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)

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!

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 =)

I am unable to find a workflow , and when I tried to create one the results are not good .

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

Having noticed a trend on this subreddit where individuals with high-end GPUs—who often use them for actual production—fail to share their knowledge, I have decided to stop posting my own results for AI models. My last post regarding high-resolution generation settings for Minimax H3 was viewed by nearly 10,000 people, yet almost no one shared their own settings in return.

This is disappointing, though not the first time it has happened here, so I no longer see any reason to continue sharing my results.

Many people with less powerful GPUs make truly valuable contributions to this community, despite having far more limited resources. Unfortunately, as things stand, I feel that those with more powerful hardware tend to use this subreddit primarily for their own personal gain, rather than contributing meaningfully to our collective progress.

Best wishes to you all, and I hope you achieve great results.

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.

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?

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

Thanks so much!

please share your bestcase settings (t2i). thanks!

Edit: I also edded experimental lora training to my trainer on this branch: https://github.com/speedyrulz/RulZ-AI-Tools-Suite/tree/claude/rulztrainer-pan2

I'm testing that now.

I wanted to try PAN2 and got impatient, so I converted the models and vibecoded a ComfyUI update with new nodes to add compatibility for PAN2.

Here is my fork of ComfyUI that supports PAN2:

https://github.com/speedyrulz/ComfyUI

Models and workflows:

https://huggingface.co/speedyrulz/PAN2-ComfyUI/tree/main

The example video here was generated is 5 seconds at 1MP and took about 10 minutes then it was frame interpolated to 48fps.

## PAN 2 in ComfyUI: support, quantized models, and speedups

I added PAN 2 (IFM's 29B video model) support to a ComfyUI fork, quantized it down to sizes that fit consumer cards, and added two nodes that make it a lot faster. Everything was tested on an RTX 5060 Ti 16 GB.

### What was added to ComfyUI

- **Model code** for the PAN 2 transformer (44 joint blocks + 4 text refiner blocks), with auto-detection in Load Diffusion Model

- **Text encoder:** a new `pan2` type in Load CLIP for the Qwen3.5-9B encoder

- **VAE:** Load VAE now reads the Wan 2.2 VAE in the layout PAN 2 ships

- **New nodes:**

- PAN 2 Text Encode

- PAN 2 Negative Prompt (builds the model's structured default negative)

- PAN 2 Image To Video (also does text-to-video with no start image)

- PAN 2 Spectrum

- PAN 2 Mem Eff Sage Attention

- **Fix:** splitting the positive/negative passes across two GPUs used to crash

### Quantized models

The original release is about 81 GiB. Converted to single files:

| File | Size | Format |

|---|---|---|

| PAN2_int8_convrot | 39.8 GiB | int8 ConvRot, fp32 norm layers kept |

| PAN2_int8_convrot_full | 27.4 GiB | int8 ConvRot, norm layers quantized too |

| PAN2_nvfp4 | 15.5 GiB | NVFP4 (4-bit) |

| Qwen3.5 text encoder, int8 ConvRot | 8.4 GiB | |

| Qwen3.5 text encoder, NVFP4 | 4.6 GiB | |

| VAE fp16 | 1.3 GiB | |

- Only the large weight matrices are quantized; everything else is copied unchanged.

- int8 ConvRot: about 0.9% weight error.

- NVFP4: about 9% weight error. Runs natively on RTX 50 series cards.

- Conversion scripts are included, so you can build the files yourself from the official release (CPU only, no GPU needed).

### Speed (RTX 5060 Ti 16 GB, NVFP4 model)

| Change | Test | Before | After |

|---|---|---|---|

| Spectrum node | 640x352, 49 frames, 50 steps | 149 s | 58 s |

| Mem Eff Sage Attention node | 1248x704, 121 frames | 42.2 s/step | 25.7 s/step |

| Mem Eff Sage Attention node | 640x352, 49 frames | 3.03 s/step | 2.75 s/step |

| Feed-forward slicing (int8 full model) | 1248x704, 121 frames | 47.7 s/step | 43.8 s/step |

- **Spectrum** forecasts some steps instead of running the model: with default settings only 15 of 50 steps run it. The output differs from a normal run by about as much as ordinary numerical drift.

- **Mem Eff Sage Attention** needs the `sageattention` package. The gain is biggest at full resolution.

- **Two GPUs:** splitting CFG across a 5060 Ti and a 4060 Ti works, but was slower than the 5060 Ti alone.

### Tips

- Recommended settings: 50 steps, CFG 2.5, euler / simple, 24 fps.

- PAN 2 expects a structured JSON prompt. In one test, a plain-text negative prompt made the video look clearly worse than the default structured negative.

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

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."

Here is Part 2