mirror of https://github.com/vladmandic/automatic
112 lines
4.9 KiB
Python
112 lines
4.9 KiB
Python
import os
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import transformers
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import diffusers
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from huggingface_hub import auth_check
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from modules import shared, devices, sd_models, model_quant, modelloader, 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='Transformer', device_map=True)
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fn = None
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if shared.opts.sd_unet is not None and shared.opts.sd_unet != 'Default':
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from modules import sd_unet
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if shared.opts.sd_unet not in list(sd_unet.unet_dict):
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shared.log.error(f'Load module: type=Transformer not found: {shared.opts.sd_unet}')
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return None
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fn = sd_unet.unet_dict[shared.opts.sd_unet] if os.path.exists(sd_unet.unet_dict[shared.opts.sd_unet]) else None
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if fn is not None and 'gguf' in fn.lower():
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shared.log.error('Load model: type=HiDream format="gguf" unsupported')
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transformer = None
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# from modules import ggml
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# transformer = ggml.load_gguf(fn, cls=diffusers.HiDreamImageTransformer2DModel, compute_dtype=devices.dtype)
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elif fn is not None and 'safetensors' in fn.lower():
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shared.log.debug(f'Load model: type=HiDream transformer="{repo_id}" quant="{model_quant.get_quant(repo_id)}" args={load_args}')
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transformer = diffusers.HiDreamImageTransformer2DModel.from_single_file(fn, cache_dir=shared.opts.hfcache_dir, **load_args)
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# elif model_quant.check_nunchaku('Transformer'):
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# shared.log.error(f'Load model: type=HiDream transformer="{repo_id}" quant="Nunchaku" unsupported')
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# transformer = None
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else:
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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' 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_encoders(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=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' and text_encoder_3 is not None:
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sd_models.move_model(text_encoder_3, 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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llama_repo = shared.opts.model_h1_llama_repo if shared.opts.model_h1_llama_repo != 'Default' else 'meta-llama/Meta-Llama-3.1-8B-Instruct'
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shared.log.debug(f'Load model: type=HiDream te4="{llama_repo}" quant="{model_quant.get_quant_type(quant_args)}" args={load_args}')
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auth_check(llama_repo)
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text_encoder_4 = transformers.LlamaForCausalLM.from_pretrained(
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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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tokenizer_4 = transformers.PreTrainedTokenizerFast.from_pretrained(
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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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if shared.opts.diffusers_offload_mode != 'none' and text_encoder_4 is not None:
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sd_models.move_model(text_encoder_4, devices.cpu)
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return text_encoder_3, text_encoder_4, tokenizer_4
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def load_hidream(checkpoint_info, diffusers_load_config={}):
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repo_id = sd_models.path_to_repo(checkpoint_info.name)
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login = modelloader.hf_login()
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try:
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auth_check(repo_id)
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except Exception as e:
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shared.log.error(f'Load model: repo="{repo_id}" login={login} {e}')
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return False
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transformer = load_transformer(repo_id, diffusers_load_config)
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text_encoder_3, text_encoder_4, tokenizer_4 = load_text_encoders(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=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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transformer=transformer,
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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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cache_dir=shared.opts.diffusers_dir,
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**load_args,
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)
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sd_hijack_te.init_hijack(pipe)
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del text_encoder_3
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del text_encoder_4
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del tokenizer_4
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del transformer
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devices.torch_gc()
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return pipe
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