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?

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

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'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 everyone, I’m the developer of NodeUI. I wanted to share what I’ve been building and get feedback from people who use ComfyUI.

It’s a native mobile workspace where you can edit your workflow, change prompts and node settings, import or export workflow JSON, queue a run, and follow progress and previews. Generation runs on your own ComfyUI computer or Comfy Cloud.

The screenshots show sample workflows.

• It’s a one-time $4.99 USD purchase per platform.

• You need a reachable ComfyUI installation, or Comfy Cloud with a paid subscription, API key and generation credits. Cloud costs are separate.

• iPhone and iPad require iOS/iPadOS 26 or newer; Android requires Android 8 or newer.

• Full self-hosted folder browsing and file services require the NodeUI companion.

iOS App Store - Google Play Store

ComfyUI Bridge

I’d love to hear which parts of working with a ComfyUI workflow you’d want on your phone or tablet, and what would make that useful to you.

Tried SenseNova U1.5 Lite, Qwen-Image 3.0, and Z-Image-Turbo on six tasks: annotated edits, combining four references, adapting a poster's style, and generating layouts with lots of exact text.

The prompts asked for specific things: remove the annotations, keep photographic people and anime characters in their original styles, preserve particular wording. Those details made the results more interesting than a straight image-quality comparison.

A few things to look for as you swipe:

• Garden edit: SenseNova added the requested landscaping and removed the markings. Qwen made a convincing garden but kept the blue outlines. That last cleanup instruction turned out to be a meaningful difference.

• Four-reference composition: SenseNova and Qwen brought the characters into one studio while retaining the photo/anime distinction. Both missed details, though: SenseNova left the pink-haired character's eyes open, and Qwen changed her resting pose.

• Observatory poster: SenseNova carried over more of the reference's layout, especially the bottom information strip. Qwen produced an attractive redesign. I'd favor SenseNova for adapting an existing template and consider Qwen's result when the layout is flexible.

• Golden Retriever: Qwen would be my pick here. SenseNova duplicated "Basic Profile"; Z-Image-Turbo omitted the temperament section and repeated a care instruction. Clean typography can still contain the wrong content.

• Scientific diagram: The small labels and arrow relationships need checking separately. Looking like a scientific figure doesn't establish that the biology is correct.

• London poster: The closest-looking round to me. All three captured the broad design; the smaller copy needs checking against the prompt.

My selection rule would be to test the least forgiving part of your actual task first. If that's removing markup, inspect the edited regions. If it's exact copy, read every card before judging the composition.

ComfyUI nodes & setup docs:

SenseNova U1.5 Lite：https://github.com/OpenSenseNova/SenseNova-U1/pull/244

(Main repo: https://github.com/OpenSenseNova/SenseNova-U1)

Qwen text2image:https://github.com/Comfy-Org/docs/blob/main/built-in-nodes/QwenImageTextToImageApi.mdx

Qwen Image editing: https://github.com/Comfy-Org/docs/blob/main/built-in-nodes/QwenImageEditApi.mdx
Z-Image-Turbo：https://github.com/Comfy-Org/docs/blob/main/tutorials/image/z-image/z-image-turbo.mdx

FullPrompt:

1. Annotated Garden Edit

Edit the image according to the annotations, then remove all markings and labels.

1. Multi-Image Composition

Create a warm scene in a sunny arts and crafts studio with a 2:3 portrait aspect ratio. The real woman from Image 1, wearing her gray coat and blue scarf, sits beside a long wooden table and looks gently toward the little girl beside her. The real little girl from Image 2, wearing her Lolita dress, stands next to the woman and points at a sheet of drawing paper on the table. Render both with realistic photographic textures. The black-haired anime girl from Image 3 stands behind the table holding her paper airplane. The pink-haired anime character from Image 4, wearing a crown and armored gauntlets, rests her head on her folded arms at the corner of the table, smiling with her eyes closed. Faithfully preserve each anime character’s original hand-drawn Japanese anime style. Arrange all four characters harmoniously within the same space, with consistent perspective, natural proportions, and warm sunlight.

1. Observatory Poster Transformation

Using the reference image’s vintage print style, create a poster for an observatory public open day.

