controlnet_stablediffusion_scenetextEraser

This is the trained model for the controlnet-stablediffusion for the scene text eraser (Diff_SceneTextEraser) We have to customize the pipeline for controlnet-stablediffusion-inpaint

Here is the training and inference code for Diff_SceneTextEraser

For direct inference

step 1: Clone the GitHub repo to get the customized ControlNet-StableDiffusion-inpaint Pipeline Implementation

git clone https://github.com/Onkarsus13/Diff_SceneTextEraser

Step2: Go into the repository and install repository, dependency

cd Diff_SceneTextEraser
pip install -e ".[torch]"
pip install -e .[all,dev,notebooks]

Step3: Run python test_eraser.py OR You can run the code given below

 from diffusers import (
    UniPCMultistepScheduler, 
    DDIMScheduler, 
    EulerAncestralDiscreteScheduler,
    StableDiffusionControlNetSceneTextErasingPipeline,
    )
import torch
import numpy as np
import cv2
from PIL import Image, ImageDraw
import math
import os

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model_path = "onkarsus13/controlnet_stablediffusion_scenetextEraser"

pipe = StableDiffusionControlNetSceneTextErasingPipeline.from_pretrained(model_path)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)

# pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
generator = torch.Generator(device).manual_seed(1)

image = Image.open("<path to scene text image>").resize((512, 512))
mask_image = Image.open('<path to the corrospoinding mask image>').resize((512, 512))

image = pipe(
    image,
    mask_image,
    [mask_image],
    num_inference_steps=20,
    generator=generator,
    controlnet_conditioning_scale=1.0,
    guidance_scale=1.0
).images[0]
image.save('test1.png')