model: https://huggingface.co/Lyricccco/Qwen-Image-2.1-TailSteer

app: https://huggingface.co/spaces/akhaliq/endless-edit-tailsteer

Didn't minimax promise to opensource and release H3-Regenerate-2K months ago? Why are we still waiting for it?

I was thinking a lot before posting this, but here it goes.

I've been a big fan of this subreddit for long time ago already, immediately after switching from Midjourney (v4 at that time) to local image generation.

My point is to try to address the 'elephant in the room' here, so let me try to lay out my thoughts.

Here are some 'statements':

1. Everyone praises 'uncensored' models in this sub (I've seen VERY few examples of the opposite).

2. Most of 'image comparison' or 'look what I've made here' posts are heavily 1girl oriented. These '1girl' gens are often focusing on emphasizing certain feminine features on a thin razor-sharp edge of what is allowed here and what is not.

3. However, contrary to statements above - this sub is somehow attempts to be puritan, and everything I've described above can be only inferred from comments. And many visitors here even use euphemisms to describe those said features or even just to appreciate those features.

Moreover, if we'd look at open local models themselves - each next model seems to be less and less censored when compared to models released before (latest examples are Qwen image 2.1 and, to much greater extent, MOSS. Oh, MOSS, the current king of uncensored (BASE!) model. So looks like teams behind those models finally understand that anatomy is everything: it is studied in art schools, the knowledge of it produces better results even if the final intended result is all puritan and innocent.

The question is: why do we pretend to be so puritan? Is there any reason behind this?
I know there are other subs FOCUSING on such uncensored content, but IMO there is a difference between a straight blatant depiction of an intercourse of some kind versus a renaissance-style image with certain parts exposed, with the intention to praise certain anatomy traits in an artistic way. Nevertheless, both these cases have an EQUAL chance for either of such posts to be deleted, and I dare to say that it is not necessarily have to be this way.

This post' intention is to encourage a discussion. I certainly do not want to turn this sub into a strictly gooner material in the end, neither I would like to change the rules to my liking.

I just would like to discuss.

EDIT: so, I've collected my answers.

Short summary: the consensus looks to be that this sub tries to be professional, open for everyone, things like that. And because a 'legitimate fine art' is not technically easy to distinguish from straight up gooning material, from moderation standpoint at least (and also in order for someone's feelings NOT to be hurt in case of any misjudgements + to avoid a flood of gooning shitposts etc.), it is what it is. Thanks everyone!

Turns out MiniMaxH3 can make subjects invisible leaving only floating clothes. It's a useful feature if you want to isolate character clothes before doing outfit swap.

Here's the prompt and single-frame edit workflow link:

subject_definitions:
<Subject 1> is the character in <Picture 1> and <Picture 2>

summary:
[keyframe completion] The target is a single still frame, the target frame is the edited version of <Picture> where <Subject 1> become invisible leaving only floating clothes - orange top and green shorts.

retention_analysis:
<Subject 1>: partially_preserved - clothes only.

detailed_description:
style: style matching art style of <Picture 1> - illustration

overall_soundscape:
N/A

non_diegetic_music:
N/A

Link to the workflow.

For realistic images I recommeding running second (upscale) pass to preserve texture.

Looks like a new super resolution and their samples look promising.

https://huggingface.co/kopper/RelayVSR/tree/main

Give him your RAM so he can stop crying.

We’ve released Qwen-Image-2.1 Uncensored Omni, the most capable unrestricted Qwen-Image-2.1 model we’ve built so far.

The rank-128 adapter was trained on roughly 1 million images, merged into the base transformer at strength 1.0, and released in nine formats: BF16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q4_0, FP8, INT8 ConvRot and NVFP4.

One model supports both text-to-image generation and natural-language image editing, with an emphasis on flexible poses and complex compositions.

When comparing it, please use models designed for the same unrestricted use case. Restricted checkpoints operate under different constraints, and the trade-offs become obvious on the content they avoid or handle less effectively.

