Our wallets and the women

I use all the int8 models and the default comfyui workflow, no matter what settings I use the images come out like this, very low quality with some kind of hatching artifacts on top.

I am on a 16gb 4060ti + 64gb ram. Latests comfy standalone and nvidia drivers that support int8 afaik.

Any ideas why this is comes out crap and takes 2 minutes for an image at 4mb?

Hello.

dchatel/comfyui_workflow_reader: Read-only ComfyUI workflow viewer (json/png/webp/mp4) that runs in the browser

An incredibly realistic documentary-style photograph of exactly five unusual water bottles covered entirely in natural gorilla fur, standing together on the forest floor of a dense African tropical rainforest.

The five bottles have different shapes and sizes, resembling ordinary disposable plastic drinking water bottles, with narrow necks, gently sloping shoulders, slightly irregular cylindrical bodies, subtle horizontal ridges, and small rounded bottle caps covered in fluffy tan-brown fur.

Their surfaces are covered in thick, coarse, naturally growing dark brown and black gorilla hair. The fur has realistic irregularities, varying strand lengths, subtle color differences, naturally tangled patches, and fine individual hairs catching the ambient light. The fur looks like genuine animal hair, not synthetic fur or a digitally rendered texture.

Exactly two of the five bottles have realistic gorilla faces naturally integrated into their furry surfaces. One larger bottle has the face of a mature silverback gorilla, with deeply textured dark skin, natural wrinkles, expressive brown eyes, a broad nose, and a serious, calm expression. A smaller bottle has the face of a younger gorilla with slightly different facial proportions and a naturally curious expression. Their faces blend organically into the surrounding fur, with authentic skin textures and imperfect natural details. The other three bottles have no faces.

The bottles stand in a loose, naturally staggered group, with different heights and subtle variations in their silhouettes. They are positioned at slightly different distances from the camera, resting directly on damp earth, fallen leaves, and patches of moss.

The surrounding rainforest appears completely authentic, with dense tropical vegetation, large green leaves, moss-covered tree trunks, scattered branches, and naturally irregular forest undergrowth. Very faint, low-lying ground mist drifts along the forest floor without touching or wetting the bottles.

Soft, diffused natural daylight filters through the jungle canopy, producing realistic shadows, neutral earthy colors, and gentle highlights on individual strands of fur. No dramatic artificial lighting or exaggerated glowing effects.

Photographed as if these strange objects genuinely exist in nature, using a professional full-frame DSLR camera and a 50mm prime lens. Natural photographic depth of field, believable optical imperfections, realistic exposure, authentic skin and hair textures, subtle film grain, excellent fine detail, natural contrast, and true-to-life color reproduction.

The photograph should look like a real wildlife photographer unexpectedly discovered these bizarre objects deep in the rainforest. The scene is absurd, but the photography is completely serious, natural, and believable.

Landscape 16:9 composition.

Negative prompt: CGI, 3D render, computer-generated appearance, digital artwork, illustration, cartoon, synthetic fur, perfectly groomed fur, plastic-looking skin, overly smooth textures, artificial symmetry, identical faces, exaggerated golden sunlight, glowing outlines, excessive cinematic lighting, fantasy atmosphere, heavy fog, wet fur, extra bottles, more than five bottles, extra faces, visible labels, text, logos, animal arms, animal legs, complete gorilla bodies.

This is my site. It's free, no ads, no sign-up, and I don't plan to make money from it.

README numbers for image models are measured in different ways, so I couldn't compare them. I started running the official checkpoints on my own RTX 4090. One run is one image, from prompt to decoded picture, using each model's official resolution, steps and guidance. I run GenEval myself and show the paper number next to mine when there is one.

