bug fix

.gitignore

@@ -0,0 +1,4 @@
+results/*
+pretrained_models/*
+gradio_cached_examples/*
+generated/*

app.py

@@ -1,38 +1,297 @@
 import gradio as gr
+import cv2
 import os
+import torch
+import argparse
+import os
+import sys
+import yaml
+import datetime
+sys.path.append(os.path.dirname(os.getcwd()))
+from pipelines.sd_controlnet_rave import RAVE
+from pipelines.sd_multicontrolnet_rave import RAVE_MultiControlNet
+import shutil
+import subprocess
+import utils.constants as const
+import utils.video_grid_utils as vgu
+import warnings
+warnings.filterwarnings("ignore")
+import pprint 
+import glob
+
+
+def init_device():
+    device_name = 'cuda' if torch.cuda.is_available() else 'cpu'
+    device = torch.device(device_name)
+    return device
+
+def init_paths(input_ns):
+    if input_ns.save_folder == None or input_ns.save_folder == '':
+        input_ns.save_folder = input_ns.video_name
+    else:
+        input_ns.save_folder = os.path.join(input_ns.save_folder, input_ns.video_name)
+    save_dir = os.path.join(const.OUTPUT_PATH, input_ns.save_folder)
+    os.makedirs(save_dir, exist_ok=True)
+    save_idx = max([int(x[-5:]) for x in os.listdir(save_dir)])+1 if os.listdir(save_dir) != [] else 0
+    input_ns.save_path = os.path.join(save_dir, f'{input_ns.positive_prompts}-{str(save_idx).zfill(5)}')
+    
+    
+    if '-' in input_ns.preprocess_name:
+        input_ns.hf_cn_path = [const.PREPROCESSOR_DICT[i] for i in input_ns.preprocess_name.split('-')]
+    else:
+        input_ns.hf_cn_path = const.PREPROCESSOR_DICT[input_ns.preprocess_name]
+    input_ns.hf_path = "runwayml/stable-diffusion-v1-5"
+    
+    input_ns.inverse_path = os.path.join(const.GENERATED_DATA_PATH, 'inverses', input_ns.video_name, f'{input_ns.preprocess_name}_{input_ns.model_id}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}')
+    input_ns.control_path = os.path.join(const.GENERATED_DATA_PATH, 'controls', input_ns.video_name, f'{input_ns.preprocess_name}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}')
+    os.makedirs(input_ns.control_path, exist_ok=True)
+    os.makedirs(input_ns.inverse_path, exist_ok=True)
+    os.makedirs(input_ns.save_path, exist_ok=True)
+    return input_ns
+    
+def install_civitai_model(model_id):
+    full_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id, '*')
+    if len(glob.glob(full_path)) > 0:
+        full_path = glob.glob(full_path)[0]
+        return full_path
+    install_path = os.path.join(const.CWD, 'CIVIT_AI', 'safetensors')
+    install_path_model = os.path.join(const.CWD, 'CIVIT_AI', 'safetensors', model_id)
+    diffusers_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id)
+    convert_py_path = os.path.join(const.CWD, 'CIVIT_AI', 'convert.py')
+    os.makedirs(install_path, exist_ok=True)
+    os.makedirs(diffusers_path, exist_ok=True)
+    subprocess.run(f'wget https://civitai.com/api/download/models/{model_id} --content-disposition --directory {install_path_model}'.split())
+    model_name = glob.glob(os.path.join(install_path, model_id, '*'))[0]
+    model_name2 = os.path.basename(glob.glob(os.path.join(install_path, model_id, '*'))[0]).replace('.safetensors', '')
+    diffusers_path_model_name = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id, model_name2)
+    print(model_name)
+    subprocess.run(f'python {convert_py_path} --checkpoint_path {model_name}  --dump_path {diffusers_path_model_name}  --from_safetensors'.split())
+    subprocess.run(f'rm -rf {install_path}'.split())
+    return diffusers_path_model_name
+
+def run(*args):
+    batch_size = 4
+    batch_size_vae = 1
+    is_ddim_inversion = True
+    is_shuffle = True
+    num_inference_steps = 20
+    num_inversion_step = 20
+    cond_step_start = 0.0
+    give_control_inversion = True
+    model_id = 'SD 1.5'
+    inversion_prompt = ''
+    save_folder = ''
+    list_of_inputs = [x for x in args]
+    input_ns = argparse.Namespace(**{})
+    input_ns.video_path = list_of_inputs[0] # video_path 
+    input_ns.video_name = os.path.basename(input_ns.video_path).replace('.mp4', '').replace('.gif', '') 
+    input_ns.preprocess_name = list_of_inputs[1]
+
+    input_ns.batch_size = batch_size
+    input_ns.batch_size_vae = batch_size_vae
+
+    input_ns.cond_step_start = cond_step_start
+    input_ns.controlnet_conditioning_scale = list_of_inputs[2]  
+    input_ns.controlnet_guidance_end = list_of_inputs[3]  
+    input_ns.controlnet_guidance_start = list_of_inputs[4]  
+
+    input_ns.give_control_inversion = give_control_inversion  
+
+    input_ns.grid_size = list_of_inputs[5]  
+    input_ns.sample_size = list_of_inputs[6]  
+    input_ns.pad = list_of_inputs[7]
+    input_ns.guidance_scale = list_of_inputs[8]
+    input_ns.inversion_prompt = inversion_prompt
+
+    input_ns.is_ddim_inversion = is_ddim_inversion
+    input_ns.is_shuffle = is_shuffle
 
