{"course":{"slug":"ai-images","title":"Image Generation, in Practice","url":"https://addaly.com/learn/ai-images","level":"beginner","track":"create"},"lesson":{"slug":"picking-an-interface","title":"Four front ends, and which one deserves your learning time","url":"https://addaly.com/learn/ai-images/picking-an-interface","position":5,"of":69,"minutes":8,"answer":"The interface you learn decides which problems are easy, and the right choice is the simplest one that can express the workflow you actually need.","keyIdea":"The interface you learn decides which problems are easy, and the right choice is the simplest one that can express the workflow you actually need, which for finished client work usually means a node graph eventually."},"body":{"format":"markdown","text":"## They all call the same model\n\nEvery local interface runs the same weights through roughly the same code. The differences are entirely about what they make easy and what they make impossible, and choosing wrongly costs weeks.\n\n**Fooocus** hides almost everything. You type a prompt, pick a rough style, and it applies a tuned set of defaults that are good. Installation is close to one step. It is the fastest route from nothing to a decent SDXL image on a Windows machine, and it is an excellent first week. It is also a ceiling: when you need a second ControlNet or a custom step order, it cannot express that.\n\n**AUTOMATIC1111 and its faster fork, Forge**, give you a form. Prompt, negative prompt, sampler, steps, seed, and tabs for img2img, inpainting and extensions. The mental model is a settings panel, which is why most tutorials are written for it. Forge is the one to install today: same interface, noticeably better memory handling, so SDXL fits on 6 GB cards that choke under A1111.\n\n**InvokeAI** sits between the two, with a real canvas. If your work is mostly inpainting and outpainting on a single image (retouching, extending, iterating on one picture) its unified canvas is the best tool of the four, because the thing you actually do all day is \"mask this bit and regenerate\", and Invoke makes that one gesture rather than five.\n\n**ComfyUI** is a node graph. You wire a checkpoint loader into a sampler into a decoder into a save node. Nothing is implicit. The learning curve is real (most people bounce off it once), and the payoff is that a workflow becomes a file you can save, share, version and re-run in eight months and get the same result.\n\n## The honest recommendation\n\nIf you are learning: start with Forge, because the tutorials match and the feedback loop is short.\n\nIf you are delivering paid work: you will end up in ComfyUI, and starting there costs you a weekend rather than a month. The reason is not power for its own sake. It is that client work means running the same eleven-step process on image after image, and a form-based interface stores that process in your head and your notes, where it decays. A saved graph does not decay.\n\nIf your work is one image at a time, heavily hand-repaired: InvokeAI, and do not apologise for it.\n\n## The trap in shared workflows\n\nComfyUI's great convenience is that a workflow is embedded in the PNG a stranger posted, so you can drag their image onto your canvas and get their exact graph. The trap is **custom nodes**. That graph may reference twelve community node packs, three of which are abandoned and one of which conflicts with something you already have. You spend an hour in the manager resolving red boxes, and then the output still differs because their checkpoint is a merge nobody has re-uploaded.\n\nThe habit that prevents this: when a downloaded workflow works, save your own cleaned-up copy with the minimum nodes, and note the model hashes alongside it. Treat the stranger's graph as a reference, not a dependency.\n\nOne more thing worth knowing before you choose. All four of these are free and open source, and all four update aggressively. An interface that updates weekly will, at some point, change a default you were relying on (a scheduler renamed, a node's output reordered) and yesterday's look will not reproduce. This is a real cost of the free tooling and the answer is in the last lesson of this block: pin your versions and write them down.\n\n## Where the paid tools sit in this\n\nJob adverts name Photoshop, and Photoshop's generative fill is a competent, well-integrated inpainting tool with a commercially indemnified model behind it. If a workplace already pays for it, use it; there is nothing to prove by refusing.\n\nWhat matters is that none of the four interfaces above is a poor relation. They run the same class of model, and on several axes (conditioning types, workflow reuse, model choice, cost per image) the free stack does more than the subscription one. A freelancer with ComfyUI, GIMP and Inkscape on a six-year-old laptop can deliver work a studio on a full Adobe licence cannot, because the studio's tool only offers the models its vendor chose to license.\n\nThe honest split is this. Paid tools are better at *integration*: one application, one file, a predictable undo history, and somebody to complain to. Free tools are better at *reach*: any model, any conditioning, any step order, no per-image cost.\n\n## Do not learn all four\n\nThe most common mistake in the first month is installing every interface to compare them. You end up with four half-understood tools, 180 GB of duplicated checkpoints, and no finished work.