BlobCtrl
Collection
3 items • Updated
import torch
from diffusers import DiffusionPipeline
from diffusers.utils import load_image
# switch to "mps" for apple devices
pipe = DiffusionPipeline.from_pretrained("Yw22/BlobCtrl", 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]BlobCtrl
Overview
BlobCtrl enables precise, user-friendly element-level visual manipulation.
Main Features: 🦉Element-level Add/Remove/Move/Replace/Enlarge/Shrink.
Video
Watch the introduction video in our project page or YouTube.
Code
Please check our GitHub repository for code.
Model
Download the model checkpoint using huggingface_hub (Version 0.1 as example):
import os
from huggingface_hub import snapshot_download
# download blobctrl models
BlobCtrl_path = "examples/blobctrl/models"
if not (os.path.exists(f"{BlobCtrl_path}/blobnet") and os.path.exists(f"{BlobCtrl_path}/unet_lora")):
BlobCtrl_path = snapshot_download(
repo_id="Yw22/BlobCtrl",
local_dir=BlobCtrl_path,
token=os.getenv("HF_TOKEN"),
)
print(f"BlobCtrl checkpoints downloaded to {BlobCtrl_path}") )
The downloaded BlobCtrl checkpoint file (blobnet&unet_lora) can be found at BlobCtrl_path.
Demo
You can try the demo here.
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Model tree for Yw22/BlobCtrl
Base model
stable-diffusion-v1-5/stable-diffusion-v1-5