mirror of
https://github.com/vladmandic/automatic
synced 2026-09-02 02:50:47 +02:00
Disty0
48 lines
1.9 KiB
Python
48 lines
1.9 KiB
Python
import os
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import diffusers
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from modules import errors, shared, devices, sd_models, model_quant
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debug = shared.log.trace if os.environ.get('SD_LOAD_DEBUG', None) is not None else lambda *args, **kwargs: None
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def load_omnigen(checkpoint_info, diffusers_load_config={}): # pylint: disable=unused-argument
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repo_id = sd_models.path_to_repo(checkpoint_info.name)
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vae = None
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if shared.opts.sd_vae != 'Default' and shared.opts.sd_vae != 'Automatic':
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try:
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debug(f'Load model: type=OmniGen vae="{shared.opts.sd_vae}"')
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from modules import sd_vae
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# vae = sd_vae.load_vae_diffusers(None, sd_vae.vae_dict[shared.opts.sd_vae], 'override')
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vae_file = sd_vae.vae_dict[shared.opts.sd_vae]
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if os.path.exists(vae_file):
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vae_config = os.path.join('configs', 'sdxl', 'vae', 'config.json')
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vae = diffusers.AutoencoderKL.from_single_file(vae_file, config=vae_config, **diffusers_load_config)
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except Exception as e:
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shared.log.error(f"Load model: type=OmniGen failed to load VAE: {e}")
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shared.opts.sd_vae = 'Default'
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if debug:
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errors.display(e, 'OmniGen VAE:')
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load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Transformer')
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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.diffusers_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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if vae is not None:
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load_config['vae'] = vae
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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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devices.torch_gc(force=True)
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return pipe
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