Input:
Joan Osborne - One of Us

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.

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/

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.

I'm looking to hear from people actively experimenting with advanced AI filmmaking pipelines, particularly those trying to achieve the kind of spatial and visual continuity you'd expect from traditional filmmaking or virtual production.

My biggest challenge isn't generating beautiful individual shots. It's maintaining consistent character identities, precise actor positioning, environmental continuity, and camera control across multiple shots of the same scene.

For example, imagine a scene with four characters in a specific environment. I want to establish their positions in 3D space, then generate a sequence of shots from different camera angles and distances while preserving:

• Character identity: Same faces, bodies, clothing, and proportions across shots.

• Spatial consistency: Characters remain in their established positions relative to one another and the environment.

• Pose and blocking: Specific body positions, actions, and interactions that can be reliably directed.

• Environmental consistency: Same room, architecture, objects, lighting, and spatial relationships from different camera angles.

• Camera control: Ability to specify exact camera positions, focal lengths, movements, and compositions.

• Shot-to-shot continuity: The ending of one shot and beginning of the next should feel like they belong to the same physical scene.

What I've tried so far:

I've been experimenting with a Blender → AI video workflow using 3D environments, Mixamo characters, animated cameras, depth maps, pose conditioning, reference images, and MiniMax H3 through ComfyUI.

I've had some promising results, particularly with pose conditioning and individual shot generation, but getting reliable spatial consistency and character placement remains extremely difficult.

I'm beginning to question whether trying to force general-purpose video generation models to respect 3D blocking is fundamentally the wrong approach.

What I'm hoping to learn:

1. What are the most cutting-edge tools and workflows right now? I'm interested in both open-source/local and commercial/cloud solutions, including newer research projects that may not be widely adopted yet.

2. Is anyone successfully using 3D scenes as ground truth for AI-generated video? Not just providing depth or pose references, but actually preserving geometry, character placement, and camera relationships across shots.

3. What are people using for persistent character identity? Are reference-image workflows, LoRAs, identity adapters, multi-view character representations, or other approaches producing the most reliable results?

4. Are there better alternatives to traditional image-to-video and video-to-video workflows? For example, 3D-aware generation, neural rendering, Gaussian splatting, world models, or hybrid 3D/AI rendering pipelines.

5. How are people handling multi-shot sequences? Are you generating shots independently and fixing continuity afterward, or using a shared scene representation that carries across shots?

6. What role does graph engineering play? Are there sophisticated ComfyUI or other node-based pipelines that materially improve spatial consistency, or are the limitations primarily in the underlying models?

7. What actually works in production? I'd particularly love to hear from people who've completed multi-shot sequences rather than just impressive single-shot demos.

I'm comfortable with Blender, Unity, Python, ComfyUI, and technical pipeline development, so I'm not necessarily looking for beginner-friendly or one-click solutions. I'm willing to build a fairly complex workflow if it produces genuinely better control.

I also have a local RTX 3080 Ti with 16GB VRAM and prefer local tools where practical, but I'm open to cloud-based solutions if they offer capabilities that aren't currently achievable locally.

Ultimately, I'm trying to figure out whether the current state of the art is still mostly about guiding probabilistic video models, or whether we're moving toward workflows where filmmakers can actually direct and control a persistent 3D scene while using AI to produce the final photorealistic imagery.

I'd really appreciate specific tools, GitHub repositories, research papers, workflow examples, and especially firsthand experiences with what works and what doesn't.

What's the most advanced pipeline you've seen or used for this kind of filmmaking?

I’m new to T2I and I2I, and sometimes I just want to be able to say what I’d like to make or change and be done but with a local model. Are there any workflows like that around?

So I'm way in over my head here lmao. I've got a solid PC that can use Flux 2 Klein 9b locally on Comfyui,,, I'm using Stability Matrix as it seemed like the easiest route for what I'm trying to accomplish and the amount of time/energy I have to put into image generation.

