Episode 20
Architectural AI: Precision with ControlNet & ComfyUI
From hobbyist AI to high-stakes professional applications: architectural AI with ControlNet & ComfyUI.
22:49 Transcript
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- ComfyUI Node-based interface for AI image generation
- ControlNet Conditioning control for diffusion image models
- DALL-E AI image generation tool by OpenAI
- Flux Image generation model by Black Forest Labs
- LoRa Low-rank adapter fine-tuning technique
- Nano Banana Google's Gemini image generation model
- OpenPose Open-source body landmark tracking library
- Replicate Platform for running AI models
- RunPod GPU cloud rental service
- Vast.ai Cloud GPU marketplace
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New to the show? Start here#20: Architectural AI: Precision with ControlNet & ComfyUI
Welcome to another episode of My Weird Prompts, an AI generated podcast featuring the random prompts of Daniel Rosehill as answered by a revolving cast of friendly AI characters. Join us for deep dives into AI, technology, and whatever other random thing Daniel finds fascinating today. Subscribe on Spotify or your favorite podcatcher. Now, today's episode.
Hello friendly podcast hosts. Here's my question for today's episode of the show. Uh so my wife is an architect and recently has been exploring generative AI, which is the resident AI enthusiast of the house up to now has been a lot of fun. And I've been kind of diving in. Um I'm interested in it myself. Been using sort of Dali and then text to image to video um lately for my own kind of project. But mostly for me it's kind of a it's a hobby. It's sometimes actually it's it's work related. I do use it for generating graphics for blogs and writing and even uh social and collateral but that's kind of fairly straightforward to do in the sense that I feel like with Replicate, um I have a few models that I really like and they're reliable and I know what I'm doing. So it's that's kind of straightforward. For my wife's stuff, it's more advanced. She's this is her business. So she does renders um and gets out sketches or sketch like models, at least that's how they look to me.
And then looking at kind of using, everyone in the field at the moment is using AI tools to create renders and she actually uses it for ideation as well. Um but the thing about her workflow versus mine is that when she's doing stuff like in painting, if she's, she might have a render of a client site and the client uh wants to visualize what would this couch look like. So it's an in painting image to image transformation saying change the furniture, get rid of that, put in this. And what's really important in this kind of use case is a very high accuracy and fidelity. So the kind of things that might maybe difference to me, like, oh, I changed the wallpaper a bit, that's AI, you know, that's just how it is. To her, like, no, that's not something I can present to a client. So it requires very strong adherence, whether it's in painting or render generation. Um so I introduced her to Replicate, which I think is a great platform because it allows you to try out a whole bunch of models, um and see what works and then iterate on that.
That's kind of my mechanism for doing it. Um but we got we discovered or began using Comfy UI about the same time. And I'm finding it really useful because even for I think to the extent that I've got really uh impressive stuff with generative AI that I'm proud of and which I regard as true forms of creative composition. Um when my wife has asked me how did you do that, I couldn't I couldn't get that. I said it was really all about breaking down the task. Uh like I started with background removal. If there was if I was using a reference image, I didn't just use the image straight up. I cleaned it up. I might have upscaled it so that it's a good reference image. And I can't point to any one thing that worked so much as all of this breaking it down and actually then finally maybe put it through flux context uh and got something great, but I can't skip any of those steps or it won't work. And Comfy UI initially I was kind of like, uh gosh, I've used N N a bunch and uh I don't know what this category of software is called, node based workflow editors.
They're wonderful, they're so powerful, but I don't think anyone really enjoys thinking of creative processes as nodes and concatenation logic and even for that matter Python scripting. So I think to an extent they're powerful and somewhat unnatural and not really that enjoyable to use. Nevertheless, uh when I began breaking up my workflows, I really saw the value and I think where I do see a lot of value is spending only a bit of time on this in uh in Comfy UI defining your pipeline and then running it iteratively and then you don't have to then you can just kind of, you know, fine tune it. And I suspect that's what I found with N N. There's a very steep learning curve, but once you know the ropes, that's when you start having fun with it because you're not kind of you know what you're doing and you can just and it's that iteration process that really leads to success in my view. Uh my experience. So really, what I'm trying to finally get to my question.
