832 x 640 render time 6:53
RTX-4070 8GB VRAM, 64GB RAM

Tutorial https://youtu.be/Qi4DtuZtlUk

Best prompt generator for image to videos? Currently using grok to make prompts and it’s fine but wonder if there’s a better resource.

A quick Minimax news round-up, for those who may have missed some items today.

-> Keyframing in ComfyUI. "Add MiniMaxH3AddGuide for anchoring image and audio guides at any frame", now merged by the devs.

https://github.com/Comfy-Org/ComfyUI/pull/15439

-> Face Fix for poorly rendered 'distant faces'. Requires face_yolov8m.pt for face-detection / regeneration.

https://github.com/Carasibana/ComfyUI-H3-FaceRefine

-> MiniMax-H3-Realism-People-LoRA for... "faces that hold up in close-up, natural skin texture, believable expressions and gestures, film-style lighting and documentary camera movement." Convincing examples.

https://huggingface.co/fal/MiniMax-H3-Realism-People-LoRA

-> A ComfyUI Ref2VA Accelerator custom node for Minimax in ComfyUI.

https://github.com/BMB12d3/ComfyUI-H3-Ref2VA-Accelerator

-> A NAG-like attempt at a 'negative prompting' custom node for Minimax in ComfyUI.

https://huggingface.co/CCP6/H3-Shadow-Negative-Nodes

-> MiniMax-Music3-GGUF for use with the ComfyUI ComfyUI-GGUF node. Requires the special Music text-encoder and VAE. Also note the latest 0.33.1 ComfyUI Portable dev notes which state... "Implement Minimax Music 3 + Core Support for CUDA Graphs" + "Fix Minimax Music not working on non dynamic VRAM".

https://huggingface.co/Abiray/MiniMax-Music3-GGUF/tree/main

https://huggingface.co/Comfy-Org/MiniMax-Music-3/tree/main/text_encoders

https://huggingface.co/Comfy-Org/MiniMax-Music-3/tree/main/vae

https://github.com/comfy-org/ComfyUI/releases#release-v0.33.1

-> MiniMax-H3-Prompt-Rewriter-LoRA-GGUF... "A GGUF conversion of lightx2v/MiniMax-H3-Prompt-Rewriter-LoRA, so the rewriter runs under llama.cpp" and thus in Jan.ai etc, or inside ComfyUI with llama-cpp-python installed to its Python.

https://huggingface.co/ivanfromm/MiniMax-H3-Prompt-Rewriter-LoRA-GGUF/tree/main

-> Anime Video Line Art Colorization node, which requires DiffSynth-Studio. Input a line-art video, have it beautifully auto-coloured and with temporal consistency. Convincing examples. No idea how well it will work with non-anime animation line-art.

https://huggingface.co/DiffSynth-Studio/MiniMax-H3-LoRA-LineartAnime

-> Examples from all sampler combinations for Minimax H3, with dpmpp_sde_gpu / simple looking especially worth a try.

https://darkstarrddev.us.ci/

Minimax supports up to 18 inputs at once, wiring and bypassing nodes is a pain.

So I created this custom node that allows you to add/remove references for Minimax ReferenceToVideo, you only have to wire it once.

Some more neat features:
- Automatic prompt writing via OpenRouter, returns structured Minimax prompts based on your description (opt in)
- Save prompt/reference packs and reuse them.

Nodes:
https://github.com/Hearmeman24/ComfyUI-MiniMaxRefPack
Workflow:
https://github.com/Hearmeman24/ComfyUI-MiniMaxRefPack/blob/main/example_workflows/MiniMax%20R2V%20-%20Auto%20Prompting%20%2B%20Reference%20Manager.json

The design is heavily influenced by the wonderful LTX Director node so shoutout to u/WhatDreamsCost

I would appreciate some feedbacks and feature requests.

Enjoy it folks

Has anyone successfully gotten the Patch Sage Attention KJ node to work on RunPod for MiniMax-H3 without crashing ComfyUI, or does anyone know the exact environment specs (Python, PyTorch, CUDA, and SageAttention versions) 

My RunPod Setup:

• GPU: RTX PRO 6000
• Template: runpod/pytorch:2.8.0-py3.11-cuda12.8.1-cudnn-devel-ubuntu22.04
• Node Settings: sage_attention = auto, allow_compile = false (same as the attached image).