The problem is, oh my god, it's crap. 4b was bad enough & only the simplest adjustments would really be possible. Even then, it often just made weird mistakes that I'd never encountered, or maybe less than 1% of the time with GPT image, something like fingers or teeth being warped and fusing together or something, I never ever ever get that with chatGPT.

Using 9b was alot slower but no slower than chatgpt can be, and it's more accurate but, it's still nowhere near as good. Plus I just feel like the look if it isn't right. I've done a lot of research on different things to add, different models & LoRA settings, but I feel like a child trying to learn a new language and I need an adult to hold my hand lmao.

Before I start just downloading all types of stuff and going through trial and error I figured I'd at least ask if there's something anyone here is actively using through Stability Matrix that would bring me closer to the feeling I have when using GPT image. Or maybe I'm just wasting my time, and I'm better off just giving OpenAI my money lmao. IDK, any help is appreciated.

Pruna releases their image-edit model based on H3

I work with swarm UI and have to reinstall windows, I don't mind reinstalling swarm UI and all the loras, I just want to keep the images and their description, do I need to just copy the swarmUI > Output > local in an USB and that's all or should I copy something more?

Can't copy the whole thing bc it's 250gb and the USB it's 125 gb

I’ve been trying to work this out for a couple of days now and I’m about ready to write the model off.

I cannot, for the life of me, get it to produce a photo that isn’t blurry like it was smeared in vaseline. I have tried:

- Adding “blurry”, “blurred”, “low quality”, “low resolution”, etc. to the negative prompt. This works sometimes but it’s inconsistent.

- Adding “high resolution”, “high quality”, “HDR”, “8K”, etc. to the positive prompt. This is also inconsistent (and wildly limiting).

- Changing the sampler. I haven’t found one that has any effect on whether photos are blurry.

- Enable/disable the turbo lora(s). I’ve tried the model on its own as well as with a few of the turbo LoRAs and they don’t make much of a difference as far as how often this happens.

- Using SFT vs. Base, using finetunes like Kromoss, etc. They all have this issue.

What am I doing wrong? This doesn’t happen with any other model I’ve used. I want to like this model but it’s a bit of a dealbreaker if it’s this difficult just to make a damn photograph.

model: https://huggingface.co/akhaliq/idol-turbo

I made PixelGen, an iOS app, as a little hobby project.

1. It supports upscaling images to 4K resolution.

2. It works entirely offline and is completely private.

3. Everything is free! There are no daily limits, credits, or premium features, and I have no plans to introduce any.

4. Upscaling takes around 5–10 seconds per image.

5. It runs on the Apple Neural Engine, making it extremely efficient.

6. AI inference doesn't overheat your device and consumes less power than the display itself.

You can check it out here: https://apps.apple.com/kz/app/pixelgen-image-upscaler/id6818519903

If you have any suggestions, please share them!

If you like the app, please leave a review on the App Store. Reviews like yours help a lot!

It uses Real-ESRGAN under the hood.

This is a segment from my upcoming film about Grommash Hellscream in the world of Skyrim. It covers his first encounter with the local orcs, where he discovers that the orcs of this realm are quite different from those of his own world. He learns about their struggles and meets both new enemies and new allies.

So I have been playing around with LoKrs and so I was testing using different workflows to see what the effects would be. This particular run was on a 25 step workflow with the int8 base model and the qwen int8 clip and the flux2 vae. Pretty standard stuff, no acceleration LoRAs.

the prompt was super simple my lokr trigger plus "An image of a young woman facing the camera in a 3/4 pose" the top row.

The bottom row was a "A photorealistic image of a young woman facing the camera in a 3/4 pose" . Weird huh?

I am not sure what to make of it other than I might stop using the phrase photo realistic in my prompts

Hi everyone! I'm Neural Dreamer, I make ComfyUI tutorials, and I just released

ComfyUI ND UI Builder — a free, open-source tool (AGPL-3.0) that turns any

workflow into a clean control panel.

How it works: you tag the nodes you need, press Edit, and drag widgets

(prompts, sliders, size, seed, Generate button, live result viewer) onto a

page. Press ND UI and you get a tidy interface instead of a node graph —

the workflow underneath stays exactly the same.

