import os, random, time import torch from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler from tqdm import tqdm from memo.models.audio_proj import AudioProjModel from memo.models.image_proj import ImageProjModel from memo.models.unet_2d_condition import UNet2DConditionModel from memo.models.unet_3d import UNet3DConditionModel from memo.pipelines.video_pipeline import VideoPipeline from memo.utils.audio_utils import extract_audio_emotion_labels, preprocess_audio, resample_audio from memo.utils.vision_utils import preprocess_image, tensor_to_video device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") weight_dtype = torch.bfloat16 with torch.inference_mode(): vae = AutoencoderKL.from_pretrained("/content/memo/checkpoints/vae").to(device=device, dtype=weight_dtype) reference_net = UNet2DConditionModel.from_pretrained("/content/memo/checkpoints", subfolder="reference_net", use_safetensors=True) diffusion_net = UNet3DConditionModel.from_pretrained("/content/memo/checkpoints", subfolder="diffusion_net", use_safetensors=True) image_proj = ImageProjModel.from_pretrained("/content/memo/checkpoints", subfolder="image_proj", use_safetensors=True) audio_proj = AudioProjModel.from_pretrained("/content/memo/checkpoints", subfolder="audio_proj", use_safetensors=True) vae.requires_grad_(False).eval() reference_net.requires_grad_(False).eval() diffusion_net.requires_grad_(False).eval() image_proj.requires_grad_(False).eval() audio_proj.requires_grad_(False).eval() reference_net.enable_xformers_memory_efficient_attention() diffusion_net.enable_xformers_memory_efficient_attention() noise_scheduler = FlowMatchEulerDiscreteScheduler() pipeline = VideoPipeline(vae=vae, reference_net=reference_net, diffusion_net=diffusion_net, scheduler=noise_scheduler, image_proj=image_proj) pipeline.to(device=device, dtype=weight_dtype) @torch.inference_mode() def generate(input_video, input_audio, seed): resolution = 512 num_generated_frames_per_clip = 16 fps = 30 num_init_past_frames = 2 num_past_frames = 16 inference_steps = 20 cfg_scale = 3.5 if seed == 0: random.seed(int(time.time())) seed = random.randint(0, 18446744073709551615) generator = torch.manual_seed(seed) img_size = (resolution, resolution) pixel_values, face_emb = preprocess_image(face_analysis_model="/content/memo/checkpoints/misc/face_analysis", image_path=input_video, image_size=resolution) output_dir = "/content/memo/outputs" os.makedirs(output_dir, exist_ok=True) cache_dir = os.path.join(output_dir, "audio_preprocess") os.makedirs(cache_dir, exist_ok=True) input_audio = resample_audio(input_audio, os.path.join(cache_dir, f"{os.path.basename(input_audio).split('.')[0]}-16k.wav")) audio_emb, audio_length = preprocess_audio( wav_path=input_audio, num_generated_frames_per_clip=num_generated_frames_per_clip, fps=fps, wav2vec_model="/content/memo/checkpoints/wav2vec2", vocal_separator_model="/content/memo/checkpoints/misc/vocal_separator/Kim_Vocal_2.onnx", cache_dir=cache_dir, device=device, ) audio_emotion, num_emotion_classes = extract_audio_emotion_labels( model="/content/memo/checkpoints", wav_path=input_audio, emotion2vec_model="/content/memo/checkpoints/emotion2vec_plus_large", audio_length=audio_length, device=device, ) video_frames = [] num_clips = audio_emb.shape[0] // num_generated_frames_per_clip for t in tqdm(range(num_clips), desc="Generating video clips"): if len(video_frames) == 0: past_frames = pixel_values.repeat(num_init_past_frames, 1, 1, 1) past_frames = past_frames.to(dtype=pixel_values.dtype, device=pixel_values.device) pixel_values_ref_img = torch.cat([pixel_values, past_frames], dim=0) else: past_frames = video_frames[-1][0] past_frames = past_frames.permute(1, 0, 2, 3) past_frames = past_frames[0 - num_past_frames :] past_frames = past_frames * 2.0 - 1.0 past_frames = past_frames.to(dtype=pixel_values.dtype, device=pixel_values.device) pixel_values_ref_img = torch.cat([pixel_values, past_frames], dim=0) pixel_values_ref_img = pixel_values_ref_img.unsqueeze(0) audio_tensor = (audio_emb[t * num_generated_frames_per_clip : min((t + 1) * num_generated_frames_per_clip, audio_emb.shape[0])].unsqueeze(0).to(device=audio_proj.device, dtype=audio_proj.dtype)) audio_tensor = audio_proj(audio_tensor) audio_emotion_tensor = audio_emotion[t * num_generated_frames_per_clip : min((t + 1) * num_generated_frames_per_clip, audio_emb.shape[0])] pipeline_output = pipeline( ref_image=pixel_values_ref_img, audio_tensor=audio_tensor, audio_emotion=audio_emotion_tensor, emotion_class_num=num_emotion_classes, face_emb=face_emb, width=img_size[0], height=img_size[1], video_length=num_generated_frames_per_clip, num_inference_steps=inference_steps, guidance_scale=cfg_scale, generator=generator, ) video_frames.append(pipeline_output.videos) video_frames = torch.cat(video_frames, dim=2) video_frames = video_frames.squeeze(0) video_frames = video_frames[:, :audio_length] video_path = f"/content/memo-{seed}-tost.mp4" tensor_to_video(video_frames, video_path, input_audio, fps=fps) return video_path import gradio as gr with gr.Blocks(css=".gradio-container {max-width: 1080px !important}", analytics_enabled=False) as demo: with gr.Row(): with gr.Column(): input_video = gr.Image(label="Upload Input Image", type="filepath") input_audio = gr.Audio(label="Upload Input Audio", type="filepath") seed = gr.Number(label="Seed (0 for Random)", value=0, precision=0) with gr.Column(): video_output = gr.Video(label="Generated Video") generate_button = gr.Button("Generate") generate_button.click( fn=generate, inputs=[input_video, input_audio, seed], outputs=[video_output], ) demo.queue().launch(inline=False, share=False, debug=True, server_name='0.0.0.0', server_port=7860, allowed_paths=["/content"])