I lied a little: it’s not pure VHS – the Sony ProMavica MVC-5000 is a still-video camera that saves single video frames to floppy disks.

Yep, it’s another VHS-flavored LoRA—but this isn’t the washed-out like 2000s Analog Cores. Think ProMavica after a spa day: cleaner grain, moodier contrast, and even the occasional surprisingly pretty bokeh. The result lands somewhere between late-’80s broadcast footage and a ‘90s TV drama freeze-frame — VHS flavour, minus the total mud-bath.

Why bother?

• More cinematic shadows & color depth.

• Still keeps that sweet lo-fi noise, chroma wiggle, and subtle smear, so nothing ever feels too modern.

• Low-dynamic-range pastel palette — cyan shadows, magenta mids, bloom-happy highlights

You can find LoRA here: https://civitai.com/models/1738734/retrovhs-mavica-5000

P.S.: i plan to adapt at least some of my loras to Flux Kontext in the near future

I’ve made some major improvement to my custom mobile homemade SD1.5 model. All the pictures I uploaded were created purely by the model without using any loras or addition tools. All the training and pictures I uploaded were made using my phone. I have a Mac mini m4 16gb on the way so I’m excited to push the model even further. Also I’m almost done fixing the famous hand/finger issue that sd1.5 is known for. I’m striving to make it or get as close to Midjourney as I can in term of capability.

OmniAvatar released the model weights for Wan 1.3B!
To my knowledge, this is the first talking avatar project to release a 1.3b model that can be run with consumer-grade hardware of 8GB VRAM+

For those who don't know, Omnigen is an improved model based on fantasytalking - Github here: https://github.com/Omni-Avatar/OmniAvatar

We still need a ComfyUI implementation for this, as to this point, there are no native ways to run Audio-Driven Avatar Video Generation on Comfy.

Maybe the great u/Kijai can add this to his WAN-Wrapper, maybe?

The video is not mine, it's from user nitinmukesh who posted it here: https://github.com/Omni-Avatar/OmniAvatar/issues/19, along with more info, PS. he ran it with 8GB VRAM

I used NotebookLM to make chattable knowledge bases for FLUX and Wan video.

The information comes from the Banodoco Discord FLUX & Wan channels, which I scraped and added as sources. It works incredibly well at taking unstructured chat data and turning it into organized, cited information!

Links:

🔗 FLUX Chattable KB (last updated July 1)
🔗 Wan 2.1 Chattable KB (last updated June 18)

You can ask questions like:

• How does FLUX compare to other image generators?
• What is FLUX Kontext?

or for Wan:

• What is VACE?
• What settings should I be using for CausVid? What about kijai's CausVid v2?
• Can you give me an overview of the model ecosytem?
• What do people suggest to reduce VRAM usage?
• What are the main new things people discussed last week?

Thanks to the Banodoco community for the vibrant, in-depth discussion. 🙏🏻

It would be cool to add Reddit conversations to knowledge bases like this in the future.

Tools and info if you'd like to make your own:

• I'm using DiscordChatExporter to scrape the channels.
• discord-text-cleaner: A web tool to make the scraped text lighter by removing {Attachment} links that NotebookLM doesn't need.
• More information about my process on Youtube here, though now I just directly download to text instead of HTML as shown in the video. Plus you can set a partition size to break the text files into chunks that will fit in NotebookLM uploads.

Looking for a locally run image processing solution to tidy up photos of garments like the attached images, any and all suggestions welcome, thank you.

So, the problem with Flux Kontext is that it needs pairs of images. For example, if you want to train an oil painting you would need a photo of a place + a corresponding painting.

It can be slow and laborious to edit or find pairs of images.

BUT - it doesn't have to be that way.

1) Get the images in the style you want. For example, Pixar Disney style.

2) Use Flux Kontext to convert these images to a style that Flux Kontext's basic model already knows. For example, cartoon.

So, you will train a Lora on a pair of Pixar images + Pixar converted to cartoon.

3) After Lora is trained. Choose any image. Photo of New York City. Use Flux Kontext to convert this photo to cartoon.

4) Lastly, apply Lora to the cartoon photo of New York City

This is a hypothetical method

https://whatdreamscost.github.io/MediaSyncer/

I made this media player last night (or mainly AI did) since I couldn't find a program that could easily play multiple videos in sync at once. I just wanted something I could use to quickly compare generations.

It can't handle many large 4k video files (it's a very basic program), but it's good enough for what I needed it for. If anyone wants to use it there it is, or you can get a local version here https://github.com/WhatDreamsCost/MediaSyncer

Hey guys! A couple days ago, I shared a live zero shot face swapping and voice conversion project, but I thought it would be nice to let you guys know I made some big improvements on the quality of the faceswap through some pre/post processing steps. Hope you guys enjoy the project and the little demo below . Link: https://github.com/luispark6/DoppleDanger

https://reddit.com/link/1lq6ty9/video/tb7i9s60wiaf1/player

https://brianheming.substack.com/p/making-illustrated-conan-adventures-039