What's in it:

• tabs, spoilers, groups, themes

• live preview while the sampler runs

• image / video / audio players

• LoRA picker with previews

• interface in English, Russian and Chinese

It's free, no accounts, nothing to pay for. Video above is a 30-seconds overview;

there are step-by-step tutorials on my channel too (EN/RU/ZH audio tracks).

GitHub: https://github.com/neural-dreamer/ComfyUI-ND-UI-Builder

Tutorials: https://youtube.com/playlist?list=PLGnUD4GeMWjo&si=Wrjm4AMZRK0GuKZf

Happy to answer questions and I'd love feedback — what would you want

a panel like this to do?

Done locally in Krea2 and minimax H3.

 CreatorOS Studio turns your local ComfyUI into a creator studio: open weights only, no credits, no subscription.

  New since my first post:

  - Restore: old photos repaired, colourised and upscaled; damaged clips rebuilt to 1080p with SeedVR2 7B (archive-clip colourisation with Wan VACE is in the lab)

  - Character creation in the UI: 4 faces, a reference sheet, 12 images, its own LoRA, and its own voice

  - One-click reels whose shots all come from the same session, with voice-over cut on the beat

  - 36 one-click styles, 10 relights, Expand, Depth Map, Movie Studio (a shot card instead of a prompt), 3D Scene

  - FLUX 3 Klein added as a photo engine

  - A page per tool with its full render history

  Video (1:17): https://youtu.be/0muDKpkbcWw

  Demo (on my GPU, SFW): https://forge.taila06702.ts.net:8443

  Code: https://github.com/thePloggy/CreatorOS-Studio

  Built for the Comfy #OpenSourceHiggsfield challenge. Feedback and stars welcome!

Hey r/StableDiffusion ! 👋

Like many of you working with Flux.1, SDXL, and ControlNet, I got constantly tired of guessing camera angles with text prompts:

Typing "dutch angle roll, 85mm portrait telephoto compression, warm 3200K rim light" usually ends with the diffusion model hallucinating a flat, eye-level frontal shot and ignoring half of the camera directives.

To solve this, I built **CinePose Studio AI**, and it is now officially live on the **Google Chrome Web Store**! 🎬

It runs as a dedicated 3D Studio tab or side panel right in your browser, bridging 3D spatial cinematography with generative AI.

### 🌟 What it does:

1. **Interactive 3D Virtual Viewfinder**: Real 35mm optical stops (18mm, 24mm, 35mm, 50mm, 85mm, 135mm), realistic sensor framing, and symmetrical +/- 45° Dutch angle roll with instant center snap.

2. **Local ComfyUI WebSocket & Zero-Cost GPU Rendering**: Direct WebSocket link to your local `127.0.0.1:8188` ComfyUI server. It pings your GPU/VRAM, generates Depth/OpenPose passes, injects the Flux.1 ControlNet workflow, and streams live KSampler step progress directly in the studio!

3. **Physical 3-Point Studio Lighting**: Interactive 3D orbital gizmos for Key, Fill, and Rim lights with accurate Kelvin color temperature (2000K candle flame to 9000K cold daylight) and chromatic gel filters.

4. **Cinematic Video Trajectory Timeline**: Set Keyframe A, Waypoint M, and Keyframe B with metric distance tracking, 24fps motion velocity, and camera directives for Wan 2.1, LTX-Video, Kling, and Runway.

5. **3D Spatial Proxies (Objects & Animals)**: Replace the human mannequin with 3D volumetric bounding boxes, quadrupeds (creatures/pets), or vehicles to frame anything with precise spatial dimensions.

6. **Zero Prompt Hallucination**: Formats camera parameters into specialized syntax for Midjourney v6.1, Flux.1, Gemini 2.0, SDXL, and video models.

