"""Prepare the complete LTX-2.5 pipeline from pinned, licensed public weights. Run on the model-download worker. The pinned upstream converter strictly checks every component; no parameters are randomly initialized or omitted. """ import argparse import gc import functools import hashlib import importlib.util import json from pathlib import Path import shutil import subprocess import sys import urllib.request REVISION = "93deb76408feee57d04b3cfeed3ded56a7a068ee" CONVERTER = "efe46a5b177af67c64b3666da8ddddac7ee12163" FILES = { "diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors": "31eb3cad89b9e54e99dd3baf286f70825ac4f6c660a70d9184d895be76d7bff4", "text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors": "ef7243612fdae7a75cb4d5cee9433e81380675fb6c213bd98ae74a9cd16561d1", "vae/ltx-2.5-audio-vae-bf16.safetensors": "c52733d37f6a7fb7949c3dc0fb468c6cb2169e4d836983a73babb9f0d54837a5", "vae/ltx-2.5-video-vae-bf16.safetensors": "847e14ca7f3355debca0cea4eaa24ac0fbcdf0061da054ac89ca638a869ddba3", "vae/ltx-2.5-video-vae-conv-bf16.safetensors": "685b06ee3d9b2039647698fc4ea33175112462fc374e2777312c907897dfce8d", } def main(): parser = argparse.ArgumentParser(description=__doc__) parser.add_argument("--source", type=Path, required=True) parser.add_argument("--output", type=Path, required=True) args = parser.parse_args() args.source.mkdir(parents=True, exist_ok=True) args.output.mkdir(parents=True, exist_ok=True) vendor = args.source / "converter" package = vendor / "package" if not (package / "diffusers/__init__.py").exists(): subprocess.run([sys.executable, "-m", "pip", "install", "--no-deps", "--target", str(package), f"https://github.com/huggingface/diffusers/archive/{CONVERTER}.zip"], check=True) sys.path.insert(0, str(package)) script = vendor / "convert_ltx2_to_diffusers.py" if not script.exists(): urllib.request.urlretrieve(f"https://raw.githubusercontent.com/huggingface/diffusers/{CONVERTER}/scripts/convert_ltx2_to_diffusers.py", script) spec = importlib.util.spec_from_file_location("ltx_converter", script) converter = importlib.util.module_from_spec(spec) spec.loader.exec_module(converter) import torch from accelerate import init_empty_weights from huggingface_hub import hf_hub_download from safetensors import safe_open from safetensors.torch import load_file from transformers import AutoConfig, AutoModelForImageTextToText, AutoTokenizer from diffusers import FlowMatchEulerDiscreteScheduler # The source checkpoint also stores the optional keyframe-stream tag. # Keep it verbatim. Plain video decoding never reads this parameter. decoder_init = converter.LTX2VideoDiffusionDecoderModel.__init__ @functools.wraps(decoder_init) def init_with_keyframe_tag(self, *a, **kw): decoder_init(self, *a, **kw) self.decoder.register_parameter("type_emb", torch.nn.Parameter(torch.empty(128))) converter.LTX2VideoDiffusionDecoderModel.__init__ = init_with_keyframe_tag for name, digest in FILES.items(): conv = "-conv-" in name path = Path(hf_hub_download("comfyicu/LTX-2.5" if conv else "pinecoresystems/LTX-2.5-serving", name, revision="1026e00f808dbd9601911479b22d65b760737dc8" if conv else REVISION, local_dir=args.source, token=False)) with path.open("rb") as stream: actual = hashlib.file_digest(stream, "sha256").hexdigest() if actual != digest: raise ValueError(f"Checkpoint hash mismatch: {name}") print("VERIFIED", name, flush=True) for name in ("LICENSE.md", "NOTICE"): path = hf_hub_download("pinecoresystems/LTX-2.5-serving", name, revision=REVISION, local_dir=args.source, token=False) shutil.copyfile(path, args.output / name) components = {} def save(name, model): model.save_pretrained(args.output / name, safe_serialization=True, max_shard_size="2GB") components[name] = ["transformers" if name == "text_encoder" else "diffusers", type(model).