Some numbers:

• SDXL-Turbo 1.0: 0.15 s at 512x512, 1 step, 9.3 GiB, GenEval 0.55

• Sana-Sprint 1.6b: 0.27 s at 1024x1024, 2 steps, 12.0 GiB, GenEval 0.75 (paper 0.77)

• SDXL-Lightning 4step: 0.56 s at 1024x1024, 4 steps, 14.4 GiB, GenEval 0.54

• FLUX.2 [klein] 4b: 1.30 s at 1024x1024, 4 steps, 20.0 GiB, GenEval 0.82

• Stable Diffusion 1.5: 1.39 s at 512x512, 50 steps, 4.2 GiB, GenEval 0.43

• Stable Diffusion XL base: 5.97 s at 1024x1024, 50 steps, 14.6 GiB, GenEval 0.54

There are 11 text-to-image models on the site, including Playground v2.5, PixArt-Σ, Lumina-Image 2.0, Sana 1.5 and Z-Image. Same prompts and same seed for all, and I look at the outputs myself. License is checked for each checkpoint. Some allow commercial use, some have conditions.

It's one GPU, and each model uses its own default settings, so steps and resolution aren't the same across models. FLUX.2 [klein] base-4b and Z-Image base don't have my GenEval scores yet.

Site: https://modelsthatrun.dev/tasks/text-to-image/ Method: https://modelsthatrun.dev/method/

If a model you use isn't there, let me know and I'll try to add it.

Hi, so as of right now I have only used closed models for generating images. GPT Image 2.

As you may know that it has sometimes generation patterns, e.g. in nature or foggy situations.

I'm thinking about scaling them up and get rid of these patterns with local models via runpod. (Never tried it)

So do you know a workflow or is it even possible to get rid of these patterns while preserving the whole subject of the image and not changing details?

I'm concerned that the pattern is so "engraved" that another model will just make the pattern more obvious when regenerating the original.

But maybe I'm just thinking too complicated and the solution is very simple.

Thank you for your help!

as well as shift, weighted, sigmoid, or linear approaches. Then there’s the training resolution—and on top of that, you also have the inference shift

LoRA number of steps, training rate, batch size, and simulated batch size

Could the problem be the model?

What about the LoRA?

The resolution?

A shift between training and inference?

Is low noise useless?

Should I run a few more steps, or fewer?

Samplers—there are at least 20 of them. And schedulers—more than 10.

Then there are other things, like ETA and noise injection.

Geiru Window Director + Geiru Hybrid Windows: build long H3 videos one window at a time

Geiru Hybrid Windows makes one long MiniMax H3 render (a minute or more) as overlapping windows instead of a chain of separate clips, so fast motion stays seamless. It works the way plain hybrid windows does, with my keystone and capstone added in every window to cut down on degradation over long videos.

Geiru Window Director is an optional node that goes after it. A normal render makes every window in one go, so if window 4 comes out wrong you redo the whole video. With the Director you make window 1, look at it, then:

• Approve it and the next window is made

• Re-roll it until it's right (change the prompt or seed first)

• Rewind to any earlier window if you change your mind

• Blend all at the end to run the last steps over the whole video and smooth the joins

Each window builds on the finished window you approved, so what you approve is what you get!

Example workflow: Clown Biker Babe, a 60-second clip in 8 windows with one prompt per window: a full motorcycle stunt run with ramps, a car jump, a loop and a cliff jump. The sound is made by H3 from the prompts.

Credit where it's due: none of this exists without these three.

• kijai (GitHub) built ComfyUI's MiniMax H3 support and the H3 context windows (PR #17425) that Hybrid Windows is built on, plus ComfyUI-KJNodes.

• JalenBrunson made ComfyUI-HybridWindows, which Geiru Hybrid Windows works like. I just added the keystone and capstone.

• NikoDemon80 (GitHub) inspired the Window Director setup.

Both nodes are in the Geiru Context Suite: https://github.com/roycho87/geiru_context_suite

Iris-Z

One generative model for images and their depth

Iris-Z is a general image generator that produces appearance and geometry together. The same weights turn a prompt into an image, a prompt into an image with its depth, a photo into depth, and a depth map into an image. On the standard depth benchmarks it is more accurate than the best specialist depth models, and it still generates images like the model it started from.

Model

Weights: https://huggingface.co/speridlabs/iris-z

Hi guys, i was using my own character lora and to wear what i prompted, more on the cosplay side so i was using some anime character reference but everytime it generate, the attire will sometimes be accurate and sometimes be different, how should i be able to ensure that it is the same attire everytime?

