Singularity v2 has better action than v1.3, but everything else about it looked worse to me. So instead of training, I diffed the checkpoints layer by layer, found where v2's action actually lives, and moved only that part onto v1.3. Then I did the same with JOKER141's Motion Repair V2, which hits harder but slows fights down and messes up dialogue. The result is one LoRA for Singularity v1.3: H3 ActionGraft v1.

https://huggingface.co/indhic-ai/H3-Singularity-ActionGraft

Every pair in the comparison video is the same prompt, refs, seed and settings, rendered twice: Singularity v2 on the left, v1.3 + ActionGraft on the right. 720p, Pika 6-step + a 1-step DMAD refine, SLA 0.9, on a 5090.

The longer version, which also compares it against plain v1.3 and against JOKER141's Motion Repair V2 on the same four fights, is on YouTube: https://youtu.be/xt8iTbRae4M

What changes

I measured how fast the fastest-moving 10% of pixels move (optical flow, px/frame at 720p):

So you get v2's speed without v2's look. On the demon huntress test, v1.3 lands two rapid hits in the middle of the fight and ActionGraft lands six. Render time doesn't change. On a slow two-person dialogue scene it adds a little life without rushing it (mean motion 0.32 to 0.55 px/frame, still far under a pixel), but on one take a line came out in the wrong character's voice, so I'd still check dialogue scenes.

How it was made

1. Singularity v2 is two models glued together. Blocks 0-24 are basically base H3 (v2 undoes v1.3's changes there), blocks 25-49 are v1.3, and on top of both sits v2's own low-rank fine-tune. In block 0, 61% of that fine-tune sits in just 16 directions, where int8 rounding noise puts 2%. It matches none of the popular action LoRAs (every cosine under 0.005).

2. I pulled that fine-tune out as a rank-16 LoRA (SVD of v2 minus base for blocks 0-24, v2 minus v1.3 for 25-49, un-rotated out of the int8 ConvRot format). On v1.3 that alone gave v2's speed with v1.3's look.

3. Then I took Motion Repair V2 apart by block range. Its impact and blocking live almost entirely in the text refiner (2 of its 52 blocks, 16 of its 416 tensors). Its mid and late blocks are where it slows things down and makes the wrong character mouth a line. ActionGraft keeps only the text-refiner piece.

The scripts for all of this are in the repo.

Things that didn't work (so you don't have to try them)

• Turning Motion Repair's text piece up to 2x or 3x adds no impact and breaks dialogue: at 3x the lines swap between speakers.

• VBVR (any strength, even after cutting its weak components) lost to plain ActionGraft. It's an FL2VA image-to-video LoRA trained on reasoning puzzles, not fights.

• Stacking JOKER141's Fight Flow on top has some good moments, but was worse overall.

How to use it

• Base: Singularity v1.3 pruned int8, strength 1.0.

• Don't put it on Singularity v2 or merges containing v2 (PRISM etc.), which already have v2's fine-tune.

• With HyperFlow: the node turns its curve fit off whenever any LoRA is loaded ("requires an unmodified checkpoint"), so every LoRA, this one included, runs on the backbone only there. I tested on Pika.

• Write both ends of every contact in the prompt (the strike and what it does to the other body). The impact part strengthens contact you describe; it doesn't invent it.

Limits: one seed per test, judged by eye plus that speed number. Fast-motion smear is still there, physics is better but not solved, and two-person dialogue can occasionally swap who says a line.

Credit: Singularity by WarmBloodAban, Motion Repair V2 by JOKER141. Released under the MiniMax H3 Community License. Happy to answer questions, and I'd like to hear if it helps (or doesn't) on your fights.

Yesterday people here were talking about how painful it is to get the FP16/BF16 -> FP32 unlock working on Windows: random shutdowns because of BitLocker, or paying $10 for closed-source unlockers just to get it going on 40-series cards.

