LTX-Video

LTX-Video is a diffusion transformer designed for fast and real-time generation of high-resolution videos from text and images. The main feature of LTX-Video is the Video-VAE. The Video-VAE has a higher pixel to latent compression ratio (1:192) which enables more efficient video data processing and faster generation speed. To support and prevent finer details from being lost during generation, the Video-VAE decoder performs the latent to pixel conversion and the last denoising step.

You can find all the original LTX-Video checkpoints under the Lightricks organization.

Click on the LTX-Video models in the right sidebar for more examples of other video generation tasks.

The example below demonstrates how to generate a video optimized for memory or inference speed.

Refer to the Reduce memory usage guide for more details about the various memory saving techniques.

The LTX-Video model below requires ~10GB of VRAM.

import torch
from diffusers import LTXPipeline, AutoModel
from diffusers.hooks import apply_group_offloading
from diffusers.utils import export_to_video

# fp8 layerwise weight-casting
transformer = AutoModel.from_pretrained(
    "Lightricks/LTX-Video",
    subfolder="transformer",
    torch_dtype=torch.bfloat16
)
transformer.enable_layerwise_casting(
    storage_dtype=torch.float8_e4m3fn, compute_dtype=torch.bfloat16
)

pipeline = LTXPipeline.from_pretrained("Lightricks/LTX-Video", transformer=transformer, torch_dtype=torch.bfloat16)

# group-offloading
onload_device = torch.device("cuda")
offload_device = torch.device("cpu")
pipeline.transformer.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type="leaf_level", use_stream=True)
apply_group_offloading(pipeline.text_encoder, onload_device=onload_device, offload_type="block_level", num_blocks_per_group=2)
apply_group_offloading(pipeline.vae, onload_device=onload_device, offload_type="leaf_level")

prompt = """
A woman with long brown hair and light skin smiles at another woman with long blonde hair.
The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek.
The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and 
natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage
"""
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=768,
    height=512,
    num_frames=161,
    decode_timestep=0.03,
    decode_noise_scale=0.025,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

Compilation is slow the first time but subsequent calls to the pipeline are faster.

import torch
from diffusers import LTXPipeline
from diffusers.utils import export_to_video

pipeline = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video", torch_dtype=torch.bfloat16
)

# torch.compile
pipeline.transformer.to(memory_format=torch.channels_last)
pipeline.transformer = torch.compile(
    pipeline.transformer, mode="max-autotune", fullgraph=True
)

prompt = """
A woman with long brown hair and light skin smiles at another woman with long blonde hair.
The woman with brown hair wears a black jacket and has a small, barely noticeable mole on her right cheek.
The camera angle is a close-up, focused on the woman with brown hair's face. The lighting is warm and 
natural, likely from the setting sun, casting a soft glow on the scene. The scene appears to be real-life footage
"""
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"

video = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=768,
    height=512,
    num_frames=161,
    decode_timestep=0.03,
    decode_noise_scale=0.025,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=24)

Notes

Refer to the following recommended settings for generation from the LTX-Video repository.
- The recommended dtype for the transformer, VAE, and text encoder is torch.bfloat16. The VAE and text encoder can also be torch.float32 or torch.float16.
- For guidance-distilled variants of LTX-Video, set guidance_scale to 1.0. The guidance_scale for any other model should be set higher, like 5.0, for good generation quality.
- For timestep-aware VAE variants (LTX-Video 0.9.1 and above), set decode_timestep to 0.05 and image_cond_noise_scale to 0.025.
- For variants that support interpolation between multiple conditioning images and videos (LTX-Video 0.9.5 and above), use similar images and videos for the best results. Divergence from the conditioning inputs may lead to abrupt transitionts in the generated video.

LTX-Video 0.9.7 includes a spatial latent upscaler and a 13B parameter transformer. During inference, a low resolution video is quickly generated first and then upscaled and refined.

