PixArt-Sigma-1024px_512px-animetune

Pruned Model fp16 (1.15 GB):inference Model 200epoch.Pruned Model bf16 (9.13 GB):Diffusers model for fine tuning + onetrainer config data.Training Data (86.05 KB):comfyui workflow.● I trained with a dataset of 400,000 images.I feel like the stability has improved compared to before.Also, please note that aside from the first two images, the sample images this time were generated using SD1.5 with i2i.Lately, I’ve been enjoying the fusion of Pixart’s composition and SD1.5’s style — it’s a lot of fun.I also created an SD1.5 merge model for i2i, so feel free to try it if you’re interested.The following is the same description as before.I’ve also uploaded a few workflows for reference.The sample images have embedded workflows viewable in ComfyUI, but recently they’ve been converted to JPG to save space, so some may not load. Installing the extension below will allow you to check them.<a target="_blank" rel="ugc" href="https://github.com/Goktug/comfyui-saveimage-plus">https://github.com/Goktug/comfyui-saveimage-plus</a>There are workflows for automatic prompt generation using TIPO, experimental workflows for general quality prompts, and simplified workflows. There's no single correct approach to inference, so experimenting with different methods can be interesting.While tags have many constraints, natural language allows more freedom for instructions. It might be worth exploring your ideal quality prompts.The 1024px model might ultimately provide superior inference, but reaching that point involves many failures and takes too much time. Continuously generating 1024px images without knowing the outcome is frustrating. This model is designed to support that process.This model has several potential uses:● Since it has fast inference speed and tag compatibility, it can be used for prompt testing before running inference on a 1024px model.● Use the 512px model to create a good composition, then upscale with the 1024px model.● Merge the 512px model with the 1024px model to generate around 768px, balancing speed and detail. (Practicality is uncertain, as it may not work reliably.)The standard size for this model is 512pxA ratio like 512x768 like SD1.5 is suitable.However, with a long side of 768px, it slightly breaks down. If stability is important, it’s better to base it on 512x512px and adjust the aspect ratio, such as 384x640.768px 1024px is not trained, so the result will be disastrous.The base model is very high quality even at 512px!Usually, models in the middle of pre-training or lite versions lack sufficient learning or aesthetic appeal, but this base model is different. It is the most aesthetically pleasing I have seen so far.●If you can't come up with a prompt, try using the prompt auto-generation below.<a target="_blank" rel="ugc" href="https://huggingface.co/spaces/KBlueLeaf/TIPO-DEMO">https://huggingface.co/spaces/KBlueLeaf/TIPO-DEMO</a>●Also, the model has not been trained on quality tags or negative prompts.It has not been trained on images with potentially harmful effects, like sketche or monochrome images.However, all 400,000 images are of high quality, so there is a possibility that any tag could improve quality. The more tags, the better.●There are likely tags that can be used as negative prompts.Tags related to styles, like the ones listed below, can be included in negative prompts to switch to a different style.1990s (style),00s,10s,simple background,anime screencap, realistic,figure etc...●If you find it troublesome to come up with prompts that produce stable quality, using prompts like the ones below might help stabilize the output. Ironically, tags like these end up becoming quality tags. lol" nikke, azur lane, blue archive, kancolle, virtual youtuber, arknights, girls' frontline"

pixartSigma1024px_512px07.safetensors

pixartSigma1024px_512px07.zip

pixartSigma1024px_512px07_trainingData.zip

512px_0.7

1024px_v04

512px_0.6

512px_v0.5

512px_v0.4

512px_v0.3

512px_v0.2

512px_v0.1

1024px_v0.31

1024px_v0.2

1024px_v0.1

1024px_v0.0

512px_v0.0

Files with SHA256: 0e59485e6662625dcbdc6e44fe9457413c4f9f56b604694c87237d7d9b5f1b8f