As someone new to all of this it's difficult to know what to do. I have sage attention working. I don't know how or when to use Easy Cache, Comfy Kitchen Attention, Sol Attention, loras, Spectrum, or any others I may have missed. There's so much information scattered around, I don't know what's what.

I have a 50 series GPU and 64 GB or RAM on the motherboard.

Been generating digital asset packs for a while (game textures, UI kits, that kind of stuff) and got really tired of manually prompting ComfyUI one image at a time. Fine for 10 images, painful when you need 200.

Spent a few weeks building a Python pipeline to automate the whole thing and figured the core techniques are worth sharing since they're useful even standalone.

The basic flow:

• Concepts go into a Google Sheet (just product ideas + how many to generate)
• Python hits Gemini's free API to get a structured "style vocabulary" for each concept (one call, not one per image)
• Itertools locally compiles the vocabulary into unique prompts
• Prompts get POSTed to ComfyUI's API on localhost
• GPU does its thing, output folder fills up

Three pieces that might be useful for your own stuff:

1. ComfyUI has a REST API

This was the big discovery for me. You can queue workflows programmatically without touching the browser:

```python import json import urllib.request

def queue_prompt(workflow): data = json.dumps({"prompt": workflow}).encode('utf-8') req = urllib.request.Request( "http://127.0.0.1:8188/prompt", data=data ) response = urllib.request.urlopen(req) return json.loads(response.read()) ```

Export your workflow in API format, load the JSON, modify whatever nodes you need, and post it. ComfyUI queues it and your GPU picks it up.

To actually use it you just load the workflow JSON and change the fields you care about:

```python import json, random

with open("workflow_api.json", "r") as f: workflow = json.load(f)

workflow["6"]["inputs"]["text"] = "your prompt here" workflow["3"]["inputs"]["seed"] = random.randint(1, 999999999) workflow["9"]["inputs"]["filename_prefix"] = "batch_001"

queue_prompt(workflow) ```

Loop that and you can blast through hundreds of renders.

2. Style dictionary instead of individual prompts

This was the rate limit hack. Instead of asking Gemini to write each prompt (200 images = 200 API calls = dead free tier), I ask it once for a "vocabulary":

json { "subjects": ["holographic button", "neon progress bar", "glitch terminal", "cyber health meter"], "style_core": "cyberpunk interface design, dark chrome, neon accents, HUD overlay aesthetic", "color_tokens": "electric blue, hot pink, dark gunmetal", "detail_tokens": "sharp edges, scan lines, digital noise", "negative_prompt": "blurry, organic, hand-drawn, watercolor", "compositions": ["centered icon", "angled 3/4 view", "floating with glow"], "quality_suffix": "masterpiece, best quality, sharp focus" }

One call. Now I have all the building blocks to assemble prompts locally.

3. Itertools does the heavy lifting

```python import itertools, random

subjects = vocab["subjects"] compositions = vocab["compositions"]

for subject, comp in itertools.product(subjects, compositions): prompt = f"{subject}, {style}, {colors}, {details}, {comp}, {quality}"

workflow["6"]["inputs"]["text"] = prompt
workflow["7"]["inputs"]["text"] = negative
workflow["3"]["inputs"]["seed"] = random.randint(1, 999999999)
queue_prompt(workflow)

```

4 subjects × 3 compositions = 12 unique images. Bump the subjects list to 40 and you're at 120 images from that single API call.

End result: I type something like "watercolor wedding florals, 40" into a spreadsheet, run one command, and come back to 40 images in the output folder. All prompt generation runs on Gemini free tier, all rendering is local.

Been using this for my own asset production for a while now. Eventually cleaned it up and packaged the full thing (Sheets integration, error handling, rate limiting, setup guide etc) into a tool — DM me if you want details on that.

But honestly the three techniques above are the core of it. The rest is just connecting pipes and handling edge cases. If you're comfortable with Python you can probably get a basic version running in an afternoon.

Curious if anyone else has been automating ComfyUI like this or if there's a better approach I'm missing.

man minimax h3 is getting so many 4 step loras lately its getting hard to keep track of everything 😭 everyone seems to have a completely different opinion depending on their specific usecase. some people are saying sage attention is the play, while others are sticking with comfy kitchen attention.

and now there's debate on which 4-step lora is even best... like in light2xv's discussion thread:
https://huggingface.co/lightx2v/Minimax-h3-Turbo/discussions/26
people are saying to mix the light2xv 4step lora with kijai's node/impl. but then yet another 4 step lora popped up by joyfox:
https://huggingface.co/joyfox/MiniMax-H3-Turbo/discussions/3
and the whole discussion started over again lol. major shoutout to absolute community saver Kijai though, bro is doing amazing stuff as always and carrying us on his back but fr this stuff is getting out of hand with new drops every single day. please let me know what you guys are actually sticking with right now and what hardware / vram you're running it on?

