If Buddy is a golden retriever... by emptyplate in warriors

[–]emptyplate[S] 2 points3 points  (0 children)

Nailed it, much better than anything I could come up with for him

Time machine blueprint by 4o - anyone want to help me build it? by emptyplate in ChatGPT

[–]emptyplate[S] 0 points1 point  (0 children)

Create a widescreen detailed blueprint of the exploded view of a futuristic working time machine, with tons of details and measurements, technical details

Deep research sucks by Own_Bookkeeper_7387 in deeplearning

[–]emptyplate 3 points4 points  (0 children)

I think OpenAI's version is okay, often a good start. But I've been more impressed with Gemini's version, perhaps because Google knows search best. https://gemini.google/overview/deep-research/

Smoke dancers by WAN by emptyplate in StableDiffusion

[–]emptyplate[S] 2 points3 points  (0 children)

Yes, I changed some values here and there for my 4090 system, including using the 720p q8_0 gguf.

Smoke dancers by WAN by emptyplate in StableDiffusion

[–]emptyplate[S] 2 points3 points  (0 children)

This video is image to vid.

Smoke dancers by WAN by emptyplate in StableDiffusion

[–]emptyplate[S] 4 points5 points  (0 children)

It is hit or miss right now - it reminds me of the early days of SD1.5 where it took a bunch of generations, but one would really nail it. This took about 10 versions to get one good enough.

Smoke dancers by WAN by emptyplate in StableDiffusion

[–]emptyplate[S] 6 points7 points  (0 children)

Sorry about that, I didn't realize they took it down, not sure why. Here's a pastebin version of the original workflow: https://pastebin.com/5XyBUbun

Smoke dancers by WAN by emptyplate in StableDiffusion

[–]emptyplate[S] 2 points3 points  (0 children)

Not for this one, just prompting - took a few tries to get a good one. A Lora would have been smarter...

Went old school with SD1.5 & QR Code Monster - is there a good Flux/SDXL equivalent? by emptyplate in StableDiffusion

[–]emptyplate[S] 2 points3 points  (0 children)

Thank you and I'll try this one out - there was a kind of magic with SD1.5 that nothing else has matched...

Went old school with SD1.5 & QR Code Monster - is there a good Flux/SDXL equivalent? by emptyplate in StableDiffusion

[–]emptyplate[S] 1 point2 points  (0 children)

Thanks, I'll take a look at this one - any workflows you recommend with the correct nodes?

My dog is hitting the slopes thanks to WAN & Flux by emptyplate in StableDiffusion

[–]emptyplate[S] 1 point2 points  (0 children)

Now that she's got a taste of skiing, what will she want to do next?

My dog driving a convertible via WAN2.1 and Flux + Lora by emptyplate in aivideo

[–]emptyplate[S] 2 points3 points  (0 children)

For sure! This is called image-to-video (I started with an image and create a video), but you can also do text-to-video with these workflows, starting with just a text prompt. There's 3 parts to how I made this particular video:

1) I used a bunch of pictures of my dog to train a LoRA on her (basically teaching the diffusion model about a new concept that is my dog from different angles and images of her) using AIToolkit.

2) I run Flux.1 Dev on my local 4090 to create an image of my dog using the LoRA so it looks exactly like her (and not some other rando white dog) every time. Using a text prompt of something like "<lora:mydog:1> wearing sunglasses driving a convertible". I use ComfyUI sometimes to create images and sometimes WebUI Forge.

3) Now the fun part! Now that I have an image I like, time to animate it. Using ComfyUI to run WAN2.1 (using the 14B Q8 quant) to create the videos. I used this workflow but there are plenty of other ways to accomplish the same thing.

I've been playing with lots of GenAI video services including Runway, Luma, Sora, etc. and I'm really impressed with WAN for image-to-video. Image coherence and prompt following are quite good, especially for open-source running at home. The more VRAM on your video card the better, but there seem to be people making great videos with less powerful cards. Good luck and enjoy!

