Update README.md

README.md

@@ -4,4 +4,66 @@ license: mit
 This is the trained model for the controlnet-stablediffusion for the scene text eraser.
 We have to customised the pipeline for the controlnet-stablediffusion-inpaint
 
-you will find the code to run the model [here](https://github.com/Onkarsus13/Diff_SceneTextEraser)
+you will find the code to run the model [here](https://github.com/Onkarsus13/Diff_SceneTextEraser)
+
+For direct inference
+
+step 1: Clone the github repo to get the customized ControlNet-StableDiffusion-inpaint Pipeline implimentation
+
+`git clone https://github.com/Onkarsus13/Diff_SceneTextEraser`
+
+Step2: Go into the repository
+
+```
+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
+
+```python
+ 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')
+
+```