Stable Diffusion IPAdapter V2 Outfit Change For eCommerce Fashion Niche

hello everyone so in this video I will share how we can use IP adapter version two the latest version of Ip adapter will be able to use it to generate clothing demos for your e-commerce store if you're running a fashion Niche e-commerce website you can try out this workflow and showcase your clothing products as demos with different poses utilizing IP adapter and control net now the basic concept is quite simple first we're going to load up the control net checkpoint models so we'll need the stable diffusion 1.5 checkpoint models whichever ones you prefer I prefer the realism style checkpoints for this particular use case so we're able to generate realistic looking people wearing the outfits from your product catalog secondly we'll have a very straightforward text prompt describing the full body of a woman or man in a mod in pose it will randomly generate natural poses that you might see on Amazon or other e-commerce sites with models striking different stances like that after the text prompt we'll pass the data to a simple control net instance we're just using one control net here for this version of the workflow to keep things streamlined for getting started we're utilizing the open pose with DW pre-processor to process our input image as you can see I have an example input image here as a POS reference for fashion items we're not using to heavy of a strength setting just giving the AI a little more freedom to create something slightly different from the original Post in our reference while still being influenced by it we'll pass that data into the conditioning so there are three data inputs we're passing from this control net step the model data the control net text prompt and then the negative positive conditioning images then we move on to this group which is the IP adapter component here we're receiving the model data from our previous control net group we pass that model data into the IP adapter unified loader to let it initialize our IP adapter plus the high strength IP adapter models this allows us to receive the IP adapters Advanced custom nodes and in here we start creating the outfit for our gener ated AI image as you can see I have a mask loader for inpainting the mask region and we're referencing this image as the outfit that will be masked onto the character in this example we have a black long sleeve t-shirt that would be masked onto the figure but one thing I need to mention is that these outputs aren't always perfectly generated there can be some artifacts or imperfections especially around the edges and boundaries of where the clothing is being being imp painted onto the body so we may need to do some manual touchups in postprocessing however this workflow allows us to very quickly generate a diverse array of clothing demo images showing our products on models in different poses all with just a few inputs and some simple prompting it's a great way to build out your products visual assets for e-commerce marketing if you're in the fashion industry from there we just need to run the IP adapter pipeline passing in all of our initialized data from the previous steps the output will be our final generated image with the clothing item seamlessly added onto the posst character that we can use for our website ads or whatever we need that's the highlevel overview of how to leverage IP adapter V2 and control net together for automating the creation of clothing demo visuals it's a really handy workflow especially for small businesses or solo e-commerce entrepreneurs who may not have resources for professional product photography shoots this AI approach lets you mock up realistic product imagery very efficiently the AI generated image result won't have the exact same pose and character as this mask image this mask image is just helping IP adapter reference the positions to place the clothing item onto the corresponding masked areas in the inpainting editor so right here we have have the mask editor interface it's very similar to the inpainting method we've used before where we highlight the area we want to place the new outfit piece the original character is wearing a pink T-shirt and we're masking that area to replace it with a long sleeve black T-shirt once you click save that will be temporarily stored in the workflows load image node the next step is to do the same process for the lower part of the outfit the pants in this example I have have pink colored pants that will be located on the lower masked area of the character's body this masked region will be covered up with the pan clothing item now in the settings there are some new options in the latest IP adapter version for the first part with the black T-shirt I'm using linear and concast as the weights type and the combined embedded type obviously we have to set the weights to 1.0 up top here and I'm using V only for the embedding scaling so that's a fairly standard setup for the first IP adapter instance but then for the second part here we need to do something a bit differently we have to use the KH plus Dov type you can also try the kuv T with kentu and the other three types to experiment but so far I found this cable V type works best for the second IP adapter instance when masking multiple items onto one image and you have to set the weights type to raise a in rather than using linear so that's another approach to play around with IP adapter settings and one thing to remember with the new IP adapter V2 is that you can use the IP adapter unified loader to share the same models the IP adapter models in one processing node So within this whole IP adapter group area we've initialize the IP adapter plus models then we can pass that IP adapter d data to the second IP adapter unified loader instance in this second loader we don't need to tell it to load up separate IP adapter models again we can just reuse the same model objects saving a lot of memory from a programming perspective we can treat this as a reusable object that can be shared across different functions pretty handy okay so after we've masked the outfit pieces onto the character letting the AI understand what needs to be inpainted into those Mass regions we pass the model and IP adapter output data into our first sampling group in this first sampling group we use the case sampler which receives the positive and negative conditioning images we also share the same seed number across samples and create an empty latent image I typically generate four or five images each run with the same seed so we can cherry-pick the best one we then get the latent vae e code and our first sampled image output which is actually saved in the save image node we should probably use the preview node instead to save some storage space on our hard drive so I'll change that to preview image then the output latent image and actual image data get passed to group two for further refinement in this second yellow group we have the same K12 sampler structure but here we received the latent image from the first sampling group's output and we'll further process that image in this second KOST sampler iteration now again in here we are creating the empty latent image in the first group so therefore we get the output of the latent image which we pass to the second group to perform further sampling and add more details then we'll go to our detail enhancers in the detailers we're having the face detailer from the paint pack from this group we're creating the face detailer and using the ultral litics detector provider selecting the face YOLO models we have detailers for faces hands full person Fashions Etc but here we're only selecting the face detailer so we're enhancing just the character's face in this detailer group then we'll pass it on to the next one which is enhancing the Fashions therefore if there are any imperfections with the clothing generation it will help us enhance the outfit appearance on the character in here we're