mirror of
https://github.com/vladmandic/automatic
synced 2026-09-18 16:54:33 +02:00
@@ -34,6 +34,7 @@
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- one of the best upscalers (traditional, non-diffusion) available today!
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- available via *process -> upscale -> chainner*
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- **Changes**
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- Update all core requirements
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- Support Remote VAE with *Omnigen, Lumina 2 and PixArt*
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- Add `--trace` command line param that enables trace logging
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- Use Diffusers version of *OmniGen*
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@@ -73,6 +74,7 @@
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- Fix process batch with batch count
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- Fix process batch double image save
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- Fix unapply texture tiling
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- Fix nunchaku batch support
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- Suppress torch empty logging
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- Improve TAESD live preview downscale handling
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@@ -1,13 +1,10 @@
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# MIT-Han-Lab Nunchaku: <https://github.com/mit-han-lab/nunchaku>
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# TODO nunchaku: cache-dir for transformer and t5 loader
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# TODO nunchaku: batch support
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from installer import log, pip
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from modules import devices
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ver = '0.2.0'
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ver = '0.3.1'
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ok = False
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@@ -138,6 +138,7 @@ def load_quants(kwargs, repo_id, cache_dir, allow_quant):
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nunchaku_repo = 'mit-han-lab/nunchaku-t5/awq-int4-flux.1-t5xxl.safetensors'
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shared.log.debug(f'Load module: quant=Nunchaku module=t5 repo="{nunchaku_repo}" precision={nunchaku_precision}')
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kwargs['text_encoder_2'] = nunchaku.NunchakuT5EncoderModel.from_pretrained(nunchaku_repo, torch_dtype=devices.dtype)
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kwargs['text_encoder_2'].quantization_method = 'SVDQuant'
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elif 'text_encoder_2' not in kwargs and model_quant.check_quant('TE'):
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quant_args = model_quant.create_config(allow=allow_quant, module='TE')
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if quant_args:
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