Dreamlook

Dreamlook is a hosted Dreambooth finetuning service for Stable Diffusion 1.5 and SDXL that trains full models, not just LoRA, in minutes. It targets avatar and pet apps, portrait and character models, virtual photoshoots and style replication, with a generation endpoint and a scalable finetuning API.

Dreamlook is a hosted finetuning service for Stable Diffusion, built around Dreambooth training rather than prompt engineering. Users upload a small set of images of a person, pet, object or style, pick a base model, and the service produces a full checkpoint that reproduces the subject with high resemblance. Training runs on the provider's own infrastructure, so there is no GPU quota hunting, no Colab session and no local setup. The homepage puts a 1'500 step SDXL run at roughly ten minutes. The output is the distinguishing part. Dreamlook trains full models, not only LoRA adapters, and it can extract LoRA files from a finished model to cut download size. More than 30 base models are available for training, and users can compare base models side by side before committing to a run. Generation happens on the same platform at 1024x1024 for both SD1.5 and SDXL, with ControlNet for precise posing and Offset Noise for darker, low key images. Typical uses named on the site are virtual photoshoots, pet avatar apps, stock imagery and QR code styling. The product is really two things. For individuals, it is a browser interface for producing a personal model: portraits, pets, a consistent character, a house style. For developers, it is a finetuning API intended to sit behind an avatar app or other product without managing instances. The free 50 tokens on signup let a newcomer test the pipeline before buying credits, and pricing is credit based with per run and per image rates rather than a monthly seat. Compared with running Dreambooth locally or on a rented GPU, Dreamlook trades control over the training stack for speed and convenience. Compared with LoRA only services, it targets higher fidelity at a higher per run cost. Anyone who needs custom sampler settings, custom datasets pipelines or full infrastructure access will find it opinionated; anyone who wants a fast, repeatable subject model and an API to call it from will find the trade reasonable.