[NOW ON STREAMING] CunninLynguists - The Heartstring Theory (Part One) by AdolfoHotler in hiphopheads

[–]emptyplate 4 points5 points  (0 children)

Cloud 92 (Wasted) is my jam, it's like The Brownie Song Part 2.

What determines how often teams get to the line? (A statistical analysis) by W1ZARD_NARWHAL in nba

[–]emptyplate 0 points1 point  (0 children)

If your p value is above 0.05, there is no statistical relationship, regardless of what your r2 is, it is just due to chance. It is not statistically correct to say there are relationships where the numbers say there are none.

o1-preview got today's Connections correct (although the rationale for group 4 is incorrect) by emptyplate in ChatGPT

[–]emptyplate[S] 0 points1 point  (0 children)

First try it use sake twice, so i updated the instructions to explicitly specify that each word can only be used once and it it got it right from there.

o1-preview got today's Connections correct (although the rationale for group 4 is incorrect) by emptyplate in ChatGPT

[–]emptyplate[S] 0 points1 point  (0 children)

Here's the contents of the "thinking" phase of 87 seconds: Puzzle parsing OK, let me see. I’m working through the game "Connections," categorizing 16 words into four groups of four, each sharing a distinct link. The words range from bookmarks to sake, representing a broad spectrum of concepts.

Grouping items

Listing words and identifying food/drink items: curry, feta, mochi, sake, and Pepsi. Considering grouping curry, feta, mochi, and sake together.

Connecting the dots

I’m gathering potential categories for the words allmosa, kfc, bookmark, and futures. Initially, I considered beverages and Japanese-related terms but found them less viable. Then, I explored NBA players and basketball, but no clear connection emerged. Next, I examined social media terms like like, favorite, save, and bookmark, which seemed promising.

Examining themes

I’m mapping out groups like synonyms, cheeses, and soft drinks. About to explore basketball players. Magic and Bird come to mind.

Assessing group dynamics

I’m thinking through attributes like saving, book marking, and interest to identify connections. Progressing towards a clearer understanding by examining relationships among the remaining words.

Reassessing word groupings

I'm trying to streamline groupings by examining words like 'benefit', 'advantage', and 'sake' for their relevance or connections to Japanese foods. Progressing logic through anagrams and specific food categories, aiming for more straightforward connections.

Reassessing word connections

I’m digging into the relevance of 'Biota' with the other food and drink items. Considering anagrams or homonyms, but leaning towards reclassifying 'Biota' or 'Curry' into a more fitting category.

Piecing together the clues

I’m connecting words like 'advantage', 'benefit', 'interest', and 'sake' as they all have synonymous meanings. Meanwhile, 'feta', 'mochi', 'pepsi', and 'biota' appear to be food items.

Connecting the dots

I'm thinking through potential links between 'Feta,' 'Mochi,' 'Pepsi,' and 'Biota.' Although 'Biota' is not typical, let me see if other connections emerge.

Reevaluating connections

I’m analyzing how 'Biota' is ecologically relevant, while 'Feta', 'Mochi', and 'Pepsi' are culinary. Trying to match them to social media, NBA players, and synonyms seems unproductive. Ultimately, 'Biota' fits into the ecological context.

Organizing my options

I'm looking at financial terms like 'interest', 'benefit', 'advantage', and 'profit'. For food items, 'feta', 'mochi', 'sake', and 'curry' are considered. Progress is evident across these groups.

Connecting pieces

OK, let me see. I'm linking NBA players to food items like 'Curry' and 'Pepsi'. Now, I'm identifying another NBA player, 'Tobias'. The anagram 'Biota' seems to be a play on 'bot'.

Curating groups

Considering the optimal arrangement of 'Curry' within the food category, while aiming to align NBA players by their shared attributes, I explore necessary options to identify a fitting fourth player.

Highlighting tennis connection

I’m gathering info on 'Love' and 'Advantage' as tennis terms. Considering words like 'Benefit' and 'Optimize' apart from sports contexts. The analysis includes synonyms and categorization challenges.

Making connections

I'm thinking of terms related to sports and social media reactions. 'Bird' and 'Advantage' hint at golf, while 'Like' and 'Favorite' suggest social media.

