Flux: Open-source text-to-image model with 12B parameters
blog.fal.ai
blog.fal.ai
what we did at fal is take the model and run it on our inference engine optimized to run these kinds of models really really fast. feel free to give it a shot on the playgrounds. https://fal.ai/models/fal-ai/flux/dev
I'd suggest re-wording the blog post intro, it reads as if it was created by Fal.
Specific phrases to change:
> Announcing Flux
(from the title)
> We are excited to introduce Flux
> Flux comes in three powerful variations:
This section also comes across as if you created it
> We invite you to try Flux for yourself.
Reads as if you're the creator
This library is quite well known, 3rd most starred project in Julia: https://juliapackages.com/packages?sort=stars.
It has been around since, at least, 2016: https://github.com/FluxML/Flux.jl/graphs/code-frequency.
I hope this one doesn't stir as much discussion. It has 4000 stars, there isnt a large mass of people who view the world through the lens of "Flux is ML library". No one will end up in a "who is on first?" discussion because of it. If this line of argument is held sacrosanct, it ends up in an infinite loop until everyone gives up and starts using UUIDs.
https://en.wikipedia.org/wiki/Go!_(programming_language)
Disclosure: I work at Google but not on the Go team.
also search engines are context aware, if your search history is full of julia questions, it will know what you're searching for
Flux A is the ML library
Flux B is the T2I model
Flux C is the React library
Flux D is the physics concept of power per unit area
Flux E is the goo you put on solder
I’d bet that fine art training would further improve the compositional skills of the model, plus it would open up a range of uses that are (to me at least) a bit more interesting than just illustrations.
Does it respond to any names? I noticed SD3 removed all names to prevent recreating famous people but as a side effect lost the very powerful ability to infer styles from artist names too.
If it's unlimited or "throttled for abuse," say that. Right now, I don't know if I can try it six times or experiment to my heart's desire.
Also, everybody should remember that these models are not copyrightable and you should never agree to any license for them...
It would be nice here if you give some examples of what you call open source model. Please ;) Because the impression is that these things do not exist, it's just a dream which does not deserve such a nice term..
However, plenty of open source software exists. The fact that open source models don't exist doesn't excuse attempts to falsely claim the prestige of the phrase "open source".
You are wrong about that. It's a file with numbers. Which makes it a database or dataset and very much protected by copyright. That's why licenses are needed. For the phone book, things like open street maps, and indeed AI models.
> The fact that open source models don't exist
The fact that many people (myself included) routinely download and use models distributed under OSI approved licenses (Apache V2, MIT, etc.) makes that statement verifiably wrong. And yes, I do check the license of stuff that I use as I work with companies that care about such matters.
> As far as I know ...
Now you know better.
This is only true in jurisdictions that follow the sweat of the brow doctrine, where effort alone without creativity is considered enough for copyright. In other places, such as the USA, collections of facts are not copyrightable and a minimal amount of creativity is required for something to qualify as copyrightable. The phone book is an example that is often used, actually, to demonstrate the difference.
Not every collection of numbers is a database, and a database is not the same thing as a dataset.
Databases have limited copyright-like protection in some places. Under TRIPS, that extends to only databases that are "creative by virtue of the selection or arrangement of their contents" or something along those lines. In the US they talk specifically about curation.
ML models do not meet either requirement by any reasonable interpretation.
> The fact that many people (myself included) routinely download and use models distributed under OSI approved licenses (Apache V2, MIT, etc.) makes that statement verifiably wrong.
The "source code" of an ML model is most reasonably interpreted as including all of the training data, which are never, ever available.
Now you know better.
[On edit: By the way, the people creating these works had better hope they're outside copyright, because if not, each one of them is a derivative work of (at least some large and almost impossible to identify subset of) its training data, so they need licenses from all the copyright holders of that training material, which few of them have or can get.]
However, transformativeness is a factor in whether or not there is a fair-use exception for the derivative work. And these models are highly transformative, so this is a strong argument for their fair-use.
"Fair use" is pretty much entirely a US concept, and similar concepts in other countries aren't isomorphic to it.
The model does have a radically different form from its inputs. So you could easily imagine that being "transformative enough" for US fair use. A lot of the other fair use elements look pretty easy to apply, too. Although there's still the question of whether all the intermediate copies you made to create the model were fair use...
