mirror of https://github.com/vladmandic/automatic
68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
import transformers
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import diffusers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te
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def load_transformer(repo_id, diffusers_load_config={}):
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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model', device_map=True)
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shared.log.debug(f'Load model: type=Qwen transformer="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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transformer = diffusers.QwenImageTransformer2DModel.from_pretrained(
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repo_id,
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subfolder="transformer",
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cache_dir=shared.opts.hfcache_dir,
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**load_args,
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**quant_args,
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)
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if shared.opts.diffusers_offload_mode != 'none' and transformer is not None:
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sd_models.move_model(transformer, devices.cpu)
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return transformer
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def load_text_encoder(repo_id, diffusers_load_config={}):
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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
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shared.log.debug(f'Load model: type=Qwen te="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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text_encoder = transformers.Qwen2_5_VLForConditionalGeneration.from_pretrained(
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repo_id,
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subfolder="text_encoder",
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cache_dir=shared.opts.hfcache_dir,
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**load_args,
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**quant_args,
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)
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if shared.opts.diffusers_offload_mode != 'none' and text_encoder is not None:
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sd_models.move_model(text_encoder, devices.cpu)
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return text_encoder
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def load_qwen(checkpoint_info, 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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transformer = load_transformer(repo_id, diffusers_load_config)
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text_encoder = load_text_encoder(repo_id, diffusers_load_config)
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load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model')
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shared.log.debug(f'Load model: type=Qwen model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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cls = diffusers.QwenImagePipeline
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pipe = cls.from_pretrained(
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repo_id,
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transformer=transformer,
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text_encoder=text_encoder,
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cache_dir=shared.opts.diffusers_dir,
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**load_args,
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)
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pipe.task_args = {
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'output_type': 'np',
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}
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del text_encoder
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del transformer
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sd_hijack_te.init_hijack(pipe)
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from modules.video_models import video_vae
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pipe.vae.orig_decode = pipe.vae.decode
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pipe.vae.decode = video_vae.hijack_vae_decode
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devices.torch_gc()
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return pipe
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