mirror of https://github.com/vladmandic/automatic
46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
import os
|
|
import torch
|
|
import diffusers
|
|
from modules import shared, processing
|
|
from modules.video_models.models_def import Model
|
|
|
|
|
|
debug = shared.log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
|
|
|
|
|
|
def load_override(selected: Model):
|
|
kwargs = {}
|
|
if selected.name == 'Allegro T2V':
|
|
kwargs['vae'] = diffusers.AutoencoderKLAllegro.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir)
|
|
if selected.name == 'LTXVideo 0.9.5 I2V':
|
|
kwargs['vae'] = diffusers.AutoencoderKLLTXVideo.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir)
|
|
if 'WAN 2.1 14B' in selected.name:
|
|
kwargs['vae'] = diffusers.AutoencoderKLWan.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir)
|
|
debug(f'Video overrides: model="{selected.name}" kwargs={list(kwargs)}')
|
|
return kwargs
|
|
|
|
|
|
def set_overrides(p: processing.StableDiffusionProcessingVideo, selected: Model):
|
|
cls = shared.sd_model.__class__.__name__
|
|
# Allegro
|
|
if selected.name == 'Allegro T2V':
|
|
shared.sd_model.vae.enable_tiling()
|
|
# Latte
|
|
if selected.name == 'Latte 1 T2V':
|
|
p.task_args['enable_temporal_attentions'] = True
|
|
p.task_args['video_length'] = 16 * (max(p.frames // 16, 1))
|
|
# LTX
|
|
if cls == 'LTXImageToVideoPipeline' or cls == 'LTXConditionPipeline':
|
|
p.task_args['generator'] = None
|
|
if cls == 'LTXConditionPipeline':
|
|
p.task_args['strength'] = p.denoising_strength
|
|
# WAN
|
|
if 'Wan' in cls:
|
|
p.task_args['width'] = 16 * (p.width // 16)
|
|
p.task_args['height'] = 16 * (p.height // 16)
|
|
p.frames = 4 * (max(p.frames // 4, 1)) + 1
|
|
# LTX
|
|
if 'LTX' in cls:
|
|
p.task_args['width'] = 32 * (p.width // 32)
|
|
p.task_args['height'] = 32 * (p.height // 32)
|