133 pointsby minimaxira day ago11 comments
  • rushingcreek20 hours ago
    This is awesome, and kudos to BFL for releasing the weights. The financial sustainability of open-source is hard to get right, and giving academics this model for free while charging a reasonable licensing fee for startups is something I think makes sense if it allows BFL and others to continue releasing open-weight models.
    • doctorpangloss15 hours ago
      Would it be financially sustainable if BFL had to pay for express permission for all the image and derived-from-video content it uses? (No)
      • rushingcreek13 hours ago
        I think this is a separate issue. No model provider currently obtains express permission from the content they train on. But some model providers, like BFL, can choose to give back to the open-source/weights community even when they don't have to. I think this outcome is strictly better than them choosing not to give back, which they totally could have done.
  • treesciencebot18 hours ago
    One interesting feature that gets enabled with open weights is adding new capabilities (tasks) to these editing models. They generalize quite well with low samples (30 ish). We talk about it here https://blog.fal.ai/announcing-flux-1-kontext-dev-inference-...
    • qingcharles18 hours ago
      Absolutely. This is the version of Kontext that everyone has been waiting for. It's far more useful now. This is the first of the new generation of imagegens that allows training. Can't do that with Gemini, GPT, MJ etc.
  • vunderba20 hours ago
    Here's hoping the distilled [Dev] model can hold up reasonably well against the larger pro/max models which in a lot of ways can completely replace the relatively old-school inpainting techniques of Stable Diffusion.

    Some before/after experiments with editing images using Kontext:

    https://specularrealms.com/ai-transcripts/experiments-with-f...

  • b0a04gl21 hours ago
    this intent in licenses might lowkey prescreens who even gets to build. this creates a soft perimeter = technically open, operationally narrow. it's shaping who can even try, plus cuts out misuse but also cuts out maybe-use. over time, that subtly redefines what counts as valid experimentation
  • minimaxira day ago
    The new non-commercial license is a bit of a doozy: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev/...
    • samtheprogram21 hours ago
      If I’m understanding this correctly, you can’t run this in a commercial setting, even if you’re not creating a derivative but simply generating outputs?
      • smerrill2521 hours ago
        I believe you can buy licensing? But def not the same as 'Open Weights..'
      • stefan_21 hours ago
        The same people that claim using all of humanities creation is fair use want you to pay for a bunch of MatMul inputs that are unrecognizable to anyone after quantizing them yourself.
        • cchance21 hours ago
          Stupid question, whats to stop someone from quantizing it, shit even just barely finetuning it for 1 step and calling it something different, no ones actually checking WTF these models are based on when they're released, especially for the source models, especially if the release is not around the same time of release as the base, i'm 99% sure someone could fine tune SD3.5 a bit and release it today as Frizz 1.0 and people would just take it as a new model using the same layer structure as SD3.5 lol
          • liuliu17 hours ago
            There is a simple method to detect this: taking a model "claimed" to be trained scratch, taking the model you suspected is the original, generate a new model = claimed_model * 0.5 + suspected_model * 0.5.

            If the claimed_model is trained from scratch, the new model will have 0 capability (basically generate gibberish words or noise). If it is a derivative of the suspected model, it will do something sensible.

            It is a bit more interesting for diffusion model because you can fine-tune to a different objective, making this investigation harder to do, but not impossible.

          • elpocko20 hours ago
            Not impossible but you'd gonna have to do a bit more than that. Most people are ignorant, but not all of them. An experienced user can tell what model family was used from a bunch of generated images. Also, no one would believe a nobody who just showed up claiming to have trained a brand new diffusion model.
          • doctorpangloss15 hours ago
            FLUX watermarks its outputs.

            Additionally, certain prompts will produce nonsensical but specific outputs known only to BFL.

