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65e1c10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 | """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()
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