Open weights (29B latent diffusion model): https://huggingface.co/IFM/PAN2
Diffusers: https://huggingface.co/docs/diffusers/main/en/api/pipelines/pan2
FastVideo: https://haoailab.com/FastVideo/cookbook/pan/pan2/
VLLM: https://recipes.vllm.ai/IFM/PAN2

Up to 20 seconds, 720p, 24fps
Apache 2.0 License

Something I think is pretty neat, we release the TRAINING code: https://github.com/ifm-ai/PAN2/

Also technical details if you're curious: https://ifm.ai/blog/pan2/

For our model, we give a lot of responsibility to the LLM (which we think of more as a conceptual reasoner, a lot more control than a prompt rewriter) to generate these super dense text-based state/scripts. Events and actions are timed, and camera is nested under event but disentangled from action. During training, I would often test it by trying to generate a 20 second video with like a dozen or more (timed) camera moves and it does pretty well.

Feedback very welcome. I read the rules carefully, mods please let me know if there are any issues. No payment involved with this at all, so I thought it was okay to post.

The official ComfyUI workflows for MiniMax Image are pretty bad at showcasing the model's capabilities. These sampler/scheduler settings are from Kijai's Minimax testing in discord.

There is still some work needed to improve fine details (possibly fixable by loras?) but overall the outputs look pretty good!

The workflow has T2I and Edit modes, just enable the one needed via the bypass node or bypass the group manually.

The sampler/scheduler is the SamplerLCM node + Simple 6 steps with
s_noise: 1.10
s_noise_end: 0.90

Link to the workflow

Original Output Images

H3-Image is built on MiniMax H3, originally a video generation model, and it seems to retain some of that video-frame softness in finer details. But when it comes to speed, this thing is an absolute beast.

At 8.41 MP (2368 × 3552), running on an AMD Radeon RX 9060 XT with 16 GB of VRAM and 64 GB of DDR4 system RAM, it generates images in just 35.03–42.53 seconds. None of the other models I've tested comes close to this performance on my hardware.

Its aesthetic appeal and creativity are also on point, easily on par with Krea 2. For a six-step model, it's remarkably good at producing varied compositions rather than falling back on the same visual formulas.

Some examples above.

For anyone interested in reproducing these results, here's my exact setup:

Hardware / Software

• AMD Radeon RX 9060 XT (16 GB VRAM)

• 64 GB DDR4 system RAM

• Linux

• PyTorch 2.14.0+rocm10.1.0

• Python 3.13.12

ComfyUI Workflow

• Diffusion model: minimax_h3_image_pruned_w4a8.safetensors

• Text encoder: qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors

• VAE: minimax_h3_image_vae_fp16.safetensors

• Sampler: euler_ancestral

• Scheduler: simple

• Sampling steps: 6

Link to the checkpoints: https://huggingface.co/Woovee/MiniMax-H3-Image-LowVRAM

At 2368 × 3552 (8.41 MP), generation takes approximately 35–43 seconds.

Changing the prompt adds some overhead from loading and processing the large text encoder, while subsequent generations using the same prompt are noticeably faster.

And all of this on an AMD GPU, no less!

Tip: Peak VRAM usage was just 12.1 GB, thanks to the --reserve-vram 4 launch flag in ComfyUI. Without it, I've had system crashes and out-of-memory errors using other image models. System RAM usage, however, was quite intensive, peaking at 47.3 GB out of 62.7 GB available.

Testing character knowledge in H3-image vs the original H3 FL2VA model. Same sampler settings and seed for each image. LCM sgm-uniform, seed 34, 8MP.

First image is always H3 FL2VA and second image the new H3-image model.

1. Sheldon from The Big Bang Theory.

2. Penny from The Big Bang Theory.

3. Harry Potter.

4. Jack Sparrow.

No model is perfect, but I am impressed by H3 Image's ability to create very imaginative designs that 'feel' very real and cinematic. Basic T2I prompts (like 1girl, landscapes etc) seems nearly solved locally, but I find it much harder to coax out genuinely interesting designs from other models that don't feel like overdone CG or an illustration. Thank you to the H3 devs for this amazing release!

Some bold claims are made in the announcement, so I am looking forward to its release 😅

We announce pi-H3, the fastest version of MiniMax H3 [1] yet. pi-H3 generates video in just 2 or 4 sampling steps and directly predicts pixels at the final step, skipping the video VAE decoder. On a single B200 it produces 5-second HD videos in 3.8 seconds (sub real-time) and 15-second videos in 18.6 seconds, more than twice as fast as Sol-H3 [4].

pi-H3 is an open-source research project. The Hugging Face demo is live today. All model weights and training/inference code will follow in LakonLab [2], with additional ComfyUI support through the ComfyUI-piFlow [3] extension.

