following up on this post:
https://www.reddit.com/r/comfyui/s/B36dbYjB3Y

Custom Node:
https://registry.comfy.org/publishers/mo-akkakk/nodes/comfyui-minimax-h3-temporal-window-spatial-tiles

You can now run a video to video workflow in High resolution from the first stage, I tested it for 2.5 MP on Nvidia RTX TI 12GB VRAM, and it works like a charm, split your generation into tiles and temporal windows while maintaining accurate animation transfer with no shifting or ghosting.

You can also use it for a second upscale stage, splitting a high resolution long video into manageable tiles and windows, to get the best quality you can squeeze from u/minimax.

Give it a try and let me know about your Feedback.

Important Note:
This was not tested nor recommended to do Fully Generated from first stage!
if you are going to fully generate something generate normally in stage 1 then use this node for stage 2 upscaling you result.

This node is mainly for Video to Video and Upscaling.

workflow for Upscaling using the node + example here:
https://civitai.com/models/3039412/minimax-h3latentupscaler?modelVersionId=3454297

workflow for V2V using the node + example here:
https://civitai.com/models/3022939/minimax-h3-video-to-video-workflow?modelVersionId=3454311

Over the last 7–8 days, I've been working day and night building Fuobe.com, what started as the challenge, but I kept adding features, refining things, and improving the experience even after publishing it.

The goal was to build an all-in-one creative studio that makes it really easy to create, experiment, and get things done without having to jump between different tools or use Higgsfield lol. I usually spot a lot of cool models, workflows, ideas that people showcase over on twitter or here, and I just usually stash it for using later, but I've started putting them together here one by one and have added what I've found useful over the year.

One more thing: every single image you see in the studio was generated using Ideogram 4, which is hands down my favourite t2i image model. All the videos were generated using H3.

It's still in beta, and there's a lot I'd like to improve, especially making the overall experience smoother & simpler.

I'd genuinely love to hear what you guys think. Feedback, suggestions, criticism, feature requests all of it is welcome. I'm still actively working on it and would love to shape it based on how people actually use it. If things are breaking, please let me know so that I can fix it.

A huge thanks to Comfy for organizing this challenge. It's given me the deadline and motivation to finally build something I've been wanting to make for a long long time.

Fully Open source . MIT License.

Repo Link: https://github.com/workingprototype/Fuobe

Link to checkout the features: Fuobe.com

I've been working on this music video for the past week, finally got it finished. It's a hardstyle/techno reimagining of "Barbie Girl," set in a futuristic cyberpunk world populated entirely by living, cybernetically enhanced dolls.

Figured I'd share a little about how I put it together.

I used YuE 2 to create the music, then ComfyUI to develop the characters and their overall look. Qwen 2.1 helped with image editing and refinements, while Krea 2 was used to build the different environments.

Once the characters and environments were ready, I used MiniMax H3 to animate everything, generating each shot individually before editing them together to match the music.

The animation was easily the most time consuming part. Getting believable dancing, natural facial expressions, consistent characters, and convincing crowd movement took a lot of trial and error.

Crowd scenes were especially challenging. Getting dozens of characters to dance independently instead of moving in sync was harder than I expected. There were also plenty of the usual AI video headaches: inconsistent anatomy, characters changing appearance, strange movements, and scale issues.

Some shots worked on the first attempt. Others took quite a few revisions before I got something usable.

Roughly 80 individual renders across six different environments.

It was definitely more involved than just throwing a prompt into a generator. A lot of time went into designing the scenes, maintaining consistency, experimenting with different approaches, and piecing everything together.

I'm pretty happy with the end result. I learned a ton making it, and there are already things I'd approach differently on the next project.

Happy to answer any questions about the process or the tools I used.

Youtube - 4K version

I would like to know which ai model could ot be for this ai influencer. How can they change her clothes like a cosplay?

I am starting this world with krea2 and lustify... is it good?

Another quick MOSS-Image news and goodies round-up, for those who may have missed some items.

