app.py

app.py

@@ -1,344 +1,43 @@
-from __future__ import annotations
-import gradio as gr
-import os
-import cv2
-import numpy as np
-from PIL import Image
-from moviepy.editor import *
-from share_btn import community_icon_html, loading_icon_html, share_js
-
-import pathlib
-import shlex
-import subprocess
-
-if os.getenv('SYSTEM') == 'spaces':
-    with open('patch') as f:
-        subprocess.run(shlex.split('patch -p1'), stdin=f, cwd='ControlNet')
-
-base_url = 'https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/'
-
-names = [
-    'body_pose_model.pth',
-    'dpt_hybrid-midas-501f0c75.pt',
-    'hand_pose_model.pth',
-    'mlsd_large_512_fp32.pth',
-    'mlsd_tiny_512_fp32.pth',
-    'network-bsds500.pth',
-    'upernet_global_small.pth',
-]
-
-for name in names:
-    command = f'wget https://huggingface.co/lllyasviel/ControlNet/resolve/main/annotator/ckpts/{name} -O {name}'
-    out_path = pathlib.Path(f'ControlNet/annotator/ckpts/{name}')
-    if out_path.exists():
-        continue
-    subprocess.run(shlex.split(command), cwd='ControlNet/annotator/ckpts/')
-
-from model import (DEFAULT_BASE_MODEL_FILENAME, DEFAULT_BASE_MODEL_REPO,
-                   DEFAULT_BASE_MODEL_URL, Model)
-
-model = Model()
-
-
-def controlnet(i, prompt, control_task, seed_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold):
-    img= Image.open(i)
-    np_img = np.array(img)
-    
-    a_prompt = "best quality, extremely detailed"
-    n_prompt = "longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality"
-    num_samples = 1
-    image_resolution = 512
-    detect_resolution = 512
-    eta = 0.0
-    #low_threshold = 100
-    #high_threshold = 200
-    #value_threshold = 0.1
-    #distance_threshold = 0.1
-    #bg_threshold = 0.4
-    
-    if control_task == 'Canny':
-        result = model.process_canny(np_img, prompt, a_prompt, n_prompt, num_samples,
-                image_resolution, ddim_steps, scale, seed_in, eta, low_threshold, high_threshold)
-    elif control_task == 'Depth':
-        result = model.process_depth(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta)
-    elif control_task == 'Hed':
-        result = model.process_hed(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta)
-    elif control_task == 'Hough':
-        result = model.process_hough(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, value_threshold,
-                      distance_threshold)
-    elif control_task == 'Normal':
-        result = model.process_normal(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta, bg_threshold)
-    elif control_task == 'Pose':
-        result = model.process_pose(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta)
-    elif control_task == 'Scribble':
-        result = model.process_scribble(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, ddim_steps, scale, seed_in, eta)
-    elif control_task == 'Seg':
-        result = model.process_seg(np_img, prompt, a_prompt, n_prompt, num_samples,
-            image_resolution, detect_resolution, ddim_steps, scale, seed_in, eta)
-    
-    #print(result[0])
-    processor_im = Image.fromarray(result[0])
-    processor_im.save("process_" + control_task + "_" + str(i) + ".jpeg")
-    im = Image.fromarray(result[1])
-    im.save("your_file" + str(i) + ".jpeg")
-    return "your_file" + str(i) + ".jpeg", "process_" + control_task + "_" + str(i) + ".jpeg"
-
-def change_task_options(task):
-    if task == "Canny" :
-        return canny_opt.update(visible=True), hough_opt.update(visible=False), normal_opt.update(visible=False)
