Text-to-Video
Diffusers
Safetensors
English
LTX2Pipeline
fp8
int8
unsloth
quantized
ltx-2.5
image-to-video
video-generation
Instructions to use unsloth/LTX-2.5-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use unsloth/LTX-2.5-FP8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("unsloth/LTX-2.5-FP8", dtype=torch.bfloat16, device_map="cuda") prompt = "A blue humanoid robot with a glowing yellow ring for a face stands in a bright warehouse aisle (INT8-ConvRot)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
File size: 1,089 Bytes
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"_class_name": "LTX2VideoDiffusionDecoderModel",
"_diffusers_version": "0.40.0.dev0",
"decoder_head_dim": 64,
"decoder_model_output_type": "x0",
"decoder_num_inference_steps": 1,
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"decoder_stage_channels": [
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"decoder_stage_depths": [
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"decoder_t_emb_dim": 384,
"decoder_timestep_scale_multiplier": 1000.0,
"decoder_upsample_channel_reductions": [
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"decoder_upsample_strides": [
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"latent_channels": 128,
"out_channels": 3,
"patch_size": 4,
"scaling_factor": 1.0,
"spatial_compression_ratio": 32,
"temporal_compression_ratio": 8
}
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