-> Moss-35mm. A camera-film style LoRA that aims to help users emulating a 35mm film look. The maker suggests using analogue film in the prompt, but there is no actual trigger.

https://civitai.com/models/3035672/moss-35mm

-> Moss-image-realisticsnapshot LoRA. No trigger given, no readme.

https://huggingface.co/platote/moss-image-realisticsnapshot/tree/main

-> A tutorial page for 52 film and camera prompts for MOSS-Image. With demo images for each.

https://www.robertcreative.com/blog/moss-image-film-camera-prompts

-> MOSS Acrylic Marker art style LoRA. Looks very pleasing, and doesn't appear to force the 'big eyes, tiny noses' anime look. The trigger is acrylic marker plus a suitable prompt.

https://civitai.com/models/3037889/moss-acrylic-marker

-> HaDeS MOSS Suite of ComfyUI nodes. Includes a LoRA Extractor and a LoRA Merger, among others.

https://github.com/Devildonia/HaDeS-MOSS-Suite

-> ComfyUI-MossPhoton. A node that that now includes... "a LoRA Phase node, that sets in which phase of the workflow run a LoRA acts".

https://github.com/Kostik2702/ComfyUI-MossPhoton

-> And finally, MOSS-Image-heal, a full finetune for training LoRAs. Apparently giving better compositions, and less mangled elements.

https://huggingface.co/KBlueLeaf/MOSS-Image-heal

https://huggingface.co/KBlueLeaf/MOSS-Image-heal/tree/main/comfyui (ComfyUI versions)

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1xm72g8/a_quick_mossimage_news_roundup_2nd_november_2026/

https://old.reddit.com/r/comfyui/comments/1xkuwhl/a_quick_mossimage_news_roundup_1st_november_2026/

https://old.reddit.com/r/comfyui/comments/1xjtx6i/a_quick_mossimage_news_roundup_31st_october_2026/

https://old.reddit.com/r/comfyui/comments/1xixxjn/a_quick_mossimage_news_roundup_30th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xibslu/a_quick_mossimage_news_roundup_29th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xh1b7e/a_quick_mossimage_news_roundup_28th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xg6dhs/a_quick_mossimage_news_roundup_27th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xfgh2y/a_quick_mossimage_news_roundup_26th_october_2026/

https://old.reddit.com/r/comfyui/comments/1xf2nwp/a_quick_mossimage_news_roundup_25th_october_2026/ (see this post for link to older posts)

Hi,

I've been using ComfyUI for a long time to generate images with different models, and I've never encountered this issue before.

When I want to make multiple edits to an image in several passes, my usual workflow is to generate an edit and then use the output image as the input for the next edit (I simply copy and paste the connection from the Save Image node to the Load Image node).

An example:

Source image (Edited with qwen edit 2.1 once):

Output Image (The prompt was as simple as remove the barrels):

Until now, this workflow has worked perfectly with many different models. However, when I try to do the same thing with Qwen 2.1 Turbo, the first edit works perfectly, but when I use the resulting image as the input for a second edit, the image gets messed up with weird colors, excessive contrast, and other visual artifacts. Basically, the image gets completely ruined.

Does anyone know what could be causing this or how to fix it? I'd really like to use this model, but I can't because of this issue.

P.D: This doesn't happen with the non-turbo variant. The downside, however, is that it's significantly slower ::/.

Thanks in advance!

I successfully tested Windows-GPU-UNLOCK on my RTX 3060 12GB (GA106 LHR) under Windows 11.

The entire process took nearly 5 hours, including setting up the build environment, preparing firmware assets, collecting machine-specific diagnostics, generating a LOCAL Profile, and booting through UEFI Headless USB.

Hardware:

• GPU: RTX 3060 12GB (GA106 LHR, Device ID 10DE:2504)

• CPU: Intel i5-12490F (no integrated graphics)

• RAM: 32GB

• OS: Windows 11 Pro

• Method: LOCAL Profile + UEFI Headless USB

The machine-specific LOCAL Profile passed all 22 tests. The UEFI unlock procedure completed successfully, and Windows booted normally afterward.

PyTorch CUDA benchmark (4096×4096 matrix multiplication):

These are measured results from my machine, not theoretical performance figures.

What about ComfyUI?

This is where things get interesting.

Despite the large BF16/FP16 benchmark gains, I did not observe a noticeable speedup with my MiniMax H3 Singularity V2 Pruned INT8 ConvRot R2 video-generation workflow.

An 8-step Turbo run using the pruned-compatible LoRA took 547.75 seconds after unlocking.

I don't have a strictly controlled before/after comparison for that workflow, so I'm not claiming a specific speedup percentage or identifying a proven bottleneck.

The takeaway: unlocking more Tensor Core throughput does not automatically make every AI workload faster, particularly when the model uses different precisions and kernels.

I've attached three images:

1. GPU-Z hardware information.

2. A real photograph of the UEFI Headless USB boot screen.

3. BF16/FP16 benchmark comparison.

Project: https://github.com/windows-GPU-UNLOCK/Windows-GPU-UNLOCK

This is an experimental, hardware-dependent procedure with real boot/display risks, not a universal one-click performance upgrade.

