mirror of
https://github.com/vladmandic/automatic
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CalamitousFelicitousness
b37e275212
Adds pipelines/ernie/ernie_lora.py with try_load_lora/lokr/loha/oft entry points modeled on the z-image native loader, wires it into lora_load.load_safetensors, and adds 'ernieimage' to allow_native. ERNIE attention is not fused (separate to_q/to_k/to_v/to_out.0), so the loader skips the qkv-split machinery the z-image loader needs and supports all four families uniformly. Recognized prefixes are diffusion_model., transformer., and lora_unet_; PEFT lora_A/lora_B keys are normalized to lora_down/lora_up. Verified against PEFT, LoKR, and Kohya/AIT files in the wild with zero unmapped or shape-mismatched modules.
70 lines
2.2 KiB
Python
70 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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'ernieimage',
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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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