Bubbliiiingandhkunzhe
9f34f7c8c5
* Update Flow * Update Flow * Update Flow * add image recaptioning * Fix bug in t2v * update train_reward_lora.py * update reward training * Update V5.1 and mix multi text_encoders to one pipeline * Update V5.1 training Code * Update ComfyUI * Update Comment * Delete files * update reward training * Update Readme * fix extract frames in compute_semantic_consistency * Update Readme && Remove to in prediction * Update Demo * Update Readme * Update ui * support vae gradient checkpointing in reward training * Update Training Readme --------- Co-authored-by: hkunzhe <huangkunzhe.hkz@alibaba-inc.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
import time
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import torch
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from easyanimate.api.api import (infer_forward_api,
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update_diffusion_transformer_api,
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update_edition_api)
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from easyanimate.ui.ui import ui, ui_eas, ui_modelscope
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if __name__ == "__main__":
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# Choose the ui mode
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ui_mode = "normal"
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# GPU memory mode, which can be choosen in ["model_cpu_offload", "model_cpu_offload_and_qfloat8", "sequential_cpu_offload"].
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# "model_cpu_offload" means that the entire model will be moved to the CPU after use, which can save some GPU memory.
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#
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# "model_cpu_offload_and_qfloat8" indicates that the entire model will be moved to the CPU after use,
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# and the transformer model has been quantized to float8, which can save more GPU memory.
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#
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# "sequential_cpu_offload" means that each layer of the model will be moved to the CPU after use,
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# resulting in slower speeds but saving a large amount of GPU memory.
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#
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# EasyAnimateV1, V2 and V3 support "model_cpu_offload" "sequential_cpu_offload"
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# EasyAnimateV4, V5 support "model_cpu_offload" "model_cpu_offload_and_qfloat8" "sequential_cpu_offload"
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# EasyAnimateV5.1 support "model_cpu_offload" "model_cpu_offload_and_qfloat8"
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GPU_memory_mode = "model_cpu_offload_and_qfloat8"
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# Use torch.float16 if GPU does not support torch.bfloat16
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# ome graphics cards, such as v100, 2080ti, do not support torch.bfloat16
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weight_dtype = torch.bfloat16
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# Server ip
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server_name = "0.0.0.0"
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server_port = 7860
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# Params below is used when ui_mode = "modelscope"
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edition = "v5.1"
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# Config
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config_path = "config/easyanimate_video_v5.1_magvit_qwen.yaml"
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# Model path of the pretrained model
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model_name = "models/Diffusion_Transformer/EasyAnimateV5.1-12b-zh-InP"
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# "Inpaint" or "Control"
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model_type = "Inpaint"
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# Save dir
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savedir_sample = "samples"
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if ui_mode == "modelscope":
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demo, controller = ui_modelscope(model_type, edition, config_path, model_name, savedir_sample, GPU_memory_mode, weight_dtype)
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elif ui_mode == "eas":
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demo, controller = ui_eas(edition, config_path, model_name, savedir_sample)
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else:
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demo, controller = ui(GPU_memory_mode, weight_dtype)
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# launch gradio
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app, _, _ = demo.queue(status_update_rate=1).launch(
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server_name=server_name,
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server_port=server_port,
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prevent_thread_lock=True
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)
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# launch api
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infer_forward_api(None, app, controller)
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update_diffusion_transformer_api(None, app, controller)
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update_edition_api(None, app, controller)
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# not close the python
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while True:
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time.sleep(5) |