mirror of
https://github.com/vladmandic/automatic
synced 2026-09-01 18:46:53 +02:00
Vladimir Mandic
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import diffusers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te
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from modules.logger import log
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from pipelines import generic
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def load_omnigen(checkpoint_info, diffusers_load_config=None): # pylint: disable=unused-argument
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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_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
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load_config.pop('cache_dir', None)
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quant_config.pop('cache_dir', None)
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log.debug(f'Load model: type=OmniGen repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
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if repo_id is None or repo_id.lower() == 'none':
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return None
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transformer = diffusers.OmniGenTransformer2DModel.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_config,
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**quant_config,
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)
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load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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load_config.pop('cache_dir', None)
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quant_config.pop('cache_dir', None)
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pipe = diffusers.OmniGenPipeline.from_pretrained(
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repo_id,
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transformer=transformer,
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cache_dir=shared.opts.diffusers_dir,
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**load_config,
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)
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sd_hijack_te.init_hijack(pipe)
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devices.torch_gc(force=True, reason='load')
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return pipe
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def load_omnigen2(checkpoint_info, diffusers_load_config=None): # pylint: disable=unused-argument
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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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from pipelines.omnigen2 import OmniGen2Pipeline, OmniGen2Transformer2DModel, Qwen2_5_VLForConditionalGeneration
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["omnigen2"] = OmniGen2Pipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["omnigen2"] = OmniGen2Pipeline
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["omnigen2"] = OmniGen2Pipeline
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generic.set_pipeline('OmniGen2', OmniGen2Pipeline)
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load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Model')
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load_config.pop('cache_dir', None)
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quant_config.pop('cache_dir', None)
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log.debug(f'Load model: type=OmniGen2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
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if repo_id is None or repo_id.lower() == 'none':
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return None
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transformer = OmniGen2Transformer2DModel.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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trust_remote_code=True,
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**load_config,
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**quant_config,
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)
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load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='TE')
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load_config.pop('cache_dir', None)
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quant_config.pop('cache_dir', None)
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mllm = Qwen2_5_VLForConditionalGeneration.from_pretrained(
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repo_id,
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subfolder="mllm",
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cache_dir=shared.opts.hfcache_dir,
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trust_remote_code=True,
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**load_config,
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**quant_config,
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)
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pipe = OmniGen2Pipeline.from_pretrained(
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repo_id,
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# transformer=transformer,
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mllm=mllm,
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cache_dir=shared.opts.diffusers_dir,
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trust_remote_code=True,
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**load_config,
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)
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pipe.transformer = transformer # for omnigen2 transformer must be loaded after pipeline
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sd_hijack_te.init_hijack(pipe)
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devices.torch_gc(force=True, reason='load')
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return pipe
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