Spaces:
Paused
Paused
File size: 13,441 Bytes
0659b98 7c35e92 0659b98 72a0cd6 7c35e92 6825e25 7c35e92 366a2ee 7c35e92 72a0cd6 7c35e92 95687fc 7c35e92 0659b98 c8296fc 7c35e92 0659b98 c8296fc 9e96e5e 7c35e92 0659b98 9e96e5e 0659b98 9e96e5e 0659b98 c8296fc 0659b98 7c35e92 0659b98 7c35e92 0659b98 c8296fc 0659b98 7c35e92 0659b98 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 |
import os
import torch
import cv2
import subprocess
from datetime import datetime
from pathlib import Path
import gradio as gr
import numpy as np
from huggingface_hub import hf_hub_download, snapshot_download
# -----------------------------
# Setup paths and env
# -----------------------------
HF_HOME = "/app/hf_cache"
os.environ["HF_HOME"] = HF_HOME
os.environ["TRANSFORMERS_CACHE"] = HF_HOME
os.makedirs(HF_HOME, exist_ok=True)
PRETRAINED_DIR = "/app/pretrained"
os.makedirs(PRETRAINED_DIR, exist_ok=True)
# -----------------------------
# Step 1: Optional Model Download
# -----------------------------
def download_models():
expected_model = os.path.join(PRETRAINED_DIR, "RAFT/raft-things.pth")
if not Path(expected_model).exists():
print("⚙️ Downloading pretrained models...")
try:
subprocess.check_call(["bash", "download/download_models.sh"])
print("✅ Models downloaded.")
except subprocess.CalledProcessError as e:
print(f"Model download failed: {e}")
else:
print("✅ Pretrained models already exist.")
def visualize_depth_npy_as_video(npy_file, fps):
# Load .npy file
depth_np = np.load(npy_file) # Shape: [T, 1, H, W]
tensor = torch.from_numpy(depth_np)
T, _, H, W = tensor.shape
# Prepare video writer
video_path = "depth_video_preview.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter(video_path, fourcc, fps, (W, H)) # 10 FPS
for i in range(T):
frame = tensor[i, 0].numpy()
norm = (frame - frame.min()) / (frame.max() - frame.min() + 1e-8)
frame_uint8 = (norm * 255).astype(np.uint8)
colored = cv2.applyColorMap(frame_uint8, cv2.COLORMAP_INFERNO)
out.write(colored)
out.release()
return video_path
# -----------------------------
# Step 1: Get Anchor Video
# -----------------------------
def get_anchor_video(video_path, fps, num_frames, target_pose, mode,
radius_scale, near_far_estimated,
sampler_name, diffusion_guidance_scale, diffusion_inference_steps,
prompt, negative_prompt, refine_prompt,
depth_inference_steps, depth_guidance_scale,
window_size, overlap, max_res, sample_size,
seed_input, height, width, aspect_ratio_inputs,
init_dx, init_dy, init_dz):
temp_input_path = "/app/temp_input.mp4"
output_dir = "/app/output_anchor"
video_output_path = f"{output_dir}/masked_videos/output.mp4"
captions_text_file = f"{output_dir}/captions/output.txt"
depth_file = f"{output_dir}/depth/output.npy"
if video_path:
os.system(f"cp '{video_path}' {temp_input_path}")
try:
theta, phi, r, x, y = target_pose.strip().split()
except ValueError:
return f"Invalid target pose format. Use: θ φ r x y", None, None
logs = f"Running inference with target pose: θ={theta}, φ={phi}, r={r}, x={x}, y={y}\n"
w, h = aspect_ratio_inputs.strip().split(",")
h_s, w_s = sample_size.strip().split(",")
command = [
"python", "/app/inference/v2v_data/inference.py",
"--video_path", temp_input_path,
"--stride", "1",
"--out_dir", output_dir,
"--radius_scale", str(radius_scale),
"--camera", "target",
"--mask",
"--target_pose", theta, phi, r, x, y,
"--video_length", str(num_frames),
"--save_name", "output",
"--mode", mode,
"--fps", str(fps),
"--depth_inference_steps", str(depth_inference_steps),
"--depth_guidance_scale", str(depth_guidance_scale),
"--near_far_estimated", str(near_far_estimated),
"--sampler_name", sampler_name,
"--diffusion_guidance_scale", str(diffusion_guidance_scale),
"--diffusion_inference_steps", str(diffusion_inference_steps),
"--prompt", prompt if prompt else "",
"--negative_prompt", negative_prompt,
"--refine_prompt", refine_prompt,
"--window_size", str(window_size),
"--overlap", str(overlap),
"--max_res", str(max_res),
"--sample_size", h_s.strip(), w_s.strip(),
"--seed", str(seed_input),
"--height", str(height),
"--width", str(width),
"--target_aspect_ratio", w.strip(), h.strip(),
"--init_dx", str(init_dx),
"--init_dy", str(init_dy),
"--init_dz", str(init_dz),
]
try:
