BLIP-Diffusion is a new model for generating and editing images based on text prompts and subject images. Unlike previous models, it uses a pre-trained multimodal encoder to represent the subject, allowing for efficient fine-tuning and better preservation of subject details. The model enables the generation of new images based on text prompts and subject images, even without prior training on specific subjects. It also supports image manipulation, style transfer, and editing guided by subject images. The model is trained in two stages to learn subject representation and can be combined with other techniques for more control over the generation and editing process. Overall, BLIP-Diffusion provides a flexible and efficient approach to generate and edit images with specific subjects.
Want to figure out what a good prompt might be to create new images like an existing one?
The CLIP Interrogator is here to get you answers!
This version is specialized for producing nice prompts for use with Stable Diffusion 2.0 using the ViT-H-14 OpenCLIP model!
A guide on how to train embeddings with textual inversion to learn a person's likeness. The guide assumes the use of the Automatic1111 Web UI and basic knowledge of embedding-related terminology. The guide explains what embeddings are, their advantages over other options such as models, hypernetworks, and LoRAs, and how they work by creating keywords that trick the model into creating the desired output. The guide also provides suggested settings for training embeddings and explains how to fix common problems.
Get an approximate text prompt, with style, matching an image. Optimized for stable-diffusion (clip ViT-L/14)). The resource is an adapted version of the CLIP Interrogator notebook by @pharmapsychotic, which uses OpenAI CLIP models to analyze an image's content and suggest text prompts to create more similar images. The results are combined with BLIP caption to provide suggested text prompts.
Example :
a cat wearing a suit and tie with green eyes, a stock photo by Hanns Katz, pexels, furry art, stockphoto, creative commons attribution, quantum wavetracing
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