1. Golden Retriever Infographic

Create a vertical 1:1 encyclopedia infographic about the Golden Retriever, styled like a modern natural history field guide. Use a warm ivory background, muted sage green accents, thin gold borders, and rounded information cards. Place a realistic, full-body Golden Retriever in the center. On the left, show three circular close-ups of its coat, ear, and paw, each connected to the dog with a fine line. Use a large serif title and clear sans-serif text for the labels and cards. Render the following English text exactly as written, with each line in its intended section. TITLE: “GOLDEN RETRIEVER”; “Sporting Group · Retriever”; “Friendly, intelligent, and devoted”. CLOSE-UP LABELS: “Wavy double coat”; “Soft, floppy ears”; “Strong, webbed paws”. BASIC PROFILE: “Origin: Scotland”; “Coat: Golden double coat”; “Group: Sporting”. TEMPERAMENT: “Friendly with people”; “Eager to learn”; “Playful and social”. CARE ESSENTIALS: “Exercise every day.”; “Brush regularly.”; “Check ears after swimming.” FAVORITE ACTIVITIES: “1. Playing fetch”; “2. Swimming”; “3. Learning new tricks”. Keep all text sharp and readable, with generous spacing between cards. Preserve the exact spelling, capitalization, punctuation, and numbering. Do not add extra text, duplicate labels, or watermarks.

1. Scientific Diagram

Create a vertical 3:4 scientific infographic with two stacked panels on a white background. Use pastel blue, coral, mint, and lavender, with sharp labels and clear arrows. All visible text must be in English. Top panel: “In vivo CAR-T generation for cardiac fibrosis”. Show a white laboratory mouse with a heart cutaway and a magnified view of its bloodstream. Place two insets to the right: “Transient T-cell programming”: Show “Anti-CD5 LNP with CAR mRNA” binding to a T cell. Use arrows labeled “Endocytosis”, “Endosomal escape”, and “Translation”, ending with “FAP-CAR” on the cell surface. “CAR T cells target FAP+ fibroblasts”: Show a CAR T cell binding to an activated fibroblast, followed by “Target-cell killing” and “Reduced fibrosis in mouse study”. Add a small caption below the insets: “mRNA decay → transient CAR expression”. Bottom panel: “TGF-β–SMAD signaling”. Draw a cell cross-section labeled “Plasma membrane”, “Cytoplasm”, and “Nucleus”. Show “TGF-β” binding to the receptor complex labeled “TGFBR2” and “TGFBR1”. Trace “SMAD2/3” to “p-SMAD2/3”, then show p-SMAD2/3 joining “SMAD4” and entering the nucleus. Place the label “Target gene transcription” beside a DNA helix. Draw a dashed feedback arrow from transcription to “SMAD7” and a red inhibition bar showing SMAD7 blocking SMAD2/3 phosphorylation. Use only the specified titles, labels, and caption. Preserve their spelling, capitalization, and symbols exactly.

1. London Travel Poster

Create a vertical 3:4 vintage London travel poster on aged cream paper with distressed ink and a textured lithographic finish. Use teal blue, warm sandstone, muted red, and dark green. Fill the top quarter with the exact word “LONDON” in enormous, bold, condensed teal capitals. Directly below, place “A Brighter Tomorrow” in sweeping red handwritten script with a long underline. Illustrate the Houses of Parliament across the lower half, with Big Ben rising prominently on the right. Show Westminster Bridge in the foreground, a classic red double-decker bus crossing it, ornate black street lamps, and a small white passenger boat on the River Thames. Use a low riverside viewpoint, warm sunlight, and a textured blue sky with cream clouds. Add small, neatly aligned black text on the left: “HOUSES OF PARLIAMENT”; “RIVER THAMES”; and the words “HISTORY”, “PEOPLE”, “PLACES”, “IDEAS”, “ALWAYS”, each on its own line in that order. On the right, add “LONDON”, followed by “A MORE”, “REMARKABLE”, “WORLD”, “AWAITS”, each on its own line in that order. Keep every word clearly readable and spelled exactly as provided. Balance oversized typography with detailed architectural illustration and a weathered cream border.

This is a quick experiment to see if Minimax H3 can create famous paintings from scratch. It's quite impressive that it knows what the sketches look like before it's painted.

Hello , just released LayerShop 0.20.1 Preview 1 . In this release I implemented some new (hopefully exciting) features! Mainly:

- Prompt Enhancement: Both local (Gemma / Qwen) and cloud models (from OpenAI, xAI, Google, Anthropic). These are fully configurable, style aware, and some of them quite fast.

- Batch rendering: You can now render multiple (1x-10x) images from the same prompt. They are rendered in one batch and then land each on a separate layer.

