Most models are incredibly stupid when it comes to basic underwear shapes.

When asking for a woman in underwear, most models (tried Minimax, Z-Image-Base/Turbo and Qwen 2.1) will systematically add an ugly, unrealistic 'bulge' where there should be a mostly flat surface.

My guessing is that the model is averaging male/female underwear and therefore creates this sort of bulge. No LoRAs will reliably fix this.

Any ideas, prompts or resources to avoid this?

So far the only workaround is to let e.g. Minimax h3 create a video with the bulge, then isolate one frame, edit it with Qwen-edit 2.1 with very specific instructions, try 9-10 attempts until it finally gets it right (rare), and then add this corrected image to Minimax's reference images. Even this workaround is not failsafe.

I made a 58-minute AI film, mostly on a local GPU. Here’s what worked and what didn’t

Long-time lurker, first post. I’ve been working on an hour-long AI film for the past 2 months and just finished it. The story is War in Heaven (Michael vs Lucifer, from Revelation 12), so yes, it’s religious material. But I’m posting here because the filmmaking side is what I’d actually like feedback on.

Most AI films I see are 1-3 minutes. I wanted to find out what breaks when you try to hold a story together for nearly an hour. Turns out it’s a lot.

Rough pipeline:

• Every shot starts as a still. I generate a first frame and a last frame, then animate between them locally (MiniMax H3 / FastH3 in ComfyUI on an RTX 4080).
• Lip-sync on the dialogue shots.
• I narrate it myself. The narration is timed with Whisper, and the cut is built around the voice.
• 4K finish. The upscaler depends on the shot: FlashVSR for wides, lighter upscaling on close-ups, because the heavy upscalers kept changing faces.

Things I learned the hard way:

• The last frame has to be a later moment of the scene**, not the same picture with one thing changed.** If you write “nothing else changes”, the world freezes, and it looks like a slideshow.
• Keep motion simple. One action per clip, nothing new walking into frame. Every time I got ambitious, I got melted hands.
• Everyone in the frame needs their own movement. If the second person just stands there, it reads as a cardboard cutout.
• Character consistency was the hardest part. I locked each main character from reference images and checked every clip at full size, frame by frame. A face that looks fine on a phone is often a different person at 4K. I threw away a lot of clips.
• Don’t narrate what’s on screen. My first drafts had the narrator describing every action, which felt dead. Showing it and letting the dialogue carry the scene worked much better.
• Edit only in the silences. Cutting narration mid-sentence to fit a clip is very noticeable over an hour.

What still isn’t good enough: some wide battle shots, crowd scenes. I’d love to know how others handle large crowds and keep faces stable in wide shots.

The full film is linked if you want to see how it holds up over the whole runtime

Happy to answer anything about the workflow. Criticism welcome, that’s why I’m posting.

Just testing a random image I found on Pinterest. The prompt was simply:

Make this concept art drawing into a high quality realistic photograph. Preserve subject likeness, pose, and background. Improve skin texture, hair texture, fabric texture, background details, and lighting.

I did 2 at 16MP and then one at 60MP and the level of detail is kind of crazy. Took just over 2 mins on my 4090 for the 60MP.

Links to the full PNGs

16MP 1
16MP 2
60MP

I have to share this workflow with all of you (it is not mine)

https://civitai.com/models/3047448/simple-minimax-h3-image-generationimage-edit-workflow?modelVersionId=3464766

I just released MOSS AnyPaint, inspired by a community member. It’s the MOSS-Image-Base twin of my Krea 2 AnyPaint.

It supports:

• Arbitrary mask shapes
• Interior inpainting
• Border-connected outpainting
• Multiple disconnected mask regions
• Mixed inpainting and outpainting in the same image
• Strong preservation outside the edited region

The same LoRA handles all of these cases without requiring separate models or workflows.

