import os
import cv2
import numpy as np
from PIL import Image
from diffusers.models.attention_processor import XFormersAttnProcessor
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
import torch
from diffusers import AutoencoderKLTemporalDecoder, EulerDiscreteScheduler
from animation.modules.attention_processor import AnimationAttnProcessor
from animation.modules.attention_processor_normalized import AnimationIDAttnNormalizedProcessor
from animation.modules.face_model import FaceModel
from animation.modules.id_encoder import FusionFaceId
from animation.modules.pose_net import PoseNet
from animation.modules.unet import UNetSpatioTemporalConditionModel
from animation.pipelines.inference_pipeline_animation import InferenceAnimationPipeline
import random
import gradio as gr
import gc
from datetime import datetime
from pathlib import Path
pretrained_model_name_or_path = "checkpoints/stable-video-diffusion-img2vid-xt"
revision = None
posenet_model_name_or_path = "checkpoints/Animation/pose_net.pth"
face_encoder_model_name_or_path = "checkpoints/Animation/face_encoder.pth"
unet_model_name_or_path = "checkpoints/Animation/unet.pth"
def load_images_from_folder(folder, width, height):
images = []
files = os.listdir(folder)
png_files = [f for f in files if f.endswith('.png')]
png_files.sort(key=lambda x: int(x.split('_')[1].split('.')[0]))
for filename in png_files:
img = Image.open(os.path.join(folder, filename)).convert('RGB')
img = img.resize((width, height))
images.append(img)
return images
def save_frames_as_png(frames, output_path):
pil_frames = [Image.fromarray(frame) if isinstance(frame, np.ndarray) else frame for frame in frames]
num_frames = len(pil_frames)
for i in range(num_frames):
pil_frame = pil_frames[i]
save_path = os.path.join(output_path, f'frame_{i}.png')
pil_frame.save(save_path)
def save_frames_as_mp4(frames, output_mp4_path, fps):
print("Starting saving the frames as mp4")
height, width, _ = frames[0].shape
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # 'H264' for better quality
out = cv2.VideoWriter(output_mp4_path, fourcc, fps, (width, height))
for frame in frames:
frame_bgr = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
out.write(frame_bgr)
out.release()
def export_to_gif(frames, output_gif_path, fps):
"""
Export a list of frames to a GIF.
Args:
- frames (list): List of frames (as numpy arrays or PIL Image objects).
- output_gif_path (str): Path to save the output GIF.
- duration_ms (int): Duration of each frame in milliseconds.
"""
# Convert numpy arrays to PIL Images if needed
pil_frames = [Image.fromarray(frame) if isinstance(
frame, np.ndarray) else frame for frame in frames]
pil_frames[0].save(output_gif_path.replace('.mp4', '.gif'),
format='GIF',
append_images=pil_frames[1:],
save_all=True,
duration=125,
loop=0)
def generate(
image_input: str,
pose_input: str,
width: int,
height: int,
guidance_scale: float,
num_inference_steps: int,
fps: int,
frames_overlap: int,
tile_size: int,
noise_aug_strength: float,
decode_chunk_size: int,
seed: int,
):
gc.collect()
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
output_dir = Path("outputs")
output_dir = os.path.join(output_dir, timestamp)
if seed == -1:
seed = random.randint(1, 2**20 - 1)
generator = torch.Generator(device=device).manual_seed(seed)
pipeline = InferenceAnimationPipeline(
vae=vae,
image_encoder=image_encoder,
unet=unet,
scheduler=noise_scheduler,
feature_extractor=feature_extractor,
pose_net=pose_net,
face_encoder=face_encoder,
).to(device=device, dtype=dtype)
validation_image_path = image_input
validation_image = Image.open(image_input).convert('RGB')
validation_control_images = load_images_from_folder(pose_input, width=width, height=height)
num_frames = len(validation_control_images)
face_model.face_helper.clean_all()
validation_face = cv2.imread(validation_image_path)
validation_image_bgr = cv2.cvtColor(validation_face, cv2.COLOR_RGB2BGR)
validation_image_face_info = face_model.app.get(validation_image_bgr)
if len(validation_image_face_info) > 0:
