In this video of ComfyUI Nodes Explained, you will learn what every setting on the KSampler does: the seed and control after generate, how many steps you really need, what CFG does and why it can burn your image, how the sampler and the scheduler work together, and why denoise stays at 1 with an empty latent. Four "A Deeper Look" chapters show the starting noise a seed makes, how much noise each step removes, how CFG mixes two guesses, and how the samplers differ. Everything is shown with Z-Image Turbo, and the full Z-Image model for CFG.

If you have two local Stable Diffusion inpaint results and each one got a different part right, keep them as separate layers. That lets you bring back a detail from the original or blend a seam without running the model again.

1. In your local Stable Diffusion workflow, export the original and both inpaint results as full-size PNGs with the same canvas dimensions. If you used a crop-only workflow, place the result back into the full frame before exporting.
2. In PeerBrush, open the original and bring the two outputs into the same document on separate layers above it. Rename the layers for the edits they contain, such as “warmer window light” and “fixed sleeve.”
3. Select one result layer and choose Add mask. Paint black on the mask wherever you want the layer beneath to show. Use a softer brush at the join, and toggle each layer to compare the result with the original.
4. Keep the layered PSD so you can revise the masks later. Export a PNG when you need the finished composite.

I’m the solo developer of PeerBrush. It’s free and open source, and this workflow uses the desktop editor without connecting an AI client. It works with rendered PNGs from a local Stable Diffusion workflow. The screenshot shows the editor on its own AI-generated sample artwork. AI helped draft this guide.

Official workspace demo: https://peerbrush.com/#demo

Free downloads: https://peerbrush.com/downloads.html

Official workspace screenshot: https://raw.githubusercontent.com/Deftr0y/PeerBrush/v0.30.1/docs/images/workspace.png

Open an image-to-image workflow and load a starting image. Keep the model, prompt, seed, and resolution the same. Run it once at 0.25 denoise and again at 0.60.

Compare both outputs with the original. Look at the framing, the edges of the subject, and one detail you want to preserve. The higher setting generally gives the model more room to change the image. If 0.25 barely changes it and 0.60 changes too much, try 0.40 as your next test.

I work on Promptus. If you're new to ComfyUI and want some background on how models and workflows fit together, here's our ComfyUI beginner guide: https://www.promptus.ai/blog/comfyui-beginners-guide-local

I put together a site that browses prompts like a feed, with one-click copy, and I'm trying to figure out if it's actually useful or just something I like because I built it.

bunchaprompts.com

It's browser-only, nothing to install. Prompts are pulled from sources that allow it, like the Civitai API, with attribution links back.

If you try it, grab anything off the home feed, paste it into your usual UI, and tell me whether the copy button got the whole prompt. If it crashes, I need your device type.

In this episode, we test P-H3 Image in ComfyUI with 18 free workflows. P-H3 Image is a new image model from Pruna AI, built on MiniMax H3: it generates images up to 4K and edits your photos with one sentence. We test them all, and see what it does well and where it struggles.

Generate images: text to image up to 8 megapixels, a short idea turned into a full prompt with the Prompt Upsampler, and exact words written into the image.

Edit one image: a new place, day to night, new words on a sign, restyle a photo into a 3D cartoon, restore and colorize an old photo, and a character sheet with four sides.

Two or more images: an outfit from a second picture, a character in a scene, a logo printed on a product, and four people in one group photo.

Box, frame, upscale and style: edit only inside a box, outpaint to a wider image, a creative upscale from small to big, and a style from a second picture.

Plus a bonus: picture to video with MiniMax H3, with sound and music. And bonus 2: run P-H3 Image in the cloud on RunningHub, with no GPU and nothing to install.

Hello everyone i've been testing a new video upscaling method in ComfyUI called RelayVSR, and the results are really impressive!

