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origina model @ https://civitai.com/models/502468/bigasp
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bigASP 🐍 v2.0
A photorealistic SDXL finetuned from base SDXL on over 6 MILLION high quality photos for 40 million training samples. Every photo was captioned using JoyCaption and tagged using JoyTag. This imbues bigASP 🐍 with the ability to understand a wide range of prompts and concepts, from short and simple to long and detailed, while generating high quality photographic results.
This is now the second version of bigASP 🐍. I'm excited to see how the community uses this model and to learn its strengths and weaknesses. Please share your gens and feedback!
Features
Both Natural Language and Tag based prompting: Version 2 now understands not only booru-style tags, but also natural language prompts, or any combination of the two!
SFW and NSFW: This version of bigASP 🐍 includes 2M SFW images and 4M NSFW images. Dress to impress? Or undress to impress? You decide.
Diversity: bigASP 🐍 is trained on an intentionally diverse dataset, so that it can handle generating all the colors of our beautiful species, in all shapes and sizes. Goodbye same-face!
Aspect ratio bucketing: Widescreen, square, portrait, bigASP 🐍 is ready to take it all.
High quality training data: Most of the training data consists of high quality, professional grade photos with resolutions well beyond SDXL's native resolution, all downloaded in their original quality with no additional compression. bigASP 🐍 won't miss a single pixel.
Large prompt support: Trained with support for up to 225 tokens in the prompt. It is BIG asp, after all.
(Optional) Aesthetic/quality score: Like version 1, this model understands quality scores to help improve generations, e.g. add score_7_up, to the start of your prompt to guide the quality of generations. More details below.
What's New (Version 2)
Added natural language prompting, greatly expanding the ability to control the model, resolve a lot of complaints about v1, and lots, lots more concepts can now be understood by the model.
Over 3X more images. 6.7M images in version 2 versus 1.5M in version 1.
SFW support. I added 2M SFW images to the dataset, both so bigASP can be more useful as well as expanding its range of understanding. In my testing so far, bigASP is excellent at nature photography.
Longer training. Version 1 felt a bit undertrained. Version 2 was trained for 40M samples versus 30M in version 1. This seems to have tighten up the model quite a bit.
Score tags are now optional! They were randomly dropped during training, so the model will work just fine even when they aren't specified.
Updated quality model. I updated the model used to score the images, both to improve it slightly and to handle the new range of data. In my experience the range of "good" images is now much broader, starting from score_5. So you can be much more relaxed in what scores you prompt for and hopefully get even more variety in outputs than before.
More male focused data. It may come as a surprise to many, but nearly 50% of the world population is male! Kinda weird to have them so underrepresented in our models! Version 2 has added a good chunk more images focused on the male form. There's more work to be done here, but it's better than v1.
Recommended Settings
Sampler: DPM++ 2M SDE or DPM++ 3M SDE
Schedule: Kerras or Exponential. ⚠️ WARNING ⚠️ Normal schedule will cause garbage outputs.
Steps: 40
CFG: 2.0 or 3.0
Perturbed Attention Guidance (available in at least ComfyUI), can help sometimes so I recommend giving it a try. It is especially helpful for faces and more complex scenes. However it can easily overcook an image, so turn down CFG when using PAG.
⚠️ WARNING ⚠️ If you're coming from Version 1, this version has much lower recommended CFG settings.
Supported resolutions (with image count for reference):
832x1216: 2229287
1216x832: 2179902
832x1152: 762149
1152x896: 430643
896x1152: 198820
1344x768: 185089
768x1344: 145989
1024x1024: 102374
1152x832: 70110
1280x768: 58728
768x1280: 42345
896x1088: 40613
1344x704: 31708
704x1344: 31163
704x1472: 27365
960x1088: 26303
1088x896: 24592
1472x704: 17991
960x1024: 17886
1088x960: 17229
1536x640: 16485
1024x960: 15745
704x1408: 14188
1408x704: 12204
1600x640: 4835
1728x576: 4718
1664x576: 2999
640x1536: 1827
640x1600: 635
576x1664: 456
576x1728: 335
Prompting
bigASP 🐍, as of version 2, was trained to support both detailed natural language prompts and booru-tag prompting. That means all of these kinds of prompting styles work:
A photograph of a cute puppy running through a field of flowers with the sun shining brightly in the background. Captured with depth of field to enhance the focus on the subject.
Photo of a cute puppy, running through a field of flowers, bright sun in background, depth of field
photo (medium), cute puppy, running, field of flowers, bright sun, sun in background, depth_of_field
If you've used bigASP v1 in the past, all of those tags should still work! But now you can add natural language to help describe what you want in more words than just tags.
