Earlier quoted context omitted.
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I'm selectively lazy for sure. I'm working full time right now. Like, all the time except for sleep
It sounds you’re not lazy enough, that’s not healthy longer term.
FLUX.1 Kontext
51–60 of 140 posts
Re: FLUX.1 Kontext
#52Earlier quoted context omitted.
Interesting, would you mind sharing? (imgur allows free image uploads, quick drag and drop) I do have a "works on my machine"* :) -- prompt "Model F keyboard", all settings disabled, on the smaller model, seems to have substantially more than no idea: https://imgur.com/a/32pV6Sp (Google Images comparison included to show in-the-wild "Model F keyboard", which may differ from my/your expected distribution) * my machine…
Your Google Images search indicates the original problem of models training on junk misinformation online. If AI scrapers are downloading every photo that's associated with "Model F Keyboard" like that, the models have no idea what is an IBM Model F, or its distinguishing characteristics, and what is some other company's, and what is misidentified. https://commons.wikimedia.org/wiki/Category:IBM_Model_F_Keyb... Speci…
I agree with you vehemently.
Another way of looking at it is, insistence on complete verisimilitude in an image generator is fundamentally in error.
I would argue, even undesirable. I don't want to live in a world where a 45 year old keyboard that was only out for 4 years is readily imitated in every microscopic detail.
I also find myself frustrated, and asking myself why.
First thought that jumps in: it's very clear that it is in error to say the model has no idea, modulo there's some independent run that's dramatically different from the only one offered in this thread.
Second thought: if we're doing "the image generators don't get details right", it would seem to be there a lot simpler examples than OPs, and it is better expressed that way - I assume it wasn't expressed that way because it sounds like dull conversation, but it doesn't have to be!
Third thought as to why I feel frustrated: I feel like I wasted time here - no other demos showing it's anywhere close to "no idea", its completely unclear to me whats distinctive about a IBM Model F Keyboard, and the the wikipedia images are worse than Google's AFAICT.
Re: FLUX.1 Kontext
#53I'm debating whether to add the FLUX Kontext model to my GenAI image comparison site. The Max variant of the model definitely scores higher in prompt adherence nearly doubling Flux 1.dev score but still falling short of OpenAI's gpt-image-1 which (visual fidelity aside) is sitting at the top of the leaderboard. I liked keeping Flux 1.D around just to have a nice baseline for local GenAI capabilities. https://genai-sh…
Nice site! I have a suggestion for a prompt that I could never get to work properly. It's been a while since I tried it, and the models have probably improved enough that it should be possible now.
A knight with a sword in hand stands with his back to us, facing down an army. He holds his shield above his head to protect himself from the rain of arrows shot by archers visible in the rear.
Re: FLUX.1 Kontext
#54Don’t understand the remove from face example. Without other pictures showing the persons face, it’s just using some stereotypical image, no?
Look more closely at the example. Clearly there is an opportunity for inference with objects that only partially obscure.
Re: FLUX.1 Kontext
#55Anyone have a guess as to when the open dev version gets released? More like a week or a month or two I wonder.
Re: FLUX.1 Kontext
#56Technical report here for those curious: https://cdn.sanity.io/files/gsvmb6gz/production/880b07220899...
Seems implementation is straightforward (very similar to everyone else, HiDream-E1, ICEdit, DreamO etc.), the magic is on data curation (which details are lightly shared).
I haven't been following image generation models closely, at a high level is this new Flux model still diffusion based, or have they moved to block autoregressive (possibly with diffusion for upscaling) similar to 4o?
Re: FLUX.1 Kontext
#57Don’t understand the remove from face example. Without other pictures showing the persons face, it’s just using some stereotypical image, no?
They chosen Asian traits that Western beauty standards fetishize that in Asia wouldn't be taken serious at all.
I notice American text2image models tend to generate less attractive and more darker skinned humans where as Chinese text2image generate attractive and more light skinned humans.
I think this is another area where Chinese AI models shine.
Re: FLUX.1 Kontext
#58It still has no idea what a Model F keyboard looks like. I tried prompts and editing, and got things that weren't even close.
I got it working when I provided an image of a Model F keyboard. This is the strength of the model, provide it an input image and it will do some magic
Disclaimer: I work for BFL
Re: FLUX.1 Kontext
#59Currently am testing this out (using the Replicate endpoint: https://replicate.com/black-forest-labs/flux-kontext-pro ). Replicate also hosts "apps" with examples using FLUX Kontext for some common use cases of image editing: https://replicate.com/flux-kontext-apps It's pretty good: quality of the generated images is similar to that of GPT-4o image generation if you were using it for simple image-to-image generations…
> Generation is speedy at about ~4 seconds per generation May I ask on which GPU & VRAM? edit: oh unless you just meant through huggingface's UI
The open weights variant is "coming soon" so the only option is hosted right now.
Re: FLUX.1 Kontext
#60Earlier quoted context omitted.
Not necessarily. "As you may see, this is a Chinese lady. You have seen a number of Chinese ladies in your training set. Imagine the face of this lady so that it won't contradict the fragment visible on the image with the snowflake". (Damn, it's a pseudocode prompt.)
yes, so a stereotypical image. my point is best illustrated if you look at all of the photos of the woman.
Even if you provide another image (which you totally can btw) the model is still generalizing predictions enough that you can say it's just making a strong guess about what is concealed.
I guess my main point is "this is where you draw the line? at a mostly accurate reconstruction of a partial of someone's face?" this was science fiction a few years ago. Training the model to accept two images (which it can, just not for explicit purposes of reconstructing (although it learns that too )) seems like a very task-specific, downstream way to handle this issue. This field is now about robust, general ways to emerge intelligent behavior not task specific models.