Spaces:
Runtime error
Runtime error
| 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 | |
| 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() |