from diffusers import StableDiffusionPipeline import torch import requests from PIL import Image from io import BytesIO from diffusers import StableDiffusionImg2ImgPipeline device = "cpu" #model_path = "weights" #model_id_or_path = "runwayml/stable-diffusion-v1-5" model_id = "rikdas/weights" pipe = StableDiffusionPipeline.from_pretrained(model_id).to(device) pipe2 = StableDiffusionImg2ImgPipeline.from_pretrained(model_id).to(device) import gradio as gr def generate_txt2img(prompt): return pipe(prompt, num_inference_steps=25, guidance_scale=7.5).images[0] def generate_img2img(img, prompt): image = Image.fromarray(img) return pipe2(prompt=prompt, image=image, strength=0.75, guidance_scale=7.5).images[0] with gr.Blocks() as demo: with gr.Tab("Text2Image"): inp_txt = gr.Text(showlabel=False, placeholder="Enter your prompt here...") btn = gr.Button("Generate") out_img = gr.Image() btn.click(fn=generate_txt2img, inputs=[inp_txt], outputs=[out_img]) with gr.Tab("Image2Image"): inp_img = gr.Image() inp_txt2 = gr.Text(showlabel=False,placeholder="Enter your prompt here...") btn2 = gr.Button("Generate") out_img2 = gr.Image() btn2.click(fn=generate_img2img, inputs=[inp_img, inp_txt2], outputs=[out_img2]) demo.launch(debug=True)