Spaces:
Running
on
Zero
Running
on
Zero
File size: 7,128 Bytes
6cd5133 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 |
import os, random, time
from huggingface_hub import snapshot_download
# Download models
os.makedirs("checkpoints", exist_ok=True)
# List of subdirectories to create inside "checkpoints"
subfolders = [
"vae",
"wav2vec2",
"emotion2vec_plus_large"
]
# Create each subdirectory
for subfolder in subfolders:
os.makedirs(os.path.join("checkpoints", subfolder), exist_ok=True)
snapshot_download(
repo_id = "memoavatar/memo",
local_dir = "./checkpoints"
)
snapshot_download(
repo_id = "stabilityai/sd-vae-ft-mse",
local_dir = "./checkpoints/vae"
)
snapshot_download(
repo_id = "facebook/wav2vec2-base-960h",
local_dir = "./checkpoints/wav2vec2"
)
snapshot_download(
repo_id = "emotion2vec/emotion2vec_plus_large",
local_dir = "./checkpoints/emotion2vec_plus_large"
)
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("./checkpoints/vae").to(device=device, dtype=weight_dtype)
reference_net = UNet2DConditionModel.from_pretrained("./checkpoints", subfolder="reference_net", use_safetensors=True)
diffusion_net = UNet3DConditionModel.from_pretrained("./checkpoints", subfolder="diffusion_net", use_safetensors=True)
image_proj = ImageProjModel.from_pretrained("./checkpoints", subfolder="image_proj", use_safetensors=True)
audio_proj = AudioProjModel.from_pretrained("./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="./checkpoints/misc/face_analysis", image_path=input_video, image_size=resolution)
output_dir = "./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="./checkpoints/wav2vec2",
vocal_separator_model="./checkpoints/misc/vocal_separator/Kim_Vocal_2.onnx",
cache_dir=cache_dir,
device=device,
)
audio_emotion, num_emotion_classes = extract_audio_emotion_labels(
model="./checkpoints",
wav_path=input_audio,
emotion2vec_model="./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(analytics_enabled=False) as demo:
with gr.Column():
gr.Markdown("# MEMO")
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(share=False, show_api=False, show_error=True) |