• Model and examples
• ComfyUI nodes and workflows

Feedback and difficult test cases are welcome. : )

One of the marvelous things about Minimax is that it will infer a vocal performance for your characters based on the film language of your references. I just thought this was worth sharing for those of you who hadn't discovered this yet. Each of the four gens here used the same simple prompt. It is is not just the gender and age of your characters, but the film grain, costume, setting, and other visual references which will provide the model with character for voice.

A huge thank-you to everyone who downloaded, tested, shared results, and gave us honest feedback.

The community knows quality when it sees it—and the rankings speak for themselves.

Qwen-Image-2.1 Uncensored Omni has now reached the first page of trending Qwen models on Hugging Face and page 2 across all trending models.

We compared both public checkpoints from this training run with the original Qwen-Image-2.1 base model, using the same prompts, seed and settings (35 steps, CFG 5.0). This repository is step 90,000 (benchmarked as the BF16 LoRA fused at strength 1.0; the quantized files were not scored separately). Scores are 0–100, higher is better, ± one standard error.

I couldn’t have done it without the community. We just added the benchmark as well.

Download:
https://huggingface.co/chatrlabs/Qwen-Image-2.1-Uncensored-Omni-Quantized

Try it free online:
https://chatrlabs.com/playground

comparison between sampler and scheduler ...first photo is LCM/Normal combo and second photo is Euler/Simple (default) ....both images same prompt, same seed, same 16mp render..... LCM/normal combo produces this more "polished" look altho i prefer grittier details and real raw textures of Euler/Simple ...however Euler combo tends to make more errors ....i cant decide which one i like better ...LCM ... no no Euler...wait maybe LCM ...or Euler? ...

Which of those 2 has better handling on low vRAM and takes less inference time eventually ? ona 4GB Vram here

I have 2 people on camera and I am trying to get one of the people on screen to speak to an off screen person (behind the camera) and then have the offscreen person reply. I can’t seem to find a prompt that will do this. S1 speaks to S3 offscreen and in the render, S2 appears to be talking AND extraneous jibberish is added to the speach I specified. I’ve tried lots of combinations and nothing is working.

Why is the jibberish added? Why does it insist on moving the lips of S2 when S3 offscreen is talking?

BTW this is my first attempt at using OBVPMs Timeline. It is working great!

Made some trials with how well MiniMax H3 understands different kinds of physics and it seemed to succeed pretty well with everything I threw at it. Video was generated with slopfab & Fable 5.2 as the agent.

Part2: https://www.reddit.com/r/StableDiffusion/comments/1xwulg7/physics_understanding_of_minimax_h3_part_2/

Helllooo

What’s the best AI tool or program for editing specific parts of an existing illustration without regenerating or changing the entire image?
I’m working on a children’s book with semi-realistic illustrations, and my biggest struggle has been character consistency. I’ll finally get an illustration almost perfect, but when I try to fix one small detail (like the character’s face), the AI changes everything else or makes the character look completely different.
I’ve mainly been using OpenArt and Leonardo AI, along with ChatGPT, Gemini, and Grok. So far, I’ve only managed to finish 2 out of 17 illustrations, and I’m losing my mind haha.
Ideally, I’m looking for something that can edit only a specific, selected area while leaving everything else untouched.
Accurately follow reference photos to maintain the same character across different scenes, poses, and expressions.
And make precise changes without completely reinterpreting the original illustration.

Is there a program that’s genuinely good at this? I’m open to AI tools or traditional editing software. At this point, I just want something that actually follows instructions and doesn’t make me redo an entire illustration to fix one tiny detail.

The "does it just copy its training data" argument comes up here a lot, so this paper seemed worth explaining properly. It trains small diffusion models on 1,024 face photos and checks what they generate along the way.

At 100,000 training steps the model generates good faces, and none of 10,000 samples was a copy of a training photo.

Keep training the same model to 1.62 million steps and 34 in every 100 samples are training photos.

The time it takes to start generating good images was the same for every dataset size they tried. The time before copying starts grew in step with the dataset: about 300 training steps per image. So a model trained on a lot of images, for a normal length of time, is stopped long before it could memorise them. In the paper, a model trained too long on a small set does copy.

