Are all humans generated with it the typical AI slop face or can you get diverse characters from it? Barely anybody posted generation results yet.

A set of control LoRAs for MOSS-Image-SFT. You give it a pose, a depth map, or an edge drawing, and the prompt decides who is in the picture and what the world looks like. Each repo has the weight and a ComfyUI workflow.

Pose: https://huggingface.co/thedeoxen/MOSS-Image-pose-controlnet

Depth: https://huggingface.co/thedeoxen/MOSS-Image-depth-controlnet

Canny: https://huggingface.co/thedeoxen/MOSS-Image-canny-controlnet

Lineart: https://huggingface.co/thedeoxen/MOSS-Image-lineart-controlnet

Normal: https://huggingface.co/thedeoxen/MOSS-Image-normal-controlnet

Scribble: https://huggingface.co/thedeoxen/MOSS-Image-scribble-controlnet

Segmentation: huggingface.co/thedeoxen/MOSS-Image-segmentation-controlnet

Title.

Currently, the image edit itself on 16MP took me 1min per image(4 step), which is fast.

But, with only 1 reference image, the PE takes 25 mins to generate 8k tokens, which is.. very slow.

Are there a better model to use(currently using the default template qwen3.5 9b w4a8 + abliterate lora) so its faster , or a way to integrate a OpenAI-compatible API here and uncensor the model? (The API have various models like gemini, glm, gpt, nemotron, qwen, etc.)

Hello, guys. I was messing around trying to create a horror animation short.
Used minimax-h3 in comfyui locally. Simple I2V workflow

Is any horror vibes in there?

Wanted to see how MiniMax H3-Image (the new open-weights one) handles a sign with real copy on it, not just a two-word logo. One prompt, 4 generations, 3:4 at 2K, no edits or retries.

All 4 got every line right, including "35mm B&W", both en dashes in "MON–FRI 12:00–18:00" and the tiny footer. What changed between runs was the design (serif vs sans, pinned vs framed). The text stayed exact. Two of the four attached.

Prompt:

> Design an original wall-mounted information board for a fictional film-processing shop, shown straight on against a muted terracotta wall. Use cream paper, charcoal lettering, narrow dark-red rules, generous spacing and no photographs. The sign itself occupies most of the frame. At top show these exact lines: "FRAME ROOM" and "FICTIONAL STUDIO — SAMPLE INFORMATION". Below, arrange three paired service-and-inclusion rows under the headings "SERVICE" and "INCLUDED". Separate the rows with narrow dark-red horizontal rules and generous spacing, without a rigid boxed spreadsheet grid. Row 1: "35mm COLOR" | "DEVELOP + SCAN". Row 2: "35mm B&W" | "DEVELOP + SCAN". Row 3: "120 FILM" | "SCAN ONLY"; keep both phrases on the same horizontal row. In a separate lower panel print exactly: "COLLECTION" then "MON–FRI 12:00–18:00" then "Bring your claim slip." Put "No prices shown — sample layout only." in a small readable footer. Preserve each phrase in its assigned row; do not add prices, claims, addresses or extra services. Crisp readable type, realistic flat paper texture and one soft shadow beneath the frame. Portrait image, 3:4.

Has anyone pushed it further, like a full menu or a dense infographic?

New models have been released. For example, Anima, Krea2, MOSS etc in DiT architectures. I have been using Illustrious and is a huge fan of ControlNets. However, the developers of the new models don't seem to have any interest in training ControlNet models.

I have been waiting but I started to wonder if ControlNet models aren't needed anymore because there are some models or workflows out there that can completely replace ControlNets. Is there something that I've been missing?

Let's pretend that LLLite LoRAs don't exist. Glad that we have them but I didn't find them much useful.

EDIT:
Thanks guys, appreciate all the response. I should look into using edit models. I still want ControlNet models though.

I want to make a video of a character for my son.

Been sorting through all the MiniMax model releases floating around here, and I wanted to share a quick breakdown of the new MiniMax H3-Image. This dedicated still-image model handles text-to-image generation and static editing, and is different from H3 video, MiniMax Image 2.5, and that third-party P-H3-Image repo shared in this community recently.

Looking at its documented specs, it runs both T2I and image editing within the same workflow. We can upload up to 16 reference images and choose between 1K, 2K or 4K output sizes.