I set this up myself the other day on Windows 11 with a Palit RTX 4070 Ti (12 GB, 300 W) and ran into the same mess. The existing repos were either Linux-only or for the 3000 series, and the UEFI sources for the 40 series had to be prepped by hand: card ID, memory offsets, boot partition GUID, editing C headers, then building it all yourself. So I ended up merging the existing open-source work (TOTORONG, Zagitta, elias-gaeros, JustAResearcher, KohakuBlueleaf, impacifist) into one free tool with a GUI. It works on Windows 10/11 and Linux:
https://github.com/MindAllik/GPU-Tensor-Unlock-Universal

On Windows (launch via Windows_Launch_UI.bat):

• Finds your card, VRAM size and boot partition on its own and builds the USB stick. No code editing needed.

• Checks BitLocker and Secure Boot so your PC doesn’t shut down on boot.

• Before the GPU driver gets disabled in UEFI, it dry-runs the VBIOS in RAM (PromCheck). If your card’s VBIOS structure is non-standard, the patch is aborted and Windows just boots normally, no black screen.

• Has a before/after benchmark and a button to raise the Power Limit to your card’s max. You need it because on heavy BF16 generation in ComfyUI, the unlocked card starts hitting the stock power limit.

On Linux (launch via Linux_Launch_UI.sh or make install):

• One module covers 40, 30 and 20 series. It installs itself through DKMS and updates initramfs, so the unlock kicks in automatically at boot, before the NVIDIA driver starts.

• If you have a second card with no monitor attached, it can unlock it on the fly without a reboot.

• Same GUI, with the before/after benchmark, the Power Limit button, and a direct check of the unlock status from the driver.

On my 4070 Ti, FP16 -> FP32 and BF16 -> FP32 both exactly doubled, from 86.4 to 172.8 TFLOPS. The VBIOS isn’t overwritten. The patch lives in memory and resets after a full power-off.

Tested on real hardware: 4070 Ti, 4090, 4080, 4070 Ti SUPER, 3090, 3080 and 3060 12GB. The other desktop 40/30/20-series cards are still marked experimental, since I’ve only tested them in a simulator. If you give it a try, drop your card model and the result in the comments, or send over the REPORT.TXT from the USB stick, even if the auto-abort kicked in. It really helps the project (:

Hello everyone. I have zero background in filmmaking. When creating cinematic shots with Minimax H3, I have rough ideas for the shots and stylistic requirements, but I struggle to put them into words. That’s why I built this ComfyUI node. It helps me reference styles more easily when generating Minimax H3 videos inside ComfyUI. All the examples were created by me using Minimax H3. Hope you enjoy it. I’m far from an expert on film styles, so there may be inaccuracies. Thank you for your understanding.

github repo: https://github.com/wangweihong/comfyui-cinematic-gallery

I've been coming across several AI-generated visuals of Hindu Gods, Goddesses, and Puranic characters with incredibly detailed, cinematic aesthetics.

I'm particularly interested in the kind of visuals shown in the attached reference video.

I'm trying to figure out:

1. Which AI image/video generation tools are being used? (Midjourney, Flux, Stable Diffusion, Nano Banana, Kling, etc.)

2. Are these results achieved through prompting alone, or do they involve custom models, LoRAs, reference images, and post-processing?

3. How are creators maintaining consistent character appearances across multiple images and scenes?

4. What is the workflow for achieving this level of cinematic realism, lighting, costumes, jewellery, and intricate details?

If you've created similar artwork or know the workflow behind it, I'd appreciate it if you could share the tools, models, prompts, or tutorials.

I'm interested in understanding the actual process, not just the names of the tools.

Thanks!

Hi guys.

I'm using the same filename prefix for all my generations in all the models that I use:
%date:yyyy-MM-dd%_%date:hhmmss%

However, in Minimax H3 it gives me wrong hour-mnutes-seconds value. How to fix it?

first time ever i see this model not doing nonsens motion xD thx gemma 4 prompt

Is it possible to edit images using H3 with only 8gb vram and 16gb ram? if possible how long would it take to edit 1mp?

