Text-to-Image
Diffusers
PyTorch
StableDiffusionPipeline
stable-diffusion
diffusion-models-class
dreambooth-hackathon
landscape
classical-art
Instructions to use avuhong/DB_Hokusai_Monet_style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use avuhong/DB_Hokusai_Monet_style with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("avuhong/DB_Hokusai_Monet_style", dtype=torch.bfloat16, device_map="cuda") prompt = "a painting in $M## style of a fishing village under a cherry blossom forest at sunset" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
DreamBooth model for the painting of mixed style between Claude-Monet and Hokusai
This is a Stable Diffusion model fine-tuned to generate mixed styled paintings between Claude-Monet and Hokusai taught to Stable Diffusion with DreamBooth.
It can be used by modifying the instance_prompt: a painting in $M## style of
This model was created as part of the DreamBooth Hackathon 🔥. Visit the organisation page for instructions on how to take part!
Description
This is a Stable Diffusion model fine-tuned on paintings of both Claude-Monet and Hokusai.
Examples
Since it's more for landscape painting, the image size matters. I found that 512*1024 normally gave interesting results. Check out this gallery for more generated images: https://www.vuhongai.com/classicalart-ai
Usage
from diffusers import StableDiffusionPipeline
pipeline = StableDiffusionPipeline.from_pretrained('avuhong/DB_Hokusai_Monet_style')
prompt = "a painting in $M## style of a fishing village under a cherry blossom forest at sunset"
image = pipe(prompt,
num_inference_steps=200,
guidance_scale=5,
height=512, width=1024,
).images[0]
image
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