Update app.py

app.py

@@ -1,50 +1,3 @@
-import torch
-from diffusers import StableDiffusionPipeline, DDIMScheduler
 import gradio as gr
 
-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()
+gr.load("hysts/ControlNet-v1-1", src="models/MVRL/GeoSynth").launch()