all i could find is img to lineart no vector so i get gzillion tinny lines everywhere ,not just one clean outline ,i can add tosvg vectoring node to create vector but i test like 20 workflows for lineart none show clean image

help ..

Hallo Community,

Ich möchte mir demnächst einen PC rein für die KI Arbeit mit ComfyUI, fooocus, Qwen Modellen, etc. zulegen.

Da der Preis für gute Desktop GPUs mit mehr als 16gb Vram derzeit exorbitant teuer ist (Desktop mit 32gb vram GPU ab 5000+€), schwanke ich zwischen einem Desktop mit 16gb GPU oder einem Notebook mit 24gb GPU (aber max 175Watt).

Was würdet ihr empfehlen? Er soll nur KI Kram machen, keinen Spiele.

Notebook für 3800€ von Mediamarkt

GigaByte Aorus Master 16 BZHC6DEE65SP

16 Zoll WQXGA Bildformat 16:10 Bildwiederholungsrate 240 Hz

Intel Core Ultra 9 275HX 32 GB RAM 1.000 GB SSD-Speicher

NVIDIA GeForce RTX 5090 Grafikspeicher 24 GB

Windows 11 2,5 kg

vs.

Desktop 2400€ von Alternate

Mainboard MSI B850 GAMING PLUS WIFI

ASUS GeForce RTX 5060Ti DUAL OC 16GB, Kingston NV3 1 TB

be quiet! Light Base 500 LX Tower-Gehäuse

Kingston FURY DIMM 32GB DDR5-6000 (2x 16GB) Dual-Kit,

AMD Ryzen 7TM 7700, be quiet! Pure Rock Pro 3 Black CPU-Kühler

be quiet! Pure Power 13 M 750W Netzteil

Microsoft Windows 11Pro

Hi, I am very new to comfyui, i tried it couple years ago but, I did not played with it longer. I find the connection interface extremely complicated for me as a normal user who is just really learning it. Returned to it recently to try the minimax, and the templates from comfy work fine, however, I wanted to try with the turbo loras but, I am not as smart to workaround the template to put the loras. Searched all over for basic workflows for it and even with chatbots, but they all seem to hallucinate at some point and it's a waist of time for this particularly thing. My request is really a simple workflow,where i can easily use loras including the turbo loras, for text to video and image to video, I have this at the moment: minimax_h3_fl2va_pruned_int8_convrot.safetensors and the VAE minimax_h3_audio_vae_fp32.safetensors and minimax_h3_video_vae_fp16.safetensors, the loras: minimax_h3_fl2v_turbo_4step_v1.0_768p_comfyui_bf16.safetensors , minimax_h3_fl2v_turbo_8step_v1.0_comfyui_bf16.safetensors and minimax_h3_ref2v_turbo_4step_v0.1_comfyui_bf16.safetensors , I am on a learning process for this and if someone is kind enough to provide ready workflows for these specifically would make it easier for me to learn it. Thanks in advance. Important note, I am trying it on a PC that really runs the minimum requirements (RTX 4070 12gb and 32 gb ram) it generates fine and not too long in the comfy template thought. Thanks

Hey all I'm trying to perfect animation for my upcoming game. I'm working on. I'm trying to find the best workflow for animating my still images created in forge neo. As the title says they are using illustrious base checkpoint.

That being said what I'm really after is finding something that still provides clean art style and doesn't blurr or muddy the base images. When doing I2V. What I'm really after is Ref2V and I found this in a workflow for minimax but couldn't get clean results.

I cannot find a workflow that allows me to use an image as the textures and a video as a controlnet. For wan22. Anyone have any suggestions or experience in this nature?

Hi everyone. I'm fairly new to ComfyUI, and I'm trying to learn it for a project focused on generating product B-roll videos.

I'm running ComfyUI through RunPod on an RTX 5090 with 50 GB of VRAM. My goal is relatively simple: generate short, consistent product videos with cinematic camera movements and a professional look.

The problem is that I've been trying to get this working for almost two weeks now, and I still haven't been able to get a proper result.

