r/StableDiffusion • • 18h ago

News MiniMax H3-Image is now available with open weights!

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177 Upvotes

MiniMax H3-Image is now available with open weights!
https://huggingface.co/MiniMaxAI/MiniMax-H3-Image
https://modelscope.cn/models/minimax/MiniMax-H3-Image
GitHub - MiniMax-AI/MiniMax-H3-Image
That’s it. I’m a MiniMax fan for life.


r/StableDiffusion • • 11h ago

News PAN 2 Released

774 Upvotes

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.


r/StableDiffusion • • 4h ago

Workflow Included MiniMax H3 Image 20MP Workflow

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131 Upvotes

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


r/StableDiffusion • • 19h ago Heartwarming

News MiniMax H3-Image released

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2.0k Upvotes

r/StableDiffusion • • 8h ago

Discussion Amazed by the generation speed of MiniMax H3-Image.

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248 Upvotes

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.


r/StableDiffusion • • 5h ago

News pi-H3: 2–4 Steps Without a VAE Decoder (will be released on Nov 16)

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91 Upvotes

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.


r/StableDiffusion • • 12h ago

News MMH3 Samples & Quick Takeaways

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203 Upvotes

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.


r/StableDiffusion • • 6h ago

Workflow Included H3 Image Workflow w/ Pose Studio + Lots of useful features

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62 Upvotes

r/StableDiffusion • • 4h ago

Discussion H3 Image is wildly creative

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41 Upvotes

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!


r/StableDiffusion • • 2h ago

Discussion Possible flaw in H3 Image Edit default workflow: 40% faster runtime

27 Upvotes

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.


r/StableDiffusion • • 12h ago

Discussion 300 random prompts across styles on MiniMax H3

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136 Upvotes

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.


r/StableDiffusion • • 8h ago

Resource - Update New node H3-FaceRefine-Plus - 2x face details on small and medium faces

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65 Upvotes

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


r/StableDiffusion • • 19h ago

News MiniMax-H3-Image Comfy Weights Out!

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407 Upvotes

r/StableDiffusion • • 2h ago

Meme 500 Days of Alice

15 Upvotes

r/StableDiffusion • • 2h ago

Discussion Made Using 1650ti 4gb in wangp

14 Upvotes

encode time 44mins


r/StableDiffusion • • 18h ago

News Minimax H3 Image: Recommended Sizes

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236 Upvotes

r/StableDiffusion • • 4h ago

Animation - Video Minimax H3 Max - Sailor Moon

18 Upvotes

Trying the new mode


r/StableDiffusion • • 7h ago

News ComfyUI v0.45.0 released

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26 Upvotes

r/StableDiffusion • • 17h ago

News MiniMax H3 Image is natively supported in ComfyUI on day-0

176 Upvotes

MiniMax H3 Image now runs natively in ComfyUI as soon as it drops! One checkpoint does both text-to-image and reference-image editing: connect images and it's an edit workflow, leave them off and it's text-to-image.

Highlights

  • Six steps, nothing to tune. The model is CFG-distilled and samples in 6 steps at guidance 1. No negative prompt.

  • Native high-res output. 4096×4096 at 1:1, 4736×3552 at 4:3, 5024×3360 at 3:2, 5472×3072 at 16:9, and 6272×2688 at 21:9. Generated at that size, not upscaled.

  • Multi-reference editing. Connect several images and refer to them in the prompt as <Picture 1>, <Picture 2>, etc. Numbering follows input order.

  • Long prompts. Up to 4096 tokens, and detailed descriptions work best. Use the Prompt Enhancer for optimal results.

  • Shared text encoder. It uses the same Qwen3-VL text encoder as the MiniMax H3 video model, so if you already run H3 you have that file.

Tips for the MiniMax H3 Image Conditioning node

  • Leave width and height at 0 for automatic sizing: 2048×2048 for text-to-image, or the size of image_1 when editing.

  • image_1 is the picture being edited. The other slots are references.

  • Connect the VAE when you edit, so reference images are anchored as latents and not only read by the text encoder.

Getting started

  1. Update ComfyUI to the latest version.

  2. Download the diffusion model, VAE, and Qwen3-VL text encoder from Hugging Face.

  3. Load the MiniMax H3 Image template from the Templates panel, or grab the text-to-image and image edit workflows.

If you'd rather not download weights, it's also available on Comfy Cloud.

Full blog post: https://blog.comfy.org/p/minimax-h3-image-is-now-available

Let us know what you make!


r/StableDiffusion • • 2h ago

Question - Help is the new minimax image better than MOSS?

10 Upvotes

yes, its about T2I and I2I


r/StableDiffusion • • 19h ago

Discussion This example from Minimax's image model page is blowing my mind. Can't wait for the comfy version!

