Update app.py

app.py

@@ -1,6 +1,44 @@
-import gradio as gr
-import os
+import torch
+from diffusers import StableDiffusionPipeline, DDIMScheduler
 
-hf_token = os.environ.get("hf_token")
+pipe = StableDiffusionPipeline.from_pretrained("MVRL/GeoSynth")
+scheduler = DDIMScheduler.from_pretrained("stabilityai/stable-diffusion-2-1-base")
+pipe.scheduler = scheduler
 
-gr.load("models/MVRL/SD-GeoSynth", hf_token=hf_token).launch()
+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("## Control Stable Diffusion with Depth Maps")
+ with gr.Row():
+ with gr.Column():
+ prompt = gr.Textbox(label="Prompt")
+ run_button = gr.Button(label="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").style(height='auto')
+ 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()