-    elif task == "Hough" :
-        return canny_opt.update(visible=False),hough_opt.update(visible=True), normal_opt.update(visible=False)
-    elif task == "Normal" :
-        return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=True)
-    else :
-        return canny_opt.update(visible=False),hough_opt.update(visible=False), normal_opt.update(visible=False)
-
-def get_frames(video_in):
-    frames = []
-    #resize the video
-    clip = VideoFileClip(video_in)
-    
-    #check fps
-    if clip.fps > 30:
-        print("vide rate is over 30, resetting to 30")
-        clip_resized = clip.resize(height=512)
-        clip_resized.write_videofile("video_resized.mp4", fps=30)
-    else:
-        print("video rate is OK")
-        clip_resized = clip.resize(height=512)
-        clip_resized.write_videofile("video_resized.mp4", fps=clip.fps)
-    
-    print("video resized to 512 height")
-    
-    # Opens the Video file with CV2
-    cap= cv2.VideoCapture("video_resized.mp4")
-    
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    print("video fps: " + str(fps))
-    i=0
-    while(cap.isOpened()):
-        ret, frame = cap.read()
-        if ret == False:
-            break
-        cv2.imwrite('kang'+str(i)+'.jpg',frame)
-        frames.append('kang'+str(i)+'.jpg')
-        i+=1
-    
-    cap.release()
-    cv2.destroyAllWindows()
-    print("broke the video into frames")
-    
-    return frames, fps
-
-
-def convert(gif):
-    if gif != None:
-        clip = VideoFileClip(gif.name)
-        clip.write_videofile("my_gif_video.mp4")
-        return "my_gif_video.mp4"
-    else:
-        pass
-
-
-def create_video(frames, fps, type):
-    print("building video result")
-    clip = ImageSequenceClip(frames, fps=fps)
-    clip.write_videofile(type + "_result.mp4", fps=fps)
-    
-    return type + "_result.mp4"
-
-
-def infer(prompt,video_in, control_task, seed_in, trim_value, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold, gif_import):
-    print(f"""
-    ———————————————
-    {prompt}
-    ———————————————""")
-    
-    # 1. break video into frames and get FPS
-    break_vid = get_frames(video_in)
-    frames_list= break_vid[0]
-    fps = break_vid[1]
-    n_frame = int(trim_value*fps)
-    
-    if n_frame >= len(frames_list):
-        print("video is shorter than the cut value")
-        n_frame = len(frames_list)
-    
-    # 2. prepare frames result arrays
-    processor_result_frames = []
-    result_frames = []
-    print("set stop frames to: " + str(n_frame))
-    
-    for i in frames_list[0:int(n_frame)]:
-        controlnet_img = controlnet(i, prompt,control_task, seed_in, ddim_steps, scale,  low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold)
-        #images = controlnet_img[0]
-        #rgb_im = images[0].convert("RGB")
-  
-        # exporting the image
-        #rgb_im.save(f"result_img-{i}.jpg")
-        processor_result_frames.append(controlnet_img[1])
-        result_frames.append(controlnet_img[0])
-        print("frame " + i + "/" + str(n_frame) + ": done;")
-
-    processor_vid = create_video(processor_result_frames, fps, "processor")
-    final_vid = create_video(result_frames, fps, "final")
-
-    files = [processor_vid, final_vid]
-    if gif_import != None:
-        final_gif = VideoFileClip(final_vid)
-        final_gif.write_gif("final_result.gif")
-        final_gif = "final_result.gif"
-
-        files.append(final_gif)
-    print("finished !")
