mirror of
https://github.com/vladmandic/automatic
synced 2026-09-05 20:40:44 +02:00
Vladimir Mandic
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
import time
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import diffusers
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import transformers
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from modules import shared, devices, errors, timer, sd_models, model_quant, sd_hijack_vae
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from modules.logger import log
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from pipelines import generic
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class SDXSPipeline(diffusers.DiffusionPipeline):
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name = "SDXSPipeline"
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def hijack_encode_text(prompt: str | list[str]):
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jobid = shared.state.begin('TE Encode')
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t0 = time.time()
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try:
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prompt = shared.sd_model.refine_prompts(prompt)
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except Exception as e:
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log.error(f'Encode prompt: {e}')
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errors.display(e, 'Encode prompt')
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try:
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res = shared.sd_model.orig_encode_text(prompt)
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except Exception as e:
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log.error(f'Encode prompt: {e}')
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errors.display(e, 'Encode prompt')
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res = None
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t1 = time.time()
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timer.process.add('te', t1-t0)
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shared.sd_model = sd_models.apply_balanced_offload(shared.sd_model)
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shared.state.end(jobid)
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return res
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def load_sdxs(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=SDXS repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
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text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3_5ForConditionalGeneration, load_config=diffusers_load_config, allow_shared=False)
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generic.set_pipeline('SDXS', SDXSPipeline)
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if repo_id is None or repo_id.lower() == 'none':
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return None
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pipe = SDXSPipeline.from_pretrained(
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repo_id,
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text_encoder=text_encoder,
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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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pipe.task_args = {
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'generator': None,
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'output_type': 'np',
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}
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pipe.orig_encode_text = pipe.encode_text
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pipe.encode_text = hijack_encode_text
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sd_hijack_vae.init_hijack(pipe)
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del text_encoder
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devices.torch_gc(force=True, reason='load')
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return pipe
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