Instructions to use tolgadev/tolgadreamsinbooth with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use tolgadev/tolgadreamsinbooth with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("tolgadev/tolgadreamsinbooth", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
"TolgaDreamsInBooth" is fine-tuned version of Dreambooth text-to-image model
- This model trained with TheLastBen's fast-DreamBooth notebook
- Labelled myself as "tkrut11" while training. You can use this label to create my portrait based images.
Sample prompt:
detailed portrait of tkrut11 Holographic Futuristic sci-fi fashion cyberpunk, (neotokyo), synthwave, (aesthetics), futuristic, bladerunner movie scene by ismail inceoglu dragan bibin hans thoma greg rutkowski Alexandros Pyromallis Nekro Rene Margitte illustrated Perfect face, fine details, realistic shaded, fine-face, pretty face sharp chine
External usage :
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("tkurtulus/tolgadreamsinbooth-concept")
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