+    input_ns.negative_prompts = list_of_inputs[9]  
+    input_ns.num_inference_steps = num_inference_steps
+    input_ns.num_inversion_step = num_inversion_step
+    input_ns.positive_prompts = list_of_inputs[10] 
+    input_ns.save_folder = save_folder
 
-def combine(a, b):
-    return a + " " + b
+    input_ns.seed = list_of_inputs[11]  
+    input_ns.model_id = const.MODEL_IDS[model_id] 
+    # input_ns.width = list_of_inputs[23] 
+    # input_ns.height = list_of_inputs[24] 
+    # input_ns.original_size = list_of_inputs[25]
+    diffusers_model_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models')
+    os.makedirs(diffusers_model_path, exist_ok=True)
+    if 'model_id' not in list(input_ns.__dict__.keys()):
+        input_ns.model_id = "None"
+    
+    if str(input_ns.model_id) != 'None':
+        input_ns.model_id = install_civitai_model(input_ns.model_id)
 
+    
+    device = init_device()
+    input_ns = init_paths(input_ns)
 
-def mirror(x):
-    return x
+    input_ns.image_pil_list = vgu.prepare_video_to_grid(input_ns.video_path, input_ns.sample_size, input_ns.grid_size, input_ns.pad)
 
+    print(input_ns.video_path)
+    input_ns.sample_size = len(input_ns.image_pil_list)
+    print(f'Frame count: {len(input_ns.image_pil_list)}')
 
-with gr.Blocks() as demo:
+    controlnet_class = RAVE_MultiControlNet if '-' in str(input_ns.controlnet_conditioning_scale) else RAVE
+    
 
-    txt = gr.Textbox(label="Input", lines=2)
-    txt_2 = gr.Textbox(label="Input 2")
-    txt_3 = gr.Textbox(value="", label="Output")
-    btn = gr.Button(value="Submit")
-    btn.click(combine, inputs=[txt, txt_2], outputs=[txt_3])
+    CN = controlnet_class(device)
 
