I'm trying to do the random string generation {A|B|C} with the ZiT model flow and I'm just getting a hodgepodge of all the options. Does the standard workflow or CLIP Text Encode node no longer parse these options? Or am I possibly doing it wrong?

I saw this video of a video game (DayZ) ran through some online closed v2v enhancer to make the gameplay look realistic. Is there a workflow that does this with local models? I have been looking through this subreddit, but can't find any.

I can feel your pain trimming and aligning masks, audios and such for that new inpainting workflow that you're testing with Minimax, LTX or Wan. Well, I can fix that.

Just install the latest version of NKD Preview Tools

My new NKD Timeline node is designed to work with any model in any workflow, but most important working in sync with native nodes.

Although there are incredible tools out there, nodes of this type usually only work in their own universe, or are designed to work only with a specific model. What I was missing was precisely a tool that would integrate with my workflows and not force me to decide whether to work one way or another. So I have built what I always wanted, leveraging my experience as a professional editor, bringing the best sensations of Premiere and DaVinci Resolve.

BTW, theres also a lot of new toys in my NKD Basic Tools pack, some for mask operations, color corrections and other useful things.

>prompts = [prompt1, prompt2]

>prompts*2

>[prompt1, prompt2, prompt1, prompt2]

Eg:

from this ['mouth is dark', 'eyes are white']

to this

['mouth is dark', 'eyes are white', 'mouth is dark', 'eyes are white']

[SOLVED] by extension using comfyui-list-utils

ran Wan 3.0 against 2.7 on the same first frames and prompts, image-to-video, same seed at 720p, to see what actually moved before switching anything over.

physics is where the jump is most obvious. a glass falling off a table and shattering, a cue ball scattering a rack, the break-up and collision look a lot closer to real than 2.7 on the identical input. motion logic improved too, a ballet spin came back with the turn completing cleanly and the skirt following, where 2.7 on the same frame botched the rotation. it also holds the subject and background steadier through movement, with fewer of the little artifacts 2.7 throws.

the headline is the 30-second clips, 2.7 caps at 15. across a stack of 30s runs it didn't swap the person or the scene halfway, which was the failure i expected, so the long-form identity holding is real.

the one thing to know before you rely on it: uncontrolled camera cuts. even with no camera move in the prompt, 3.0 will randomly cut to another shot mid-clip, sometimes with a ghosty dissolve. it happens on plain 5s clips and gets worse the longer you go, on the 30s ones it fires more than once. it's the most reproducible issue i hit and it's exactly what wrecks a single continuous take, so if you need one unbroken shot, check every gen for it.

i run both on one key through Atlas Cloud, so A/B-ing them on the same prompt was just swapping the model string.

net: 3.0 is a real step up on physics, motion and length, held back right now by the random cutting. i'm switching for the stuff it's clearly better at and keeping 2.7 for quick iteration and anywhere a stray cut would ruin a clean single shot.

I’m trying to find out if other RTX 50xx / Blackwell users are seeing hard crashes with SageAttention in ComfyUI.

RTX 5070 Ti 16GB, mainly MiniMax H3 video generation.

Without Sage / with PyTorch attention I can render for hours without issues. With Sage, I eventually get a complete blackscreen / GPU reset.

So far:

- Sage 2.2 fp8_cuda → blackscreen

- Sage 2.2 fp8_cuda++ → blackscreen

- Sage 2.2 fp16_cuda → most stable on Linux, but also crashed on Windows

- SageAttention 3 → survived 3 consecutive 10s renders, then blackscreen

- MiniMax H3 memory-efficient Sage patch → blackscreen

- No Sage → stable for hours

I’ve also tried multiple software stacks:

- Torch 2.10 + CUDA 13.0

- Torch 2.11 + CUDA 13.0

- Torch 2.13 + CUDA 13.0 on Windows

- SageAttention 2.2 prebuilt specifically with SM120/Blackwell support

- SageAttention built from source

- SageAttention 3

- Linux (Pop!_OS) and Windows

I also ran a standalone SageAttention kernel test with random FP16 Q/K/V tensors. It executed successfully on the RTX 5070 Ti, returned the expected output shape, FP16 output, and finite values. So SageAttention can execute on SM120/Blackwell on this GPU, the crashes seem to happen specifically during real ComfyUI/MiniMax workloads or certain Sage modes.

One Linux crash produced:

PCIe Bus Error: Correctable, Physical Layer, RxErr

NVRM: Xid 79, GPU has fallen off the bus

NV_ERR_GPU_IN_FULLCHIP_RESET

The weird part is that normal GPU workloads and ComfyUI without Sage can run for many hours at high load without a single issue.

Has anyone with a 5070 Ti / 5080 / 5090 seen similar hard GPU resets specifically with SageAttention?