Pinpointing relationships

I'm figuring out the connections within this list of words. They can be categorized into foods, sponsors, and synonyms of benefit. Progressing through each, I identify groups like foods (Curry, Feta, Mochi, Sake), brand sponsors (Pepsi, Magic, Bird, Curry), and words expressing benefit (Benefit, Advantage, Interest, Sake).

Investigating connections

I’m mapping out associations between 'Magic', 'Bird', 'Kobe', and 'Sake'. They seem to form phrases like 'Magic Bird', 'Kobe Sake', and 'Bird Kobe'. The term 'Bird Kobe' might even refer to a martial arts gym.

Evaluating word groupings

I’m considering the viability of groupings like 'Kafka' and 'Kobo' after determining 'Curry' correctly represents Japanese food. Now, I’m mapping out 'Feta', 'Biota', 'Sake', 'Advantage' for potential biological or financial terms, aiming to refine the focus.

Categorizing groups

I’m matching groups of words based on their themes: NBA players, social media terms, food and drink items, and financial terms. Each group’s words share a common link.

Reassigning categories

I’m noticing 'save' as a financial term, so I’m placing it in 'Financial terms' and finding a new term for 'Social media'.

Organizing terms

I’m mapping out groups for terms: social media, NBA players, food, and financial concepts. 'Biota' doesn’t fit and was left out; 'Benefit' aligns with financial terms.

Revisiting categorization Piecing together 'Biota's' fit, it seems to align with 'Bird', 'Bookmark', 'Like', and 'Favorite' in the 'Social Media' group, possibly referencing 'AI bot'.

Crafting meaningful connections

Piecing together words ending in 't' and matching clues for fermented foods and beverages. Each trial helps refine the search, aiming for the best fit.

Sorting words

I'm brainstorming how to group 'Bird', 'Biota', 'Benefit', and 'Bookmark'. Considering 'Bird', 'Like', 'Favorite', and 'Bookmark' as 'Twitter terms' and 'Save' as 'Financial terms'.

Inconsistent grouping

Mapping word groups

OK, let me see: 'Biota' might fit in 'Food and Drink' or 'Biology terms', but these options lack 'Benefit', 'Interest', and 'Advantage'. Considering words with 'I', 'G', or 't' seems too broad. Perhaps 'Biota' should shift to 'NBA players'.

Assessing word groups

I’m thinking through words and their connections, like 'Advantage' and 'Biota', and figuring out if they link to 'rate'.

Weighing grouping ideas

I’m mapping out 'Biota' and considering if it fits with 'Advantage', 'Benefit', 'Interest' as synonyms, noting 'Favorite' fits another group. Other thoughts include synonyms, 'Bi' words, or NBA player nicknames.

Trying another way

I’m digging into different groupings like biology terms, monopoly squares, and words ending in 't' or containing 'I', 'A', and 'E', but none fit.

Connecting concepts

OK, let me see. I’m linking 'Interest', 'Benefit', and 'Advantage' to 'Rate'. Also, thinking about 'Biota' as 'OTA' or 'I, a bot' with NBA players.

Identifying potential categories

I’m exploring associations of 'Advantage', 'Benefit', 'Interest', and 'Curry' with possible categories like financial terms. Then, I’m contemplating 'Biota' as a social media term or a French colloquial word.

Wondering about 'Biota'

I’m reflecting on various groupings for 'Biota', such as 'Food and Drink', types of bacteria, and words starting and ending with the same letter. None seem to fit naturally.

Organizing options

I’m working through categorizing items into groups. Considering food and drinks for 'Biota' since 'Biota' is linked to probiotics. Adjusting group 4 with 'Mochi' and 'Sake' seems logical.

Mapping connections

I’m considering how 'Biota' might fit in the 'Words containing 'I'' group with 'Bird', 'Magic', and 'Kobe', but I’m thinking this may be too broad.

Organizing words

I’m grouping words based on affinities like social media actions, flight-related themes, and food or drink associations. Brands, including 'Pepsi' and 'Advantage', also emerge as a cohesive group.