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feat(video): quantize modular components on load

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The modular loader passes sdnq quantization configs to load_components
as a per-component dict: transformers take the model config, the text
encoder takes the te config, and components without an entry load
unquantized. Pre-quantized repositories keep their own config, which
diffusers detects before a passed config applies.
This commit is contained in:
CalamitousFelicitousness
2026-08-08 00:17:43 +01:00
parent 6375b42ff7
commit d0da21adce
1 changed files with 16 additions and 1 deletions
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modules/video_models/video_modular.py
+16 -1
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@@ -1,7 +1,7 @@
import time
import logging
import torch
from modules import shared, errors, devices
from modules import shared, errors, devices, model_quant
from modules.logger import log
@@ -38,10 +38,25 @@ def load_modular_pipe(repo_cls, repo: str, workflow: str | None = None, revision
)
# workflow selection stays out of from_pretrained: pruning the blocks tree to one task
# would disable runtime auto-dispatch between them; only the component fetch is restricted
load_kwargs = {}
quant_config = {}
quant_args = model_quant.create_config(module='Model')
if 'quantization_config' in quant_args:
quant_config['transformer'] = quant_args['quantization_config']
quant_config['transformer_ref'] = quant_args['quantization_config']
te_args = model_quant.create_config(module='TE')
if 'quantization_config' in te_args:
quant_config['text_encoder'] = te_args['quantization_config']
if quant_config:
# per-component dict without a default entry: only the listed components quantize while
# loading, everything else loads unquantized
load_kwargs['quantization_config'] = quant_config
log.debug(f'Load modular: quant={next(iter(quant_config.values())).__class__.__name__} modules={list(quant_config)}')
pipe.load_components(
workflow=workflow,
dtype=devices.dtype,
cache_dir=shared.opts.hfcache_dir,
**load_kwargs,
**offline_args,
)
loaded = [name for name, component in pipe.components.items() if component is not None]