I’ve found RuneXX’s workflows a helpful starting point for different types of video generation with LTX2.3. Is there a respective “definitive” source of workflows but for WAN?
(Thank you to u/Reckless_Venom1507 for the suggestion about RuneXX)
I’ve found RuneXX’s workflows a helpful starting point for different types of video generation with LTX2.3. Is there a respective “definitive” source of workflows but for WAN?
Running into a lot of issues setting things up. I’m planning on scrapping everything so far and starting fresh, and "doing things right" this time. What approach have you taken that you’ve found helpful when doing a fresh setup, and then make adjustments to get the generations going?
Available hardware:
40gb VRAM across 3x GPUs (2x5060ti16gb, 1x2060super8gb)
256gb ddr4 ram
Background rant:
Running into a lot of issues setting up media generation models like LTX2.3 via ComfyUI. All I want to do is figure out how to load a workflow from civitai, make minor adjustments to fit my hardware, and then generate media. Then measure speed/quality, and iterate from there.
But man, the whole setup is so frustratingly complicated. I have experience running LLMs locally with llama.cpp, and adjusting the run with different flags on startup. But when it comes to things like video generation, it just seems like a whole other beast. Kijai, multiGPU, GGUF, VAE, high/low, etc etc etc, I can never seem to get things setup appropriately, even though it seems like it should be simple.
I'm sure that there is good information on Reddit threads, but even searching through all the threads there is just such an insane amount of information, fringe situations, variables to consider, its not really helpful to be honest. Even trying to enlist Claude Code's help, but still feeling like I'm spinning my wheels.
I know it’s such a faux pas to ask a noobie question like “how I do dis?”, but I’m getting to the point where things just really haven’t been working well and I need to check with the wisdom of the community
Running into a lot of issues setting things up. I’m planning on scrapping everything so far and starting fresh, and "doing things right" this time. What approach have you taken that you’ve found helpful when doing a fresh setup, and then make adjustments to get the generations going?
Available hardware:
40gb VRAM across 3x GPUs (2x5060ti16gb, 1x2060super8gb)
256gb ddr4 ram
Background rant:
Running into a lot of issues setting up media generation models like LTX2.3 via ComfyUI. All I want to do is figure out how to load a workflow from civitai, make minor adjustments to fit my hardware, and then generate media. Then measure speed/quality, and iterate from there.
But man, the whole setup is so frustratingly complicated. I have experience running LLMs locally with llama.cpp, and adjusting the run with different flags on startup. But when it comes to things like video generation, it just seems like a whole other beast. Kijai, multiGPU, GGUF, VAE, high/low, etc etc etc, I can never seem to get things setup appropriately, even though it seems like it should be simple.
I'm sure that there is good information on Reddit threads, but even searching through all the threads there is just such an insane amount of information, fringe situations, variables to consider, its not really helpful to be honest. Even trying to enlist Claude Code's help, but still feeling like I'm spinning my wheels.
I know it’s such a faux pas to ask a noobie question like “how I do dis?”, but I’m getting to the point where things just really haven’t been working well and I need to check with the wisdom of the community
Stacking 4-5 GPUs (1x2060super, 4x5060ti) on a gigabyte mc62-g40 mobo, which has 7x PCIe 16x slots. Might end up actually taking out the 2060super in favor of the 4x5060ti, but they seem pretty close to each other when they’re plugged in with only one PCIe slot of space between them. Is this like a fire hazard or might mess up the life span of the GPUs over time, or is this not a big deal as long as I undervolt a bit? Wondering if I’m overthinking all this lol
Has anyone been able to run Q4 or Q5 of XiaomiMiMo/MiMo-V2.5, with functioning multimodal capability as well as MTP, through llamacpp? Only AesSedai’s gguf quants appear to have mmproj, and it is unclear if it has MTP layers preserved or not.
I have only 40gb of vram, but 256gb of 4-channel ddr4 ram, so I’m not expecting any great inference speed, but I’m intrigued by the model’s strength and multimodal capabilities so wanted to give it a go. Looks like MTP on llamacpp is still in draft branch, so I’ll have to use that it seems.
Already watched all the most popular anime (FMA brotherhood, frieren, AoT, death note, JJK, monster, oshi no ko, mushoku tensei, code geass, parasyte etc) and I’m getting into an age range where it is not appealing to watch anime about middle/high school anymore
Wondering if there are good seinen anime or anime for adults that people would rec? I really enjoy good animation especially
My favorite anime of all time is hinamatsuri haha, but recently I’ve really enjoyed heavenly delusion. Dorohedro was a bit too weird for me.
I set up Hermes agent with honcho memory system, all locally. Running into a lot of issues with honcho though. attribution bugs, over-extraction, and observation bloat; anyone else familiar with these issues?
The deriver is incorrectly attributing user facts to the AI. I tried getting Hermes to patch src/deriver/prompts.py but it still produces observations like eg "hermes likes bagels" when the fact is about me. The observation extractor seems to not distinguish speaker roles reliably. Has anyone solved this? Is there a deriver prompt tweak that forces proper speaker disambiguation?
The deriver is logging things like message timestamps, language use patterns, and transient states ("is hungry", "is relaxed") as persistent facts. I've tried adjusting the deriver prompt to be more selective, but it keeps generating noise. What thresholds or prompt instructions do you use to keep only signal?
I'm getting the same fact extracted multiple times across sessions with slight rewording. My observation DB went from a few hundred to 2600+ entries before hermes noticed it and manually cleaned it down to ~700. Is there a dedup or consolidation strategy that works well? I'm wondering if I should be post-processing with a curator or if the deriver itself can be configured to check for existing entries before extracting.
Since Honcho already stores persistent memory and observations, is Obsidian still worth integrating? Or does Honcho make a separate note-taking vault redundant for most use cases?
Setup: Local Honcho instance, hybrid mode, Qwen3.6 models (vLLM + llama.cpp), PostgreSQL backend
Hermes agent chat model & honcho dialectic chat model: Qwen3.6 27b NVFP4 MTP
Honcho chat model for everything else (deriver, etc): qwen3.6 35b a3b q4
Honcho embedding model: Qwen3 embedding 0.6b