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a70f4ae
1
Parent(s):
60630de
Update app.py
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app.py
CHANGED
@@ -6,6 +6,7 @@ from utils import video_to_frames, add_dict_to_yaml_file, save_video, seed_every
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from tokenflow_pnp import TokenFlow
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from preprocess_utils import *
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from tokenflow_utils import *
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# load sd model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_id = "stabilityai/stable-diffusion-2-1-base"
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@@ -51,6 +52,11 @@ def get_example():
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]
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return case
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def prep(config):
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# timesteps to save
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@@ -115,6 +121,7 @@ def preprocess_and_invert(input_video,
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n_timesteps = 50,
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batch_size: int = 8,
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n_frames: int = 40,
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inversion_prompt:str = '',
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):
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@@ -131,13 +138,22 @@ def preprocess_and_invert(input_video,
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preprocess_config['steps'] = steps
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preprocess_config['batch_size'] = batch_size
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preprocess_config['save_steps'] = int(n_timesteps)
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preprocess_config['n_frames'] = n_frames
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preprocess_config['seed'] = seed
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preprocess_config['inversion_prompt'] = inversion_prompt
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preprocess_config['frames'] = video_to_frames(input_video)
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preprocess_config['data_path'] = input_video.split(".")[0]
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-
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if randomize_seed:
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seed = randomize_seed_fn()
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seed_everything(seed)
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@@ -150,7 +166,7 @@ def preprocess_and_invert(input_video,
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inverted_latents = gr.State(value=total_inverted_latents)
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do_inversion = False
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return frames, latents, inverted_latents, do_inversion
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def edit_with_pnp(input_video,
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@@ -167,6 +183,7 @@ def edit_with_pnp(input_video,
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pnp_f_t: float = 0.8,
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batch_size: int = 8, #needs to be the same as for preprocess
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n_frames: int = 40,#needs to be the same as for preprocess
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n_timesteps: int = 50,
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gudiance_scale: float = 7.5,
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inversion_prompt: str = "", #needs to be the same as for preprocess
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@@ -189,7 +206,7 @@ def edit_with_pnp(input_video,
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if do_inversion:
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frames, latents, inverted_latents, do_inversion =
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input_video,
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frames,
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latents,
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@@ -201,7 +218,10 @@ def edit_with_pnp(input_video,
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n_timesteps,
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batch_size,
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n_frames,
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inversion_prompt)
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do_inversion = False
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@@ -221,7 +241,6 @@ def edit_with_pnp(input_video,
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# demo #
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########
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intro = """
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<div style="text-align:center">
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<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
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@@ -233,8 +252,6 @@ intro = """
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</div>
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"""
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with gr.Blocks(css="style.css") as demo:
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gr.HTML(intro)
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@@ -282,10 +299,12 @@ with gr.Blocks(css="style.css") as demo:
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with gr.Column(min_width=100):
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inversion_prompt = gr.Textbox(lines=1, label="Inversion prompt", interactive=True, placeholder="")
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batch_size = gr.Slider(label='Batch size', minimum=1, maximum=
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value=8, step=1, interactive=True)
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n_frames = gr.Slider(label='Num frames', minimum=2, maximum=200,
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value=24, step=1, interactive=True)
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n_timesteps = gr.Slider(label='Diffusion steps', minimum=25, maximum=100,
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value=50, step=25, interactive=True)
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n_fps = gr.Slider(label='Frames per second', minimum=1, maximum=60,
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@@ -336,13 +355,15 @@ with gr.Blocks(css="style.css") as demo:
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n_timesteps,
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batch_size,
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n_frames,
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inversion_prompt
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],
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outputs = [frames,
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latents,
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inverted_latents,
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do_inversion
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])
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run_button.click(fn = edit_with_pnp,
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@@ -359,6 +380,7 @@ with gr.Blocks(css="style.css") as demo:
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pnp_f_t,
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batch_size,
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n_frames,
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n_timesteps,
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gudiance_scale,
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inversion_prompt,
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from tokenflow_pnp import TokenFlow
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from preprocess_utils import *
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from tokenflow_utils import *
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import math
