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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) |