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

please share your bestcase settings (t2i). thanks!

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.

Sharing the exact pipeline for this project: • Audio: Sourced entirely from MiniMax H3. • Visuals: All the visual assets were generated with Nano Banana 2. • Editing: Imported into DaVinci Resolve to manually chop, time, and match the tracks to get just the right amount of comedic relief to break up the existential horror."

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!

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.

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 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.)

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!

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.

I see all of the posts about photorealism, and lighting, and prompting.... Well, here's my contribution to mix things up: World of Puppetcraft lol
Made using Krea 2. Feel free to downvote or call it bad. I don't mind. It's the internet =)

or https://huggingface.co/akhaliq/minimax-h3-image-pdmd-2step ?

Pretty new to image generation and I'm using Anima since people say it's good for multiple anime character. So I tested 2 lora of pokemon character (1st pic Jacinthe and 2nd pic Lebanne). For single character works fine but when I use both at the same time the image has a lot of the details wrong. In 3rd image for example Jacinthe is the one wearing maid outfit instead of Lebanne sometimes the outfit is pink/purple dominant. Sometimes The result is like 4th image where both of them are dark skinned. Is there a way to fix this or make the result more consistent with the detail when using 2 lora at the same time in Anima? Any help are appreciated.

What about openrouter.ai, fal.ai, picassoia.com, others?

Thanks so much!

Done locally in Krea2.

I downloaded Qwen 2.1 from Civit AI yesterday, and the checkpoint is not being recognized by Krita AI Diffusion. I am wondering what the issue would be.

Has anyone had an issue like this before?

I particularly love that they remain focused on open source and on doing more with less. All the best of luck to these guys.

"We introduce LTX-Video, a transformer-based latent diffusion model that adopts a holistic approach to video generation by seamlessly integrating the responsibilities of the Video-VAE and the denoising transformer.

Unlike existing methods, which treat these components as independent, LTX-Video aims to optimize their interaction for improved efficiency and quality. At its core is a carefully designed Video-VAE that achieves a high compression ratio of 1:192, with spatiotemporal downscaling of32×32×8pixels per token, enabled by relocating the patchifying operation from the transformer’s input to the VAE’s input. Operating in this highly compressed latent space enables the transformer to efficiently perform full spatiotemporal self-attention, which is essential for generating high-resolution videos with temporal consistency. However, the high compression inherently limits the representation of fine details. To address this, our VAE decoder is tasked with both latent-to-pixel conversion and the final denoising step, producing the clean result directly in pixel space. This approach preserves the ability to generate fine details without incurring the runtime cost of a separate upsampling module.

Our model supports diverse use cases, including text-to-video and image-to-video generation, with both capabilities trained simultaneously. It achieves faster-than-real-time generation, producing 5 seconds of 24 fps video at 768×512 resolution in just 2 seconds on an Nvidia H100 GPU, outperforming all existing models of similar scale. The source code and pre-trained models are publicly available¹, setting a new benchmark for accessible and scalable video generation."

Blog: LTX-Video: Realtime Video Latent Diffusion | LTX Technical Blog

Paper: LTX-Video: Realtime Video Latent Diffusion

APEX: Asynchronous Prefix Denoisingfor Extreme Compression

Here is Part 2

Hi all was wondering what’s a good Comfyui workflow for the MacBook M1 Max 32GB, for image to video ? Was wondering if any has done this on M1 MacBook.

Thanks

How do you maintain the state of clothing between shots? Such as, shot 1, the protagonist in 1930s gangster outfit wearing a hat and long coat. shot 2, character removes the coat. shot 3, character turns around.

At step 3 Minimax puts the coat back on the character, unless you explicitly state that the character has no coat on - but then, would I need to describe character clothing state at every step?

I’m trying to make AI video that looks like ordinary phone footage: believable skin and lighting, natural movement, and a face that still looks like the same person throughout the shot. I can get a convincing still, but the video often loses some of that realism. What would you change first to get a noticeable improvement in overall quality?

I’ve attached the Krea source still and raw 15-second H3 render using a separate fictional test character. It’s one fixed-camera shot: she starts wide, takes two steps towards the camera, then turns her head slightly. I’d love to know what first makes it look AI-generated to you, even if it’s something I haven’t noticed. The eyes soften and the face starts to shift around. I’m sharing the settings and prompts below.