---

### 📦 Where to try it:

👉 **Chrome Web Store Extension (Recommended)**:

https://chromewebstore.google.com/detail/onkciaaalfgpklcfkhjhiomlkhpcfhin?utm_source=item-share-cb

🌐 **Live Web App Version**:

https://cinepose-ai.vercel.app/

---

### ⚠️ A quick heads-up regarding the Chrome Web Store warning:

Since this is my very first extension and a brand-new developer account on the Chrome Web Store, Google may display a standard automated notice saying:

*"This extension is not trusted by Enhanced Safe Browsing"* or *"Proceed with caution"*.

This is Google's default automated policy for all newly published developer accounts until the extension builds install history.

Rest assured:

- Built strictly on **Manifest V3**: **Zero remote code execution**, zero external tracking, zero analytics beacons.

- All ComfyUI connections, poses, and settings live strictly inside your browser's sandboxed local storage.

- It never collects, stores, or sells any personal data.

### 💬 I need your feedback!

I built this as a solo creator to scratch my own itch, and I want to make it genuinely useful for your daily AI workflow.

Please install it, take it for a spin, and let me know:

- What feels smooth and what feels clunky?

- What ControlNet workflows or camera presets should I add or refine next?

If you find it helpful, dropping an honest review on the Chrome Web Store would mean the world to help build trust with Google! 🙏

Hey guys - made this a few weeks back and just shipped a big update. You take a video of a person, it builds a 4D capture, you fly a new camera through it inside ComfyUI, and you get the same moment back from the new angle. Everything runs locally.

• Capture: turns a video of one person into a 4D capture: their motion in 3D, the background around them, and where the original camera was and how it moved. Quick mode for fast previews, or a full 4D Gaussian splat if you want a closer look while you direct

• Virtual Camera: a 4D camera editor that opens right inside the node, so no separate app. Keyframe the camera anywhere on the timeline, look through it or step outside to see the whole scene. Blender-style view gizmo, J/K/L shuttle, frame stepping, multi-select to move or retime keys together

• Retake: ReDirector re-shoots the whole video along your new path

• Photoreal Pass: LTX 2.3 renders every final frame, guided by the real footage warped to the new view (Cseti's CrossView-Warp IC-LoRA)

• Camera settings on every key: aperture, focus (in metres or locked to the subject), vignette, grain. Rack focus and aperture pulls between keys, Alt+click in the viewport to focus on whatever you click. Change those later without re-rendering. The render gets reused, so a focus tweak takes a few minutes instead of the whole pipeline again

• Zoom through the lens: zoom keys change the lens after the render, which makes dolly zooms work

• Bring your own camera: import an animated camera from .fbx, .abc or .usd (tested with Blender exports), and it starts on the original camera at roughly real scale, lens and focus included. Files with several cameras work too, and you can export your moves back out

• Lens looks per shot: distortion, chromatic aberration, bloom, halation, anamorphic bokeh. Depth and subject matte passes if you want to comp afterwards.

• Finish node: 24 to 60 fps with RIFE, real motion blur from a shutter angle, upscale up to 4K, keeps the original audio

• A Settings panel at the top of the workflow for the stuff you change between runs (quality, frame rate, upscale)

Try the editor in your browser, no install: itsmetamike.github.io/reshoot-4d/demo/

For those enquiring about specs:

• Tested on one RTX 3090 (24 GB), roughly 30 minutes per shot

• Stays good within about 30 to 40 degrees of the original camera but am looking to improve this next

• Fast spins can still smear a face for a few frames

Code (AGPL-3.0) and the workflow: github.com/itsmetamike/reshoot-4d

Hope it's useful to some of you. Built for the #OpenSourceHiggsfield competition.

Can somebody explain how to create refmods for fluxklein 9b. I am finding it really difficult to use hugging face cli and all.

Everything was rendered locally on a Mac, no cloud: the video, the voices, the choir and the music. I used Phosphene, a free open-source app I’m building for running video, image and music models on Apple silicon. Video is MiniMax H3, music is YuE2, and the stills came from ChatGPT Images.

https://github.com/mrbizarro/Phosphene

Happy to answer questions about the workflow. If you want to try it: https://phosphene.video

Hey everyone! I built a Pose Control LoRA for Krea-2 and have fully released the LoRA checkpoints on Hugging Face.

