Instructions to use Kijai/LTX2.3_comfy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusion Single File
How to use Kijai/LTX2.3_comfy with Diffusion Single File:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
Will there be an "fp8_input_scaled" version for the Dev model?
#21
by jool454 - opened
Thanks for everything.
The official light tricks model should already be that, though it's a whole checkpoint:
edit) I deleted fp8 model on my repo since it overlapped with here, but I'll leave the extraction code here for reference.
import sys
import json
import torch
from safetensors.torch import safe_open, save_file
def cut_safetensors(input_path, output_path):
with safe_open(input_path, framework="pt", device="cpu") as f:
metadata = f.metadata()
config = json.loads(metadata.get('config', '{}'))
for key in ['vae', 'audio_vae', 'vocoder']:
if key in config:
del config[key]
metadata['config'] = json.dumps(config)
quant_meta = json.loads(metadata.get('_quantization_metadata', '{"layers": {}}'))
quant_layers = quant_meta.get("layers", {})
del metadata['_quantization_metadata']
new_state_dict = {}
prefix = "model.diffusion_model."
for key in f.keys():
if key.startswith(prefix):
new_state_dict[key] = f.get_tensor(key)
base_key = key.replace(".weight", "")
if base_key in quant_layers:
quant_info = quant_layers[base_key]
json_data = json.dumps(quant_info).encode("utf-8")
new_tensor = torch.tensor(list(json_data), dtype=torch.uint8)
new_state_dict[f"{base_key}.comfy_quant"] = new_tensor
save_file(new_state_dict, output_path, metadata=metadata)
input_path, output_path = sys.argv[1:3]
if __name__ == "__main__":
cut_safetensors(input_path, output_path)
Added my extraction here as well.
Kijai changed discussion status to closed