Daniel sent us this one, and I have to say, it's the kind of question that sits right at the intersection of what we love — the actual technical guts behind the "magic" buttons everyone's clicking. He's been working with generative AI for diagrams, using NanoBanana for those crisp text renderings, and he keeps hitting the same wall: the image is ninety-eight percent perfect, but there's one typo, and tossing the whole generation feels like burning down the house because a door hinge squeaks. His question is essentially, what's actually happening under the hood when tools like Canva or Google Photos let you click on something, delete it, move it, or change a facial expression — and if you wanted to replicate that in something like ComfyUI, what are the actual technical ingredients you'd be hunting for?
Oh, this is exactly the kind of question that makes me wish I had a whiteboard, because the answer sits at this fascinating collision point between computer vision, diffusion models, and some genuinely clever engineering that most people never think about. And before we dive in — quick note, today's script is coming to us courtesy of DeepSeek V four Pro.
Hope it handles our tangents better than I do.
Let's start with the thing Daniel nailed in his prompt, which I think is the conceptual key to the whole thing. He said the UI presents a canvas, you click on it, and you specify a precise reference within that image. That sounds trivial — oh, you clicked on a thing — but that click is doing an enormous amount of work. What's actually happening is that the tool is running a segmentation model in real time. When your cursor lands on what looks to you like a person's face or a block of text, the model is generating a pixel-level mask that says "this is the object, these are its boundaries, this is what the user means by 'this thing.
The click isn't just an X-Y coordinate. It's more like a query that returns a territory.
And the quality of that segmentation is what separates the tools that feel magical from the ones that feel like a frustrating art project. Google's Magic Editor uses a combination of their own segmentation models — some of this traces back to work they published on interactive segmentation, where the model predicts object boundaries based on a single click or tap. Canva's magic tools sit on top of similar infrastructure. The actual architecture varies, but the principle is the same: a lightweight vision transformer or a convolutional network trained on massive datasets of labeled objects, so when you click on a coffee cup in a photo, it knows where the edges of the cup end and the table begins.
This is one of those things where "massive datasets" is doing a lot of heavy lifting, because the difference between a good mask and a bad one is whether the model has seen enough examples of coffee cups at weird angles, partially occluded by someone's hand, or in weird lighting.
And that's actually the first ingredient if you're trying to replicate this in ComfyUI. You need a segmentation node. Segment Anything, or S. , from Meta, has been the go-to for a while. The original paper dropped in April twenty twenty-three, and it was a step change because it could segment objects it had never seen before, using what they called a promptable design. You give it a point, a box, or a rough mask, and it produces a detailed segmentation mask. two paper followed, and now there are ComfyUI custom nodes that wrap S.
Ingredient one is a segmentation model that can turn "I clicked here" into a precise mask. What's ingredient two?
Let's take Daniel's first use case, because I think it's the simpler one conceptually. He talks about clicking on pseudotext and hitting delete. What he's describing is inpainting — but not the crude version where you just fill a selected area with surrounding colors. Modern AI inpainting uses a diffusion model that's conditioned on the entire image except the masked region, and it generates new pixels for the masked area that are contextually coherent. The model is essentially asking: "Given everything I see around this hole, what should go in the hole to make this look like a natural, unedited image?
The "everything I see around this hole" part is crucial, because early inpainting tools were basically smart clone stamps. They'd sample nearby textures and blend them. But if you're removing a person from a beach photo, the model needs to understand that behind the person there should be sand and ocean, not just extrapolated from the five pixels of sand touching the person's elbow.
That's exactly the distinction. The diffusion-based inpainting models — and this is where Stable Diffusion's inpainting pipeline, or dedicated models like LaMa, which stands for Large Mask Inpainting, come in — they use the unmasked portion of the image as conditioning. During the denoising process, they only generate new content for the masked region, but they "see" the full image context. LaMa was particularly interesting because it used fast Fourier convolutions to maintain global context even when the mask was huge — like removing a person who takes up thirty percent of the frame. The model could reconstruct repeating patterns or architectural structures because it understood the image at multiple scales simultaneously.
Let me make sure I'm tracking the ComfyUI shopping list here. You'd need a S. node for generating the mask from a click, and then an inpainting node that takes the original image, the mask, and a prompt — probably something like "remove object, fill with natural background" — and runs the diffusion process only on the masked area.