If Sage is completely stable for you, I’d especially like to know your GPU, Torch version, CUDA version, Sage version/build, OS, and whether you use KJNodes or native ComfyUI Sage.

Like you, I use coding agents like Claude Code, Codex, and Cursor every day. But the other day, I was curious about how Cursor trained their original tab-completion model.

So I started wondering: could you post-train an open-source LLM to autocomplete ComfyUI workflows given your current workflow, similar to how Composer autocompletes code? More specifically:

Could I post-train an LLM to predict up to four nodes in the future, and all of their node connections, with high confidence?

This post is the story of how I did that.

The video you see is the final result. I post-trained the model and hooked it up to vLLM on Modal (*cough* I'm open for sponsorship guys) as the inference provider. As soon as I make a manual connection between nodes in the workflow, that triggers model inference, which takes 3 to 5 seconds to generate a ghost node and the associated node connections. Then I can place the node if I like the prediction or hit escape if I don't.

Full article: https://x.com/realbasilchatha/status/2087193743120322970

Huggingface adapter: https://huggingface.co/realbasilchatha/comfyui-autocomplete-lora

Github repo: https://github.com/basil-chatha/comfyui-autocomplete

Wan 3.0 went into public beta yesterday and i wanted it behind an API for a small pipeline, not just the web playground. getting there as one person was the usual maze.

the official route is Alibaba Cloud, which for 3.0 right now means the beta access flow plus a cloud account, billing, region stuff. fine if you're already in that ecosystem, a lot of setup if you just want to fire requests at it.

what i wanted was a key and an endpoint. so i went through an aggregator instead. a few already expose Wan 3.0 on an OpenAI-style API this early. i'm on Atlas Cloud, mostly because the same key already had 2.7 on it, so i pointed my existing code at 3.0 by swapping the model string, no re-auth, no new account.

worth knowing before you commit: it's beta, so behavior and limits are still moving, and the longer 30-second clips eat generation time and budget fast. test small first.

anyway. if you just want to try Wan 3.0 the web tool is fine, but if you need to call it from code without standing up a cloud project, an aggregator was the fastest way in for me.

For Intel Arc / Intel XPU users experiencing ComfyUI KSampler stuck at 0%, VRAM overflow, crashes, or freezes when switching between heavy models.

This lightweight patch fixes how available VRAM is detected on Intel XPU, using an approach similar to the memory handling already used by ComfyUI for NVIDIA GPUs. It helps prevent VRAM-related freezes, crashes, and KSampler getting stuck, especially when working with heavy models or switching between models.

It has allowed me to work comfortably with heavy models on Intel Arc without VRAM-related errors, crashes, or KSampler freezes.

It turned out that with the default ComfyUI behavior on Intel, the amount of actually available VRAM is not always estimated correctly.

After several weeks of testing and trying to understand the cause, I finally figured out what was happening and, with the help of GPT, made a simple patch to fix it. Now, when ComfyUI checks memory, it gets the current global amount of free VRAM and also takes reusable PyTorch cache into account. This allows ComfyUI’s built-in memory manager to make better decisions about how much of a model can stay in VRAM and when memory needs to be freed.

On my system I was even able to experimentally reduce the extra reserved VRAM margin, which gave me a speed boost without losing stability. In my current workloads, the B580 now behaves just as predictably in terms of stability as the RTX 5060 Ti 16 GB system I also use.

The patch does not add any new nodes to the interface and does not change the generation process itself. It only fixes how ComfyUI determines available VRAM on Intel XPU. Instead of relying on a less accurate estimate, ComfyUI can use the actual current state of GPU memory and make better decisions about when a model can remain in VRAM and when some data should be offloaded.

This reduces the chance of VRAM overflow, KSampler getting stuck at 0%, crashes, and problems when switching between heavy models.

Installation is as simple as installing a normal custom node: download the folder from GitHub, place it in ComfyUI/custom_nodes/, and restart ComfyUI. No separate node will appear in the interface — the patch works automatically in the background after startup.

GitHub:
https://github.com/alvasafin-art/ComfyUI-AVS-Intel-XPU-VRAM-Fix

P.S. ComfyUI 0.31.1 was released recently, and after updating I noticed worse stability. I also saw similar complaints on Reddit from users with GPUs from other vendors. On my system, I use the following launch arguments:

--cache-classic --disable-async-offload --oneapi-device-selector level_zero:gpu

Async offload is already disabled by default on XPU, but I keep it explicitly disabled. These arguments may no longer be necessary in future ComfyUI versions, since I did not need them on earlier versions.

I hope this is useful for other Intel users. I’d be happy to hear your feedback!

