initial gradio app

app.py

@@ -1,10 +1,83 @@
-
 import gradio as gr
-description = "BigGAN text-to-image demo."
-title = "BigGAN ImageNet"
-interface = gr.Interface.load("huggingface/osanseviero/BigGAN-deep-128", 
-            description=description,
-            title = title,
-            examples=[["american robin"]]
-)
-interface.launch()
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+from datasets import load_dataset
+from PIL import Image  
+import re
+import os
+
+auth_token = os.getenv("auth_token")
+model_id = "CompVis/stable-diffusion-v1-4"
+device = "cpu"
+#pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token, revision="fp16", torch_dtype=torch.float16)
+pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=auth_token)
+pipe = pipe.to(device)
+
+def infer(prompt, samples, steps, scale, seed):        
+    generator = torch.Generator(device=device).manual_seed(seed)
+    images_list = pipe(
+        [prompt] * samples,
+        num_inference_steps=steps,
+        guidance_scale=scale,
+        generator=generator,
+    )
+    images = []
+    safe_image = Image.open(r"unsafe.png")
+    for i, image in enumerate(images_list["sample"]):
+        if(images_list["nsfw_content_detected"][i]):
+            images.append(safe_image)
+        else:
+            images.append(image)
+    return images
+    
+
+
+block = gr.Blocks()
+
+with block:
+    with gr.Group():
+        with gr.Box():
+            with gr.Row().style(mobile_collapse=False, equal_height=True):
+                text = gr.Textbox(
+                    label="Enter your prompt",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Enter your prompt",
+                ).style(
+                    border=(True, False, True, True),
+                    rounded=(True, False, False, True),
+                    container=False,
+                )
+                btn = gr.Button("Generate image").style(
+                    margin=False,
+                    rounded=(False, True, True, False),
+                )
+        gallery = gr.Gallery(
+            label="Generated images", show_label=False, elem_id="gallery"
+        ).style(grid=[2], height="auto")
+
+        advanced_button = gr.Button("Advanced options", elem_id="advanced-btn")
+
+        with gr.Row(elem_id="advanced-options"):
+            samples = gr.Slider(label="Images", minimum=1, maximum=4, value=4, step=1)
+            steps = gr.Slider(label="Steps", minimum=1, maximum=50, value=45, step=1)
+            scale = gr.Slider(
+                label="Guidance Scale", minimum=0, maximum=50, value=7.5, step=0.1
+            )
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=2147483647,
+                step=1,
+                randomize=True,
+            )
+        text.submit(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
+        btn.click(infer, inputs=[text, samples, steps, scale, seed], outputs=gallery)
+        advanced_button.click(
+            None,
+            [],
+            text,
+        )
+        
+block.launch()