Instructions to use davidfant/sd-ecom-brightswimwear with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidfant/sd-ecom-brightswimwear with Diffusers:

pip install -U diffusers transformers accelerate

import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image

# switch to "mps" for apple devices
pipe = AutoPipelineForInpainting.from_pretrained("davidfant/sd-ecom-brightswimwear", dtype=torch.float16, device_map="cuda")

img_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo.png"
mask_url = "https://raw.githubusercontent.com/CompVis/latent-diffusion/main/data/inpainting_examples/overture-creations-5sI6fQgYIuo_mask.png"

image = load_image(img_url).resize((1024, 1024))
mask_image = load_image(mask_url).resize((1024, 1024))

prompt = "a tiger sitting on a park bench"
generator = torch.Generator(device="cuda").manual_seed(0)

image = pipe(
  prompt=prompt,
  image=image,
  mask_image=mask_image,
  guidance_scale=8.0,
  num_inference_steps=20,  # steps between 15 and 30 work well for us
  strength=0.99,  # make sure to use `strength` below 1.0
  generator=generator,
).images[0]

Notebooks
Google Colab
Kaggle

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davidfant
/

sd-ecom-brightswimwear

stable-diffusion-inpainting, 4/5 breast size (12000 steps)