convert_original_to_comfy.py

#!/usr/bin/env python3
"""
Standalone converter for Causal Forcing training checkpoints (.pt) to
ComfyUI-compatible .safetensors.

No dependency on the ComfyUI codebase — all conversion logic is self-contained.

Requirements:
    pip install torch safetensors

Usage:
    # Framewise model (uses EMA weights, num_frame_per_block=1 by default):
    python convert_causal_forcing_standalone.py \
        --input checkpoints/framewise/causal_forcing.pt \
        --output models/causal_forcing_framewise.safetensors

    # Chunkwise model (uses non-EMA weights, num_frame_per_block=3):
    python convert_causal_forcing_standalone.py \
        --input checkpoints/chunkwise/causal_forcing.pt \
        --output models/causal_forcing_chunkwise.safetensors \
        --no-ema --num-frame-per-block 3
"""

import argparse
import json
import logging

import torch
from safetensors.torch import save_file

log = logging.getLogger(__name__)

PREFIXES_TO_STRIP = ["model._fsdp_wrapped_module.", "model."]

_MODEL_KEY_PREFIXES = (
    "blocks.", "head.", "patch_embedding.", "text_embedding.",
    "time_embedding.", "time_projection.", "img_emb.", "rope_embedder.",
)


def extract_state_dict(state_dict: dict, use_ema: bool = True) -> dict:
    """
    Extract and clean a Causal Forcing state dict from a training checkpoint.

    Handles three checkpoint layouts:
      1. Training checkpoint with top-level generator_ema / generator keys
      2. Already-flattened state dict with model.* prefixes
      3. Already-converted ComfyUI state dict (bare model keys)

    Returns a state dict with keys matching the CausalWanModel / WanModel layout.
    """
    if "head.modulation" in state_dict and "blocks.0.self_attn.q.weight" in state_dict:
        return state_dict

    raw_sd = None
    order = ["generator_ema", "generator"] if use_ema else ["generator", "generator_ema"]
    for wrapper_key in order:
        if wrapper_key in state_dict:
            raw_sd = state_dict[wrapper_key]
            log.info("Extracted '%s' with %d keys", wrapper_key, len(raw_sd))
            break

    if raw_sd is None:
        if any(k.startswith("model.") for k in state_dict):
            raw_sd = state_dict
        else:
            raise KeyError(
                f"Cannot detect Causal Forcing checkpoint layout. "
                f"Top-level keys: {list(state_dict.keys())[:20]}"
            )

    out_sd = {}
    for k, v in raw_sd.items():
        new_k = k
        for prefix in PREFIXES_TO_STRIP:
            if new_k.startswith(prefix):
                new_k = new_k[len(prefix):]
                break
        else:
            if not new_k.startswith(_MODEL_KEY_PREFIXES):
                log.debug("Skipping non-model key: %s", k)
                continue
        out_sd[new_k] = v

    if "head.modulation" not in out_sd:
        raise ValueError("Conversion failed: 'head.modulation' not found in output keys")

    return out_sd


def convert_and_save(input_path: str, output_path: str, use_ema: bool = True,
                     num_frame_per_block: int = 1):
    print(f"Loading {input_path} ...")
    state_dict = torch.load(input_path, map_location="cpu", weights_only=False)
    out_sd = extract_state_dict(state_dict, use_ema=use_ema)
    del state_dict

    dim = out_sd["head.modulation"].shape[-1]
    num_layers = 0
    while f"blocks.{num_layers}.self_attn.q.weight" in out_sd:
        num_layers += 1
    print(f"Detected model: dim={dim}, num_layers={num_layers}, keys={len(out_sd)}")

    transformer_config = {"causal_ar": True}
    if num_frame_per_block > 1:
        transformer_config["num_frame_per_block"] = num_frame_per_block
    metadata = {
        "config": json.dumps({"transformer": transformer_config}),
    }
    save_file(out_sd, output_path, metadata=metadata)
    print(f"Saved to {output_path}")


if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Convert Causal Forcing checkpoint to ComfyUI safetensors (standalone)"
    )
    parser.add_argument("--input", required=True, help="Path to the training .pt checkpoint")
    parser.add_argument("--output", required=True, help="Output .safetensors path")
    parser.add_argument(
        "--no-ema", action="store_true",
        help="Use 'generator' instead of 'generator_ema' (default: use EMA)",
    )
    parser.add_argument("--num-frame-per-block", type=int, default=1,
                        help="Frames per AR block (1=framewise, 3=chunkwise)")
    parser.add_argument("-v", "--verbose", action="store_true", help="Enable debug logging")
    args = parser.parse_args()

    logging.basicConfig(
        level=logging.DEBUG if args.verbose else logging.INFO,
        format="%(levelname)s: %(message)s",
    )

    convert_and_save(args.input, args.output, use_ema=not args.no_ema,
                     num_frame_per_block=args.num_frame_per_block)