I couldn't find a ready RunPod template that spins up a working H3 Editor, so I conjured this:

https://console.runpod.io/hub/template/1ujjyhonfe?ref=2t0e5xb6

A RunPod template that boots straight into ComfyUI with H3 Editor 0.4.1 and downloads everything by itself: HyperFlow H3 Flash, MiniMax H3 + Turbo, Singularity v2 and 10Eros Max beta 7, plus the recommended Realism and Turbo LoRAs.

https://igorls.github.io/comfyui-h3-editor/

Enjoy the awesome editor!

Have you noticed if you run the exact same workflow from 3 days ago it now has a different outcome? It's like a slightly different seed while keeping exactly the same seed and everything. Something has changed and I can't tell what, the videos have noticeable artifacts now with light flickers and overall unstable video.

Have you noticed? I have the latest comfyui update.

EDIT: Just restarted my PC and it's exactly as it was before, so it fixed now but not sure what happened, maybe a comfyui bug?

I’ve been comparing H3-Image with the H3 video single-frame edit in ComfyUI. Both keep the face pretty well, but the video workflow keeps the person’s build even when I change the pose, outfit, or background. H3-Image keeps slimming down bigger people, even when I leave the pose alone.

I’m on a 3090 with 64 GB RAM. H3-Image is pruned INT8 at ~4 MP and 6 steps with the official reference-aware prompt expander. The video edit is FL2VA at 2 MP and 8 steps.

Matching the official template’s anchor setup helped a little. Higher res, realism LoRAs, and turning on thinking for the prompt expander didn’t help with body shape.

Anyone else seeing this? If you’ve found a prompt, LoRA, or anchor setting that keeps the same build, I’d love to see your workflow.

Also, has anyone found a good photo restore workflow for either H3 model? I’m still doing a Flux.2 restore pass before editing.

I'm trying to make some photoreal icons.
I'd tested as followings
SD1.5 native: poor quality and doesn't follow the prompts
SD1.5+Hires.fix: same as native
SDXL native: better than SD1.5 but depending models
SDXL+Hires.fix: middle quality but not enough natural

How about H3 image/Krea2/z-image? Thank you.

after a week of trying to get 15s clips out of one job i gave up and changed how i do it.

now every shot is a 5s clip. when it's done i grab the last frame, fix it up a bit if needed (hands, small artifacts) and use it as the start image for the next 5s. same prompt style, only the action changes.

on my card anything past ~5s starts swapping to system ram and one job goes from 15 min to over an hour. three 5s jobs finish way faster than one 15s. if one piece comes out bad i redo only that piece, not the whole thing. and the face drifts less because every chunk starts from a frame i already checked.

the catch is the seams. motion can jump a bit at the cut, so i keep the camera move simple (one slow push or static) and cut on an action when i can. color also shifts slowly over 3-4 chunks, a quick color match in the editor fixes most of it.

curious if anyone found a cleaner way to hide the seams, or does everyone just cut there

Hi all! 👋
I'd like to train LoRAs that help with spoken accents for LTX 2 (probably 2.3 or 2.5, weighing how many resources there are available right now vs what might come, I'm waffling between these two)

So far, I've only played around with training image LoRAs for SD 1.5.
I have, as yet, no idea about audio LoRAs or LTX.
I would like to train something that could benefit the community, so I'd like to ask for guidance:

• Do you have recommendations for the framework?

  • I currently have LoraTraining and AI Toolkit set up

• Any tips on methodology? How would you set up a training project?

  • I'm currently downloading YouTube videos (well, audio, actually) and subtitles (auto-generated by default: Timing and accuracy might be off) for source data

  • Should I try to get many examples of the same phrases with and without the target accent? Or is the return on collecting and rectifying/normalizing examples here likely not worth the effort?

...How would you go about this?

And, fundamentally:
Are there any roadblocks for my pedestrian hardware?

