I haven't had any luck making a good-enough FP8 safetensors version of this fun little merge yet, but I'm still working on it, and will share if successful. This is an Int8 ConvRot merge of the latest version of Boogu Image Edit Turbo, plus a little something extra to help with unlocking its fuller potential. For purely artistic reasons, of course. Boogu is actually a pretty impressive little family of models, and I like using this merge, so I figured maybe share, and help raise awareness.
I've tested each flavor in various ways. The Image model can edit, the Edit model can generate, and both come in Turbo versions, plus you can even use the Base version with a Turbo LoRA to get better diversity with detail and composition. It's a groovy thing. I tried the turbo editing model, liked it enough to look for a way to get it to do just a little more natural beauty (boobies) too, and so here we are. I daresay Boogu can outperform Klein 9b, and I didn't expect anything within my grasp to do that so soon. It's a pretty smart model for its size class, especially if you already know how to talk to a Qwen3 text encoder.
Boogu is a 10-billion-parameter model, so it's a little bit heavier than Klein 9b, and it uses a Qwen3VL 8b text encoder. I prefer the Heretic/Abliterated version, just because otherwise it can balk at some prompts (it's extremely sensitive and prudish for a text encoder, but then I guess it's seen a lot in its time). And I know I probably just lost everyone in the "potato" crowd as soon as they saw "10b" and "8b", but hear me out. I'm practically on a potato, too. I'm using a 3060 ti with only 8GB of VRAM, and I'm able to use this and it's as fast as some of my other smaller models.
Despite the diffusion model and text encoder each being almost 10 gigs in size, I get generation times of around 2-3 seconds per step, and editing times of about 5 seconds per step, and I get usable outputs at only 4 steps. Thank you, ComfyUI, for native Int8 support. So, even with a smaller GPU, this might work for you, too. And apparently, you can go as low as 3 steps. You can use as many as 8 or probably 12, but I usually just use 4 steps.
I favor this one particular version of the model, since I use Boogu more for editing than generating, but it does a fine job of both (all samples were generated using it, and then some were edited). If it had more LoRA support already, this model would stand a fair chance of putting my beloved Klein on a shelf. All those hundreds of LoRAs, all put away in the old archive SDD, and I haven't even tried them all yet D:
Much like Flux2 Klein, editing tends to produce a little bit of color shift (depending on what's being added/taken away/changed), but you can mediate most of this (and prevent staticky artifacts) with just the denoise settings. I find a denoise of .87 works best for me, but you should always experiment on your own, as well. It probably helps to use more steps than I do, also, or do a detail-fix upscale pass, or something. It's still new, and I'm still learning it, too.
I'm including my own personal-use copy of a Heretic/Abliterated Qwen3VL 8b text encoder (and I can't recall where I got it or I'd provide a link and credit), along with the Flux1 VAE, which you might need to rename to "Boogu_VAE" if you are using my sample workflow or any of the sample images' workflow innards (or just change it in the workflow). The VAE link in my listing goes to the official Civitai page for Boogu-Image Base, but just download the VAE and rename it (if necessary) and make sure your models are in the right folders, and you should be good to go.
I'm also including a sample workflow, along with a pack of blank image latents to use with it. I like using blank images, because then I can simply use the same workflow for genning and editing, and I never have to remember which multiples of 8 or 16 to type into that stupid blank latent node this way. Drop one into the Load Image node and you're all set.
I've set the workflow's denoise to 0.87, because this seems to work best when editing, and helps prevent some of the artifacting and color drift that can otherwise occur. You can set it to 1.0 for generating images, but always set it lower for editing. You can experiment with this and see if you can get it to work better than I have. Almost all of my sample images were generated with a denoise less than 1.
And lastly, though this isn't really a NSFW model (it's general-purpose), it's capable of doing some NSFW stuff, so please prompt responsibly. If you generate or edit wrongness into the AI-osphere it will all be on you anyway, so do think first. I'm just giving you a tool, it's up to you to use it responsibly.
You can thank this guy for the added bits, though he claims no credit in his listing. Give him some credit (or Buzz) anyway, because thanks to him, this model knows how to do bewbies. This is a fun as well as useful model, and I'm looking forward to seeing it get more attention in the future. It would be nice to see it get more LoRA support from the community.
Boogu is licensed under the Apache2.0 license. So yay~! Maybe that will help.
And eventually I'll get around to writing a simple guide for all of this crap. I know it can be confusing for the less-initiated.
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Comments (11)
For forgiveness like you, I still believe in humanity, thank you, I guess with 15 of Vram it works.
I have 8, and I can use it, the Int8 works well on some older GPUs. ComfyUI helps a great deal, also.
@PheebyKatz But make me understand, why can you run it with 8gb of Vram, if the set of files form 19gb of Vram together practically?
How do they make him run models like this?
I'm waiting for an explanation, because I want to do it with ltx or Wan.
@chamo9009 here are a couple of links, which might help. I'm not here to argue or sell you anything, feel free to read it for yourself, or not, and in the meantime I will keep running 20 gigs of models on my 8GB and thinking it's wonderful compared to waiting 10 minutes for a single image. And for what it's worth, I use Wan2.2 for animations.
https://blog.comfy.org/p/dynamic-vram-in-comfyui-saving-local
@PheebyKatz I get it! I have seen the articles and it looks like it's a new setup for comfyui which avoids OOM errors. I have to install the libraries and extension, Isn't it?
@chamo9009 Just update ComfyUI. I use the Easy Install version myself, makes it much easier. https://github.com/Tavris1/ComfyUI-Easy-Install It also includes/updates the Pixaroma node pack, which has some really good and useful advanced nodes.
@PheebyKatz But I think I need a lot of ram :(, what I did find is that your generations can be even faster! Watch:https://huggingface.co/Winnougan/Comfy-Qwen3-VL-INT8/blob/main/qwen3vl_4b_int8_convrot.safetensors
There is a 4b encoder, Int8 convrot, that is, if you use the 8b encoder, with this you reduce the use of 4gb of Vram!!
I've never tried using the smaller text encoder, I had assumed it had a different architecture and wouldn't work. Of course a smaller model with fewer parameters (half at 4 billion) will give different results. There are smaller quantizations of the same models, like nvfp4, but the quality is a bit lower in outputs. I'm happy with what I've got, but if you find a faster setup that works for you, then hey, go for it and have fun. I am always experimenting, I think everyone should.
@PheebyKatz My brother, I managed to run it locally and experimented on Google colab using script! That is, it runs in a free environment like Boogu Image Edit! With only 15 Vram and 10 ram. But, latency is very slow, edits with your settings take at most 200 seconds per image. I don't know if there's a way to do it faster, and clearly using dynamic vram mode.
@chamo9009 I'm glad you got it to work, at least! I'm still looking for ways to make it faster without ruining the quality.
@PheebyKatz Yes, at least it works, and it's not so bad, it edits well and it's worth it both locally and in Google Colab. Also, G.C comes out for free, I think it's the 1st edition and generation model at the same time that runs in a free environment like Colab, I'm glad I discovered it, I hope you get the model to run faster! I will be attentive to your notifications.






