mirror of
https://github.com/vladmandic/automatic
synced 2026-09-08 22:08:42 +02:00
CalamitousFelicitousness
7e5d040c4c
LTXCaps gains a `variant` field ('0.9', '2.0', '2.3') replacing
`is_ltx_2_3`; variant-specific branches check `caps.variant == '2.3'`
instead of grepping the model name.
ltx_util.load_upsample_2x mirrors load_upsample's contract so the 2.x
path owns a module-level cache and stops reloading ~2.3 GB every run.
The cached pipe is stamped with a synthetic
`CheckpointInfo('ltx-upsampler-2.x')` so it gets its own OffloadHook
slot and can go through apply_balanced_offload without invalidating
the main pipe's module map. The hardcoded `.to('cpu')` and post-pass
torch_gc are gone; the second apply_balanced_offload handles spill.
video_overrides comment on OzzyGT LTX-2.3 connectors states plainly
that mirrors pack weights twice by design; ignore_patterns is the
surgical workaround, not an hf_hub bug.
Drive-by: load_upsample log line used `__class__.__name__` (always
'type'); switched to `__name__`.
107 lines
5.1 KiB
Python
107 lines
5.1 KiB
Python
import os
|
|
import torch
|
|
import diffusers
|
|
from modules import shared, processing, devices
|
|
from modules.logger import log
|
|
from modules.video_models.models_def import Model
|
|
|
|
|
|
debug = log.trace if os.environ.get('SD_VIDEO_DEBUG', None) is not None else lambda *args, **kwargs: None
|
|
|
|
|
|
def load_override(selected: Model, **load_args):
|
|
kwargs = {}
|
|
# Allegro
|
|
if 'Allegro T2V' in selected.name:
|
|
kwargs['vae'] = diffusers.AutoencoderKLAllegro.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir, **load_args)
|
|
# LTX
|
|
if 'LTXVideo 0.9.5 I2V' in selected.name:
|
|
kwargs['vae'] = diffusers.AutoencoderKLLTXVideo.from_pretrained(selected.repo, subfolder="vae", torch_dtype=torch.float32, cache_dir=shared.opts.hfcache_dir, **load_args)
|
|
# OzzyGT LTX-2.3 mirrors pack connectors/ twice by design: sharded (*-00001-of-0000N +
|
|
# .index.json) and unsharded diffusion_pytorch_model.safetensors of the byte-identical
|
|
# weights. snapshot_download faithfully fetches both; diffusers' component loader picks
|
|
# sharded when the index is present. ignore_patterns skips the ~6.3 GB unsharded copy
|
|
# without reaching for a cleaner upstream mirror.
|
|
ltx2_redundant_connector_repos = {
|
|
'OzzyGT/LTX-2.3',
|
|
'OzzyGT/LTX-2.3-sdnq-dynamic-int4',
|
|
}
|
|
if selected.repo in ltx2_redundant_connector_repos:
|
|
kwargs['ignore_patterns'] = ['connectors/diffusion_pytorch_model.safetensors']
|
|
# LTX2TextConnectors weights are byte-identical across all 2.3 variants (verified by blob
|
|
# hash). Pre-load from a canonical repo so per-variant fetches skip connectors/ entirely.
|
|
# FP16 variants share OzzyGT/LTX-2.3; SDNQ variants share the pre-quantized mirror.
|
|
ltx2_connectors_cls = None
|
|
try:
|
|
from diffusers.pipelines.ltx2 import LTX2TextConnectors
|
|
ltx2_connectors_cls = LTX2TextConnectors
|
|
except ImportError as e:
|
|
log.warning(f'Video load: LTX2TextConnectors unavailable ({e}); dedup of LTX-2.3 connectors disabled')
|
|
if ('LTXVideo 2.3' in selected.name and shared.opts.te_shared_t5 and ltx2_connectors_cls is not None):
|
|
conn_repo = 'OzzyGT/LTX-2.3-sdnq-dynamic-int4' if 'SDNQ' in selected.name else 'OzzyGT/LTX-2.3'
|
|
log.debug(f'Video load: module=connectors repo="{conn_repo}" cls={ltx2_connectors_cls.__name__} shared={shared.opts.te_shared_t5}')
|
|
kwargs['connectors'] = ltx2_connectors_cls.from_pretrained(
|
|
conn_repo,
|
|
subfolder='connectors',
|
|
torch_dtype=devices.dtype,
|
|
cache_dir=shared.opts.hfcache_dir,
|
|
ignore_patterns=['connectors/diffusion_pytorch_model.safetensors'],
|
|
**load_args,
|
|
)
|
|
# WAN
|
|
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, **load_args)
|
|
if ('A14B' in selected.name) or ('14B VACE' in selected.name):
|
|
if shared.opts.model_wan_stage == 'combined':
|
|
kwargs['boundary_ratio'] = shared.opts.model_wan_boundary
|
|
elif shared.opts.model_wan_stage == 'high noise':
|
|
kwargs['transformer_2'] = None
|
|
kwargs['boundary_ratio'] = 0.0
|
|
elif shared.opts.model_wan_stage == 'low noise':
|
|
kwargs['boundary_ratio'] = 1000.0
|
|
kwargs['transformer'] = None
|
|
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))
|
|
# SkyReels
|
|
if 'SkyReelsV2DiffusionForcing' in cls:
|
|
p.task_args['overlap_history'] = 17
|
|
# LTX
|
|
ltx_i2v_classes = ('LTXImageToVideoPipeline', 'LTXConditionPipeline', 'LTX2ImageToVideoPipeline', 'LTX2ConditionPipeline')
|
|
if cls in ltx_i2v_classes:
|
|
p.task_args['generator'] = None
|
|
if cls == 'LTXConditionPipeline':
|
|
p.task_args['strength'] = p.denoising_strength
|
|
if 'LTX' in cls:
|
|
p.task_args['width'] = 32 * (p.width // 32)
|
|
p.task_args['height'] = 32 * (p.height // 32)
|
|
# 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
|
|
# WAN VACE
|
|
if 'WanVACEPipeline' in cls:
|
|
if (getattr(p, 'init_images', None) is not None) and (len(p.init_images) > 0):
|
|
p.task_args['reference_images'] = p.init_images
|
|
# WAN 2.2-5B
|
|
if 'WAN 2.2 5B' in selected.name:
|
|
shared.sd_model.vae.disable_tiling()
|
|
# Kandinsky 5
|
|
if 'Kandinsky 5.0 Lite 5s' in selected.name:
|
|
# p.task_args['time_length'] = 5
|
|
pass
|
|
if 'Kandinsky 5.0 Lite 10s' in selected.name:
|
|
# p.task_args['time_length'] = 10
|
|
shared.sd_model.transformer.set_attention_backend("flex")
|