+    CN.init_models(input_ns.hf_cn_path, input_ns.hf_path, input_ns.preprocess_name, input_ns.model_id)
+    
+    input_dict = vars(input_ns)
+    pp = pprint.PrettyPrinter(indent=4)
+    pp.pprint(input_dict)
+    yaml_dict = {k:v for k,v in input_dict.items() if k != 'image_pil_list'}
+
+    start_time = datetime.datetime.now()
+    if '-' in str(input_ns.controlnet_conditioning_scale):
+        res_vid, control_vid_1, control_vid_2 = CN(input_dict)
+    else: 
+        res_vid, control_vid = CN(input_dict)
+    end_time = datetime.datetime.now()
+    save_name = f"{'-'.join(input_ns.positive_prompts.split())}_cstart-{input_ns.controlnet_guidance_start}_gs-{input_ns.guidance_scale}_pre-{'-'.join((input_ns.preprocess_name.replace('-','+').split('_')))}_cscale-{input_ns.controlnet_conditioning_scale}_grid-{input_ns.grid_size}_pad-{input_ns.pad}_model-{os.path.basename(input_ns.model_id)}"
+    res_vid[0].save(os.path.join(input_ns.save_path, f'{save_name}.gif'), save_all=True, append_images=res_vid[1:], loop=10000)
+    control_vid[0].save(os.path.join(input_ns.save_path, f'control_{save_name}.gif'), save_all=True, append_images=control_vid[1:], optimize=False, loop=10000)
+
+    yaml_dict['total_time'] = (end_time - start_time).total_seconds()
+    yaml_dict['total_number_of_frames'] = len(res_vid)
+    yaml_dict['sec_per_frame'] = yaml_dict['total_time']/yaml_dict['total_number_of_frames']
+    with open(os.path.join(input_ns.save_path, 'config.yaml'), 'w') as yaml_file:
+        yaml.dump(yaml_dict, yaml_file)
+    
+    return os.path.join(input_ns.save_path, f'{save_name}.gif'), os.path.join(input_ns.save_path, f'control_{save_name}.gif')
+
+
+def output_video_fn(video_path, text_prompt):
+    fold_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos")
+    video_path = os.path.join(fold_path, os.path.basename(video_path).replace('input', 'output'))
+    print(video_path, text_prompt)
+    return video_path
+
+block = gr.Blocks().queue()
+with block:
     with gr.Row():
-        im = gr.Image()
-        im_2 = gr.Image()
+        gr.Markdown('## RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models')
+    with gr.Row():
+        with gr.Column():
+            with gr.Row():
+                input_path = gr.File(label='Upload Input Video', file_types=['.mp4'], scale=1)
+                
+                inputs = gr.Video(label='Input Video', 
+                                    format='mp4',
+                                    visible=True,
+                                    interactive=False,
+                                    scale=5)
+                input_path.upload(lambda x:x, inputs=[input_path], outputs=[inputs])
+  
+            with gr.Row():
+                example_input = gr.Video(label='Input Example', 
+                                    format='mp4',
+                                    visible=True,
+                                    interactive=False)
+                
+                example_output = gr.Video(label='Output Example', 
+                                    format='mp4',
+                                    visible=True,
+                                    interactive=False)
+                # input(os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos", "exp_input_1.mp4"))
+            ex_prompt = gr.Textbox(label='Text Prompt', interactive=False)
+            with gr.Row():
+                ex_list = []
+                ex_prompt_dict = {
+                    '1': "A black panther",
+                    '2': "A medieval knight",
+                    '3': "Swarovski blue crystal swan",
+                    '4': "Switzerland SBB CFF FFS train",
+                    '5': "White cupcakes, moving on the table",
+                }
+                for i in range(1,6):
+                    ex_list.append([os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos", f"exp_input_{i}.mp4"), ex_prompt_dict[str(i)]])  
+                
+                ex = gr.Examples(
+                    examples=ex_list,
+                    inputs=[example_input, ex_prompt],
+                    outputs=example_output,
+                    fn=output_video_fn,
+                    cache_examples=True,)
+                
+            
+
+        with gr.Column():
+            with gr.Row():
+                result_video = gr.Image(label='Edited Video',
+                                        interactive=False)
+                control_video = gr.Image(label='Control Video',
+                                        interactive=False)
 