Sorry, I'm a bit of a rambler or I use these as kind of thoughts and uh thoughts and questions. My question in this uh podcast is um ControlNets. When we're talking about sort of precision editing, we start hearing a lot about ControlNets and of course LoRAs as well, which I use, but I'm more I I'm not familiar with ControlNets. And what uh I don't really understand with Comfy UI is we have API nodes, right? So we can run a part of our workflow in the cloud. And we can do we can mix and match, which is amazing. I can do background removal locally and then do that in the cloud. But how how can those things be be used together? Like Nano Banana, if I have a node for that, um I don't really understand how I can kind of um um how I can use a control net with that. Like are you able to use a control net take an image on your source and combine it locally um a control net on your local computer with the actual diffusion model in the cloud.
Does it not work like that? I know this is a very rookie question, uh but I don't quite understand how in these workflows, um there's a there's, you know, a limit to what one can do locally and I think that from a cost standing standpoint, uh I think my wife agrees with me that you know, for when you're looking at four cents per render, that's not the kind of money that we're going to be avoiding the cloud to save that money. It's more like it's more efficient to do this part of the workflow here. Um so let's do it locally and then send it up to the cloud. But if you want to get to this, get into this kind of more advanced stuff where you're beginning to work with ControlNets and your own LoRAs in your workflow, um does that orchestration happen from your local Comfy UI instance and you're running all this in the cloud, in which case it kind of makes me wonder why wouldn't you just run your Comfy UI um itself, the instance in the cloud.
Like what's the point of having the instance run locally if all it's doing is just connecting dots um in the cloud. So those are my points of confusion, I guess, both for my workflow and for hers. I look forward to your thoughts.
Welcome back to AI conversations. Today we're diving into a fascinating area where creativity meets extreme precision, and it's all thanks to a great question from one of our listeners. He mentioned his wife is an architect, deeply exploring generative AI for her professional work, and he's seen firsthand the difference between hobbyist use and high stakes, client facing applications.
And that distinction is absolutely critical. What might be acceptable for a fun personal project, a slight imperfection, a bizarre blending, simply won't cut it when you're presenting a multi-million dollar architectural design. The stakes are incredibly high and the tools need to deliver.
Exactly. So our listener wants to understand how advanced AI tools, specifically ControlNets and LoRAs, fit into these complex professional workflows, especially when you start thinking about combining local processing with cloud-based inference. It's a deep dive into accuracy, efficiency, and how these powerful components truly orchestrate together.
It's a fantastic question because it really touches upon the evolving landscape of AI powered design and the technical considerations that move it from novelty to professional utility. We'll unpack how these tools enable the kind of granular control and fidelity that architects, designers, and other professionals demand. It's not just about generating an image, it's about generating the rightness, the rightness, the rightness, the rightness, the right image with precise specifications.
Right. Because for architects, it's not just about a pretty picture. It's about envisioning a space, a structure, a functional design that has to be both aesthetically pleasing and structurally sound, all while meeting a client's exact vision. Our listener mentioned his wife needs to visualize things like what would this specific couch look like in this exact space? That requires an entirely different level of control than say, generate a cool sci-fi cityscape.
Absolutely. That's where the power of tools like ControlNet and LoRAs, especially within a flexible framework like Comfy UI, truly shines. Standard text to image models, while impressive, often struggle with maintaining spatial coherence, specific object placement, or consistent style across iterations. They're designed for broad interpretation, not precise execution.
Okay, so let's start with ControlNet, since our listener mentioned he's familiar with LoRAs, but not as much with ControlNet. Herman, what is ControlNet and why is it so crucial for precision editing?
Think of ControlNet as giving the generative AI a set of precise instructions or a blueprint to follow. Normally, when you give a text prompt to a diffusion model, it generates an image based solely on that text, often with a lot of creative freedom. ControlNet allows you to feed additional input into the model, an image specifically processed in a certain way to guide the generation process structurally.
So, instead of just saying a modern living room, I could say a modern living room and give it a sketch of the room's layout or even a depth map from a 3D model.
Precisely. That sketch or depth map becomes the ControlNet input. There are many types of ControlNets, each specialized for different kinds of structural guidance. For example, a Canny ControlNet can take an edge detection map of an image and force the generated output to follow those exact edges. A depth ControlNet uses depth information to maintain the 3D structure. OpenPose can control character poses.
That's fascinating. So, for an architect, if they have an existing client site render, they could extract a Canny edge map or a depth map from it, then use that with ControlNet to generate new design elements, like placing that specific couch, ensuring it respects the existing room geometry.
Exactly. Without ControlNet, if you tried to add a couch via inpainting, the AI might distort the surrounding walls or furniture. With ControlNet guiding the structural elements, it understands the spatial constraints and can intelligently integrate the new object. It's like giving the AI a very detailed coloring book rather than just telling it to draw something.