In fact, I'll even concede that a court could find that a model wasn't a derivative work of its inputs to begin with, and not even have to get to the fair use question. The argument would be that the model doesn't actually reproduce any of the creative elements of any particular training input.
I do think a finding like that would be a much bigger stretch than a finding that the model was copyrightable. I could easily see a world where the model was found derivative but was not found copyrightable. And it's actually not clear to me at all that the model has to be copyrightable to infringe the copyright in something else, so that's another mess.
Somewhat related, even if the model itself isn't infringing, it's definitely possible to have most models create outputs that are very similar to (some specific examples in) their training data... in ways that obviously aren't transformative. Outputs that might compete with the original training data and otherwise fail to be fair use. So even if the model is in the clear, users might still have to watch out.
What criteria for copyright protection are they missing?
I can tell you a secret. What you call 'open source' models are impossible. Because massive randomness is a part of training process. They are not reproducible. Having everything you cannot even tell if the given model was trained on the given dataset. Copyright is a different thing.
And a bad news, what's coming is even worst. Those will be the whole things with self awareness and personal experience. They can be copied, but not reproduced. More over, it's hard or almost impossible to detect if something undeclared was planted in their 'minds'.
All together means 'open source' model in strict interpretation is a myth, great idea which happen to be not. Like Turing test.
> However, plenty of open source software exists.
Attempt to switch topic detected.
PS: as for that massive downvote, I even wasn't rude, don't care. This account will be abandoned soon regardless, like all before and after.
The Llama models aren't. Some of the Mistral models are (the Apache 2 ones). Microsoft Phi-3 is - it's MIT.
I've decided to draw my personal line at Open Source Initiative compliance for the license they release the model itself under.
I respect the opinion that it's not truly open source unless they release the training data as well, but I've decided not to make that part of my own personal litmus test here.
My reasoning is that knowing something is "open source" helps me decide what I legally can or cannot do with it when building my own software. Not having access to the training data downs affect my legal rights, it just affects my ability to recompile myself. And I don't have millions of dollars of GPUs so that isn't so important to me, personally.
Tough beans? There's lots of actual software that can't be open source because it embeds stuff with incompatible restrictions, but nobody tries to redefine "open source" because of that.
... and, on a vaguely similar-flavored note, you'd better hope that the models you're using end up found to be noninfringing or fair use or something with respect to those "unlicensed data", because otherwise you're in a world of hurt. It's actually a lot easier to argue that the models aren't copyrightable than it is to argue that they're not derivative of the input.
> I've decided to draw my personal line at Open Source Initiative compliance for the license they release the model itself under.
You're allowed to draw your personal line about what you'll use anywhere you want, but that doesn't mean that you should try to redefine "open source" or support anybody who does.
Never underestimate the value of getting hordes of unpaid workers to refine your product. (See also React, others)
I'd prefer "false advertising" - it's more direct and without the culture war baggage.
That said, I don't think outputs of the model are derivative works of it, any more than the model is a derivative of its training data, so it's not clear to me they can actually enforce what you do with them.
Are you talking about https://en.wikipedia.org/wiki/Database_right or plain old copyright?
I'm no IP lawyer, but I've always thought that copyright put "requirements" on the artefact (i.e the threshold of originality), not the process.
In my jurisdiction we have database rights, meaning that you get IP protections for the artefact based on the work put into the process. For example a database of distances between adress pairs or something is probably not copyrightable, but can be protected under database rights if enough work was done to compile the data.
EDIT: Saw in another place in thread speaking about the https://en.wikipedia.org/wiki/Sweat_of_the_brow doctrine, relates to Database rights. (Neither of which notably are not applicable in the U.S)
The only thing that's really specified about the model itself is its architecture, which is (1) dictated by function, and (2) usually deeply stereotyped.
Fair enough, but those datasets are also primarily copyrighted material. If the software here merely transforms the input material (which I agree it does), then the output is a derivative work.
If I take a string of data from a true hardware RNG, XOR it with a Taylor Swift song, and throw away the original random stream, is the resulting fundamentally random bit string still a derivative work of the song? As with the ML model, you can't recognize the song in it. And as with at least some training examples in the inputs of most ML models, you can't recover the song from it either.
It feels like the test for whether X is derivative for copyright purposes should include some kind of attention to whether X is a creative work at all. Maybe not, but then what test do you use?
