RuneXX

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LTX-2.3-Workflows

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Sampler you can try others too. LCM or Euler both seems to work well.
In the official workflow for LTX2.0 it used to be Euler, and Res_2s in the 2nd pass upscale, and in ComfyUI official its Euler and gradient_estimation in the 2nd pass upscale (if i remember correctly).

For LTX-2.3 they seem to have tried yet another different samplers it looks like. I havent tried those.
The model just came out, so I might change, and free to try and experiment with different samplers yourself too ;-) It gives slightly different results.

Personally i feel LCM or Euler gives good result and good speed (at least for LTX-2.0)
I wouldn't get too caught up in the sampler part, many of them are extremely similar, that its hard to even tell any difference ;-)

Res_2s is a good sampler that might give better quality, but it can be quite slow.
(why they used ClownSampler instead of KSampler i dont really know, its also not a big difference. But maybe since that sampler adds the Res_2s, that is not part of regular ComfyUI if i remember right )

But most of all, image and video models work with many different samplers, so it can for sure be a bit up to personal preference ;-)
And it wouldnt hurt to set them same as LTX-2 did for sure. I will try those too as well, see if its worth it vs the speed (if res_2s). Euler/ Euler ancestral cfg should be quite fast though.
I actually use euler_ancestral often myself too.

Thanks so much for sharing your knowledge! :)

The res_2s sampler - I think - requires (or did once) the RES4LYF node pack, so I understand they may not have wanted to use a sampler that was going to give many users "missing node" errors.

The results for me, changing samplers, are quite notable, from usable (lcm) to not usable (res_2s with LTX_2.3, oddly, since this was the optimal sampler for me with LTX-2.0... very similar workflow, using the Q8 GGUFs with all of Kijai's auxiliary safetensors [VAE, Text Encoder, ]).

I'll keep experimenting.

Gemini had this to say, interestingly....

To get cleaner, more professional-looking video out of LTX-2.3, it is necessary to understand how the architecture of the model interacts with different samplers, and why using res_2s twice caused an over-baked result.

The Mechanics of Samplers and Rectified Flow

LTX-2.3, like Flux and Stable Diffusion 3, uses a Rectified Flow (RF) architecture. Unlike older latent diffusion models that step through complex noise schedules, RF models are trained to map a straight line from pure noise to the clean image.

1st-Order Solvers (euler, lcm)
These samplers calculate the trajectory at the current step and draw a straight line to the next step.

• euler: The native, mathematically "correct" solver for Rectified Flow models. It provides the cleanest, most structurally sound base without adding artificial contrast.
• lcm: Latent Consistency Models are designed to skip multiple steps at once. It naturally smooths out the generation. This is why your lcm passes looked good and avoided the "over-baked" look—it inherently favors smooth, continuous gradients over sharp, high-frequency details. The trade-off is that lcm can sometimes look slightly soft or "plasticky" and lack fine texture.

2nd-Order Solvers (res_2s, dpmpp_2m, heun)
These samplers calculate the trajectory, look ahead to the next step, and then apply a correction to curve the path.

• res_2s: This is a highly aggressive 2nd-order solver specifically tuned to pull intense detail out of RF models.
• The Over-Baking Issue: 2nd-order solvers inject high-frequency detail and micro-contrast. When you run res_2s on the first pass, it creates a very sharp, contrast-heavy base latent. When you pass that latent into the upscale node and run res_2s again on the second pass, the sampler compounds that micro-contrast. The model tries to add sharp detail on top of already sharp detail, resulting in a "deep-fried," over-saturated, or over-baked visual output.

Ancestral / SDE Solvers (euler_ancestral, dpmpp_2m_sde)
These inject a small amount of new noise at every step to prevent the image from becoming too smooth. While great for still images, SDE (Stochastic Differential Equation) and Ancestral samplers should generally be avoided for video generation. The injected noise is random at each step, which translates to severe temporal flickering and boiling textures across the video frames.

Recommended Sampler Configuration

For a professional, cinematic output, you want to separate the responsibilities of the two passes. Pass 1 should build clean geometry and motion. Pass 2 should add texture and crispness.

Pass 1 (Base Generation)

• **Set to: euler**
• Why: You need perfect temporal consistency and clean geometry without aggressive contrast. euler will follow the LTX-2.3 flow trajectory exactly as trained. It will give you a solid, stable base video with no artifacting.

Pass 2 (Latent Upsampler / Refinement)

• **Set to: euler OR res_2s**
• Why: When the latent is upscaled, it becomes slightly soft. The second pass only runs over a fraction of the total sigmas (denoising strength) to add high-resolution details back in.
• If you want a highly cinematic, smooth, and natural look (like film), use euler again.
• If you want an incredibly sharp, highly detailed look (like digital 4K video or hyper-realism), use res_2s or dpmpp_2m. Because the first pass was handled by euler, the base is clean enough that the 2nd-order solver will only enhance the fine textures (like skin pores or fabric) without over-baking the global contrast.

If you use a 2nd-order solver on the second pass and it still feels slightly too harsh, you need to lower the starting sigma (denoise strength) of the second pass, rather than changing the sampler.

Yeah that sounds about right ;-)
My "favorite" is often euler at 1st pass, and euler ancestral at 2nd pass (or just LCM both)

I havent tried the euler_cfg ones they now use, but i suspect they are not very different

Gemini basically said euler at both is fine ;-) or res_2s. But i have to agree with Gemini, that sometimes res_2s just over-bakes the image. Specially skin texture. It might sometimes give more details, but as a general-purpose safe choice Euler is often the go to I think ;-) Rarely can you go wrong with Euler. But sometimes you can enhance the details a bit with other samplers

• in the Two Stage workflow, they use "euler_ancestral_cfg_pp" in the 1st stage and "euler_cfg_pp" in the 2nd.

Those works quite well. Will compare with Euler /LCM, seems similar, but got nice results from a couple of random runs