| """Generate image-to-video clips with Wan 2.2 TI2V-5B for every row of prompts.jsonl. |
| |
| Each row is rendered once per seed at native 720p (121 frames, 24 fps, 50 steps). |
| Existing clips are skipped, so the script can be re-run after an interruption. |
| Timings go to out/log.csv. |
| |
| python gen.py --limit 1 # pilot: first clip only |
| python gen.py # everything |
| """ |
| import argparse |
| import csv |
| import json |
| import time |
| from pathlib import Path |
|
|
| import torch |
| from diffusers import AutoencoderKLWan, WanImageToVideoPipeline |
| from diffusers.utils import export_to_video, load_image |
|
|
| HERE = Path(__file__).parent |
| MODEL_ID = "Wan-AI/Wan2.2-TI2V-5B-Diffusers" |
| |
| NEGATIVE = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走" |
|
|
|
|
| def main(): |
| ap = argparse.ArgumentParser() |
| ap.add_argument("--prompts", default=HERE / "prompts.jsonl", type=Path) |
| ap.add_argument("--images", default=HERE / "images-resize", type=Path) |
| ap.add_argument("--out", default=HERE / "out", type=Path) |
| ap.add_argument("--seeds", default=[0, 1], type=int, nargs="+") |
| ap.add_argument("--limit", type=int, help="stop after this many clips") |
| args = ap.parse_args() |
|
|
| rows = [json.loads(line) for line in args.prompts.read_text().splitlines() if line.strip()] |
| jobs = [(r, s) for r in rows for s in args.seeds] |
| if args.limit: |
| jobs = jobs[: args.limit] |
| args.out.mkdir(exist_ok=True) |
|
|
| t0 = time.time() |
| vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32) |
| pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16).to("cuda") |
| print(f"model loaded in {time.time() - t0:.0f}s", flush=True) |
|
|
| log_path = args.out / "log.csv" |
| new_log = not log_path.exists() |
| with open(log_path, "a", newline="") as f: |
| log = csv.writer(f) |
| if new_log: |
| log.writerow(["clip", "image", "group", "condition", "seed", "seconds"]) |
| for i, (row, seed) in enumerate(jobs, 1): |
| name = f"{Path(row['image']).stem}__{row['condition']}__s{seed}.mp4" |
| dst = args.out / name |
| if dst.exists(): |
| print(f"[{i}/{len(jobs)}] skip {name}", flush=True) |
| continue |
| image = load_image(str(args.images / row["image"])) |
| t = time.time() |
| frames = pipe( |
| image=image, |
| prompt=row["prompt"], |
| negative_prompt=NEGATIVE, |
| height=image.height, |
| width=image.width, |
| num_frames=121, |
| num_inference_steps=50, |
| guidance_scale=5.0, |
| generator=torch.Generator("cuda").manual_seed(seed), |
| ).frames[0] |
| export_to_video(frames, str(dst), fps=24) |
| secs = time.time() - t |
| log.writerow([name, row["image"], row["group"], row["condition"], seed, f"{secs:.1f}"]) |
| f.flush() |
| print(f"[{i}/{len(jobs)}] {name} {secs:.0f}s", flush=True) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
