mirror of
https://github.com/vladmandic/automatic
synced 2026-09-06 13:00:44 +02:00
CalamitousFelicitousness
4af3a57741
Dispatch anima loras through a dedicated native loader covering kohya, bfl/ai-toolkit, and hybrid (bfl with alpha plus qwen3 text encoder) formats. Cosmos 2.0 path rename is mirrored from diffusers in flat (underscore) form so rewritten paths match network_layer_mapping keys without further conversion. Split model_type from cosmos to anima so a future base-cosmos2 lora path stays separable. Update flow_models, taesd supported list, and the taesd wanvideo bucket so samplers and preview decoding keep working after the split. Extend assign_network_names_to_compvis_modules to walk pipe.llm_adapter under the lora_llm_adapter_ prefix, and add llm_adapter to default_components so activate and deactivate include it for anima models while staying inert elsewhere via the existing getattr guards.
69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
from modules import shared
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force_hashes_diffusers = [ # forced always
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# '816d0eed49fd', # flash-sdxl
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# 'c2ec22757b46', # flash-sd15
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# '22c8339e7666', # spo-sdxl-10ep
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# 'aaebf6360f7d', # sd15-lcm
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# '3d18b05e4f56', # sdxl-lcm
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# 'b71dcb732467', # sdxl-tcd
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# '813ea5fb1c67', # sdxl-turbo
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# '5a48ac366664', # hyper-sd15-1step
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# 'ee0ff23dcc42', # hyper-sd15-2step
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# 'e476eb1da5df', # hyper-sd15-4step
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# 'ecb844c3f3b0', # hyper-sd15-8step
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# '1ab289133ebb', # hyper-sd15-8step-cfg
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# '4f494295edb1', # hyper-sdxl-8step
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# 'ca14a8c621f8', # hyper-sdxl-8step-cfg
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# '1c88f7295856', # hyper-sdxl-4step
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# 'fdd5dcd1d88a', # hyper-sdxl-2step
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# '8cca3706050b', # hyper-sdxl-1step
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]
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allow_native = [
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'sd',
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'sdxl',
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'sd3',
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'f1',
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'chroma',
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'zimage',
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'anima',
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]
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force_classes_diffusers = [ # forced always
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'FluxKontextPipeline', 'FluxKontextInpaintPipeline',
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]
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fuse_ignore = [
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'hunyuanvideo',
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]
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def get_method(shorthash=''):
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use_diffusers = shared.opts.lora_force_diffusers or (shared.sd_model.__class__.__name__ in force_classes_diffusers) or (shared.sd_model_type not in allow_native)
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if len(shorthash) > 4:
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use_diffusers = use_diffusers or any(x.startswith(shorthash) for x in force_hashes_diffusers)
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nunchaku_dit = hasattr(shared.sd_model, 'transformer') and 'Nunchaku' in shared.sd_model.transformer.__class__.__name__
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nunchaku_unet = hasattr(shared.sd_model, 'unet') and 'Nunchaku' in shared.sd_model.unet.__class__.__name__
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use_nunchaku = nunchaku_dit or nunchaku_unet
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if use_nunchaku:
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return 'nunchaku'
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elif use_diffusers:
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return 'diffusers'
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else:
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return 'native'
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def disable_fuse():
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if hasattr(shared.sd_model, 'quantization_config'):
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return True
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if hasattr(shared.sd_model, 'transformer') and hasattr(shared.sd_model.transformer, 'quantization_config'):
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return True
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if hasattr(shared.sd_model, 'transformer_2') and hasattr(shared.sd_model.transformer_2, 'quantization_config'):
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return True
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if hasattr(shared.sd_model, '_lora_partial'):
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return True
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return shared.sd_model_type in fuse_ignore
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