__name__] print("SAVED", name, flush=True) text_path = args.source / "text_encoders/gemma4-12b-with-proj-ltx-2.5-bf16.safetensors" with safe_open(text_path, framework="pt") as source: config = AutoConfig.for_model(**json.loads(source.metadata()["gemma_config"])) tokenizer = args.output / "tokenizer" tokenizer.mkdir(exist_ok=True) for key in source.keys(): if key == "tokenizer_json" or key.startswith("hf_asset__"): name = "tokenizer.json" if key == "tokenizer_json" else key.removeprefix("hf_asset__") (tokenizer / name).write_bytes(source.get_tensor(key).numpy().tobytes()) text_weights, projections = {}, {} for key in source.keys(): if key.startswith("text_embedding_projection."): projections[key] = source.get_tensor(key) elif key.startswith("model."): text_weights["model.language_model." + key.removeprefix("model.")] = source.get_tensor(key) elif key.startswith("vision_model."): text_weights["model.embed_vision." + key.removeprefix("vision_model.")] = source.get_tensor(key) elif key.startswith("multi_modal_projector."): text_weights["model.embed_vision.multimodal_embedder." + key.removeprefix("multi_modal_projector.")] = source.get_tensor(key) elif key.startswith("audio_projector."): text_weights["model.embed_audio." + key.removeprefix("audio_projector.")] = source.get_tensor(key) text_weights["lm_head.weight"] = text_weights["model.language_model.embed_tokens.weight"] with init_empty_weights(): text_encoder = AutoModelForImageTextToText.from_config(config) text_encoder.load_state_dict(text_weights, strict=True, assign=True) text_encoder.tie_weights() save("text_encoder", text_encoder) del text_encoder, text_weights AutoTokenizer.from_pretrained(tokenizer, local_files_only=True) components["tokenizer"] = ["transformers", "AutoTokenizer"] gc.collect() state = {key.removeprefix("model.diffusion_model."): value for key, value in load_file(args.source / "diffusion_models/ltx-2.5-22b-distilled-transformer-bf16.safetensors").items()} state.update(projections) save("transformer", converter.convert_ltx2_transformer(state, "2.5")) save("connectors", converter.convert_ltx2_connectors(state, "2.5", config.text_config)) del state, projections gc.collect() save("vae", converter.convert_ltx2_video_vae( load_file(args.source / "vae/ltx-2.5-video-vae-conv-bf16.safetensors"), "2.5", False)) gc.collect() save("diffusion_decoder", converter.convert_ltx2_diffusion_video_vae( load_file(args.source / "vae/ltx-2.5-video-vae-bf16.safetensors"), "2.5")) gc.collect() audio = load_file(args.source / "vae/ltx-2.5-audio-vae-bf16.safetensors") save("audio_vae", converter.convert_ltx2_audio_vae( {k.removeprefix("audio_vae."): v for k, v in audio.items() if k.startswith("audio_vae.")}, "2.5")) save("vocoder", converter.convert_ltx2_vocoder( {k.removeprefix("vocoder."): v for k, v in audio.items() if k.startswith("vocoder.")}, "2.5")) scheduler = FlowMatchEulerDiscreteScheduler(use_dynamic_shifting=False, base_shift=0.95, max_shift=2.05, base_image_seq_len=1024, max_image_seq_len=4096, shift_terminal=None) scheduler.save_pretrained(args.output / "scheduler") components["scheduler"] = ["diffusers", type(scheduler).__name__] (args.output / "model_index.json").write_text(json.dumps({"_class_name": "LTX2Pipeline", "_diffusers_version": "0.41.0.dev0", **components}, indent=2) + "\n") (args.output / "conversion.json").write_text(json.dumps(dict( original_model="Lightricks/LTX-2.5", original_revision="49b093a39c19169a8a81721dcc541e07e945232f", public_distribution="pinecoresystems/LTX-2.5-serving", revision=REVISION, conv_vae_distribution="comfyicu/LTX-2.5", conv_vae_revision="1026e00f808dbd9601911479b22d65b760737dc8", converter_revision=CONVERTER, source_sha256=FILES, validation="Every component loaded with strict=True; all source file SHA256 values verified.", text_encoder_adapter="Lossless parameter-name mapping, including vision/audio embeddings and tied LM head."), indent=2) + "\n") if __name__ == "__main__": main()