mirror of
https://github.com/vladmandic/sdnext.git
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61 lines
3.0 KiB
Python
61 lines
3.0 KiB
Python
# using https://github.com/rootonchair/diffuser_layerdiffuse
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from huggingface_hub import hf_hub_download
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from safetensors.torch import load_file
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from modules.layerdiffuse.layerdiffuse_model import TransparentVAEDecoder
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from modules.layerdiffuse.layerdiffuse_loader import load_lora_to_unet, merge_delta_weights_into_unet
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from modules import shared, errors, devices
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def apply_layerdiffuse_sd15(pipeline):
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vae_model_path = hf_hub_download('LayerDiffusion/layerdiffusion-v1', 'layer_sd15_vae_transparent_decoder.safetensors', cache_dir=shared.opts.hfcache_dir)
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transparent_vae = pipeline.vae
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transparent_vae.__class__ = TransparentVAEDecoder
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transparent_vae.set_transparent_decoder(load_file(vae_model_path))
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pipeline.vae = transparent_vae
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lora_model_path = hf_hub_download('LayerDiffusion/layerdiffusion-v1','layer_sd15_transparent_attn.safetensors', cache_dir=shared.opts.hfcache_dir)
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load_lora_to_unet(pipeline.unet, lora_model_path, frames=1, device=devices.device, dtype=devices.dtype)
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def apply_layerdiffuse_sdxl_attn(pipeline):
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vae_model_path = hf_hub_download('LayerDiffusion/layerdiffusion-v1', 'vae_transparent_decoder.safetensors', cache_dir=shared.opts.hfcache_dir)
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transparent_vae = pipeline.vae
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transparent_vae.__class__ = TransparentVAEDecoder
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transparent_vae.set_transparent_decoder(load_file(vae_model_path))
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pipeline.vae = transparent_vae
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pipeline.load_lora_weights('rootonchair/diffuser_layerdiffuse', weight_name='diffuser_layer_xl_transparent_attn.safetensors')
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def apply_layerdiffuse_sdxl_conv(pipeline):
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model_path = hf_hub_download('LayerDiffusion/layerdiffusion-v1', 'vae_transparent_decoder.safetensors', cache_dir=shared.opts.hfcache_dir)
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transparent_vae = pipeline.vae
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transparent_vae.__class__ = TransparentVAEDecoder
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transparent_vae.set_transparent_decoder(load_file(model_path))
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pipeline.vae = transparent_vae
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lora_model_path = hf_hub_download('rootonchair/diffuser_layerdiffuse', 'diffuser_layer_xl_transparent_conv.safetensors', cache_dir=shared.opts.hfcache_dir)
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lora_state_dict = load_file(lora_model_path)
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merge_delta_weights_into_unet(pipeline, lora_state_dict)
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def apply_layerdiffuse():
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if not shared.native:
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return
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try:
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if shared.sd_model_type == 'sd':
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shared.log.info(f'LayerDiffuse: class={shared.sd_model.__class__.__name__}')
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apply_layerdiffuse_sd15(shared.sd_model)
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elif shared.sd_model_type == 'sdxl':
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# shared.log.info(f'LayerDiffuse: class={shared.sd_model.__class__.__name__} type=attn')
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# apply_layerdiffuse_sdxl_attn(shared.sd_model)
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shared.log.info(f'LayerDiffuse: class={shared.sd_model.__class__.__name__} type=conv')
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apply_layerdiffuse_sdxl_conv(shared.sd_model)
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else:
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shared.log.warning(f'LayerDiffuse: class={shared.sd_model.__class__.__name__} not supported')
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shared.sd_model.layerdiffusion = True
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except Exception as e:
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shared.log.error(f'LayerDiffuse: {e}')
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errors.display(e, 'LayerDiffuse')
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