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Runtime error
Linoy Tsaban
commited on
Commit
•
e24d40d
1
Parent(s):
9ad499d
add ddpm inversion
Browse files
app.py
CHANGED
@@ -61,20 +61,22 @@ def prep(config):
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model_key = "stabilityai/stable-diffusion-2-depth"
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toy_scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
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toy_scheduler.set_timesteps(config["save_steps"])
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-
print("config[save_steps]", config["save_steps"])
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timesteps_to_save, num_inference_steps = get_timesteps(toy_scheduler, num_inference_steps=config["save_steps"],
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strength=1.0,
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device=device)
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-
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# seed_everything(config["seed"])
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if not config["frames"]: # original non demo setting
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save_path = os.path.join(config["save_dir"],
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f'sd_{config["sd_version"]}',
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Path(config["data_path"]).stem,
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f'steps_{config["steps"]}',
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f'nframes_{config["n_frames"]}')
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os.makedirs(os.path.join(save_path, f'latents'), exist_ok=True)
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add_dict_to_yaml_file(os.path.join(config["save_dir"], 'inversion_prompts.yaml'), Path(config["data_path"]).stem, config["inversion_prompt"])
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# save inversion prompt in a txt file
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with open(os.path.join(save_path, 'inversion_prompt.txt'), 'w') as f:
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@@ -82,43 +84,53 @@ def prep(config):
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else:
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save_path = None
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-
model = Preprocess(device,
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vae=vae,
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text_encoder=text_encoder,
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scheduler=scheduler,
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tokenizer=tokenizer,
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unet=unet)
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-
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-
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num_steps=model.config["steps"],
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save_path=save_path,
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batch_size=model.config["batch_size"],
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timesteps_to_save=timesteps_to_save,
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inversion_prompt=model.config["inversion_prompt"],
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)
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-
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-
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def preprocess_and_invert(input_video,
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frames,
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latents,
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inverted_latents,
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seed,
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randomize_seed,
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do_inversion,
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-
# save_dir: str = "latents",
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steps,
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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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sd_version = "2.1"
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-
height = 512
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weidth: int = 512
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-
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if do_inversion or randomize_seed:
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preprocess_config = {}
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preprocess_config['H'] = height
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@@ -134,30 +146,37 @@ def preprocess_and_invert(input_video,
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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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if randomize_seed:
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seed = randomize_seed_fn()
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seed_everything(seed)
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frames, latents, total_inverted_latents, rgb_reconstruction = prep(preprocess_config)
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-
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-
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-
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-
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-
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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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frames,
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latents,
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inverted_latents,
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seed,
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randomize_seed,
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do_inversion,
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steps,
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prompt: str = "a marble sculpture of a woman running, Venus de Milo",
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# negative_prompt: str = "ugly, blurry, low res, unrealistic, unaesthetic",
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pnp_attn_t: float = 0.5,
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@@ -183,14 +202,18 @@ def edit_with_pnp(input_video,
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config["pnp_attn_t"] = pnp_attn_t
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config["pnp_f_t"] = pnp_f_t
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config["pnp_inversion_prompt"] = inversion_prompt
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if do_inversion:
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frames, latents, inverted_latents, do_inversion = preprocess_and_invert(
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input_video,
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frames,
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latents,
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inverted_latents,
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seed,
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randomize_seed,
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do_inversion,
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@@ -198,7 +221,8 @@ 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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@@ -207,12 +231,13 @@ def edit_with_pnp(input_video,
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seed_everything(seed)
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-
editor = TokenFlow(config=config,pipe=tokenflow_pipe, frames=frames.value, inverted_latents=inverted_latents.value)
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edited_frames = editor.edit_video()
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-
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# path = export_to_video(edited_frames)
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return
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########
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# demo #
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@@ -238,6 +263,7 @@ with gr.Blocks(css="style.css") as demo:
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frames = gr.State()
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inverted_latents = gr.State()
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latents = gr.State()
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do_inversion = gr.State(value=True)
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with gr.Row():
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@@ -252,15 +278,7 @@ with gr.Blocks(css="style.css") as demo:
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label="Describe your edited video",
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max_lines=1, value=""
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)
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-
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# with gr.Group(elem_id="share-btn-container"):
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# community_icon = gr.HTML(community_icon_html, visible=True)