\n\nPick one on the rule above, and give it two weeks without looking sideways. The skills that actually transfer (reading denoise strength, judging a mask edge, knowing when a frame is unrecoverable) are not interface skills at all, and every hour spent on the interface question is an hour not spent acquiring them."},"check":{"question":"A studio produces the same eleven-step retouch-and-composite process on roughly forty product images a month, handed between two people. Which interface choice best fits, and why?","options":[{"text":"Fooocus, because its curated defaults produce a consistent house look without either operator having to configure samplers or step counts by hand.","correct":false,"tempting":"Consistency here comes from repeating a specific process, not from defaults; Fooocus deliberately hides the steps the studio needs to control and share."},{"text":"ComfyUI, because the eleven steps become one saved graph both people run identically.","correct":true},{"text":"AUTOMATIC1111 or Forge, because a settings form is faster to operate than a node graph once the eleven steps have been committed to memory by both operators.","correct":false,"tempting":"Fine for one operator on one day; the process lives in memory and notes, which is exactly what fails when work is handed between two people over months."},{"text":"InvokeAI, because its unified canvas is the strongest inpainting and outpainting surface of the four and this is fundamentally retouching work.","correct":false,"tempting":"True about single-image repair, and the wrong axis: the problem stated is repeatability across forty images and two operators, not canvas ergonomics."}]},"prev":{"slug":"what-the-files-are","title":"A model is four kinds of file, and mixing them up wastes an evening","url":"https://addaly.com/learn/ai-images/what-the-files-are"},"next":{"slug":"the-first-hour","title":"The first hour, and the four errors everybody gets","url":"https://addaly.com/learn/ai-images/the-first-hour"},"authorship":"Founder and series editor: Dr Mustafa Mun. Written by AI models to a written standard. Exam keys checked blind by a second model: 3,665 of 3,666 agreed. Academic review invited.","review":{"status":"Draft, open for academic review"},"cite":"Addaly (2026) 'Four front ends, and which one deserves your learning time', in Image Generation, in Practice. Edited by M. Mun. Addaly. Available at: https://addaly.com/learn/ai-images/picking-an-interface (Accessed: 13 October 2026).","licence":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"reader":{"schema":1,"title":"Four front ends, and which one deserves your learning time","courseTitle":"Image Generation, in Practice","module":{"index":1,"slug":"the-kit","title":"The kit, and what each piece of it costs you"},"answer":null,"keyIdea":"The interface you learn decides which problems are easy, and the right choice is the simplest one that can express the workflow you actually need, which for finished client work usually means a node graph eventually.","hero":{"kind":"plate","image":null,"alt":null,"caption":null},"blocks":[{"t":"h2","id":"they-all-call-the-same-model","runs":[{"text":"They all call the same model"}]},{"t":"p","runs":[{"text":"Every local interface runs the same weights through roughly the same code. The differences are entirely about what they make easy and what they make impossible, and choosing wrongly costs weeks."}]},{"t":"p","runs":[{"text":"Fooocus","b":true},{"text":" hides almost everything. You type a prompt, pick a rough style, and it applies a tuned set of defaults that are good. Installation is close to one step. It is the fastest route from nothing to a decent SDXL image on a Windows machine, and it is an excellent first week. It is also a ceiling: when you need a second ControlNet or a custom step order, it cannot express that."}]},{"t":"p","runs":[{"text":"AUTOMATIC1111 and its faster fork, Forge","b":true},{"text":", give you a form. Prompt, negative prompt, sampler, steps, seed, and tabs for img2img, inpainting and extensions. The mental model is a settings panel, which is why most tutorials are written for it. Forge is the one to install today: same interface, noticeably better memory handling, so SDXL fits on 6 GB cards that choke under A1111."}]},{"t":"p","runs":[{"text":"InvokeAI","b":true},{"text":" sits between the two, with a real canvas. If your work is mostly inpainting and outpainting on a single image (retouching, extending, iterating on one picture) its unified canvas is the best tool of the four, because the thing you actually do all day is “mask this bit and regenerate”, and Invoke makes that one gesture rather than five."}]},{"t":"p","runs":[{"text":"ComfyUI","b":true},{"text":" is a node graph. You wire a checkpoint loader into a sampler into a decoder into a save node. Nothing is implicit. The learning curve is real (most people bounce off it once), and the payoff is that a workflow becomes a file you can save, share, version and re-run in eight months and get the same result."}]},{"t":"h2","id":"the-honest-recommendation","runs":[{"text":"The honest recommendation"}]},{"t":"p","runs":[{"text":"If you are learning: start with Forge, because the tutorials match and the feedback loop is short."