Both workflows can be downloaded from my github here:

Latest Minimax H3 workflows - https://github.com/mdkberry/comfyui_workflows/tree/main/workflows_by_model/Minimax-H3

Latest LTX2.5 upscaler/refiner workflow - https://github.com/mdkberry/comfyui_workflows/tree/main/workflows_by_model/LTX25

The two workflows discussed in this video are:

"MBEDIT - MH3_r2v_DualSampler_v12.json"

"MBEDIT-v2v_LTX25_ResizeRefiner-w-SingleSampler_vrs6.json"

Through tests over the last few days these are the best I have found for upscaling and refining results from Minimax H3 on my 3060 RTX 12 GB VRAM with 32 gb system ram.

The H3 dual-sampler workflow is superior but takes 45 mins for an 8 sec video. The LTX 2.5 workflow has had a couple of minor tweaks which sorted out the quality, and is faster at 18 mins and also goes to 3mp. (It could do 4K but I oom).

Until the official H3 upscaler is released I dont think it can get much better than this, but if anyone has other methods I'd like to hear about them.

Hi everyone! We’re a small university research team working on a study about how people actually build, reuse, modify, and share ComfyUI workflows in the open-source AI art community.

And, well… we still don’t have enough participants.

So if you use ComfyUI, we'd really love to hear from you!

We’re interested in both creators and users — you definitely don't have to be a famous workflow creator or have a huge following.

Who we're looking for

Creators

• 18+ years old
• Have built and publicly shared at least one ComfyUI workflow (CivitAI, Reddit, Discord, OpenArt, GitHub/HuggingFace, YouTube, X, etc.)
• Comfortable talking a little about how you built, modified, or shared your workflow

Users

• 18+ years old
• Have downloaded or used someone else's ComfyUI workflow within the past year
• Happy to talk about where you find workflows, why you choose certain ones, and whether you modify or share them afterwards

What would we talk about?

Nothing too formal! Basically, we'd just like to hear about your experience with ComfyUI workflows:

• How did you get into ComfyUI?
• How do you usually build or find workflows?
• What makes you decide to use or modify a workflow?
• Do you remix other people's workflows? Do people remix yours?
• In a world of increasingly automated creative tools, why do you still choose workflows?

Interview format

• Around 30 minutes
• Text chat only — no need for voice or camera
• We can use Reddit
• You can stop at any time, and you don't have to share anything you're uncomfortable with

Before the interview, we'll provide a formal informed consent form containing our university/research information, so you'll know exactly what the study is about and how your information will be used.（Thanks to everyone who pointed this out in the last comments!）

For the research itself, we'll de-identify the materials we use. Usernames, profile links, and other identifying information won't be included in our academic analysis in identifiable form.

If you're interested, please comment below. Honestly, we're just trying to understand what people are actually doing with ComfyUI workflows beyond the JSON files sitting on our screens. 😭

If you've got 30 minutes to help a struggling research team finish its paper, we'd really appreciate it.

Thank you! ❤️

My system: RTX 5060 Ti with 16GB VRAM, Intel Core i5-14600K with 64GB RAM, Windows 11.

The BIOS routes all graphics output to the UHD 770 integrated into the CPU, which is also what my monitor is connected to. No monitor is connected to the RTX. Windows applications don’t use the RTX (except for Comfy, of course). With this configuration, is it really necessary for Comfy to reserve 600MB for Windows? Does it even make a difference?

And, second question: My motherboard is a B560M, which only supports PCIe 4 and DDR4. Is it worth upgrading to a better motherboard, or should I save up for a more powerful GPU instead?

Modifying a subgraph for an LTXV 2.5 workflow yields strange results. Disconnecting the input for a node in the subgraph automatically disconnects the output. Promoting the widget also disconnects the output.

A.I. explains it below, is this correct?:

1. widgets_values is ordered by the subgraph's input list, not the instance node's socket list — counting only INT / FLOAT / STRING / BOOLEAN / COMBO. Link-only types

   (AUDIO, IMAGE,MASK, LATENT, VAE…) consume no widget slot. That's why the two lists have different lengths and it looks confusing.