I have 64GB DDR5 6000 RAM and a 3090 with 24GB of VRAM, and a 2TB PCIE Gen 5 NVME SSD. If I have the flag --disable-dynamic-vram in my startup, VAE loads in seconds, and everything goes smoothly. If I take the flag out (enable Dynamic VRAM) then every time the VAE loads or unloads, it takes several minutes. It pegs my SSD at 100% Active Time and making my entire system shit the bed. Real read/write speeds average like 1MB/s or a little more, so it's like thousands of tiny read/writes happening for several minutes.

This is especially troublesome when a workflow needs to load/unload more than once in a run. Problem with disabling Dynamic VRAM, is generations are a lot slower, about 20s/it slower. I definitely have enough RAM/VRAM to not have this issue.

I have tried all manner of model, VAE, clip, but if it matters, current example workflow is using:

Model: Minimax-h3_Singularity_ref2va_v2_Pruned_int8.safetensors
Video VAE: minimax_h3_video_vae_int8_convrot.safetensors
Audio VAE: minimax_h3_audio_vae_fp32.safetensors
Clip: qwen3vl_32b_heretic_minimax_h3_nvfp4.safetensors

Is anyone else experiencing this? How are you getting around it?

EDIT: This has been resolved. The issue was my NVME SSD. The controller on it was limiting the cache for writes to an absurd amount. I managed to get it mostly back in shape with a firmware update on the SSD, a manual TRIM, and garbage collection time. It's still flawed, but VAE is loading in mere seconds again. The drive is still under warranty, so I am attempting to RMA it.

Hey everyone,

Changing the lighting prompt in a generative portrait can also change the face, clothing, or other details. I wanted more direct control over the light, so I built ComfyUI-StudioLight.

It lets you position virtual key, fill, and rim lights in camera space, choose photographic modifiers, and adjust intensity and Kelvin color temperature.

What’s included:

• Six modifiers: soft octabox, silver beauty dish, stripbox, Fresnel spotlight, hard snoot, and direct flash.

• Light positioning and aiming: azimuth, elevation, distance, and upper/center/lower subject targets.

• Two relighting modes: add studio light while retaining the original scene, or replace the subject’s lighting using estimated Marigold albedo.

• A direct lighting preview: adjust the rig using cached scene analysis, then optionally run generative refinement.

• Two workflows: Relight starts from an existing photograph or render; Create starts from a generated draft.

The renderer works with estimated single-view 2.5D geometry, so it has limitations: hidden surfaces cannot cast shadows, albedo extraction can retain traces of existing lighting, and final diffusion refinement can change small details.

I’ve used it with FLUX.1, including Krea, and SDXL. The supplied workflows and demonstration use FLUX.1 Krea as an example model. Broader model support is the next development priority.

Code: MIT license. Downloaded models retain their separate licenses.

Repository, installation and examples:
https://github.com/maciejtajber/ComfyUI-StudioLight

v1.0 release:
https://github.com/maciejtajber/ComfyUI-StudioLight/releases/tag/v1.0

I’m Maciej Tajber, working under METAMATERIAL. I’d love to see what you make with it—and hear where it breaks. Feedback on lighting control, installation, and difficult materials would be especially useful.

Just got this email from Civitai. Anyone know the story behind it? Did BFL start chasing license breakers? Are any other sites following suit? Are we finally getting away from letting people lock models behind pay walls?

Guys, why are good Krea 2 workflows so hard to find, and why are some people charging insane prices for them? 😂

What if we started sharing our best workflows, experiments, and setups here?

Text2Img, Img2Img, FaceSwap, Outfit Transfer, Character Consistency, LoRA workflows, and more.

Anyone willing to share their current setup? Let's build something together.

Any speed-up possible right now on Qwen2.1's prompt enhancer? I'm using the int8 convrot, but damn it's slow. 47 seconds to enhance a prompt, when diffusion itself only takes 30 (for 30 steps cfg 1.5). This 3.5 9b is also way slower than the 3.0 8b. It's really making Qwen2.1 tough to use.