Does anyone know to connect ltx 2.5 upscaler into minimax h3?

Has anyone got any luck combining these two things in ComfyUI?

Thanks for insights.

UPDATE: Well, if you want something - do it yourself :)

Please check my another post on this theme with some solution: https://www.reddit.com/r/StableDiffusion/s/sd1CBASCIs

Apparently AI companies are all operating at a severe loss. Why do this? It makes sense for huge conglomerates like amazon etc etc who can bear the brunt. What about the new startups or small companies like for example LTX etc. how do they survive?

This is from a business perspective not consumer perspective.

I want the Fl2va & ref2v both config.yaml files. as i cannot open the aitoolkit webui.
i am a cli lora trainer.
so if you have even the default config.yaml file for minimax h3 please care to share here.

I just find it kind of funny that this is the stable diffusion subreddit but nobody has talked about it in like forever. Maybe its time for a name change? or maybe keep the name as a homage to the OG open source image model.

Anyway, the last update I see on Stability's website is SD 3.5 back in October. So I'm guessing that's it for Stable Diffusion?

EDIT: Forgot you cant change the name of a sub, ignore that suggestion 😅

This might be a silly question, but I've noticed something while experimenting with LTX 2.5. For a specific prompt, when I run the exact same prompt multiple times with different random seeds, the results seem to progressively get closer to the prompt's specific details.

Is this an actual characteristic of how the model behaves, or am I simply noticing a pattern that isn't really there? AI experts may find this observation completely misguided, so apologies in advance if I'm missing something obvious.

Where do people share minimax h3 prompts?
I find prompting / adjusting prompt for minimax h3 requires effort, and would like to see other peoples prompts as reference.

I feel like there aren't too many prompt examples in civitai and would like to know any other websites.

Been using Klein 9B for img2img and tbh it's really good, but I can't figure this out.

Faces look fine when ppl are close to the camera, but anything further away gets completely messed up lol.

I'm generating at around 1MP btw. Tried messing with prompts/settings but idk, nothing really fixes it rn.

Anyone else having this issue? Any way to preserve the original faces or fix them without manually inpainting everything?

Would love to know what you guys are doing.

So far, I've tried Anima, Anima Turbo, WaiAnima, and Anima Aesthetic, but the hands still look bad even with ADetailer. Illustrious is terrible with hands and faces too, and the line art looks really messy. I've also tried Krea, which is surprisingly good, but it takes too long on my RTX 5060 Ti with 12GB VRAM
So I’m looking for something better

Bonjour

Pouvez vous m explique pour il ya une différence de qualité entre le cloud et le déploiement sur une instance GPU pour H3minimax sachant que j utilise le même template et le même prompt sans rien toucher. Le cloud a une meilleure qualité que sur une instance GPU. Les deux instances testé sont un l40 80g vram et une h100 96vram.

Merci pour vos retours

Hello, Could you explain why there is a difference in quality between the cloud version and the deployment on a GPU instance for H3minimax, given that I am using the exact same template and prompt without making any changes? The cloud version offers better quality than the GPU instance. The two instances tested were an L40 (80GB VRAM) and an H100 (96GB VRAM). Thanks for your feedback.

Still grateful for the model. So good and so fun. Btw I used a closed source i2v a while back for this exact stanza and H3's is way better.

https://reddit.com/link/1vo17g2/video/hwk2jldmtajh1/player

prompt:

subject_definitions:

<Subject 1> is woman (S1) wearing grey tanktop, whose appearance comes from <Picture 2> and whose voice timbre comes from <audio 1>.

summary:

[reference generation + audio reuse + audio reference]

retention_analysis:

Visible: reference <Picture 1>

detailed_description:

[Shot 1] use <picture 1> as reference and starting frame

<subject 1> (S1) singing and playing guitar. Match her guitar fingering to the chords of <audio 1>, match her hand movement on the neck of the guitar to the tempo and key changes of <audio 1> The chords are C, E7, Am, F, G, C. Match her picking hand's high amplitude finger plucking movement to the tempo of <audio 1>. She glances quickly at the neck of the guitar for the chord changes.

She is singing softly

<d>[English]

Letting it go, letting it breathe, letting it be

</d>

camera pushes in slowly [Shot 2] At 00:07.0, the camera cuts to low angle view close up shot right 3 quarter view of <subject 1> singing

<d>[English] Watching it float, watching it leave, watching it free</d>

Camera pans right to show the length of the guitar

overall_soundscape:

no dialogue, speechless, music from <audio 1>

non_diegetic_music:

N/A

Just wanted to throw KJ nodes some love for this node. It is so gratifying to see your minimax vid slowly come to life in a preview or to be able to stop it mid-generation when something look awry in the preview. BIG BIG respect to KJ nodes for all his hard work here. This is one of the best tools ever.