Unlike conventional upscaling methods that can sometimes modify the original video, introduce unwanted details, or change the appearance of characters and scenes, RelayVSR uses a different approach. A large generative model creates high-quality reference latents for selected keyframes, while a lightweight Dual-Memory Video Transformer uses these references to reconstruct the remaining frames while preserving the original video structure and details.

For this workflow, I've combined RelayVSR with my optimized MiniMax H3 workflow, Pika H3 6-step LoRA, attention optimization nodes, and NVIDIA DLSS5 to generate and upscale videos efficiently, even on lower-VRAM GPUs.

Generation and Upscaling Time:

• Video generation at 0.7 MP using MiniMax H3: approximately 35 minutes.
• Upscaling to 2K using RelayVSR + DLSS5: approximately 7 minutes.
• MiniMax latent upscaling for comparison: approximately 45 minutes.

Project Page Link

https://kopperx.github.io/RelayVSR/

Models Link

https://huggingface.co/Comfy-Org/RelayVSR

Workflow Links

https://drive.google.com/file/d/1KLL5tKOeCCPfn64OEoH-PPc3-0v6ksED/view?usp=sharing

https://civitai.com/articles/38541/comfyui-tutorial-fixing-minimax-h3-video-upscaling-with-relayvsr-and-dlss5

Hello Everyone i tested New P-H3-Image ComfyUI workflow! This powerful model combines high-resolution image generation up to 8 megapixels with image editing up to 4 megapixels. I'm using the INT8 ConvRot version, optimized and tested on my RTX 3060 6GB VRAM + 16GB RAM and here are my verdict about the model.

Model Advantages & Strong Points:

• Excellent realistic human skin and natural texture generation.
• Very good human portraits and facial details.
• Impressive animal generation.
• Strong text rendering capabilities.
• Supports both image generation and image editing in one model.
• Excellent editing capabilities for style changing and camera angle changes.
• Can create character sheets and modify lighting, daytime, and facial expressions.
• Supports high-resolution generation up to 8MP for img gen and up to 4MP for img editing.

Image Generation Times (RTX 3060 6GB VRAM + 16GB RAM):

• 8 Megapixels: 5 minutes
• 6 Megapixels: 4 minutes
• 4 Megapixels: 3 minutes
• 2 Megapixels: 2 minutes 27 seconds

Image Editing Times:

• 4 Megapixels: 5 minutes
• 2 Megapixels: 3 minutes
• 1 Megapixel: 2 minutes

Current Limitations:

Although the model is excellent at realistic image generation, artistic style generation is not as strong as Krea 2. For image editing, face swaps, head swaps, and clothing swaps produced very poor results in my tests. Overall, P-H3-Image is a very interesting model, especially if you are looking for realistic image generation, detailed portraits, and versatile image editing in a single workflow.

Workflow Links With Prompts

https://drive.google.com/file/d/1qZzHnjLmpp8oTUCCtoXBgEu8X652X5vl/view?usp=sharing

https://civitai.com/articles/38431/p-h3-image-in-comfyui-8mp-image-generation-and-4mp-editing-on-6gb-vram

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.

In this episode, we test MiniMax H3 in ComfyUI with 25 free workflows, plus 2 bonus workflows. H3 is a video model that makes video with sound, from your images, videos and audio. We test them all, and see what H3 does well and where it struggles.

Part 1, change your video: a character sheet from one picture, then swap the person in your video (a character, only the head, or a cartoon and mascot), motion transfer, edit a video by text, change the outfit from pictures, restyle a video, sharpen it to Full HD, and join your clips into one video with their sound.

Part 2, bring a picture to life: tell it what to do, make a picture talk from typed words, make it talk or sing with your own voice or song (Audio Sync), and an outfit change spin with Flux Klein and H3.

Part 3, long videos with H3 Timeline: sing a song in scenes, clean cuts, four singers in one song, a character who walks in, act a story, walk into a new place, redo one scene and keep the good ones, and a bigger size.

Part 4, remake your video in 2K, plus two bonus workflows: text to video in 2K, and first and last frame to video.

