import gradio as gr import torch import streamlit as st from PIL import Image import numpy as np from io import BytesIO from diffusers import StableDiffusionImg2ImgPipeline device="cpu" pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=st.secrets['USER_TOKEN']) pipe.to(device) def resize(w_val,l_val,img): img = Image.open(img) img = img.resize((w_val,l_val), Image.Resampling.LANCZOS) #img = img.resize((value,value), Image.Resampling.LANCZOS) return img def infer(source_img, prompt, guide, steps, seed, Strength): generator = torch.Generator('cpu').manual_seed(seed) source_image = resize(512, source_img) source_image.save('source.png') image_list = pipe([prompt], init_image=source_image, strength=Strength, guidance_scale=guide, num_inference_steps=steps) images = [] safe_image = Image.open(r"unsafe.png") for i, image in enumerate(image_list["sample"]): if(image_list["nsfw_content_detected"][i]): images.append(safe_image) else: images.append(image) return image gr.Interface(fn=infer, inputs=[gr.Image(source="upload", type="filepath", label="Raw Image"), gr.Textbox(label = 'Prompt Input Text'), gr.Slider(2, 15, value = 7, label = 'Guidence Scale'), gr.Slider(10, 50, value = 25, step = 1, label = 'Number of Iterations'), gr.Slider( label = "Seed", minimum = 0, maximum = 2147483647, step = 1, randomize = True), gr.Slider(label='Strength', minimum = 0, maximum = 1, step = .05, value = .5) ], outputs='image').queue(max_size=10).launch(enable_queue=True)