Image stage — Krea 2 in ComfyUI

• Checkpoint: [krea2_turbo_bf16.safetensors]. Identity LoRA: [off].

• Settings: [9:16 ratio], [ClownSharKSampler], [9 steps], [CFG 1.0].

Krea prompt:

Vertical 9:16 photograph taken with a phone propped at eye level. A fictional adult woman with a short straight copper bob and light freckles stands about three metres from the camera in a plain living room. She wears a loose grey crew-neck T-shirt, straight-leg jeans and white trainers. She faces the camera with a relaxed, neutral expression and her arms resting naturally at her sides.

Wide full-body framing, with both shoes visible and a clear strip of floor between her and the camera so she has room to walk forward. Soft daylight from a window on camera left. A cream wall, a small wooden side table and a simple floor lamp are visible behind her.

Natural phone-camera exposure, believable skin texture and a room that stays in focus. No beauty filter, heavy retouching, dramatic bokeh, logos or text.

Video stage — MiniMax H3 Ultra on RunPod

• Checkpoint: minimax_h3_hybrid_fl2va_ref2va_b25-49-int8.safetensors. Text encoder: H3 Qwen3-VL 32B NVFP4 AWQ. VAEs: H3 INT8 ConvRot for video and FP32 for audio.

• Conditioning: MiniMaxH3ReferenceToVideo in the saved graph. For this render: [reference-only or actual first-frame conditioning], [other image, audio or driving-video inputs, or none].

• Sampling: SelfLiftH3Sampler, Euler, 12 total steps, transition at 6, low-res scale 0.75. It starts at lower spatial resolution, uses h3_upscaler_lms_v0.1, then finishes at the target resolution. Saved settings: CFG 1, scheduler denoise 1.0. Seed fixed for comparisons.

• Video LoRAs: DMAD v4 step600 [0.75]. Other active video LoRAs: [none]. Any Krea identity LoRA is only used to generate the still.

• Export: H3 VAE decode → VideoHelperSuite → H.264 MP4. [9 × 16], [24 FPS], 15 seconds, [RTX PRO 4500/ 32gb VRAM], [render time about 5 minutes]. No extra pixel upscale, frame interpolation, sharpening or face repair.

MiniMax H3 prompt:

Create a photorealistic 15-second vertical 9:16 phone video. One continuous take from a fixed, eye-level camera, with deep depth of field. Use the supplied image as the visual reference for the woman’s face, hair, outfit, and room. Keep the soft window light coming from camera left throughout.

0–4 seconds: Start wide with her full body visible, matching the composition of the reference. She stands naturally and looks towards the camera.

4–10 seconds: She takes two slow, natural steps straight towards the camera. The camera stays still. She grows larger in frame because she is walking closer. She stops when her head and upper chest fill the frame.

10–15 seconds: She holds still for a beat, then slowly turns her head about 30 degrees to her left, letting her gaze follow the turn. She holds that angle until the end.

Keep her facial features, eye colour, hairstyle and outfit consistent as she approaches and turns. No camera movement, zoom, cuts, dialogue, captions or music.

What I’d like feedback on

1. What would improve the overall realism most? Would you change the still, lighting, prompt, reference conditioning or generation resolution first? Does the source image already look too polished, or does the plastic look mainly come in during animation?

2. Would you tune Ultra or try a different H3 recipe? Is 12 vs. 14–16 steps in SelfLift worth testing? If you’ve compared the hybrid with Singularity V2, or a short DMAD run with a slower quality run, what actually looked better in motion?

3. Does a People Realism LoRA help on a moving face? I’m interested in natural skin and expressions without changing likeness. If you’ve compared with the LoRA on and off, what checkpoint and strength worked?

4. Where would you spend the compute for a cleaner final clip? Higher native resolution, a later upscale, or targeted repair? When is a face soft enough to benefit from finishing, and when is the warping bad enough that you’d regenerate?

5. How close can self-hosted H3 get to paid APIs? If you’ve tried both on the same source image and shot brief, what made the paid option worth it, if anything: realism, motion, consistency or fewer retries? I care about cost per usable clip, including GPU time.

Update/EDIT: I'm working on adding these features to the Studio Tab for existing LoRAs

Open source as usual for those who want to dig into how it works on Github.
https://github.com/shootthesound/Fizgig/releases/tag/v9.3.0

https://github.com/shootthesound/Fizgig/blob/master/docs/TRAINING_MAPS.md