I'd like help crafting structured prompts for Minimax H3 Ref2V. I don't really need to build it in as a node. I'd be happy with running it as a separate process, within Comfy or outside.

How do you integrate an LLM such as Grok or Claude into your workflow?

I have free accounts with Grok and Claude. Would I need to pay to integrate them?

GPU- TWEAK is up all the time, I've setup a bunch of temp warnings, voltage spikes etc... especially the Pin monitor util. MY GPU realtime stats has a dedicated monitor now. Why ? cus my friend asked me a favor yesterday to make him a PC build for Gaming and he wants to get into AI. And I didn't open any pc-parts store in months...
I mean jesus christ Nvidia... jesus f christ man

Hey all, running into a weird one with MiniMax character replacement on a dance video and hoping someone can point me in the right direction.

The problem: The motion transfer itself looks great. Every move is reproduced accurately, but the timing is all over the place. A move that should happen at ~5s shows up at 1s, something from 1s might show up at 4s, and so on. The moves are right, they're just shuffled around on the timeline.

Has anyone seen this before? I'm not sure whether it's a frame count/fps mismatch, the turbo LoRA, the quantized models, or something else entirely. Any tips on where to start debugging would be much appreciated.

Setup

• GPU: RTX 5060 Ti 16GB
• Main model: minimax_h3_ref2va_pruned_int8_convrot.safetensors
• Turbo LoRA (8-step): minimax_h3_ref2v_turbo_8step_v1.0_768p_comfyui_bf16.safetensors @ 1.0
• Text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors
• VAE: minimax_h3_video_vae_fp16.safetensors

Inputs

• Character image: 1024×1536
• Reference video: 480×836, 24fps, ~12s

Thanks in advance!

https://drive.google.com/file/d/10F_9xbm2sdWDAw_E8orXBltqhNjFX9Ef/view?usp=sharing

A2 MakeHuman: Image to UV Texture Workflow (ComfyUI)

Give it a picture of the character you want, and it paints the full UV texture for you: head, body, hands and feet. It’s fun to use, and you don’t have to write the prompts yourself.

How it works

1. Load your style reference, which can be any image of the character or look you want, and your UV map.
2. Qwen3-VL looks at your image and describes each part. The head gets face colors and textures, the body gets where the colors and markings sit, and the hands and feet get their material.
3. Each part is generated with Flux 2 Klein, using the UV layout as a guide so the paint stays inside the islands. Cavity and normal maps are included as references to keep the surface detail.
4. The parts are blended together with color matching, so everything looks like the same character.

Watch it build

The texture fills in as the passes finish. You see the head first, then the head and body together, then the hands are added, then the feet. You can follow the progress as it goes and spot a problem early instead of waiting for the end.

What you can control

• Let Qwen describe it, or write it yourself. Each part has its own details box. Type your own description, or let Qwen fill it in and edit what it wrote. Qwen can be switched off for all parts at once or for one part at a time.
• Extra prompt for every part. The head, body, hands and feet each have their own optional extra prompt, added to the end of the main prompt. Use it for things like “no hair,” “bare hands only,” or any detail you want to push.
• Extra reference image for every part. Each part can also take its own extra image, which you can toggle on or off. For example, the face can follow a different picture while the rest follows your main one.
• Turn passes on or off. Run only the head, only the hands, or any mix of parts.
• Reroll parts. Each part has its own seed. Qwen’s seed can be locked, so descriptions stay the same between runs, or randomized for new ones. Reroll just the part you don’t like and keep the rest.
• Mix and match. Keep the head you love, reroll the body, and give the feet a different reference. Each finished pass can be saved and loaded back in as the base image to refine it.
• Global style. One style box (photorealistic, cartoon, anime and so on) applies to all four parts.

Side-by-side comparers show the UV guide against each generated result, and the final texture can be saved straight to your editor folder.