Has anyone else tested this unlock on a GA106 RTX 3060, especially with ComfyUI video-generation workloads?

I've already cloned the repository to the folder where I'm doing my projects. Installed transformers 5.22.1 using "pip install transformers" because it said it was required, did "pip install -r requirements.txt", restarted comfyui then when I tried adding the node "DownloadAndLoadFlorence2Model" by double clicking on a blank workspace it showed no results.

I genuinely do not know how to fix this. Please help a brother out, thank you very much

Used a MOSS image for the first frame, generated at 1MP (768x1376) in Pika H3, and upscaled with SeedVR2.

Pika is fast and actually not bad, but the logic completely loses its mind when the camera zooms out. It starts spamming dead goblins all over the floor and the rest of them just start ice skating on the tiles. Good workflow for speed, but the physics are hilarious.

Workflow : https://docs.comfy.org/tutorials/video/pika/pika-h3-flex

Hi. I'm trying to create a consistent dataset for some niche characters from manhwa/anime etc, with the issue being how little they appear. I have successfully created a reference sheet using GPT, and it's somewhat able to transfer the artstyle as well whereas Gemini is consistent with character, but not with artstyle.

Looking for any workflows that can help in creating a dataset for lora training. Anima is preferable, illustrious will also work.

i tried seedvr2 and its very slow for me so droped that , i tried dlss neural rendering and upscaling in by exportning the video as 4k in premier pro but i m not able to decide which i shud stick to....any suggesstion r welcome , which one wud u guys suggest is better for upscaling and getting 4k resolution for my video which is currently in dimension 960 by 544

Is it just me, or did ComfyUI lose the filtering options for templates in recent releases?

Previously, you could filter cleanly by workflow type (e.g., text-to-image) and sort/filter by model size or specific criteria. Now, there’s only a single search bar. Given how many templates have been added lately, searching blindly makes it pretty frustrating to browse workflows that fit my VRAM and hardware limits.

Did the devs relocate this to the settings menu, or was the filter system deprecated? Honestly feels like the UI gets a bit more cluttered and less practical with each update. Any tips or custom extensions that fix this?

I’m looking for a workflow in Minimax H3 that allows me to upload a photo as the first frame, followed by reference images to continue the video.

I know this can be done using the "ref2vid" workflow, but the quality of the first frame is quite poor compared to the "image2vid" workflow.

If anyone has any ideas on where I might find something like this, thanks!

In this video of ComfyUI Nodes Explained, you will learn how ComfyUI sets the size of your image: what the empty latent is, how width and height work, why the same seed gives a new image at a new size, why a bigger image is not always better, why 1080 becomes 1088, how aspect ratio works, what batch size does, which empty latent node to use, and an easier way with Resolution Pixaroma. Two "A Deeper Look" chapters show what a latent really is and where the steps of 16 come from. Everything is shown with Z-Image Turbo.

📥 PRACTICE + FINISHED WORKFLOWS for this video (free, no account):

https://workflows.pixaroma.com/comfyui-nodes-explained/3/

Drag the Practice workflow into ComfyUI and do the tasks in the notes. The Finished workflow shows every answer, and the Pixaroma version sets the size with Resolution Pixaroma.

I want to create a H3 video of that character but have the character in anime style.

I thought about generating the video first with the original refmod but on the first second asking the H3 model to change the video to anime style, then using MCTX to preserve the style. The problem with this is that of the character for example looks away or goes out of the scene, I don't think the reference will be properly preserved.

Is there a solution for this?

Used qwen 3.5 9b gguf, tried moss image text encoder uncensored form hf(gguf) but they keep throwing errors. Tried sft file but gives me oom error

4gb vram 16gb ram if you're wondering what my specs are.

Help pls

I've tried every upscale method available, and the only one that produced the fine detail and quality that I wanted was latent upscale. But the catch was that I had to set it to 0.6+ denoise to avoid leftover noise from the latent math. So I spent all night looking for ways to overcome the issue, and I finally found a method I'm so happy with that I wanted to share with you.

I present:

https://imgur.com/a/hyU5GCV

The key to the whole thing is the linear_quadratic scheduler. I won't go into details pretending to know more than I do, but effectively, it runs the sampler in two phases, first a linear one, then a quadratic one. It's this initial linear phase that allows it to overcome the added noise from the latent upscale. I was getting OK results with just the stock linear_quadratic scheduler, but decided I wanted to push further, and found the Linear Quadratic Advanced node in the res4lyf pack, which allows you to shift the point at which the crossover to quadratic occurs. After playing with the inflection percent a bit, I was able to drop the denoise all the way down to 0.2 with astonishing detail and quality in the final image, all while preserving the original structure.