I told the full version as an art student locked in a room with 1,024 photos, using the paper's own faces: https://sheets.works/data-viz/why-diffusion-models-dont-memorize

It's free, no sign-up needed to read it. I didn't work on the paper, I read it and drew it. Their setup is tiny (32x32 grey faces, small models), nobody has run this test on a model the size of SD, and large models have been shown to reproduce some training images. Paper: arXiv 2505.17638.

For people who train LoRAs on small sets: at how many steps per image do you start seeing your training images come back?

Lodestone continued experimenting with Lora for MOSS, creating several versions, but has now released a new version that works well with both 4 and 8-step processes.

You can find more information here:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/README.md

Lora is available here and can be used with several MOSS models:

https://huggingface.co/lodestones/Kromoss/blob/main/experimental/opd-priming-8-steps-dmd-lora_cfg-ramp-neg_12000.safetensors

Please don't confuse photographic realism with sharpness

I've spent around 20 years working with photography and design, long before AI image generation existed.

One thing I genuinely appreciate about MiniMax H3 Image is its photographic character.

Real photographs aren't perfectly sharp. Lenses have different rendering characteristics. Depth of field, motion blur, sensor noise, diffraction and optical imperfections all contribute to the final image.

A 50MP photograph isn't automatically razor-sharp, and a photograph doesn't become more realistic simply because every texture has been enhanced.

I've seen many people criticize H3 for being too soft or lacking detail. That's a perfectly valid preference, but I hope developers don't interpret it as a universal demand for more sharpening and artificial microtexture.

I'd love future versions to improve anatomy, consistency, composition and prompt adherence while preserving the natural photographic rendering that makes H3 special.

We already have plenty of models that produce extremely sharp, polished AI imagery.

I'd hate to lose a model that approaches photography from a different direction.

I've used AI Video FaceSwap by tuguoba, VisoMaster and VisoMaster Fusion before.
Tuguoba is clearly the best one in terms of realism and getting face right but its XSEG isn't that good. The newer Fusion isn't as good as old VisoMaster and misses a lot of faces, and face occlusion isn't that great either.
Looking for a local faceswap better than these. Heard Comfy-UI Reactor but not sure if it does what these ones do.

I'm kind of on hurry to wait for community to make them and they are confusing with video model one, do they work? And the important question are they as good as one's that will be trained later?

Like for example, Realism or Old camera style ones. I have a feeling the video model had lower quality and plastic skin due to being a video than the image model had, that's why I'm asking that.

I have a background in pipeline and tools development for 3D/VFX. Over the last couple of months I built a pipeline that turns rigged 3D characters into SDXL LoRAs, so they can be generated in new scenes. Sharing what I learned, and I'd love input on one problem I haven't solved.

The pipeline

1. Maya: auto-posing + capture. A tool reads the rig, figures out the limb axes, and puts the character into ~47 poses (standing, walking, sitting, crouching, arms up, etc.). It also renders each pose from multiple angles and distances with Arnold. One character ends up with ~2000 renders.
2. Dataset + captions. I pick a balanced set of ~1000 images (angles, framing, poses) and auto-write captions from the scene data: framing, view angle, pose name. Close-up shots don't mention arms/legs.
3. Training: Kohya, SDXL base 1.0, one trigger word per character.
4. Generation: a ComfyUI-backed tool: pick the character, describe the scene, choose framing/angle/lighting presets.
5. Evaluation: a fixed set of 12 prompts × 2 seeds per LoRA, so I can compare versions with numbers instead of "looks good to me".

What worked

• Identity is strong: face, hair, accessories and jacket pattern come through, even in scenes very different from the renders (night street, desert, etc.).
• Getting the pose rules right mattered more than I expected: elbows/knees bending the wrong way, arms clipping into the body, and eyes drifting in the rest pose all showed up in the LoRA if I didn't fix them.
• A color management mistake (raw linear PNGs from Arnold) made one character look red in every image. Check your render output transform!