}]},{"t":"p","runs":[{"text":"If you are delivering paid work: you will end up in ComfyUI, and starting there costs you a weekend rather than a month. The reason is not power for its own sake. It is that client work means running the same eleven-step process on image after image, and a form-based interface stores that process in your head and your notes, where it decays. A saved graph does not decay."}]},{"t":"p","runs":[{"text":"If your work is one image at a time, heavily hand-repaired: InvokeAI, and do not apologise for it."}]},{"t":"h2","id":"the-trap-in-shared-workflows","runs":[{"text":"The trap in shared workflows"}]},{"t":"p","runs":[{"text":"ComfyUI’s great convenience is that a workflow is embedded in the PNG a stranger posted, so you can drag their image onto your canvas and get their exact graph. The trap is "},{"text":"custom nodes","b":true},{"text":". That graph may reference twelve community node packs, three of which are abandoned and one of which conflicts with something you already have. You spend an hour in the manager resolving red boxes, and then the output still differs because their checkpoint is a merge nobody has re-uploaded."}]},{"t":"p","runs":[{"text":"The habit that prevents this: when a downloaded workflow works, save your own cleaned-up copy with the minimum nodes, and note the model hashes alongside it. Treat the stranger’s graph as a reference, not a dependency."}]},{"t":"p","runs":[{"text":"One more thing worth knowing before you choose. All four of these are free and open source, and all four update aggressively. An interface that updates weekly will, at some point, change a default you were relying on (a scheduler renamed, a node’s output reordered) and yesterday’s look will not reproduce. This is a real cost of the free tooling and the answer is in the last lesson of this block: pin your versions and write them down."}]},{"t":"h2","id":"where-the-paid-tools-sit-in-this","runs":[{"text":"Where the paid tools sit in this"}]},{"t":"p","runs":[{"text":"Job adverts name Photoshop, and Photoshop’s generative fill is a competent, well-integrated inpainting tool with a commercially indemnified model behind it. If a workplace already pays for it, use it; there is nothing to prove by refusing."}]},{"t":"p","runs":[{"text":"What matters is that none of the four interfaces above is a poor relation. They run the same class of model, and on several axes (conditioning types, workflow reuse, model choice, cost per image) the free stack does more than the subscription one. A freelancer with ComfyUI, GIMP and Inkscape on a six-year-old laptop can deliver work a studio on a full Adobe licence cannot, because the studio’s tool only offers the models its vendor chose to license."}]},{"t":"p","runs":[{"text":"The honest split is this. Paid tools are better at "},{"text":"integration","i":true},{"text":": one application, one file, a predictable undo history, and somebody to complain to. Free tools are better at "},{"text":"reach","i":true},{"text":": any model, any conditioning, any step order, no per-image cost."}]},{"t":"h2","id":"do-not-learn-all-four","runs":[{"text":"Do not learn all four"}]},{"t":"p","runs":[{"text":"The most common mistake in the first month is installing every interface to compare them. You end up with four half-understood tools, 180 GB of duplicated checkpoints, and no finished work."}]},{"t":"p","runs":[{"text":"Pick one on the rule above, and give it two weeks without looking sideways. The skills that actually transfer (reading denoise strength, judging a mask edge, knowing when a frame is unrecoverable) are not interface skills at all, and every hour spent on the interface question is an hour not spent acquiring them."}]}],"check":{"question":[{"text":"A studio produces the same eleven-step retouch-and-composite process on roughly forty product images a month, handed between two people. Which interface choice best fits, and why?"}],"options":[{"text":[{"text":"Fooocus, because its curated defaults produce a consistent house look without either operator having to configure samplers or step counts by hand."}],"correct":false,"tempting":[{"text":"Consistency here comes from repeating a specific process, not from defaults; Fooocus deliberately hides the steps the studio needs to control and share."}]},{"text":[{"text":"ComfyUI, because the eleven steps become one saved graph both people run identically."}],"correct":true,"tempting":null},{"text":[{"text":"AUTOMATIC1111 or Forge, because a settings form is faster to operate than a node graph once the eleven steps have been committed to memory by both operators."}],"correct":false,"tempting":[{"text":"Fine for one operator on one day; the process lives in memory and notes, which is exactly what fails when work is handed between two people over months."}]},{"text":[{"text":"InvokeAI, because its unified canvas is the strongest inpainting and outpainting surface of the four and this is fundamentally retouching work."}],"correct":false,"tempting":[{"text":"True about single-image repair, and the wrong axis: the problem stated is repeatability across forty images and two operators, not canvas ergonomics."}]}],"order":[3,0,2,1],"why":null},"prev":{"slug":"what-the-files-are","title":"A model is four kinds of file, and mixing them up wastes an evening","minutes":8},"next":{"slug":"the-first-hour","title":"The first hour, and the four errors everybody gets","minutes":8}}}