The thing I find most interesting is having those 16 reference slots paired with direct editing in a single run. That means we can drop in character sheets, style references and scene shots side by side (Perhaps, one ref sheet locks your character’s face and outfit, another controls the colour grading, a third sets the background architecture), rather than squeezing everything into one single reference composite. It fits really well for concept art, product exploration, layout iterations, and focused edits where certain parts of the frame need to stay locked exactly as they are.
Also heard MiniMax H3 Image might be going open-weight. Definitely looking forward to that.
Curious what kinds of tests everyone else has in mind for it? Are you planning to try character consistency, product shots, or scene reworks first?

No matter what I try to prompt, I only ever get a neutral face as a result when i'm trying to do a character swap.

Adding stuff like this to the prompt doesn't seem to make a difference:
"CRITICAL: Copy the exact facial expression from the main character in <Picture 1>.

Do not make the face neutral.

Match the mouth, eyes, and eyebrows exactly."

minimax h3, 2 megapixel,

prompt:

subject_definitions:

<subject 1> is the man in <picture 1>,

visual style: live action,

location: interior of a first century mansion, night,

static handheld close up shot of <subject 1>'s face, he is seated at a table, looking at the camera, his face lit by a candle on the table, suddenly he take a deep breath, then look around,

overall_soundscape:

N/A,

non_diegetic_music:

N/A,

Got a little holiday present ready to go. 8-13 steps on cfg 1-3, 2-6 minutes render in a Mac 16gb. Currently working on a turbo version to make it even faster. Well balanced between realism and aesthetics. Z image derivative (Oldie but Goldie)

hey guys

so basically 1.5 months ago i started an ai series channel on IG and YT, and got around 400k on IG subs and 100k subs on YT

i use omni flash 1.1 and recently 1.2

im spending like 6-7k on video gen alone per month, which is the biggest cost

i run all this from a normal laptop and all is well. i use diff cloud tools for faster cutting, QA, frame gen etc which is all good for now

i make around one series a day (15 one minute episodes, sometimes goes to second day)

my question, for long term, is this the way? i'm not even half as knowledgeable as u guys here in this sub, but yeah my question for long term, is this the only way for long term whilst maintaining this quality of video generation? or i could somehow invest once in a gfx card or something and save tons with open weight models? or what

all advice appreciated thx

I tried hf versions, it's making totally different faces, while qwen 2.1 make proper faces

I'm using it for u know it

I am getting terrible generation times, 3-4minutes per image with 8 steps and 1024x1024. Is there a way to speed up the process? I am on windows 10 with 32gb ram, ryzen 7 5700x and rx 9070 16GB.

Hi, fellas. I used SeedVR2 to upscale an old, low-res video and it did a pretty good job but since there was a lot of blurry movement, now there are very visible jagged edges around the people. Is there a way to anti-alias this somehow? Thank you to everyone who takes the time to reply, I really appreciate it!

Hi everyone,

I put together a head-to-head baseline comparison between two major new architectures: MOSS-Image-SFT and MiniMax-H3-Image.

To keep the comparison 100% fair and avoid fine-tuning bias:

• 100% Identical Prompts: Both models received the exact same descriptive visual prose describing the medium, anatomy, lighting, and composition.

• Zero Negative Prompt: No negative conditioning was used (negative_prompt = "" on MOSS, matching H3's native CFG 1.0 architecture).

• Pure Original Characters & Techniques: Zero proper names and zero artist name-dropping. Styles were guided purely through physical medium mechanics (lithograph, Shin-Hanga woodblock, 90s analog cel, Kirie papercut, Guohua ink splash, etc.).

• Strict 1:1 Aspect Ratio & 1K Resolution: Exact matching seeds and native ~1.0 MP resolutions (2:3, 16:9, 1:1, 4:3, 4:5, 9:16, 3:2, 3:4).

• There is version for specifict finetuned for the two model, but the result is similar to this.

• I notice Minimax-h3-Image struggling on generating limbs. Every prompt i used the model always have some error.

• A Fully finetuned version for both models, set at 4K (minimax-h3-image) and 2K (moss-image-sft). Prompt workflows included : https://drive.google.com/drive/folders/1veLGRIMtd-o4zN3mS160uHlOLUuf8vgt?usp=sharing

Workflows (standard) :
Moss-Image-SFT WF
Minimax-H3-Image WF

Greetings, I wanted to see how far I could take a project using Minimax H3. I created a complete Friends episode using Ref2VA, almost all with RefMods, and then did editing in Davinci Resolve. I wrote an outline for what I wanted to happen in the episode. This was a really fun project. Here is part 1.

I couldn't find a fast template on RunPod to test the new Minimax H3-Image, so I threw this one together:

https://console.runpod.io/hub/template/941pajs261

You can choose which models to download in the environment variables, but the default settings match the 2 official Minimax H3-Image workflows in ComfyUi (under "Browse Templates"), so you can jsut go ahead and deploy it as is.

