add an experimental option to apply loras to outputs rather than inputs
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@ -166,7 +166,10 @@ def lora_forward(module, input, res):
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for lora in loaded_loras:
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module = lora.modules.get(lora_layer_name, None)
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if module is not None:
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res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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if shared.opts.lora_apply_to_outputs and res.shape == input.shape:
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res = res + module.up(module.down(res)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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else:
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res = res + module.up(module.down(input)) * lora.multiplier * (module.alpha / module.up.weight.shape[1] if module.alpha else 1.0)
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return res
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@ -3,7 +3,7 @@ import torch
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import lora
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import extra_networks_lora
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import ui_extra_networks_lora
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from modules import script_callbacks, ui_extra_networks, extra_networks
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from modules import script_callbacks, ui_extra_networks, extra_networks, shared
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def unload():
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@ -28,3 +28,8 @@ torch.nn.Conv2d.forward = lora.lora_Conv2d_forward
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script_callbacks.on_model_loaded(lora.assign_lora_names_to_compvis_modules)
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script_callbacks.on_script_unloaded(unload)
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script_callbacks.on_before_ui(before_ui)
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shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {
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"lora_apply_to_outputs": shared.OptionInfo(False, "Apply Lora to outputs rather than inputs when possible (experimental)"),
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}))
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