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Runtime error
Runtime error
envs
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app.py
CHANGED
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@@ -20,13 +20,13 @@ import spaces
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from PIL import Image
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from omegaconf import OmegaConf
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from einops import rearrange, repeat
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from torchvision import transforms
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from transformers import CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, DDIMScheduler
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from pipelines.pipeline_imagecoductor import ImageConductorPipeline
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from modules.unet import UNet3DConditionFlowModel
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from utils.gradio_utils import ensure_dirname, split_filename, visualize_drag, image2pil,
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from utils.utils import create_image_controlnet, create_flow_controlnet, interpolate_trajectory, load_weights, load_model, bivariate_Gaussian
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from utils.lora_utils import add_LoRA_to_controlnet
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from utils.visualizer import Visualizer, vis_flow_to_video
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@@ -382,9 +382,12 @@ class ImageConductor:
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eval_mode = True,
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).videos
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outputs_path = os.path.join(output_dir, f'output_{i}_{id}.mp4')
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vis_video = (rearrange(sample[0], 'c t h w -> t h w c') * 255.).clip(0, 255)
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torchvision.io.write_video(outputs_path, vis_video, fps=8, video_codec='h264', options={'crf': '10'})
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return {output_image: visualized_drag, output_video: outputs_path}
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@@ -619,10 +622,13 @@ with block:
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examples_type = gr.Textbox(label="Examples Type (Ignore) ", value="", visible=False)
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with gr.Column(scale=7):
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output_video = gr.Video(
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with gr.Row():
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from PIL import Image
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from omegaconf import OmegaConf
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from einops import rearrange, repeat
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from torchvision import transforms,utils
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from transformers import CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, DDIMScheduler
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from pipelines.pipeline_imagecoductor import ImageConductorPipeline
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from modules.unet import UNet3DConditionFlowModel
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from utils.gradio_utils import ensure_dirname, split_filename, visualize_drag, image2pil, save_videos_grid
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from utils.utils import create_image_controlnet, create_flow_controlnet, interpolate_trajectory, load_weights, load_model, bivariate_Gaussian
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from utils.lora_utils import add_LoRA_to_controlnet
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from utils.visualizer import Visualizer, vis_flow_to_video
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eval_mode = True,
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).videos
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# outputs_path = os.path.join(output_dir, f'output_{i}_{id}.mp4')
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# vis_video = (rearrange(sample[0], 'c t h w -> t h w c') * 255.).clip(0, 255)
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# torchvision.io.write_video(outputs_path, vis_video, fps=8, video_codec='h264', options={'crf': '10'})
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outputs_path = os.path.join(output_dir, f'output_{i}_{id}.gif')
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save_videos_grid(sample[0][None], outputs_path)
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return {output_image: visualized_drag, output_video: outputs_path}
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examples_type = gr.Textbox(label="Examples Type (Ignore) ", value="", visible=False)
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with gr.Column(scale=7):
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# output_video = gr.Video(
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# label="Output Video",
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# width=384,
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# height=256)
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output_video = gr.Image(label="Output Video",
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height=256,
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width=384,)
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with gr.Row():
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