Okay, that makes a lot of sense. And where do LoRAs fit into this picture? Our listener mentioned using them.
LoRAs, or Low-Rank Adaptation of Large Language Models, are essentially small, specialized sets of weights that can be loaded on top of a base diffusion model. They fine-tune the model for very specific styles, characters, or objects without having to retrain the entire large model.
So, if an architect has a preferred style of furniture or wants to visualize a specific brand's aesthetic, they could use a LoRA that's been trained on that style or brand.
You got it. You could have a mid-century modern furniture LoRA, or a brutalist architecture LoRA, or even a LoRA for a specific type of fabric or material. When combined with ControlNet, you're achieving both structural precision and stylistic consistency. ControlNet dictates where things go and their basic form, while LoRA dictates how they look, their specific aesthetic details. It's about achieving both form and texture, location and style, all in one go.
This really speaks to our listener's point about needing very high accuracy and fidelity for his wife's work. It's not just about generating an image, but generating an image that precisely reflects a client's vision and the architectural constraints.
Right. And this brings us to the next crucial tool in this advanced workflow, Comfy UI. Our listener brought up how he initially found node-based editors unnatural and not enjoyable, but then recognized their power. This is a common sentiment.
Yeah, I totally get that. Node-based interfaces can look intimidating at first, like you're programming rather than creating. It's a maze of boxes and wires.
It can be, but for complex, multi-step generative AI workflows, Comfy UI offers unparalleled flexibility and transparency. Unlike more abstract user interfaces where you input a prompt and click a button, Comfy UI exposes every single step of the diffusion process as a node.
So, when our listener talked about breaking down tasks, like background removal, then upscaling, then applying a model, Comfy UI basically lets you literally build that sequence visually.
Exactly. You connect nodes for loading an image, then a node for background removal, then an upscaling node, then a ControlNet preprocessor node, then the ControlNet model itself, then the sampler, and finally the save image node. Each step is explicit. This modularity is key for professional applications because it allows for, one, one transparency. You see exactly what's happening at each stage. One trends, reproducibility. You can save and share your exact workflow, ensuring consistent results. Three, flexibility. You can swap out models, preprocessors, or parameters at any point without redoing the entire process. Four, debugging. If something goes wrong,
Hmm, I can see how that would be powerful for an architect. They need to show iterations to clients, and being able to replicate a specific look or tweak just one element of a complex workflow would be incredibly valuable. It's a lot more robust than just tweaking a text prompt and hoping for the best.
Precisely. It turns the generative process into a visual programming environment, allowing for complex logic and conditional flows that would be impossible with simpler interfaces. It's how you move from generate an image to engineer a precise visual solution.
Okay, so we've got ControlNet for structural guidance, LoRAs for stylistic consistency, and Comfy UI for building and managing these complex, multi-step workflows. Now, for the listener's core question, how do these pieces fit together when you're mixing local and cloud processing? Can I run a local ControlNet with a cloud-based diffusion model?
This is where it gets interesting, and it highlights a common misconception. Generally, ControlNet, LoRAs, and the base diffusion model all, all need to reside and run together on the same inference machine.
Wait, really? So I can't like run ControlNet locally on my desktop and then send that ControlNettified request to a cloud-based Stable Diffusion model?
Not in the way you're probably imagining, where ControlNet is a completely separate process sending a pre-processed signal to a remote model. ControlNet is an extension of the diffusion model itself. It operates during the diffusion process, guiding each step of the image generation based on its input. Sending data back and forth between a local ControlNet and a remote diffusion model for every single step of the inference would be astronomically inefficient, slow, and complex to manage.
Okay, so let me make sure I understand. ControlNet isn't just generating an output that then gets fed into the diffusion model once. It's like a constant chaperone during the entire generative journey.
That's a great analogy. It's actively influencing the noise prediction at each step of the reverse diffusion process. So, for the vast majority of practical applications, if you're using a ControlNet and a LoRA, they need to be loaded alongside the base diffusion model on the same hardware that is performing the actual image generation.
Got it. So that means if I want to leverage cloud processing for the heavy lifting, my entire Comfy UI workflow, including the ControlNet and LoRAs, would typically run in the cloud.
Correct. Many cloud providers now offer instances specifically tailored for Comfy UI, where you can upload your entire workflow graph, your custom models, LoRAs, and ControlNets, and run the whole pipeline remotely. This leverages the cloud's powerful GPUs and scalability.
