SDv1.5 SD15-VinageStyle model, trained by Norod78 in two parts.

First Stable-Diffusion v1.5 fine-tuned for 10k steps using Huggingface Diffusers train_text_to_image script upon Norod78/vintage-blip-captions then it underwent further fine tuning with Dreambooth using the same images as the ones in the dataset but rather then having it blip-captioned, it was split into "Vintage style", "Vintage face" and "Pulp cover" concepts.

Dreambooth model was trained with TheLastBen's fast-DreamBooth notebook

Because the model was first fined-tuned on the whole dataset and only then it was fine-tuned again to learn each individual concept, you can use prompts without Trigger-Words and still get a subtle "Vintage" touch

Trigger-Words are: "Vintage", "Vintage style", "Vintage face", "Pulp cover"

A few sample pictures generated with this mode (more available here):

A photo of Gal Gadot as wonderwoman, Vintage style, very detailed, clean, high quality, sharp image.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3486356206, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 2

A photo of Gal Gadot as wonderwoman fighting against Cthulhu, Vintage, very detailed, clean, high quality, sharp image, ,Naoto Hattori.Negative prompt: grainy, blurry, text, watermark, inconsistent, smudged.Steps: 40, Sampler: DPM++ 2M Karras, CFG scale: 7.5, Seed: 3408435550, Face restoration: CodeFormer, Size: 512x512, Model hash: 33006be6, Model: VintageStyle, Batch size: 4, Batch pos: 3