mirror of
https://github.com/vladmandic/automatic
synced 2026-09-11 23:56:44 +02:00
Vladimir Mandic
83 lines
3.4 KiB
Python
83 lines
3.4 KiB
Python
import os
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import time
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import transformers
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import diffusers
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from modules import shared, devices, sd_models, timer, model_quant, modelloader
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def hijack_encode_prompt(*args, **kwargs):
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t0 = time.time()
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if 'max_sequence_length' in kwargs:
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kwargs['max_sequence_length'] = os.environ.get('HIDREAM_MAX_SEQUENCE_LENGTH', 256)
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res = shared.sd_model.orig_encode_prompt(*args, **kwargs)
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t1 = time.time()
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timer.process.add('te', t1-t0)
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# shared.log.debug(f'Hijack: te={shared.sd_model.text_encoder.__class__.__name__} time={t1-t0:.2f}')
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
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return res
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def load_hidream(checkpoint_info, diffusers_load_config={}):
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modelloader.hf_login()
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repo_id = sd_models.path_to_repo(checkpoint_info.name)
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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Transformer', device_map=True)
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shared.log.debug(f'Load model: type=HiDream transformer="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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transformer = diffusers.HiDreamImageTransformer2DModel.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':
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transformer = transformer.to(devices.cpu)
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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=HiDream te3="{repo_id}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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text_encoder_3 = transformers.T5EncoderModel.from_pretrained(
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repo_id,
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subfolder="text_encoder_3",
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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':
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text_encoder_3 = text_encoder_3.to(devices.cpu)
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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='LLM', device_map=True)
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shared.log.debug(f'Load model: type=HiDream te4="{shared.opts.model_h1_llama_repo}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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tokenizer_4 = transformers.PreTrainedTokenizerFast.from_pretrained(
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shared.opts.model_h1_llama_repo,
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cache_dir=shared.opts.hfcache_dir,
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**load_args,
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)
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text_encoder_4 = transformers.LlamaForCausalLM.from_pretrained(
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shared.opts.model_h1_llama_repo,
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output_hidden_states=True,
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output_attentions=True,
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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':
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text_encoder_4 = text_encoder_4.to(devices.cpu)
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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=HiDream model="{checkpoint_info.name}" repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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pipe = diffusers.HiDreamImagePipeline.from_pretrained(
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repo_id,
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text_encoder_3=text_encoder_3,
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text_encoder_4=text_encoder_4,
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tokenizer_4=tokenizer_4,
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transformer=transformer,
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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.orig_encode_prompt = pipe.encode_prompt
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pipe.encode_prompt = hijack_encode_prompt
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devices.torch_gc()
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return pipe
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