Update app.py

app.py

@@ -1,198 +1,25 @@
 import gradio as gr
-import torch
-import os
-import spaces
-import uuid
 
-from diffusers import AnimateDiffPipeline, MotionAdapter, EulerDiscreteScheduler
-from diffusers.utils import export_to_video
-from huggingface_hub import hf_hub_download
-from safetensors.torch import load_file
-from PIL import Image
-from gradio_client import Client, file
-from moviepy.editor import VideoFileClip, AudioFileClip, concatenate_videoclips
 
-
-# using tango2 via Gradio python client 
-client = Client("declare-lab/tango2")
-
-# Constants
-bases = {
-    "ToonYou": "frankjoshua/toonyou_beta6",
-    "epiCRealism": "emilianJR/epiCRealism"
-}
-step_loaded = None
-base_loaded = "epiCRealism"
-motion_loaded = None
-
-# Ensure model and scheduler are initialized in GPU-enabled function
-if not torch.cuda.is_available():
-    raise NotImplementedError("No GPU detected!")
-
-device = "cuda"
-dtype = torch.float16
-pipe = AnimateDiffPipeline.from_pretrained(bases[base_loaded], torch_dtype=dtype).to(device)
-pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, timestep_spacing="trailing", beta_schedule="linear")
-
-# Safety checkers
-from safety_checker import StableDiffusionSafetyChecker
-from transformers import CLIPFeatureExtractor
-
-safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker").to(device)
-feature_extractor = CLIPFeatureExtractor.from_pretrained("openai/clip-vit-base-patch32")
-
-def check_nsfw_images(images: list[Image.Image]) -> list[bool]:
-    safety_checker_input = feature_extractor(images, return_tensors="pt").to(device)
-    has_nsfw_concepts = safety_checker(images=[images], clip_input=safety_checker_input.pixel_values.to(device))
-    return has_nsfw_concepts
-
-# Function 
-@spaces.GPU(enable_queue=True)
-def generate_image(prompt, base, motion, step, progress=gr.Progress()):
-    global step_loaded
-    global base_loaded
-    global motion_loaded
-    print(prompt, base, step)
-
-    if step_loaded != step:
-        repo = "ByteDance/AnimateDiff-Lightning"
-        ckpt = f"animatediff_lightning_{step}step_diffusers.safetensors"
-        pipe.unet.load_state_dict(load_file(hf_hub_download(repo, ckpt), device=device), strict=False)
-        step_loaded = step
-
-    if base_loaded != base:
-        pipe.unet.load_state_dict(torch.load(hf_hub_download(bases[base], "unet/diffusion_pytorch_model.bin"), map_location=device), strict=False)
-        base_loaded = base
-
-    if motion_loaded != motion:
-        pipe.unload_lora_weights()
-        if motion != "":
-            pipe.load_lora_weights(motion, adapter_name="motion")
-            pipe.set_adapters(["motion"], [0.7])
-        motion_loaded = motion
-
-    progress((0, step))
-    def progress_callback(i, t, z):
-        progress((i+1, step))
-
-    output = pipe(prompt=prompt, guidance_scale=1.0, num_inference_steps=step, callback=progress_callback, callback_steps=1)
-
-    has_nsfw_concepts = check_nsfw_images([output.frames[0][0]])
-    if has_nsfw_concepts[0]:
-        gr.Warning("NSFW content detected.")
-        return None
-
-    name = str(uuid.uuid4()).replace("-", "")
-    video_path = f"/tmp/{name}.mp4"
-    export_to_video(output.frames[0], video_path, fps=10)
-    
-    audio_path = tango2(prompt)
-    final_video_path = fuse_together(audio_path, video_path)
-    
-    return final_video_path
-
-
-def tango2(prompt):
-    results = client.predict(
-      prompt=prompt,
-      steps=100,
-      guidance=3,
-      api_name="/predict"
-    )
-    return results
-
-def fuse_together(audio, video):
-    
-    # Load your video and audio files
-    video_clip = VideoFileClip(video)
-    audio_clip = AudioFileClip(audio)
-    
-    # Loop the video twice
-    looped_video = concatenate_videoclips([video_clip, video_clip])
-    
-    # Cut the audio to match the duration of the looped video
-    looped_audio = audio_clip.subclip(0, looped_video.duration)
-    
-    # Set the audio of the looped video to the adjusted audio clip
-    final_video = looped_video.set_audio(looped_audio)
-    
-    # Write the result to a file (output will be twice the length of the original video)