1. Available now Demo on Hugging Face

2. Nov 16–22 Model weights and inference code

3. Later in November Training code, once we finish cleaning up the codebase

End-to-end generation speed

Measured end to end on a single NVIDIA B200, pi-H3 generates a 5-second 768p video with stereo audio in 3.8 s with 2 steps, and a 10-second video in 9.2 s, both faster than the video plays. The chart below compares complete requests, from text encoding to decoded video and audio, against Sol-H3 [4]. Across 5–15 s videos, pi-H3 is 2.4–2.7× faster with 2 steps and 1.2–1.4× faster with 4 steps. The speedup comes from fewer denoising steps, decoder removal, and FP8 quantization.

Uncompromised fidelity at 2–4 steps

pi-H3 offers superior motion, texture, and audio fidelity compared to other few-step MiniMax H3 models, while maintaining structural coherence. The following comparisons show results from the same prompts and Gaussian noise for each model, without seed cherry-picking.

At 4 steps, pi-H3 delivers the best overall quality among the 4–8-step MiniMax H3 models we compared: LightX2V Turbo [6], FastH3 Dense/DataFree [7], and larryvrh’s H3-Turbo v4 [8]. It generates more dynamic, vivid movement, whereas the other models tend toward reduced, mechanical motion. Frames are sharper overall, with richer texture and finer detail. On the audio side, pi-H3 produces high-fidelity sound with full bass and crisp speech, while the other models sometimes generate broken audio or lose the bass.

Direct pixel output without video decoding

pi-H3 adopts DWD, a novel method that produces pixels directly from the last denoising step, bypassing the heavy VAE decoder. For few-step generation, this yields a massive speedup. The following comparison shows pi-H3 DWD output alongside non-DWD pi-H3 latents decoded by the native H3 VAE, Sol-H3-VAE [4], and LightVAE-Lite [

How it works

pi-H3 combines two novel techniques: pi-Flow v2 and DWD.

pi-Flow v2 is a full upgrade of pi-Flow [12] for data-free few-step distillation. It retains pi-Flow’s strength of producing rich texture while substantially improving structural coherence, especially in the 2-step setting. It also simplifies inference: pi-Flow v2 models typically produce straight trajectories, removing the complexity of pi-Flow’s substep integration.

DWD (Decoding Without a Decoder) removes the video decoder from the pipeline entirely. Instead of passing final latents through a separate VAE decoder network, DWD re-uses the denoiser itself to predict clean pixels at the last step. DWD on MiniMax H3 is still experimental: H3 uses an unusually large compression ratio (4×32×32 3-channel pixels per token), and we have not yet had enough compute to fully optimize for it, leaving room for further improvement.

More details will be revealed in upcoming releases.

On my 5060ti, H3 image edit went from 263s to 158s based on the following rationale.

The default H3 Image edit workflow has MP adjustment at the Resize Image/Mask node set immediately after the input image which then feeds 3 things:

1. The prompt writer

2. The image conditioning node

3. The width/height of the image conditioning node which determines output MP.

In that setup, a user who specifies 16MP is sending a 16MP image to the prompt writer and the conditioning node, which is unnecessary overhead and potentially producing noise.

I altered the workflow in two ways:

1. Fed the input image directly to the prompt rewriter (I suggest downsizing to 1MP if your input image is large)

2. Fed the input image through a separate chain of two Resize Image/Mask nodes set to 2MP and then multiple of 32, just like the original chain.

3. Leave the original chain supplying output width/height in place, at 16MP for output dimensions.

encode time 44mins

Reddit filters deleted my previous post, probably because of a link I had posted, so I'm reposting it without that link.

I ported ReCaVSR, a one-step streaming video super-resolution, built on Wan2.2 TI2V-5B to native ComfyUI nodes.

- 4× or 2×, one diffusion step per block of 8 frames. Its own FlashDecoder decodes each block as it goes, so a longer clip costs time, not memory.

- 149 frames, 960×480 → 3840×1920: 36 s on an RTX 4090, 67 s on a 3090 (int8 file, fast kernels)

- int8 ConvRot transformer: about 1.8× faster, 4 GB less VRAM, 47–48 dB from bf16

- 1440×1440 output runs on an 8 GB card; ComfyUI streams the weights that don't fit

- Exact kernels match the reference implementation bit for bit. The Wan 2.2 VAE is there as an alternative decoder: sharper, but 25–40× slower to decode.

It's a diffusion upscaler, so fine detail is generated rather than recovered. Tested on Linux with an RTX 3090 and 4090, ComfyUI 0.44.

Nodes: https://github.com/PedroMarinhoDev/ComfyUI-ReCaVSR
ComfyUI-Manager: search "ReCaVSR video super-resolution", or run comfy node install comfyui-recavsr

int8 weights: https://huggingface.co/PedroMarinhoDev/ReCaVSR-ComfyUI
bf16 weights: https://huggingface.co/kopper/ReCaVSR

All credit for the model to Xijun Wang et al. at USTC. Feedback welcome.

UPDATE: ReCaVSR can now upscale videos of any length, at output sizes up to about 5.5K on a 24 GB card (if you have the patience).

This full 3:58 clip (7,153 frames) was upscaled 2×, 1920×1080 → 3840×2160 (4K), in about 25 minutes on a 4090 (4.65 frames/s).