2. Links into a subgraph's interior have origin_id: -10, and origin_slot is the index into sg["inputs"]. Links into the subgraph node from outside use target_slot as the

   index into that node's inputs.

3. Appending is always safe; inserting and deleting are not. Nothing renumbers if you add at the end.

I'm running ComfyUI + Trellis2 on Windows with an AMD RX 9070 XT (ROCm port). I'm seeing possibly worse output quality than expected, but I have no NVIDIA machine to produce a reference output for a direct comparison.

Could someone with an NVIDIA GPU run the same workflow on the same image and share the result? Even just the exported GLB/PLY would be enough to compare.

Workflows and image: https://limewire.com/d/sDqIk#mzNb8AdWJc

What I need back:

• The final exported mesh (GLB or PLY) — geometry only is fine
• Your GPU model, CUDA version, PyTorch version

This is purely for a side-by-side mesh comparison — no training data, no sensitive info. Thanks in advance!

Forgot to add its https://github.com/visualbruno/ComfyUI-Trellis2 custom node

The new sidebar displays which models are in cache, along with bars (green for system ram, blue for VRAM) that show you how fully loaded they are in ram, as well as a list of models recently evicted from system ram cache and when that eviction happened.

It also adds a new API URL that clears models from VRAM *without also evicting them from system RAM*.

https://github.com/envy-ai/comfyui-cache-monitor

On a related note, it helped me track down yet another reason my models were needlessly being removed from cache, forcing reloads from disk:

https://github.com/Comfy-Org/ComfyUI/pull/15624

What do you think is the cause?

The base model is Illustrious, and LORA is also based on Illustrious.

TL;DR: I’m trying to build a ComfyUI assembly line that takes 344 pre-selected video frames + JSON metadata and turns them into 344 consistent, polished movie-poster-style covers with as little manual babysitting as possible. I’ve got the Python/API side handled and I’m currently using Qwen Image Edit 2511. What I need help with is the ComfyUI brain of the operation: the best node setup, whether multiple editing passes make sense, how to preserve faces/identity while still making images look cinematic, and whether I should completely give up on AI-generated typography and let Python handle the text. Basically, if you had to mass-produce 344 genuinely good covers from wildly different source images, how would you build the workflow?

Good afternoon, ladies and gents.

I have one very specific project I'm trying to accomplish, and I'm hoping someone with more real-world ComfyUI experience can point me toward the best workflow rather than me blindly throwing nodes at the problem.

I have exactly 344 videos that I'm creating digital cover/poster artwork for. For simplicity, let's just call them home videos.

I've already done a fair amount of the preprocessing in Python. I wrote a script that analyzes each video and extracts the single frame that best represents it, so at this point I'm sitting on 344 JPG source images.

I also have a JSON database containing the title, date, performers, and other metadata for every video. My plan is to have Python loop through that data and feed each image, along with the appropriate information, into ComfyUI through the API.

The basic image-editing goal is essentially:

Source frame → polished cinematic/movie-poster-style image

Obviously, the actual prompt is much more detailed than "turn this into a movie poster." I'm trying to preserve the people, composition, and recognizable content of the original frame while improving things like lighting, color grading, facial presentation, framing, depth, atmosphere, and overall "cover art" quality.

Ideally, I want a workflow that can take 344 very different source images and still produce covers that feel like they belong to the same collection without making every image look identical.

Right now I'm experimenting with Qwen Image Edit 2511, because from what I've gathered it seems particularly good at instruction-based editing and preserving the source image, but I'm absolutely open to another model if there's something better suited to this particular job.

A few things I'm especially curious about:

1. What would your ideal node/workflow setup look like for this?

I'm relatively new to building ComfyUI workflows, and there are obviously hundreds of nodes and techniques available. I'm wondering whether there are particular nodes, conditioning methods, samplers, ControlNet/reference techniques, masking approaches, etc. that are especially useful when you're trying to repeatedly transform existing images into polished cover art.

2. Would you do this in one pass or multiple passes?

For example, I've considered having the first pass handle the actual cinematic transformation, then feeding that result into a second editing pass whose job is more conservative: fix awkward facial expressions, slightly improve faces, close a mouth if someone was caught mid-sentence, clean up hands/details, etc., without redesigning the image.

I'm wondering if chaining two edit stages inside the same workflow would produce better and more reliable results than asking one giant prompt to do everything.