result = subprocess.run(command, capture_output=True, text=True, check=True)
logs += result.stdout
except subprocess.CalledProcessError as e:
logs += f"Inference failed:\n{e.stderr}{e.stdout}"
return None, logs
caption_text = ""
if os.path.exists(captions_text_file):
with open(captions_text_file, "r") as f:
caption_text = f.read()
depth_video_path = visualize_depth_npy_as_video(depth_file, fps)
return str(video_output_path), logs, caption_text, depth_video_path
# -----------------------------
# Step 2: Run Inference
# -----------------------------
def inference(
fps, num_frames, controlnet_weights, controlnet_guidance_start,
controlnet_guidance_end, guidance_scale, num_inference_steps, dtype,
seed, height, width, downscale_coef, vae_channels,
controlnet_input_channels, controlnet_transformer_num_layers
):
model_path = "/app/pretrained/CogVideoX-5b-I2V"
ckpt_path = "/app/out/EPiC_pretrained/checkpoint-500.pt"
video_root_dir = "/app/output_anchor"
out_dir = "/app/output"
command = [
"python", "/app/inference/cli_demo_camera_i2v_pcd.py",
"--video_root_dir", video_root_dir,
"--base_model_path", model_path,
"--controlnet_model_path", ckpt_path,
"--output_path", out_dir,
"--controlnet_weights", str(controlnet_weights),
"--controlnet_guidance_start", str(controlnet_guidance_start),
"--controlnet_guidance_end", str(controlnet_guidance_end),
"--guidance_scale", str(guidance_scale),
"--num_inference_steps", str(num_inference_steps),
"--dtype", dtype,
"--seed", str(seed),
"--height", str(height),
"--width", str(width),
"--num_frames", str(num_frames),
"--fps", str(fps),
"--downscale_coef", str(downscale_coef),
"--vae_channels", str(vae_channels),
"--controlnet_input_channels", str(controlnet_input_channels),
"--controlnet_transformer_num_layers", str(controlnet_transformer_num_layers),
]
try:
result = subprocess.run(command, capture_output=True, text=True, check=True)
logs = result.stdout
except subprocess.CalledProcessError as e:
logs = f"❌ Step 2 Inference Failed:\nSTDERR:\n{e.stderr}\nSTDOUT:\n{e.stdout}"
return None, logs
video_output = f"{out_dir}/00000_{seed}_out.mp4"
return video_output if os.path.exists(video_output) else None, logs
# -----------------------------
# UI
# -----------------------------
demo = gr.Blocks()
with demo:
gr.Markdown("## 🎬 EPiC: Cinematic Camera Control")
with gr.Tabs():
with gr.TabItem("Step 1: Camera Anchor"):
with gr.Row():
with gr.Column():
with gr.Row():
near_far_estimated = gr.Checkbox(label="Near Far Estimation", value=True)
pose_input = gr.Textbox(label="Target Pose (θ φ r x y)", placeholder="e.g., 0 30 -0.6 0 0")
fps_input = gr.Number(value=24, label="FPS")
aspect_ratio_inputs=gr.Textbox(value= "3,4",label="Target Aspect Ratio (e.g., 2,3)")
init_dx = gr.Number(value=0.0, label="Start Camera Offset X")
init_dy = gr.Number(value=0.0, label="Start Camera Offset Y")
init_dz = gr.Number(value=0.0, label="Start Camera Offset Z")
num_frames_input = gr.Number(value=49, label="Number of Frames")
radius_input = gr.Number(value = 1.0, label="Radius Scale")
mode_input = gr.Dropdown(choices=["gradual"], value="gradual", label="Camera Mode")
sampler_input = gr.Dropdown(choices=["Euler", "Euler A", "DPM++", "PNDM", "DDIM_Cog", "DDIM_Origin"], value="DDIM_Origin", label="Sampler")
diff_guidance_input = gr.Number(value=6.0, label="Diffusion Guidance")
diff_steps_input = gr.Number(value=50, label="Diffusion Steps")
depth_steps_input = gr.Number(value=5, label="Depth Steps")
depth_guidance_input = gr.Number(value=1.0, label="Depth Guidance")
window_input = gr.Number(value=64, label="Window Size")
overlap_input = gr.Number(value=25, label="Overlap")
maxres_input = gr.Number(value=720, label="Max Resolution")
sample_size = gr.Textbox(label="Sample Size (height, width)", placeholder="e.g., 384, 672", value="384, 672")
seed_input = gr.Number(value=43, label="Seed")
height = gr.Number(value=480, label="Height")
width = gr.Number(value=720, label="Width")
prompt_input = gr.Textbox(label="Prompt")
neg_prompt_input = gr.Textbox(label="Negative Prompt", value="The video is not of a high quality, it has a low resolution. Watermark present in each frame. The background is solid. Strange body and strange trajectory.")