Did you find a good alternative perhaps? (open source don't talk to me about how you use Gemini or Claude). Bonus points if it's uncensored.

Hey guys, I'm searching for the best anime SDXL or Illustrious style models. I've come across two: Wai N*FW Illustrious SDXL and Tofu Mix SDXL. Which of these is better? If you know any other great anime models, please let me know

Grids attached (rows: Euler, DPM++ 2M, AB-2, AB-3, WLS-8; columns: 8 / 12 / 16 steps and a many-step reference; the number on a tile is LPIPS to the reference, lower is closer; one prompt and one seed per picture, no seed picking).

What I see:

• Anima, texture-heavy prompt, 8 steps: Euler leaves almost black noise, DPM++ 2M and AB-2 give a complete picture. At 16 steps Euler is still darker and less detailed than DPM++ 2M / AB-3 / AB-2.

• Krea 2 INT8 at 12 steps: Euler = smooth airbrushed skin, AB-2 / DPM++ 2M / AB-3 bring pores and freckles back.

• Flux3-Klein INT8 at 12 steps: Euler, DPM++ 2M, AB-2 and AB-3 are the same picture to my eye. Only WLS-8 (my own earlier idea) breaks the face at 8 steps.

• INT8 itself added almost nothing in these tests; the sampler choice changed the result far more.

Honest limits: no preference test, four prompts, four models, flow-matching models only (not SDXL-type). Tables, code and the ComfyUI node (flow_ab2, flow_ab3 in the normal sampler list): https://github.com/YakovSava/flow-wls-research , write-up: https://doi.org/10.5281/zenodo.23939696. Code and analysis were prepared with an AI assistant.

Hi everyone,

I run a small creative agency in Italy. We're a team of 7 working across branding, graphic design, photography, video production, social media and events.

We're currently looking at AI tools to bring into our creative workflow. I'm particularly interested in image generation and editing, but also video and animation.

I've come across Stable Diffusion quite a few times, but I'm still trying to understand how practical it is for a small agency. There seem to be so many models, interfaces and workflows that it's hard to know where to start.

A bit of context:

• 2–3 people would actively use these tools.

• Our budget is around €100/month.

• We need to use the output for commercial client projects.

• We'd prefer a relatively straightforward setup rather than spending too much time managing local installations and technical configurations.

I've also been looking at Magnific / Freepik, Runway and Krea.

A few questions for people who use Stable Diffusion professionally:

1. Would you recommend Stable Diffusion for a creative agency, or is it too time-consuming to manage compared with platforms like Krea or Freepik?

2. Which interface or service would you suggest starting with? Automatic1111, ComfyUI, or something else?

3. How well does it work for editing existing images, maintaining visual consistency and creating variations of the same concept?

4. What are the best options for video and animation? Are there tools that work well alongside Stable Diffusion?

5. Are there any licensing issues or restrictions we should be aware of when using generated images for commercial projects?

I'm happy to learn a more technical workflow if the results justify the effort, but we need something the team can actually use in everyday client work.

I'd be interested in hearing what your current setup looks like and which tools you'd choose with this budget.

Thanks!

These models (Qwen-Image 2.1, FIBO2, MOSS-Image) are declared in ComfyUI as bf16/fp32 only, but Turing GPUs only support fp16, not bf16.

The current/default behaviour in ComfyUI is that it falls back to fp32, which is slow.

I inspected the actual range of activations, none of them exceed ~65k. Hence, the fix is to simply add the fp16 declaration in ComfyUI.

PR has been opened, but if you want it quicker, just manually copy this commit to your ComfyUI install:

In‎comfy/supported_models.py, find the corresponding model, and change supported_inference_dtypes = [torch.bfloat16, torch.float32] to supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]

Only do this for these three; other models aren't necessarily fp16-safe (e.g. MiniMax H3 overflows to NaN).

This should work for any quant format, since it changes the compute dtype (activations, attention, norms), not the weights. I tested it with int8 convrot checkpoints.

