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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 videox_fun.api.api import (infer_forward_api,
update_diffusion_transformer_api)
from videox_fun.ui.controller import flow_scheduler_dict
from videox_fun.ui.wan2_2_fun_ui import ui, ui_client, ui_host
if __name__ == "__main__":
# Choose the ui mode
# "normal" refers to the standard UI, which allows users to click to switch models, change model types, and more.
# "host" represents the hosting mode, where the model is loaded directly at startup and can be accessed via
# the API to return generation results.
# "client" represents the client mode, offering a simple UI that sends requests to a remote API for generation.
ui_mode = "normal"
# GPU memory mode, which can be chosen in [model_full_load, model_cpu_offload, model_cpu_offload_and_qfloat8, sequential_cpu_offload].
# model_full_load means that the entire model will be moved to the GPU.
#
# 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 = "sequential_cpu_offload"
# Compile will give a speedup in fixed resolution and need a little GPU memory.
# The compile_dit is not compatible with the fsdp_dit and sequential_cpu_offload.
compile_dit = False
# 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
# Server ip
server_name = "0.0.0.0"
server_port = 7860
# Config path
config_path = "config/wan2.2/wan_civitai_i2v.yaml"
# Params below is used when ui_mode = "host"
# Model path of the pretrained model
model_name = "models/Diffusion_Transformer/Wan2.2-Fun-A14B-InP"
# "Inpaint" or "Control"
model_type = "Inpaint"
if ui_mode == "host":
demo, controller = ui_host(GPU_memory_mode, flow_scheduler_dict, model_name, model_type, config_path, compile_dit, weight_dtype)
elif ui_mode == "client":
demo, controller = ui_client(flow_scheduler_dict, model_name)
else:
demo, controller = ui(GPU_memory_mode, flow_scheduler_dict, config_path, compile_dit, weight_dtype)
def gr_launch():
# 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)
gr_launch()
# not close the python
while True:
time.sleep(5) |