pip install -U diffusers transformers accelerate
import torch
from diffusers import DiffusionPipeline
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Dushwe/mineral-colour", dtype=torch.bfloat16, device_map="cuda")
prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
image = pipe(prompt).images[0]mineral-colour on Stable Diffusion via Dreambooth
token
mineral-colour
Here are the images used for training this concept:
inference
from torch import autocast
from diffusers import StableDiffusionPipeline
import torch
import diffusers
from PIL import Image
def image_grid(imgs, rows, cols):
assert len(imgs) == rows*cols
w, h = imgs[0].size
grid = Image.new('RGB', size=(cols*w, rows*h))
grid_w, grid_h = grid.size
for i, img in enumerate(imgs):
grid.paste(img, box=(i%cols*w, i//cols*h))
return grid
pipe = StableDiffusionPipeline.from_pretrained("Dushwe/mineral-colour").to("cuda")
prompt = 'A little girl in china chic hanfu walks in the forest, mineral-colour'
images = pipe(prompt, num_images_per_prompt=1, num_inference_steps=50, guidance_scale=7.5,torch_dtype=torch.cuda.HalfTensor).images
grid = image_grid(images, 1, 1)
grid
generate samples
Chinese palace, 4k resolution, mineral-colour
beginning of autumn, autumn, forests, scenery, background, landscape, woodland, trees,mineral-colour
You run your new concept via diffusers
Colab Notebook for Inference. Don't forget to use the concept prompts!
- Downloads last month
- -
# Gated model: Login with a HF token with gated access permission hf auth login