Create app.py

app.py

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+import gradio as gr
+import inspect
+import warnings
+import numpy as np
+from typing import List, Optional, Union
+import requests
+from io import BytesIO
+from PIL import Image
+import torch
+from torch import autocast
+from tqdm.auto import tqdm
+from diffusers import StableDiffusionImg2ImgPipeline
+
+access_token = "TOKEN"
+# Go to hugging face, your profile "SETTINGS" options, click on "Access tokens", and then generate ur token from there. Paste it over the top
+
+# load the pipeline
+device = "cuda"
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "CompVis/stable-diffusion-v1-4",
+    revision="fp16", 
+    torch_dtype=torch.float16,
+use_auth_token=access_token
+).to(device)
+
+
+
+
+
+
+def generate(img, strength, seed, prompt):
+    # Convert the seed to an integer
+    seed = int(seed)
+
+    img1 = np.asarray(img)
+    img2 = Image.fromarray(img1)
+
+    # Check that the input image is a valid image
+    if not isinstance(img2, Image.Image):
+        raise ValueError("Invalid input image")
+
+    # Resize the image
+    init_image = img2.resize((768, 512))
+
+    # Create a list to store the 4 output images
+    images = []
+
+    # Use the GPU if available, otherwise use the CPU
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    with autocast(device):
+        # Generate the 4 output images using the pipe function
+        for i in range(2):
+            # Initialize the generator with a random seed
+            generator = torch.Generator(device=device).manual_seed(seed*i)
+
+            # Call the pipe function and store the output image
+            output_image = pipe(prompt=prompt, init_image=init_image, strength=strength, guidance_scale=7.5, generator=generator, batch_size=128).images[0]
+
+            # Check that the output image is a valid image
+            if not isinstance(output_image, Image.Image):
+                raise ValueError("Invalid output image")
+
+            images.append(output_image)
+
+    return [images[0], images[1]]
+
+
+gr.Interface(
+    
+    generate,
+    title = 'Image to Image using Diffusers',
+    inputs=[
+        gr.Image(elem_id = "input-image"),
+        gr.Slider(0, 1, value=0.05, label ="Strength (keep close to 0 for minimal changes)"),
+        gr.Slider(50, 700, value=75, label ="Seed"),
+        gr.Textbox(label="Prompt (leave blank if you want minimal changes)"),
+    ],
+    outputs = [
+        gr.Image(elem_id="output-image"),
+        gr.Image(elem_id="output-image"),
+
+        ], css = "#output-image, #input-image, #image-preview {border-radius: 40px !important; background-color : gray !important;} "
+).launch(share=True, debug=True)