diff_scenetexteraser

https://github.com/onkarsus13/diff_scenetexteraser

Science Score: 44.0%

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Last synced: 10 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: Onkarsus13
License: apache-2.0
Language: Python
Default Branch: main
Size: 5.95 MB

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Stars: 0
Watchers: 1
Forks: 1
Open Issues: 0
Releases: 0

Created almost 3 years ago · Last pushed about 2 years ago

Metadata Files

Readme Contributing License Code of conduct Citation

README.md

This is the trained model for the controlnet-stablediffusion for the scene text eraser. We have to customized the pipeline for the controlnet-stablediffusion-inpaint

To training the model we had to use the SCUT-Ensnet dataset

Installation

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

You can get the changes in the official repository

Inference

python test_eraser.py

Check the Inference code and Colab Notebook

```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")

modelpath = "onkarsus13/controlnetstablediffusion_scenetextEraser"

pipe = StableDiffusionControlNetSceneTextErasingPipeline.frompretrained(modelpath)

pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

pipe.to(device)

pipe.enablexformersmemoryefficientattention()

pipe.enablemodelcpu_offload()

generator = torch.Generator(device).manual_seed(1)

image = Image.open("").resize((512, 512)) mask_image = Image.open('').resize((512, 512))

image = pipe( image, maskimage, [maskimage], numinferencesteps=20, generator=generator, controlnetconditioningscale=1.0, guidance_scale=1.0 ).images[0]

image.save('test1.png')

```

You will find the models checkpoints here

Owner

Name: ONKAR Susladkar
Login: Onkarsus13
Kind: user

Repositories: 66
Profile: https://github.com/Onkarsus13

Artifitial Inteegence | Deep learning | Computer Vision | Natural language Prosessing |

Citation (CITATION.cff)

cff-version: 1.2.0
title: 'Diffusers: State-of-the-art diffusion models'
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Patrick
    family-names: von Platen
  - given-names: Suraj
    family-names: Patil
  - given-names: Anton
    family-names: Lozhkov
  - given-names: Pedro
    family-names: Cuenca
  - given-names: Nathan
    family-names: Lambert
  - given-names: Kashif
    family-names: Rasul
  - given-names: Mishig
    family-names: Davaadorj
  - given-names: Thomas
    family-names: Wolf
repository-code: 'https://github.com/huggingface/diffusers'
abstract: >-
  Diffusers provides pretrained diffusion models across
  multiple modalities, such as vision and audio, and serves
  as a modular toolbox for inference and training of
  diffusion models.
keywords:
  - deep-learning
  - pytorch
  - image-generation
  - diffusion
  - text2image
  - image2image
  - score-based-generative-modeling
  - stable-diffusion
license: Apache-2.0
version: 0.12.1

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