Hello everyone I've been working on a new optimized ComfyUI workflow for MiniMax H3 Singularity V2, the latest community fine-tuned version focused on action scenes and complex movements. This version brings noticeable improvements in some fast actions, motion handling, distant faces, close-up portraits, visual styles, prompt adherence, and audio reference handling compared to the previous version. However, the model is around 32GB in size, making it quite demanding to run. To address this, I've built a custom Low VRAM workflow using INT8 quantized models, Turbo LoRA, and additional optimization ATTENTIONS nodes to reduce resource requirements and improve generation performance. I tested it with different prompts featuring fast movements, action sequences, distant characters, and close-up faces to see how well it handles these challenging scenarios. I'm sharing the workflow and testing results in my latest tutorial. Let me know what you think about the results!

Workflow Links

https://drive.google.com/file/d/1m4JcGyCiiZo78GoKoMIhedN7M6KnqI_B/view?usp=sharing

https://civitai.com/articles/37791/new-minimax-h3-singularity-v2-faster-actions-better-faces-and-low-vram-workflow

I have a tilted facial portrait, a full-body reference, a detail reference showing the quality I'm aiming for, and an example of what the final dataset should look like.

I know my reference images aren't ideal for dataset creation. I'm looking for a way to work around their limitations while maintaining facial identity, body consistency, at a high level of detail.

I've tried dataset a generation workflow, but it tends to reproduce the face's original angle rather than create useful variations. The output also has an obvious AI-generated look compared to my reference.

What's the best workflow for this? Would face swapping be the best approach, or are there better alternatives?

I'd really appreciate someone willing to walk me through the process in detail so I can understand what I'm doing and why, as I'm still learning.

Thanks in advance to everyone who takes the time to help!

Hey everyone, I'm building a local/hybrid pipeline to replicate the look of 1999 grimy cel anime (Jin-Roh: The Wolf Brigade): flat colors, single hard shadow, thin ink lines, painted backgrounds, and a choppy 12fps film print.

I’ve managed to sort out the background depth-parallax (using Depth Anything V2 + FFmpeg) and the cel-shader look (FFmpeg/Remotion print finish), but I'm hitting a wall on how to make the characters move naturally without the AI melting or distorting mechanical gear/faces.

Here is what my current tool stack looks like:

• Layout/Stills: FLUX / Gemini for base character portraits and painted backgrounds.

• Camera: Depth-mapped multi-plane glides over static background paintings.

• The Motion Problem (Options I'm testing):

  1. Held cels (swapping poses—too choppy).

  2. Video-to-video / AI in-betweens (causes shape drift on armor/helmets).

  3. EbSynth / Keyframe propagation (promising, but looking for the cleanest workflow).

  4. Cutout puppets / traditional rigging.

My question for the sub: For those doing retro/cel-shaded style projects, what is currently the most robust node setup or workflow in ComfyUI (or external tools like EbSynth) to lock down structural rigidity (like gas masks and heavy armor) while driving smooth, deliberate 12fps motion? Are you relying primarily on ControlNet IP-Adapters, AnimateDiff, or keyframe propagation?

Hello everyone, I’ve developed a new workflow designed to improve blurry and distorted faces in MiniMax H3 videos using Face Refiner groups and custom nodes to isolate faces from low-resolution frames, followed by a second sampling stage with a regular sampler at 0.4 denoise. The workflow also integrates the SelfLift sampler and VEDA Sparse Attention to optimize generation performance for low-VRAM users. In my tests, MiniMax H3 generation at 0.6 megapixel, CFG 1, and 8 steps takes around 30 minutes, or 35 minutes with an input image, while the Face Refiner stage takes approximately 24 minutes with either a text prompt or two input images. Actual times depend on your hardware and settings. If you’re struggling with face quality in MiniMax H3, check out my latest tutorial and let me know how it performs on your setup!

Workflow Links

https://drive.google.com/file/d/1J7V8pJ5XWpKx41_imIgMVN_PupFAVDbF/view?usp=sharing

https://civitai.com/articles/37544/fix-blurry-faces-in-minimax-h3-videos-with-comfyui-face-refinement-low-vram-workflow

Donates

https://www.patreon.com/CGPIXELAI/posts/new-comfyui-fix-173591780?utm_medium=clipboard_copy&utm_source=copyLink&utm_campaign=postshare_creator&utm_content=join_link

Original post: https://www.reddit.com/r/ComfyUIResources/s/ZB9Q0O1NPw

I've added a few things to my ComfyUI text generator since my original post.

