mirror of https://github.com/vladmandic/automatic
84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
import os
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import importlib.util
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import transformers
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import diffusers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
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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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shared.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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# resolve local path for custom pipeline modules
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local_path = sd_models.path_to_repo(checkpoint_info, local=True)
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pipeline_file = os.path.join(local_path, 'pipeline.py')
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adapter_file = os.path.join(local_path, 'llm_adapter', 'modeling_llm_adapter.py')
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if not os.path.isfile(pipeline_file):
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shared.log.error(f'Load model: type=Anima missing pipeline.py in "{local_path}"')
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return None
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if not os.path.isfile(adapter_file):
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shared.log.error(f'Load model: type=Anima missing llm_adapter/modeling_llm_adapter.py in "{local_path}"')
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return None
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# dynamically import custom classes from the model repo
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pipeline_mod = _import_from_file('anima_pipeline', pipeline_file)
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adapter_mod = _import_from_file('anima_llm_adapter', adapter_file)
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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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shared.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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**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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