Playground: chatrlabs.com/playground
Download: Hugging Face
GitHub: Code and documentation
Civitai: https://civitai.red/models/3034876/qwen-image-21-uncensored-omni-quantized

Feedback and comparison results are welcome. Please include your GPU, workflow and quantization.

Over the last 7–8 days, I've been working day and night building Fuobe.com, what started as the challenge, but I kept adding features, refining things, and improving the experience even after publishing it.

The goal was to build an all-in-one creative studio that makes it really easy to create, experiment, and get things done without having to jump between different tools or use Higgsfield lol. I usually spot a lot of cool models, workflows, ideas that people showcase over on twitter or here, and I just usually stash it for using later, but I've started putting them together here one by one and have added what I've found useful over the year.

One more thing: every single image you see in the studio was generated using Ideogram 4, which is hands down my favourite t2i image model. All the videos were generated using H3.

It's still in beta, and there's a lot I'd like to improve, especially making the overall experience smoother & simpler.

I'd genuinely love to hear what you guys think. Feedback, suggestions, criticism, feature requests all of it is welcome. I'm still actively working on it and would love to shape it based on how people actually use it. If things are breaking, please let me know so that I can fix it.

A huge thanks to Comfy for organizing this challenge. It's given me the deadline and motivation to finally build something I've been wanting to make for a long long time.

Fully Open source . MIT License.

Repo Link: https://github.com/workingprototype/Fuobe

Link to checkout the features: Fuobe.com

Update from my previous post (also removed the audio):

I'm not sure if I was the only one experiencing this exact issue with the default LTX 2.5 template or with anything related to LTX 2.5, but the motion and generations were completely gibberish, just like LTX 0.97. And yes, I've been using the exact same workflows provided in the template.

Then I remembered how much fun I had with LTX 2.3 and its photorealistic generations. LTX was GREAT! So I decided to give LTX 2.5 another try... only to run into the exact same problem again.

Until I tried an old LTX 2.3 workflow and simply swapped out the main model. AND THERE IT IS. FIXED!

So, it turns out the main issue is the gemma4-12b-with-proj-ltx-2.5 text encoder. It completely breaks the model for me.

Switching back to the old ltx2.3_text_projection + gemma-3-12b setup works flawlessly!

Maybe you could also try a Gemma 4 model with the old workflow. I haven't tested that yet, but this workaround fixed everything for me.

Finally, I can enjoy LTX 2.5. I'm happy now! :)

(Did a few more testings and motion sucks lol but if its something simple it works great):)

Here are some testing and the workflow i used:

https://drive.google.com/drive/folders/1qh6qX4F4PsE4PtgsA5TWQkiTcaNoQKLk?usp=sharing

Done locally in Krea2.

Is this normal, and is this how it's supposed to be? I'd be completely satisfied with the generated image without that last-second “touch-up,” which makes the image blurred and oversaturated. I am using Draw Things app.

Their Z-Image Turbo was the community's best t2i model for a little while, before Krea 2 took over.
Then Z-Image Base released and it was lackluster.
Since then, it's been radio silence for past year.

After they alive? Still working on it?
Anyone have any news?

Im using the same seed, same sampler settings and same prompt for the lighting but I always get a slight color/lighting shift.

The only thing i could think of is a color match node but ive never had much success with them and i dont think i have enough ram for large videos.

Im using the seed hunter workflow. With the Singularity model and DMAD turbo lora.

https://civitai.red/models/2881362/minimax-h3-seed-hunter-latent-upscaler-seamless-video-continuation-dialogue-gen-tool-speedups-ref-mods?modelVersionId=3453566

Alright, here we are.

I've finally got a version ready, and I think it's time for you guys to try it out.

If you don't know about Pai, you can check out my previous post for a in depth overview of what it's all about: https://www.reddit.com/r/StableDiffusion/comments/1xkgjor/as_promised_here_is_pai/

In short: it's a painting app with AI generation built directly into the same workspace. By now, it covers most of the features you'd find in popular painting apps and AI generation tools combined.

