Update app.py

app.py

@@ -1,3 +1,50 @@
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler
 import gradio as gr
 
-gr.load("hysts/ControlNet-v1-1", src="MVRL/GeoSynth").launch()
+pipe = StableDiffusionPipeline.from_pretrained("MVRL/GeoSynth")
+# scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base")
+# pipe.scheduler = scheduler
+
+def process(prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta):
+    generator = torch.manual_seed(seed)
+    output_images = pipe(prompt,
+                         height=image_resolution,
+                         width=image_resolution,
+                         num_inference_steps=ddim_steps,
+                         guidance_scale=scale,
+                         negative_prompt=n_prompt,
+                         num_images_per_prompt=num_samples,
+                         eta=eta,
+                         generator=generator,
+                        ).images
+    return output_images
+
+block = gr.Blocks().queue()
+with block:
+    with gr.Row():
+        gr.Markdown(
+            """
+            # GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis
+            Srikumar Sastry*, Subash Khanal, Aayush Dhakal, Nathan Jacobs (*Corresponding Author)<br>
+            """
+        )
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt")
+            run_button = gr.Button(value="Run")
+            with gr.Accordion("Advanced options", open=True):
+                num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+                image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=7.5, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+                eta = gr.Number(label="eta (DDIM)", value=0.0)
+                n_prompt = gr.Textbox(label="Negative Prompt",
+                                      value='')
+        with gr.Column():
+            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery")
+    ips = [prompt, n_prompt, num_samples, image_resolution, ddim_steps, scale, seed, eta]
+    run_button.click(fn=process, inputs=ips, outputs=[result_gallery])
+
+block.launch()