That's the basic stack, yeah. But there's a subtlety that makes the difference between "that kind of worked" and "that looks seamless." The inpainting model needs to know about the transition boundary. You can't just cut a hard-edged mask and fill it, because you'll get visible seams where the generated content meets the original. What the good tools do is feather the mask edges, or use "mask conditioning" where the model is specifically trained to blend at the boundaries. In ComfyUI, you'd typically add a mask blur node or use the "grow mask" and "feather mask" operations to create a soft transition zone. It's one of those things that sounds like a minor detail but is actually the difference between a tool people pay for and a tool people abandon after one try.
It's funny how often the "magic" in these tools is really just a stack of five or six well-tuned parameters that nobody tells you about because they seem too mundane to mention.
That stack is exactly what I want to get into, because Daniel's second use case — the "move it" or "drag to change posture" feature — that's where the stack gets sophisticated. When you grab someone's head in Google Photos and tilt it up, there's a lot more happening than inpainting. You're asking the model to understand the three-dimensional structure of a human head, rotate it in space, and then generate the new appearance from a different angle — all while preserving the person's identity and the lighting conditions of the original photo.
That sounds almost like a mini three-D render happening under the hood, but with generative filling in the gaps.
It effectively is. What Google described — and they've been somewhat cagey about the full architecture, but they've published enough to piece together the approach — is that their Magic Editor uses a combination of depth estimation and what they call "view synthesis." When you select a person or an object, the system first estimates a depth map of the selected region. That gives it a rough three-D understanding of the surface geometry. Then, when you drag to rotate or reposition, it's essentially re-rendering that geometry from a new viewpoint. But the raw re-rendering would have holes and artifacts — occluded regions that weren't visible in the original image — and that's where the generative model fills in the gaps.
It's like: estimate depth, rotate the geometry, identify the new pixels that need to be invented, and then inpaint those pixels using the surrounding context as conditioning.
That's the core loop. And there's a specific paper from Google Research in twenty twenty-three called "Drag Your GAN" — and then a follow-up called "DragDiffusion" — that really kicked off this whole drag-based editing paradigm. The idea is that you place a pair of points: a source point and a target point. The model then iteratively moves the content at the source point toward the target point, updating the image at each step, while trying to keep everything else consistent. Under the hood, it's optimizing in the latent space of a G. or a diffusion model — nudging the latent representation so that certain features shift position while the rest of the image stays anchored.
When you say "optimizing in the latent space," you mean it's not just generating a new image from scratch. It's doing a kind of guided search through the space of possible images that are similar to the original but with the specified change.
And this is computationally more expensive than a single forward pass through a diffusion model. When you drag a point in one of these tools, the model might be running dozens or hundreds of small optimization steps behind the scenes. That's why these features tend to be server-side rather than on-device — though Google has been pushing more of this onto device with their Tensor chips, specifically because they've optimized these loops to run efficiently on mobile hardware.
For Daniel's ComfyUI replication project, we're now adding a depth estimation node and potentially a DragDiffusion-style node to the shopping list. Is DragDiffusion available as a ComfyUI custom node?
It is, actually. There's a community node called ComfyUI-DragDiffusion that wraps the functionality. But I should mention — and this is where the practical advice comes in — it's not as polished as what you get in Google Photos. The open-source implementations tend to be slower, and the quality can be hit or miss depending on the image. You'll get better results on faces and human figures, which makes sense because the underlying models have seen millions of those, and worse results on unusual objects or complex scenes with a lot of occlusions.
Which brings up a broader point. A lot of the "magic" in Canva and Google Photos isn't just the models — it's the engineering around picking the right model for the right task, and falling back gracefully when things don't work. If I click "remove" on a person in Canva and it looks terrible, Canva probably has some heuristic that says "try the inpainting model with different parameters" or "try a content-aware fill as a fallback." When you're building your own pipeline in ComfyUI, you're the one who has to build that logic.
And it's something I think a lot of people miss when they try to move from commercial tools to open-source workflows. The model is only one piece. The product engineering — the error handling, the parameter tuning, the preprocessing, the postprocessing — that's where most of the perceived quality lives. Let me give you a concrete example. When Canva's Magic Eraser removes an object, it doesn't just run inpainting and call it done. It often runs a super-resolution pass on the inpainted region to make sure the detail level matches the surrounding area. It might run a color-matching pass to correct for any slight tonal shifts. It might even run multiple inpainting variations and pick the best one using a perceptual quality metric. None of that is the model — it's all the engineering wrapper.