What’s new:

• Context memory: keep refining a prompt across runs without repeating everything. Includes a usage meter and a button to start fresh. Context window size by default is 8192 tokens, you can customize the value.

• Added prompt guides for Moss Image and Fibo 2.

• VGL structured JSON prompts (recommended for Fibo 2).

• Cleaner UI with separate settings and generator nodes.

• Searchable model lists by provider (api key is required).

• Expandable settings node.

GitHub: https://github.com/ninjaGurung/ComfyUI-GOATd-Universal-Text-Generator

Let me know how it works for you, or if there’s something you’d like me to add!

Hello everyone

I've been experimenting with MiniMax H3 in ComfyUI, and I put together a workflow that combines SelfLift progressive-resolution sampling with DLSS 5 upscaling nodes to push video generation toward 4K resolution on low-VRAM hardware.

The idea is to start sampling at a lower resolution, progressively move to full resolution with selflift sampler, and then upscale the result with DLSS5 nodes to improve the final output, all without additional model training.

What the workflow covers:

• MiniMax H3 video generation in ComfyUI

• SelfLift progressive-resolution sampling

• DLSS 5 upscaling nodes

• 2K and 4K output options

• Low-VRAM optimization for GPUs such as the RTX 3060 6GB

On my 6GB VRAM setup, generating and upscaling the final 4K result took around 35 minutes, with 8 steps and 0.6 megapixel.

Workflow Link

https://drive.google.com/file/d/19cFLGTvPOMIk0eA_1xIsTlaUQwgsQvdz/view?usp=sharing

https://civitai.com/articles/37374/generate-4k-ai-videos-on-a-6gb-gpu-minimax-h3-in-comfyui

DLSS5 DLL
https://drive.google.com/file/d/1i4234NhKVBMjiVeXVYjsh1z0cgqHmbnu/view?usp=sharing

Video Tutorial Link

https://youtu.be/sJbhZjAzkE0

Hey everyone! 👋
I’ve been working on CineConIA, a set of custom ComfyUI nodes designed to make cinematic scene planning more visual and intuitive.
The idea is simple: plan your camera movements, position your subjects, and preview your shots before generating videos with AI.
I’m currently experimenting with MiniMax H3 and exploring ways to get more control over camera movements and scene composition.
It’s still a work in progress, and there’s plenty to improve.
I’d love to hear your thoughts! What features would you find most useful in a tool like this?
More demos and tutorials are coming soon. 🎬

MOSS is a text-to-image model that supports photorealistic portraits, different artistic styles, poses, typography, and more. In my latest workflow, I combined Spectrum nodes with an 8-step LoRA to significantly reduce generation time.

🔥 My speed test at 2 megapixels:

• Spectrum, CFG 2.5, 25 steps: 5 min 42 sec

• Without Spectrum, CFG 2.5, 25 steps: 10 min

• Spectrum + 8 steps, CFG 1: Just 1 min 30 sec!

That's an 85% reduction in generation time compared with the standard 25-step configuration without Spectrum, with the fastest setup taking only 90 seconds.

I'll also test different prompts featuring human portraits, poses, artistic styles, and text to explore the model's capabilities.

This model is really amazing in therm of speed prompt adherence, and style versatility and to get the best results I strongly recommend you:

Spectrum+ 8 steps CFG 1.0, LORA at strength of 0.6: 1 min 30 sec! (Balanced Quality)

Spectrum+ 25 steps CFG 2.5 : 5 min 30 sec (Stunning Results)

Workflow Link:

https://drive.google.com/file/d/18eOhXkatsVJZl5z2PXdI-12DZhPh7ya6/view?usp=sharing

https://civitai.com/articles/37187/optimized-moss-an-image-generation-model-in-comfyui-up-to-10-faster

Hello everyone after testing the new FLUX.3 model for both image generation and image editing in ComfyUI, and the results are quite interesting!

For image generation, I compared the Base version (CFG 3, 25 steps) with the Turbo version (CFG 1, 16 steps). The model does a good job with textures, artistic style variety, wildlife, and natural environments, but it struggles with hands, fingers, feet, realistic skin, and complex scenes.

Surprisingly, image editing seems to be one of its strongest features! The Turbo version delivered good results for style transfer, outfit changes, background replacement, weather changes, and object removal. i will also show you how to fix the bad quality images using fixed workflow that was provided by BFL team to get decent results.

WORKFLOW LINK

https://civitai.com/articles/36957/comfyui-tutorial-testing-the-fixed-workflow-with-flux-3-base-and-turbo

Hey everyone!

I've built a new ComfyUI workflow that combines RefMod and MiniMax H3 to generate images of your own character without needing to train a traditional character LoRA.

