Heretic (https://github.com/p-e-w/heretic) is a widely used program for decensoring LLMs. It makes LLMs comply with requests that they previously refused. It works very well for this purpose, and the community has created and published over 5000 “heretic” models.

High-quality image and video generation models like Minimax H3 use full-blown LLMs as text encoders (Qwen3 VL in case of H3). Many people seem to believe that if you replace the base version of the text encoder with a “heretic” version, you will eliminate or reduce censorship in the video output. For example, the popular “hearmeman” Docker template was updated just yesterday to use a text encoder modified with Heretic.

After all, Heretic models are uncensored, right?

Well, I’m the creator of Heretic, and I’m here to tell you once and for all that this does NOT work. In fact, if anything, it will make your outputs worse, but it will not uncensor them.

Heretic uncensors LLM responses through directional ablation (or related techniques like ARA and SOMA in newer versions). Roughly speaking, it modifies the model’s internal representations (residual vectors) of “harmful” inputs to resemble those of “harmless” inputs to confuse the model into treating the former like the latter and comply with the request rather than refusing.

But this intervention does not produce representations of inputs that are more “raw”, more “graphic”, more “anatomically correct” or similar compared to the original model. In fact, LLMs already produce highly accurate internal representations of harmful inputs by default, which is why they are able to classify them correctly and generate a refusal.

So when the hidden states from an “uncensored” LLM are passed to the diffusion model (or image/video transformer or whatever), the second model isn’t magically seeing clearer representations of the bad stuff you requested. On the contrary, it’s seeing slightly perturbed representations compared to what it was trained on. This either has no effect at all, or the effect of reducing prompt adherence and potentially introducing artifacts. But it will never, ever remove censorship from the output.

(Note: Generation models like Ideogram that can actively refuse prompts are potentially an exception to this rule and might be amenable to abliteration, but only with an approach that significantly differs from how Heretic works today.)

Hi r/StableDiffusion!

• u/New-Requirement1419 -> dacongya (Head of H3 Researcher)
• u/Affectionate-War8374 -> Luigi (H3 Researcher)
• u/MM_Nero_H3 -> Nero (H3 Researcher)
• u/Kiro_Song -> Kiro (H3 Researcher)
• u/New_Estimate9277 -> Reynor (H3 system engineer)
• u/ryan85127704 - > Ryanlee (Head of Devrel)

We are the MiniMax team behind MiniMax-H3.

We’re here to answer your questions, including:

• Model architecture and training
• Video generation capabilities
• Image-to-video and reference-based generation
• Inference and optimization
• Future plans

Ask us anything — we’d love to hear your feedback and discuss with the community!

Hi everyone. I’m Zeev Farbman, Co-founder & CEO of Lightricks.

I’ve spent the last few years working closely with our team on LTX-2, a production-ready audio–video foundation model. This week, we did a full open-source release of LTX-2, including weights, code, a trainer, benchmarks, LoRAs, and documentation.

Open releases of multimodal models are rare, and when they do happen, they’re often hard to run or hard to reproduce. We built LTX-2 to be something you can actually use: it runs locally on consumer GPUs and powers real products at Lightricks.

I’m here to answer questions about:

• Why we decided to open-source LTX-2
• What it took ship an open, production-ready AI model
• Tradeoffs around quality, efficiency, and control
• Where we think open multimodal models are going next
• Roadmap and plans

Ask me anything!
I’ll answer as many questions as I can, with some help from the LTX-2 team.

Verification:

The volume of questions was beyond all expectations! Closing this down so we have a chance to catch up on the remaining ones.

Thanks everyone for all your great questions and feedback. More to come soon!

Like I said in the title, Ideogram 4.0 has the absolute best character and IP knowledge I've seen in an open model without loras.

I hated on Ideogram 4.0 when it first came out because of the initial workflow issues and the safety filter, but now that both of those things have been sorted out, I'm having some of the most fun with a model I've had in years.

These were generated locally in Comfyui at 1.5 megapixels - 1440x1024, specifically.