If you're Daniel, and you're trying to fix a single typo in an otherwise perfect NanoBanana-generated diagram, you're looking at a pipeline that's: one, use a text detection model to precisely locate the typo region — and this is important, because clicking on text is different from clicking on an object; two, generate a tight mask around just the incorrect characters; three, run inpainting with a prompt that specifies the correct text; and four, run some kind of quality check to make sure the inpainted text is actually legible and correct.
That text detection step is worth dwelling on, because it connects back to something Daniel mentioned about NanoBanana's character-level pipeline. The reason text generation has historically been so bad in diffusion models is that diffusion models don't understand characters — they understand visual patterns. They can produce things that look text-like from a distance, but up close it's gibberish. What NanoBanana did differently, and what tools like Ideogram have been doing, is they added explicit text rendering modules that operate at the character level. They're not just hoping the diffusion process happens to produce legible text — they're constraining it.
When you're doing inpainting on text, you have the same problem in reverse. The inpainting model doesn't know it's supposed to be generating text — it just sees "fill this hole with something that looks like the surrounding area." If you want it to produce correct text in the inpainted region, you need to give it a prompt that specifies the exact characters, and even then, you're at the mercy of whether the model can actually render those characters correctly.
This is where I've seen a really clever approach in the ComfyUI community. Instead of trying to inpaint the text directly, some workflows do what's called "text replacement via masking and re-rendering." You detect the incorrect text region, mask it out, and then use a dedicated text-to-image model that's specifically fine-tuned for text rendering — like one of the Flux variants or an Ideogram model — to generate just the text patch. Then you composite that patch back into the original image with careful blending at the edges. It's effectively treating the text region as a separate generation task with a specialized model, rather than asking a general-purpose inpainting model to handle text.
That's a nice example of the principle Daniel was getting at — understanding the technical breakdown so you can reproduce it yourself. You're not using one magic model. You're orchestrating three or four specialized tools, each doing what they're best at.
Orchestration is really the word. If you look at a tool like ComfyUI, the whole paradigm is a visual programming language for this exact kind of orchestration. You have nodes for loading models, nodes for segmentation, nodes for masking, nodes for inpainting, nodes for upscaling, nodes for compositing. You're building a pipeline where each node is a specialized function, and the art is in how you connect them and tune the parameters.
Let me pull on a thread Daniel raised that I think gets at something deeper. He talked about the frustration of throwing away a whole generation because of one typo, and how that's a "shot in the dark" with probabilistic technology. That probabilistic nature is the fundamental tension here, isn't it? These models are designed to produce varied, creative outputs. When you need deterministic, surgical precision on one specific element, you're fighting the model's nature.
That's the core tension, and it's why the "click and edit" paradigm is so powerful. When you do a full image-to-image pass — taking the whole image and saying "fix the typo" — the model is free to change anything. It might fix the typo but also subtly alter the colors, shift the layout, change a background element. The more you constrain the model to only modify the masked region, the more deterministic the edit becomes. But here's the tradeoff: the tighter the mask, the less context the model has to work with, and the harder it is to produce a seamless result.
There's a Goldilocks zone for mask size. Too big, and you risk collateral changes. Too small, and the inpainting looks pasted on.
And finding that Goldilocks zone is one of those things that commercial tools have spent enormous engineering effort on. They're dynamically adjusting the mask based on the content. If you're removing a person from a complex background, the mask might automatically expand to include some of the surrounding area to give the inpainting model enough context. If you're fixing a small blemish on a face, the mask stays tight because the surrounding skin texture is highly consistent and easy to extrapolate.
Let's talk about the "move and pose" use case a bit more, because I think it's the most technically impressive of the bunch. Daniel mentioned rotating someone's head or changing their posture. What's actually happening at the pixel level when you drag someone's skull upward?
There are really two approaches being used in production today, and they're often combined. The first is the depth-based view synthesis I mentioned. The second is what's called "keypoint-based deformation." For human faces and bodies, there are well-established keypoint detectors — facial landmarks, body pose estimators like OpenPose or MediaPipe — that can identify specific points: the corner of the eye, the tip of the nose, the shoulder joint. When you drag on a person's head, the system is actually detecting these keypoints, calculating how they would move if the head were rotated in three-D space, and then warping the image to match the new keypoint positions.
