I am perplexed. Facebook is flooded with Matrix house party photos. I saw other celebrities in simillar setting. Is that made with ChatGPT drawing feature? Or is it Flux? How did they made multiple different characters? I am lost :D

Hello there!

A month ago I generated and modeled a few character designs and worldbuilding thingies. I found a local 3d printing person that offered colourjet printing and got one of the characters successfully printed in full colour! It was quite expensive but so so worth it!

i was actually quite surprised by the texture accuracy, here's to the future of miniature printing!

HuggingFace: https://huggingface.co/HiDream-ai/HiDream-I1-Full
GitHub: https://github.com/HiDream-ai/HiDream-I1

From their README:

HiDream-I1 is a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds.

Key Features

• ✨ Superior Image Quality - Produces exceptional results across multiple styles including photorealistic, cartoon, artistic, and more. Achieves state-of-the-art HPS v2.1 score, which aligns with human preferences.
• 🎯 Best-in-Class Prompt Following - Achieves industry-leading scores on GenEval and DPG benchmarks, outperforming all other open-source models.
• 🔓 Open Source - Released under the MIT license to foster scientific advancement and enable creative innovation.
• 💼 Commercial-Friendly - Generated images can be freely used for personal projects, scientific research, and commercial applications.

We offer both the full version and distilled models. For more information about the models, please refer to the link under Usage.

https://preview.redd.it/5uptmrurwlte1.png?width=1822&format=png&auto=webp&s=41a915671ecd579651d7bec87ac5ce50ad6e591d

https://preview.redd.it/zl5op96wylte1.png?width=1822&format=png&auto=webp&s=ad524ed823a8c91245ba718b714ee5d07835b6a4

https://preview.redd.it/7ldcza6wylte1.png?width=1822&format=png&auto=webp&s=c8d1e1fd573d1b55ff013df76dad589deb99aa87

https://preview.redd.it/i68tva6wylte1.png?width=1822&format=png&auto=webp&s=e5c01df6d26870dcbcbe7f061a5658e0d49c320a

Github: https://github.com/MoonGoblinDev/Civicomfy

So when using Runpod I ran into a problem of how inconvenient downloading model in ComfyUI on a cloud gpu server. So I make this downloader. Feel free to try, feedback, or make a PR!

Hey, I built a image viewer that automatically extracts prompt data from PNG and JPEG files — including prompt, negative prompt, and settings — as long as the info is embedded in the image (e.g. from Forge, ComfyUI, A1111, etc.). You can browse folders, view prompts directly, filter, delete images, and there’s also a fullscreen mode with copy functions. If you have an image where nothing is detected, feel free to send it to me along with the name of the tool that generated it. The tool is called ImagePromptViewer. GitHub: https://github.com/LordKa-Berlin/ImagePromptViewer Feel free to check it out if you're interested. 

https://preview.redd.it/6m116qebylte1.png?width=2560&format=png&auto=webp&s=1c77f7a5c981ba7312d7170e5f3c74107f90728a

https://preview.redd.it/z6jmfj6cylte1.png?width=2560&format=png&auto=webp&s=ef50c3472c8dc7e3c5635fd62ae446d79aa880a3

Hey community! While we all love generating amazing 2D images, the world of Image-to-3D is also heating up. A big challenge there is getting high-quality, detailed 3D models out. We wanted to share TripoSF, specifically its core VAE (Variational Autoencoder) component, which we think is a step towards better 3D generation targets. This VAE is designed to reconstruct highly detailed 3D shapes.

What's cool about the TripoSF VAE? * High Resolution: Outputs meshes at up to 1024³ resolution, much higher detail than many current quick 3D methods. * Handles Complex Shapes: Uses a novel SparseFlex representation. This means it can handle meshes with open surfaces (like clothes, hair, plants - not just solid blobs) and even internal structures really well. * Preserves Detail: It's trained using rendering losses, avoiding common mesh simplification/conversion steps that can kill fine details. Check out the visual comparisons in the paper/project page! * Potential Foundation: Think of it like the VAE in Stable Diffusion, but for encoding/decoding 3D geometry instead of 2D images. A strong VAE like this is crucial for building high-quality generative models (like future text/image-to-3D systems).

What we're releasing TODAY: * The pre-trained TripoSF VAE model weights. * Inference code to use the VAE (takes point clouds -> outputs SparseFlex params for mesh extraction). * Note: Running inference, especially at higher resolutions, requires a decent GPU. You'll need at least 12GB of VRAM to run the provided examples smoothly.

What's NOT released (yet 😉): * The VAE training code. * The full image-to-3D pipeline we've built using this VAE (that uses a Rectified Flow transformer).

We're releasing this VAE component because we think it's a powerful tool on its own and could be interesting for anyone experimenting with 3D reconstruction or thinking about the pipeline for future high-fidelity 3D generative models. Better 3D representation -> better potential for generating detailed 3D from prompts/images down the line.

Check it out: * GitHub: https://github.com/VAST-AI-Research/TripoSF * Project Page: https://xianglonghe.github.io/TripoSF * Paper: https://arxiv.org/abs/2503.21732

Curious to hear your thoughts, especially from those exploring the 3D side of generative AI! Happy to answer questions about the VAE and SparseFlex.

Wondering if this will work also for image and video generation and not just LLMs. With LLMs we could always groupt our GPUs together to run larger models, but with video and image generation, we are mostly limited to a single GPU, which makes this enticing to run larger models, or more frames and higher resolution videos. Doesn't seem that bad, considering the possibilities we could do with video generation with 128GB. Will it work or is it just for LLMs?

Howdy! Hopefully this is the right subreddit for this - if not please tell refer me to a better spot!

I am an ecology student working with a beaver conservation foundation and we are exploring possibilities of creating an AI model that will take a before photo of a landowner's stream (see 1st photo) and modify it to approximate what it could look like with better management practices and beaver presence (see next few images). The key is making it identifiable, so that landowners could look at it and be better informed at how exactly our suggestions could impact their land.

Although I have done some image generation and use LLMs with some consistency, I have never done anything like this and am looking for some suggestions on where to start! From what I can tell, I should probably fine-tune a model and possibly make a LoRA, since untrained models do a poor job (see last photo). I am working on making a database with photos such as the ones I posted here, but I am not sure what to do beyond that.

Which AI model should I train? What platform is best for training? Do I need to train it on both "before" and "after" photos, or just "after"?

Any and all advice is greatly appreciated!!! Thanks

https://docs.google.com/spreadsheets/d/1543rZ6hqXxtPwa2PufNVMhQzSxvMY55DMhQTH81P8iM/edit?usp=sharing