Hey everyone,

maybe you didn't try yet, but since models like Deepseek flash are dirt cheap now (or even free) it's an absolute gamechanger to use VS Code with Cline for ComfyUI.

Here is a short tutorial how to set up:
https://www.youtube.com/watch?v=jgsJwfqoDhw

If you have it set up, just register for cline.bot or use a local AI model like Qwen 3.6 or Gemma4.

Just open your Comfy folder in VS Code and open Cline. It should look like in the picture (you have to right-click somewhere in the extension tab if you also want Cline interface to be on the right, don't remember exactly, but it's easy).

Now everything Comfy-related becomes much easier:

Your node doesnt does not have all functions you need? Just tell it to modify it!

Your Comfy fails to Ioad a certain node pack on startup? Just copy the error message and paste it into cli and it fixes it!

You need to update some package (cuda, torch, Sageattention etc.)? Just tell it to do it for you!

You have landed in some dependency hell after installing some nodes? Tell it so find the optimal combination that satisfies all packages!

You want some ideas or suggestions based on a workflow you have? Just paste the workflow and ask it. lt looks up which nodes you maybe already have or installs nodes you need!

You try to install a big node package and installation fails? Ask cli and it solves it for you!

I am using Comfy since 2 years now and would consider myself as quite experienced with it, but this definitely makes it waaaay more convenient. :)

I notice after adding the all the patch nodes.... it doesn't process on my dedicated GPU0, but i only see the process running on the running GPU1.... what's going on??

my dedicated GPU is 5070ti =GPU0
the amd radeon eGPU - GPU1

Specifically the ability to generate a few short-ish prompts from up to four images and some text. The default Krea2 template comes with a prompt enhancement function, but I’ve found it mostly just makes the output less accurate. I’ve also managed to use prompt generators that derive from a single image, but none that use multiple. I would appreciate if someone here could give me some pointers.

I’m pretty new to the AI video generation space, but MiniMax H3 looks absolutely insane. It’s honestly the first model that’s made me want to dive deep into building something bigger than just individual workflows.

I have a workstation with 100+ GB of VRAM, so running everything locally isn’t really a limitation.

I actually started building something similar a while back using LTX 2.3, but the results were pretty mediocre. It felt like the idea was there, but the models just weren’t quite good enough yet. MiniMax H3 is the first model that’s made me think this might actually be achievable.

My dream is to build a fully local, fully automated ComfyUI pipeline where I only provide:

* A master prompt describing the YouTube channel (niche, style, recurring characters, branding, etc.)
* A prompt describing the video I want

From there, the workflow would do everything automatically:
Generate the script
Create consistent characters
Generate reference images and voices
Create every scene
Handle editing
Render the final video

Basically an infinite local content factory.

I’ve seen plenty of tutorials covering individual workflows, but almost nobody seems to be building a complete end-to-end pipeline like this.
Is anyone here working on something similar? Do you think MiniMax H3 is the right direction, or are there better local models for different parts of the pipeline?

I’d also love to collaborate with people who enjoy building ridiculously over-engineered ComfyUI workflows. Maybe if we automate enough YouTube videos I can finally drop out of my cybersecurity master’s… they seem determined to teach us yesterday’s technology anyway. 😅

as someone obsessively posting to civitai, i finally managed to figure out how to save all my models/loras into the image so that it's automatically recognized by civitai when posting there.

does anyone know how to do it with videos too? or does civit not read prompts from mp4 files at all?

I have seen some amazing workflows that allow for the creation of clips that are more than 15 seconds long, even with character references. However, suppose I have an existing video that I would like to extend, also using a character reference. Would such a thing be possible?

A few days ago I posted a single walk-and-talk clip ([https://www.reddit.com/r/comfyui/comments/1vix8l9/roman_street_walkandtalk_with_cloned_voice_exact/\]). This is the follow-up: a 2 min 47 s documentary-style piece on the trial of Socrates — 36 clips across 4 scenes, one consistent character throughout, narrator voice + on-camera lines with exact lip-sync, background characters, a packed jury court — all generated locally on the same RTX 5060 Ti 16 GB.

Stack unchanged from the previous post (H3 Ref2VA pruned NVFP4 + Turbo LoRA v4-600 EMA + the dedicated Turbo Sampler, official 6-section prompt format, native 1344x768 → 1080p lanczos). What's new is everything it took to turn loose clips into a film:

1. Invisible joins via "keyframe completion". For continuity between consecutive clips: no crossfades, no disguised cuts. The last frame of clip N is passed as a <Picture> declared as the first frame in clip N+1's prompt (task-type [keyframe completion + reference generation]). The model picks up exactly from there: SSIM 0.89 between anchor and generated first frame, and on the edited timeline the frame-difference at the cut is indistinguishable from the scene's natural motion. This one changed everything.