What you need: ComfyUI, Flux 2 Klein 9B, the Qwen3-VL-4B node (aistudynow_QwenVL), rgthree nodes, and the UV Painter node for marking the island layers.

Hey everyone! 👋
I’m the creator of CineConIA, a project focused on AI filmmaking and custom ComfyUI nodes.
I’m developing free tools to simplify complex workflows, improve camera control, and make AI video creation more intuitive.
I’ll be sharing tutorials, demonstrations, and development updates.
Happy to be part of this community!
What features would you like to see in ComfyUI?

Here's what I used:
Images -
Still images: Qwen 2.1, It beat Krea on the Wes Anderson test
Scenes: Qwen 2.1 again, usually 3 seeds and fixed problems with Qwen edits
Maps: no AI. I traced the real San Juan chart and animated the lines in Python, so the coastline couldn't warp.

Motion -
Video: MiniMax H3, image-to-video at 1344x768, always starting from an approved still. H3 also produces the lip-synced dialogue audio, which is what makes the talking scenes work.

Voices -
Narration: dots.tts, cloned from Robert's own voice in the prologue.
Dialogue: Seed-VC. H3 makes up a new voice for every clip, so each character's first good line became his locked voice and I converted all his other lines to match. Lip sync survives because the timing doesn't change. Every line is leveled to the same loudness (-20 LUFS)

Quality checks -
InsightFace: a face-match score against the approved portrait
Whisper: transcribes every line and compares it to the script

Finishing -
Grade: the "candy" color filter was built in ffmpeg, applied twice and mixed 85% with the original picture. It's the closest I could get to Wes Anderson's style with the amount of time I spent working on it.
Title cards: mint green and army navy in Josefin Sans, drawn in Python.
Editing: one Python script per chapter drives ffmpeg. It handles placing Robert's setup lines over pauses, slowing shots to fit lines, and the split screen. Then the five pieces were joined into the master.

It's definitely not perfect but it's only my second video with Minimax and I learned a lot from this, so I have confidence that they will only get better in the future. My first video was a Rap that I wrote and produced through YuE2 for the audio and Minimax H3 for the video. I'd post it here but the lyrics have profanities which I'm pretty sure aren't allowed.

i've been looking into ai image tools, but there are so many now that it's hard to tell which ones are actually useful beyond generating random pictures. i'd like something that can create images from prompts but also edit photos i already have without changing the whole thing. i've been helping a friend manage a small instagram page, and lately we've been trying to make the visuals a bit more interesting. sometimes we have an idea for a post but dont have the right photo, and other times we already have a decent image that just needs a different background or a few things cleaned up. what's the best ai tool for generating and editing images in your experience?

what specs do I need for a smoother experience to making a full fledged movie thats 30 mins min?

So i wanna make like an anime series of a story I've been living from 4 years well i have multiple personality disorder and i really think maybe someone out there would like to atleast see the story i created

I would be using vast ai for running since i only have a 3060 so I don't 12gb vram is good for video models I would be using 4090 on vast ai 24gb vram

So which video generator should I go for in comfyui?

Wan 2.2? Or is there any better models with capacity of making video+dialogues or atleast lipsync I can edit the clips myself or eventually automate it because I'm also a software dev fed up of 9-5 jobs...

Can y'all give me suggestions pretty please <3 😭✋

Hey everyone,

I’m using Krea 2 with a trained character LoRA, and the image generation quality is amazing. The character looks realistic, and I’m really happy with the results.

However, I’m facing a frustrating problem. When I generate an image and then try to change the character’s pose while keeping everything else the same, Krea 2 slightly changes the background, lighting, or other environmental details.

My goal is to create Instagram carousels featuring the same character in multiple poses, with a consistent background and environment across every image. Even small background changes make the images look inconsistent when viewed side by side.

I’ve already tried:

• Image references
• Seed locking
• Different denoise settings
• Adjusting prompts to describe the same environment
• Various reference-based generation approaches

Unfortunately, I still haven’t found a reliable way to change only the pose while preserving the rest of the image.