validation_image_face_info = sorted(validation_image_face_info, key=lambda x: (x['bbox'][2] - x['bbox'][0]) * (x['bbox'][3] - x['bbox'][1]))[-1]
validation_image_id_ante_embedding = validation_image_face_info['embedding']
else:
validation_image_id_ante_embedding = None
if validation_image_id_ante_embedding is None:
face_model.face_helper.read_image(validation_image_bgr)
face_model.face_helper.get_face_landmarks_5(only_center_face=True)
face_model.face_helper.align_warp_face()
if len(face_model.face_helper.cropped_faces) == 0:
validation_image_id_ante_embedding = np.zeros((512,))
else:
validation_image_align_face = face_model.face_helper.cropped_faces[0]
print('fail to detect face using insightface, extract embedding on align face')
validation_image_id_ante_embedding = face_model.handler_ante.get_feat(validation_image_align_face)
# generator = torch.Generator(device=accelerator.device).manual_seed(23123134)
decode_chunk_size = decode_chunk_size
video_frames = pipeline(
image=validation_image,
image_pose=validation_control_images,
height=height,
width=width,
num_frames=num_frames,
tile_size=tile_size,
tile_overlap=frames_overlap,
decode_chunk_size=decode_chunk_size,
motion_bucket_id=127.,
fps=7,
min_guidance_scale=guidance_scale,
max_guidance_scale=guidance_scale,
noise_aug_strength=noise_aug_strength,
num_inference_steps=num_inference_steps,
generator=generator,
output_type="pil",
validation_image_id_ante_embedding=validation_image_id_ante_embedding,
).frames[0]
out_file = os.path.join(
output_dir,
f"animation_video.mp4",
)
for i in range(num_frames):
img = video_frames[i]
video_frames[i] = np.array(img)
png_out_file = os.path.join(output_dir, "animated_images")
os.makedirs(png_out_file, exist_ok=True)
save_frames_as_mp4(video_frames, out_file, fps)
export_to_gif(video_frames, out_file, fps)
save_frames_as_png(video_frames, png_out_file)
seed_update = gr.update(visible=True, value=seed)
return out_file, seed_update
with gr.Blocks(theme=gr.themes.Soft(), analytics_enabled=False) as demo:
gr.Markdown("""
StableAnimator
⚠️ This demo is for academic research and experiential use only.
""")
with gr.Row():
with gr.Column():
with gr.Group():
image_input = gr.Image(label="Reference Image", type="filepath")
pose_input = gr.Textbox(label="Driven Poses", placeholder="Please enter your driven pose directory here.")
with gr.Group():
with gr.Row():
width = gr.Number(label="Width (supports only 512×512 and 576×1024)", value=512)
height = gr.Number(label="Height (supports only 512×512 and 576×1024)", value=512)
with gr.Row():
guidance_scale = gr.Number(label="Guidance scale (recommended 3.0)", value=3.0, step=0.1, precision=1)
num_inference_steps = gr.Number(label="Inference steps (recommended 25)", value=20)
with gr.Row():
fps = gr.Number(label="FPS", value=8)
frames_overlap = gr.Number(label="Overlap Frames (recommended 4)", value=4)
with gr.Row():
tile_size = gr.Number(label="Tile Size (recommended 16)", value=16)
noise_aug_strength = gr.Number(label="Noise Augmentation Strength (recommended 0.02)", value=0.02, step=0.01, precision=2)
with gr.Row():
decode_chunk_size = gr.Number(label="Decode Chunk Size (recommended 4 or 16)", value=4)
seed = gr.Number(label="Random Seed (Enter a positive number, -1 for random)", value=-1)
generate_button = gr.Button("🎬 Generate The Video")
with gr.Column():
video_output = gr.Video(label="Generate The Video")
with gr.Row():
seed_text = gr.Number(label="Video Generation Seed", visible=False, interactive=False)
gr.Examples([
["inference/case-1/reference.png","inference/case-1/poses",512,512],
["inference/case-2/reference.png","inference/case-2/poses",512,512],
["inference/case-3/reference.png","inference/case-3/poses",512,512],
["inference/case-4/reference.png","inference/case-4/poses",512,512],
["inference/case-5/reference.png","inference/case-5/poses",576,1024],
], inputs=[image_input, pose_input, width, height])
generate_button.click(
generate,
inputs=[image_input, pose_input, width, height, guidance_scale, num_inference_steps, fps, frames_overlap, tile_size, noise_aug_strength, decode_chunk_size, seed],