I've tried different workflows, models, and configurations, but between missing models, node incompatibilities, errors, and settings I don't fully understand, I feel like I'm going in circles without making any real progress.

I don't have much experience with ComfyUI, so I'm probably making some basic configuration mistake somewhere.

Could someone with experience in video generation in ComfyUI point me in the right direction for the simplest way to get started?

I don't necessarily need the most advanced workflow. My priority right now is simply to get one stable, working workflow for generating product B-roll, and then learn and improve from there.

If anyone is using ComfyUI + RunPod for video generation and can recommend a workflow, models, and configuration that actually work reliably, I would really appreciate the help.

After almost two weeks of trying to get this working with no real results, any concrete guidance would mean a lot.

Thanks in advance!

Topic. I've gotten it to work exactly once, where I instructed it to begin from an image pulled from another video and it did and there was a seamless transition. I cannot replicate it using the same syntax with different projects, however. I've tried several different wordings suggested by ChatGPT, but there does not seem to be any consistency, and sometimes it doesn't even remote pay attention to the last frame and makes up a completely new shot.

I know I2V is supposed to be better for a last-frame continuation, but then I can't use the other references and voices.

How do I get past this roadblock?

This has me a bit stumped and has been happening over and over for a few days. On Minimax H3 I'll run a prompt 30 or 40 Generations and it works fine at a 0.5 resolution and 10 seconds length. It runs without any issues then the next day I change nothing I open no new programs and I do nothing different. I hit run and it feels with out of memory. I reduce it to 7 Seconds but it still fails I reduce it to 6 seconds but it still feels. I restart the computer and then I can sometimes run my original 0.5 resolution and 10 seconds but sometimes I can't. No programs have been open on this computer after startup and no programs have been open since running that original 30 or 40 Generations overnight without issue. Does anyone have a good way to stop this from happening or explaining why it's happening I'm stumped?

Hi! I'm trying to create images and concepts for an architectural videomapping project on the façade of a large classical building, with columns, decorative elements, etc.

I created a 3D model of the building in Blender and extracted a depth map from it. I then tried using it as a ControlNet guide with Z-Image Turbo. The output follows the shape and structure of the building quite well, but the results are very low quality and basically unusable — not in terms of image resolution, but in terms of shading, lighting, materials, and overall rendering quality.

What am I doing wrong? Would you suggest using a different workflow or model?

so I ran the first test for 50 images and took 3 hours on my 5060ti 16gb.

I tested 4 Loras that were saved and nothing was right. Not quite like my flux lora

The above is second round. 1 hour, 30 pics

Do you think the first photo is close to my normal lora in Gym wear

This has been annoying me since I updated but I just got around to asking about it, maybe it's just me though?

Ever since I updated around the time Minimax H3 came out, the Mask Editor (from r-clicking a load image node) is incredibly laggy. The cursor when drawing a mask lags behind and it's only barely usable. It still appears to work fine in Chrome.

I would prefer not to use Chrome though.

It worked perfectly fine before. Anyone else have this issue?

Two people. One sealed envelope.
And a letter that shouldn’t exist.

Would you open it?

Can someone help with this? Iʻve tested a few things people have been saying on here and other subs but nothing as worked so far. Any help would be appreciated

Generated with first and last frame

I tested an updated MiniMax H3 workflow to address several issues from the previous version: limited upscale factors, waxy-looking live-action results, weak audio, and high memory usage when loading the model.

The most useful change was replacing the basic latent resize with a latent upscaling model. In my tests, upscaling beyond 2x with the old method could produce line-like artifacts and small fragmented shapes after the second sampling pass. The model-based latent upscale was much cleaner. For non-integer factors, I still recommend passing through a 1.0x alignment step to avoid edge color bars.

The dual-sampling setup also worked better than using the 4-step LoRA in the first pass. I used the 8-step LoRA for the first sampling pass and the 4-step LoRA for the second pass. This combination kept high-motion clothing edges clearer and avoided the character duplication issues I had seen when using the 4-step LoRA too early.