            • 42lux13 hours ago
              There is no watermarking in flux. The only artifacts that remain are vae artifacts. The vae is Apache licensed and used by many models now. So you can't identify the specific model.
              • 11 hours ago
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          • SV_BubbleTime17 hours ago
            I forget which, but some HiDream maybe was called out for this when it happened to generate basically the same dude in front of the same archway when compared against flux.
            • liuliu17 hours ago
              HiDream is a separate architecture. OTOH, it might be finetuned on FLUX generated data, we will never know.
            • doctorpangloss15 hours ago
              HiDream is trained on AI generated outputs.
        • ronsor19 hours ago
          Quite frankly, I still believe that these model licenses are dubiously enforceable at best, and I'm skeptical that models are copyrightable at all.
        • whywhywhywhy19 hours ago
          The double standard is frankly disgusting.

          I'm actually all for open training but I think it's only fair you treat the model as your treated the life's work of others.

    • Hizonner12 hours ago
      License for what? They don't have a copyright except maybe in the easily reimplemented Python code.

      Model weights are not copyrightable creative works, no matter how much various companies wish they were.

      At least they're not copyrightable until either legislatures extend the list of what's copyrightable, or courts have definitively show their willingness to reinterpret the words in the existing definitions far outside of their established legal meanings, their established meanings in common speech, and/or any sane analogy to those established meanings.

      Yes, I am aware that collections and databases are copyrightable. Models don't have the elements required for a copyrightable collection or database. I'm also aware that software is copyrightable. Models don't have the elements required for copyrightable software. They just flat out aren't works of authorship in any way. How much effort goes into creating them is irrelevant; that's not part of what defines a copyrightable work.

  • kristopolousa day ago
    I was at a hackathon with this thing last weekend in SF at bfl. It's a pretty good system.
    • HanClinto20 hours ago
      What sorts of things were built with it?
      • kristopolous17 hours ago
        I think this should work: https://docs.google.com/spreadsheets/d/1cxh9oA1ZHkzGRMKutVNb...

        I was at the top of the list ... pitched it poorly. That night I made a party game to practice: https://pitchanary.com/

        The rules might need some work.

        • HanClinto16 hours ago
          Wow, this is a seriously good turnout for the hackathon. Thank you for posting this list, it's fun to look through these!
          • kristopolous14 hours ago
            It made me realize that the more I believe in the quality of what I'm producing, the more I try to let the product speak for itself and the less I explain it and the poorer I do.

            It's no stretch to say that the hackathons I won, all the projects were janky and the hackathons I lost, all the products worked well and did exactly what I said.

  • whatevsmate21 hours ago
    Neat, I plan to check this out.

    I really want an AI to jam with on a canvas rather than to just have it generate the final results.

    I have been hoping someone would pick up on the time series forecasting innovations in the LLM space, combine them with data from e.g. the Google quick draw dataset, and turn that into a real-time “painting partner” experience, kind of like chatting with an LLM through brush strokes.

    • vunderba20 hours ago
      Using the kontext models in Fal.ai shows you a nice slider of the before and after edits and has a button that lets you set the edited image as the new source so you can continue to make changes.

      Now that BFL has released a dev model, I'd love to see a Kontext plugin for Krita given that it already has one for Stable Diffusion though!

      https://github.com/Acly/krita-ai-diffusion

      • dragonwriter19 hours ago
        The Krita plugin is a bridge to ComfyUI which can already run Flux and presumably will have native support for Kontext (dev) within a week or so, and the plugin already has limited support for using Flux, so Kontext in the existing plugin (rather than requiring a new one) seems a fairly reasonable expectation.
  • thetoon19 hours ago
    What amount of VRAM is this supposed to work with?
    • SV_BubbleTime17 hours ago
      Today… about 18-20GB.

      Tomorrow… like 4GB if you have an hour.

      • dragonwriter15 hours ago
        > Today… about 18-20GB.

        There's an FP8 version that's the default for the ComfyUI template that's in the release that just came out with Kontext support that I've seen reports of running in 12GB or less, and which I'm running at this moment in 16GB.

  • popalchemist16 hours ago
    License is a major bummer.
  • 20 hours ago
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  • oTsanony16 hours ago
    Yo guys, I think I might’ve found a chill and straightforward way to openly generate NSFW stuff using flux1-context on ComfyUI.