#!/usr/bin/env python3 """Run ReCamMaster on ONE (clip, target_view) pair. ReCamMaster wants: source_video : (T,3,H,W) in [-1,1] -- 41 frames from front.mp4 target_camera: (1, num_anchors, 12) bf16 -- per-anchor (3x4) flattened prompt + negative_prompt num_frames=41, stride=4 -> 11 anchors == our T_anchor_front[11,4,4] The 11 anchors of T_anchor_front are world_from_front_cam (world = front_cam_0). We re-express each target view's anchor pose RELATIVE to source_first_pose (= T_anchor_front[0] = identity), which is: rel[t] = inv(T_anchor_front[0]) @ T_world_from_view_anchor[t] = T_world_from_view_anchor[t] (since T_anchor_front[0] = I) Output: 41-frame mp4 at 832x480, 16fps, in evalWM/results/recammaster/... """ from __future__ import annotations import argparse import json import sys import time from pathlib import Path import imageio.v3 as iio import numpy as np import torch import torch.nn.functional as F EVALWM_ROOT = Path("/scratch/project/prj-02-phai-lab/lulin/longtail/evalWM") RECAM_ROOT = Path("/scratch/project/prj-02-phai-lab/lulin/longtail/ReCamMaster") sys.path.insert(0, str(EVALWM_ROOT / "run_baselines")) sys.path.insert(0, str(RECAM_ROOT)) from io_utils import OUTPUT_FPS, OUTPUT_HEIGHT, OUTPUT_WIDTH, N_OUTPUT_FRAMES # noqa: E402 from trajectory import SENSOR_FROM_TAG, load_clip_geometry, ANCHOR_INDICES_41 # noqa: E402 DEFAULT_NEGATIVE_PROMPT = ( "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 build_target_pose_embedding(geo: dict, view: str, num_anchors: int = 11) -> torch.Tensor: """Construct (1, num_anchors, 12) pose embedding for `view`. Base case (num_anchors=11) uses the 11 GT anchors at frame indices [0,4,..,40]. If num_anchors > 11, the extra anchors are padded with the last GT anchor (= frozen camera at t=40). This pairs with last-frame video padding so the scene is "frozen" in the padded tail rather than getting an extrapolated pose. """ sensor = SENSOR_FROM_TAG[view] T_view_41 = geo["T_world_from_cam_41_by_sensor"][sensor] # (41,4,4) T_front_anchor0 = geo["T_anchor_front_11"][0] # (4,4) ~identity inv0 = np.linalg.inv(T_front_anchor0) anchor_poses = T_view_41[ANCHOR_INDICES_41] # (11,4,4) rel = inv0[None] @ anchor_poses # (11,4,4) if num_anchors > 11: pad = np.repeat(rel[-1:], num_anchors - 11, axis=0) rel = np.concatenate([rel, pad], axis=0) elif num_anchors < 11: rel = rel[:num_anchors] rel_3x4 = rel[:, :3, :].astype(np.float32) # (A,3,4) rel_flat = rel_3x4.reshape(num_anchors, 12) # (A,12) return torch.from_numpy(rel_flat).unsqueeze(0).to(torch.bfloat16) # (1,A,12) def load_source_video(front_mp4: str, target_h: int, target_w: int, num_frames: int = N_OUTPUT_FRAMES) -> torch.Tensor: """Returns (T,3,H,W) float32 in [-1,1]. Pads with last frame if source is shorter.""" arr = iio.imread(front_mp4) # (T,H,W,3) uint8 arr = arr[:num_frames] if arr.shape[0] < num_frames: pad = np.repeat(arr[-1:], num_frames - arr.shape[0], axis=0) arr = np.concatenate([arr, pad], axis=0) t = torch.from_numpy(arr).permute(0, 3, 1, 2).float() / 255.0 # (T,3,H,W) if t.shape[2] != target_h or t.shape[3] != target_w: t = F.interpolate(t, size=(target_h, target_w), mode="bilinear", align_corners=False) return (t * 2.0 - 1.0) def run_one(args): from diffsynth import ModelManager, WanVideoReCamMasterPipeline, save_video