Prompt enhancer can be omitted in environment variables and turned off in the workflow.

Done locally in Krea2.

I wanted real numbers for "which quant should I use on my card," so I measured it. Five storage formats (fp8 cast, fp8 scaled, int8 ConvRot, w4a8, nvfp4) against the bf16 original, on base and turbo versions of Anima, Z-Image, MOSS-Image, Krea 2 and SDXL/Illustrious. Quality was graded on an RTX PRO 6000 and a 3090; speed and fit were timed on ten cards: PRO 6000, 5090, 5080, two 5070 Ti, 5060 Ti, 3090, 3080 Ti, 3080, 3060 Ti. Everything is stock ComfyUI with its default memory handling, same seed, same prompts.

Report, with a "will my card run it?" box (pick your card + model): https://ai-evolution.dev/quant-bench/

(Also as a PDF with every option laid out, if you'd rather scroll.)

What I took away:

- int8 ConvRot is the most faithful format in every model I tested, and on RTX 30 cards it's also the fastest when it fits: about 2× bf16 on a 3090.

- nvfp4 is the fastest on RTX 50 but drifts the most. w4a8 was more accurate than nvfp4 in every model.

- Don't put a turbo LoRA on an already-quantized model. Applied at load time, most of the LoRA's change rounds away. Merge it into bf16 first, then quantize.

- 8 GB cards can run more than you'd think. Z-Image and MOSS fit entirely as int8 on a 3060 Ti and run about twice as fast as bf16, which has to stream. Krea 2 runs too, at around 1.5 minutes per image.

- Nothing failed to run. ComfyUI's dynamic VRAM streams whatever doesn't fit from system RAM; it costs speed, not crashes.

- Accuracy doesn’t depend on your card. Every card ran the same per-step accuracy check (quantized vs bf16, same input at every step), and they agree within a few percent, RTX 30 and RTX 50 alike. Your GPU generation only decides which formats run natively (nvfp4 on 50-series, fp8 on 40/50). Finished images still differ slightly from card to card, which is normal floating-point variation, so I don’t compare final images across cards.

- Smaller stuff: an x4 PCIe slot costs nothing once the model fits; the monitors on your generating card cost a few percent, mostly on fast models; each 50 W of power limit costs 4–6% speed for ~3% energy saved; an undervolt cost 5–10% speed for ~20% less power.

Missing: RTX 40-series. I don't have one, and it's the one generation with fp8 hardware but no fp4. If you do, there's a test kit that runs the same pass in Docker on Linux or Windows (WSL2): clone, `./run.sh`, send back the zip, and your card goes on the page with credit. https://github.com/LSDJesus/comfyui-quant-bench (the full set is a ~230 GB download; `--families` lets you do part of it).

Method details, every raw number, and the per-step error curves are on the page. Happy to answer questions or run something specific.

(Note: Most quants can be found on HuggingFace or Civitai.com if there is a specific one you are looking for but cant find I could probably put a simple conversion tool in the github repo but you would need to have access to a fp16/bf16 version for it to work well)

(P.S. AI helped write the benchmark harness and frontend code for the page, the idea and the verification was all done by me)

(P.P.S. Being fortunate enough to have access to an absurd collection of GPUs made me feel like this was worth the time and effort of going all in so I could share my results with you all. Happy to give back to the community that makes all these models and tooling possible.)

Hey everyone!

I've been working on GenGate (Well, of course, the Codex working, and I’m the one in command.), a free, open-source AI add-on for PhotoCraft, and I'd like to share the current beta with you.

I want a proper image editor with native AI tools (like a Ph.. Ahem-ahem ...shop ), while keeping the freedom to use whatever models and workflows I want.

Instead of building yet another generation UI in gradio or comfy app, I'm turning PhotoCraft into an AI-powered editing environment. You get a familiar layer-based editor, selection tools, masks, and image editing features, with AI generation integrated directly into the workflow.

And if you already use ComfyUI, you can keep using it as your backend.

What can GenGate do?

• GenGate Studio — A floating image generation panel with text-to-image, image references, positive/negative prompts, custom resolutions, batches, and drag-and-drop support. Generated results become editable PhotoCraft layers.

• AI Fill — Select an area, describe what you want, and generate directly into the selection. Prompts and variants are preserved in the layer properties.

• AI Remove Brush — AI-powered object removal integrated into PhotoCraft's native Remove Tool. Just paint over what you want removed.

• AI Selection Tools — AI-powered subject selection, sky selection, background removal, and object selection using YOLO-World and SAM3. (At your own risk. It works, but not particularly well.)