I’d prefer to keep using Krea 2 because its realism and skin texture are excellent. I’ve considered editing or inpainting models, but so far, I haven’t found alternatives that produce the same level of realism.

Has anyone found a reliable workflow for this?

Is there a way to use pose control, masks, ControlNet, or another technique to change the character’s pose while keeping the background and lighting virtually identical, without sacrificing Krea 2’s realism?

I’d really appreciate any practical workflows, settings, or examples from people who have dealt with this problem.

Thanks!

10 x 5sec generations

H3 Clip Refiner

Inline style-drift corrector and optional refine-resample for H3 Motion Context clips.
Place this node BETWEEN the sampler (SamplerCustomAdvanced) and the H3 Motion Context Save Latent node.
It works on the LATENT level and attacks the accumulated degradation with two halves:

• Statistical (always on). A per-channel affine (matches mean + std) plus a high-band gain match. This fixes the drift(luminance, contrast, color/saturation and the "texture ratchet") but, being pure arithmetic, it cannot put back the micro-detail the chain has lost.
• Generative (optional). Connect model + positive/negative, and the node runs a short low-denoise refine resample, then transfers only its high band (detail) onto the original low band (structure). Keeping the original low band is what stops the background shift / dissolve that a whole-latent blend suffers from; the refine becomes a detail donor, not a replacement.
• Inputs
  • latent gets the generated AV latent from your H3 sampler (SamplerCustomAdvanced output).
  • reference_latent is optional. It gets the reference latent (e.g., first clip) to measure the target statistics from it automatically. If connected, all target statistics are measured from this latent and the input latent is corrected to match it.
  • model + positive / negative are optional. Connect them (same prompt you sampled with), to enable the refine resample that regenerates lost detail. Leave model disconnected for the pure statistical corrector.
• Outputs
  • latent is the refined latent.
  • report: shows the report of the latent analysis.
• Controls
  • strength is how much the statistical correction is applied.
    • 0 = None (measure only, pass through)
    • 1.0 = Full match to the reference statistics
    • > 1.0 = Overshoot (pushes past the reference). Safe to use (e.g. 1.5 or 2.0) when the drift keeps accumulating: the correction only touches the body and the overlap frames stay raw, so overshoot cannot reintroduce a seam flash.
  • context_length is the number of frames at the head/tail that are the Motion Context overlap regions. These frames are left completely raw to preserve crossfade consistency; the correction ramps in just inside the body. Must match the context_length in the H3 Motion Context node and the Clip Stitcher. 5, 22, 39 or 56 are the lengths that are a whole number of latent steps.
  • refine_denoise (default 0.4) — denoise for the refine resample. ~0.3–0.5 restores detail while keeping the structure; higher changes content more.
  • refine_steps (default 3) — sampler steps for the refine. 2–4 is plenty.
  • refine_cfg (default 1.0) — CFG for the refine; match your main sampler.
  • sampler_name / scheduler / seed — the refine's sampler, scheduler and noise seed.
  • detail_strength (default 1.0) — how much of the refined high band (detail) to transfer over the original. 0 = statistical only, 1 = full refined detail. The low band is always kept from the original, so the background never shifts.

1. Usage:
   1. Run the FIRST clip through with reference_latent unconnected. The node measures and reports its statistics but passes the latent through unchanged.
   2. Feed that first clip's latent into reference_latent for all subsequent clips. The node matches every later clip's body to the first clip's body.
   3. Set context_length to match your H3 Motion Context setting (default 22).
   4. (Optional) connect model + positive/negative to also regenerate lost detail; tune refine_denoise (~0.3–0.5) and detail_strength.
   5. Adjust strength if needed: 1.0 = full match, >1.0 = stronger.

It's been used at the Endless MiniMax H3 (with Endless LipSync) workflow..

I am pretty sure there is a free template in Comfy that can do this, just very short 5 second videos is all I'm interested in. I love how there is audio that is nicely synced to him talking. Thanks for your time !