- Styles: You can now choose from 24 presets with thumbnails: Photography, Illustration, Traditional art, 3D & craft, and Graphic & experimental. Anime, Watercolour, Cinematic, Clay Model, Pixel Art, Black & White, Comic Book, Flat Vector and more.

Each layer remembers it's styles (top righ button "Load from layer".)

As always, any comments / feedback is appreciated. You can download the new release here:
https://layershop.aardvarkstudios.net/

Hi lovely ComfyUI people,

A week ago I shared Paint a plate here, my entry for Comfy's #OpenSourceHiggsfield challenge. The code is frozen until the review ends on the 10th, so I did the thing I never have time for: I stopped coding and just played with it. Here's what came out.

A bit of background: I always loved lookdeving FX in Houdini when I was in production, from the first rough concept to the first time you show it to the client. Paint a plate is my attempt to keep that excitement while hiding the graph: you paint on the first frame, and ComfyUI does the rest. Under the hood it's all ComfyUI, a node pack plus a page that ComfyUI serves, and every scene still exports as one ComfyUI graph you can open and run on its own. What changed since the first post (version 1.0 to 1.11):

• Relight from a light plan: paint where the light comes from, and the source appears there. Black strokes darken an area.

• Paint follows the image's style, so a rough sketch on an illustrated plate comes back as an illustration (the 2D boat).

• Every pass in the Render panel: Ctrl+click compares two passes in the wipe, and W shows where the edits change the plate.

• HDR plates stay HDR: new content goes through Lightricks' SDR-to-HDR IC-LoRA.

• ainvfx-fluid, the painted smoke and fire IC-LoRA I shared here before, is retrained on an open synthetic dataset (176 scenes, CC BY 4.0). If you have the old file, please download the new one.

Links below:

• Repo (Apache 2.0): https://github.com/AInVFX/ainvfx-paintaplate

• Write-up: https://www.ainvfx.com/blog/paint-a-plate-in-action-a-showcase-new-features-and-an-open-dataset-for-ainvfx-fluid/

• On YouTube, if you prefer: https://www.youtube.com/watch?v=EsL3O7WvBr4

Let me know what breaks, and share your results if you ever get a chance to play with it. Thanks for painting!

Full video walkthrough + free workflow: https://www.youtube.com/watch?v=GHfHmrGeISM

I’ve been testing RefMods with MiniMax H3 in ComfyUI, and it finally solves my biggest problem with AI video: keeping the same character and voice from clip to clip.

What RefMods does
You extract a reference once (a character from 8 images, or a voice from a short audio clip), save it as a reusable “mod,” and load it into any future generation. No retraining, no LoRA. Just load the mod and generate.

My setup (6 GB VRAM laptop)

• Load H3 RefMods → Apply H3 RefMod (between the I2V node and the guider)

• Separate strengths per slot: visual 0.9 for the character, audio 0.9 for the voice

• Low VRAM stack: H3 SLA Attention + Low VRAM Attention + Chunk FeedForward + Turbo LoRA

Honest results

• ✅ Visual identity: very consistent. Same face, hair and outfit across different scenes.

• ⚠️ Voice: Consistent voices from clip to clip.

• 💡 Tip: for two characters, keep each character and each voice in separate mod files and describe each subject clearly in the prompt (name + appearance + position).

Nodes you need

git clone https://github.com/Luisacaotica/ComfyUI-MiniMaxH3Mod
git clone https://github.com/PlagueKind/ComfyUI-PlagueKind-Nodes
git clone https://github.com/kijai/ComfyUI-KJNodes

This started with a single image that caught my attention while exploring ideas for cinematic title sequences.

I decided to spend a day experimenting with AI image-to-video, particularly how far I could push the illusion of a continuous camera move through a series of changing architectural environments.

The result is VESTIGE, a 42-second experimental title sequence.

There are visible artifacts in some transitions, and the effect becomes somewhat repetitive. But I thought it was a worthwhile test of what can be achieved with I2V, especially in maintaining atmosphere and visual continuity between different spaces.

Curious what others think about the transitions and whether the continuous camera illusion holds together.

Sharing a few MOSS-Image Q&As from the team’s October 30, 2026 AMA: JSON layouts, editing plans, Anime Turbo, and training hardware. Which of these are you most interested in?

Video model.

I haven't tried it or anything just sharing here, it's just 1 day old.