I do recognize the possibility that the models might not themselves be eligible for copyright as independent works, yet still infringe copyright in the training inputs. It seems messy, but not impossible.
... and as I said elsewhere, it's also messy that while you generally can't recover every training input from the model, you can usually recover something very close to some of the training inputs.
It's not a copy of it, and when you distribute it you're not distributing the original. So it's not a derivative for copyright purposes.
It can still be a derivative for other legal purposes. Judges don't appreciate it when you do funny math tricks like that and will see through them.
> It feels like the test for whether X is derivative for copyright purposes should include some kind of attention to whether X is a creative work at all. Maybe not, but then what test do you use?
Yes, that's how US copyright law works. (well sort of…)
Being a transformative work of something makes it less of a copy of it, the more transformed it is, since it falls under fair use exemptions or is clearly a different category of thing.
If a model was a derivative of its training data, then Google snippets/thumbnails would be derivatives of its search results and would be illegal too. Unless you wrote a new law to specifically allow them.
In other countries (Germany, Japan) fair use is weaker, but model training has laws specifically making it legal in certain circumstances, and presumably so do Google snippets.
A compressed (or normally encrypted) version wouldn't be a copy that way, either, but I would still absolutely go down for distributing it. The difference is that the compression can be reversed to recover the original. Even lossy compression would create such a close derivative that nobody would probably even bother to make the distinction.
You're right that "math games" don't work in the law, but that cuts both ways. If you do something that truly makes the original unrecoverable and in fact undetectable, and if nothing salient to the legal issues at hand about the new version derives from the original, then judges are going to "see through" the "math trick" of pretending that it is a derivative.
> then Google snippets/thumbnails would be derivatives of its search results
Thumbnails are legally derivative works, in the US and probably most other places. In the US, they're protected by the fair use defense, and in other places they're protected by whatever carveouts those places have. But that doesn't mean they're not derivative works.
In fact, if I remember the US "taxonomy" correctly, thumbnails are infringing. It's just that certain kinds of ingfringement are accepted because they're fair use.
If thumbnails weren't derivative works at all, then the question of fair use wouldn't arise, because there can be no infringement to begin with if the putatively infringing work isn't either derivative or a direct copy.
Where thumbnails are different from ML models is that they're clearly works of authorship. In a thumbnail, you can directly see many of the elements that the author put into the original image it's derived from.
The questions are (a) whether ML models are works of authorship to begin with (I say they're not), and (b) whether something that's not a work of authorship can still be a derivative work for purposes of copyright infringment (I'm not sure about that).
So far as I know, neither one is the subject of either explicit legislation or definitive precedent in most of the world, including the US.
Since it costs millions to produce one of these models, it's not just taking the software and running it to compile them.
Thanks for pointing that out @Hizonener
Bummer. After seeing what was generated in the blog post I was excited to try it! Now feeling disappointed.
I was hoping it'd be more like https://play.go.dev.
Good luck.
Remarkably better than the "DrawThings" iPhone app (my only reference point).
Recently Claude began to allow generation of SVG drawings, and asking it to draw a unicorn and later add extra tails or horns worked correctly.
A fork exists in physical space and it's pretty intuitive to understand what it can do. These models exist within digital space and are incredibly opaque by comparison.
That sounds interesting! Were the results somewhat clean and clear SVG or rather a mess that just looked decent?
For what it's worth, I've previously asked in the Stable Diffusion Discord server for help generating a "lamb with seven horns and seven eyes" but the members there were also unsuccessful.
> A Gary Larsen, "Far Side" comic of a racoon disguising itself by wearing a fedora and long trench coat. The raccoon's face is mostly hidden by the fedora. There are extra paws sticking out of the front of the trench coat from between the buttons, suggesting that the racoon is in fact a stack of several raccoons.
Every human I've ever described this to has no problem picturing what I mean. It's a classic comic trope. AIs still struggle.
The intelligence just doesn't "fit" in there.
Personally I'm curious to see what would happen if someone burnt $100M of compute time on training a truly enormous image generator model, something the same-ish size as GPT4...
But AIs learn and therefore create in exactly the same way as humans, ostensibly on the same data. How can this be possible? /s
The first attempt at this based on https://reductress.com/post/my-boyfriends-are-always-two-kid... ... really misunderstood the image. This may also be part of the problem.
