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Update app.py
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app.py
CHANGED
@@ -1,7 +1,9 @@
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import gradio as gr
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import os
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import yaml
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import huggingface_hub
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huggingface_hub.hf_hub_download(
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repo_id='yzd-v/DWPose',
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@@ -39,12 +41,68 @@ directory_path = './models'
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# Print the directory contents
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print_directory_contents(directory_path)
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def infer(
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demo = gr.Interface(
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fn = infer,
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inputs = [gr.
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outputs = [gr.Textbox()]
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)
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import gradio as gr
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import os
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import yaml
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import tempfile
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import huggingface_hub
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import subprocess
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huggingface_hub.hf_hub_download(
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repo_id='yzd-v/DWPose',
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# Print the directory contents
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print_directory_contents(directory_path)
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def infer(ref_video_in, ref_image_in):
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# Create a temporary directory
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with tempfile.TemporaryDirectory() as temp_dir:
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print("Temporary directory created:", temp_dir)
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# Define the values for the variables
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ref_video_path = ref_video_in
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ref_image_path = ref_image_in
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num_frames = 72
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resolution = 576
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frames_overlap = 6
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num_inference_steps = 25
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noise_aug_strength = 0
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guidance_scale = 2.0
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sample_stride = 2
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fps = 15
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seed = 42
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# Create the data structure
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data = {
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'base_model_path': 'stabilityai/stable-video-diffusion-img2vid-xt-1-1',
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'ckpt_path': 'models/MimicMotion_1-1.pth',
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'test_case': [
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{
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'ref_video_path': ref_video_path,
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'ref_image_path': ref_image_path,
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'num_frames': num_frames,
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'resolution': resolution,
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'frames_overlap': frames_overlap,
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'num_inference_steps': num_inference_steps,
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'noise_aug_strength': noise_aug_strength,
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'guidance_scale': guidance_scale,
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'sample_stride': sample_stride,
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'fps': fps,
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'seed': seed
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}
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]
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}
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# Define the file path
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file_path = os.path.join(temp_dir, 'config.yaml')
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# Write the data to a YAML file
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with open(file_path, 'w') as file:
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yaml.dump(data, file, default_flow_style=False)
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print("YAML file 'config.yaml' created successfully in", file_path)
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# Execute the inference command
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command = ['python', 'inference.py', '--inference_config', file_path]
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result = subprocess.run(command, capture_output=True, text=True)
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# Print the command output
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print("Command output:", result.stdout)
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print("Command errors:", result.stderr)
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return "done"
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demo = gr.Interface(
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fn = infer,
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inputs = [gr.Video(type="filepath"), gr.Image(type="filepath")],
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outputs = [gr.Textbox()]
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)
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