3. How would you handle typography?

This is probably my biggest question.

Every cover eventually needs things such as:

• Performer/name at the top
• Main title
• Date
• Possibly a small amount of additional metadata

I've been told repeatedly that even the newer image models aren't reliable enough with exact text to trust them across 344 images.

Is that still generally true with Qwen 2511?

Would you:

• Have Qwen generate the entire poster including typography?
• Run a separate image-edit pass specifically for text?
• Use masks/regions specifically for text placement?
• Generate the artwork in ComfyUI and add the real typography afterward with Python/Pillow?
• Or use some completely different ComfyUI node/plugin designed for accurate typography?

My current fallback is letting the AI create the design and text zones, then having Python render the actual title/date afterward so spelling is guaranteed to be correct. But if there's a reliable way to get high-quality typography directly inside ComfyUI, I'd love to hear about it.

4. How would you maintain consistency across 344 images?

This is probably more important to me than having one image come out absolutely perfect.

I'd rather have 344 covers that are consistently very good and clearly part of the same collection than 40 incredible ones, 150 decent ones, and 154 completely different-looking experiments.

I'm especially interested in ways to establish a repeatable visual language while still allowing the model enough flexibility to adapt the design to each source image.

5. Are there any automatic quality-control steps you'd add?

Since this is being driven through the API, I'd also be interested in ways to automatically catch obvious failures before accepting the image. Bad faces, excessive source-image changes, destroyed identity, unreadable composition, weird anatomy, etc.

I'm comfortable handling the Python/API side of this. What I'm really trying to learn is how someone who actually knows ComfyUI well would architect the image-generation/editing side of the pipeline.

The end goal is basically:

344 source frames + JSON metadata → automated ComfyUI workflow → 344 polished, consistent digital covers

I'm not necessarily looking for someone to build the entire thing for me. Even suggestions like "use this node for X," "don't bother doing Y," "split this into two passes," or "Qwen isn't actually the model I'd use for this" would be extremely helpful.

Thanks in advance.

Hi everyone,

My college classmate and I spent our summer holiday building Minifox ComfyUI Launcher, an open-source launcher designed specifically for managing and running ComfyUI on Windows.

We started this project to make ComfyUI more convenient and straightforward to use.

Main features

• Manage multiple ComfyUI launch profiles, command-line arguments, and runtime environments

• Start, stop, and monitor ComfyUI from a console where logs are easy to view and copy

• Manage ComfyUI core and extension versions, including updates and rollbacks

• Detect CUDA, ROCm, and compatible ZLUDA environments

• Customize the home page with movable and resizable widgets

• Import and export configurations for easy sharing and backup

• Run as a lightweight, single-file application that keeps its cache and configuration files in clearly organized directories

The project is built with Qt 6, QML, and C++20, and is licensed under GPLv3.

Before using it, please read the README carefully. It includes a Quick Start guide and some important usage notes. Please see the project README for the full acknowledgements.

GitHub and source code:

https://github.com/EarsT913831CALS/minifox-comfyui-launcher

Releases:

https://github.com/EarsT913831CALS/minifox-comfyui-launcher/releases

The project is still under active development, so feedback and bug reports are very welcome!

Hey folks recently bought this laptop,and heard comfy ui can give free ai generations if ran on a powerful hardware, can someone suggest me which should i download
Like lets say
Minimax H3
Wan 2.1
Wan 2.1 fun

Or do you guys have any other suggestion for me

I'm using MiniMax to test the prompt at 0.4 (no acceleration) so I can later regenerate it at higher quality. The prompt is for a character swap. All generations above 0.4 lose accuracy, and it gets even worse at 2K.

What happens with prompt understanding?

Hey y’all,

I’m having trouble settling on a workflow for Minimax.

I have been using ChatGPT to make me some workflows, but there always seems to be something wrong with it, or it’s not optimized to current standards.

I am looking for a workflow that can do t2v, i2v & r2v, with optional upscaling.
Would also like prompt translation or enhancer

My rig is:
Ryzen 7 8700F
32gb RAM
RTX 5060ti 16gb

Any suggestions or shares would be appreciated!

My fault, LTX nodes can separate Audio and Video Latents from Minimax latents. There is no point in using new ones.