The image shows an imaginative, whimsical illustration of a character composed of two parts. The upper part features a man dressed in a long, elegant gray coat, wearing a bowler hat and round sunglasses, with a sophisticated white polka-dot ascot tie. His face has a subtle smile. The lower part of the character transitions seamlessly into a smaller figure of a cat, appearing to wear striped pants, with its tail visible. The entire character combines human and feline elements, creating a surreal, anthropomorphic appearance. The illustration is in black and white, emphasizing a stylized, cartoon-like design.
And that provided:
The image captures a whimsical and secretive scene featuring three dwarves stacked in a totem formation, each attempting to conceal their nature under a large brown cloak. The top dwarf has a bright, cheerful expression and blond hair, holding the cloak wide to mimic wings, and is dressed in black armor adorned with teal gems and matching earrings. The middle dwarf displays a fierce expression, sporting a bushy orange beard, and is also clad in similar dark armor with teal embellishments. The bottom dwarf, an older figure with a long white beard, is adorned in a royal dark outfit with gold accents and a small crown, clasping a glowing white orb. This trio of dwarves, each with distinctive fantasy armor, unites in a playful attempt to disguise their stature and nature, adding an element of adventure and mystery to the scene.
That produced https://imgur.com/a/Of9FsJl
I suspect the orange beard came from the previous part in the session. But that might be an approach to take in trying to describe it in a way that can be used.
It’s more like a giant telescope of many lenses (the latents from the prompts) and you’re adjusting the lenses to bring a possible reality of many into focus.
"a woman lying on her back wearing a blouse and shorts."
But it wouldn't render the image - i instead got a NSFW warning. That's one way to hide the fact that it cannot render it properly i guess...
PS: after a few tries it rendered "a woman lying on her back" correctly.
It is very fast and very good at rendering text, and appears to have a text encoder such that the model can handle both text and positioning much better: https://x.com/minimaxir/status/1819041076872908894
A fun consequence of better text rendering is that it means text watermarks from its training data appear more clearly: https://x.com/minimaxir/status/1819045012166127921
There is a PR to that repo for a diffusers implementation, which may run on a cheap L4 GPU w/ enable_model_cpu_offload(): https://huggingface.co/black-forest-labs/FLUX.1-schnell/comm...
* NVIDIA Jetson AGX Orin Dev. Kit with 64 GB shared RAM.
* Default configuration for flux-dev. (FP16, 50 steps)
* 33GB GPU RAM usage.
* 4 minutes 20 seconds per image at around 50 Watt power usage.
I could write “with text that says Shutterstock” in the prompt but that doesn’t necessairly mean the dataset contains that
The image linked has a traditional www watermark in the lower-left as well. Even something innocous as a "Super Mario 64" prompt shows a copyright watermark: https://x.com/minimaxir/status/1819093418246631855
Of all of the instances on HN of Godwin's law playing out that I've ever seen, this one is the new cake-taker.
This is like the fifth time I see someone paraphrasing Niemöller in an ai context, and it's exhausting. It's also near impossible to take the paraphraser seriously.
More to the point, AI is a tool. I could just as well infringe on vanity fair IP using ms-paint. Someone more artistic than me could make a oil-on-canvas copy of their logo too.
Or, to turn your own annoying "argument" against you:
First they came for AI models, and I did not speak out, because I wasn't using them. Then they came for Photoshop, and I did not speak out, because I had never learned to use it. Then they came for for oil and canvas, and now there are no art forms left for me.
As to your use of the argument in the other direction, I’d say it doesn’t work very well because no one with any power is coming for those things.
Whether you are paraphrasing or referencing to a famous confessional poem dealing with the Holocaust, the only reasonable interpretation is that you're comparing with the Holocaust. Even if you were unaware of the phrases origins, that's how anyone who does know where it comes from will interpret it. See other comments drawing the same conclusion for reference.
Again. Ai is a tool. It can produce illegal material, just like a pencil can, or a brush with oil and canvas. How are they different? They are not.
It's like GRRM complaining that LLMs can reproduce chunks of text from his books "they fed my novels into it" Oh yeah? It's definitely not all the parts of your book quoted in millions of places online, including several dedicated wiki style sites? That wouldn't be it, right?
https://www.vanityfair.com/verso/static/vanity-fair/assets/l...
