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Runtime error
| 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(768, 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) | |