-    
-    return final_vid, gr.Accordion.update(visible=True), gr.Video.update(value=processor_vid, visible=True), gr.File.update(value=files, visible=True), gr.Group.update(visible=True)
-
-
-def clean():
-    return gr.Accordion.update(visible=False),gr.Video.update(value=None, visible=False), gr.Video.update(value=None), gr.File.update(value=None, visible=False), gr.Group.update(visible=False)
-
-title = """
-    <div style="text-align: center; max-width: 700px; margin: 0 auto;">
-        <div
-        style="
-            display: inline-flex;
-            align-items: center;
-            gap: 0.8rem;
-            font-size: 1.75rem;
-        "
-        >
-        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
-            ControlNet Video
-        </h1>
-        </div>
-        <p style="margin-bottom: 10px; font-size: 94%">
-        Apply ControlNet to a video 
-        </p>
-    </div>
-"""
-
-article = """
-    
-    <div class="footer">
-        <p>
-        Follow <a href="https://twitter.com/fffiloni" target="_blank">Sylvain Filoni</a> for future updates 🤗
-        </p>
-    </div>
-    <div id="may-like-container" style="display: flex;justify-content: center;flex-direction: column;align-items: center;margin-bottom: 30px;">
-        <p>You may also like: </p>
-        <div id="may-like-content" style="display:flex;flex-wrap: wrap;align-items:center;height:20px;">
-            
-            <svg height="20" width="148" style="margin-left:4px;margin-bottom: 6px;">       
-                 <a href="https://huggingface.co/spaces/fffiloni/Pix2Pix-Video" target="_blank">
-                    <image href="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue" src="https://img.shields.io/badge/🤗 Spaces-Pix2Pix_Video-blue.png" height="20"/>
-                 </a>
-            </svg>
-            
-        </div>
-    
-    </div>
-    
-"""
-
-with gr.Blocks(css='style.css') as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.HTML(title)
-        gr.HTML("""
-                <a style="display:inline-block" href="https://huggingface.co/spaces/fffiloni/ControlNet-Video?duplicate=true"><img src="https://img.shields.io/badge/-Duplicate%20Space-blue?labelColor=white&style=flat&logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABAAAAAQCAYAAAAf8/9hAAAAAXNSR0IArs4c6QAAAP5JREFUOE+lk7FqAkEURY+ltunEgFXS2sZGIbXfEPdLlnxJyDdYB62sbbUKpLbVNhyYFzbrrA74YJlh9r079973psed0cvUD4A+4HoCjsA85X0Dfn/RBLBgBDxnQPfAEJgBY+A9gALA4tcbamSzS4xq4FOQAJgCDwV2CPKV8tZAJcAjMMkUe1vX+U+SMhfAJEHasQIWmXNN3abzDwHUrgcRGmYcgKe0bxrblHEB4E/pndMazNpSZGcsZdBlYJcEL9Afo75molJyM2FxmPgmgPqlWNLGfwZGG6UiyEvLzHYDmoPkDDiNm9JR9uboiONcBXrpY1qmgs21x1QwyZcpvxt9NS09PlsPAAAAAElFTkSuQmCC&logoWidth=14" alt="Duplicate Space"></a> 
-                """, elem_id="duplicate-container")
-        with gr.Row():
-            with gr.Column():
-                video_inp = gr.Video(label="Video source", source="upload", type="filepath", elem_id="input-vid")
-                video_out = gr.Video(label="ControlNet video result", elem_id="video-output")
-                
-                with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
-                    community_icon = gr.HTML(community_icon_html)
-                    loading_icon = gr.HTML(loading_icon_html)
-                    share_button = gr.Button("Share to community", elem_id="share-btn")
-                
-                with gr.Accordion("Detailed results", visible=False) as detailed_result:
-                    prep_video_out = gr.Video(label="Preprocessor video result", visible=False, elem_id="prep-video-output")
-                    files = gr.File(label="Files can be downloaded ;)", visible=False)
-                
-            with gr.Column():
-                #status = gr.Textbox()
-                
-                prompt = gr.Textbox(label="Prompt", placeholder="enter prompt", show_label=True, elem_id="prompt-in")
-                
-                with gr.Row():
-                    control_task = gr.Dropdown(label="Control Task", choices=["Canny", "Depth", "Hed", "Hough", "Normal", "Pose", "Scribble", "Seg"], value="Pose", multiselect=False, elem_id="controltask-in")
-                    seed_inp = gr.Slider(label="Seed", minimum=0, maximum=2147483647, step=1, value=123456, elem_id="seed-in")
-                
-                with gr.Row():
-                    trim_in = gr.Slider(label="Cut video at (s)", minimun=1, maximum=5, step=1, value=1)
-                
-                with gr.Accordion("Advanced Options", open=False):
-                    with gr.Tab("Diffusion Settings"):
-                        with gr.Row(visible=False) as canny_opt:
-                            low_threshold = gr.Slider(label='Canny low threshold', minimum=1, maximum=255, value=100, step=1)
-                            high_threshold = gr.Slider(label='Canny high threshold', minimum=1, maximum=255, value=200, step=1)
