Create app.py

app.py

@@ -0,0 +1,51 @@
+"""
+Adapted from https://huggingface.co/spaces/stabilityai/stable-diffusion
+"""
+
+import torch
+
+import time
+
+import gradio as gr
+
+from constants import css, examples, img_height, img_width, num_images_to_gen
+from share_btn import community_icon_html, loading_icon_html, share_js
+
+from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler
+
+model_ckpt = "stabilityai/stable-diffusion-2-base"
+scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
+pipe = StableDiffusionPanoramaPipeline.from_pretrained(
+     model_ckpt, scheduler=scheduler, torch_dtype=torch.float16
+)
+
+pipe = pipe.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+
+def generate_image_fn(prompt: str, guidance_scale: float) -> list:
+    start_time = time.time()
+    prompt = "a photo of the dolomites"
+    image = pipe(prompt, guidance_scale=guidance_scale).images
+    end_time = time.time()
+    print(f"Time taken: {end_time - start_time} seconds.")
+    return image
+
+
+description = "This Space demonstrates MultiDiffusion Text2Panorama using Stable Diffusion model. You can use it for generating custom pokemons. To get started, either enter a prompt and pick one from the examples below. For details on the fine-tuning procedure, refer to [this repository]()."
+article = "This Space leverages a T4 GPU to run the predictions. We use mixed-precision to speed up the inference latency."
+gr.Interface(
+    generate_image_fn,
+    inputs=[
+        gr.Textbox(
+            label="Enter your prompt",
+            max_lines=1,
+            placeholder="a photo of the dolomites",
+        ),
+        gr.Slider(value=40, minimum=8, maximum=50, step=1),
+    ],
+    outputs=gr.Gallery().style(grid=[2], height="auto"),
+    title="Generate custom pokemons",
+    description=description,
+    article=article,
+    examples=[["a photo of the dolomites", 40]],
+    allow_flagging=False,
+).launch(enable_queue=True)