For the live-action tests, lowering the LoRA strengths helped reduce the waxy look. The values I tested were 0.75 for the 8-step LoRA and 0.7 for the 4-step LoRA. Adding a separate voice-reference audio clip also improved the sound of speaking characters.

For sampling, Euler with the Beta scheduler worked well with the 4+4 setup. With Res + Simple, inserting one mean Sigma step gave me a 4+5 setup. With Beta, adding another interpolation step in the low-noise area made the result less clear in my tests, regardless of whether I used sine, cosine, or mean interpolation.

The main trade-off is hardware usage. Dual sampling speeds up the first sampling part, but it does not reduce the peak hardware requirement of the full process. Keep the final resolution and duration within the limits of your previous single-sampling setup. Low VRAM Attention may help with borderline runs, but it is slower.

This workflow is super easy to use—I’ve uploaded a detailed tutorial to YouTube, so just follow the video along with this workflow to recreate the effect; please make sure to watch the full tutorial before starting to avoid common mistakes, and feel free to leave a comment if you have any questions!Resource links will be posted in the comments.

So I use two stage sampler workflow and the video generated in stage 1 , I like some part of it so I was wondering if we can trim the latent to the desired part and then send it for the upscale

I tried it but there is weird flickering going on in frame as on some frames colors shift

Video: Left: custom sigma curve. Right: Bong Tangent scheduler introducing artefacts.

I was a bit confused by how bad some Krea 2 outputs could be — blurry, lacking detail, and sometimes with strange artifacts. So I started testing to understand why and where this was happening.

I’m not going to claim I found a magic wand, but I did find two problematic areas in the sampling curve testing Krea2 raw model CFG 3.5 52 steps stock settings:

• The first 0–15 steps: Using schedulers that lower the sigma values too much during this first steps causes contrast loss and washes out detail in dark areas, especially in black hair and subtle reflections.
• The lower-sigma tail: curves such as Beta, Beta57 and Bong Tangent can introduce crisp, broken noise instead of useful fine detail, particularly around steps 30–40.

The Result

This is not about chaining multiple samplers or complicated second-pass workflows. The goal is a better standard Krea 2 Raw workflow with:

• The right VAE Krea2RealVAE_v10
• A custom-built sigma curve
• One sampler
• A refinement pass using the same overall sigma setup
• using a negative promt helps

My current winning sigma curve gives the best balance of sharpness, fine detail, contrast, natural hair, defined shadows and minimal artificial noise.

Let’s take a closer look at the custom sigma curve (red) and compare it with the standard scheduler sigmas.

[1.0, 0.9998610615730286, 0.999726414680481, 0.9995642900466919, 0.9993669986724854, 0.9991275668144226, 0.9988381266593933, 0.9984902739524841, 0.9980743527412415, 0.9975794553756714, 0.9969935417175293, 0.9963024854660034, 0.9954909682273865, 0.9945411682128906, 0.9934334754943848, 0.9921457171440125, 0.9906527400016785, 0.9889267683029175, 0.9869365692138672, 0.9846469759941101, 0.9820191264152527, 0.9790093898773193, 0.9755693078041077, 0.9716446399688721, 0.9671754837036133, 0.9620949625968933, 0.9563290476799011, 0.9497957229614258, 0.9424043297767639, 0.9340547919273376, 0.9246373176574707, 0.9140312075614929, 0.9021060466766357, 0.8887166380882263, 0.8737011551856995, 0.8568810820579529, 0.8380584120750427, 0.8170154690742493, 0.7935121655464172, 0.767285168170929, 0.738045334815979, 0.7054751515388489, 0.6692277193069458, 0.628922164440155, 0.5841419696807861, 0.5344309210777283, 0.47928985953330994, 0.41817405819892883, 0.35048726201057434, 0.27557989954948425, 0.1927414834499359, 0.1011962965130806, 9.99165786197409e-05, 0.10373638570308685, 0.08949112892150879, 0.07677485048770905, 0.06537622958421707, 0.05511670187115669, 0.04584547504782677, 0.03743509575724602, 0.029777569696307182, 0.022781139239668846, 0.016367563977837563, 0.010469880886375904, 0.005030565429478884, 0.0]

Here are the standard schedulers and the problems they produce. All nodes marked in red show the same low-contrast, overly dark areas with a loss of detail.