It's like a digital puppet, where the strings are these keypoints, and the AI is filling in the gaps where the warp creates holes or distortions.
That's a good way to think about it. The warp handles the gross movement — the head shifts to the new position — but it can't handle things like "what does the side of the face look like now that it's rotated into view?" or "what's behind the ear that was previously hidden?" Those occluded regions are where the generative inpainting kicks in. And this combination of warping plus inpainting is what makes the results look natural rather than like a bad Photoshop liquify job.
This is where the identity preservation becomes critical. If you're changing someone's head pose in a family photo, you need the person to still look like themselves. How do they handle that?
This is an active research area, and the best approaches use what's called "identity conditioning." There are models specifically trained to preserve facial identity across transformations — things like ArcFace embeddings or dedicated identity-preserving adapters for diffusion models. The idea is that you extract a compact vector representation of the person's face from the original image, and you feed that as an additional conditioning signal to the inpainting or generation model. The model is told: "Generate new pixels for this region, and make sure they match this identity vector." It's the same technology that powers those "AI headshot" tools where you upload a few photos of yourself and get professional-looking portraits in different poses and outfits.
The shopping list for a full "move and pose" pipeline is getting substantial. You need keypoint detection, depth estimation, a warping node, an identity-preserving inpainting model, and a blending node to composite everything back together. This is not a weekend project.
It's not, but the ComfyUI ecosystem has actually made it surprisingly accessible. There are workflows floating around that combine MediaPipe for keypoint detection, Depth Anything for depth estimation, and then either Stable Diffusion or Flux with ControlNet for the guided inpainting. ControlNet is doing a lot of the heavy lifting here — it lets you condition the generation on things like depth maps, pose skeletons, or edge maps, so the model knows "generate new pixels that are consistent with this three-D structure and this pose.
ControlNet was a genuine breakthrough when it dropped. I remember when that paper came out in early twenty twenty-three from Stanford — it suddenly made diffusion models controllable in a way they hadn't been before. You could feed it a canny edge map or a depth map, and it would respect that structure while still being creative with the details.
The reason it's so relevant to Daniel's question is that ControlNet is essentially the bridge between the deterministic world of UI clicks and the probabilistic world of generative AI. When you click on something and say "move this," the UI is generating a set of structural constraints — a depth map, a pose skeleton, an edge map — that represent the desired output. ControlNet takes those constraints and says "I'll generate an image that satisfies these constraints while still looking natural." It's the closest thing we have to a deterministic interface on top of a probabilistic model.
Let me poke at something. Daniel mentioned that these tools often bake "minute instructions" into little buttons — "fix this person's smile," "make the sky more dramatic," things like that. Those buttons are essentially pre-written prompts that the user doesn't see. But there's an interesting design question here: when does it make more sense to expose the prompt, and when does it make more sense to hide it behind a button?
And I think the answer depends on the user's mental model. If you're a designer who understands prompts and wants fine-grained control, you want the prompt exposed. That's the ComfyUI and Automatic1111 approach — here's the prompt field, craft it carefully. But for the average user who just wants to remove a photobomber from their beach photo, the prompt is an implementation detail. They don't want to think about tokens and conditioning scales. They want to click "remove" and have it work.
The risk of exposing the prompt is that users will write bad prompts and blame the tool. The risk of hiding it is that power users feel constrained. It's the classic tension between simplicity and control.
What's interesting is that some tools are now landing in the middle. Canva's Magic Studio, for example, has both the one-click buttons and a prompt field for more advanced edits. Google Photos mostly hides the prompt but lets you add text descriptions for certain types of edits. It's a spectrum, and the tools are gradually sliding toward more user control as people get more comfortable with the technology.
Let's bring this back to Daniel's specific use case, because I think there's a practical recommendation buried in all of this. He's generating tech diagrams with NanoBanana, he's got one typo, and he wants to fix it without regenerating the whole thing. What's the most reliable ComfyUI pipeline for that today?
I'd recommend a pipeline with four stages. Stage one: text detection. Use a dedicated optical character recognition, or O. , model to precisely locate the text regions. PaddleOCR is a good open-source option that's fast and accurate, and there are ComfyUI nodes that wrap it. Stage two: mask generation. Convert the O. bounding boxes into precise masks for the incorrect text. Stage three: inpainting with a text-capable model. This is the tricky part. You want to use a model that's specifically good at rendering text — Flux with a text-focused fine-tune, or one of the Ideogram-inspired workflows. Your prompt should specify the exact correct text, and you should use a fairly high guidance scale to keep the model from going off-script. Stage four: quality verification. This is the part most people skip. Run a second O. pass on the inpainted region to verify that the correct text is now present. If it's not, adjust the mask or the prompt and try again.
