mirror of
https://github.com/vladmandic/automatic
synced 2026-09-04 03:50:44 +02:00
Vladimir Mandic
73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import sys
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import diffusers
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import transformers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
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from modules.logger import log
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from pipelines import generic
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def load_vibe(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_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
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log.debug(f'Load model: type=VIBE repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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from pipelines.vibe import VIBESanaEditingModel, VIBESanaEditingPipeline, VIBESanaImagePipeline
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diffusers.VIBESanaEditingPipeline = VIBESanaEditingPipeline
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diffusers.VIBESanaEditingModel = VIBESanaEditingModel
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generic.set_pipeline('VIBE', VIBESanaEditingPipeline)
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sys.modules['vibe.transformer.vibe_sana_editing'] = diffusers # monkey patch since hf model_index.json points to custom class path
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from pipelines.vibe import VIBE_SPEC
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transformer = generic.load_transformer(
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repo_id,
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cls_name=VIBESanaEditingModel,
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load_config=diffusers_load_config,
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allow_quant=False,
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native_spec=VIBE_SPEC,
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)
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text_encoder = generic.load_text_encoder(
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repo_id,
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cls_name=transformers.Qwen3VLForConditionalGeneration,
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load_config=diffusers_load_config,
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allow_quant=False,
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allow_shared=False,
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)
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if repo_id is None or repo_id.lower() == 'none':
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return None
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processor = transformers.Qwen3VLProcessor.from_pretrained(
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repo_id,
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subfolder='tokenizer',
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cache_dir=shared.opts.hfcache_dir,
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)
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pipe = VIBESanaEditingPipeline.from_pretrained(
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repo_id,
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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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tokenizer=processor,
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**load_args,
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)
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del transformer
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del text_encoder
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del processor
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['vibe-sana'] = VIBESanaEditingPipeline
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING['vibe-sana'] = VIBESanaImagePipeline
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pipe.task_args = {
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'output_type': 'np',
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}
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sd_hijack_te.init_hijack(pipe)
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sd_hijack_vae.init_hijack(pipe)
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devices.torch_gc(force=True, reason='load')
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return pipe
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