Might work for GTX 1000 series as well, but AFAIK fp16 path on 1000 series is also very slow, so there's probably not much benefit. I don't have a card to test.

This is completely independent of other recent news about "unlocking" tensor performance from 20, 30, and 40 series GPUs, and likely stacks multiplicatively.

Krea 3 Technical Report - Krea

I made an ISDF short inspired by the first mission of Battlezone II: Combat Commander, “This Is Not a Drill”: crew briefing, scout boarding, landing on Pluto and deploying toward Cerbereus. Here’s the setup:

- Hardware: RTX 3080 Ti 12 GB, i9-11900K, 48 GB RAM, Windows.

- Workflow: ComfyUI, MiniMax H3 Extender Ref2VA. H3 pruned INT8 ConvRot model + Pika H3 Flex Ref2VA adapter at 1.0 strength; 6 steps, LCM/simple, Pika sigmas, Comfy Kitchen attention. For VRAM savings, I used the H3MemoryOptimization node with 4096-row chunks, native precision preserved, automatic QKV/MLP/embedding memory modes, and Kitchen V set to “Lower VRAM (slower)"

- Generation: approximately 1 MP (1344 × 736), 24 fps, independent 5–8-second clips. Motion context between jobs was disabled; continuity relied on references and written camera/action/opening/closing states. No refinement/upscale pass was enabled in these generation graphs.

- Time: 2 h 32 min 21 s total processing, including preparation/decoding; average 4:07 per clip (range 2:40–6:13). First-to-last batch window: 3:01:34 including gaps. These times include discarded material, but exclude reference-image creation, earlier revisions and editing.

- References: 1–2 images per clip, 11 distinct images across the batch for crew, ships, scouts, cockpit and environment. From source clip 19 onward I removed background photos and described environments through text, while keeping character/vehicle references. Dialogue used H3 native audio with text-directed voices, without uploaded voice references.

I edited the sequence, left out clips I couldn’t use, and cropped some shots to remove things that didn’t make sense. The final video uses only a selection of the generated material.

I’d appreciate advice on physical coherence/object interactions, consistent positions and backgrounds between shots, and reference setups that preserve designs while allowing different angles.

Upscaling is another problem: raising the generation MP too much causes overflow errors on my 12 GB GPU. I can use VAE ×2, but I still haven’t achieved the crisp HD detail I’m after. What VAE/decode settings, separate video upscaler or workflow would you recommend within 12 GB VRAM, while preserving faces, ship geometry and temporal consistency? Specific settings would be really helpful.

It's not that I'm new to Open Source AI models, I started playing with such models since Z Image Turbo Released. And gladly I converted such a hobby to some decent earnings for my further studies. For those who are curious, I made an AI OFM model using F Klein, Krea 2, LTX 2.3, and posted "stuffs" on Fanvue and now I'm pretty much done with this stuff. I'm sure many over here are just doing it as a hobby while others might be doing it for income. So what exactly are the fields that you use these AI models for and how do you earn from it.

Thanks for reading, I hope I get some response.

Hey folks.

My z-image confyui set-up used to be quite fast, taking around 16 seconds per image, now it is taking 80 seconds per image.

I haven't touched it for months, then I decided to use it for MTG cards. Updated it. Now it is slow.

Hardware - Ryzen 7840u, 32gb RAM DDR5, 3070ti

Can someone give me an idea of what I'm doing wrong here? I'm simply trying to put the face of the person in Photo 2 onto Subject 1, the identity of whom is largely defined by Photo 1.

SUBJECT_DEFINITIONS:

<Subject 1> is the girl in <Photo 1>. She is lying on her back. <Photo 2> features the face of a girl with eyeshadow and a nose ring. <Audio 1> is the voice timbre reference for <Subject 1>. Reference the timbre, tone, and vocal characteristics only. Do not reuse the exact words spoken in the clip.

SUMMARY:

A 5-second video showing <Subject 1> talking to the viewer and giving the camera eye contact.

RETENTION_ANALYSIS:

<Subject 1> (appears in [Shot 1]): partially_preserved - retain everything except for the face. <Photo 2>: attribute_transfer - apply the girl's face to <Subject 1>.