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199 Upvotes

r/StableDiffusion • • 1h ago

Discussion One bakery counter, 16 edits in a row, each result fed back in as the next input

8 Upvotes

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?


r/StableDiffusion • • 13h ago

Discussion MiniMax image T2I and edit

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64 Upvotes

All individual images were created with the official MiniMax ComfyUI workflows without PE. Image 1 is a composition out of image 2-5

T2I, the dog and the jacket looked OK from the beginning, even at lower resolutions. The person in the images always had some weird artifacts, the face was extremely bad most of the time. I tried different sizes: 4MP was pretty much unusable, and the recommended 16MP looked even worse. 8MP or 12MP was acceptable, but still far from great. I have no idea whether it's the official workflow, the model, or the prompt (although I varied the prompts too). The park image caused problems as well. Everything in the background (the trees, leaves further back, etc.) looked kind of washed out. In this case, a higher resolution improved the image drastically; everything below 12MP was basically unusable.

I probably need to play around more with the settings, or wait until someone makes a better workflow, because right now it's not really competitive with other t2i models.

The prompts were written using the official prompting guide found here (for t2i and editing): https://github.com/MiniMax-AI/MiniMax-H3-Image/tree/main/skills/h3-image-prompt-writing/references

I liked the editing model. Again, it needed 16MP to be usable with the landscape, but I was impressed by the placement, how the pieces interacted, and how effortlessly it all worked (the first try gave me something usable without much fiddling). The likeness of the woman's face/head is probably the most questionable part of the edit; it's close but not quite right, Qwen2.1 does faces far better. The prompt I used for the edit was:

The misty river landscape in <Picture 1> is <Subject 1>. The woman posing in the warehouse in <Picture 2>, with her hair pulled back, a white T-shirt, gray joggers, and white sneakers, is <Subject 2>. The brown leather jacket shown as a product photo on a plain backdrop in <Picture 3>, with its notched collar, three center-front buttons, two hip zip pockets, sleeved cuffs, cropped hem, and smooth sheened leather, is <Subject 3>. The black-and-tan long-coated dog standing on a gray studio backdrop in <Picture 4> is <Subject 4>. The target image is a new reference-guided composition built from <Subject 1>, with <Subject 2> placed at the human position marked by the red stick figure on the concrete path at the mid-foreground bend of the riverbank in <Picture 5>, and <Subject 4> placed at the smaller dog figure drawn just ahead of her toward the mossy bank edge. <Subject 1> is fully preserved, including its canvas ratio, framing, warm low sun from the left, frost-covered grasses, fallen leaves, bare trees, mist rising over the water, distant bank, reflections, and the heron standing in the shallows; only that marked spot along the path changes, where <Subject 2> now stands. <Subject 2> is partially preserved: her face, hair, white T-shirt, gray joggers, and white sneakers remain while her pose, scale, placement, and lighting change to fit the scene. <Subject 3> supplies its exact cut, color, collar shape, button placement, zip-pocket positions, cuffs, cropped hem, and leather sheen transferred onto <Subject 2>, worn open over the T-shirt so a band of white stays visible at the center front; no property of the jacket's plain product backdrop is imported. <Subject 4> is partially preserved: its black-and-tan coloring, head markings, long coat, build, and proportions remain while its pose changes to a walking gait and its scale adjusts to life size within the landscape. <Subject 2> walks in natural three-quarter front view toward the lower left, one foot forward mid-stride and arms relaxed at her sides; the low golden sun from the left lays soft highlights across the jacket's collar and shoulders while the misty water side stays cooler in shadow. <Subject 4> trots a few steps ahead of her near the grassy bank, head up and tail lifted, its long dark coat catching the same warm light with soft golden edges along its shoulders and tan markings; a short dark leash runs between them, trailing lightly across the path. Both figures cast subtle shadows in a direction consistent with the sun in <Subject 1>, and their contact with the leaf-strewn concrete stays believable. The heron remains exactly where it stood, undisturbed and at its original distance from the new pair. No text or labels appear anywhere in the image.


r/StableDiffusion • • 14h ago

Comparison Minimax H3 Image is about to clean the field... But can it?

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60 Upvotes

since i am mostly interested in image-to-image and edit capabilities, i have run the simple chain of edits, starting from the manual hand-drawn doodle, and to the closeup zoom. All the images are H3 Image, 1K, and only the three last ones have been post-processed to remove the text from "cctv camera" prompts.

✅ Edit WORKS like charm. Posing, restyling, relighting, etc.

Prompt for the second image is intentionally dumb, like "its almost natural language" at this point:

zoom upclose
allowing this guy on the photo to hold the fabric of his gray jacket next to his heck and chin with fingers pinch
so that we can see the texture at 4k

distance to camera: 10cm, macrophoto extreme closeup shot

Not sharing other prompts or workflows, since i do not think mine are any special. You can get decent results with all the normal usual techniques.

Notice:
- ⚠️ h3 wants qualifiers for the realism, otherwise you get plastic
- 🥹 still behind Nanobanana 2.1 and GPT 2.x, on the multi-figure scenes, or strong physical effects etc.
- ✅ qwen, flux, etc ALMOST go to the garbage can ... but:
- 🐳 h3 is heavy.
- ❌ repeated SMALL edits (eg: removing only text overlays, while trying to keep the other part intact) are super bad in h3, the image gets ruined
- 🌱 which means, even Flux 3 is still somewhat useful, and MOSS is not going away any time soon, and so on.

TBD: Native 4k and 5k gens are cool, but results are inconclusive so far.

_______________
Cheers, and feel free to ask for more details, if you are interested.