Instructions to use Lightricks/LTX-2.5-Diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lightricks/LTX-2.5-Diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lightricks/LTX-2.5-Diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
diffusion_decoder differs from the current ltx-2.5-video-vae and lacks decoder.type_emb
Hi! While porting the SDR-To-HDR IC-LoRA (with seam keyframes) to diffusers, we compared this repo's diffusion_decoder with the original Lightricks/LTX-2.5 vae/ltx-2.5-video-vae-bf16.safetensors. The two don't match:
- Missing tag: the original file has the keyframe tag
decoder.type_emb(BF16, [128]), and this repo's decoder does not. - Different weights: none of the 161 decoder tensors that can be matched by name are equal. The relative differences range from 2β6% on
conv_in/conv_outto 10β40% onw_down,context_projandupsamples.*. The latent statistics are identical.
On a decoder-only check with identical inputs and noise against Lightricks' own decoder (49 frames, 480x736), this repo's decoder matches at 44.4 dB for a plain decode and 35.2 dB with keyframes. The original VAE converted to diffusers matches at 62.2 dB and 68.6 dB.
The diffusers converter is updated to carry decoder.type_emb and set decoder_keyframe_type_embedding=True in https://github.com/huggingface/diffusers/pull/14975. Would you consider re-converting diffusion_decoder from the current original VAE with it? Happy to share the comparison script.