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179 lines (154 loc) · 6.79 KB
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#!/usr/bin/env python3
"""Convert the Echo BF16 release checkpoint into a Diffusers Modular Pipeline repository."""
import argparse
import gc
import json
from pathlib import Path
import torch
from convert_ltx2_to_diffusers import (
convert_ltx2_audio_vae,
convert_ltx2_connectors,
convert_ltx2_transformer,
convert_ltx2_video_vae,
convert_ltx2_vocoder,
get_model_state_dict_from_combined_ckpt,
)
from safetensors.torch import load_file
from diffusers import __version__
def resolve_checkpoint(path: Path) -> Path:
path = path.expanduser().resolve()
if path.is_file():
return path
manifest_path = path / "checkpoint.json"
if not manifest_path.is_file():
raise FileNotFoundError(f"Checkpoint manifest not found: {manifest_path}")
manifest = json.loads(manifest_path.read_text(encoding="utf-8"))
precision = str(manifest.get("precision", "")).lower()
if precision not in {"bf16", "bfloat16"}:
raise ValueError(
"Echo conversion supports the BF16 release only. The FP8 and FP4 releases use custom packed "
f"kernels and cannot be represented by this converter, got precision={precision!r}."
)
model_filename = manifest.get("files", {}).get("model")
if not model_filename:
raise ValueError(f"Checkpoint manifest has no `files.model`: {manifest_path}")
return path / model_filename
def save_component(model, output_path: Path, name: str, max_shard_size: str) -> None:
model.to(torch.bfloat16).save_pretrained(
output_path / name,
safe_serialization=True,
max_shard_size=max_shard_size,
)
def component_entry(library: str, class_name: str, repo: str, subfolder: str | None):
return [
None,
None,
{
"type_hint": [library, class_name],
"pretrained_model_name_or_path": repo,
"subfolder": subfolder,
"variant": None,
"revision": None,
},
]
def write_modular_index(output_path: Path, base_model: str, repo_id: str | None) -> None:
echo_repo = repo_id or str(output_path)
index = {
"_class_name": "EchoModularPipeline",
"_diffusers_version": __version__,
"_blocks_class_name": "EchoBlocks",
"text_encoder": component_entry("transformers", "Gemma3ForConditionalGeneration", base_model, None),
"tokenizer": component_entry("transformers", "GemmaTokenizerFast", base_model, None),
"connectors": component_entry("ltx2", "LTX2TextConnectors", echo_repo, "connectors"),
"vae": component_entry("diffusers", "AutoencoderKLLTX2Video", echo_repo, "vae"),
"audio_vae": component_entry("diffusers", "AutoencoderKLLTX2Audio", echo_repo, "audio_vae"),
"transformer": component_entry("diffusers", "LTX2VideoTransformer3DModel", echo_repo, "transformer"),
"vocoder": component_entry("ltx2", "LTX2VocoderWithBWE", echo_repo, "vocoder"),
}
(output_path / "modular_model_index.json").write_text(json.dumps(index, indent=2) + "\n", encoding="utf-8")
def convert(
checkpoint: Path,
output_path: Path,
base_model: str,
repo_id: str | None,
max_shard_size: str,
) -> None:
source = resolve_checkpoint(checkpoint)
output_path = output_path.expanduser().resolve()
output_path.mkdir(parents=True, exist_ok=True)
print(f"Loading Echo checkpoint from {source}", flush=True)
combined = load_file(str(source), device="cpu")
component_states = {
"dit": get_model_state_dict_from_combined_ckpt(combined, "model.diffusion_model"),
"vae": get_model_state_dict_from_combined_ckpt(combined, "vae"),
"audio_vae": get_model_state_dict_from_combined_ckpt(combined, "audio_vae"),
"vocoder": get_model_state_dict_from_combined_ckpt(combined, "vocoder"),
}
del combined
missing = [name for name, state in component_states.items() if not state]
if missing:
raise ValueError(f"Echo checkpoint is missing components: {missing}")
tensor_counts = {}
dit_state = component_states.pop("dit")
transformer = convert_ltx2_transformer(dict(dit_state), version="2.3")
tensor_counts["transformer"] = len(transformer.state_dict())
save_component(transformer, output_path, "transformer", max_shard_size)
del transformer
gc.collect()
connectors = convert_ltx2_connectors(dict(dit_state), version="2.3")
tensor_counts["connectors"] = len(connectors.state_dict())
save_component(connectors, output_path, "connectors", max_shard_size)
del connectors, dit_state
gc.collect()
vae = convert_ltx2_video_vae(component_states.pop("vae"), version="2.3", timestep_conditioning=False)
tensor_counts["vae"] = len(vae.state_dict())
save_component(vae, output_path, "vae", max_shard_size)
del vae
gc.collect()
audio_vae = convert_ltx2_audio_vae(component_states.pop("audio_vae"), version="2.3")
tensor_counts["audio_vae"] = len(audio_vae.state_dict())
save_component(audio_vae, output_path, "audio_vae", max_shard_size)
del audio_vae
gc.collect()
vocoder = convert_ltx2_vocoder(component_states.pop("vocoder"), version="2.3")
tensor_counts["vocoder"] = len(vocoder.state_dict())
save_component(vocoder, output_path, "vocoder", max_shard_size)
del vocoder
gc.collect()
write_modular_index(output_path, base_model=base_model, repo_id=repo_id)
report = {
"schema": "echo.diffusers.conversion.v1",
"source": str(source),
"precision": "bfloat16",
"tensor_counts": tensor_counts,
"base_model": base_model,
"model_repo": repo_id,
"vocoder_output_sample_rate": 48000,
}
(output_path / "conversion_report.json").write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
print(f"Echo conversion complete: {output_path}", flush=True)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--checkpoint", type=Path, required=True, help="Echo BF16 checkpoint or release folder.")
parser.add_argument("--output-path", type=Path, required=True, help="Destination Diffusers model directory.")
parser.add_argument(
"--base-model",
default="google/gemma-3-12b-it",
help="Gemma model repo/path containing the text encoder and tokenizer at its root.",
)
parser.add_argument(
"--repo-id",
default=None,
help="Future Hub repo id for the converted Echo components. Defaults to the local output path.",
)
parser.add_argument("--max-shard-size", default="5GB")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
convert(
checkpoint=args.checkpoint,
output_path=args.output_path,
base_model=args.base_model,
repo_id=args.repo_id,
max_shard_size=args.max_shard_size,
)