device = "cuda" row = json.loads(Path(args.clip_json).read_text()) geo = load_clip_geometry(row) pose_embed = build_target_pose_embedding(geo, args.view).to(device) source_video = load_source_video(row["front_mp4"], args.height, args.width).to(device) # Load prompt if row.get("text_emb_pt"): prompt = torch.load(row["text_emb_pt"], map_location="cpu", weights_only=False).get("prompt", "") else: prompt = "A driving scene viewed from a vehicle-mounted camera." # Build pipeline once per script invocation (single (clip, view)) model_manager = ModelManager(torch_dtype=torch.bfloat16, device="cpu") model_manager.load_models([ str(RECAM_ROOT / "models" / "Wan-AI" / "Wan2.1-T2V-1.3B" / "diffusion_pytorch_model.safetensors"), str(RECAM_ROOT / "models" / "Wan-AI" / "Wan2.1-T2V-1.3B" / "models_t5_umt5-xxl-enc-bf16.pth"), str(RECAM_ROOT / "models" / "Wan-AI" / "Wan2.1-T2V-1.3B" / "Wan2.1_VAE.pth"), ]) pipe = WanVideoReCamMasterPipeline.from_model_manager(model_manager, device=device) # Inject the projection layers added by ReCamMaster dim = pipe.dit.blocks[0].self_attn.q.weight.shape[0] for block in pipe.dit.blocks: block.cam_encoder = torch.nn.Linear(12, dim).to(device).to(torch.bfloat16) block.projector = torch.nn.Linear(dim, dim).to(device).to(torch.bfloat16) block.cam_encoder.weight.data.zero_() block.cam_encoder.bias.data.zero_() block.projector.weight = torch.nn.Parameter(torch.eye(dim).to(device).to(torch.bfloat16)) block.projector.bias = torch.nn.Parameter(torch.zeros(dim).to(device).to(torch.bfloat16)) state_dict = torch.load( RECAM_ROOT / "models" / "ReCamMaster" / "checkpoints" / "step20000.ckpt", map_location="cpu", weights_only=False, ) if "state_dict" in state_dict: state_dict = state_dict["state_dict"] # Filter ReCamMaster heads only into dit blocks missing, unexpected = pipe.dit.load_state_dict(state_dict, strict=False) print(f"[recam] loaded ckpt: missing={len(missing)} unexpected={len(unexpected)}") video = pipe( prompt=prompt, negative_prompt=DEFAULT_NEGATIVE_PROMPT, source_video=source_video, target_camera=pose_embed, cfg_scale=args.cfg_scale, num_inference_steps=args.num_steps, seed=args.seed, height=args.height, width=args.width, num_frames=N_OUTPUT_FRAMES, tiled=True, ) # Save out_path = Path(args.output_mp4) out_path.parent.mkdir(parents=True, exist_ok=True) tmp = out_path.with_suffix(".tmp.mp4") save_video(video, str(tmp), fps=OUTPUT_FPS, quality=5) tmp.rename(out_path) print(f"[recammaster_one] wrote {out_path}") def parse_args(): ap = argparse.ArgumentParser() ap.add_argument("--clip-json", required=True) ap.add_argument("--view", required=True, choices=["cross_left", "cross_right", "rear_left", "rear_right", "rear_tele"]) ap.add_argument("--output-mp4", required=True) ap.add_argument("--num-steps", type=int, default=50) ap.add_argument("--cfg-scale", type=float, default=5.0) ap.add_argument("--guidance", type=float, default=1.0) # ignored, recammaster uses cfg_scale ap.add_argument("--height", type=int, default=480) ap.add_argument("--width", type=int, default=832) ap.add_argument("--seed", type=int, default=0) return ap.parse_args() def main(): t0 = time.time() run_one(parse_args()) print(f"[recammaster_one] elapsed {time.time()-t0:.1f}s") if __name__ == "__main__": main()