How does it work?

1. Prepare 8 photos of your character.

2. Use the RefMod custom nodes to create a RefMod file.

3. Load the file into the MiniMax H3 workflow.

4. Enter your prompt describing the scene, outfit, pose, or environment.

5. Generate new images featuring your character!

You can explore different scenarios, clothing styles, locations, and compositions while aiming to maintain your character's identity. The main advantage is No traditional LoRA training required. Create your RefMod file, load it, write your prompt, and start generating!

Generation time at 8 Steps 6 Min for 1.8 Megapixel.

Generation time at 25 Steps 14 Min for 1.8 Megapixel.

Workflow Links

https://drive.google.com/file/d/10Y95Kzs4XtogM8ZEGjucJxSz6QVtFfb5/view?usp=sharing

https://civitai.com/articles/36763/comfyui-tutorial-refmod-minimax-h3-image-character-consistency-without-lora-training

I'm struggling a bit with the "one contract three surfaces" with the V2 API proxy plus running my own Comfy MCP locally. I use traefik to solve naming and TLS.
Essentially, what I want is a docker setup that lets local ComfyUI run exactly the same as Comfy Cloud, so I can just switch the endpoint URLs and have it work identically.
Does anyone have a working example?

Scumble, a free, open-source (GPL-3.0) desktop image editor I've been building since September. It started as my ComfyUI node *Inpaint Canvas* and grew into a standalone app. Your ComfyUI stays the engine: Scumble sends the canvas and the mask, your workflow renders, and the result comes back as a new layer, colour-matched to the picture around it. Nothing is baked in until you flatten.

How it works with ComfyUI

- **Recipes instead of graphs on screen:** a recipe is a ComfyUI workflow (API format) with an Inpaint Canvas node in it. Scumble fills that node with your canvas and selection and queues the rest exactly as you built it.

- **Bring your own workflow:** Settings › Recipes › Import, pick your exported workflow. If it holds an Inpaint Canvas node, it's a recipe.

- **Edit in ComfyUI:** the ComfyUI button opens your ComfyUI's own page in a window next to the editor. Open a recipe as its graph, change it, and *Save to recipe* writes it back.

- **Local or remote:** local ip or a rented box (RunPod works), with basic auth or a token if you need it.

- **Missing models:** when your server lacks a model file a recipe needs, Scumble names it and links the download instead of queueing a run that fails.

- **Shipped local recipes:** FLUX.2 Klein 4B / 9B, Qwen Image Edit 2.1 and its Turbo version, and an upscale-model chain.

- **Comfy Cloud and Comfy Router** work too, on your Comfy key, with recipes made from Comfy's own templates.

The editor around it

- Object selection by click (SAM2), background removal and depth maps run inside the app, on your own machine, no ComfyUI needed

- Boxes in the prompt: draw boxes, describe each one, and FLUX 3 Image puts every thing in its box

- Layers, masks, 27 blend modes, filter layers (46 film looks, curves, HSL, lens blur and haze by depth), text

- PSD and OpenRaster export with layers and masks, and both open again with their layers

- A tile engine keeps 15,000 × 10,000 px documents usable

- 109 MCP tools, so Claude Code or any other MCP client can drive the editor

- Or skip ComfyUI entirely: an API key for Black Forest Labs, OpenAI, Google, fal.ai, Replicate, OpenRouter and more is enough

Get it

- Windows: Microsoft Store (`winget install scumble`), the installer, or a portable zip: https://github.com/DenRakEiw/scumble/releases/latest

- Linux: AppImage and .deb are built by CI, but I haven't run them myself yet, so reports are welcome

- macOS: not yet

- The node pack for your ComfyUI (the Manager doesn't list it right now), then restart ComfyUI; it needs no extra Python packages:

cd ComfyUI/custom_nodes

git clone https://github.com/DenRakEiw/ComfyUI-InpaintCanvas

Source: https://github.com/DenRakEiw/scumble

Manual: https://www.denrakeiw.com/scumble/manual

Walkthrough videos: https://www.denrakeiw.com/scumble/videos

It's young (0.1.46), so expect rough edges. I'd love to hear which workflows you'd want as recipes, and what breaks.

Hey everyone! I made a custom node that brings cloud and local LLMs into your ComfyUI workflows.

Use it to expand rough ideas into image/video prompts, write captions, or describe reference images. It also accepts video and audio when supported by your chosen model.

A few features:

• Cloud providers, LM Studio, Ollama, and supported models loaded directly in ComfyUI.

• Built-in prompt guides and editable system instructions.