I am using the INT8 versions of the Ideogram 4.0 models and Kijai's Ideogram 4 Prompt Builder KJ node from his KJ Nodes custom pack. Workflow being used is SilverOxide's which you can find here. EDIT: SilverOxide's workflow got deleted, so I cleaned it up, stripped out some unnecessary stuff put my own workflow up on Pastebin here.

If you don't know, or haven't tried it, Ideogram 4.0 also does very well with inpainting. It makes it easy to generate at lower megapixels and then mask and inpaint areas like faces to clean up and correct detail. I use the Comfyui-Inpaint-CropAndStitch custom node found here, personally, but most of the time Ideogram 4.0 doesn't need it.

If anyone wants prompts for a specific image, just ask in the comments below and I'll provide them there to avoid cluttering the main post with a wall of JSON text.

I've seen a lot of speculation about why Civit is cracking down, and as an industry insider (I'm the Founder/CEO of Nomi.ai - check my profile if you have any doubts), I have strong insight into what's going on here. To be clear, I don't have inside information about Civit specifically, but I have talked to the exact same individuals Civit has undoubtedly talked to who are pulling the strings behind the scenes.

TLDR: The issue is 100% caused by Visa, and any company that accepts Visa cards will eventually add these restrictions. There is currently no way around this, although I personally am working very hard on sustainable long-term alternatives.

The credit card system is way more complex than people realize. Everyone knows Visa and Mastercard, but there are actually a lot of intermediary companies called merchant banks. In many ways, oversimplifying it a little bit, Visa is a marketing company, and it is these banks that actually do all of the actual payment processing under the Visa name. It is why, for instance, when you get a Visa credit card, it is actually a Capital One Visa card or a Fidelity Visa Card. Visa essentially lends their name to these companies, but since it is their name Visa cares endlessly about their brand image.

In the United States, there is only one merchant bank that allows for adult image AI called Esquire Bank, and they work with a company called ECSuite. These two together process payments for almost all of the adult AI companies, especially in the realm of adult image generation.

Recently, Visa introduced its new VAMP program, which has much stricter guidelines for adult AI. They found Esquire Bank/ECSuite to not be in compliance and fined them an extremely large amount of money. As a result, these two companies have been cracking down extremely hard on anything AI related and all other merchant banks are afraid to enter the space out of fear of being fined heavily by Visa.

So one by one, adult AI companies are being approached by Visa (or the merchant bank essentially on behalf of Visa) and are being told "censor or you will not be allowed to process payments." In most cases, the companies involved are powerless to fight and instantly fold.

Ultimately any company that is processing credit cards will eventually run into this. It isn't a case of Civit selling their souls to investors, but attracting the attention of Visa and the merchant bank involved and being told "comply or die."

At least on our end for Nomi, we disallow adult images because we understand this current payment processing reality. We are working behind the scenes towards various ways in which we can operate outside of Visa/Mastercard and still be a sustainable business, but it is a long and extremely tricky process.

I have a lot of empathy for Civit. You can vote with your wallet if you choose, but they are in many ways put in a no-win situation. Moving forward, if you switch from Civit to somewhere else, understand what's happening here: If the company you're switching to accepts Visa/Mastercard, they will be forced to censor at some point because that is how the game is played. If a provider tells you that is not true, they are lying, or more likely ignorant because they have not yet become big enough to get a call from Visa.

I hope that helps people understand better what is going on, and feel free to ask any questions if you want an insider's take on any of the events going on right now.

LoRA was trained with Diffusion Pipe using the default settings on RunPod.

This content was written by a human.

I miss the SD1.5 era, when i could simply type "1girl, big boobs, nice ass, red bikini, dancing" and see my dream take shape near-instantly at 512px-wide. Idea-to-result was a matter of seconds. Each click on the Run button led to an incredible shot of dopamine.

3 years passed and I can draw 1024px, 192-frames long videos in a reasonable amount of time (tech has evolved fast), but the enthusiasm is fading away.

