r/StableDiffusion

Trying to get some of those music videos with kind of side stories?

This took me an embarrasing amount of time planning and figuring out what to do, and I just couldn't be bothered to finish the entire song... Is a lot! I hope you like it! I'll keep making more if you don't! lovee!

LTX 2.5 has a known smearing problem. It's really bad, and makes almost every output of LTX completely unusable. Sorry LTX, but the default model really is just shit. Minimax beats LTX in every area, obviously, but especially when it comes to smearing (or in case of Minimax, lack thereof). However, I recently found out that you can actually significantly reduce the smearing in LTX and actually get usable outputs from it. It came from adventuring this node pack for MiniMax, where it is meant to clean up smearing artifacts in MiniMax H3.

I thought this was interesting, so I converted these nodes (at least some of the nodes that matter) to work with LTX 2.3/2.5. Here is the repo with the custom node I made. The workflow is also in that repo (be aware there is some spaghetti (this was just for myself really, and it shows qwq), and you will need some custom nodes (eg. KJNodes, Comfy UI Easy Media, etc)). Just use the manager to install the missing custom nodes.

Using these nodes makes a clear difference. A stark difference, it's almost unbelievable. This makes it look like a generational improvement, closing the gap on MiniMax H3 in terms of temporal stability (ain't no way in hell its ever matching the instruction following or general capabilities of H3 lmao).

Essentially, the jerk oracle creates new "hold" frames based on the amount of smearing per frame. Some frames have larger "hold" amounts. We pipe that into a new sampling step, which improves the smearing. After the sampling is finished, we chop off the added "hold" frames so that we are left with just the original frame count, but now these frames actually have better consistency. You can read all about how the process works in the author's original implementation if you want more insight onto how this functions.

I had mistakenly been using a base for H3 prompting from some random tip / example by someone. It worked ok, I thought. But I was getting a bit frustrated because almost every time I was making a longer series of clips with dialogue, it kept adding random gibberish to fill out time, or making the wrong person speak. I thought it was just a "feature" of H3 and lived with it. But then I realised what was missing, so I added the actual ref2v prompt guide to my LLM and difference was staggering. I could make long series of 30x15 sec clips, and the dialogue was perfect just as the script said, no gibberish was added in any place, and the emotional beats and reactions worked much better too.

Believe it :) Dont just use whatever prompting. It matters more than one might think.

https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.md

Last week my post got a ton of questions about the LTX 2.5 workflow. so here's the follow-up.. After running a bunch of tests, the one I'd recommend right now is this:

https://github.com/Lightricks/ComfyUI-LTXVideo/blob/master/example_workflows/2.5/LTX-2.5_ICLoRA_Union_Control_Distilled.json

It's been the most consistent one I've tried for V2V so far

drop your results below if you give it a shot! and have a great WEEKENDDDDDDD!!!

I was going to wait until around Christmas to purchased but took the plunge in July for 11,500 and I was upset that I didnt catch it @ $8,000. Now the Blackwell pro 6000 is inching towards $20,000 and are sold out. Are consumers and hobbyist like you and I are buying these up or datacenters? I would think datacenters would go for the b200 and up. However, Im browsing around and see you guys and girls doing remarkable ai diffusion with just a 3060. Im impressed with this community.

I keep having fun with ref2va model.

RTX 3060, 64 Gb RAM. I use ref2v Turbo 4 step Lora paired with Sol Attention and Minimax H3 Memory Effecient Sage Attention at 6 steps. It takes about 2 minutes per second of generation.

A few people asked how VPipe compares with h3.c, so I ran them side by side on the same machine with the same settings.

Machine: base 15” M5 MacBook Air, 16GB RAM

MiniMax H3 settings:

* 960×544

* 124 frames

* 6 DiT steps

Results:

* VPipe: 12m 15s

* h3.c: 16m 22s

So on this particular matched workload, VPipe finished in about 25% less wall-clock time.

The video shows both the generation process and the final outputs side by side, so you can also compare the resulting quality rather than just the timing.

VPipe is not an MinimaxH3-specific implementation — it’s an Apache-2.0 open-source multimodal pipeline/runtime with a native Metal inference backend for Apple Silicon. MiniMax H3 is just one of the workloads I’ve been optimizing recently.

GitHub: https://github.com/tgo-app-dev/vpipe

Interested in feedback on both the performance comparison and the output differences.

I have no idea why it does these so well. I could watch these all day.

Hello Everyone! Let me share the newest version of my camera control LTX IC-LoRA. This node and LoRA can be used in a V2V workflow to change the camera position or movement of an existing video clip. I've put a lot of work into this version, I hope you'll enjoy it.

You can download the model here: https://huggingface.co/Cseti/LTX2.3-22B_IC-LoRA-CrossView-Warp_v2
Node + example workflow can be found here: https://github.com/cseti007/ComfyUI-CrossViewWarp
A lame tutorial video I made to help how to use the node can be found here: https://www.youtube.com/watch?v=7QAapT9xMgM

Using the workflow from Nekodificador and Ablejones in Discord:
https://discord.com/invite/dstjQYQNt
https://ln5.sync.com/dl/47c351f50#msqfrnfr-am3rr8fx-v7qm3ah9-xw222n3c
For complex scenes like this with to much people is easy just to do a manual mask instead of SAM.