-                        
-                        with gr.Row(visible=False) as hough_opt:
-                            value_threshold = gr.Slider(label='Hough value threshold (MLSD)', minimum=0.01, maximum=2.0, value=0.1, step=0.01)
-                            distance_threshold = gr.Slider(label='Hough distance threshold (MLSD)', minimum=0.01, maximum=20.0, value=0.1, step=0.01)
-                        
-                        with gr.Row(visible=False) as normal_opt:
-                            bg_threshold = gr.Slider(label='Normal background threshold', minimum=0.0, maximum=1.0, value=0.4, step=0.01)
-                        
-                        ddim_steps = gr.Slider(label='Steps', minimum=1, maximum=100, value=20, step=1)
-                        scale = gr.Slider(label='Guidance Scale', minimum=0.1, maximum=30.0, value=9.0, step=0.1)
-                    
-                    with gr.Tab("GIF import"):
-                        gif_import = gr.File(label="import a GIF instead", file_types=['.gif'])
-                        gif_import.change(convert, gif_import, video_inp, queue=False)
-
-                    with gr.Tab("Custom Model"):
-                        current_base_model = gr.Text(label='Current base model',
-                                             value=DEFAULT_BASE_MODEL_URL)
-                        with gr.Row():
-                            with gr.Column():
-                                base_model_repo = gr.Text(label='Base model repo',
-                                                      max_lines=1,
-                                                      placeholder=DEFAULT_BASE_MODEL_REPO,
-                                                      interactive=True)
-                                base_model_filename = gr.Text(
-                                     label='Base model file',
-                                     max_lines=1,
-                                     placeholder=DEFAULT_BASE_MODEL_FILENAME,
-                                     interactive=True)
-                            change_base_model_button = gr.Button('Change base model')
-                        
-                        gr.HTML(
-                            '''<p>You can use other base models by specifying the repository name and filename.<br />
-                                  The base model must be compatible with Stable Diffusion v1.5.</p>''')
-                
-                        change_base_model_button.click(fn=model.set_base_model,
-                                                       inputs=[
-                                                           base_model_repo,
-                                                           base_model_filename,
-                                                       ],
-                                                       outputs=current_base_model, queue=False)
-                
-                submit_btn = gr.Button("Generate ControlNet video")
-        
-        inputs = [prompt,video_inp,control_task, seed_inp, trim_in, ddim_steps, scale, low_threshold, high_threshold, value_threshold, distance_threshold, bg_threshold, gif_import]
-        outputs = [video_out, detailed_result, prep_video_out, files, share_group]
-        #outputs = [status]
-        
-        
-        gr.HTML(article)
-    control_task.change(change_task_options, inputs=[control_task], outputs=[canny_opt, hough_opt, normal_opt], queue=False)
-    submit_btn.click(clean, inputs=[], outputs=[detailed_result, prep_video_out, video_out, files, share_group], queue=False)
-    submit_btn.click(infer, inputs, outputs)
-    share_button.click(None, [], [], _js=share_js)
-
-    
-    
-demo.queue(max_size=12).launch()
+from setuptools import setup, find_packages
+
+setup(
+  name = 'phenaki-pytorch',
+  packages = find_packages(exclude=[]),
+  version = '0.3.0',
+  license='MIT',
+  description = 'Phenaki - Pytorch',
+  author = 'Phil Wang',
+  author_email = 'lucidrains@gmail.com',
+  long_description_content_type = 'text/markdown',
+  url = 'https://github.com/lucidrains/phenaki-pytorch',
+  keywords = [
+    'artificial intelligence',
+    'deep learning',
+    'transformers',
+    'attention mechanisms',
+    'text-to-video'
+  ],
+  install_requires=[
+    'accelerate',
+    'beartype',
+    'einops>=0.6',
+    'ema-pytorch>=0.1.1',
+    'opencv-python',
+    'pillow',
+    'numpy',
+    'sentencepiece',
+    'torch>=1.6',
+    'torchtyping',
+    'torchvision',
+    'transformers>=4.20.1',
+    'tqdm',
+    'vector-quantize-pytorch>=0.10.15'
+  ],
+  classifiers=[
+    'Development Status :: 4 - Beta',
+    'Intended Audience :: Developers',
+    'Topic :: Scientific/Engineering :: Artificial Intelligence',
+    'License :: OSI Approved :: MIT License',
+    'Programming Language :: Python :: 3.6',
+  ],
+)