-    btn = gr.Button(value="Mirror Image")
-    btn.click(mirror, inputs=[im], outputs=[im_2])
+            with gr.Row():  
+                preprocess_list = ['depth_zoe', 'lineart_realistic', 'lineart_standard', 'softedge_hed']
+                preprocess_name = gr.Dropdown(preprocess_list,
+                                            label='Control type',
+                                            value='depth_zoe')
+                guidance_scale = gr.Slider(label='Guidance scale',
+                                minimum=0,
+                                maximum=40,
+                                step=0.1,
+                                value=7.5)
+            with gr.Row():
+                seed = gr.Slider(label='Seed',
+                                minimum=0,
+                                maximum=2147483647,
+                                step=1,
+                                value=0,
+                                randomize=True)
+            with gr.Row():
+                positive_prompts = gr.Textbox(label='Positive prompts')
+                negative_prompts = gr.Textbox(label='Negative prompts')
+            run_button = gr.Button(value='Run All')
+            with gr.Accordion('Configuration',
+                              open=False):
+                with gr.Row():
+                    controlnet_conditioning_scale = gr.Slider(label='ControlNet conditioning scale',
+                              minimum=0.0,
+                              maximum=1.0,
+                              value=1.0,
+                              step=0.01)
+                    controlnet_guidance_end = gr.Slider(label='ControlNet guidance end',
+                              minimum=0.0,
+                              maximum=1.0,
+                              value=1.0,
+                              step=0.01)
+                    controlnet_guidance_start = gr.Slider(label='ControlNet guidance start',
+                              minimum=0.0,
+                              maximum=1.0,
+                              value=0.0,
+                              step=0.01)
+                    
+                with gr.Row():
+                    grid_size = gr.Slider(label='Grid size (n x n)',
+                                minimum=2,
+                                maximum=3,
+                                value=3,
+                                step=1)
+                    sample_size = gr.Slider(label='Number of grids',
+                                minimum=1,
+                                maximum=10,
+                                value=2,
+                                step=1)
+                    pad = gr.Slider(label='Pad',
+                                minimum=1,
+                                maximum=10,
+                                value=1,
+                                step=1)
 
-    gr.Markdown("## Text Examples")
-    gr.Examples(
-        [["hi", "Adam"], ["hello", "Eve"]],
-        [txt, txt_2],
-        txt_3,
-        combine,
-        cache_examples=True,
-    )
+
+    inputs = [input_path, preprocess_name, controlnet_conditioning_scale, controlnet_guidance_end, controlnet_guidance_start, grid_size, sample_size, pad, guidance_scale, negative_prompts, positive_prompts, seed]
+    
+    run_button.click(fn=run,
+                     inputs=inputs,
+                     outputs=[result_video, control_video])
 
 if __name__ == "__main__":
-    demo.launch()
+
+    block.launch(share=True)