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# loading_icon = gr.HTML(loading_icon_html, visible=False)
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# share_button = gr.Button("Share to community", elem_id="share-btn", visible=True)
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-
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-
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# with gr.Row():
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# inversion_progress = gr.Textbox(visible=False, label="Inversion progress")
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with gr.Row():
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run_button = gr.Button("Edit your video!", visible=True)
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@@ -274,8 +292,10 @@ with gr.Blocks(css="style.css") as demo:
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randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
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gudiance_scale = gr.Slider(label='Guidance Scale', minimum=1, maximum=30,
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value=7.5, step=0.5, interactive=True)
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steps = gr.Slider(label='Inversion steps', minimum=10, maximum=
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value=
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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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@@ -284,7 +304,7 @@ with gr.Blocks(css="style.css") as demo:
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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=
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n_fps = gr.Slider(label='Frames per second', minimum=1, maximum=60,
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value=10, step=1, interactive=True)
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@@ -300,6 +320,11 @@ with gr.Blocks(css="style.css") as demo:
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fn = reset_do_inversion,
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outputs = [do_inversion],
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queue = False)
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inversion_prompt.change(
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fn = reset_do_inversion,
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@@ -326,6 +351,7 @@ with gr.Blocks(css="style.css") as demo:
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frames,
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latents,
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inverted_latents,
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seed,
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randomize_seed,
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do_inversion,
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@@ -333,11 +359,13 @@ 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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@@ -347,10 +375,12 @@ with gr.Blocks(css="style.css") as demo:
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frames,
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latents,
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inverted_latents,
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seed,
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randomize_seed,
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do_inversion,
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steps,
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prompt,
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pnp_attn_t,
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pnp_f_t,
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@@ -360,7 +390,7 @@ with gr.Blocks(css="style.css") as demo:
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gudiance_scale,
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inversion_prompt,
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n_fps ],
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outputs = [output_video, frames, latents, inverted_latents, do_inversion]
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)
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gr.Examples(
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@@ -371,4 +401,5 @@ with gr.Blocks(css="style.css") as demo:
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)
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demo.queue()
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demo.launch()
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model_key = "stabilityai/stable-diffusion-2-depth"
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toy_scheduler = DDIMScheduler.from_pretrained(model_key, subfolder="scheduler")
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toy_scheduler.set_timesteps(config["save_steps"])
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timesteps_to_save, num_inference_steps = get_timesteps(toy_scheduler, num_inference_steps=config["save_steps"],
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strength=1.0,
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device=device)
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+
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# seed_everything(config["seed"])
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if not config["frames"]: # original non demo setting
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save_path = os.path.join(config["save_dir"],
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+
f'inversion_{config[inversion]}',
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f'sd_{config["sd_version"]}',
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Path(config["data_path"]).stem,
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f'steps_{config["steps"]}',
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f'nframes_{config["n_frames"]}')
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os.makedirs(os.path.join(save_path, f'latents'), exist_ok=True)
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if opt[inversion] == 'ddpm':
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os.makedirs(os.path.join(save_path, f'latents'), exist_ok=True)
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add_dict_to_yaml_file(os.path.join(config["save_dir"], 'inversion_prompts.yaml'), Path(config["data_path"]).stem, config["inversion_prompt"])
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# save inversion prompt in a txt file
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with open(os.path.join(save_path, 'inversion_prompt.txt'), 'w') as f:
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else:
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save_path = None
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model = Preprocess(device,
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config,
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vae=vae,
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text_encoder=text_encoder,
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scheduler=scheduler,
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tokenizer=tokenizer,
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unet=unet)
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+
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frames_and_latents, rgb_reconstruction = model.extract_latents(
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num_steps=model.config["steps"],
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save_path=save_path,
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batch_size=model.config["batch_size"],
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timesteps_to_save=timesteps_to_save,
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inversion_prompt=model.config["inversion_prompt"],
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inversion_type=model.config["inversion"],
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skip_steps=model.config["skip_steps"],
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reconstruction=model.config["reconstruct"]
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)
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if model.config["inversion"] == 'ddpm':
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frames, latents, total_inverted_latents, zs = frames_and_latents
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return frames, latents, total_inverted_latents, zs, rgb_reconstruction
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+
else:
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frames, latents, total_inverted_latents = frames_and_latents
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return frames, latents, total_inverted_latents, rgb_reconstruction
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+
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def preprocess_and_invert(input_video,
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frames,
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latents,
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inverted_latents,
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+
zs,
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seed,
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randomize_seed,
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do_inversion,
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steps,
