mirror of https://github.com/vladmandic/automatic
87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
import sys
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import importlib.util
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import transformers
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import diffusers
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import huggingface_hub as hf
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
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from modules.logger import log
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from pipelines import generic
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def _import_from_file(module_name, file_path):
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spec = importlib.util.spec_from_file_location(module_name, file_path)
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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return mod
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def load_anima(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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load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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log.debug(f'Load model: type=Anima repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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# download custom pipeline modules from repo
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try:
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pipeline_file = hf.hf_hub_download(repo_id, filename='pipeline.py', cache_dir=shared.opts.diffusers_dir)
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adapter_file = hf.hf_hub_download(repo_id, filename='llm_adapter/modeling_llm_adapter.py', cache_dir=shared.opts.diffusers_dir)
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except Exception as e:
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log.error(f'Load model: type=Anima failed to download custom modules: {e}')
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return None
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# dynamically import custom classes and register in sys.modules so
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# Diffusers' from_pretrained can resolve them via trust_remote_code
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adapter_mod = _import_from_file('modeling_llm_adapter', adapter_file)
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sys.modules['modeling_llm_adapter'] = adapter_mod
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pipeline_mod = _import_from_file('pipeline', pipeline_file)
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sys.modules['pipeline'] = pipeline_mod
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AnimaTextToImagePipeline = pipeline_mod.AnimaTextToImagePipeline
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AnimaLLMAdapter = adapter_mod.AnimaLLMAdapter
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# load components
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transformer = generic.load_transformer(repo_id, cls_name=diffusers.CosmosTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
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text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3Model, load_config=diffusers_load_config, subfolder="text_encoder", allow_shared=False)
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shared.state.begin('Load adapter')
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try:
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llm_adapter = AnimaLLMAdapter.from_pretrained(
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repo_id,
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subfolder="llm_adapter",
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cache_dir=shared.opts.diffusers_dir,
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torch_dtype=devices.dtype,
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)
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except Exception as e:
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log.error(f'Load model: type=Anima adapter: {e}')
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return None
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finally:
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shared.state.end()
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tokenizer = transformers.AutoTokenizer.from_pretrained(repo_id, subfolder="tokenizer", cache_dir=shared.opts.diffusers_dir)
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t5_tokenizer = transformers.AutoTokenizer.from_pretrained(repo_id, subfolder="t5_tokenizer", cache_dir=shared.opts.diffusers_dir)
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# assemble pipeline
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pipe = AnimaTextToImagePipeline.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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llm_adapter=llm_adapter,
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tokenizer=tokenizer,
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t5_tokenizer=t5_tokenizer,
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cache_dir=shared.opts.diffusers_dir,
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trust_remote_code=True,
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**load_args,
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)
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del text_encoder
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del transformer
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del llm_adapter
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sd_hijack_te.init_hijack(pipe)
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sd_hijack_vae.init_hijack(pipe)
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devices.torch_gc()
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return pipe
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