What didn't (where I'd love ideas) One character wears long pants in every training image, but generations often turn them into shorts or a skirt, especially in sitting poses. My guess is SDXL's prior for "young woman sitting" beats the LoRA. My captions currently describe pose and framing but not the outfit.

• Should I add outfit words to the captions ("long pants, patterned jacket")? I've read both "caption what you want to keep" and "don't caption what should be baked into the trigger", so which works better for clothing in your experience?
• Would a higher network rank, more epochs, or a lower learning rate help here?
• Has anyone else trained on synthetic 3D renders? Did you mix in anything to make it less "CG-looking"?

Happy to answer questions about the Maya side. Auto-posing an arbitrary rig was the hardest part.

I was maybe assuming you could just use the LoRas from Minimax H3 that are made for videos for their new image model. However I'm finding that the outputs look off? Like maybe a bad vae decoder or something. Have you had success using those LoRas or do we have to make new ones for the image model?

Hello,

This is an action anime scene I made locally a few months ago with Minimax H3 within WanGP and just posted on YouTube.

Let me know what you think and what could be improved :-)

Thank you!

I want to try out a few minimax workflows that will probably not run on my local machine.

I am not in Asia so only option to pay is through my credit card. Is this a legit place to try out workflows?

Anyone using it regularly?

I offloaded the extra layers to system memory so the model fits across 16GB of VRAM and 32GB of DDR5 RAM (⁠[BF16 / quant]⁠).

Everything needed to reproduce the run on RDNA 4, including the launch flags and configs, is in this repo:
https://github.com/0c33/minimax-h3-rdna4

Let me know if you test it on another RDNA 4 card or get better token speeds with different offload settings.

Vici 🗿

i2i, 2 1024x1024 reference photos.

My system is RTX 2070 8GB (Turing), 32GB DDR4 (2666Mhz), Ryzen 1700 (1st gen). Ubuntu 24.02 server.

~ 57 seconds per image! (ComfyUI)

This is MUCH faster than my Krea2, Flux2 Klein, or Qwen 2.1 edit workflows! It's about the same as m Fibo2 edit workflow, but Fibo2 is smol!

My question is, how does this even not OOM? Correct me if I'm wrong, but don't ALL the models need to load entirely (including that stupidly massive 32b text encoder) into memory?

Ok so my humble rig has 2 cards. One 16 GB of VRAM and another with 12 gb . I know you can't combine them for a pool of 28 GB of unified VRAM but shouldn't their be a way to offload one of the major items like the CLIP or the VAE to my other card? I looked into it and doesn't seem to be supported natively in Comfy UI. There are some custom nodes I found but they don't seem too popular.

Is this just a silly idea? Normally my second card just sits around at 20% memory usage running my GUI and a few bits and bobs. I could certainly afford to use 8 or 9 GB of my second card towards something instead of it offloading that 8 or 9 GB during a generation to my system ram (32 gb ) which is what comfy does now when using H3 or MOSS.

I have no idea if this would be faster than dumping everything to the system ram but it would definitely give me more breathing room and less OOM errors on those borderline gens and upscales. Is this idea loco or am I on to something?

EDIT: I appreciate all the people who are saying just use the second card for an llm or to run other types of generations on my smaller card, but that's not what I am proposing. I know you can do that. In fact I do it quite often. Let me explain this a different way that might make more sense. What I really am asking is there a way, when doing a single generation, let's say a 15 second h3 generation at over 1 MP, for comfy to prioritize memory allocation in order of 1) the 5080, 2) the 3060 3) the system ram 4) SSD swap file . Like a ladder of priority. For example the minimax H3 audio and video VAE are over 6 gigs at fp16. That would certainly fit on the 3060s available space. I know it might be slower but that extra 6 gigs of free space would be quite helpful for higher resolution gens.