mirror of https://github.com/vladmandic/automatic
38 lines
2.1 KiB
Python
38 lines
2.1 KiB
Python
import transformers
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import diffusers
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def load_meissonic(checkpoint_info, diffusers_load_config={}):
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from modules import shared, devices, modelloader, sd_models
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from modules.meissonic.transformer import Transformer2DModel as TransformerMeissonic
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from modules.meissonic.scheduler import Scheduler as MeissonicScheduler
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from modules.meissonic.pipeline import Pipeline as PipelineMeissonic
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from modules.meissonic.pipeline_img2img import Img2ImgPipeline as PipelineMeissonicImg2Img
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from modules.meissonic.pipeline_inpaint import InpaintPipeline as PipelineMeissonicInpaint
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modelloader.hf_login()
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fn = sd_models.path_to_repo(checkpoint_info.path)
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cache_dir = shared.opts.diffusers_dir
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diffusers_load_config['variant'] = 'fp16'
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diffusers_load_config['trust_remote_code'] = True
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model = TransformerMeissonic.from_pretrained(fn, subfolder="transformer", cache_dir=cache_dir, **diffusers_load_config)
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vqvae = diffusers.VQModel.from_pretrained(fn, subfolder="vqvae", cache_dir=cache_dir, **diffusers_load_config)
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text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained(fn, subfolder="text_encoder", cache_dir=cache_dir)
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# text_encoder = transformers.CLIPTextModelWithProjection.from_pretrained("laion/CLIP-ViT-H-14-laion2B-s32B-b79K", cache_dir=cache_dir)
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tokenizer = transformers.CLIPTokenizer.from_pretrained(fn, subfolder="tokenizer", cache_dir=cache_dir)
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scheduler = MeissonicScheduler.from_pretrained(fn, subfolder="scheduler", cache_dir=cache_dir)
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pipe = PipelineMeissonic(
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vqvae=vqvae.to(devices.dtype),
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text_encoder=text_encoder.to(devices.dtype),
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transformer=model.to(devices.dtype),
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tokenizer=tokenizer,
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scheduler=scheduler,
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)
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diffusers.pipelines.auto_pipeline.AUTO_TEXT2IMAGE_PIPELINES_MAPPING["meissonic"] = PipelineMeissonic
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diffusers.pipelines.auto_pipeline.AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["meissonic"] = PipelineMeissonicImg2Img
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diffusers.pipelines.auto_pipeline.AUTO_INPAINT_PIPELINES_MAPPING["meissonic"] = PipelineMeissonicInpaint
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devices.torch_gc()
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return pipe
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