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# load sd model
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_id = "stabilityai/stable-diffusion-2-1-base"
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]
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return case
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def largest_divisor(n):
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for i in range(2, int(math.sqrt(n)) + 1):
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if n % i == 0:
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return n // i
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return n
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def prep(config):
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# timesteps to save
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n_timesteps = 50,
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batch_size: int = 8,
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n_frames: int = 40,
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n_seconds: int = 1,
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inversion_prompt:str = '',
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):
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preprocess_config['steps'] = steps
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preprocess_config['batch_size'] = batch_size
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preprocess_config['save_steps'] = int(n_timesteps)
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#preprocess_config['n_frames'] = n_frames
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preprocess_config['seed'] = seed
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preprocess_config['inversion_prompt'] = inversion_prompt
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preprocess_config['frames'], frames_per_second = video_to_frames(input_video)
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preprocess_config['data_path'] = input_video.split(".")[0]
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total_vid_duration = preprocess_config['frames']/frames_per_second
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if(total_vid_duration < 1):
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preprocess_config['n_frames'] = preprocess_config['frames']
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else:
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preprocess_config['n_frames'] = frames_per_second/n_seconds
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if preprocess_config['n_frames'] % batch_size != 0:
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preprocess_config['batch_size'] = largest_divisor(batch_size)
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if randomize_seed:
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seed = randomize_seed_fn()
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seed_everything(seed)
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inverted_latents = gr.State(value=total_inverted_latents)
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do_inversion = False
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return frames, latents, inverted_latents, do_inversion, preprocess_config['batch_size'], preprocess_config['n_frames']
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def edit_with_pnp(input_video,
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pnp_f_t: float = 0.8,
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batch_size: int = 8, #needs to be the same as for preprocess
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n_frames: int = 40,#needs to be the same as for preprocess
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n_seconds: int = 1,
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n_timesteps: int = 50,
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gudiance_scale: float = 7.5,
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inversion_prompt: str = "", #needs to be the same as for preprocess
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if do_inversion:
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frames, latents, inverted_latents, do_inversion, batch_size, n_frames = preprocess_and_invert(
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input_video,
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frames,
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latents,
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n_timesteps,
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batch_size,
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n_frames,
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n_seconds,
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inversion_prompt)
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config["batch_size"] = batch_size
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config["n_frames"] = n_frames
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do_inversion = False
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# demo #
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########
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intro = """
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<div style="text-align:center">
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<h1 style="font-weight: 1400; text-align: center; margin-bottom: 7px;">
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</div>
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"""
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with gr.Blocks(css="style.css") as demo:
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gr.HTML(intro)
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with gr.Column(min_width=100):
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inversion_prompt = gr.Textbox(lines=1, label="Inversion prompt", interactive=True, placeholder="")
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batch_size = gr.Slider(label='Batch size', minimum=1, maximum=100,
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value=8, step=1, interactive=True, visible=False)
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n_frames = gr.Slider(label='Num frames', minimum=2, maximum=200,
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value=24, step=1, interactive=True, visible=False)
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n_seconds = gr.Slider(label='Num seconds', info="How many seconds of your video to process",
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minimum=1, maximum=2, step=1)
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n_timesteps = gr.Slider(label='Diffusion steps', minimum=25, maximum=100,
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value=50, step=25, interactive=True)
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n_fps = gr.Slider(label='Frames per second', minimum=1, maximum=60,
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n_timesteps,
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batch_size,
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n_frames,
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n_seconds,
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inversion_prompt
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],
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outputs = [frames,
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latents,
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inverted_latents,
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do_inversion,
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batch_size,
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n_frames
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])
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run_button.click(fn = edit_with_pnp,
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pnp_f_t,
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batch_size,
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n_frames,
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n_seconds,
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n_timesteps,
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gudiance_scale,
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inversion_prompt,
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