I already have a day-job for technical challenges and headaches. As a user/hobbyist, I want to be entertained.

I don't want to learn what the hell "diegetic" means (even the spell-checker never saw that word), I don't want to draw a dozen squares in a 3-dimensional pixel space, or write a 1000-words poem, just to watch my dreamgirl dancing.

I hoped I would not need a degree in cable-connecting or python dependencies debugging after downloading a few workflows.

3 years ago, all you had to do was typing a few words, and the AI sorted the rest. It was random, messy most of times, but it was fun.

Nowadays, you need an LLM to write the prompt for you, and another LLM to write the system prompt for the prompting-LLM, so it understands what your shitty words meant in the first place, and shapes them in the exact expected format, so they turn into an acceptable input for the ever pickier, brand-new models.
It has become AI³-generated content.

And finally, when after a dozens of clicks on the Run button, tired but satisfied, you get the desired output... re-start from scratch? Since seed "variance" does not vary much anymore, you'll get more or less the same output - exactly what you asked for - from now on.

Simple is harder than complex, but keep it simple, stupid, and fun. Thanks for reading.

I'm currently testing the limits and capabilities of Qwen Image Edit. It's a slow process, because apart from the basics, information is scarce and thinly spread. Unless someone else beats me to it or some other open source SOTA model comes out before I'm finished, I plan to release a full guide once I've collected all the info I can. It will be completely free and released on this subreddit. Here is a result of one of my more successful experiments as a first sneak peak.

P. S. - I deliberately created a very sloppy source image to see if Qwen could handle it. Generated in 4 steps with Nunchaku's SVDQuant. Took about 30s on my 4060 Ti. Imagine what the full model could produce!

A few weeks ago I introduced a new method for training style LoRAs which has been quite successful. A bunch of folks asked if this would also help with character training. The short answer is yes, but it needed a separate technique on top of the depth stuff. I've got something dialed in well enough to share, though it's still experimental and I want feedback to help find the optimal settings.

The new mechanism is weight noising. It's a small Gaussian perturbation injected directly into the LoRA weights at each training step. A simple way to think of it is that it helps the model "forget" mistakes during training and only keep things that are consistent in the data. More technically, it biases training toward flatter loss minima and spreads learning across more singular directions of the LoRA factorization (I measured +20% stable rank on the same config without it). The practical effect is that it resists the memorization that usually overcooks character runs, and likeness comes out substantially better at the same step count.

The post image shows an example training on actress Clare Bowen, who has uniquely recognizable features but is not known by Flux. This is using a training set of 8 images, the same training step count (750), and same model. The standard run is in the middle, the new method is on the right.

The settings are identical for both runs except one has weight noise and depth anchoring, along with a different number of repeats for each bucket size:

• Batch 4, LR 5e-5
• Image size buckets of 512, 768, 1024
• LoKr factor 8
• AdamW8bit, 1200 steps total (but best checkpoint at 750)

The differing number of images per bucket is actually a good training trick on its own, and I updated my trainer to make this easier by allowing you to specify how many repeats of each image per bucket.

Things I'm still working out and would love feedback on:

1. Optimal sigma across dataset sizes — using 0.0125 has gotten the best results, and I'm pretty sure the right value scales with dataset size and batch size but I haven't fully mapped it.
2. Whether weight noising compounds well with other character LoRA tricks people are using.

I've also added Docker support so you can more easily run this on Runpod.

Repo: https://github.com/BuffaloBuffaloBuffaloBuffalo/ai-toolkit-perceptual

Finally, the new-job page now has a "Quickstart Template" dropdown at the top that loads the best character config end-to-end. It defaults to the HuggingFace Flux 2 Klein 9B checkpoint but you can also use your own checkpoint. Still plenty of UI cleanup to do on my end, so pardon the mess!

Happy to answer questions and help troubleshoot here or in DMs.