app_.py

@@ -1,319 +0,0 @@
-import gradio as gr
-import cv2
-import os
-import torch
-import argparse
-import os
-import sys
-import yaml
-import datetime
-sys.path.append(os.path.dirname(os.getcwd()))
-from pipelines.sd_controlnet_rave import RAVE
-from pipelines.sd_multicontrolnet_rave import RAVE_MultiControlNet
-import shutil
-import subprocess
-import utils.constants as const
-import utils.video_grid_utils as vgu
-import warnings
-warnings.filterwarnings("ignore")
-import pprint 
-import glob
-
-
-def init_device():
-    device_name = 'cuda' if torch.cuda.is_available() else 'cpu'
-    device = torch.device(device_name)
-    return device
-
-def init_paths(input_ns):
-    if input_ns.save_folder == None or input_ns.save_folder == '':
-        input_ns.save_folder = input_ns.video_name
-    else:
-        input_ns.save_folder = os.path.join(input_ns.save_folder, input_ns.video_name)
-    save_dir = os.path.join(const.OUTPUT_PATH, input_ns.save_folder)
-    os.makedirs(save_dir, exist_ok=True)
-    save_idx = max([int(x[-5:]) for x in os.listdir(save_dir)])+1 if os.listdir(save_dir) != [] else 0
-    input_ns.save_path = os.path.join(save_dir, f'{input_ns.positive_prompts}-{str(save_idx).zfill(5)}')
-    
-    
-    if '-' in input_ns.preprocess_name:
-        input_ns.hf_cn_path = [const.PREPROCESSOR_DICT[i] for i in input_ns.preprocess_name.split('-')]
-    else:
-        input_ns.hf_cn_path = const.PREPROCESSOR_DICT[input_ns.preprocess_name]
-    input_ns.hf_path = "runwayml/stable-diffusion-v1-5"
-    
-    input_ns.inverse_path = os.path.join(const.GENERATED_DATA_PATH, 'inverses', input_ns.video_name, f'{input_ns.preprocess_name}_{input_ns.model_id}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}')
-    input_ns.control_path = os.path.join(const.GENERATED_DATA_PATH, 'controls', input_ns.video_name, f'{input_ns.preprocess_name}_{input_ns.grid_size}x{input_ns.grid_size}_{input_ns.pad}')
-    os.makedirs(input_ns.control_path, exist_ok=True)
-    os.makedirs(input_ns.inverse_path, exist_ok=True)
-    os.makedirs(input_ns.save_path, exist_ok=True)
-    return input_ns
-    
-def install_civitai_model(model_id):
-    full_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id, '*')
-    if len(glob.glob(full_path)) > 0:
-        full_path = glob.glob(full_path)[0]
-        return full_path
-    install_path = os.path.join(const.CWD, 'CIVIT_AI', 'safetensors')
-    install_path_model = os.path.join(const.CWD, 'CIVIT_AI', 'safetensors', model_id)
-    diffusers_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id)
-    convert_py_path = os.path.join(const.CWD, 'CIVIT_AI', 'convert.py')
-    os.makedirs(install_path, exist_ok=True)
-    os.makedirs(diffusers_path, exist_ok=True)
-    subprocess.run(f'wget https://civitai.com/api/download/models/{model_id} --content-disposition --directory {install_path_model}'.split())
-    model_name = glob.glob(os.path.join(install_path, model_id, '*'))[0]
-    model_name2 = os.path.basename(glob.glob(os.path.join(install_path, model_id, '*'))[0]).replace('.safetensors', '')
-    diffusers_path_model_name = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models', model_id, model_name2)
-    print(model_name)
-    subprocess.run(f'python {convert_py_path} --checkpoint_path {model_name}  --dump_path {diffusers_path_model_name}  --from_safetensors'.split())
-    subprocess.run(f'rm -rf {install_path}'.split())
-    return diffusers_path_model_name
-
-def run(*args):
-    list_of_inputs = [x for x in args]
-    input_ns = argparse.Namespace(**{})
-    input_ns.video_path = list_of_inputs[0] # video_path 
-    input_ns.video_name = os.path.basename(input_ns.video_path).replace('.mp4', '').replace('.gif', '') 
-    input_ns.preprocess_name = list_of_inputs[1]
-
-    input_ns.batch_size = list_of_inputs[2]  
-    input_ns.batch_size_vae = list_of_inputs[3]
-
-    input_ns.cond_step_start = list_of_inputs[4]
-    input_ns.controlnet_conditioning_scale = list_of_inputs[5]  
-    input_ns.controlnet_guidance_end = list_of_inputs[6]  
-    input_ns.controlnet_guidance_start = list_of_inputs[7]  
-
-    input_ns.give_control_inversion = list_of_inputs[8]  
-
-    input_ns.grid_size = list_of_inputs[9]  
-    input_ns.sample_size = list_of_inputs[10]  
-    input_ns.pad = list_of_inputs[11]
-    input_ns.guidance_scale = list_of_inputs[12]
-    input_ns.inversion_prompt = list_of_inputs[13]
-
-    input_ns.is_ddim_inversion = list_of_inputs[14]  
-    input_ns.is_shuffle = list_of_inputs[15]  
-
-    input_ns.negative_prompts = list_of_inputs[16]  
-    input_ns.num_inference_steps = list_of_inputs[17]  
-    input_ns.num_inversion_step = list_of_inputs[18]  
-    input_ns.positive_prompts = list_of_inputs[19] 
-    input_ns.save_folder = list_of_inputs[20]  
-
-    input_ns.seed = list_of_inputs[21]  
-    input_ns.model_id = const.MODEL_IDS[list_of_inputs[22]] 
-    # input_ns.width = list_of_inputs[23] 
-    # input_ns.height = list_of_inputs[24] 
-    # input_ns.original_size = list_of_inputs[25]
-    diffusers_model_path = os.path.join(const.CWD, 'CIVIT_AI', 'diffusers_models')