outputs=[video_output, seed_text],
)
if __name__ == "__main__":
feature_extractor = CLIPImageProcessor.from_pretrained(pretrained_model_name_or_path, subfolder="feature_extractor", revision=revision)
noise_scheduler = EulerDiscreteScheduler.from_pretrained(pretrained_model_name_or_path, subfolder="scheduler")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(pretrained_model_name_or_path, subfolder="image_encoder", revision=revision)
vae = AutoencoderKLTemporalDecoder.from_pretrained(pretrained_model_name_or_path, subfolder="vae", revision=revision)
unet = UNetSpatioTemporalConditionModel.from_pretrained(
pretrained_model_name_or_path,
subfolder="unet",
low_cpu_mem_usage=True,
)
pose_net = PoseNet(noise_latent_channels=unet.config.block_out_channels[0])
face_encoder = FusionFaceId(
cross_attention_dim=1024,
id_embeddings_dim=512,
# clip_embeddings_dim=image_encoder.config.hidden_size,
clip_embeddings_dim=1024,
num_tokens=4, )
face_model = FaceModel()
lora_rank = 128
attn_procs = {}
unet_svd = unet.state_dict()
for name in unet.attn_processors.keys():
if "transformer_blocks" in name and "temporal_transformer_blocks" not in name:
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
# print(f"This is AnimationAttnProcessor: {name}")
attn_procs[name] = AnimationAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank)
else:
# print(f"This is AnimationIDAttnProcessor: {name}")
layer_name = name.split(".processor")[0]
weights = {
"to_k_ip.weight": unet_svd[layer_name + ".to_k.weight"],
"to_v_ip.weight": unet_svd[layer_name + ".to_v.weight"],
}
attn_procs[name] = AnimationIDAttnNormalizedProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=lora_rank)
attn_procs[name].load_state_dict(weights, strict=False)
elif "temporal_transformer_blocks" in name:
cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = unet.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = unet.config.block_out_channels[block_id]
if cross_attention_dim is None:
attn_procs[name] = XFormersAttnProcessor()
else:
attn_procs[name] = XFormersAttnProcessor()
unet.set_attn_processor(attn_procs)
# resume the previous checkpoint
if posenet_model_name_or_path is not None and face_encoder_model_name_or_path is not None and unet_model_name_or_path is not None:
print("Loading existing posenet weights, face_encoder weights and unet weights.")
if posenet_model_name_or_path.endswith(".pth"):
pose_net_state_dict = torch.load(posenet_model_name_or_path, map_location="cpu")
pose_net.load_state_dict(pose_net_state_dict, strict=True)
else:
print("posenet weights loading fail")
print(1/0)
if face_encoder_model_name_or_path.endswith(".pth"):
face_encoder_state_dict = torch.load(face_encoder_model_name_or_path, map_location="cpu")
face_encoder.load_state_dict(face_encoder_state_dict, strict=True)
else:
print("face_encoder weights loading fail")
print(1/0)
if unet_model_name_or_path.endswith(".pth"):
unet_state_dict = torch.load(unet_model_name_or_path, map_location="cpu")
unet.load_state_dict(unet_state_dict, strict=True)
else:
print("unet weights loading fail")
print(1/0)
vae.requires_grad_(False)
image_encoder.requires_grad_(False)
unet.requires_grad_(False)
pose_net.requires_grad_(False)
face_encoder.requires_grad_(False)
total_vram_in_gb = torch.cuda.get_device_properties(0).total_memory / 1073741824
print(f'\033[32mCUDA version：{torch.version.cuda}\033[0m')
print(f'\033[32mPytorch version：{torch.__version__}\033[0m')
print(f'\033[32mGPU Type：{torch.cuda.get_device_name()}\033[0m')
print(f'\033[32mGPU Memory：{total_vram_in_gb:.2f}GB\033[0m')
if torch.cuda.get_device_capability()[0] >= 8:
print(f'\033[32mSupports BF16, use BF16\033[0m')
dtype = torch.bfloat16
else:
print(f'\033[32mBF16 is not supported, use FP16. The 5B model is not recommended\033[0m')
dtype = torch.float16
device = "cuda" if torch.cuda.is_available() else "cpu"
demo.queue().launch(inline=False, share=False, debug=True, server_name='0.0.0.0', server_port=7860, allowed_paths=["/content"])