refine_prompt_input = gr.Textbox(label="Refine Prompt", value=" The video is of high quality, and the view is very clear. ")
with gr.Column():
video_input = gr.Video(label="Upload Video (MP4)")
step1_button = gr.Button("▶️ Run Step 1")
step1_video = gr.Video(label="[Step 1] Masked Video")
step1_captions = gr.Textbox(label="[Step 1] Captions", lines=4)
step1_logs = gr.Textbox(label="[Step 1] Logs")
step1_depth = gr.Video(label="[Step 1] Depth Video", visible=False) # Hidden by default
with gr.TabItem("Step 2: CogVideoX Refinement"):
with gr.Row():
with gr.Column():
with gr.Row():
controlnet_weights_input = gr.Number(value=0.5, label="ControlNet Weights")
controlnet_guidance_start_input = gr.Number(value=0.0, label="Guidance Start")
controlnet_guidance_end_input = gr.Number(value=0.5, label="Guidance End")
guidance_scale_input = gr.Number(value=6.0, label="Guidance Scale")
inference_steps_input = gr.Number(value=50, label="Num Inference Steps")
dtype_input = gr.Dropdown(choices=["float16", "bfloat16"], value="bfloat16", label="Compute Dtype")
seed_input2 = gr.Number(value=42, label="Seed")
height_input = gr.Number(value=480, label="Height")
width_input = gr.Number(value=720, label="Width")
num_frames_input2 = gr.Number(value=49, label="Num Frames")
fps_input2 = gr.Number(value=24, label="FPS")
downscale_coef_input = gr.Number(value=8, label="Downscale Coef")
vae_channels_input = gr.Number(value=16, label="VAE Channels")
controlnet_input_channels_input = gr.Number(value=6, label="ControlNet Input Channels")
controlnet_layers_input = gr.Number(value=8, label="ControlNet Transformer Layers")
with gr.Column():
step2_video = gr.Video(label="[Step 2] Final Refined Video")
step2_button = gr.Button("▶️ Run Step 2")
step2_logs = gr.Textbox(label="[Step 2] Logs")
step1_button.click(
get_anchor_video,
inputs=[
video_input, fps_input, num_frames_input, pose_input, mode_input,
radius_input, near_far_estimated,
sampler_input, diff_guidance_input, diff_steps_input,
prompt_input, neg_prompt_input, refine_prompt_input,
depth_steps_input, depth_guidance_input,
window_input, overlap_input, maxres_input, sample_size,
seed_input, height, width, aspect_ratio_inputs,
init_dx, init_dy, init_dz
],
outputs=[step1_video, step1_logs, step1_captions, step1_depth] # ← updated here
)
step2_button.click(
inference,
inputs=[
fps_input2, num_frames_input2,
controlnet_weights_input, controlnet_guidance_start_input,
controlnet_guidance_end_input, guidance_scale_input,
inference_steps_input, dtype_input, seed_input2,
height_input, width_input, downscale_coef_input,
vae_channels_input, controlnet_input_channels_input,
controlnet_layers_input
],
outputs=[step2_video, step2_logs]
)
if __name__ == "__main__":
download_models()
demo.launch(server_name="0.0.0.0", server_port=7860) |