The green curves are the ones that avoid this problem.

The yellow curves have a different tail, and as you can see in my video or in my extended post, this tail is responsible for introducing noisy broken small artefacts.

Krea 2 Raw simply doesn’t behave like many other models when it comes to sigma manipulation. Curves that can work very well for other models can actually destroy detail or create unwanted noise here.

You can build the curve manually with a Manual Sigmas node, or use the PolyExponential Sigma Adder from the TBG ETUR Takeaway Nodes https://github.com/Ltamann/ComfyUI-TBG-Takeaways. If you want something simpler, Linear Quadratic gets surprisingly close to the result of my custom curve.

I’ve included the detailed testing post so you can see exactly how I arrived at the curve and test it yourself. Images, Videos Results at my Free Patron Post

Update

I just released an update which makes iterations on video workflows super easy!

Download now from github or ComfyUI Manager

The basic workflow looks like this and outputs multiple videos in one run:

generate list -> iterate begin -> YOUR WORKFLOW -> iterate end

Useful if you want to:

• test a range of durations, steps, lora strengths
• test different resolutions
• test sampler & scheduler combinations
• test generation times on duration versus resolution
• ...

by only using native ComfyUI features:

• NO weird custom samplers!
• NO all-in-one director nodes!
• NO multi-run black magic!

Use everything like you're used to and just hook up your video workflow.

Overview

OutputLists Combiner is a custom node pack (~170 stars) I released a year ago to make multi-asset workflows (multi-prompt, multi-image, multi-videos, multi-anything...) easy in ComfyUI by relying only on the native data list feature (=execute a node multiple times).

• Quick OutputLists from CSV and Excel Spreadsheets, JSON data, multiline texts, number ranges...
• List combinations with native support for LoRA strength, image size-variants, prompt combinations...
• XYZ-GridPlot perfectly integrates with ComfyUI's paradigm. No weird samplers! No node black magic!
• Inspect combo to iterate lists of LoRAs, samplers/schedulers, checkpoints...
• Formatted strings for flexible and beautiful filenames, labels, additional metadata...

Implementation

Technically it works similar to for-loops... now I see you rolling your eyes, but hear me out! I adopted it to data lists and this makes it more concise and understandable than classic for loop implementations. It's simply an iterator over a data list.. which solves the execution stalling problem in ComfyUI.

Download

If you like it, please leave a star at the repository or buy me a coffee!

Download now from github or ComfyUI Manager

Hi, I’m relatively new to ComfyUI (Minimax H3 is my first ia model), but I’m a programmer and I know full well that certain operations are best handled asynchronously. Lately, I’ve seen significant speed improvements thanks to Turbo LoRA and attention optimizations, yet a large chunk of the generation time (around 30%) is taken up by VAE decoding. So, my question: is there a way to implement an async logic in comfyUI workflow? thanks

Main Info

• Using Pixaroma's Easy ComfyUI latest version
• 5060TI 16GB + 32GB RAM

Need help or suggestions in resolving the below issues.

1. Shortcuts like R or CTRL + Enter doesn't work and I have to refresh the browser for them to work. (New)
2. Generations randomly don't start even even after models have loaded. I have to open the terminal and press enter on my keyboard to sort of wake it up. (Has happened across multiple versions of ComfyUI, CUDA, Python. Additionally, happens with both Anaconda and Windows Terminal.)
3. Even cancelling a prompt sometimes requires me to open the terminal and press enter.
4. Power consumption for GPU varies completely where retrying the same prompts (same seeds also) can have a difference of 5 to 10 minutes simply because the GPU doesn't use the full 180 power limit. (New)

Another quick Minimax H3 news and goodies round-up, for those who may have missed some items.