If you need some ideas for how to write prompts that bigASP understands well, try running some of your favorite images through JoyCaption: https://huggingface.co/spaces/fancyfeast/joy-caption-alpha-two bigASP v2 was trained using JoyCaption (Alpha Two) to generate short, medium, long, etc descriptive captions, so any of those JoyCaption settings will work well to help you out.
I also recommend checking out the metadata for any of the images in the gallery to get some ideas. I always upload my images with the ComfyUI workflow when possible.
Finally, scoring. bigASP 🐍 v2, like v1 and inspired by the incredible work of PonyDiffusion, was trained with "score tags". This means it understands things like score_8_up, which specify the quality of the image you want generated. All images in bigASP's dataset were scored from 0 to 9, with 0 completely excluded from training, and 9 being the best of the best. So when you write something like score_7_up at the beginning of your prompt, you're telling bigASP "I want an image that's at least a quality of 7."
Unlike v1, this version of bigASP does not require specifying a score tag in your prompt. If you don't, bigASP is free to generate across its wide range of qualities, so expect both good and bad! But I highly recommend putting a score tag of some kind at the beginning of your prompt, to help guide the model. I usually just use score_7_up, which guides towards generally good quality, while still giving bigASP some freedom. If you want only the best, score_9. If you want more variety, score_5_up. Hopefully that makes sense! You can specify multiple score tags if you want, but one is usually enough. And you can put lower scores in the negative to see if that helps. Something like score_1, score_2, score_3.
NOTE: If you use a score tag, it must be at the beginning of the prompt.
NOTE: If you want booru style tags in your prompt, don't forget that some tags use parenthesis, for example photo (medium). And many UI's use parenthesis for prompt weighting! So don't forget to escape the parenthesis if you're using something like ComfyUI or Auto1111, i.e. photo \(medium\).
Example prompts
SFW, chill sunset over a calm lake with a distant mountain range, soft clouds, and a lone sailboat.
score_7_up, Photo of a woman with blonde hair, red lipstick, and a black necklace, sitting on a bed, masturbating, watermark
score_8, A vibrant photo of a tropical beach scene, taken on a bright sunny day. The foreground features a wooden railing and lush green grass, with a large, twisted palm tree in the center-right. The background showcases a sandy beach with turquoise waters and a clear blue sky. The palm tree has long, spiky leaves that contrast with the smooth, curved trunk. The overall scene is peaceful and inviting, with a sense of warmth and relaxation.
score_8_up, A captivating and surreal photograph of a cafe with neon lights shining down on a handsome man standing behind the counter. The man is wearing an apron and smiling warmly at the camera. The lighting is warm and glowing, with soft shadows adding depth and detail to the man. Highly detailed skin textures, muscular, cup of coffee, medium wide shot, taken with a Canon EOS.
score_7_up, photo \(medium\), 1girl, spread legs, small breasts, puffy nipples, cumshot, shocked
score_7_up, photo (medium), long hair, standing, thighhighs, reddit, 1girl, r/asstastic, kitchen, dark skin
score_8_up, photo \(medium\), black shirt, pussy, long hair, spread legs, thighhighs, miniskirt, outside
Photo of a blue 1960s Mercedes-Benz car, close-up, headlight, chrome accents, Arabic text on license plate, low quality
Prompting (Advanced)
Like version 1, this version of bigASP understands all of the tags that JoyTag uses. Some might find it useful to reference this list of tags seen during v2's training, including the number of times that tag occurs: https://gist.github.com/fpgaminer/0243de0d232a90dcae5e2f47d844f9bb
Of course, version 2 now understands more natural prompting, thanks to JoyCaption. That means there's a gold mine of new concepts that this version of bigASP 🐍 understands. Many people found the tag list from v1 to be helpful in exploring the model's capabilities. For natural language, a similar approach is to do "n-gram" analysis on the corpus of captions the model saw during training. Basically, this finds common combinations of "words". Here's that list, including the number of times each fragment of text occurs: https://gist.github.com/fpgaminer/26f4da885cc61bede13b3779b81ba300
The first column of the n-gram list is the number of "words" considered, from 1 up to 7. The second column is the text. The third column is the number of times that particular combination of words was seen (which might be higher than the number of images, since they can occur multiple times in a single caption). Note that "noun chunks" are counted as a single word in this analysis. Examples: "her right side", "his body", "water droplets". All of those are considered a single "word", since they represent a consolidated concept. So don't be surprised if