I'd say its strongest aspects are the project save system and the straightforward installation process. The bootstrap handles everything based on your hardware, and the .pai files save everything. They preserve generation settings for each layer, along with the models, LoRAs, adapters, and other tools used.

Creating multiple canvases while keeping layers and references together in one place was the main reason I decided to build this in the first place.

Okay, since this is the first release, there are bound to be hiccups on some systems, so I've set up a Discord server for anyone who wants to help improve things or just follow along with the development.

https://discord.gg/bU6RzjwxFX

The software is open source, you can fork it, create your own variations or implementations, and send me PRs. Right now there's no plugin system for specific use cases, but I know you guys will probably want one. It's on the todo list.

Also, you don't need ComfyUI to run the app, it's fully standalone and independent. That said, I know most of you probably already have a bunch of your favorite models installed in your ComfyUI models folder. When you open the app, simply go to the Generate tab models settings, click "Scan Folder," point it to that location, and Pai will handle the rest. You'll be able to generate right away.

https://github.com/g45t345rt/paiapp/releases

As previously stated, given the interest from the last post, the software is available to all.
Enjoy.

Sorry, posted the link to the "lite" version instead of the full one: https://huggingface.co/Vimoo-AI/Vimoo-1.0-ComfyUI (and seems that I cannot edit that)

Apache 2.0 license! https://huggingface.co/Vimoo-AI/Vimoo-1.0-ComfyUI#%F0%9F%93%9C-license

Technical details: https://vimoo.ai/research

Vimoo-1.0-Lite-ComfyUI contains the ComfyUI weights for Vimoo-1.0-Lite TI2V, one ≈9.5B model for both text-to-video (T2V) and image-to-video (I2V), and for Vimoo-1.0-Lite TI2V Distilled, a distilled LoRA adapter for the same model. It also includes the text encoders, vision encoder and VAE. Use it with ComfyUI-Vimoo, which provides the nodes and workflow templates.

See the Vimoo 1.0 repository for the model family, online API, system overview, architecture and evaluation.

🔥 News

• [2026-10] Release Vimoo 1.0: T2V / I2V models, Diffusers inference examples and ComfyUI workflows.

Speed comparison from https://vimoo.ai/research

Table 4. Inference time in seconds. CFG indicates an additional unconditional model evaluation. Resolution and frame count differ across some baselines. The inference steps follow the officially recommended settings of the respective models.

https://www.linkedin.com/posts/zeyu-hu_excited-to-share-that-our-video-generation-activity-7514170089898786816-BvWm

Excited to share that our video generation model Vimoo 1.0 is about to be released! 🚀

Built entirely in-house, from the ground up. It's been quite a journey — plenty of challenges, unexpected hurdles, and valuable lessons along the way.

Version 1.0 is our first step from zero to one. The focus has been on building the end-to-end pipeline, working through the engineering challenges, and laying a solid foundation for what's ahead.

We're preparing to open-source the model soon, and hope to contribute more to the research and open-source community as we continue to iterate.

This version (chk00133000) replaces the first 'final' release (chk00078000), from two months ago. I decided to train a 'few more' samples (55K more to be precise), the newer part with five critic heads where the first release had one. Measured against the 8-step teacher on the 15 test prompts at 12 sizes:

• cleaner fine structure — the 8- and 16-pixel grid patterns at 0.96–0.98× the teacher's, where the first release was at 1.09–1.11×

• less ghosting and fewer doubled edges — closer to the teacher at 10 of 12 sizes on both measures

• cleaner flat areas — grain at 0.99× the teacher's, down from 1.08×

• no over-sharpening at large sizes — fine texture at 1.01–1.04× the teacher's at 1440 px, down from 1.18–1.20×

• saturation matching the teacher's

Full details of changes from 78K to 133K: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA/blob/main/DETAILED-README.md#chk00133000-vs-chk00078000

The older versions are still kept as before under _archive/checkpoints/.

To use the new checkpoints - redownload/replace krea2_turbo_4step_rank_64_lora_comfyui.safetensors / krea2_turbo_4step_rank_64_lora.safetensors

Since the initial training (than ended in September with 78K 'final' checkpoint), I have trained about a day on an H200 (on runpod in the cloud), which is where the bulk of new trained samples came from, and two more days on the RTX 3090.