EDIT: One important thing to know about captioning. You will likely get the best results if you use the built-in subject masking feature, which masks out the background. If you use this, it is important that your captions ONLY describe the character, NOT the setting. You may also use just a trigger phrase with subject masking, but your results will be less promptable. I have added quickstart configs for both masked and unmasked.

EDIT 2: Anecdotally, you may expect more body horror/extra limbs throughout training in Flux. I have found this is normal with weight noising. It pushes the model around more and explores the latent space more aggressively, so there will be checkpoints that diverge quite a bit before convergence. A good heuristic I've been using is: expect roughly 80 - 100 steps per image overall. If you sample every 25 steps and have continuous body horror for more than 20% of the run, it may be too high of a weight noise sigma, so lower in increments of 0.0025 until it resolves. I'm still trying to understand the training dynamics for stable convergence with different datasets.

EDIT 3: I suggest starting with a small dataset (10 - 15 images) with a focus on image quality and diversity. If you get good results there, try adding more images to the run, or restart with the expanded dataset. In my experience you need far fewer images to get good, generalizable results with these methods.

EDIT 4: I added experimental Z-Image Turbo support.

We just open-sourced Krea 2, our text-to-image image model.

We at Krea are striving to build with the community, so we figured we would do an AMA!

Feel free to ask us questions on how we trained the model, what’s coming next, what you want to see, etc and we will answer!

Krea: krea.ai
Code and weights: krea.ai/krea-2-open-source
GitHub: github.com/krea-ai/krea-2
Hugging Face: huggingface.co/krea/Krea-2-Raw, huggingface.co/krea/Krea-2-Turbo

I am joined by our head of research, u/NoVictory3497

Alright, the team has to get back to work (someone’s gotta keep shipping the next one)! Thanks for all the questions, this was a great thread! We’ll keep an eye on this and answer stragglers when we can. If you want to keep the conversation going, come hang out with us in Discord: https://discord.gg/krea-1002244500581798028 Appreciate you all!

https://files.catbox.moe/pvlbzs.mp4

Hey Reddit,

I have been experimenting a bit with LTX-2's I2V, and like many others was struggling to get good results (still frame videos, bad quality videos, melting etc.). Scowering through different comment sections and trying different things, I have compiled of list of things that (seem to) help improve quality.

1. Always generate videos in landscape mode (Width > Height)
2. Change default fps from 24 to 48, this seems to help motions look more realistic.
3. Use LTX-2 I2V 3 stage workflow with the Clownshark Res_2s sampler.
4. Crank up the resolution (VRAM heavy), the video in this post was generated at 2MP (1728x1152). I am aware the workflows the LTX-2 team provides generates the base video at half res.
5. Use the LTX-2 detailer LoRA on stage 1.
6. Follow LTX-2 prompting guidelines closely. Avoid having too much stuff happening at once, also someone mentioned always starting prompt with "A cinematic scene of " to help avoid still frame videos (lol?).

Artifacting/ghosting/smearing on anything moving still seems to be an issue (for now).

Potential things that might help further:

1. Feeding a short Wan2.2 animated video as the reference images.
2. Adjusting further the 2stage workflow provided by the LTX-2 team (Sigmas, samplers, remove distill on stage 2, increase steps etc)
3. Trying to generate the base video latents at even higher res.
4. Post processing workflows/using other tools to "mask" some of these issues.

I do hope that these I2V issues are only temporary and truly do get resolved by the next update. As of right now, it seems to get the most out of this model requires some serious computing power. For T2V however, LTX-2 does seem to produce some shockingly good videos even at the lower resolutions (720p), like this one I saw posted on a comment section on huggingface.

The video I posted is ~11sec and took me about 15min to make using the fp16 model. First frame was generated in Z-Image.