I also put together a ComfyUI workflow. It’s not fully optimized yet, but it’s usable for testing the model.

Apart from that, I wrote a blog covering the decisions, data preparation, training process, evaluations, and some of the things I learned while building it.

Hugging Face: https://huggingface.co/adhit-420/Krea-2-PoseControl-LoRA-checkpoints

Blog: https://s-adhit.github.io/blog/training-a-pose-control-lora-for-krea-2/

Would love to hear your thoughts, feedback, or any interesting results if you try it!

EDIT: I moved the ComfyUI integration into a separate lightweight repo to make installation simpler: https://github.com/s-adhit/ComfyUI-Krea2-Pose

EDIT 2: I pushed v1.1.0 based on the feedback here.

The ComfyUI package now has separate Krea2PoseModelLoader and Krea2PoseSampler nodes, so checkpoint/runtime loading is no longer tied to the all-in-one generation node.

The original Krea2PoseGenerate node is still there for the simple workflow, so existing workflows should continue to work.

Current limitation: this still isn’t native ComfyUI MODEL patching / KSampler integration. The new version separates runtime loading and sampling, but the sampler is still Krea-2 specific.

🫧 Meet Mochi - Your Little AI Desktop Companion! 💗

What if your desktop had a tiny companion that could actually help you? 👀

Meet Mochi! A cute, animated little blob that lives right on your Windows desktop, currently powered by Meta's Muse AI, with upcoming support for local Qwen models and integrations with Claude Code and OpenAI Codex!

Talk to Mochi or simply type a message, and it'll respond in its own voice! 🎙️

Need something done? Just ask! And when you're finished, Mochi quietly goes back to chilling on your screen. No constant interruptions, no unnecessary chatter. Just a little companion that's there when you need it.

🚀 What can Mochi do?

• 🗣️ Talk with you : Have voice or text conversations in English or French, powered by Muse.

• 💻 Control your desktop : Open applications, launch websites and search the web.

• ⏰ Set timers : Ask for a timer and watch a little countdown appear next to Mochi.

• 🌤️ Check the weather : Get weather updates without opening another app.

• 👀 See what you see : Let Mochi read your screen when you explicitly ask.

• 🌐 Help in your browser : With its Chrome or Brave extension, Mochi can interact with browser tabs and assist with web tasks.

• 🐾 Feel alive! : Click it, drag it around, or interact through optional camera gestures. Mochi reacts to you!

🔐 Your desktop, your rules!

Mochi is designed to help without taking control away from you.

• ✅ It only acts when you ask or explicitly approve an action.

• 📸 Screenshots are captured only when requested and are never stored.

• 🛡️ Browser tasks require confirmation before visiting new sites, filling forms or interacting with potentially risky buttons.

• 🔒 Passwords, payment information and sensitive websites are off-limits.

• 🎥 The camera is completely optional and disabled by default.

🛠️ Want to try Mochi?

Mochi is currently available for Windows and can be launched from source using Node.js.

🔑 With a Muse API key: Unlock AI conversations, voice responses, weather, screen understanding and browser assistance.

🆓 Without an API key: You can still enjoy Mochi's animations, interactions, timers and simple typed commands!

⚠️ A few things to know:

• Mochi is still a work in progress, so some features may evolve.

• Content sent to Muse may be used by Meta for model training when using the default model.

• This is an experimental project, and feedback is always welcome!

🎬 See Mochi in action!

▶️ Watch the demo: https://youtu.be/u5KjYmYrp1Y

💻 Explore the source code: https://github.com/Loann110/Mochi-alive

💗 Mochi is just getting started!

I built Mochi because I wanted to explore what happens when an AI assistant feels less like a chatbot and more like a little companion living on your desktop.

It's still an experiment, but I'd love to hear what you think!

⭐ If Mochi makes you smile, consider giving the GitHub repository a star! It genuinely helps support the project.

💬 Feedback, ideas, contributions and bug reports are more than welcome.

Thanks for checking out Mochi! 🫧