That verification step is clever. It's automating the "squint at the screen and hope it looks right" part.
It's exactly the kind of engineering wrapper I was talking about earlier. The commercial tools do this automatically — they generate multiple inpainted variations, run O. on each one, and pick the one where the text is most legible and correct. There's no reason you can't build the same logic into a ComfyUI workflow, it just takes more nodes.
You mentioned Flux a couple of times. For listeners who haven't been tracking the model landscape — and it moves fast — Flux is the image generation model from Black Forest Labs, founded by the team that originally created Stable Diffusion. It launched in mid twenty twenty-four, and it's been notable for having much better text rendering than previous diffusion models. Not quite at the NanoBanana or Ideogram level of character-level determinism, but substantially better than Stable Diffusion three or DALL-E three.
That improvement comes from architectural changes in how the model processes text conditioning. Without getting too deep into the weeds, Flux uses a different text encoder architecture that preserves more fine-grained information about character sequences. Previous models would encode "hello" into a semantic vector that captured the meaning but lost the spelling. Flux retains more of the character-level information, which means it can actually reproduce specific letter sequences instead of just producing something that looks vaguely like text.
Which is exactly what you need when you're doing inpainting on a typo. You're not asking the model to understand what the text means — you're asking it to reproduce specific characters in a specific font and color and position.
And this is where I want to circle back to something Daniel said that I think is the real insight behind his question. He said "we're always on a quest to take the magic of AI and break it down technically, to understand it and reproduce it ourselves without relying on expensive cloud subscriptions." That's not just about saving money. It's about control. When you understand what's happening under the hood, you can fix things when they break. You can customize the pipeline for your specific use case. You can combine tools in ways the commercial products never anticipated.
There's a philosophical point here too. The more you break down the "magic" into its component parts, the more you realize that what looks like intelligence is really a stack of well-engineered solutions to specific sub-problems. Segmentation, depth estimation, keypoint detection, inpainting, super-resolution, blending — none of these is magic on its own. The magic is in the integration.
The integration is getting easier. Two years ago, building a pipeline like the one we just described would have required writing a lot of custom code. Today, you can do most of it in ComfyUI with community nodes. The barrier to entry is dropping fast, and I think we're heading toward a world where the distinction between "user" and "developer" for these tools gets increasingly blurry.
Let me throw out a slightly contrarian thought. As these pipelines get more accessible, there's a risk that people treat them as black boxes even when they're built from open components. You can drag and drop nodes in ComfyUI without understanding what each node actually does, and when something goes wrong — when the inpainting looks weird or the pose change distorts the face — you don't know which node to debug. The "magic" problem doesn't go away just because the components are open source.
That's fair. But I think the difference is that with an open-source pipeline, the possibility of understanding is there. With a closed commercial tool, you literally cannot look inside. With ComfyUI, you can inspect the intermediate outputs at every stage — the segmentation mask, the depth map, the keypoints, the raw inpainting before blending. You can learn what each component does by watching it work. That's a fundamentally different relationship with the technology, even if most users never go that deep.
It's the difference between driving a car and being able to pop the hood. Most people won't pop the hood, but the fact that you can changes something about how the tool feels to use.
And for someone like Daniel, who's an active open-source developer, popping the hood is the whole point. He's not just trying to fix a typo in a diagram. He's trying to understand the stack so he can build on it.
Let's talk about one more technical angle before we wrap up, because Daniel mentioned something in his prompt that I think deserves attention. He talked about the "fill in" problem — the difference between Microsoft Paint's block-color fill and intelligent inpainting. There's actually an interesting middle ground here that a lot of people don't know about, which is the evolution of inpainting before diffusion models.
Oh, this is a great tangent. Before diffusion-based inpainting took over, the state of the art was things like PatchMatch, originally a paper from twenty oh-nine by Connelly Barnes and others. The idea was brilliantly simple: for each pixel on the boundary of the hole, find the most similar patch elsewhere in the image, and copy it. Then move inward, layer by layer, always matching against known pixels. It worked shockingly well for textures and repetitive patterns, but it failed completely on anything that required semantic understanding — it couldn't invent a new face or remove a person and generate the background behind them, because those pixels didn't exist anywhere else in the image.