(available without sign-in) FLUX.1 [schnell] (Apache 2.0, open weights, step distilled): https://fal.ai/models/fal-ai/flux/schnell
(requires sign-in) FLUX.1 [dev] (non-commercial, open weights, guidance distilled): https://fal.ai/models/fal-ai/flux/dev
FLUX.1 [pro] (closed source [only available thru APIs], SOTA, raw): https://fal.ai/models/fal-ai/flux-pro
>FLUX.1 [schnell]: A distilled version of the base model that operates up to 10 times faster
It should also be noted that "schnell" is the German word for "fast".
> Models
> We are offering three models:
> FLUX.1 [pro] the base model, available via API
> FLUX.1 [dev] guidance-distilled variant
> FLUX.1 [schnell] guidance and step-distilled variant
something about pro must be better than dev or it wouldn't be made API-only, but what exactly, how does guidance distilling affect pro it and what quality remains in dev?
Well, I was wondering about bias in the model, so I entered "a president" as the prompt. Looks like it has a bias alright, but it's even more specific than I expected...
[0] https://fal.media/files/elephant/gu3ZQ46_53BUV6lptexEh.png
If this runs locally, this is very very close to that in terms of both image quality and prompt adherence.
I did fail at writing text clearly when text was a bit complicated. This ideogram image's prompt for example https://ideogram.ai/g/GUw6Vo-tQ8eRWp9x2HONdA/0
> A captivating and artistic illustration of four distinct creative quarters, each representing a unique aspect of creativity. In the top left, a writer with a quill and inkpot is depicted, showcasing their struggle with the text "THE STRUGGLE IS NOT REAL 1: WRITER". The scene is comically portrayed, highlighting the writer's creative challenges. In the top right, a figure labeled "THE STRUGGLE IS NOT REAL 2: COPY ||PASTER" is accompanied by a humorous comic drawing that satirically demonstrates their approach. In the bottom left, "THE STRUGGLE IS NOT REAL 3: THE RETRIER" features a character retrieving items, complete with an entertaining comic illustration. Lastly, in the bottom right, a remixer, identified as "THE STRUGGLE IS NOT REAL 4: THE REMI
Otherwise, the quality is great. I stopped using stable diffusion long time ago, the tools and tech around it became very messy, its not fun anymore. Been using ideogram for fun but I want something like ideogram that I can run locally without any filters. This is looking perfect so far.
This is not ideogram, but its very very good.
If this thing can mint memes with captions in it on a single node I guess that’s the weekend gone.
Thanks for the useful review.
See: https://www.reddit.com/r/StableSwarmUI/comments/1ei86ar/flux... (SwarmUI is cross platform and runs on macs, and linux)
Would love to see an AI company attack engineering diagrams head on, my current hunch is that they just aren't in the training dataset (I'm very tempted to make a synthetic dataset/benchmark)
That seems like a good use for a speech driven assistant that know how to use PC desktop software. Just talk to a CAD program and say what you want. This seems like a long way off but could be very useful.
SD2 was more consistent about the word appearing in the image. "hat" would add hats more reliably. Context started to matter a little bit.
SD3 seems to be getting a lot better at the idea of scene composition, so now specific entities can be prompted to wear hats. Not perfect, but noticeably improved from SD2.
Extrapolating from that, we're still a few generations from being able to describe things with the precision of an engineering diagram - but we're heading in the right direction at a rapid clip. I doubt there needs to be any specialist work yet, just time and the improvement of general purpose models.
Prompt: two square boxes at a distance of 3.5mm. Both boxes have the same size, 10cm.
"An upside down house" -> regular old house
"A horse sitting on a dog" -> horse and dog next to eachother
"An inverted Lockheed Martin F-22 Raptor" -> yikes https://fal.media/files/koala/zgPYG6SqhD4Y3y_E9MONu.png
"A horse sitting on a dog" doesn't work but "A dog sitting on a horse" works perfectly.
> Convey compassion and altruism through scene details.
I have seen a lot of promises made by diffusion models.
This is in a whole different world. I legitimately feel bad for the people still a StabilityAI.
The playground testing is really something else!
The licensing model isn’t bad, although I would like to see them promise to open up their old closed source models under Apache when they release new API versions.
The prompt adherence and the breadth of topics it seems to know without a finetune and without any LORAs, is really amazing.
Gave me a credit of 2USD to play with.
The best prompt adherence on the market right now BY FAR is DALL-E 3 but it still falls down on more complicated concepts and obviously is hugely censored - though weirdly significantly less censored if you hit their API directly.