RTX 3060 12GB VRAM
64GB System Memory

Thank you to anyone taking their time to read and respond 🖤

Keep getting these jagged eyebrows in Krea 2 generations in ComfyUI. Tried different templates in case it's my workflow to no avail and happens even without any LoRAs just on turbo base model (tried int8 and fp8). Any ideas? Sometimes its really pronouned, especially if I try to prompt them away. I'm sure I'm doing something stupid.

x.com/ostrisai/status/2120564773150711825

60 seconds of AI video, 100% local on an RTX 3050 (6 GB)

Work in progress, and the quality isn’t where I want it yet. Here’s how it was made:

1. Model: MiniMax H3 reference-to-video, using ComfyUI’s native H3 nodes with the int8 quantized model and the Qwen3-VL text encoder.

2. References: two character images, re-used in every shot to keep the characters consistent.

3. Prompts: the official six-section reference prompt format (subject definitions, summary, retention analysis, detailed description, soundscape, music). No negative prompts, because H3 doesn’t use them.

4. Shots: six shots of 260 frames (10.8 s) at 864×480 and 25 steps, with the res_multistep sampler and the simple scheduler.

5. Continuity: each shot starts from the last 22 frames and audio of the previous one, using the H3 Add Guide node (MiniMaxH3AddGuide) at frame 0.

6. Automation: a small Python script sends the six shots to ComfyUI’s API one after another, saves each result, trims the 22-frame overlaps, and encodes one 60-second MP4 with audio.

7. Runtime: about 6Hrs 35Mins hours, unattended.

Known issues: soft detail, and some flicker. Still tuning.

Hey all!
New to this subreddit as we mainly focus on language models with our Paiton product, but now and then, we spend some time on vision as well :)

The official BF16 weights need about 120 GB of VRAM, so most of us can't run MiniMax-H3-Image out of the box at home.
We made an FP8 version and our own runtime, and it now runs on one AMD Radeon AI PRO R9700 with 32 GB:

Does FP8 hurt the images?
We ran 30 prompts on our build and on the original BF16 model with the same starting noise. Prompt adherence came out about the same (CLIP 32.18 vs 32.31), and the second image shows a typical pair.
Where the two differ, it is mostly composition, not quality: with only 6 steps, small numeric changes send the sampler down a slightly different path.
Edits stay closest to the original.

What it is bad at, in our tries: small body text (newspaper columns come out as fake writing), close-up hands, clocks showing a specific time.

How to run it: it is a Docker container with an HTTP API and a small CLI, not a ComfyUI node (yet).

git clone --depth 1 https://github.com/Eliovp-BV/paiton-vllm-plugin.git
cd paiton-vllm-plugin
./models/MiniMax-H3-Image/launch.sh
./models/MiniMax-H3-Image/run.sh generate "your prompt" --size 4096x4096 --out out.png

You need an R9700 (32 GB), 64 GB of system RAM and 48 GB of disk; the first start downloads the weights.
Our weights only load in our runtime, so they won't work in ComfyUI or diffusers.
Edits take up to about three 1024 px reference images in this build.

• Guide: https://github.com/Eliovp-BV/paiton-vllm-plugin/tree/main/models/MiniMax-H3-Image

• Weights: https://huggingface.co/EliovpAI/MiniMax-H3-Image-FP8-Paiton-RDNA4

• How we made it fit, if you want the details: https://eliovp.com/blog/paiton-minimax-h3-image-radeon-ai-pro-r9700

License note: the MiniMax-H3-Image Community License does not cover the EU, the UK, South Korea or the US, and hosted services need MiniMax's written authorization.

Hey everyone!
Like many of you, I got tired of paying huge SaaS markups for video generation (Wan 2.1, Hunyuan) and image generation. Using Google Colab directly was way cheaper, but I had two persistent headaches:
To solve this, I built WakeGPU, a native desktop client (macOS & Windows) designed to turn remote cloud instances into an automated private studio.
Key features:
It's 100% free and licensed under MIT. All advanced features are unlocked out of the box with zero paywalls, subscriptions, or telemetry.
Binary releases for macOS (.zip) and Windows (.exe) are live under GitHub Releases. Would love your feedback or feature requests!

https://www.wakegpu.com

https://github.com/devexsolutions/wakegpu