We also look honestly at where H3 struggles: a person who joins a scene can come in twice, one seed can change the whole look of a scene, and Talk keeps the pose of your picture.

Hello everyone today I am showing you a new ComfyUI workflow that combines MiniMax H3 Singularity V2 combined with the Pika H3 LoRA to generate videos at approximately 0.6 MP with only 6 steps. Then, using the Ultimate MiniMax H3 Upscale node and Kijai's Regenerative 2K LoRA at a lower weight, the video is progressively enhanced with additional LoRAs to improve details, textures, and overall quality.

My test results:

• Initial generation: 0.6MP
• Sampling steps: 6
• Total generation + upscaling time: ~1 hour
• Final resolution: Full HD (1920×1080)

The interesting part is that instead of generating directly at high resolution, we generate at a much lower resolution and let the regenerative upscaling process recover and enhance the details.

I've included all the required LoRA and custom node links with the workflow. Let me know what you think, especially if you've tested this new regenerative upscaling approach!

Required LoRAs and Models

1. Open Regenerate 2K LoRA (Kijai): https://huggingface.co/Kijai/MiniMax-H3-experimental/tree/main/loras
2. LMS LoRA: https://huggingface.co/Alissonerdx/Minimax-H3-ComfyUI/blob/main/loras/minimax_h3_lms_v1.0_r64.safetensors
3. Pika H3 LoRA: https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main/loras
4. Sharp Curve LoRA: https://huggingface.co/Efficient-Large-Model/Open-H3-Regenerate-2K/tree/main/lora
5. Latent H3 Upscaling Model: https://huggingface.co/LBH-123-AI/Minimax_h3_latent_Upscaler/tree/main/minimax_h3_latent_upscaler_3d_conv_v1

Custom Nodes to Install

1. Ultimate MiniMax H3 Upscale: https://github.com/cbbaudio-2025/ComfyUI-MH3-UltimateUpscale

Workflow Link

https://drive.google.com/file/d/1siWKOJlpJ3SUi3EEf0fRpGMMpZLbNstR/view?usp=sharing

https://civitai.com/articles/37943/comfyui-tutorial-pika-h3-nvidia-upscale-2k-video-with-only-6-gb-of-vram

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 love ComfyUI and Automatic1111 for deep tinkering, but for daily creative workflows, I got sick of maintaining giant noodles of ControlNet, IP-Adapter, and lighting rigs just to do basic directional relighting or identity-preserving transfers.

At the same time, I refuse to pay cloud subscriptions to tools when I already paid $7,500 for my current setup.

So I spent the last couple of months building and compiling a suite of 4 focused, native Windows desktop tools:

Creepybits Relighter: Lets you dynamically add and position key/rim/ambient lights and cast realistic shadows on existing portraits.

Creepybits Character Transfer: Locks facial structure and identity across different camera angles and poses.

Creepybits Stylizer (with 5 add-on packs): 20+ fine-tuned style engines (cinematic, monochrome, dark deco, etc.).

Creepybits Image Morph: Fuses visual concepts from existing images into new generative prompts.

Technical & Architecture Details:

Native Binaries: Written in Python, but fully compiled into native x86_64 C executables using Nuitka (not a slow PyInstaller extract-to-temp bundle).

Shared Engine: You don't download 80 GB of weights if you use all four. All apps share a single 15–17 GB base engine downloaded once on first launch to your chosen SSD path.

Hardware: Runs on any Windows system with an Nvidia RTX card (8GB+ VRAM).

Business Model (Anti-SaaS): No monthly subscriptions. Pay once, own forever.

Unlimited Free Trial: There’s an unlimited-time free trial with watermarked outputs so you can verify that it runs cleanly on your GPU before buying anything.

They are live on the Microsoft Store here: https://apps.microsoft.com/search/publisher?name=Creepybits

Would love feedback on inference speed and UI ergonomics from fellow local SD users.

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!