mirror of https://github.com/vladmandic/automatic
42 lines
1.7 KiB
Python
42 lines
1.7 KiB
Python
import transformers
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import diffusers
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from modules import shared, sd_models, devices, model_quant
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from modules.logger import log
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from pipelines import generic
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def load_hunyuandit(checkpoint_info, diffusers_load_config=None):
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if diffusers_load_config is None:
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diffusers_load_config = {}
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repo_id = sd_models.path_to_repo(checkpoint_info)
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sd_models.hf_auth_check(checkpoint_info)
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# import torch # override for hunyuandit
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# devices.dtype = torch.float16
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# devices.dtype_vae = torch.float16
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# devices.dtype_unet = torch.float16
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# diffusers_load_config['torch_dtype'] = devices.dtype
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load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
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log.debug(f'Load model: type=HunyuanDiT repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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transformer = generic.load_transformer(repo_id, cls_name=diffusers.HunyuanDiT2DModel, load_config=diffusers_load_config)
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repo_te = 'Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers' if 'HunyuanDiT-v1' in repo_id else repo_id
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text_encoder_2 = generic.load_text_encoder(repo_te, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder_2", allow_shared=False) # this is not normal t5
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pipe = diffusers.HunyuanDiTPipeline.from_pretrained(
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repo_id,
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transformer=transformer,
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text_encoder_2=text_encoder_2,
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safety_checker=None,
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feature_extractor=None,
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cache_dir=shared.opts.diffusers_dir,
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**load_args,
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)
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del text_encoder_2
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del transformer
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# sd_hijack_te.init_hijack(pipe)
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devices.torch_gc(force=True, reason='load')
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return pipe
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