mirror of
https://github.com/vladmandic/automatic
synced 2026-08-25 22:20:46 +02:00
Vladimir Mandic
99 lines
3.9 KiB
Python
99 lines
3.9 KiB
Python
import os
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import sys
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import transformers
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import diffusers
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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_bria(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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repo_lc = repo_id.lower()
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# FIBO family is upstream diffusers-based, so use native classes directly.
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if 'fibo' in repo_lc:
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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=BriaFibo 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.bria import BRIA_FIBO_SPEC
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transformer = generic.load_transformer(
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repo_id,
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cls_name=diffusers.BriaFiboTransformer2DModel,
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load_config=diffusers_load_config,
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allow_quant=False,
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native_spec=BRIA_FIBO_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.SmolLM3ForCausalLM,
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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 'fibo-edit' in repo_lc:
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cls = diffusers.BriaFiboEditPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['bria-fibo'] = cls
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING['bria-fibo'] = cls
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING['bria-fibo'] = cls
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else:
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cls = diffusers.BriaFiboPipeline
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING['bria-fibo'] = cls
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generic.set_pipeline('Bria', cls)
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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 = cls.from_pretrained(
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repo_id,
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transformer=transformer,
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text_encoder=text_encoder,
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cache_dir=shared.opts.diffusers_dir,
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**load_args,
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)
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from pipelines.bria import prompt_to_json
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pipe.before_prompt_encode = prompt_to_json.before_prompt_encode
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pipe.task_args = {
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'output_type': 'np',
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}
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else:
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sys.path.append(os.path.join(os.path.dirname(__file__), 'bria'))
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from pipelines.bria.bria_pipeline import BriaPipeline
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from pipelines.bria.transformer_bria import BriaTransformer2DModel
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diffusers.BriaPipeline = BriaPipeline
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diffusers.BriaTransformer2DModel = BriaTransformer2DModel
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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=Bria 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.bria import BRIA_SPEC
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transformer = generic.load_transformer(repo_id, cls_name=BriaTransformer2DModel, load_config=diffusers_load_config, native_spec=BRIA_SPEC)
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text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
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generic.set_pipeline('Bria', BriaPipeline)
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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 = BriaPipeline.from_pretrained(
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repo_id,
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transformer=transformer,
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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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del text_encoder
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del transformer
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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()
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return pipe
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