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It generated (via a prompt) an image of a space ship landing on a remote planet.
I asked an edit, "The ship itself should be more colourful and a larger part of the image".
And it replaced the space-ship with a container vessel.
It had the chat history, it should have understood I still wanted a space-ship, but it dropped the relevant context for what I was trying to achieve.
I'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…
Adding it would also provide a fair assessment for a leading open source model.
The site is a great idea and features very interesting prompts. :)
I vibed up a little chat interface https://kontext-chat.vercel.app/
> show me a closeup of… Investigators will love this for “enhance”. ;)
Can it generate chess? https://manifold.markets/Hazel/an-ai-model-will-successfully...
Earlier quoted context omitted.
It seems more accurate than 4o image generation in terms of preserving original details. If I give it my 3D animal character and ask it for a minor change like changing the lighting, 4o will completely mangle the face of my character, it will change the body and other details slightly. This Flux model keeps the visible geometry almost perfectly the same even when asked to significantly change the pose or lighting
gpt-image-1 (aka "4o") is still the most useful general purpose image model, but damn does this come close. I'm deep in this space and feel really good about FLUX.1 Kontext. It fills a much-needed gap, and it makes sure that OpenAI / Google aren't the runaway victors of images and video. Prior to gpt-image-1, the biggest problems in images were: - prompt adherence - generation quality - instructiveness (eg. "put the…
OpenAI models are expensive to train because it’s beneficial for OpenAI models to be expensive and there is no incentive to optimize when they’re gonna run in a server farm anyway.
Probably a bunch of teams never bothered trying to replicate Dall-E 1+2 because the training run cost millions, yet SD1.5 showed us comparable tech can run on a home computer and be trained from scratch for thousands or fine tuned for cents.
I'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…
I tried this out and a hilarious "context-slip" happened: https://imgur.com/a/gT6iuV1 It generated (via a prompt) an image of a space ship landing on a remote planet. I asked an edit, "The ship itself should be more colourful and a larger part of the image". And it replaced the space-ship with a container vessel. It had the chat history, it should have understood I still wanted a space-ship, but it dropped the releva…
Earlier quoted context omitted.
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…
is it mostly accurate though? how would you know? suppose you had an asian woman whose face is entirely covered with snow. sure you could tell AI to remove the snow and some face will be revealed, but who is to say it's accurate? that's why traditionally you have a reference input.