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| import os | |
| import sys | |
| import time | |
| import torch | |
| current_file_path = os.path.abspath(__file__) | |
| project_roots = [os.path.dirname(current_file_path), os.path.dirname(os.path.dirname(current_file_path)), os.path.dirname(os.path.dirname(os.path.dirname(current_file_path)))] | |
| for project_root in project_roots: | |
| sys.path.insert(0, project_root) if project_root not in sys.path else None | |
| from cogvideox.api.api import (infer_forward_api, | |
| update_diffusion_transformer_api, | |
| update_edition_api) | |
| from cogvideox.ui.controller import flow_scheduler_dict | |
| from cogvideox.ui.wan_fun_ui import ui, ui_eas, ui_modelscope | |
| if __name__ == "__main__": | |
| # Choose the ui mode | |
| ui_mode = "eas" | |
| # GPU memory mode, which can be choosen in [model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload]. | |
| # model_cpu_offload means that the entire model will be moved to the CPU after use, which can save some GPU memory. | |
| # | |
| # model_cpu_offload_and_qfloat8 indicates that the entire model will be moved to the CPU after use, | |
| # and the transformer model has been quantized to float8, which can save more GPU memory. | |
| # | |
| # sequential_cpu_offload means that each layer of the model will be moved to the CPU after use, | |
| # resulting in slower speeds but saving a large amount of GPU memory. | |
| GPU_memory_mode = "model_cpu_offload" | |
| # Use torch.float16 if GPU does not support torch.bfloat16 | |
| # ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16 | |
| weight_dtype = torch.bfloat16 | |
| # Config path | |
| config_path = "config/wan2.1/wan_civitai.yaml" | |
| # Server ip | |
| server_name = "0.0.0.0" | |
| server_port = 7860 | |
| # Params below is used when ui_mode = "modelscope" | |
| model_name = "models/Diffusion_Transformer/Wan2.1-Fun-1.3B-InP" | |
| # "Inpaint" or "Control" | |
| model_type = "Inpaint" | |
| # Save dir of this model | |
| savedir_sample = "samples" | |
| if ui_mode == "modelscope": | |
| demo, controller = ui_modelscope(model_name, model_type, savedir_sample, GPU_memory_mode, flow_scheduler_dict, weight_dtype, config_path) | |
| elif ui_mode == "eas": | |
| demo, controller = ui_eas(model_name, flow_scheduler_dict, savedir_sample, config_path) | |
| else: | |
| demo, controller = ui(GPU_memory_mode, flow_scheduler_dict, weight_dtype, config_path) | |
| # launch gradio | |
| app, _, _ = demo.queue(status_update_rate=1).launch( | |
| server_name=server_name, | |
| server_port=server_port, | |
| prevent_thread_lock=True | |
| ) | |
| # launch api | |
| infer_forward_api(None, app, controller) | |
| update_diffusion_transformer_api(None, app, controller) | |
| update_edition_api(None, app, controller) | |
| # not close the python | |
| while True: | |
| time.sleep(5) |