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import time
import torch
from easyanimate.api.api import (infer_forward_api,
update_diffusion_transformer_api,
update_edition_api)
from easyanimate.ui.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_and_qfloat8"
# 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
# Params below is used when ui_mode = "modelscope"
edition = "v5"
# Config
config_path = "config/easyanimate_video_v5_magvit_multi_text_encoder.yaml"
# Model path of the pretrained model
model_name = "models/Diffusion_Transformer/EasyAnimateV5-12b-zh-InP"
# "Inpaint" or "Control"
model_type = "Inpaint"
# Save dir
savedir_sample = "samples"
if ui_mode == "modelscope":
demo, controller = ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype)
elif ui_mode == "eas":
demo, controller = ui_eas(edition, config_path, model_name, savedir_sample)
else:
demo, controller = ui(GPU_memory_mode, weight_dtype)
# 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) |