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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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skip_steps: int = 15,
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):
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sd_version = "2.1"
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+
height: int = 512
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weidth: int = 512
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+
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if do_inversion or randomize_seed:
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preprocess_config = {}
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preprocess_config['H'] = height
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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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+
preprocess_config['inversion'] = 'ddpm'
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preprocess_config['skip_steps'] = skip_steps
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preprocess_config['reconstruct'] = False
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+
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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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+
frames, latents, total_inverted_latents, zs, rgb_reconstruction = prep(preprocess_config)
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+
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frames = gr.State(value = frames)
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latents = gr.State(value = latents)
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inverted_latents = gr.State(value = total_inverted_latents)
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zs = gr.State(value = zs)
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do_inversion = False
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return frames, latents, inverted_latents, zs, do_inversion
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def edit_with_pnp(input_video,
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frames,
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latents,
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inverted_latents,
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zs,
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seed,
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randomize_seed,
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do_inversion,
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steps,
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+
skip_steps: int = 15,
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prompt: str = "a marble sculpture of a woman running, Venus de Milo",
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# negative_prompt: str = "ugly, blurry, low res, unrealistic, unaesthetic",
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pnp_attn_t: float = 0.5,
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config["pnp_attn_t"] = pnp_attn_t
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config["pnp_f_t"] = pnp_f_t
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config["pnp_inversion_prompt"] = inversion_prompt
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config["inversion"] = "ddpm"
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config["skip_steps"] = skip_steps
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+
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if do_inversion:
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+
frames, latents, inverted_latents, zs, do_inversion = preprocess_and_invert(
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input_video,
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frames,
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latents,
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inverted_latents,
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+
zs,
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seed,
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randomize_seed,
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do_inversion,
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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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+
skip_steps)
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do_inversion = False
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seed_everything(seed)
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+
editor = TokenFlow(config=config,pipe=tokenflow_pipe, frames=frames.value, inverted_latents=inverted_latents.value, zs= zs.value)
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edited_frames = editor.edit_video()
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+
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+
edit_video_path = f'tokenflow_PnP_fps_{n_fps}.mp4'
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+
save_video(edited_frames, edit_video_path, fps=n_fps)
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# path = export_to_video(edited_frames)
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+
return edit_video_path, frames, latents, inverted_latents, zs, do_inversion
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########
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# demo #
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frames = gr.State()
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inverted_latents = gr.State()
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latents = gr.State()
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+
zs = gr.State()
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do_inversion = gr.State(value=True)
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with gr.Row():
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label="Describe your edited video",
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max_lines=1, value=""
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)
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+
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with gr.Row():
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run_button = gr.Button("Edit your video!", visible=True)
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randomize_seed = gr.Checkbox(label='Randomize seed', value=False)
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gudiance_scale = gr.Slider(label='Guidance Scale', minimum=1, maximum=30,
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value=7.5, step=0.5, interactive=True)
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+
steps = gr.Slider(label='Inversion steps', minimum=10, maximum=200,
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+
value=50, step=1, interactive=True)
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+
skip_steps = gr.Slider(label='Skip Steps', minimum=5, maximum=25,
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+
value=5, step=1, interactive=True)
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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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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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value=10, step=1, interactive=True)
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fn = reset_do_inversion,
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outputs = [do_inversion],
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queue = False)
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+
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+
steps.change(
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fn = reset_do_inversion,
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+
outputs = [do_inversion],
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+
queue = False)
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inversion_prompt.change(
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fn = reset_do_inversion,
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frames,
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latents,
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inverted_latents,
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+
zs,
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seed,
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randomize_seed,
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do_inversion,
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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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363 |
+
skip_steps
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],
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outputs = [frames,
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latents,
|
367 |
inverted_latents,
|
368 |
+
zs,
|
369 |
do_inversion
|
370 |
|
371 |
])
|
|
|
375 |
frames,
|
376 |
latents,
|
377 |
inverted_latents,
|
378 |
+
zs,
|
379 |
seed,
|
380 |
randomize_seed,
|
381 |
do_inversion,
|
382 |
steps,
|
383 |
+
skip_steps,
|
384 |
prompt,
|
385 |
pnp_attn_t,
|
386 |
pnp_f_t,
|
|
|
390 |
gudiance_scale,
|
391 |
inversion_prompt,
|
392 |
n_fps ],
|
393 |
+
outputs = [output_video, frames, latents, inverted_latents, zs, do_inversion]
|
394 |
)
|
395 |
|
396 |
gr.Examples(
|
|
|
401 |
)
|
402 |
|
403 |
demo.queue()
|
404 |
+
|
405 |
demo.launch()
|