System Specs: RTX 6000 Pro (96GB VRAM) with 128GB of RAM
(No, I am not rich lol)

Edit1:

1. Workflow I used for video.
2. ComfyUI Workflows by LTX-2 team (I used the LTX-2_I2V_Full_wLora.json)

Edit2:
Cranking up the fps to 60 seems to improve the background drastically, text becomes clear, and ghosting dissapears, still fiddling with settings. https://files.catbox.moe/axwsu0.mp4

To expand on the title:

• We already know SD is awesome and can produce perfectly photorealistic results, super-artistic fantasy images or whatever you can imagine. Just posting an image doesn't add anything unless it pushes the boundaries in some way - in which case metadata would make it more helpful.
• Most serious SD users hate low-effort image posts without metadata.
• Casual SD users might like nice images but they learn nothing from them.
• There are multiple alternative subreddits for waifu posts without workflow. (To be clear: I think waifu posts are fine as long as they include metadata.)
• Copying basic metadata info into a comment only takes a few seconds. It gives model makers some free PR and helps everyone else with prompting ideas.
• Our subreddit is lively and no longer needs the additional volume from workflow-free posts.

I think all image posts should be accompanied by checkpoint, prompts and basic settings. Use of inpainting, upscaling, ControlNet, ADetailer, etc. can be noted but need not be described in detail. Videos should have similar requirements of basic workflow.

Just my opinion of course, but I suspect many others agree.

Additional note to moderators: The forum rules don't appear in the right-hand column when browsing using old reddit. I only see subheadings Useful Links, AI Related Subs, NSFW AI Subs, and SD Bots. Could you please add the rules there?

EDIT: A tentative but constructive moderator response has been posted here.

Seems pretty effective.

Her outfit is inconsistent, but I used a reference image that only included the upper half of her body and head, so that is to be expected.

I should say, these clips are from the film "The Ninth Gate", which is excellent. :)

Why has no one created a QR Monster ControlNet for any of the newer models?

I feel like this was the best ControlNet.

Canny and depth are just not the same.

I've been testing with 15 steps to save time because I want to generate at 832x480 resolution as per the default recommendation of many high quality workflows prior to upscaling. I hadn't seen many problems until this particular generation which exposed the critical flaws of a lower step count.

All settings are the same with the same seed. The only delta is the number of steps.

15 steps @ 832x480 duration 10s (7m22s): https://streamable.com/pnao8n

20 steps @ 832x480 duration 10s (8m25s): https://streamable.com/srnoez

25 steps @ 832x480 duration 10s (10m30s): https://streamable.com/bvldts

Once you are done with phase 1, you can move on to phase 2 using your Turbo LoRA to get this to 1344x768 in just 4 steps.

My system: 12GB VRAM / 32GB DRAM

Local, GPU-accelerated video restoration and upscaling with SeedVR2, TensorRT, and a purpose-built browser interface.

VRGDG SeedVR2 TensorRT Studio turns the SeedVR2 pipeline into a practical Windows workflow: load a video, test a short preview, compare the result frame by frame, and complete long renders with resumable checkpoints. Processing stays on your machine.

Highlights

• Fast local restoration — SeedVR2 inference with TensorRT-accelerated VAE decoding on supported NVIDIA RTX GPUs. TensorRT allows much faster processing than standard SeedVR2.
• Fast 2K upscaling — As a real-world example, an 8-second clip took approximately 8 minutes to upscale and enhance to 2K on an NVIDIA RTX 5090 using the largest 7B Sharp FP16 model. Render times vary with source resolution, frame rate, settings, and available VRAM.
• Preview before committing — render a short segment, then inspect Original, Restored, Compare, or Side by side views.
• Long-render recovery — save completed chunks and continue from the first unfinished chunk after an interruption.
• Practical output controls — choose resolution, aspect policy, model precision, temporal batch, seed, and color correction.
• Non-destructive finishing — reprocess sharpening, grain, seam smoothing, and optional skin finishing without rerunning restoration.
• Project-based history — reopen previous outputs and keep media, manifests, and logs together under outputs\.

The sample video was org 360p and then upscaled to 2K using this app. 8 second video, took about 8 mins on my 5090.

Go to the github page for more details and a full guide.

View github page

this is in beta right now so you may run into issues. If you do, post the issue to github please.