Enhancing a Whole Set Without a Folder Upload
Batch work here is a question of how many runs are in flight, not how many files you can hand over at once.
Most people arriving here want a folder picker, and it is better to say at the top that there is not one. Fast Enhancer takes a single PNG or JPG per run, at 8 credits and an estimated 10 to 25 seconds each. Two hundred images means two hundred runs, started one file at a time.
What makes that manageable is concurrency rather than multi-upload. The Free plan runs one task at a time at standard speed; Starter runs two at once; Pro adds a third slot and moves your runs ahead in the queue; Studio runs six and includes API access. The files still go in individually, but several are being processed in the same moment instead of waiting behind each other. It is also worth being clear about what a batch does not deliver: the panel describes upscaling and sharpening without publishing any magnification factor, so if every file in the set also needs to reach a specific larger size, handle that on the AI image upscaler page rather than expecting it from a batch here.
What it does
Built for this specific job, not a generic editor.
One file per run
The tool accepts a single PNG or JPG at a time. There is no multi-select, no folder drop and no archive upload, so a batch is a sequence of individual runs rather than one action.
Up to six in flight on Studio
Parallel task slots are what paid plans add: two on Starter, three on Pro and six on Studio, which keeps the queue moving instead of leaving you waiting on each file in turn.
A priority queue from Pro upwards
Pro moves your runs ahead of standard ones as well as adding a third slot, which matters when a set has a deadline rather than a spare afternoon.
API access on Studio
Studio includes API access, which is the route worth taking once a set is large enough that starting each run by hand has stopped being a reasonable way to spend an afternoon.
How it works
Four steps, no timeline to learn.
Count the files and multiply by eight
Credits are the binding constraint on a batch, not time. Fifty images costs 400 credits and exhausts Starter; one hundred costs 800, which most of Pro covers; five hundred costs 4,000, which is Studio.
Test one representative image first
Set members usually share one problem because they share one origin. Spend 8 credits learning how the whole batch will behave before committing several hundred to it.
Keep your slots filled
Start as many runs as your plan allows and add another each time one finishes. On Free that is one at a time, which is fine for a handful of files and slow for a catalogue.
Spot-check rather than trusting uniformity
A set that looks consistent often is not — sizes differ, backgrounds differ and some files were re-saved. Check images from different parts of the batch before assuming they all behaved the same way.
What people use it for
The jobs this actually gets used for.
Catalogue refreshes
A supplier or archive set in which every image suffered the same treatment on the way out, so one test run predicts how the remaining hundreds will land.
Older library images
Blog and documentation assets published years ago at sizes and compression settings nobody would choose now, brought back up to a standard that still holds.
Event and gallery deliveries
A photographer’s export of several hundred files where the exposure and framing are fine and only the fine detail came out soft.
Generated stills waiting for review
A run of AI-produced images kept at 2 credits each, with the shortlisted few worth 8 credits apiece for the clarity pass before they go anywhere public.
Why throughput and volume are separate purchases
Parallel slots shorten the wait. They do not make a single image cheaper.
The word bulk is used carelessly and it collapses two different things into one expectation. The first is how many files you can hand over in a single action, and on that the answer here is one. The second is how many of those runs are being processed at the same moment, which is exactly what the paid tiers change. Buying a plan in the expectation of a folder picker and finding a queue instead is the most common version of this misunderstanding.
Credits are the constraint that does not move. Six parallel slots do not reduce the cost of a file by a single credit: each image is still 8 credits, so a set of five hundred needs the Studio allowance no matter how quickly it runs. Parallelism buys hours back. Only credits buy volume, and anyone planning a large batch should do that arithmetic before starting rather than discovering the limit at file two hundred.
The saving that genuinely matters is the test run. Because a batch usually shares one origin, it also shares one failure mode, and a single 8-credit trial tells you whether the set is worth processing at all. If it is not — if the files lack detail rather than merely losing it — you have spent eight credits finding that out instead of several hundred.
Credits and plans
Every tool draws on one shared credit balance.
Every image costs 8 credits, unchanged by plan or by how many are running at once. Free gives 30 credits a month with no card, which is three files, at one task at a time and standard speed. Starter is $9 for 400 credits and two tasks in flight — fifty images; Pro is $19 for 1,000 credits, three tasks and a priority queue — 125 images; Studio is $59 for 4,000 credits, six tasks and API access — 500 images.
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Related tools
Closely related pages on the same problem.
Frequently asked questions
Can I upload a folder, and which formats are accepted?
No folder drop, no multi-select and no archive upload — one PNG or JPG per run. Anything in another format has to be exported first, and PNG is preferable where your converter offers it.
What does a batch actually cost?
Eight credits per image regardless of plan. Fifty files is 400 credits, which uses up Starter; a hundred is 800; five hundred is 4,000, the Studio allowance. Parallel slots speed it up; they do not change that arithmetic.
How many images can process at the same time?
One on Free, two on Starter, three on Pro and six on Studio. Pro and above also move ahead in the queue, and Studio adds API access for sets large enough to warrant automating them.
Some files in the set look unchanged — why?
Usually because they were not the same file you thought they were. Sizes differ, some were re-saved more often than others, and images whose fine detail was destroyed rather than weakened will improve very little. Test one from each distinct origin before running the rest.
Can this make every file in the set bigger as well as clearer?
Do not plan around it. The panel describes upscaling and sharpening but publishes no magnification factor, so there is nothing to budget or to promise for output dimensions. If the set needs defined larger sizes, the AI image upscaler page addresses that up to 4K.
More OpenFilmo tools
Other generators and utilities in the same workspace.