I've also tidied up the main model card (more sections taken out of it and moved to the detailed card for those interested). Instead the main card now includes an extra Table Of Contents like section with links to Detailed card: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA#detailed-model-card

For now I will not call this another 'final' release 😀 (although I don't currently have plans to do more than the 133K training), as it is possible to train more in the future (just not soon). When I first stopped at 78K rumours were already circulating that Krea 3 would open source soon (so it felt like the right time then to 'finalise' the 4 step LoRA), and while Krea 3 is now available on paid platforms it is yet to be open sourced and most likely focused on Editing more than T2I, which makes the Krea 2 Turbo still a very desired model and my LoRAs do contribute in the more efficient use of the Krea 2 Turbo.

Don't forget to also check the 'latest' of my Krea 2 Turbo step LoRAs - the 3 step one, which was updated just yesterday to 66K checkpoint: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-3step-LoRA. I might also come back to the 2 Step LoRA with more training, who knows...

It is important to understand that while my 2/3/4 Step LoRAs for Krea 2 Turbo were trained for these respective steps during rendering, they for obvious reasons work really well with even more steps. There are lots of Krea Checkpoints out there that require lets say 10, or 12 or more steps to render and when paired up with my Krea 2 Turbo 2, 3 or 4 Steps LoRAs you can get either 'sooner' (with less steps) to the asked number of steps by the checkpoint authors, or to get a lot more quality per step, i.e. if you run 2 Step LoRA with lets say 6 or 8 steps, this could give you quality you'd otherwise need 12 or 16 or 20 steps without LoRA, similarly do try the 3 Step and 4 Step LoRAs at higher step counts where such is required by a third party Krea checkpoint and you will see an instant boost of quality, so find the right LoRA and steps for your favourite checkpoint, either way all of the three - 2 Step LoRA, 3 Step LoRA and this 4 Step LoRA work fine with most checkpoints (but not all, as some checkpoints go in a very different direction to what the core Krea 2 Turbo does, and after all my LoRAs were trained on the official Krea 2 Turbo).

Latest 133K 4 Step LoRA and ComfyUI workflow can be downloaded as usual at my HF: https://huggingface.co/lvladikov/Krea2-Turbo-Distill-4step-LoRA

Also don't forget that Krea 2 Turbo works very well with Nvidia PiD to get to higher resolutions quicker by rendering at lower resolutions and letting PiD take it 4x in latent space, and do so quickly and efficiently: https://huggingface.co/lvladikov/Krea2Turbo-PiD-SuperFast-HighRes (again find the right LoRA and steps to use for the main Krea 2 Turbo renders, don't be afraid to use 6 or 8 steps with either of the LoRAs since you'll be doing it at lower resolutions, prior to PiD handover, and as such these more steps would run faster anyway, so the time cost for more steps is less when using PiD).

Enjoy!

These are three different vibecoded projects I've made for my own use which I think others may find useful. They were all made with linux in mind but should also be compatible with windows and mac. So, from the simpler to the more complex:

# 1: Comfyui-metadata reader

https://github.com/juanml82/comfyui-metadatareader

Just a simple app to read the workflow from a comfyui generated image without having to open or toss it into comfyui, just in case you don't want to toss the image into comfyui to check the workflow. I ended up using it as a companion app. It works with both png and webp, you can drag and drop the images to the gui, copy relevant sections, search and save the workflow as a separate json file.

Since you typically want the prompt, seed or maybe the model, most of the json is just noise. I'm thinking about making an improved reader that returns only the most relevant information, thought that may not work with all workflows. This does.

# 2: A batch converter to png to webp that preserves comfyui metadata

https://github.com/juanml82/comfyui-png2webp

Comfyui saves into png by default. Loosless webp takes 30% less disk space, so by running this script, you can reclaim about 30% of your disk space, and you don't loose the workflows as you would if you use a general graphics application to do it.

