forked from Hitmare/Eris_api_tensor_patch
parent
1d24924c3a
commit
e0000cab58
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@ -1,92 +1,94 @@
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import torch.nn
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import ldm.modules.diffusionmodules.openaimodel
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from modules import script_callbacks, shared, devices
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unet_options = []
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current_unet_option = None
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current_unet = None
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def list_unets():
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new_unets = script_callbacks.list_unets_callback()
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unet_options.clear()
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unet_options.extend(new_unets)
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def get_unet_option(option=None):
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option = option or shared.opts.sd_unet
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if option == "None":
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return None
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if option == "Automatic":
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name = shared.sd_model.sd_checkpoint_info.model_name
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options = [x for x in unet_options if x.model_name == name]
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option = options[0].label if options else "None"
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return next(iter([x for x in unet_options if x.label == option]), None)
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def apply_unet(option=None):
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global current_unet_option
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global current_unet
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new_option = get_unet_option(option)
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if new_option == current_unet_option:
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return
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if current_unet is not None:
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print(f"Dectivating unet: {current_unet.option.label}")
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current_unet.deactivate()
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current_unet_option = new_option
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if current_unet_option is None:
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current_unet = None
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if not shared.sd_model.lowvram:
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shared.sd_model.model.diffusion_model.to(devices.device)
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return
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shared.sd_model.model.diffusion_model.to(devices.cpu)
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devices.torch_gc()
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current_unet = current_unet_option.create_unet()
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current_unet.option = current_unet_option
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print(f"Activating unet: {current_unet.option.label}")
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current_unet.activate()
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class SdUnetOption:
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model_name = None
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"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
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label = None
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"""name of the unet in UI"""
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def create_unet(self):
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"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
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raise NotImplementedError()
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class SdUnet(torch.nn.Module):
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def forward(self, x, timesteps, context, *args, **kwargs):
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raise NotImplementedError()
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def activate(self):
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pass
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def deactivate(self):
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pass
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def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
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if current_unet is not None:
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return current_unet.forward(x, timesteps, context, *args, **kwargs)
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return ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui(self, x, timesteps, context, *args, **kwargs)
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import torch.nn
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from modules import script_callbacks, shared, devices
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unet_options = []
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current_unet_option = None
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current_unet = None
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original_forward = None # not used, only left temporarily for compatibility
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def list_unets():
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new_unets = script_callbacks.list_unets_callback()
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unet_options.clear()
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unet_options.extend(new_unets)
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def get_unet_option(option=None):
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option = option or shared.opts.sd_unet
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if option == "None":
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return None
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if option == "Automatic":
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name = shared.sd_model.sd_checkpoint_info.model_name
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options = [x for x in unet_options if x.model_name == name]
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option = options[0].label if options else "None"
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return next(iter([x for x in unet_options if x.label == option]), None)
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def apply_unet(option=None):
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global current_unet_option
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global current_unet
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new_option = get_unet_option(option)
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if new_option == current_unet_option:
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return
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if current_unet is not None:
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print(f"Dectivating unet: {current_unet.option.label}")
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current_unet.deactivate()
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current_unet_option = new_option
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if current_unet_option is None:
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current_unet = None
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if not shared.sd_model.lowvram:
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shared.sd_model.model.diffusion_model.to(devices.device)
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return
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shared.sd_model.model.diffusion_model.to(devices.cpu)
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devices.torch_gc()
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current_unet = current_unet_option.create_unet()
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current_unet.option = current_unet_option
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print(f"Activating unet: {current_unet.option.label}")
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current_unet.activate()
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class SdUnetOption:
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model_name = None
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"""name of related checkpoint - this option will be selected automatically for unet if the name of checkpoint matches this"""
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label = None
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"""name of the unet in UI"""
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def create_unet(self):
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"""returns SdUnet object to be used as a Unet instead of built-in unet when making pictures"""
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raise NotImplementedError()
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class SdUnet(torch.nn.Module):
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def forward(self, x, timesteps, context, *args, **kwargs):
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raise NotImplementedError()
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def activate(self):
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pass
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def deactivate(self):
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pass
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def create_unet_forward(original_forward):
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def UNetModel_forward(self, x, timesteps=None, context=None, *args, **kwargs):
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if current_unet is not None:
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return current_unet.forward(x, timesteps, context, *args, **kwargs)
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return original_forward(self, x, timesteps, context, *args, **kwargs)
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return UNetModel_forward
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