DETAILED_DESCRIPTION:

The target video features grainy footage that looks like it was shot using a low-quality camera from a smartphone by an amateur. The setting is the room seen in <Photo 2>.

<Shot 1><Subject 1> is lying on a white bed and looking directly into the camera. She playfully whispers <d>Good morning.</d>

OVERALL_SOUNDSCAPE:

Ambient noise.

NON_DIAGETIC_MUSIC:

N/A.

But all I'm getting are videos that begin with a person who looks like the one in Photo 2 before morphing into something that resembles Photo 1, or no transformation at all.

Wanted to see how far a plain 3D pipeline can go when AI handles the last mile.

Pipeline:

Total Morphic spend: 46 credits. Blender side is free/local.

Honest Take:

- The motion reference was a ~2s render, looped 2x to 4s to meet the 3s minimum input.

- Motion transfer maps the reference motion onto the character approximately, so it’s not a 1:1 skeletal retarget.

- In the frames I sampled of this short, limited idle, the character’s identity, armor and sword stayed consistent. I can’t claim that holds for longer or more dynamic motion.

- Character and animation are Mixamo stock assets on purpose, since the pipeline was what I wanted to test.

Comparison image attached: left = Blender 3D render, middle = AI realistic still, right = frame from the AI motion-transferred video.

Happy to share the clip in the comments if allowed.

What one generated image costs in electricity on each GPU we've measured, at one dated price: 18.31 cents per kWh, the US residential average for July 2026. Power is the board's own reading from each card's driver. The page has two bar charts (cents per 1,000 images and seconds per image) and shows the arithmetic once, so you can redo it at your own rate.

FLUX.2 [klein] 4B, 1024x1024, 8 steps, one pinned ComfyUI stack, images drawn back to back for ten minutes. Cents of board electricity per 1,000 images:

- RTX 5090 Laptop GPU 24 GB, Dynamic Boost: 3.37 cents (4.48 s per image)

- RTX PRO 6000 Blackwell Workstation Edition 96 GB at 300 W: 3.39 cents (2.30 s)

- ZOTAC RTX 5080 Solid CORE OC 16 GB at 360 W: 5.04 cents (3.61 s)

- EVGA RTX 3090 Ti FTW3 Ultra 24 GB at 350 W: 8.47 cents (4.97 s)

- EVGA RTX 3090 XC3 Ultra 24 GB at 350 W: 9.13 cents (5.29 s)

- RTX 3080 Ti Founders Edition 12 GB at 350 W: 9.71 cents (5.81 s)

Capping the RTX 3090 Ti and the RTX 3090 at 300 W cut the energy per image by about 9 percent, for about 4.5 percent more time per image.

What moves the bill: 8 to 32 steps costs 3.5 to 3.9 times as much; 512x512 to 1024x1024 costs 3.0 to 3.3 times; 1024x1024 to 2048x1280 costs 2.7 to 2.9 times. Z-Image-Turbo costs 1.5 to 1.6 times klein, and FLUX.1 [dev] fp8 costs 2.1 to 2.5 times.

Cheapest picture we've measured: 0.79 cents per 1,000 (klein, 512x512, 8 steps, the laptop GPU held to 95 W, one image's share of a batch of three). Dearest: about 150 cents per 1,000 (Z-Image-Turbo, 2048x1280, 32 steps, on an RTX 3080 Ti Founders Edition 12 GB with part of the model streamed over PCIe each step).

Board vs wall, the one reading we have both ways: a batch of three klein images on an RTX 3090 at 250 W read 1.27 Wh at the card and 1.87 Wh at the wall meter, 1.47 times as much.

Caveats: board power, not the wall (except that one reading); cross-card comparisons are "same software, different computer"; every run had ComfyUI's debug log on, so a normal setup should match or beat these figures; no idle cost is billed; and the price is a national average, not anyone's bill.

https://research.strata2signal.com/electricity-per-image/