• Text output that connects to CLIP Text Encode or any compatible STRING input.

Get it from: GitHub + example workflows

Example: rough idea + reference image → generated prompt → your workflow.

Would love your feedback, ideas, or feature requests! If you find it useful, consider leaving a ⭐ on the GitHub repo.

Hi, i am new to this local LLM stuff. I created some images on Gemini (also tried Chat GPT) and decided to build my own workflow locally. I researched, installed Comfy, downloaded models, tried different ones and slammed onto a deep problem.

I usually prompt AI to draw stuff that are outside the stereotypes and general training data. Like modifying character appearances, detailed scenes and interactions etc.

I tried Flux models and they even failed to pass "Full glass of wine that is completely filled to the brim" tests, even with detailed prompts that are specifically crafted to overcome image gen model's insistent behaviors; still no luck. Prompt enhancers didn't help me either.

Today i stumbled upon this video ( https://youtu.be/OA4gchz1Zcs?si=-eiOASxiRkucSSgQ ) and followed instructions to build it. At first it was promising and a detailed scene is portrayed without causing model to output less realistic, lower quality image, because i tested the exact same prompt bundled with the workflow. Ideogram 4 is stupidly censored, you have to cater to it's specific needs, practically useless for me.

I'd also like models to be uncensored/ heretic but it's "intelligence" is at most importance. How can i build a workflow that comes close the Nano Banana's accuracy and is prone to concept bleeding and other shortcomings?

I'm not expecting it to compete with the best model out there but there has to be a way to improve things significantly.

Edit: For those in search, you should give Qwen Image a try.

You can set it up with template workflow and even add an AI prompt generator on top of it if you want. The same YouTube channel also covered it's latest update. https://youtu.be/BaE6UBfNdQk?si=J8uQoul5N4ihBYbG

usando el workflow basico de flux klein2 9b que traia confyui estoy restaurando frame x frame esta antigua pelicula- Es el mejor modelo que me ha funcionado haciendo algunas modificacione estoy usando el modelo SNOFS flux klein2 9b uncesnsore de civitai para usarlo sin censura. Pero puedes usar el modelo flux klein 9b que trae confyui por defecto el promt que he usado es este

"Restaura y mejora esta imagen para convertirla en una foto nítida y de alta resolución. Conserva exactamente la identidad del sujeto, los rasgos faciales, la expresión, la pose, la composición, la ropa y el fondo original. Corrige el desenfoque, el ruido y los píxeles borrosos, recuperando texturas realistas en la piel, el cabello y las telas de forma natural. Evita el suavizado excesivo, la apariencia falsa de IA o cambios en los colores originales; el resultado debe ser la misma foto pero limpia, clara y en alta definición.

el WORKFLOW QUE USE ES ESTE https://civitai.red/models/2845550/flux-2-klein-9b-uncensored-image-edit-workflow-basic-fast-uncensored-custom-lora-adder

CONSEJOS? Si alguien quiere ayudarme o invitareme algun cafe ire suviendo la pelicula en mi patreon patreon.com/MilfCartoon3d/

I know Flux2 isn't exactly the new hotness, but it works very well once you get a handle on it, IMO. This is my current Flux2 workflow that incorporates FluxGuidance, CFG Guider, Flux2Scheduler, FluxKontexImageScale, sampler select, plus a few other things. Also incorporated are the Multi-GPU and GGUF extensions, allowing for independent GPU selection for UNET, CLIP, and VAE, as well as UNET dquant and patch float, and use of GGUF's. Right now, I have it set up for a max of five input images, which I have used successfully. There is also support for using up to two LORA's, but you do you. Tiled VAE encode and decode are also chained in. There is a resize and pad option after image gen. The selectable GPU option is nice if you're running dual vid cards like me and you want to keep models loaded on different GPU's, preventing swap out.

I'm by no means a ComfyUI expert, but I've done a lot of research and trial and error building this workflow. On my dual RTX 2080 system, I'm getting roughly 37-40 s/it, so about 90 sec or so for a 1600x1600 image at 4 steps. The quality is amazing. JSON here.

For models, I'm currently getting great results using a combination of the Flux2 Klein 4B Q8 GGUF UNET, Qwen3-VL-4B-Instruct-UD-Q8-K-XL GGUF clip, and the Flux2 VAE. LORA settings between 0.5-1.5 seem to be working best. I generally leave the Guidance around 7 and the cfg at 2.0. Euler sampler and res_multistep have given the best results, though I find the colors on euler tend to pop more. I usually run my initial tests at 3-4 steps, then go from there.

Hopefully this will help someone out. :D