mirror of
https://github.com/vladmandic/automatic
synced 2026-09-03 19:40:47 +02:00
vladmandic
67 lines
2.7 KiB
Python
67 lines
2.7 KiB
Python
import transformers
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import diffusers
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from modules import shared, devices, sd_models, model_quant, sd_hijack_te
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from modules.logger import log
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from pipelines import generic
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def load_step1x_edit(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=Step1XEdit repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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# Load text encoder (Qwen2.5-VL - available in transformers)
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text_encoder = generic.load_text_encoder(repo_id,
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cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config
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)
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# Load processor for Qwen2.5-VL
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processor = transformers.Qwen2_5_VLProcessor.from_pretrained(repo_id,
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cache_dir=shared.opts.hfcache_dir,
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subfolder='processor'
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)
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# Step1XEditPipeline and Step1XEditTransformer2DModel are custom classes not in current diffusers
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# Try direct pipeline class first, fall back to trust_remote_code
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pipe_cls = getattr(diffusers, 'Step1XEditPipeline', 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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text_encoder=text_encoder,
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processor=processor,
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**load_args,
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)
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else:
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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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text_encoder=text_encoder,
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processor=processor,
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trust_remote_code=True,
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**load_args,
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)
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del text_encoder
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del processor
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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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""" Reference
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"StepFun Step1X-Edit v1.2": {
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"path": "stepfun-ai/Step1X-Edit-v1p2",
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"preview": "stepfun-ai--Step1X-Edit-v1p2.jpg",
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"desc": "Multimodal image editing model using Step1X transformer architecture with Qwen2.5-VL text encoding, trained with Flow Matching scheduler for high-quality in-context image edits and refinements.",
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"skip": true,
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"extras": "sampler: Default",
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"size": 8.58,
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"date": "2025 October"
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},
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"""
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