mirror of https://github.com/vladmandic/automatic
75 lines
3.1 KiB
Python
75 lines
3.1 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
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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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# 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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if shared.opts.model_wan_stage == 'combined':
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kwargs['boundary_ratio'] = shared.opts.model_wan_boundary
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elif 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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if cls == 'LTXImageToVideoPipeline' or cls == 'LTXConditionPipeline':
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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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