[Feature request] LTX-Video v0.9.6 15x faster inference than non-distilled model.

[nitinmukesh]

Is your feature request related to a problem? Please describe.
No problem. This request is Low priority. As and when time allows.

Describe the solution you'd like.
Please support the new release of LTX-Video 0.9.6

Describe alternatives you've considered.
Original repo have support but it is easier to use with diffusers

Additional context.
April, 15th, 2025: New checkpoints v0.9.6:
Release a new checkpoint ltxv-2b-0.9.6-dev-04-25 with improved quality
Release a new distilled model ltxv-2b-0.9.6-distilled-04-25
15x faster inference than non-distilled model.
Does not require classifier-free guidance and spatio-temporal guidance.
Supports sampling with 8 (recommended), 4, 2 or 1 diffusion steps.
Improved prompt adherence, motion quality and fine details.
New default resolution and FPS: 1216 × 704 pixels at 30 FPS
Still real time on H100 with the distilled model.
Other resolutions and FPS are still supported.
Support stochastic inference (can improve visual quality when using the distilled model)
https://github.com/Lightricks/LTX-Video

Feedback on distilled model
https://www.reddit.com/r/StableDiffusion/comments/1k1xk1m/6_seconds_video_in_60_seconds_in_this_quality_is/

https://www.reddit.com/r/StableDiffusion/comments/1k1o4x8/the_new_ltxvideo_096_distilled_model_is_actually/

[amitmirdha227]

amitmirdha227/Incest

[nitinmukesh]

@yiyixuxu

import torch
from diffusers import LTXVideoTransformer3DModel, FlowMatchEulerDiscreteScheduler, LTXConditionPipeline
import os
from diffusers.utils import export_to_video

transformer = LTXVideoTransformer3DModel.from_pretrained(
    "multimodalart/ltxv-2b-0.9.6-distilled",
    subfolder="transformer",
    torch_dtype=torch.bfloat16,
    variant="bf16"
)
scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(
    "multimodalart/ltxv-2b-0.9.6-distilled",
    subfolder="scheduler"
)
pipe = LTXConditionPipeline.from_pretrained(
    "Lightricks/LTX-Video-0.9.5",
    transformer=transformer,
    scheduler=scheduler, #add or remove the scheduler to see the difference
    torch_dtype=torch.bfloat16,
)
pipe.enable_sequential_cpu_offload()
# pipe.enable_model_cpu_offload()
prompt = "A woman eating a burger"
negative_prompt = "worst quality, inconsistent motion, blurry, jittery, distorted"
generator = torch.Generator(device="cuda").manual_seed(42)
video = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=1216,
    height=704,
    num_frames=121,
    num_inference_steps=8,
    guidance_scale=1,
    generator=generator
).frames[0]

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

distilled_scheduler.mp4

BTW: This model is insane. On 8 GB VRAM
100%|█████████████| 8/8 [01:15<00:00, 9.47s/it]

[nitinmukesh]

Removed the scheduler

pipe = LTXConditionPipeline.from_pretrained(
    "Lightricks/LTX-Video-0.9.5",
    transformer=transformer,
    # scheduler=scheduler, #add or remove the scheduler to see the difference
    torch_dtype=torch.bfloat16,
)

distilled_scheduler1.mp4

[nitinmukesh]

Using LTXPipeline

FlowMatchEulerDiscreteScheduler

distilled_scheduler3.mp4

Default scheduler

distilled_scheduler4.mp4

[nitinmukesh]

Hello @yiyixuxu

Could this be considered if not a lot of changes, plz.

[nitinmukesh]

No longer needed. Distilled model is working fine with LTXPipeline (Do not use conditioning pipeline)

Refer the discussion here
#11369