chatpig is on vacation, just wait for cow-encoder

https://huggingface.co/Kijai/LTXV2_comfy if you want the seperate files like VAE/TE/diffusion model instead of a checkpoint.

Also the below script to turn the checkpoints into transformer only models (the below script only works for the fp8 checkpoints btw)

from safetensors.torch import save_file
import safetensors.torch
import torch
import json
import gc

def load_file(path):
    print(f"Loading {path}...")
    if not path.endswith(".safetensors"):
        loaded = torch.load(path)
    else:
        loaded = safetensors.torch.load_file(path)
    return loaded

model_type = "ltx-2-19b-distilled-fp8"

file_path = "ltx-2-19b-distilled-fp8.safetensors"
current_sd = load_file(file_path)

# Load original metadata
original_metadata = {}
with safetensors.torch.safe_open(file_path, framework="pt") as f:
    original_metadata = f.metadata() or {}


if "config" in original_metadata:
    config = json.loads(original_metadata["config"])
    if "transformer" in config or "scheduler" in config:
        filtered_config = {
            "transformer": config["transformer"],
            "scheduler": config["scheduler"]
        }
        #print(json.dumps(filtered_config, indent=4))
        # Update metadata with only vae config
        original_metadata["config"] = json.dumps(filtered_config)
    else:
        print("vae not found in config")
else:
    print(json.dumps(original_metadata, indent=4))

if "_quantization_metadata" in original_metadata:
    del original_metadata["_quantization_metadata"]

quant_conf = {
    "format": "float8_e4m3fn",
    }

sd_pruned = dict()
for k, v in current_sd.items():
    if k.startswith("model.") and "embeddings_connector" not in k:
        if k not in sd_pruned:
            sd_pruned[k] = v
            if "weight_scale" in k:
                sd_pruned[k.replace(".weight_scale", ".comfy_quant")] = torch.tensor(list(json.dumps(quant_conf).encode('utf-8')), dtype=torch.uint8)


# Clear memory
del current_sd
gc.collect()
torch.cuda.empty_cache()

for k, v in sd_pruned.items():
    if isinstance(v, torch.Tensor):
        print(f"{k}: {v.shape} {v.dtype}")
    else:
        print(f"{k}: {type(v)}")
save_file(sd_pruned, f"{model_type}_.safetensors", metadata=original_metadata)

ltx2 gguf #59

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