It was a very sophisticated copy-paste, but it couldn't create anything new.
And what diffusion inpainting does is fundamentally different. It's not copying from elsewhere in the image — it's generating new content from its learned distribution of what images look like. When you remove a person from a beach photo, the model isn't finding sand and ocean elsewhere in the photo and copying it. It's generating sand and ocean that are consistent with the specific lighting, perspective, and texture of your photo, based on having seen millions of beach photos during training. It's synthesis, not retrieval.
That's why the mask context matters so much. The model needs to see the surrounding sand and ocean to generate new sand and ocean that match. If you gave it a mask of the person with no surrounding context, it would generate generic sand and ocean that probably wouldn't blend.
And this is where another technique comes in that's worth mentioning: "contextual attention." Some inpainting models use attention mechanisms that explicitly look at the unmasked regions when generating each pixel in the masked region. The model is literally saying "for this pixel I'm about to generate, which unmasked pixels are most relevant?" and then using those as conditioning. It's a more sophisticated version of the PatchMatch intuition — find the relevant context and use it — but done in a learned, semantic way rather than a brute-force similarity search.
If you're building a ComfyUI pipeline and your inpainting results look like they don't match the surrounding area, one thing to try is a model that uses contextual attention, or to adjust the mask padding to give the model more surrounding context to work with.
That's a good general debugging principle for these pipelines. When the output looks wrong, think about what information the model had access to. Did it have enough context? Was the mask too tight or too loose? Was the prompt specific enough? Was the guidance scale appropriate? Most inpainting failures are not model failures — they're failures of the conditioning setup.
Which brings us back to the engineering wrapper point. The commercial tools have product teams whose entire job is to tune these conditioning parameters for common use cases. When you're building your own pipeline, you're the product team.
You're the product team, the engineer, and the quality assurance tester all in one. It's more work, but the upside is that you can handle edge cases the commercial tools never anticipated. If Canva's Magic Eraser doesn't know what to do with a complex technical diagram full of arrows and labels, you can tune your pipeline specifically for that use case. You can swap in a text-specialized model. You can add verification steps. You can iterate until it works reliably.
That's really the answer to Daniel's underlying question. The ingredients for replicating these "magic" features in ComfyUI are: segmentation, masking, depth estimation, keypoint detection, inpainting with a capable diffusion model, control mechanisms like ControlNet, and a whole lot of parameter tuning and quality verification. The individual pieces are all available. The art is in assembling them.
The assembly is getting easier every month. The ComfyUI community is incredibly active — new custom nodes drop constantly, workflows are shared and refined, and the whole ecosystem is moving toward making these pipelines more accessible. What required a research team two years ago is now something a motivated hobbyist can build on a weekend.
Not bad for a technology that, as Daniel pointed out, was perplexingly bad at rendering text just a couple of years ago.
The pace is hard to keep up with. And I think that's part of why Daniel's question resonates — there's a hunger to understand not just what the tools do, but how they do it, because understanding the "how" is what lets you stay ahead of the curve instead of just reacting to each new product launch.
Alright, I think we've given Daniel a pretty solid technical map. Before we close out — and now: Hilbert's daily fun fact.
Hilbert: The national animal of Scotland is the unicorn. It has been since the twelve hundreds, when it first appeared on the Scottish royal coat of arms. Scotland is one of the only countries in the world whose national animal is a mythical creature.
That explains a lot about Scotland, actually.
...right. Thanks, Hilbert.
One forward-looking thought before we go. We're clearly heading toward a world where these precise editing capabilities become embedded in every image tool, from professional design software down to the default photo app on your phone. The question isn't whether the technology will get better — it will. The question is whether the open-source ecosystem can keep pace with the commercial polish, or whether the convenience of "just click the magic button" will win out over the control of building your own pipeline. I suspect the answer is both — different tools for different needs — but it's worth watching.
If you're the kind of person who wants to build rather than just click, the ComfyUI ecosystem is where a lot of the action is right now. The ingredients are all there. It just takes some assembly.
This has been My Weird Prompts. Thanks to our producer Hilbert Flumingtop. You can find us at myweirdprompts.com and on Spotify. We'll catch you next time.