Show example code

import torch
from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
from diffusers.utils import export_to_video, load_video

pipeline = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-dev", torch_dtype=torch.bfloat16)
pipeline_upsample = LTXLatentUpsamplePipeline.from_pretrained("Lightricks/ltxv-spatial-upscaler-0.9.7", vae=pipeline.vae, torch_dtype=torch.bfloat16)
pipeline.to("cuda")
pipe_upsample.to("cuda")
pipeline.vae.enable_tiling()

def round_to_nearest_resolution_acceptable_by_vae(height, width):
    height = height - (height % pipeline.vae_temporal_compression_ratio)
    width = width - (width % pipeline.vae_temporal_compression_ratio)
    return height, width

video = load_video(
    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cosmos/cosmos-video2world-input-vid.mp4"
)[:21]  # only use the first 21 frames as conditioning
condition1 = LTXVideoCondition(video=video, frame_index=0)

prompt = """
The video depicts a winding mountain road covered in snow, with a single vehicle 
traveling along it. The road is flanked by steep, rocky cliffs and sparse vegetation. 
The landscape is characterized by rugged terrain and a river visible in the distance. 
The scene captures the solitude and beauty of a winter drive through a mountainous region.
"""
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
expected_height, expected_width = 768, 1152
downscale_factor = 2 / 3
num_frames = 161

# 1. Generate video at smaller resolution
# Text-only conditioning is also supported without the need to pass `conditions`
downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(downscaled_height, downscaled_width)
latents = pipeline(
    conditions=[condition1],
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=downscaled_width,
    height=downscaled_height,
    num_frames=num_frames,
    num_inference_steps=30,
    decode_timestep=0.05,
    decode_noise_scale=0.025,
    image_cond_noise_scale=0.0,
    guidance_scale=5.0,
    guidance_rescale=0.7,
    generator=torch.Generator().manual_seed(0),
    output_type="latent",
).frames

# 2. Upscale generated video using latent upsampler with fewer inference steps
# The available latent upsampler upscales the height/width by 2x
upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
upscaled_latents = pipe_upsample(
    latents=latents,
    output_type="latent"
).frames

# 3. Denoise the upscaled video with few steps to improve texture (optional, but recommended)
video = pipeline(
    conditions=[condition1],
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=upscaled_width,
    height=upscaled_height,
    num_frames=num_frames,
    denoise_strength=0.4,  # Effectively, 4 inference steps out of 10
    num_inference_steps=10,
    latents=upscaled_latents,
    decode_timestep=0.05,
    decode_noise_scale=0.025,
    image_cond_noise_scale=0.0,
    guidance_scale=5.0,
    guidance_rescale=0.7,
    generator=torch.Generator().manual_seed(0),
    output_type="pil",
).frames[0]

# 4. Downscale the video to the expected resolution
video = [frame.resize((expected_width, expected_height)) for frame in video]

export_to_video(video, "output.mp4", fps=24)

LTX-Video 0.9.7 distilled model is guidance and timestep-distilled to speedup generation. It requires guidance_scale to be set to 1.0 and num_inference_steps should be set between 4 and 10 for good generation quality. You should also use the following custom timesteps for the best results.

Base model inference to prepare for upscaling: [1000, 993, 987, 981, 975, 909, 725, 0.03].
Upscaling: [1000, 909, 725, 421, 0].

Show example code

import torch
from diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline
from diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition
from diffusers.utils import export_to_video, load_video

pipeline = LTXConditionPipeline.from_pretrained("Lightricks/LTX-Video-0.9.7-distilled", torch_dtype=torch.bfloat16)
pipe_upsample = LTXLatentUpsamplePipeline.from_pretrained("Lightricks/ltxv-spatial-upscaler-0.9.7", vae=pipeline.vae, torch_dtype=torch.bfloat16)
pipeline.to("cuda")
pipe_upsample.to("cuda")
pipeline.vae.enable_tiling()

def round_to_nearest_resolution_acceptable_by_vae(height, width):
    height = height - (height % pipeline.vae_temporal_compression_ratio)
    width = width - (width % pipeline.vae_temporal_compression_ratio)
    return height, width

prompt = """
artistic anatomical 3d render, utlra quality, human half full male body with transparent 
skin revealing structure instead of organs, muscular, intricate creative patterns, 
monochromatic with backlighting, lightning mesh, scientific concept art, blending biology 
with botany, surreal and ethereal quality, unreal engine 5, ray tracing, ultra realistic, 
16K UHD, rich details. camera zooms out in a rotating fashion
"""
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
expected_height, expected_width = 768, 1152
downscale_factor = 2 / 3
num_frames = 161

