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| import os | |
| import gradio as gr | |
| import numpy as np | |
| import torch | |
| from diffusers import DDIMScheduler | |
| from pytorch_lightning import seed_everything | |
| from masactrl.diffuser_utils import MasaCtrlPipeline | |
| from masactrl.masactrl_utils import (AttentionBase, | |
| regiter_attention_editor_diffusers) | |
| torch.set_grad_enabled(False) | |
| from gradio_app.image_synthesis_app import create_demo_synthesis | |
| from gradio_app.real_image_editing_app import create_demo_editing | |
| from gradio_app.app_utils import global_context | |
| SPACE_ID = os.getenv('SPACE_ID') | |
| TITLE = '# [MasaCtrl](https://ljzycmd.github.io/projects/MasaCtrl/)</h1>' | |
| DESCRIPTION = '<div align="center">' | |
| DESCRIPTION += f'<p>Gradio demo for MasaCtrl: <a href="https://github.com/TencentARC/MasaCtrl">[Github]</a>, <a href="https://arxiv.org/abs/2304.08465">[Paper]</a>. If MasaCtrl is helpful, please help to ⭐ the <a href="https://github.com/TencentARC/MasaCtrl">Github Repo</a> 😊</p>' | |
| DESCRIPTION += f'<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| DESCRIPTION += '</div>' | |
| with gr.Blocks(css="style.css") as demo: | |
| gr.Markdown(TITLE) | |
| gr.HTML(DESCRIPTION) | |
| model_path_gr = gr.Dropdown( | |
| ["xyn-ai/anything-v4.0", | |
| "CompVis/stable-diffusion-v1-4", | |
| "runwayml/stable-diffusion-v1-5"], | |
| value="xyn-ai/anything-v4.0", | |
| label="Model", info="Select the model to use!" | |
| ) | |
| with gr.Tab("Consistent Synthesis"): | |
| create_demo_synthesis() | |
| with gr.Tab("Real Editing"): | |
| create_demo_editing() | |
| def reload_ckpt(model_path): | |
| print("Reloading model from", model_path) | |
| global_context["model"] = MasaCtrlPipeline.from_pretrained( | |
| model_path, scheduler=global_context["scheduler"]).to(global_context["device"]) | |
| model_path_gr.select( | |
| reload_ckpt, | |
| [model_path_gr] | |
| ) | |
| if __name__ == "__main__": | |
| demo.queue().launch() | |