mirror of
https://github.com/vladmandic/automatic
synced 2026-09-17 16:24:33 +02:00
vladmandic
99 lines
4.1 KiB
Python
99 lines
4.1 KiB
Python
import transformers
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import diffusers
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from modules import shared, sd_models, sd_hijack_te, devices, model_quant
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from modules.logger import log
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from pipelines import generic
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def load_lumina(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_config, _quant_config = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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log.debug(f'Load model: type=LuminaSFT repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
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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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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_lumina2(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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if shared.opts.teacache_enabled:
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from modules import teacache
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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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log.debug(f'Load model: type=Lumina2 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
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transformer = generic.load_transformer(repo_id, cls_name=diffusers.Lumina2Transformer2DModel, load_config=diffusers_load_config)
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text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Gemma2Model, load_config=diffusers_load_config)
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load_config, _quant_args = 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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del transformer
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del text_encoder
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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_lumina_dimoo(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_config, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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log.debug(f'Load model: type=LuminaDiMOO repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_config}')
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pipe_cls = getattr(diffusers, 'LuminaDiMOOPipeline', None)
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if pipe_cls is not None:
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pipe = pipe_cls.from_pretrained(
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repo_id,
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cache_dir=shared.opts.diffusers_dir,
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**load_config,
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)
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else:
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try:
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pipe = diffusers.DiffusionPipeline.from_pretrained(
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repo_id,
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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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except Exception as e:
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raise RuntimeError(f'Lumina-DiMOO is not available in installed diffusers={diffusers.__version__}. Please update diffusers to a version that includes LuminaDiMOOPipeline.') from e
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devices.torch_gc(force=True, reason='load')
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return pipe
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""" Reference
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"AlphaVLLM Lumina DiMOO": {
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"path": "Alpha-VLLM/Lumina-DiMOO",
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"desc": "Lumina-DiMOO is an omni diffusion large language model for multimodal generation and understanding with text-to-image, image editing and understanding capabilities.",
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"preview": "Alpha-VLLM--Lumina-DiMOO.jpg",
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"skip": true,
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"extras": "sampler: Default",
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"size": 0,
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"date": "2025 September"
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},
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"""
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