Text-to-Image
Diffusers
Safetensors
StableDiffusionInpaintPipeline
Safetensors
stable-diffusion
stable-diffusion-diffusers
import torch
from diffusers import AutoPipelineForInpainting
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = AutoPipelineForInpainting.from_pretrained("andro-flock/Liberty-LibertyMain-Inpainting", 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]Liberty_LibertyMain Inpainting
Description:
This is the version you should be using for inpainting:
VAE is included in the model.
Remember de filename must finish in inpainting
CLIP is fixed.
Civitai Page: https://civitai.com/models/6937
You can use this with the 🧨Diffusers library
Diffusers
from diffusers import StableDiffusionPipeline
import torch
model_id = "andro-flock/Liberty-LibertyMain-Inpainting"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "masterpiece, best quality, 1girl, (colorful),(delicate eyes and face), volumatic light, ray tracing, bust shot ,extremely detailed CG unity 8k wallpaper,solo,smile"
image = pipe(prompt).images[0]
image.save("result.png")
- Downloads last month
- 32