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import torch |
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import gradio as gr |
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import composable_lora |
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import modules.scripts as scripts |
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from modules import script_callbacks |
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from modules.processing import StableDiffusionProcessing |
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def unload(): |
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torch.nn.Linear.forward = torch.nn.Linear_forward_before_lora |
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torch.nn.Conv2d.forward = torch.nn.Conv2d_forward_before_lora |
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if not hasattr(torch.nn, 'Linear_forward_before_lora'): |
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torch.nn.Linear_forward_before_lora = torch.nn.Linear.forward |
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if not hasattr(torch.nn, 'Conv2d_forward_before_lora'): |
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torch.nn.Conv2d_forward_before_lora = torch.nn.Conv2d.forward |
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torch.nn.Linear.forward = composable_lora.lora_Linear_forward |
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torch.nn.Conv2d.forward = composable_lora.lora_Conv2d_forward |
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script_callbacks.on_script_unloaded(unload) |
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class ComposableLoraScript(scripts.Script): |
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def title(self): |
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return "Composable Lora" |
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def show(self, is_img2img): |
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return scripts.AlwaysVisible |
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def ui(self, is_img2img): |
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with gr.Group(): |
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with gr.Accordion("Composable Lora", open=False): |
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enabled = gr.Checkbox(value=False, label="Enabled") |
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opt_uc_text_model_encoder = gr.Checkbox(value=False, label="Use Lora in uc text model encoder") |
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opt_uc_diffusion_model = gr.Checkbox(value=False, label="Use Lora in uc diffusion model") |
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return [enabled, opt_uc_text_model_encoder, opt_uc_diffusion_model] |
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def process(self, p: StableDiffusionProcessing, enabled: bool, opt_uc_text_model_encoder: bool, opt_uc_diffusion_model: bool): |
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composable_lora.enabled = enabled |
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composable_lora.opt_uc_text_model_encoder = opt_uc_text_model_encoder |
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composable_lora.opt_uc_diffusion_model = opt_uc_diffusion_model |
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composable_lora.num_batches = p.batch_size |
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prompt = p.all_prompts[0] |
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composable_lora.load_prompt_loras(prompt) |
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def process_batch(self, p: StableDiffusionProcessing, *args, **kwargs): |
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composable_lora.reset_counters() |
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