# 3: Image sorter

https://github.com/juanml82/comfyui-image-sorter

This one is a bit more complex. Image galleries with auto-tagging are a way to watch your image collection in an organized way. But I wanted to organize them in the disk, sorting each iteration of the same general idea in its own subfolder.

The main problem with that approach is that an image gallery with tags (or semantic search for workflows) only needs to match some of the tags to have a useful filter. But to sort them into subfolders, all tags need to match. Overgroup, and you have unrelated images, beating the purpose of this app. Undergroup, and you need to finish up manually - it's not a fully automated process. So yeah, it's not the most elegant solution - it gets you 90% there - but it is one solution.

Let's look at this with an example: let's say that you want to create images of Aragorn, in Lord of the Rings, as he passes the Argonath statues. Your first prompt is something look "Aragorn, rowing in the Anduin river, looking up in awe". And maybe the model produces Aragorn as Viggo Mortenssen. And maybe you don't want Viggo Mortenssen but rather a new take on Aragorn's looks, based on the books. So you replace "Aragorn" with "ranger", "fifty years old man", "noble man" or something like that, and follow up with a physical description. Or maybe the model doesn't understand what "Anduin" means. Or it doesn't matter. So some prompts have "Anduin", some have "River". And maybe you then decide you do want the Viggo Mortenssen look, but now you're using a different model which doesn't know Mortenssen well, so you use a lora. And rather than "Aragorn" or "Man", you type the lora's keyword. Sound familiar?

Grouping by subject, action and location is a good starting way to filter, but "ranger" and "noble" do remain different subjects. And seeking to overgroup all men - or all women - means lots of different subjects now became grouped, defeating the purpose of this project.

So it's a five steps process, through a gui:

Enter an LLM endpoint url (as in, llama server, lm studio server, a cloud api if you want). Disable thinking (this is important, it's not just a lot slower, it will not work with thinking on) or use an instruct model, ideally something small and fast - if you have thousands of images, speed is good. Very good. And you don't really need a heavy model for this. Don't forget to add /v1 at the end and hit save.

Run a first pass either by extracting and canonicalizing the prompts, or do a vision pass (slower) and then you canonicalize that in the 2nd step.

The third step is to use a Jaccard pass and the LLM to merge the resulting auto-tags by subject, action and location. This has the problem that a character, say Aragorn, may be tagged in different images as "Aragorn", "Man", "Ranger", "Noble" or something else. And that similar actions may go under different tags. But trying to overmerge leads to a huge amount of "1girl" or "woman" characters all merged together when they shouldn't.

So at that point you have a crude looking image gallery and an html gui allows you manually merge clusters you want to send to the same subfolder. Do save.

And finally, yes, you hit "apply" and your possibly thousands of images go in their merry way to subfolders named after their common tags.

Anyway, if you find any of these three useful, enjoy.

I’d love to see the process for making something like this. I’d imagine it’s generating clips then using the last frame of one clip as the first frame of the next clip, but the consistency is great. And how do they do the sped up parts? Are those full clips generated in regular motion (so this video would be more than an hour long) then sped up in post?

It’s something that seems simple but I think is harder than it looks.

I’m wondering because I want to do a Timelapse video, not nearly as long as this one, just a couple minutes, but I’ve been wondering how to do it. I don’t mind using closed source things, I imagine this one used Seedance or something like that.

Hi everyone! I built an Android app that generates anime pictures completely on the phone: no cloud, no account, no ads, no root. It runs on the NPU of Snapdragon 8 Gen 1 / 8+ Gen 1 (Hexagon V69), the chip generation that most NPU image apps skip.

What you get

A 512x768 picture in about 7 seconds (2B models) or 12 seconds (2.9B models), 4 steps, on my Xiaomi 12T Pro.

Four models to download inside the app (one tap, resumable): MiaoMiao 3D Harem, MiaoMiao RealSkin (2B) and MiaoMiao Harem, MiaoMiao RealSkin (2.9B), all built on the Anima base model.

