MercuryAlloy automates the build process for the Mercury browser.

I really like the Mercury browser, but I worried about the browser getting out of date, since releases of the browser seem to be build and released manually. So I threw together a set of scripts and overrides that will allow the build process to run without user interaction and on a schedule. You can modify the subscripts to move your compiled executable anywhere you want (like a web server), as well as send a custom alert upon successful build (like sending the link out via email).

This is a more technical project, but it has been a fun learning experience.

Middletown Township Chief of Police Joseph Bartorilla confirmed the suspect in the death was arrested just after 9 p.m. Tuesday.

Ah, the power of local models. Would DALL-E produce a better version? Probably. If it would produce one at all, that is.

I can kind of see why this happened, but does anyone really think there is a risk of DALL-E spitting out a reasonable instruction manual on how to build a nuclear bomb?

I tried to get SD-XL to generate an image of a frog with its eyes closed. It refused. I even cranked up the attention on closed to an absurd level, and it seemed to get sassy with me.

Should blocking a user still allow them to vote on your posts? I'd rather have nothing to do with particular users, and it seems that they continue to show up in the activity for every single post I make around kbin.

Stable Diffusion XL

Prompt: Ham Solo from Star Wars!

Having a good day?

From their website

Mistral AI team is proud to release Mistral 7B, the most powerful language model for its size to date.

Mistral 7B in short

Mistral 7B is a 7.3B parameter model that:

• Outperforms Llama 2 13B on all benchmarks
• Outperforms Llama 1 34B on many benchmarks
• Approaches CodeLlama 7B performance on code, while remaining good at English tasks
• Uses Grouped-query attention (GQA) for faster inference
• Uses Sliding Window Attention (SWA) to handle longer sequences at smaller cost

We’re releasing Mistral 7B under the Apache 2.0 license, it can be used without restrictions.

• Download it and use it anywhere (including locally) with our reference implementation
• Deploy it on any cloud (AWS/GCP/Azure), using vLLM inference server and skypilot
• Use it on HuggingFace

Mistral 7B is easy to fine-tune on any task. As a demonstration, we’re providing a model fine-tuned for chat, which outperforms Llama 2 13B chat.

When editing photos, one of the first things you'll want to do is "fix" the skin of your subjects. I've fumbled around with this for years. I use almost exclusively open source tools like GIMP for my work, which while extremely powerful, often lack most of those automatic tools and niceties present in paid products like Photoshop. So I'll share my method, which I adopted from this YouTube video.

Depending on how much you self host, you may find it hard to keep track of your devices' host names. So what are your naming conventions to keep track everything? Some people stick to descriptive names, others pick themes, like Greek mythology.

Personally, I use Japanese emperors. I've made it all the way to Seinei. Luckily I still have some breathing room to add more services and servers. Much to my wife's chagrin. :)

Full Post Text

In the spirit of full disclosure, the content of this post is heavily cribbed from this post on Reddit. However, as we've seen, the Internet is not forever. It is entirely possible that a wealth of knowledge could be lost at any time due to any number of reasons. Because I have found this particular post so helpful and find myself coming back to it over and over, I thought it would be appropriate to share this method of creating an inpainting model from any custom stable diffusion model.

Inpainting models, like the name suggests, are specialized models that excel at "filling in" or replacing sections of an image. They're especially good at decoding what a section of an image should look like based on the section of image that already exists. This is useful when you're generating images and only a small section needs to be corrected, or if you're trying to add something specific to an image that exists.

So how is this done? With a model merge. Automatic1111 has an excellent model merging tool that we'll use. Let's assume that you have a custom model called my-awesome-model.ckpt that is based on stable-diffusion-1.5.

In the A1111 Checkpoint Merger interface, follow these steps:

1. Set "Primary model (A)" to stable-diffusion-1.5-inpainting.ckpt.

2. Set "Secondary model (B)" to my-awesome-model.ckpt.

3. Set "Tertiary model (C)" to stable-diffusion-1.5.ckpt.

4. Set "Multiplier (M)" to 1.

5. Set "Interpolation Method" to Add difference.

6. Give your model a name in the "Custom Name" field, such as my-awesome-model-inpainting.ckpt.

   • Adding "-inpainting" will signal to A1111 that the model is an inpainting model. This is useful for extensions such as openOutpaint. Also, it's just a good idea to properly label your models. Because we know you're a degenerate that has hundreds of custom waifu models downloaded from CivitAI.

7. Click Merge.

And bazinga! You have your own custom inpainting stable diffusion model. Thanks again to /u/MindInTheDigits for sharing the process.

This is a Mastodon thread I created featuring devices and computers that never were, but could have been. I think my favorite might be the HD Laserdisc player called the MOID.

https://mastodon.jordanwages.com/system/media_attachments/files/110/686/620/475/187/344/original/95ff43d76ba41dd6.jpg

I just bought a "new" homelab server and am considering adding in some used/refurbished NVIDIA Tesla K80s. They have 24 GB of VRAM and tons of compute power for very cheap if you get them used.

The issue is that these cards run super hot and require extra cooling set ups. I was able to find this fan adapter kit on eBay. But I still worry that if I pop one or two of these bad boys in my server that the fan won't be enough to overcome the raw heat put off by the K80.

Have any of you run this kind of card in a home lab setting? What kind of temps do you get when running models? Would a fan like this actually be enough to cool the thing? I appreciate any insight you guys might have!

I just bought a "new" homelab server and am considering adding in some used/refurbished NVIDIA Tesla K80s. They have 24 GB of VRAM and tons of compute power for very cheap if you get them used.

The issue is that these cards run super hot and require extra cooling set ups. I was able to find this fan adapter kit on eBay. But I still worry that if I pop one or two of these bad boys in my server that the fan won't be enough to overcome the raw heat put off by the K80.

Have any of you run this kind of card in a home lab setting? What kind of temps do you get when running models? Would a fan like this actually be enough to cool the thing? I appreciate any insight you guys might have!

OverClocked ReMix is a video game music community with tons of fan-made ReMixes and information on video game music.

I figured since we're currently in a reawakening to decentralized and special purpose forums and websites, now might be a good time to remind people of OCRemix. Or help someone discover it for the first time.

TLDR We trained a series of 7B LLMs named XGen-7B with standard dense attention on up to 8K sequence length for up to 1.5T tokens. We also fine tune the models on public-domain instructional data. The main take-aways are: * On standard NLP benchmarks, XGen achieves comparable or better results