Update Space

app.py

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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)