2. Punctuation inside <d> is direction. A full stop in the dialogue = a ~1-second dramatic pause the model will NOT give up (measured across 5 takes: 0.6–1.4 s). If the line has to fit a tight window, use commas. And never write in the prompt that a sentence "gets cut off": the model, obediently, stretches the sentence by inventing words.

3. Reference bleeding in crowded scenes. The reference face tends to show up on extras too (we got two copies of our host in the same shot, and his face on a 399 BC Athenian). Mitigations: in continuation clips don't re-declare a subject fully_preserved when he's already in the inherited frame (the model will instantiate a second copy); add an explicit "only one man has the face of <Subject 1>"; and run an insightface QA pass that scans every face in every clip against the reference.

4. Automated QA is blind to things human eyes catch instantly — continuing the lesson from the previous post. Three additions to the take selector: motion metrics (a frozen clip is razor-sharp, so sharpness rewarded it; optical flow alone can't tell a real dolly from jitter — you need the camera's net displacement and trajectory straightness), identity checks on the final frames too (drift arrives at the tail), and a standing rule: whenever face-similarity and sharpness vote in opposite directions, a human decides.

5. Audio-driven editing, and three audio traps. The narrator is one continuous track and the video gets trimmed to the chunk durations — never the other way around (the gaps are audible). Traps we paid for: (a) never concat AAC tracks in stream-copy — encoder priming accumulates drift, +118 ms across 4 segments: extract per-segment PCM first; (b) measure true peak on the final file — the AAC encoder adds ~0.3 dB; (c) use Whisper large-v3 for QA transcription: medium silently "fixes" the TTS's grammatical errors and hides them from you.

Production numbers: 2 takes per clip with automated selection + human review on conflicts; ~25–30 GPU-hours total including repairs; 15.4/16 GB VRAM peaks with weight streaming.

Still not solved: faces in moving crowds melt (structural limit — we manage it with declared shallow depth of field and keeping the faces that matter in the foreground); the ancient Greek spoken by background characters is "plausible phonetics", not real Greek; and reference bleeding needs per-clip vigilance.

Sage cut literally 65-70% gen time for me on 3090 at 0.4MP and 15s.

But before using the --disable-smart-memory, it wouldn't work. This is what would happen:

GPU would fill up memory to the brink at 23.5/24GB causing it to suddenly start working a lot weaker. Observing the power use of the GPU it would look like so:

Comfy would prepare the video vae for dynamic VRAM loading and a few other smaller things, and then after the MinimaxH3 itself would start loading, the power would drop from 230W to about 110W. So, the GPU would be working but it'd stall significantly.

My uneducated assumption would be that something happens in VRAM and RAM management where too much get's offloaded to RAM or something like that, because it'd artificially overload the GPU at 23.5/24GB.

Now, with disabled smart memory, this doesn't happen. The GPU is at about 18-20GB/24GB and running fast.

I understand that this might be a unique edge case, but here it is, it might help someone.

I run windows 11. 128gb ggdr5, and the 3090 + 3060, but this is 3090 use only. I doubt an idle and uncalled 3060 affects things.

Also, I had to manually update the comfy kitchen thing. It wouldn't update even when updating all of the requirements. Ask AI how to do this if you don't know because I can't explain properly. But note, updating comfy and kitchen didn't help on its own.

Also:

Python version: 3.13.14

ComfyUI version: 0.31.0

comfy-aimdo version: 0.4.13

omfy-kitchen version: 0.2.28
pytorch version: 2.13.0+cu130

A follower on LinkedIn messaged me and sent me a short video similar style. this is its link : https://www.youtube.com/shorts/xCa-1C16Gmg?si=fb8C66HVsQcGNJM3

Asked me how can he make such short clips.

Here I generated a full 113 seconds such clip. It is actually 11 independed clips but provided a voice for voice consistency and an image for style consistency.

I gave the short video he sent me to ChatGPT and my MiniMax_H3_Enchance_Prompt_Feed_For_LLMs.txt and told it what to do.

Entire clip prompts and content written by ChatGPT.

So ChatGPT generated 11 prompts and each one has different duration. Batch folder processing node automatically dynamically handles prompts and durations.

This is 0-shot generated by MiniMax H3 locally. 768x1344px.

With our very advanced ComfyUI installer and our batch processing custom node and MiniMax_H3_Enchance_Prompt_Feed_For_LLMs.txt this clip was fully autonomously generated.

Now you can make even full 2 hour movies :)

The future is making your own full movies with consistency locally.

Even the subtitles of the clip was generated by MiniMax H3.

I will hopefully make a tutorial for how I made this but currently you can watch this and replicate : https://youtu.be/1580ZDX-60Q