-    os.makedirs(diffusers_model_path, exist_ok=True)
-    if 'model_id' not in list(input_ns.__dict__.keys()):
-        input_ns.model_id = "None"
-    
-    if str(input_ns.model_id) != 'None':
-        input_ns.model_id = install_civitai_model(input_ns.model_id)
-
-    
-    device = init_device()
-    input_ns = init_paths(input_ns)
-
-    input_ns.image_pil_list = vgu.prepare_video_to_grid(input_ns.video_path, input_ns.sample_size, input_ns.grid_size, input_ns.pad)
-
-    print(input_ns.video_path)
-    input_ns.sample_size = len(input_ns.image_pil_list)
-    print(f'Frame count: {len(input_ns.image_pil_list)}')
-
-    controlnet_class = RAVE_MultiControlNet if '-' in str(input_ns.controlnet_conditioning_scale) else RAVE
-    
-
-    CN = controlnet_class(device)
-
-    CN.init_models(input_ns.hf_cn_path, input_ns.hf_path, input_ns.preprocess_name, input_ns.model_id)
-    
-    input_dict = vars(input_ns)
-    pp = pprint.PrettyPrinter(indent=4)
-    pp.pprint(input_dict)
-    yaml_dict = {k:v for k,v in input_dict.items() if k != 'image_pil_list'}
-
-    start_time = datetime.datetime.now()
-    if '-' in str(input_ns.controlnet_conditioning_scale):
-        res_vid, control_vid_1, control_vid_2 = CN(input_dict)
-    else: 
-        res_vid, control_vid = CN(input_dict)
-    end_time = datetime.datetime.now()
-    save_name = f"{'-'.join(input_ns.positive_prompts.split())}_cstart-{input_ns.controlnet_guidance_start}_gs-{input_ns.guidance_scale}_pre-{'-'.join((input_ns.preprocess_name.replace('-','+').split('_')))}_cscale-{input_ns.controlnet_conditioning_scale}_grid-{input_ns.grid_size}_pad-{input_ns.pad}_model-{os.path.basename(input_ns.model_id)}"
-    res_vid[0].save(os.path.join(input_ns.save_path, f'{save_name}.gif'), save_all=True, append_images=res_vid[1:], loop=10000)
-    control_vid[0].save(os.path.join(input_ns.save_path, f'control_{save_name}.gif'), save_all=True, append_images=control_vid[1:], optimize=False, loop=10000)
-
-    yaml_dict['total_time'] = (end_time - start_time).total_seconds()
-    yaml_dict['total_number_of_frames'] = len(res_vid)
-    yaml_dict['sec_per_frame'] = yaml_dict['total_time']/yaml_dict['total_number_of_frames']
-    with open(os.path.join(input_ns.save_path, 'config.yaml'), 'w') as yaml_file:
-        yaml.dump(yaml_dict, yaml_file)
-    
-    return os.path.join(input_ns.save_path, f'{save_name}.gif'), os.path.join(input_ns.save_path, f'control_{save_name}.gif')
-
-
-def output_video_fn(video_path):
-    fold_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos")
-    video_path = os.path.join(fold_path, os.path.basename(video_path).replace('input', 'output'))
-    return video_path
-
-block = gr.Blocks().queue()
-with block:
-    with gr.Row():
-        gr.Markdown('## RAVE')
-    with gr.Row():
-        with gr.Column():
-            with gr.Row():
-                input_path = gr.File(label='Upload Input Video', file_types=['.mp4'], scale=1)
-                
-                inputs = gr.Video(label='Input Video', 
-                                    format='mp4',
-                                    visible=True,
-                                    interactive=False,
-                                    scale=5)
-                input_path.upload(lambda x:x, inputs=[input_path], outputs=[inputs])
-                
-            with gr.Row():
-                positive_prompts = gr.Textbox(label='Positive prompts')
-                negative_prompts = gr.Textbox(label='Negative prompts')
-            with gr.Row():  
-                preprocess_name = gr.Dropdown(const.PREPROCESSOR_DICT.keys(),
-                                            label='Control type',
-                                            value='depth_zoe')
-                guidance_scale = gr.Slider(label='Guidance scale',
-                                minimum=0,
-                                maximum=40,
-                                step=0.1,
-                                value=7.5)
-
-            with gr.Row():
-                inversion_prompt = gr.Textbox(label='Inversion prompt')
-                seed = gr.Slider(label='Seed',
-                                minimum=0,
-                                maximum=2147483647,
-                                step=1,
-                                value=0,
-                                randomize=True)
-            
-            with gr.Row():
-                model_id = gr.Dropdown(const.MODEL_IDS,
-                                    label='Model id',
-                                    value='SD 1.5')
-                save_folder = gr.Textbox(label='Save folder')
-            
-            run_button = gr.Button(value='Run All')
-            with gr.Accordion('Configuration',
-                              open=False):
-                with gr.Row():
-                    batch_size = gr.Slider(label='Batch size',
-                              minimum=1,
-                              maximum=36,
-                              value=4,
-                              step=1)
-                    batch_size_vae = gr.Slider(label='Batch size of VAE',
