ratio at runtime (set_pipeline_args), so
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# it is not set here; only the single-expert stages need load time because they drop a transformer.
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if shared.opts.model_wan_stage == 'high noise':
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kwargs['transformer_2'] = None
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kwargs['boundary_ratio'] = 0.0
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elif shared.opts.model_wan_stage == 'low noise':
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kwargs['boundary_ratio'] = 1000.0
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kwargs['transformer'] = None
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debug(f'Video overrides: model="{selected.name}" kwargs={list(kwargs)}')
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return kwargs
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def set_overrides(p: processing.StableDiffusionProcessingVideo, selected: Model):
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cls = shared.sd_model.__class__.__name__
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# Allegro
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if selected.name == 'Allegro T2V':
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shared.sd_model.vae.enable_tiling()
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# Latte
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if selected.name == 'Latte 1 T2V':
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p.task_args['enable_temporal_attentions'] = True
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p.task_args['video_length'] = 16 * (max(p.frames // 16, 1))
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# SkyReels
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if 'SkyReelsV2DiffusionForcing' in cls:
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p.task_args['overlap_history'] = 17
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# LTX
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ltx_i2v_classes = ('LTXImageToVideoPipeline', 'LTXConditionPipeline', 'LTX2ImageToVideoPipeline', 'LTX2ConditionPipeline')
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if cls in ltx_i2v_classes:
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p.task_args['generator'] = None
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if cls == 'LTXConditionPipeline':
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p.task_args['strength'] = p.denoising_strength
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if 'LTX' in cls:
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p.task_args['width'] = 32 * (p.width // 32)
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p.task_args['height'] = 32 * (p.height // 32)
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# WAN
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if 'Wan' in cls:
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p.task_args['width'] = 16 * (p.width // 16)
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p.task_args['height'] = 16 * (p.height // 16)
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p.frames = 4 * (max(p.frames // 4, 1)) + 1
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# WAN VACE
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if 'WanVACEPipeline' in cls:
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if (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
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p.task_args['reference_images'] = p.init_images
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# WAN 2.2-5B
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if 'WAN 2.2 5B' in selected.name:
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shared.sd_model.vae.disable_tiling()
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# Kandinsky 5
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if 'Kandinsky 5.0 Lite 5s' in selected.name:
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# p.task_args['time_length'] = 5
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pass
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if 'Kandinsky 5.0 Lite 10s' in selected.name:
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# p.task_args['time_length'] = 10
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shared.sd_model.transformer.set_attention_backend("flex")
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