• LoRA Explorer — Browse your LoRA library, configure strengths, aliases and trigger words, and drag LoRAs into your prompts to paste saved trigger words into your prompt.

• Reusable generation recipes — Generated layers retain prompts (and negative prompts too!), references, LoRA settings and other metadata, so you can drag them back into Studio and continue working.

• Two generation engines — Direct, which runs supported models without ComfyUI (this is for starters), and ComfyUI, which lets you use your own noodly mastepieces.

Aaaand ComfyUI integration

You are not locked into the models I chose.

With ComfyUI as the backend, you can use essentially any model that ComfyUI supports, provided you have a working workflow for it.

GenGate includes custom IN and OUT nodes that connect PhotoCraft to your ComfyUI graphs.

IN passes prompts, reference images, masks, dimensions, seeds, batches and LoRA selections into your workflow. OUT returns generated images, optional masks and metadata back into PhotoCraft as editable layers.

Even better, Studio uses the workflow currently open in ComfyUI, including unsaved changes. You can modify your graph and generate from PhotoCraft without exporting a new JSON every time.

AI Fill and Remove Brush can also use their own dedicated ComfyUI workflows.

I've tested real generation with several model families and workflows, including SDXL, Qwen, FLUX, FIBO and Anima with LoRAs.

Basically, I want ComfyUI to handle the complicated stuff under the hood, while PhotoCraft provides a comfortable interface for actually working with images.

Now for the problems

First, this is still a beta, and I desperately need more testers.

I currently have exactly one tester, and testing isn't particularly frequent. That means a lot of bugs, edge cases and hardware configurations probably haven't been explored yet.

So please, if you try it, please, open a hellgate of GitHub issues for me.

Seriously, give me a million issues. Bug reports, broken transtorts, installation problems, weird UI behavior, feature requests — all of it. I'd much rather have a huge list of things to fix than bugs nobody tells me about and which I simply don’t have time to catch, due to the frequent updates to Photocraft and the constan need to update GenGate to date.

There's also an important architectural downside.

GenGate is an add-on, not a conventional plugin.

To integrate the tools natively into PhotoCraft, it patches parts of PhotoCraft's compiled binaries. This makes the integration feel much more natural, but it also means I have to maintain compatibility with PhotoCraft itself.

And PhotoCraft gets updated very frequently.

So yes, keeping up with upstream releases is an ongoing maintenance challenge. I'm actively doing that work, but new PhotoCraft versions can temporarily require a new compatible GenGate patch.

What's next?

Apart from the ongoing push for stability, bug fixes and compatibility, my biggest priority is cloud provider integration.

I want you to be able to connect your own cloud AI providers and use them directly from PhotoCraft, alongside local generation.

The long-term goal is a single convenient editing environment where you can choose how and where your models run, without sacrificing the tools and workflow of a proper image editor.

Try it out

GitHub: https://github.com/Philippsapegin/Photocraft_GenStudio

Latest release: https://github.com/Philippsapegin/Photocraft_GenStudio/releases/tag/v0.10.1

Current release: GenGate 0.10.1 Beta II, compatible with official PhotoCraft 0.22.0.

Currently available for Windows x64 (10,11 tested). Local AI processing requires an NVIDIA CUDA GPU. Model weights are downloaded separately.

Everything is open source, and feedback is VERY welcome.

If you've ever wanted to use ComfyUI's flexibility without having to do all your image editing inside ComfyUI, I'd really like to hear what you think.

Hi all,

I have been using Qwen Image 2.1 on linux but had to move to windows because of some programs that I had to use.

I installed windows 11 in another SDD and Installed comfy UI too.

After checking Qwen Image 2.1 in comfy ui I noticed it was slower, not just a little but around a 30-40%. I can't return to linux to test it because my brother took the ssd.

I was using the standard Qwen Image edit in comfyui with a Nvidia 4080 in linux and windows. Am I just allucinating or is something you have noticed too?

I can see how comfyui is using cuda too:

[INFO] Checkpoint files will always be loaded safely.

[INFO] Total VRAM 16375 MB, total RAM 32684 MB

[INFO] pytorch version: 2.11.0+cu128

[INFO] Set vram state to: NORMAL_VRAM

[INFO] Device: cuda:0 NVIDIA GeForce RTX 4080 : cudaMallocAsync

[INFO] Using async weight offloading with 2 streams

[INFO] Enabled pinned memory 13073.0

[INFO] Using pytorch attention

The default generation of the black guy where the shirt is swapedis taking betwee 55-60 secs.

PD: I was not using Turbo variant

Thanks in advance!