-                              minimum=1,
-                              maximum=36,
-                              value=1,
-                              step=1)
-                
-                with gr.Row():
-                    is_ddim_inversion = gr.Checkbox(
-                        label='Use DDIM Inversion',
-                        value=True)
-                    is_shuffle = gr.Checkbox(
-                        label='Shuffle',
-                        value=True)
-                
-                with gr.Row():
-                    num_inference_steps = gr.Slider(label='Number of inference steps',
-                              minimum=1,
-                              maximum=100,
-                              value=20,
-                              step=1)
-                    num_inversion_step = gr.Slider(label='Number of inversion steps',
-                              minimum=1,
-                              maximum=100,
-                              value=20,
-                              step=1)
-                    cond_step_start = gr.Slider(label='Conditioning step start',
-                                minimum=0,
-                                maximum=1.0,
-                                value=0.0,
-                                step=0.1)
-                
-                with gr.Row():
-                    controlnet_conditioning_scale = gr.Slider(label='ControlNet conditioning scale',
-                              minimum=0.0,
-                              maximum=1.0,
-                              value=1.0,
-                              step=0.01)
-                    controlnet_guidance_end = gr.Slider(label='ControlNet guidance end',
-                              minimum=0.0,
-                              maximum=1.0,
-                              value=1.0,
-                              step=0.01)
-                    controlnet_guidance_start = gr.Slider(label='ControlNet guidance start',
-                              minimum=0.0,
-                              maximum=1.0,
-                              value=0.0,
-                              step=0.01)
-                give_control_inversion = gr.Checkbox(
-                    label='Give control during inversion',
-                    value=True)
-                    
-                with gr.Row():
-                    grid_size = gr.Slider(label='Grid size',
-                                minimum=1,
-                                maximum=10,
-                                value=3,
-                                step=1)
-                    sample_size = gr.Slider(label='Sample size',
-                                minimum=-1,
-                                maximum=100,
-                                value=-1,
-                                step=1)
-                    pad = gr.Slider(label='Pad',
-                                minimum=1,
-                                maximum=10,
-                                value=1,
-                                step=1)
-
-
-
-        with gr.Column():
-            with gr.Row():
-                result_video = gr.Image(label='Edited Video',
-                                        interactive=False)
-                control_video = gr.Image(label='Control Video',
-                                        interactive=False)
-
-    with gr.Row():
-        example_input = gr.Video(label='Input Example', 
-                            format='mp4',
-                            visible=True,
-                            interactive=False)
-        example_output = gr.Video(label='Output Example', 
-                            format='mp4',
-                            visible=True,
-                            interactive=False)
-        # input(os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos", "exp_input_1.mp4"))
-        gr.Markdown("## Video Examples")
-        gr.Examples(
-            examples=[os.path.join(os.path.dirname(os.path.abspath(__file__)), "example_videos", "exp_input_1.mp4")],
-            inputs=example_input,
-            outputs=example_output,
-            fn=output_video_fn,
-            cache_examples=True,)
-
-    inputs = [input_path, preprocess_name, batch_size, batch_size_vae, cond_step_start, controlnet_conditioning_scale, controlnet_guidance_end, controlnet_guidance_start, give_control_inversion, grid_size, sample_size, pad, guidance_scale, inversion_prompt, is_ddim_inversion, is_shuffle, negative_prompts, num_inference_steps, num_inversion_step, positive_prompts, save_folder, seed, model_id]
-    
-    run_button.click(fn=run,
-                     inputs=inputs,
-                     outputs=[result_video, control_video])
-
-if __name__ == "__main__":
-
-    block.launch(share=True)

gradio_cached_examples/59/log.csv

@@ -1,2 +0,0 @@
-Output Example,flag,username,timestamp
-"{""video"":{""path"":""gradio_cached_examples/59/Output Example/994f36ecf77e57c9b298/exp_output_1.mp4"",""url"":null,""size"":null,""orig_name"":""exp_output_1.mp4"",""mime_type"":null},""subtitles"":null}",,,2023-12-17 12:08:44.852661