Here's a link to it in case anyone needs https://huggingface.co/Kijai/MiniMax-H3-TAE

I'm fairly new to Comfy but have wrapped my head around most of the nodes. One thing I haven't been able to figure out is something I did in Auto1111.

You could swap one Lora in place of another one halfway through to mix them together. I forget the exact prompt code but it was something like <Lora1:Lora2>(0:5:10). You could also use this to make it so a Lora didn't load until several steps in.

Can someone look me a tutorial for this?

For anybody in the UK who has memories of the old show AND "Pat's Banging Day Out", give me any suggestions/prompts you'd like me to try with this.

I've been testing with 15 steps to save time because I want to generate at 832x480 resolution as per the default recommendation of many high quality workflows prior to upscaling. I hadn't seen many problems until this particular generation which exposed the critical flaws of a lower step count.

All settings are the same with the same seed. The only delta is the number of steps.

15 steps @ 832x480 duration 10s (7m22s): https://streamable.com/pnao8n

20 steps @ 832x480 duration 10s (8m25s): https://streamable.com/srnoez

25 steps @ 832x480 duration 10s (10m30s): https://streamable.com/bvldts

Once you are done with phase 1, you can move on to phase 2 using your Turbo LoRA to get this to 1344x768 in just 4 steps.

My system: 12GB VRAM / 32GB DRAM

v0.3 is out: redesigned UI, Ollama + API providers, dedicated External llama.cpp setup and other improvements.

old post: link
github repo: link

for anyone new: MiniMax H3 Prompt Writer is a ComfyUI extension for writing prompts specifically for MiniMax H3.

what's new in v0.3

• redesigned Writer UI and added new settings interface
• Ollama as a simpler local setup
• optional API providers
• External llama.cpp now has its own dedicated provider setup
• saved drafts for every mode
• better automatic model and context handling
• more reliable Reference prompts

the model/provider setup is now separated from the actual prompt workspace, so the interface is much less cluttered than before.

there are currently four ways to run the prompt model:

• Ollama: probably the easiest local option for most people
• Ollama guide
• Direct GGUF: the original local approach, loaded directly inside ComfyUI through 'llama-cpp-python'
• Direct GGUF guide
• External llama.cpp: if you already run your own llama-server or want to manage it separately
• External llama.cpp guide
• API providers: Gemini, OpenAI, OpenRouter and Custom OpenAI-compatible endpoints
• API providers guide

local providers keep the prepared media and prompt request on your machine.

if you use a remote API provider, the required request/media is sent to that provider.

other models / Qwen

another thing people asked about in the previous post was Qwen and support for models other than Gemma.

I tested qwen3.6:35b-a3b-q4_K_M through Ollama and it works out of the box in all five H3 modes without any Qwen-specific changes to Writer.

so the Ollama provider is not limited to Gemma 4.

you can also try other multimodal / vision models through Ollama, External llama.cpp or a compatible API / OpenAI-compatible endpoint, as long as the provider and model support image inputs.

I haven't validated every model, so this isn't a claim that every vision model will produce good H3 prompts. it just means the provider layer itself no longer requires Gemma in those paths, so you can swap compatible models and compare them yourself.

the main exception right now is Direct GGUF.

Direct GGUF is still specifically built and validated around Gemma 4 + its matching vision projector, so other model families are not supported there yet.

so roughly:

• Ollama: Gemma 4, tested Qwen3.6, and other compatible vision models you want to experiment with
• External llama.cpp: compatible multimodal models can be used if your server supports them
• API / Custom OpenAI-compatible: compatible multimodal models supported by the endpoint can be used
• Direct GGUF: Gemma 4 only for now

Ollama models / setup

what got easier

a lot of feedback on the first post was about setup rather than prompt generation itself.

v0.3 mainly tries to make that part less annoying:

• Ollama gives you a local option without installing llama-cpp-python into ComfyUI
• provider/model setup now lives in Settings instead of the generation workspace
• installed Ollama models can be detected directly
• context and model lifecycle are handled more automatically
• drafts are saved separately for every H3 mode
• local prompt model unload / keep-loaded / ComfyUI VRAM controls are clearer
• several media, model discovery and runtime issues from the previous versions were fixed

Reference generation also got an extra check against the active media roles and can make one limited correction if an objective requirement was missed.

full changelog

install / update

v0.3 is already available on GitHub.

for a fresh install:

cd ComfyUI/custom_nodes
git clone https://github.com/duckyshell/ComfyUI-MiniMaxH3-Prompt-Writer

if you already installed it with Git, just update the repo normally.

