Add stochastic sampling to FlowMatchEulerDiscreteScheduler

[apolinario]

What does this PR do?

This PR adds stochastic sampling to FlowMatchEulerDiscreteScheduler based on Lightricks/LTX-Video@b1aedddltx_video/schedulers/rf.py, which was added with th release of 0.9.6-distilled. I decoupled the next and current sigma to try to get closer to the rf.py implementation of the stochastic sampling, but a second pair of eyes on this would be great.

To try it:

importtorchfromdiffusersimportLTXVideoTransformer3DModel, FlowMatchEulerDiscreteScheduler, LTXPipelinefromdiffusers.utilsimportexport_to_videotransformer=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=LTXPipeline.from_pretrained(
"Lightricks/LTX-Video-0.9.5",
transformer=transformer,
scheduler=scheduler, #add or remove the scheduler to see the differencetorch_dtype=torch.bfloat16,
)
pipe.to("cuda")
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)

Who can review?

@yiyixuxu

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[apolinario]

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[yiyixuxu]

thanks @apolinario

[yiyixuxu]

I think just have this in config is enough no?

[apolinario]

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[yiyixuxu]

@apolinario
here it seems to reversed, no?
before:
dt = (per_token_sigmas - lower_sigmas)[..., None]

now:
dt = ower_sigmas - per_token_sigmas

[apolinario]

good catch!

[nitinmukesh]

Quick question, Should it be LTXPipeline or LTXConditionPipeline?
0.9.5 support was added in LTXConditionPipeline.

[yiyixuxu]

thanks @apolinario !

[yiyixuxu]

@nitinmukesh I think it's probably better in LTXConditionPipeline, can you try it out?

[nitinmukesh]

@apolinario

Thank you for adding the sampling.

Please could you share few sample outputs which you created. I am not getting good results so want to compare if something wrong in code.
#11359

[Ednaordinary]

Quick question, Should it be LTXPipeline or LTXConditionPipeline? 0.9.5 support was added in LTXConditionPipeline.

LTXPipeline. The model here is 0.9.6-distilled (the only one that uses the stochastic sampling as of now). "0.9.5" is included because the transformer and scheduler from 0.9.6 are inserted, which is fine because nothing else in the pipeline is different from 0.9.5 and there's currently nothing the lightricks 0.9.6 repos. 0.9.6-distilled is guidance distilled so it does not work in the condition pipeline, while 0.9.6 does

[Ednaordinary]

Also, I find increasing the schedulers shift while using distilled helps to boost coherence. This is inline with FastVideo (PCM distillation) which says to set the shift to 17. The 1.0 in https://huggingface.co/multimodalart/ltxv-2b-0.9.6-distilled/blob/main/scheduler/scheduler_config.json doesn't seem like a good default. I'm unsure what it is in the original LTX repo

Some different shifts

0.25

0.25.mp4

0.5

0.5.mp4

1.0

1.mp4

2.0

2.mp4

4.0

4.mp4

8.0

8.mp4

16.0

16.mp4

32

32.mp4

64

64.mp4

16 seems like a good default

(tested by adding pipe.scheduler._shift = 16.0 somewhere between pipe init and pipe call)

[nitinmukesh]

Thank you @Ednaordinary

The information you provided is very helpful. Getting better results than before.

distilled_scheduler1.mp4

[nitinmukesh]

249 frames

distilled_scheduler2.mp4

[Ednaordinary]

Looks great! What I've noticed so far is that the background is often very repetitive in a weird way like shown in your 249 frame example. Sometimes this can be solved by increasing the shift to an insanely large amount (think in the 200s) but that also incurs everything else that comes from running shift that high (eventually, everything just turns into blobs)

[nitinmukesh]

Sure will try that, thank you. Next gonna try if distilled support image to video (LTXImageToVideoPipeline).
The speed of the models is OoTW, all these generated on 8 GB VRAM + 16 GB RAM and only 3 minutes for 249 frames.

[nitinmukesh]

I2V is working good.

distilled_scheduler6.mp4

Result from 0.9.1 using same image
newgenai79/sd-diffuser-webui#11

[nitinmukesh]

@Ednaordinary

What do you suggest for 0.9.6 dev, LTXPipeline or Conditioning pipeline, in case you tried.

[Ednaordinary]

0.9.6 should be the condition pipeline I'm pretty sure