sdxl-gongbao@peter65374

finetuned sdxl 1.0 with food named 'gongbao chicken' images

story-dalle@adymaharana

A model trained for the task of story visualization; generating images to pair with captions in a story.

zekebooth@zeke

My own personal https://replicate.com/replicate/dreambooth

one-shot-talking-face@camenduru

one-shot-talking-face-replicate

lednet@sczhou

Joint Low-light Enhancement and Deblurring in the Dark

qinxiang@extrange

Generate photos of the most handsome man on Earth

controlnet-scribble@jagilley

Generate detailed images from scribbled drawings

plasma@andreasjansson

Generate plasma shader equations

demixing@jimothyjohn

Separate instruments and/or vocals from any song.

sentiment-analysis@curt-park

Sentiment Analysis with Texts

sdxl-ziggislide@marlonbarrios

ziggislide is a model of an adventurous character of an influencer always wearing colorful fabulous outfit, goggles and helmet, he loves water sliding boards and taking selfies. To invoke his presence use the token ziggyslide inside the prompt

audio-to-waveform@fofr

Create a waveform video from audio

plug_and_play_image_translation@daanelson

Edit an image using features from diffusion models

animatediff-v3@shimmercam

AnimateDiff v3 + SparseCtrl: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning. Created with Shimmer.

sd15-consistency-decoder-vae@anotherjesse

SD1.5 and OpenAI's Consistency Decoder

loyal-piano-m7@tomasmcm

Source: chargoddard/loyal-piano-m7 ✦ Quant: TheBloke/loyal-piano-m7-AWQ ✦ Intended to be a roleplay-focused model with some smarts and good long-context recall

sam-7b@tomasmcm

Source: SuperAGI/SAM ✦ Quant: TheBloke/SAM-AWQ ✦ SAM (Small Agentic Model), a 7B model that demonstrates impressive reasoning abilities despite its smaller size

sdxl-xmas-sweater@fofr

SDXL fine-tuned on Xmas sweaters

live-portrait@mbukerepo

Efficient Portrait Animation with Stitching and Retargeting Control

sam-vit-h@cbx1

The Segment Anything Model (SAM) is a powerful and versatile image segmentation model. It leverages a "foundation model" approach, meaning it can be used for various segmentation tasks without needing to be specifically trained for each one.