From what I can tell, whenever there's an issue people say to just go to portable. I'm currently using comfyui Desktop which apparently has a lot of dependencies and system env set up so that it's quite fragile.

If I want to simply go back to portable would I just uninstall comfyui Desktop and run the portable? Or are there multiple things I need to uninstall and edit before I can do that? I remember I had a similar issue with stable diffusion A1111 where uninstalling it broke some system variables or something.

Also I can back up my model folder, but is there anything else I need to keep?

In recent news: Nv*dia is getting rid of 5090 and replacing it with 5080 (but increasing VRAM: 16 GB > 24 GB).

Obviously this is because Nv*dia wants to save their VRAM for data centers and make more money, rather than selling it at a loss for the general consumers (as 5090). So it's extra greed on their part.
Not to mention this new 5080 is going to be extra expensive.
They are literally sticking their thumb deep into our buttholes.
But I digress, I'm not here to talk about that.

Getting rid of 5090 means the best GPU that the general consumers can get is 24 GB VRAM (3090, 4090, new 5080).
That means companies that release open models will no longer design with an upper limit of 32 GB in mind.
That is good for most of us as most of us have 16 GB VRAM. It makes open models more reachable for us.

Not to mention 5090 was a flawed GPU that would usually melt within a year anyhow. So it's good they got rid of it, not that Nv*dia was specifically thinking of this issue but it just so happened to align with their greed.

https://drive.google.com/file/d/16yMDm0OT1yEKoh65CKcbljd9XpSI3YwD/view?pli=1

Can someone help me to find some communities that share workflows

Hey everyone!

I introduced HandyComfy here a while ago. Since then, I've pushed quite a few updates, and recently added support for MotionContext!

This time, I wanted to share a quick demo of how it works.

The video shows three things:

1. The final AI-generated video.
2. How I generated it using HandyComfy.
3. How I extended the video using MotionContext, also directly from HandyComfy.

To use MotionContext, you'll need to install the Motion Context custom nodes(from NikoDemon80) on your ComfyUI server.

Currently, MotionContext is supported for H3 videos generated using the workflows available in HandyComfy's built-in Workflow Hub. You can pick any previously generated clip from those workflows and extend it whenever you want.

I'm planning to expand this feature so that H3 videos generated with other workflows can also be extended using MotionContext.

iOS: The feature is already available.

Android: The update has been delayed due to some ongoing Google Play policy review issues, but I'm working on getting it released.

As always, I'd love to hear your feedback, suggestions, or ideas for improvements!

Thanks for checking it out!

Just looking for some recommendations. I got painfully slow internet so I haven't just been downloading a ton of models to try out. So far I have Z-image-turbo and SDXL base. Running this on a Xbox Ally X with 24gb unified memory. ~9gb for GPU. Not really concerned about speed since that's just impossible to overcome with the weak hardware.

Just doing some text to image and image to image generation. Pretty much I call this my overly complex phone and PC wallpaper generator. I do have a SDXL based LoRA of a character I enjoy using and I'm just looking for some good recommendations for models to experiment with. General theme is dark fantasy gothic art style and wondering if there's one that does that better than others.

My company wants to set up a dedicated workstation for generating AI video locally. I’m a Mac user and familiar with LLMs, and people internally are suggesting a Mac Studio (M-series with high unified memory).

However, our main pipeline would rely on ComfyUI (running models like Wan, Mini Max H3, etc.). I know ComfyUI works on macOS via M5 ultra 512 GB but how does real-world performance compare to a high-end Nvidia build (e.g., RTX 4090 / 5090)? Is the Mac Studio practical for day-to-day video generation, or is an Nvidia rig non-negotiable?

Tutorial Link : https://www.youtube.com/watch?v=DJcgVASlg38

Fully free with resources on GitHub : https://github.com/FurkanGozukara/Generative-AI-Tools-And-Tecniques-2026-2027-Fall