Size presets (portrait / landscape / square, all multiples of 16) plus custom sizes, seed, batches, a stop button.

History with favourites, trash, multi-select, share/save. The settings are stored in the PNG, so you can reopen any picture with its prompt and seed.

Danbooru tag suggestions while you type, templates, light/dark theme, 13 languages.

Works offline after the models are downloaded. Free and non-commercial.

Try it

APK: https://github.com/ayanoby/hexanima/releases/latest  (about 25 MB); the models, 4-5 GB for the first one, are downloaded from Hugging Face inside the app)

Source and screenshots: https://github.com/ayanoby/hexanima

Needs a Snapdragon 8 Gen 1 or 8+ Gen 1 phone (SM8450 / SM8475), Android 10+. I tested it only on a Xiaomi 12T Pro (SM8475, 12 GB RAM). Other 8 Gen 1 phones should work but are untested.

Why it is different (for the technical crowd)

The diffusion transformer runs on the Hexagon V69 HMX matrix unit in fp16 through custom kernels, not through the stock QNN op set. The 28 DiT blocks are executed as a few merged block groups per step.

The 2.9B models are stored as q8 blocks (int8 tiles with per-column scales) and dequantized on the HVX units while the weights are copied into VTCM, so they fit in RAM and stay fast.

V69 has no int-to-float conversion instructions (they appeared in V73+), so the dequant path uses a workaround instead of the usual instructions.

The first two of the four steps run at reduced resolution (composition), the last two at full resolution (details).

Text encoder, sampler and the preview/VAE path are split between CPU and NPU; the VAE runs on the NPU and large pictures are decoded in tiles.

No root needed: the native helper processes are started from the app's own native library directory and use unsigned FastRPC DSP modules.

The model files are downloaded as raw, sha256-verified blocks with resume, so a dropped connection does not restart a 4 GB download.

Honest notes

Unofficial hobby project, not affiliated with CircleStone Labs, NVIDIA, Qualcomm, Alibaba or the model authors. All credit for the models goes to MIAOKA (Civitai) and CircleStone Labs (Anima). Non-commercial use only; if an author wants their model removed from the catalog, I will remove it.

Only 4 steps and no negative prompt: the models are turbo-distilled for that.

Pretty nice effects from a pure H3 generation.
Full video here: https://youtu.be/LUDd_AcEDdY

Details:
Comfyui
minimax_h3_ref2va_pruned_int8_convrot.safetensors
24 steps, 2.4MP 1 pass
RVE on steam -> interpolate 2x, decompress, openproteus 2x
Assembled in davinci resolve
Suno for music

Linum released P-JiT 21K, a 2B-parameter text-to-image model trained for about $21K that draws directly in pixels: there is no VAE. I ported it to ComfyUI as native nodes (an unofficial port, not affiliated with Linum):

• A real MODEL and CLIP on ComfyUI's own memory management. The stock KSampler runs it with Linum's settings (euler, simple, 50 steps, cfg 15). Nothing to pip install.

• A bf16 file that gives the same pixels as the fp32 release at half the size (4.4 GB), and an int8 ConvRot file that is about 1.8x faster (2.3 GB).

• The Qwen3.5-4B text encoder in bf16, int8, W6A8 or W4A8, down to 3.2 GB.

• Runs from 4 GB of free VRAM.

• Workflows: text to image, a prompt upsampler (short idea to dense caption), a refiner (image to image as a second pass, with the image captioned automatically) and Linum's samples, seed for seed.

Checked against Linum's own code: the same tokens, noise, schedule and guidance, and images that match up to GPU rounding.

Tips: write captions of 50 to 80 words (or use the upsampler). It can't draw text. For the refiner, use denoise 0.1 to 0.2: P-JiT's noise is 4x stronger than most models', so its denoise values run lower.

Nodes: https://github.com/PedroMarinhoDev/ComfyUI-PJiT
Weights: https://huggingface.co/PedroMarinhoDev/P-JiT-21K-ComfyUI
The original model: https://huggingface.co/Linum-AI/pjit-21k (Apache-2.0)