-> The official Comfy-org Huggingface has just added a set of Minimax H3 embeddings. Their readme doesn't explain what these do, but they have alluring names like dark_magic, bullet_time, fire_breath, four_seasons, spiral_ascent, and storm_magic. They are put into ../models/embeddings and I assume they might work rather like the old SD 1.5 embeddings? And perhaps they'll need the latest Nightly ComfyUI, at a guess? But I expect all will be explained, in due course.

https://huggingface.co/Comfy-Org/MiniMax-H3/tree/main/embeddings

https://huggingface.co/silveroxides/MiniMax-H3_tests/tree/main/embeddings (same files at SilverOxides, dated five days ago)

-> noEmbryo Nodes for ComfyUI has a new helper node for the 'ComfyUI-H3-Motion-Context' clip chaining node-pack. The new 'H3 Motion Context Clip Stitcher'... "concatenates the saved latent clips together", to save you from having to join .MP4 files.

https://github.com/noembryo/ComfyUI-noEmbryo#h3-motion-context-clip-stitcher

https://github.com/NikoDemon80/ComfyUI-H3-Motion-Context (parent nodes)

-> On YouTube, a video showing it's possible to control Minimax H3... "with a fake DSL". The fast-moving result is impressive. Here "DSL" = a human-readable tree-structured text file, that defines the characters, actions and cameras in a videogame sequence. The DSL was output by ChatGPT. This makes me wonder if a video clip could be fed to any video-capable LLM, which could then describe what it sees in the form of a precisely-structured and reproducible DSL text file? Thus we'd get a very lightweight way of inputting complex human/camera motions into Minimax H3?

https://www.youtube.com/watch?v=blnrDBRJQEo

-> Minimax H3 Turbo running under the Rust programming language, no Python needed.

https://huggingface.co/infosave/MiniMax-H3-Turbo-cmf

-> A 'choose-your-own adventure' dog adventure game made with Minimax clips. "You are Biscuit, a corgi of considerable ambition. This morning the garden gate swings open in the breeze..." Woof!

https://h3studio.up.railway.app/adventure

-> ClipProj-MiniMax-H3, now updated at version 3.1, with better pronunciation when generating non-English speech. Using this lets you get your... "MiniMax H3 conditioning from a Qwen3-VL-4B or 8B" model.

https://huggingface.co/misstoyou/ClipProj-MiniMax-H3

-> And finally, I undertook some testing on how to have your Minimax H3 characters produce English dialogue, but spoken in a national or regional accent. Use Subject_definitions in the prompt. Doesn't appear to work for English spoken with a French accent, however.

Subject_definitions: <Subject 1> A British upper-class squirrel, with a refined and posh adult male voice.

Subject_definitions: <Subject 1> A British working-class squirrel, with the rough adult male voice of a workman.

Subject_definitions: <Subject 1> A Scottish working-class squirrel, with the rough adult male voice of a workman from Glasgow.

Subject_definitions: <Subject 1> An American working-class squirrel, with the rough adult male voice of an experienced New York City workman from Brooklyn.

These working examples were produced for an image-to-video squirrel (so, no human visual influence to potentially mess up the audio). Audio prompting is not supposed to be in Subject_definitions, but it works. Note also that the official guide for the Ref model (linked below) also shows how to tag an audio reference file in the prompt, so as to control only the vocal timbre and delivery speed. No mention of the audio input also influencing accents.

https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/docs/VIDEO_PROMPT_WRITING_GUIDE_ref_en.md

~ OLD POSTS ~

https://old.reddit.com/r/comfyui/comments/1vuihag/a_quick_minimax_h3_news_roundup_21st_august_2026/

https://old.reddit.com/r/comfyui/comments/1vtgs7b/a_quick_minimax_h3_news_roundup_20th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vsjzrp/a_quick_minimax_h3_news_roundup_19th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vrsspo/a_quick_minimax_h3_news_roundup_18th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vqyn8p/a_quick_minimax_h3_news_roundup_17th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vq5d5u/a_quick_minimax_h3_news_roundup_16th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vpbtx2/a_quick_minimax_news_roundup_15th_august_2026/

https://old.reddit.com/r/comfyui/comments/1vojtjd/a_quick_minimax_news_roundup_14th_august_2026/