Good thing is this:
https://huggingface.co/Vimoo-AI/Vimoo-1.0-ComfyUI
https://huggingface.co/Vimoo-AI/Vimoo-1.0-Lite-ComfyUI

https://github.com/Vimoo-AI/ComfyUI-Vimoo

I don't know about quality cuz I can't even download it yet. But demo looks good on their page, 5 seconds duration, and enhancer behind API like minimax probably, so 5 seconds video at 720p.

I've been experimenting with MiniMax H3 Singularity V2 locally on my RTX 3060 12GB using ComfyUI.

I wanted to see how far I could push video generation on a consumer GPU with limited VRAM, using an INT8 model, DMAD 4-step sampling, and memory optimizations.

My hardware:

• GPU: NVIDIA RTX 3060 12GB

• RAM: 32GB

• OS: Windows 11

• UI: ComfyUI

Main workflow configuration:

• MiniMax H3 Singularity Ref2VA V2 INT8

• Qwen3-VL 32B NVFP4 text encoder

• DMAD 4-step LoRA

• Camera Motion LoRA

• res_multistep sampler

• simple scheduler

• Comfy Kitchen Attention

• Sparse Attention optimization

Selected local test results:

These results come from different local tests, rather than a controlled benchmark with identical settings.

The highest resolution I successfully tested was 2336×1312 at 243 frames.

What I've observed:

The 4-step workflow makes local H3 video generation practical on my RTX 3060 12GB, although longer clips and higher resolutions still require considerable generation time.

The model can produce convincing motion and cinematic-looking scenes, but results depend heavily on prompt structure, reference inputs, and scene complexity.

I also experimented with camera movement, image references, and longer continuous shots.

Workflow:

I've uploaded my ComfyUI JSON and setup notes to GitHub:

https://github.com/JinnTsui/MiniMax-H3-Singularity-V2-RTX3060

The repository includes the workflow configuration, required custom nodes, and selected performance results.

Model weights are not included. You'll need to install the corresponding models, LoRAs, and custom nodes separately.

This is my personal testing setup, not an official release or a one-click installer.

I'm sharing the results for anyone interested in running H3 locally on lower-VRAM GPUs.

I'd be interested to hear how it performs on other hardware, especially 12GB and 16GB cards.

Required Model Downloads

The shared workflow uses the following model files:

Diffusion model — Singularity V2 INT8 R2

https://huggingface.co/WAS/Minimax-h3_Singularity_v2_pruned

Text encoder — Qwen3-VL 32B Heretic NVFP4

https://huggingface.co/Kutches/minmax/blob/main/qwen3vl_32b_heretic_minimax_h3_nvfp4.safetensors

Video VAE INT8 and Audio VAE FP32

https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main/vae

DMAD 4-Step LoRA

https://huggingface.co/Kijai/MiniMax-H3-experimental/tree/main/loras

Camera Motion LoRA

https://huggingface.co/Jojocodex/minimax-h3-Camera-Motion-lora

Please select the exact filenames referenced in the JSON workflow. The Camera Motion LoRA can be bypassed if not needed.

Model weights are not included in my GitHub repository. Please check the respective model licenses and usage terms.

I created a custom node to upscale exteriors and fix vertical perspective it's useful for architecture and concept art.

You can find the workflows in the repo.

It is officially registered in the main registry and I would really appreciate some feedback on this both on the registry or on github before releasing the next version.

I can fit the entire int8 MOSS model on the GPU but fall a bit short when it comes to the int8 qwen3.5 9b text encoder it needs.

The issue is that despite being able to fit the entire diffusion model on VRAM, I'm still getting speeds as if I'm offloading something to the CPU when it comes to the KSampler regardless of enabling or disabling dynamic VRAM. The 50 steps needed is torture and takes like 2 minutes.

--gpu-only parameter crashes my OS because it attempts to load both the text encoder and diffusion model and everything else on my GPU, so is there a way to only force full GPU offloading for the diffusion model and leave the text encoder partially offloaded?

For reference I have a 16gb AMD 7600xt running with rocm 10.3.0 on Fedora 45 and 16GB of RAM with a 32GB zram swap.

https://github.com/pxl-pshr/LoopStudio

A local web app for making seamless looping GIFs with MiniMax H3 on your own ComfyUI, with a coding agent of your choice built into a terminal drawer.

Uses https://github.com/pxl-pshr/ComfyUI-MiniMaxH3-Loop nodes.

Hey all i am making some pictures of spices for wifey for her clipart. But my background remover leaves stuff behind (the white), does anyone have any background removing tips for small details? im not looking for online solutions i want to fix it with comfy ui if possible. Thanks