mirror of https://github.com/vladmandic/automatic
45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
import transformers
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import diffusers
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from huggingface_hub import file_exists
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def load_pixart(checkpoint_info, diffusers_load_config={}):
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from modules import shared, devices, modelloader, sd_models, model_quant
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modelloader.hf_login()
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repo_id = sd_models.path_to_repo(checkpoint_info.name)
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repo_id_tenc = repo_id
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repo_id_pipe = repo_id
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if not file_exists(repo_id_tenc, "text_encoder/config.json"):
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repo_id_tenc = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
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if not file_exists(repo_id_pipe, "model_index.json"):
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repo_id_pipe = "PixArt-alpha/PixArt-Sigma-XL-2-1024-MS"
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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='Model')
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transformer = diffusers.PixArtTransformer2DModel.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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load_args, quant_args = model_quant.get_dit_args(diffusers_load_config, module='TE', device_map=True)
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text_encoder = transformers.T5EncoderModel.from_pretrained(
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repo_id_tenc,
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subfolder="text_encoder",
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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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load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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pipe = diffusers.PixArtSigmaPipeline.from_pretrained(
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repo_id_pipe,
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cache_dir=shared.opts.diffusers_dir,
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transformer=transformer,
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text_encoder=text_encoder,
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**load_args,
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)
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devices.torch_gc(force=True)
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return pipe
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