Pixart-Ladies-900M V0.50: This version is a huge improvement over the others. Anatomy and consistency went up a big notch. I made a workflow and a comfyUI folder download to make it easy for those of you that never tried Pixart. This version is a great example of what I want my model to be. Photographically accurate. You can find a simple workflow here: <a target="_blank" rel="ugc" href="https://civarchive.com/articles/8315/simple-comfyui-pixart-workflow-for-pixart-ladies-900m">https://civarchive.com/articles/8315/simple-comfyui-pixart-workflow-for-pixart-ladies-900m</a> From the file section, you can download the model itself in .safetensors format or a clean comfyUI zip folder including the model itself, the FP8 T5 encoder, the VAE, a wildcard node as well as all the dependencies needed to run it. Basically, a one click download to get going right away. The comfyUI setup runs on less than 4GB of VRAM. PIxart-Sigma-900M: After weeks of testing different settings, I've come up with this version with much better anatomical details of everything below the neck. I'll be continuing this path and expanding the dataset for even better results in the future. All other versions should be obsolete next to this one. You can get the workflow by drag and dropping the pictures to your comfyUI window. Pixart-Sigma-600M: So after expanding the dataset, I wanted to try training Pixart Sigma. If you want to try it out, use this workflow: <a target="_blank" rel="ugc" href="https://civarchive.com/models/420163/abominable-spaghetti-workflow-pixart-sigma">https://civarchive.com/models/420163/abominable-spaghetti-workflow-pixart-sigma</a> Download both pixart-sigma ladies v0.15 and SD1.5 ladies v0.15 put both in your ComfyUI checkpoint folder and set both up in the workflow. The rest stays the same. What I've noticed about Pixart-Sigma is that it has a huge increase in image quality. With more training, the model will become even better. This is basically a sample or a Alpha version if you like to call it this way. Pixart is hugely undertrained when it comes to the lower body (anything below the waist) including hands, arms and legs position. Don't expect this model to properly or easily make full body shots yet. Proper anatomy of the arms when prompting for hands behind her head or arms above her head still remains somewhat horrible but it's already a nice improvement over base Pixart. Nipples and areoles are still somewhat blurry which should be fixed with more training. OLD (SD1.5): This is JUST a small finetune off base SD1.5. No merge or nothing else was added. I'm sharing it as is and I may or not improve it in the future. It is more of a proof of concept that with a small well curated and captioned dataset, it is possible to get decent human anatomy. I wished for the guys of Open Diffusion to see what's possible with such technique. (r/open_diffusion or the discord if you're interested in participating)One morning, I built a dataset of 404 images and captioned it using TagGui with a VLM in about 2 hours adjusting what didn't work by hand. The dataset consist of women over the age of 18 only. Most of them are nudes with about only 25% in skimpy clothes or lingerie. No pornographic material was used so don't expect it to be able to produce such content. The model was trained at 512 resolution for a few epochs and then at 768 for a total of 10,100 steps. The whole thing was done in less than 24 hours. My LR were pretty aggressive to make it that fast so it might lack in details and skin texture. Prompting it: headshot photograph: Give you a close up of the faceclose up portrait: give you a close up of the face but if you add breasts in the prompt, you'll have a close up from the head to the breasts.portrait: give you a shot from the head to the waistfull body portrait: from head to toeFeel free to experiment, I haven't used it much beside running a few dynamic prompt to see if it was working or not. The sample pictures are just a dump of a few pictures I generated through those prompts. Maybe you can do betterIf you want to use my model for a merge, just credit me on your post and put a link to this page.

Ladies

<ul><li>Ran more training with the expanded dataset</li><li>slightly better bodies and faces</li><li>hands are still a mess</li><li>nipple and body skin details are still underfit and need more training</li><li>Should output better results than 0.15</li></ul>

Files with SHA256: c54b8c5cf7aab400f34ed1b59afc656d5ad67f132275a84639e096a13f9e3559