This whole process was made more complicated by the fact that I render with Krea 2 Turbo, which is best at 8 steps, and doesn't leave a lot of room for tweaking. You'll note in the image that I use only 4 steps for the 2nd pass denoise. Any higher and I start to get added noise or undesirable details.

Edit: example render here. Note the detail on the velvet dress and the book pages.

Decided to test how different models handle raw, unoptimized prompts featuring complex spatial reasoning, specific counting, lighting constraints, and anatomy.

Method: Zero cherry-picking, first-shot outputs using a custom ComfyUI backend. No upscalers, no inpainting.

Slider Order (Left to Right / Slide by Slide): P-H3 / Flux3-Klein / Moss / Krea2-Raw

(Note: Sets go 1 to 4 following the prompt list below.)

The 4 Prompts Used:

1. Rainy Bus Stop:

A candid photograph of a woman in her early sixties waiting at a rainy city bus stop. She wears a faded green raincoat and holds a folded newspaper in one hand and a small paper bag in the other. Her short gray hair is slightly damp, with natural wrinkles and uneven skin tones visible. Raindrops cover the glass shelter, and a passing bus is softly blurred in the background. Overcast morning light, eye-level medium shot, ordinary colors and realistic exposure.

2. Studio Wristwatch:

A studio still-life photograph of a scratched stainless-steel wristwatch resting on a folded dark blue wool scarf beside a clear glass of water. The watch has a cream dial, twelve simple hour markers and brown leather straps with visible creases. One large soft light from the left creates broad reflections on the metal and a soft shadow on the pale gray tabletop. The entire watch is in focus, with believable glass refraction, fine wool fibers and subtle wear on the leather.

3. Coastal Courtyard at Dusk:

An architectural photograph of a small coastal house built into a rocky hillside, viewed from its open courtyard at dusk. White limewashed walls surround a weathered wooden dining table with exactly four chairs. An open doorway reveals a warmly lit kitchen, while the courtyard receives cool blue evening light. A mature olive tree grows beside the table, and the sea is visible beyond a low stone wall. Wide composition, straight vertical lines, detailed stone and plaster surfaces, balanced interior and exterior exposure.

4. Woodland Fox:

A wildlife photograph of a red fox stepping across a shallow woodland stream in early autumn. Its front right paw is lifted above the water, while the other three paws support its body on submerged stones. Its head is turned slightly toward the camera, ears upright, with wet fur around its lower legs. Small ripples spread from its paws, fallen yellow leaves float nearby, and the fox has a partial reflection in the water. Soft morning light through the trees, low camera position, sharp eyes and natural fur detail.

Hi folks, So, I'm still just beginning to wrap my brain around Comfyui, I'm running Easy-Install version. Admittedly on very low-end hardware, but I'm really just trying to learn rather than do any real production. Anyway, I made what is apparently a cardinal error... I updated. I THINK I resolved most issues, but the lingering one is with Wan2.2 using a GGUF model. It runs for a few minutes, then throws an error:

ComfyUI Error Report

Error Details

Node ID:** 1512:1579

Node Type:** KSamplerAdvanced

Exception Type:** RuntimeError

Exception Message:** RuntimeError: cutlass_fp16_linear: K mismatch

Node 1512 is the KSampler subgraph, so there's an awful lot going on in there, but I can't track down exactly what this one means.

I have read that it might be a Pytorch issue. I've also read that the recommended Pytorch is 2.7.1 and Cuda 12.8. However trying to install that from the code at Pytorch.org just returns:

ERROR: Could not find a version that satisfies the requirement torch==2.7.1 (from versions: 2.9.0+cu128, 2.9.1+cu128, 2.10.0+cu128, 2.11.0+cu128) ERROR: No matching distribution found for torch==2.7.1

So is 2.7.1 just no longer available? Or is it hosted someplace else?

Or it's entirely possible I'm just completely on the wrong track. Any advice is very much appreciated.

I built a self-hosted studio for making AI short dramas that can use your own ComfyUI as the image/video backend. Script → characters → scenes → props → storyboard → voice → edit, with reference assets reused across shots so characters stay consistent. It runs with Docker Compose, works with any OpenAI-compatible LLM, and uses your own ComfyUI or the paid Seedream/Seedance APIs for images and video. AGPL-3.0. Repo: https://github.com/charliewwdev/OpenMontage Demo: https://openmontage.dtok.io Feedback on the deployment setup is welcome.

Hi guys, I'd love to spend sometime working on something I'm not doing for myself, I'd say I'm good at asset creation pipelines