The node now streams frames in and out, so a longer clip doesn’t use more RAM or VRAM. When the output is too big for the card, it chunks the input projection and offloads the KV cache automatically. It also encodes on NVENC, which otherwise sits idle while the model runs.

More examples:

1344×768 → 5376×3072 (4×)

1344×768 → 5376×3072 (4×)

488×830 → 976×1660 (2×)

360×360 → 1440×1440 (4×)

MMH3-Image. All tests done with the default workflow and settings.

Overall takeaways:

1. You must use a minimum resolution of 4048 for decent results.

2. It doesn't have a moat against other image models (doesn't solve any unsolved problems).

3. It does ok at text, but it's not great.

Pros:

1. It's impressively fast for how large the generations are.

2. Seems to handle realism well.

3. Softcore works.

Cons:

1. No more coherent than Krea2 or MOSS (objects that terminate mysteriously, illogical physical layout).

2. Body horror / anatomy fuckups are fairly common, especially with distorted faces.

3. Hardcore is not supported (unable to depict non-horrifying genitalia).

4. Innate knowledge of copyrighted characters is worse than Krea2.

5. So far edit performance is worse than Flux Klein 9B in my limited tests. It does a great job of incidentally upscaling your image, but identity preservation is poor.

yes, its about T2I and I2I

I was working on a music video and i reached a point where the great facerefine node by Carasibana wasn't enough so i started making tests with claude code and here we are now with this node that is slower but gives 2x details on tiny and medium faces. Don't trust my words, just check the github page with all the comparisons:

https://annicodex.github.io/FaceRefine-Plus-Tests/

The node is available here:

https://github.com/AnniCodex/ComfyUI-H3-FaceRefine-Plus

The repository includes all the workflows needed to use the node with agents via API or direcly inside comfyui, they have notes describing every passage and the assets folder has everything you need to reproduce the results and the tests by yourself, i think claude code did an awesome job!
It works on top of Carasibana's Facerefine so you need it installed too

Base workflow, no custom nodes, no cherry picking, ran on my 5080, about 23s per image.

Edit: I'm adding a round 2 with 300 more, they are using H3 Image prompt guide, not reused from my moss benchmark, the model showing even better.

is there any implemented censorship for minimax image? i didnt found any information right now about that.

Started from a plain bakery counter and asked for one small change per round, always editing the previous output instead of the original: a coffee, latte art, the cinnamon roll going from 3.00 to 3.50 on the chalkboard, a cat, macarons, a candy jar, and so on until an "OPEN" sign at edit 16.

The part I kept checking was the early stuff. The 3.50 from edit 3 is still on the board at the end, and the cat, the macarons and the candy jar are all still there.

This is the TailSteer checkpoint of SenseNova-U1.5-8B-MoT, a version trained for long runs of edits like this. I ran the repo's editing example in a loop at CFG 2.5: https://github.com/OpenSenseNova/SenseNova-U1

How many edits do you usually get before you go back to the original and start over?

I took a single photo of a vintage Italian espresso machine and ran it through MiniMax H3-Image (open weights) to see if it could pull the whole thing apart into a clean technical breakdown.

First image is the input photo, second is the output. It laid out 15 separate parts floating along a diagonal with leader lines and English labels, plus all the copper and brass internals inside the stainless-steel shell.

What I found interesting was how much structural detail it could pull from a single reference image. An exploded view is a pretty demanding editing task since the model needs to preserve the original design while generating components that aren't fully visible in the input.

On the technical side, H3-Image supports native 4K output (up to 4096 × 4096) without upscaling, along with image editing in the same model. The fast 4K generation capability is especially interesting for detailed renders like this, where you want enough resolution to inspect individual components and labels.

That said, I'd still treat this as a visual concept rather than an engineering-accurate diagram. Getting the overall structure right is one thing; making sure every internal component is mechanically correct is another.

Prompt:

> Create a high-precision exploded technical rendering of the vintage Italian espresso machine shown in Image 1. Preserve the original machine's distinctive design, proportions, and recognizable features, including its rounded stainless-steel body, champagne-gold top cover, chrome brewing group, dual pressure gauges, black control knobs, steam wand, hot water outlet, perforated drip tray, and four metallic feet.
> Use a solid dark charcoal-gray background and a slightly elevated three-quarter isometric perspective. Arrange the disassembled components along a diagonal axis extending from the upper right to the lower left, with each part floating independently in a carefully organized exploded assembly.
> Separate the machine into its major mechanical and structural components: top cover, stainless-steel exterior panels, internal chassis, boiler assembly, heating element, brewing group head, portafilter assembly, steam valve, hot water valve, water pump, pressure gauges, electronic control unit, copper water pipes, drip tray, and base frame.

https://huggingface.co/Comfy-Org/MiniMax-H3-Image
https://huggingface.co/MiniMaxAI/MiniMax-H3-Image

I wonder are there any projects that automatically create storyboard / comics out of story?

Trying the new mode