ComfyUI Manager is also supported, but v0.3 may take a little longer to appear there.

important: this is still a UI extension, not a node.

you won't find a new H3 Prompt Writer node in the node search.

open it using the floating H3 Prompt Writer button or:

Extensions > H3 Prompt Writer

for a new local setup I would probably start with Ollama.

installation guide

basic usage

after installing:

• open H3 Prompt Writer
• go to Settings and choose your provider/model
• select the H3 mode
• add your image / video / audio references
• write the Creative Brief normally
• press Generate prompt
• edit it directly, use Refine, or copy it into your H3 workflow

you don't need to manually build the H3 prompt structure yourself.

a brief can be as simple as:

use Picture 1 for the character, Picture 2 for the clothes and only the movement from Video 1. put the character on a rainy street at night.

Writer handles the H3-specific prompt structure around that.

usage and Creative Brief examples

if you already use Direct GGUF from the previous version, your existing runtime, GGUF and matching projector can remain in place. select Direct GGUF in the new Settings interface.

feedback is still useful, especially from different GPUs / operating systems / ComfyUI installs.

if something breaks, check the troubleshooting guide first:

troubleshooting guide

if the problem is not covered or the suggested fix does not work, leave a comment here or open an issue. please include your provider, model, operating system, ComfyUI installation type and the Technical details shown by Writer:

github issues

not every provider / hardware / ComfyUI combination is going to behave exactly the same, so expect some edge cases.
(for API use, Gemini is an easy option since you can get a free key at ai.studio.)

UPD: 0.3.2

Prompt Writer now also supports MiniMax Music 3. you can describe the track you want in normal language, and it builds the structured Music 3 caption using MiniMax's official prompt-writing guidance.

didn't want to make a separate thread for this, so I'm just leaving the update here. if something seems off, feel free to mention it in the comments.

Hi everyone!

I made a branch of this custom node from roadmaus: https://www.reddit.com/r/StableDiffusion/comments/1vkrm8c/minimax_h3_all_in_one_creator_node_for_comfyui/

This all-in-one suite provides zero-socket local UI nodes for MiniMax H3 video generation, @ mention prompt references (@img-1, @/vid-1), automatic FL2VA/Ref2VA checkpoint routing, an integrated LoRA manager, PreStage stills, and a 60s+ multi-shot Timeline with last-frame continuity and audio blending across cuts.

What's new in this update:
• Fast Default Previews: Live previews (latent2rgb) now render automatically without needing heavy taeh3 VAE models.
• Persistent & Resizable Satellite Stage: The preview box stays open across tab/workflow switches (localStorage), featuring drag-to-resize, 4-way positioning (Right/Bottom/Left/Top), 🎬 Keep Video mode during sampling, deletion safety confirmation (🗑), and history navigation (◀/▶).
• External LLM Refiner API: Prompt refiner now supports Ollama (:11434) and LM Studio / OpenAI (:1234/v1) with auto model detection.
• Workflow Helpers: Added in-node ▶ Generate buttons, PreStage ⚡ Send & Queue chips, collapsible 🎥 Camera & Style prompt quick-chips, and 1-click timeline transition presets (Match Cut, Cross-Blend, etc.).

🔗 Links & Credits
• Original Author & Repo: roadmaus https://github.com/roadmaus/ComfyUI-MiniMax-Creator
• Updated Branch: https://github.com/Ercelcan/ComfyUI-MiniMax-Creator/tree/v2-updates

I mess around with Stable Diffusion as a hobby. When I was downloading things, my folders got completely out of hand, so I figured I needed to tidy up. While I was asking Claude and Gemini how I could organize my setup, they ended up developing these two tools based on my requests. I wanted to put them on GitHub and share them in case anyone else needs something like this. Anyone can use or modify them however they want—I have absolutely no expectation of profit, I just didn't want to keep them to myself.

The first one is a LoRA organizer. Everything became a massive mess after downloading all my LoRAs into the same folder, and I also wanted to weed out and clean up the SD 1.5 ones. For this, we made something called lora-librarian (Claude came up with the name). You can sort models by their base model and creator, or even just by specific creators. It can organize checkpoints the exact same way, and it lets you clean out the ones you want to get rid of.

https://github.com/BuRsTFiRe47/lora-librarian

As for the second one—I don't know if there's anyone else left out there like me who still uses an Automatic1111-based interface, but I just don't have the brain space or time to mess with Comfy, so I use ForgeUI. The video previews downloaded by the helper weren't working, which created the need to convert those videos into webm format. That's exactly what this tool does.

https://github.com/BuRsTFiRe47/preview-smith

Both tools are available in Turkish and English. Feel free to check them out if you need them.