mirror of
https://github.com/vladmandic/automatic
synced 2026-09-06 04:50:44 +02:00
vladmandic
91 lines
3.5 KiB
Python
91 lines
3.5 KiB
Python
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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pipe_cls = getattr(diffusers, 'VIBESanaEditingPipeline', None)
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transformer_cls = getattr(diffusers, 'VIBESanaEditingModel', None)
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text_encoder_cls = getattr(transformers, 'Qwen3VLForConditionalGeneration', None)
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processor_cls = getattr(transformers, 'Qwen3VLProcessor', None)
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if pipe_cls is not None and transformer_cls is not None and text_encoder_cls is not None and processor_cls is not None:
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transformer = generic.load_transformer(
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repo_id,
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cls_name=transformer_cls,
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load_config=diffusers_load_config,
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allow_quant=False,
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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=text_encoder_cls,
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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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processor = processor_cls.from_pretrained(repo_id, subfolder='tokenizer', cache_dir=shared.opts.hfcache_dir)
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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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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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else:
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try:
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from installer import install, installed
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if not installed('vibe', quiet=True):
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install('git+https://github.com/ai-forever/VIBE', 'vibe')
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import vibe # pylint: disable=unused-import
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except Exception as e:
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raise RuntimeError('VIBE requires either native diffusers VIBESana classes or `vibe` package') from e
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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_args,
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)
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['vibe-sana'] = pipe.__class__
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING['vibe-sana'] = pipe.__class__
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING['vibe-sana'] = pipe.__class__
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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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""" Reference
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"VIBE Image Edit": {
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"path": "iitolstykh/VIBE-Image-Edit",
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"preview": "iitolstykh--VIBE-Image-Edit.jpg",
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"desc": "VIBE is an open-source text-guided image editing model combining Sana1.5-1.6B diffusion backbone with Qwen3-VL multimodal conditioning for fast, instruction-based edits.",
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"skip": true,
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"extras": "sampler: Default, cfg_scale: 4.5, image_guidance_scale: 1.2, steps: 20",
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"size": 9.72,
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"date": "2025 December"
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},
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"""
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