Create app.py

app.py

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+import os
+import json
+import random
+from getpass import getpass
+from glob import glob
+from natsort import natsorted
+import matplotlib.pyplot as plt
+from PIL import Image
+
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from IPython.display import display
+
+
+import gradio as gr
+
+from gradio.components import Textbox, Image
+
+
+repo_name = 'mohansathya/twosd' # YOUR REPO NAME
+pipe2 = StableDiffusionPipeline.from_pretrained(repo_name, torch_dtype=torch.float16).to('cuda')
+
+
+
+def generate_query_response(prompt):
+    negative_prompt = "bad anatomy, ugly, deformed, desfigured, distorted, poorly drawn, blurry, low quality, low definition, lowres, out of frame, out of image, cropped, cut off, signature, watermark"
+    num_samples = 5
+    guidance_scale = 7.5
+    num_inference_steps = 30
+    height = 512
+    width = 512
+
+    seed = random.randint(0, 2147483647)
+    print("Seed: {}".format(str(seed)))
+    generator = torch.Generator(device='cuda').manual_seed(seed)
+
+    with autocast("cuda"), torch.inference_mode():
+        imgs = pipe2(
+            prompt,
+            negative_prompt=negative_prompt,
+            height=height, width=width,
+            num_images_per_prompt=num_samples,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            generator=generator
+        ).images
+
+    for img in imgs:
+        return img
+
+    
+# Input from user
+in_prompt = Textbox(label="Enter a prompt:")
+
+# Output response
+out_response = Image(label="Generated image:")
+    
+# Gradio interface to generate UI link
+iface = gr.Interface(
+    fn=generate_query_response, inputs=in_prompt, outputs=out_response) 
+
+# Launch the interface to generate UI
+iface.launch()