Image-to-Image
Diffusers
Safetensors
English
StableDiffusionInpaintPipeline
stable-diffusion
stable-diffusion-diffusers
inpainting
art
artistic
anime
absolute-realism
Instructions to use Lykon/absolute-reality-1.6525-inpainting with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Lykon/absolute-reality-1.6525-inpainting with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Lykon/absolute-reality-1.6525-inpainting", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Absolute reality 1.6525 inpainting
lykon/absolute-reality-1.6525-inpainting is a Stable Diffusion Inpainting model that has been fine-tuned on runwayml/stable-diffusion-inpainting.
Please consider supporting me:
- on Patreon
- or buy me a coffee
Diffusers
For more general information on how to run inpainting models with ๐งจ Diffusers, see the docs.
- Installation
pip install diffusers transformers accelerate
- Run
from diffusers import AutoPipelineForInpainting, DEISMultistepScheduler
import torch
from diffusers.utils import load_image
pipe = AutoPipelineForInpainting.from_pretrained('lykon/absolute-reality-1.6525-inpainting', torch_dtype=torch.float16, variant="fp16")
pipe.scheduler = DEISMultistepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("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)
mask_image = load_image(mask_url)
prompt = "a majestic tiger sitting on a park bench"
generator = torch.manual_seed(33)
image = pipe(prompt, image=image, mask_image=mask_image, generator=generator, num_inference_steps=25).images[0]
image.save("./image.png")
- Downloads last month
- 93
