初始化项目，由ModelHub XC社区提供模型

Model: EmpathicRobotics/vla-1.7b-qwen3-v2
Source: Original Platform

tools/encode/encode_cosmos.py

@@ -0,0 +1,95 @@
+#!/usr/bin/env python3
+"""
+Cosmos video-chunk encoder -- turns 8 real video frames into `<cosmos_N>`
+tokens (200 raw ids, no offset), the reverse of tools/decode/decode_cosmos.py.
+Reproduces the OLD (vla-1.7b-qwen3-v2 training-era) preprocessing convention
+EXACTLY, not the current/newer aspect-preserving pipeline convention
+(2026-07-23 pivot, 896 tokens/chunk) -- recovered from git history
+(commit edf25393, before 38d8e5f2 switched to aspect-preserving):
+
+    Resize((160,160))          # direct squash/stretch to 160x160, NOT an
+                                # aspect-preserving crop -- distorts aspect
+                                # ratio on purpose (this model's own training
+                                # convention, don't "fix" it here)
+    ToTensor()
+    Normalize(mean=[0.5,0.5,0.5], std=[0.5,0.5,0.5])   # -> range [-1, 1]
+
+8 frames -> stack -> permute(1,0,2,3) -> (3,8,160,160) -> unsqueeze(0) ->
+(1,3,8,160,160) -> CausalVideoTokenizer.encode() -> (1,2,10,10) == 200 ids,
+checkpoint nvidia/Cosmos-Tokenizer-DV8x16x16 (encoder.jit -- same repo
+decode_cosmos.py already downloads decoder.jit from).
+
+Usage:
+    python tools/encode/encode_cosmos.py --frames f0.png f1.png ... f7.png --output tokens.txt
+    # exactly 8 frame image paths, in temporal order
+"""
+import argparse
+import os
+import sys
+
+sys.path.insert(0, os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "decode", "vendor"))
+
+TARGET_SIZE = 160
+N_FRAMES = 8
+COSMOS_HF_REPO = "nvidia/Cosmos-Tokenizer-DV8x16x16"
+_LOCAL_CHECKPOINT_ENC = "/e/project1/reformo/nguyen38/prototype/pretrained_ckpts/Cosmos-Tokenizer-DV8x16x16/encoder.jit"
+
+
+def _resolve_checkpoint_enc() -> str:
+    if os.path.exists(_LOCAL_CHECKPOINT_ENC):
+        return _LOCAL_CHECKPOINT_ENC
+    from huggingface_hub import hf_hub_download
+    print(f"Local checkpoint not found -- downloading encoder.jit from {COSMOS_HF_REPO} "
+          f"(~350MB, cached for future runs)...")
+    return hf_hub_download(repo_id=COSMOS_HF_REPO, filename="encoder.jit")
+
+
+def encode_frames(frame_paths: list) -> list:
+    if len(frame_paths) != N_FRAMES:
+        raise ValueError(f"Expected exactly {N_FRAMES} frame paths, got {len(frame_paths)}")
+
+    import torch
+    import torchvision.transforms as T
+    from PIL import Image
+    from cosmos_tokenizer.video_lib import CausalVideoTokenizer
+
+    transform = T.Compose([
+        T.Resize((TARGET_SIZE, TARGET_SIZE)),
+        T.ToTensor(),
+        T.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+    ])
+    frames = [transform(Image.open(p).convert("RGB")) for p in frame_paths]
+    video = torch.stack(frames, dim=0)          # (T, 3, H, W)
+    video = video.permute(1, 0, 2, 3)           # (3, T, H, W)
+    video = video.unsqueeze(0)                  # (1, 3, T, H, W)
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    enc = CausalVideoTokenizer(checkpoint_enc=_resolve_checkpoint_enc()).to(device)
+    with torch.no_grad():
+        indices = enc.encode(video.to(device))[0]  # (1, 2, 10, 10)
+
+    ids = indices.reshape(-1).tolist()
+    if len(ids) != 200:
+        raise ValueError(f"Expected 200 raw ids, got {len(ids)} -- checkpoint/shape mismatch")
+    return ids
+
+
+def main():
+    ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter)
+    ap.add_argument("--frames", nargs=8, required=True, metavar="FRAME",
+                     help="Exactly 8 frame image paths, in temporal order")
+    ap.add_argument("--output", help="Optional: write comma-separated ids to this file")
+    args = ap.parse_args()
+
+    ids = encode_frames(args.frames)
+    out = ",".join(str(i) for i in ids)
+    print(f"{len(ids)} cosmos tokens:")
+    print(out)
+    if args.output:
+        with open(args.output, "w") as f:
+            f.write(out)
+        print(f"Saved: {args.output}")
+
+
+if __name__ == "__main__":
+    main()