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Update app.py
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app.py
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@@ -1,4 +1,5 @@
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import numpy as np
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import argparse
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import imageio
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@@ -60,14 +61,19 @@ def get_args():
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args = parser.parse_args()
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return args
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def infer(prompt, video_path,
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#args = get_args()
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#os.makedirs(args.output_path, exist_ok=True)
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# Height and width should be a multiple of 32
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height = (height // 32) * 32
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width = (width // 32) * 32
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if condition == "pose":
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pretrained_model_or_path = "lllyasviel/ControlNet"
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body_model_path = hf_hub_download(pretrained_model_or_path, "annotator/ckpts/body_pose_model.pth", cache_dir="checkpoints")
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@@ -132,4 +138,23 @@ def infer(prompt, video_path, output_path, condition, video_length, height, widt
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).videos
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save_videos_grid(sample, f"{output_path}/{prompt}.mp4")
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return f"{output_path}/{prompt}.mp4"
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import gradio
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import os
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import numpy as np
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import argparse
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import imageio
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args = parser.parse_args()
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return args
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def infer(prompt, video_path, condition, video_length, is_long_video):
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#args = get_args()
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#os.makedirs(args.output_path, exist_ok=True)
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# Height and width should be a multiple of 32
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output_path = ""
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height, width = 512
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height = (height // 32) * 32
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width = (width // 32) * 32
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smoother_steps = [19, 20]
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is_long_video = False
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seed = 42
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if condition == "pose":
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pretrained_model_or_path = "lllyasviel/ControlNet"
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body_model_path = hf_hub_download(pretrained_model_or_path, "annotator/ckpts/body_pose_model.pth", cache_dir="checkpoints")
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).videos
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save_videos_grid(sample, f"{output_path}/{prompt}.mp4")
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return f"{output_path}/{prompt}.mp4"
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with gr.Blocks() as demo:
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with gr.Column():
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prompt = gr.Textbox(label="prompt")
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video_path = gr.Video(source="upload", type="filepath")
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condition = gr.Textbox(label="Condition", value="depth")
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video_length = gr.Slider(label="video length", minimum=1, maximum=15, step=1, value=2)
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seed = gr.Number(label="seed", valie=42)
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submit_btn = gr.Button("Submit")
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video_res = gr.Video(label="result")
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submit_btn.click(fn=infer,
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inputs=[prompt,
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video_path,
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condition,
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video_length,
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seed,
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]
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outputs=[video_res])
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