# 1. Generate video at smaller resolution
downscaled_height, downscaled_width = int(expected_height * downscale_factor), int(expected_width * downscale_factor)
downscaled_height, downscaled_width = round_to_nearest_resolution_acceptable_by_vae(downscaled_height, downscaled_width)
latents = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=downscaled_width,
    height=downscaled_height,
    num_frames=num_frames,
    timesteps=[1000, 993, 987, 981, 975, 909, 725, 0.03],
    decode_timestep=0.05,
    decode_noise_scale=0.025,
    image_cond_noise_scale=0.0,
    guidance_scale=1.0,
    guidance_rescale=0.7,
    generator=torch.Generator().manual_seed(0),
    output_type="latent",
).frames

# 2. Upscale generated video using latent upsampler with fewer inference steps
# The available latent upsampler upscales the height/width by 2x
upscaled_height, upscaled_width = downscaled_height * 2, downscaled_width * 2
upscaled_latents = pipe_upsample(
    latents=latents,
    adain_factor=1.0,
    output_type="latent"
).frames

# 3. Denoise the upscaled video with few steps to improve texture (optional, but recommended)
video = pipeline(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=upscaled_width,
    height=upscaled_height,
    num_frames=num_frames,
    denoise_strength=0.999,  # Effectively, 4 inference steps out of 5
    timesteps=[1000, 909, 725, 421, 0],
    latents=upscaled_latents,
    decode_timestep=0.05,
    decode_noise_scale=0.025,
    image_cond_noise_scale=0.0,
    guidance_scale=1.0,
    guidance_rescale=0.7,
    generator=torch.Generator().manual_seed(0),
    output_type="pil",
).frames[0]

# 4. Downscale the video to the expected resolution
video = [frame.resize((expected_width, expected_height)) for frame in video]

export_to_video(video, "output.mp4", fps=24)

LTX-Video supports LoRAs with [~loaders.LTXVideoLoraLoaderMixin.load_lora_weights].

Show example code

import torch
from diffusers import LTXConditionPipeline
from diffusers.utils import export_to_video, load_image

pipeline = LTXConditionPipeline.from_pretrained(
    "Lightricks/LTX-Video-0.9.5", torch_dtype=torch.bfloat16
)

pipeline.load_lora_weights("Lightricks/LTX-Video-Cakeify-LoRA", adapter_name="cakeify")
pipeline.set_adapters("cakeify")

# use "CAKEIFY" to trigger the LoRA
prompt = "CAKEIFY a person using a knife to cut a cake shaped like a Pikachu plushie"
image = load_image("https://huggingface.co/Lightricks/LTX-Video-Cakeify-LoRA/resolve/main/assets/images/pikachu.png")

video = pipeline(
    prompt=prompt,
    image=image,
    width=576,
    height=576,
    num_frames=161,
    decode_timestep=0.03,
    decode_noise_scale=0.025,
    num_inference_steps=50,
).frames[0]
export_to_video(video, "output.mp4", fps=26)

LTX-Video supports loading from single files, such as GGUF checkpoints, with [loaders.FromOriginalModelMixin.from_single_file] or [loaders.FromSingleFileMixin.from_single_file].

Show example code

import torch
from diffusers.utils import export_to_video
from diffusers import LTXPipeline, AutoModel, GGUFQuantizationConfig

transformer = AutoModel.from_single_file(
    "https://huggingface.co/city96/LTX-Video-gguf/blob/main/ltx-video-2b-v0.9-Q3_K_S.gguf"
    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
    torch_dtype=torch.bfloat16
)
pipeline = LTXPipeline.from_pretrained(
    "Lightricks/LTX-Video",
    transformer=transformer,
    torch_dtype=torch.bfloat16
)

LTXPipeline

[[autodoc]] LTXPipeline

all
call

LTXImageToVideoPipeline

[[autodoc]] LTXImageToVideoPipeline

all
call

LTXConditionPipeline

[[autodoc]] LTXConditionPipeline

all
call

LTXLatentUpsamplePipeline

[[autodoc]] LTXLatentUpsamplePipeline

all
call

LTXPipelineOutput

[[autodoc]] pipelines.ltx.pipeline_output.LTXPipelineOutput