going to use the YOLO deep fashion models to specifically Target enhancing only the fashion elements so again we're using the same face detailer node but changing the model to deep fashion object detection in this group then of course we pass it on lastly we'll upscale our image here we'll use two upscaler one is going to be using the NM KD super upscale models and we're using the ultimate custom node to do one round of enhancement in here I should upscale by 1.5x size just a mild upscale but I want to add more texture details to the skin and clothing with this upscaling model then we'll lastly pass it to the 4X Ultra sharp upscaler and do a very simple upscale with that model's custom node sharpening up the overall final generated image so once you have your final result here remember to save it because this is also just a preview image if you like the image then you can right click and save it if you don't like it then you can generate again using different seed numbers and try again now this method isn't going to be creating an exact replica of the outfit details but overall the colors and outfit shapes like this long sleeve shirt or long pants it will get those main elements right but it won't replicate the exact same sewing patterns button placements zipper locations Etc you have to keep in mind that IP adapter and the AI can't just perfectly reproduce every tiny detail in a new generative image there will be some variance so I think this solution is good for creating secondary images to Showcase your product on your e-commerce site like an Amazon or Shopify store obviously your primary product image is going to be the clear white background shot of just the item itself but then the second third fourth or fifth images could utilize this workflow to generate models demonstrating how the product looks while being worn in different poses I think this is a pretty handy and fast method for e-commerce business owners to have diverse modeling views presenting their products on their online stor fronts so let's try running this with an outfit example and see what we get clicking the generate button here and we'll see the open pose detecting the pose in the first step based on our reference image so the output will have something structurally similar to this pose reference okay so we have the output already as I mentioned IP adapter doesn't always perfectly clone the exact styles from your reference so I always generate a batch of four empty latent images that way I can cherry pick the best ones from that set in the second latent group it looks like we have a better refined version like this one and it will bring that image into our face and fashion detailers those AI models are then going to help enhance the outfits and overall visual quality so all four get processed through those enhancers and then then we get the final processing in the UPS scalers here we'll see a larger version from our final generated image result with the 4X Ultra sharp upscaling applied looks like we have a solid winner out of those four initial samples let's save up this image if we like this final output I can just rightclick and save the image you can put that result image in your e-commerce stores file folders or wherever you'd like yeah so that's basically the whole process for this workflow it works with stable diffusion 1.5 and regular stable diffusion checkpoints just change the checkpoint models to sdxl if you prefer that and also change the control net models to the sdxl open pose control net type for example here I have this XL open pose control net or I could use the other one the T2i adapter XL open pose as the control net as well but for person or character generation I tend to prefer using the bass SD 1.5 models it's up to you whether you like using sdxl or not and lastly for the IP adapter the IP adapter unified loader will actually detect if you're using the sdxl version and automatically change the IP adapter model for you in V2 so you don't have to handle that part manually anymore just play around with the settings and you'll see the result showing up here in the first sample stage we get an initial output based on the reference image post then in the second sampling it enhances the face details of the model after that it enhances the outfit itself adding color variations texture details like zippers and buttons as you can see it did here this other image result is fairly good too but it doesn't match what we wanted from the input reference using that pink color for the pants so this one likely wouldn't be used over all we have this very simple easyto use comfy UI workflow that can work well for fashion and e-commerce product niches I think we could use it for showcasing other e-commerce products too as long as they're not extremely complex items with lots of small parts it works best for relatively simple products without an abundance of logos or intricate details that's what it's designed for taking inspiration from Fashion outfit photos you'd see on sites like Amazon or Shopify for example like this post where the model has her leg out to the side in a more Dynamic stance rather than just a boring straight on standing pose having some movement and varied modeling angles makes the product look more interesting at first glance for customers so yeah that's our workflow here as far as I know there's another AI model called oot diffusion that specializes in fashion clothing editing and changes like this and there are some comfy UI custom nodes out there that implement it you can Google it and find some of those already created custom nodes for OT diffusion in comy UI but I understand a lot of people have issues where those custom node implementations cause errors when booting up comfy UI which is not ideal they do have a demo page on hugging face that you can try out though I'll link that page in the description below you can test it there and I'll also try setting up those o diffusion custom nodes for comy UI myself the latest release for The O diffusion comy UI custom nodes is written in Chinese so I'll work on Translating that to English for you all to check out later too next up I'll be testing and demoing this old diffusion fashion editing AI model and is comfy UI integration from the demo page it looks very straightforward just select the model pick the outfit style you want like a yellow tank top and click run it will then change the outfit in the image accordingly very basic UI without any difficulties there but implementing it properly in comfy UI seems to have some extra steps that can cause issues for some people you can't just install it directly from the manager so I'll walk through getting that set up in another video yeah so as you can see from the demo results it looks pretty accurate so far the this model seems to be doing the best job currently for changing outfit Styles even at different angles like this you can use this model to change outfits while also handling logos or text on t-shirts really well it helps accurately transform those elements on the character's clothing without any broken or distorted artifacts which can be a challenge with regular stable diffusion as we know if we try to generate things with logos or text directly in stable diffusion it often struggles to produce a clean readable result but using specialized AI models like oot diffusion helps enhance those aspects of the final image output all right that's it for this video I'll see you all in the next one for the oat diffusion demo have a great day bye

Channel: Future Thinker @Benji

Views: 6,281

Keywords: IP Adapter v2, ControlNet, clothing demos, e-commerce store, fashion niche, fashion industry, AI-generated imagery, product photography, small businesses, solo entrepreneurs, automation, fashion products, poses, workflow, customization, tips and tricks, stable diffusion, ai art, comfyui tutorial, IPAdapter V2 Outfit Change

Id: sfsKItB1YI8

Length: 17min 49sec (1069 seconds)

Published: Thu Apr 11 2024