I quickly mocked up a few weird/complex prompts and did some side-by-side comparisons with Flux and DALL-E 3. Flux is impressive and significantly performant particularly since both the dev/shnell models have been confirmed by Black Forest to be runnable via ComfyUI.
> The fastest image generation model tailored for local development and personal use
Versus flux pro or dev models
Several iterations and these were the best I got out of schnell, dev and pro respectively for the following prompt:
"a fantasy creature with the body of a dragon and a beachball for a head, hybrid, best quality, shadows and lighting, fantasy illustration muted"
This is missing from the image. The generated image looks well, but while reading the prompt I was surpised it was missing
So your censorship investigation (via boobs) is testing a completely different, unrelated, model.
(Also you can download the model itself to check the local behaviour without extra filters. Unfortunately I don't have time to do it right now, but I'd love to know)
Rest is groping for a reason to make "model is censored [classifier made POST return black image instead of boobs]" something sensical.
...then it's not open source. At least the others are Apache 2.0 (real open source) and correctly labeled proprietary, respectively.
Photo of teen girl in a ski mask making an origami swan in a barn. There is caption on the bottom of the image: "EAT DRUGS" in yellow font. In the background there is a framed photo of obama
https://i.imgur.com/RifcWZc.png
Donald Trump on the cover of "Leopards Ate My Face" magazine
The only reason that diffusion isn't used for text is because text requires discrete outputs.
They train them by taking an image with a label, ie, "cat", and then adding some noise to it, run a training step, add more noise, run another step, and so on until the image is total (or near total) noise and still being told it's a cat.
Then, when you want to generate "cat", you start with noise, and it finds a cat in the noise and cancels some of the noise repeatedly. If you're able to watch an image get generated, sometimes you'll even see two cats on top of each other, but one ends up fading away.
Turns out, these denoisers don't require that many parameters, and if your resulting image has a few pixels that are just a tiny bit off color, you won't even notice.
If a pixel is just slightly the wrong shade of green, nobody really cares.
It looks like this is the case for LLMs, that the training quality of the data has a significant impact on the output quality of the model, which makes sense.
So the real magic is in designing a system to curate that high quality data.
You can get better results with better data, for sure. And better architecture, for sure. But raw size is really important the difference in quality for models, all else held equal, is HUGE and obvious if you play with them.
You don't have to speculate on this as you can see that custom models for SDXL for instance perform vastly better than vanilla SDXL at the same number of parameters. It's all data set and tagging.
Complete and utter UX/first impression fail. I had no desire to actualy try the model after this.
I assume you’re offering this as an API? Would be nice to have pricing page as I didn’t see one on your website.
Reddit message: https://www.reddit.com/r/StableDiffusion/comments/1ehh1hx/an...
Linked image: https://preview.redd.it/dz3djnish2gd1.png?width=1024&format=...
The prompt:
> Photo of Criminal in a ski mask making a phone call in front of a store. There is caption on the bottom of the image: "It's time to Counter the Strike...". There is a red arrow pointing towards the caption. The red arrow is from a Red circle which has an image of Halo Master Chief in it.
Some of the images I generated using schnell model with 8-10 steps using this prompt. https://imgur.com/a/3mM9tKf
This model appears to do well with fingers and hands out of the box.
-not censor it
-not be doing prompt injection
It's very easy, which is why no other firm is capable of it.
Really curious to see what other low-hanging fruits people are finding.
Lets, say that you took an image of a flower in a garden and ai has also generated an image of the same flower. When we see these pics side by side we find a lot of difference between them. Origin of my question was "how can we minimize this difference ?". How we can tell the machine that the more the magnitude of a certain parameter the more real it is, not sure if camera settings could help in this case.
So if you're wanting to experiment and have a 24GB card, have at it!
I don’t feel like base models are super useful. Most real use cases depend on being able to iterate on consistent outputs imo.
I have had a very bad experience trying to use other models to modify images but I mostly do anime shit and maybe styles are less consistently embedded into language for those models
Result (distilled schnell model) for
"Photo of Criminal in a ski mask making a phone call in front of a store. There is caption on the bottom of the image: "It's time to Counter the Strike...". There is a red arrow pointing towards the caption. The red arrow is from a Red circle which has an image of Halo Master Chief in it."