-    name = str(uuid.uuid4()).replace("-", "")
-    path = f"/tmp/{name}.mp4"
-    final_video.write_videofile(path, codec="libx264", audio_codec="aac")
-
-    return path
-
-
-# Gradio Interface
-with gr.Blocks(css="style.css") as demo:
-    gr.HTML(
-        "<h1><center>AnimateDiff-Lightning⚡ + TANGO 2</center></h1>" +
-        "<p><center>Using Gradio Python Client to combine <b>AnimateDiff Lightning</b> with <b>Tango2</b> to give Voice to your Generated Videos</center></p>" +
-        "<p><center>Refer Gradio Guide for Python Clients here :<a href='https://www.gradio.app/guides/getting-started-with-the-python-client'>Getting Started with the Gradio Python client</a></center></p>"
-    )
-    with gr.Group():
-        with gr.Row():
-            prompt = gr.Textbox(
-                label='Prompt (English)'
-            )
-        with gr.Row():
-            select_base = gr.Dropdown(
-                label='Base model',
-                choices=[
-                    "ToonYou", 
-                    "epiCRealism",
-                ],
-                value=base_loaded,
-                interactive=True
-            )
-            select_motion = gr.Dropdown(
-                label='Motion',
-                choices=[
-                    ("Default", ""),
-                    ("Zoom in", "guoyww/animatediff-motion-lora-zoom-in"),
-                    ("Zoom out", "guoyww/animatediff-motion-lora-zoom-out"),
-                    ("Tilt up", "guoyww/animatediff-motion-lora-tilt-up"),
-                    ("Tilt down", "guoyww/animatediff-motion-lora-tilt-down"),
-                    ("Pan left", "guoyww/animatediff-motion-lora-pan-left"),
-                    ("Pan right", "guoyww/animatediff-motion-lora-pan-right"),
-                    ("Roll left", "guoyww/animatediff-motion-lora-rolling-anticlockwise"),
-                    ("Roll right", "guoyww/animatediff-motion-lora-rolling-clockwise"),
-                ],
-                value="",
-                interactive=True
-            )
-            select_step = gr.Dropdown(
-                label='Inference steps',
-                choices=[
-                    ('1-Step', 1), 
-                    ('2-Step', 2),
-                    ('4-Step', 4),
-                    ('8-Step', 8)],
-                value=4,
-                interactive=True
-            )
-            submit = gr.Button(
-                scale=1,
-                variant='primary'
-            )
-    video = gr.Video(
-        label='AnimateDiff-Lightning',
-        autoplay=True,
-        height=512,
-        width=512,
-        elem_id="video_output"
-    )
-
-    prompt.submit(
-        fn=generate_image,
-        inputs=[prompt, select_base, select_motion, select_step],
-        outputs=video,
-    )
-    submit.click(
-        fn=generate_image,
-        inputs=[prompt, select_base, select_motion, select_step],
-        outputs=video,
-    )
-
-demo.queue().launch()
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(visible=False, min_width=200, scale=0) as sidebar:
+            btn1 = gr.Button("Button 1")
+            btn2 = gr.Button("Button 2")
+        with gr.Column() as main:
+            open_sidebar_btn = gr.Button("Open Sidebar", scale=0)
+            close_sidebar_btn = gr.Button("Close Sidebar", visible=False, scale=0)
+            open_sidebar_btn.click(lambda: {
+                open_sidebar_btn: gr.Button(visible=False),
+                close_sidebar_btn: gr.Button(visible=True),
+                sidebar: gr.Column(visible=True)
+            }, outputs={open_sidebar_btn, close_sidebar_btn, sidebar})
+            close_sidebar_btn.click(lambda: {
+                open_sidebar_btn: gr.Button(visible=True),
+                close_sidebar_btn: gr.Button(visible=False),
+                sidebar: gr.Column(visible=False)
+            }, outputs={open_sidebar_btn, close_sidebar_btn, sidebar})
+            gr.Markdown("# Hello Blocks")
+            gr.Markdown("Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nullam nec nulla nec nulla fermentum fermentum. Nullam nec nulla nec nulla fermentum fermentum.")
+
+demo.launch()