That makes sense. But then, what about the listener's point about doing some pre-processing locally, like background removal, and then then sending it to the cloud? Is that still viable?
Absolutely. That's a very common and efficient hybrid workflow. You can perform computationally less intensive tasks or tasks that benefit from local interactivity on your local machine. For instance, initial image capture and cleanup, taking photos, cropping, basic edits. Reference image preparation, running an image through an edge detection algorithm locally to generate the Canny map before sending it to the cloud. And of background removal or simple upscaling, if you have a decent local GPU, these can be quick. You then send the prepared inputs, your clean image, your ControlNet maps, to a cloud Comfy UI instance, which then loads the base
Ah, so the local part is about preparing the ingredients, and the cloud part is about cooking the meal with high-powered appliances.
Precisely. The orchestration the listener mentioned could involve a local Comfy UI front end that communicates with a remote Comfy UI server. The local instance acts more as a client for building and managing the workflow, pushing jobs to the powerful cloud back end. It's not about splitting the diffusion process itself between local and cloud, but about intelligently distributing the overall workflow steps.
And his point about four cents per render, that it's not worth avoiding the cloud for those savings, that makes more sense now. It's not about the marginal cost of a single render, but the overall efficiency, speed, and capabilities of the cloud for complex, high-volume work.
Exactly. For professional applications, it's rarely about penny-pinching on individual renders. It's about throughput, access to cutting-edge hardware, scalability, and uptime. If a client needs 50 high-fidelity architectural visualizations by tomorrow, a cloud-based Comfy UI setup can parallelize those renders across many GPUs, something a local machine simply can't do. The four cents per render becomes a very attractive proposition when it unlocks that kind of power and speed.
So, for architects and other professionals delving into this, what's the recommended practical approach to really leverage these tools for high-fidelity generative AI?
First, fully embrace node-based tools like Comfy UI. Overcome that initial learning curve. The transparency and control it offers are invaluable for professional, reproducible work. Second, integrate ControlNets for precise structural guidance. Understand the different types, Canny for edges, depth for 3D structure, normal for surface orientation, and how to prepare your input images for them. Third, utilize LoRAs for stylistic consistency and brand-specific elements. This ensures your output aligns with your aesthetic vision.
And regarding local versus cloud?
For computationally heavy inference, especially with multiple ControlNets or very large models, running your full Comfy UI instance in the cloud is often the most efficient. Services like RunPod, Vast.ai, or even specialized platforms for Comfy UI allow you to do this seamlessly. However, as we discussed, performing preliminary steps like input image preparation, background removal, or generating ControlNet masks locally can still save time and bandwidth, especially if your local machine is capable. The key is to think of it as a pipeline where components can be optimized for their respective best environments.
So, a hybrid approach, but specifically for pre-processing locally and then sending the prepared data to a cloud-based Comfy UI that handles the entire diffusion pipeline with all its control nets and LoRAs.
Precisely. It's about smart workflow design, not trying to split the core diffusion process across disparate environments.
This has been incredibly insightful, Herman. It really clarifies how these advanced tools are transforming professional design by offering unparalleled control. What are your thoughts on the future implications?
I anticipate even more sophisticated integrations, perhaps more intelligent orchestration layers that abstract away some of the complexity of managing local versus cloud resources while maintaining the flexibility. We'll likely see more specialized control nets tailored for specific industries like architecture or product design. The focus will continue to be on tools that bridge the gap between creative vision and precise, controllable AI output, making these once unnatural interfaces feel more intuitive over time.
It's exciting to think about. The ability to iterate with such precision and speed could fundamentally change how architects and designers approach their initial ideation and client presentations. It's a powerful step forward, truly.
Indeed. It empowers creators to push the boundaries of what's possible without sacrificing the quality and control required for professional excellence.
Herman, thank you for breaking down this complex topic so clearly. And to our listener, thank you for that fantastic question that sparked such an important discussion.
My pleasure, Corn. It's a testament to how fast this field is evolving.
Absolutely. And that's all for this episode of AI Conversations. We'll be back next time with more insights into the ever-changing world of artificial intelligence.
Thanks for listening to another episode of My Weird Prompts, an AI generated podcast featuring Daniel Rosehill's AI prompts as answered by a revolving cast of friendly AI characters including me, Cornelius the Sloth. Subscribe on Spotify or visit myweirdprompts.com.
This episode was generated with AI assistance. Hosts Herman and Corn are AI personalities. It may contain errors: disclaimer · report an error