mirror of
https://github.com/vladmandic/automatic
synced 2026-09-17 16:24:33 +02:00
Disty0
62 lines
2.2 KiB
Python
62 lines
2.2 KiB
Python
import os
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import transformers
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import diffusers
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from huggingface_hub import repo_exists
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from modules import sd_hijack_te
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def load_lumina(_checkpoint_info, diffusers_load_config={}):
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from modules import shared, devices, modelloader, model_quant
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modelloader.hf_login()
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load_config, _quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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pipe = diffusers.LuminaText2ImgPipeline.from_pretrained(
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'Alpha-VLLM/Lumina-Next-SFT-diffusers',
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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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def load_lumina2(checkpoint_info, diffusers_load_config={}):
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from modules import shared, devices, sd_models, model_quant
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repo_id = sd_models.path_to_repo(checkpoint_info.name)
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if os.path.isdir(checkpoint_info.filename) and not repo_exists(repo_id):
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repo_id = checkpoint_info.filename
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if shared.opts.teacache_enabled:
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from modules import teacache
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shared.log.debug(f'Transformers cache: type=teacache patch=forward cls={diffusers.Lumina2Transformer2DModel.__name__}')
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diffusers.Lumina2Transformer2DModel.forward = teacache.teacache_lumina2_forward # patch must be done before transformer is loaded
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load_config, quant_config = model_quant.get_dit_args(diffusers_load_config, module='Transformer')
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transformer = diffusers.Lumina2Transformer2DModel.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, module='TE', device_map=True)
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text_encoder = transformers.AutoModel.from_pretrained(
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repo_id,
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subfolder="text_encoder",
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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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pipe = diffusers.Lumina2Pipeline.from_pretrained(
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repo_id,
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cache_dir=shared.opts.diffusers_dir,
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text_encoder=text_encoder,
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transformer=transformer,
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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)
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return pipe
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