Create app.py

app.py

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+import gradio as gr
+
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline, DDIMScheduler
+from IPython.display import display
+
+model_path = WEIGHTS_DIR             # If you want to use previously trained model saved in gdrive, replace this with the full path of model in gdrive
+
+pipe = StableDiffusionPipeline.from_pretrained(model_path, safety_checker=None, torch_dtype=torch.float16).to("cuda")
+pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+pipe.enable_xformers_memory_efficient_attention()
+
+g_cuda = torch.Generator(device='cuda')
+
+def inference(prompt, negative_prompt, num_samples, height=512, width=512, num_inference_steps=50, guidance_scale=7.5, g_seed=52362):
+    with torch.autocast("cuda"), torch.inference_mode():
+        return pipe(
+                prompt, height=int(height), width=int(width),
+                negative_prompt=negative_prompt,
+                num_images_per_prompt=int(num_samples),
+                num_inference_steps=int(num_inference_steps), guidance_scale=guidance_scale,
+                generator=g_cuda.manual_seed(g_seed)
+            ).images
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            prompt = gr.Textbox(label="Prompt", value="concept art portrait of suleyman_person person, hyper realistic face, intricate, detailed, evil, strong face")
+            negative_prompt = gr.Textbox(label="Negative Prompt", value="")
+            run = gr.Button(value="Generate")
+            with gr.Row():
+                num_samples = gr.Number(label="Number of Samples", value=4)
+                guidance_scale = gr.Number(label="Guidance Scale", value=7.5)
+            with gr.Row():
+                height = gr.Number(label="Height", value=512)
+                width = gr.Number(label="Width", value=512)
+            num_inference_steps = gr.Slider(label="Steps", value=50)
+        with gr.Column():
+            gallery = gr.Gallery()
+            with gr.Row():
+                g_seed = gr.Number(label="Random Number Generator Seed", value=52362)
+
+    run.click(inference, inputs=[prompt, negative_prompt, num_samples, height, width, num_inference_steps, guidance_scale, g_seed], outputs=gallery)
+
+demo.launch(debug=True, share=True)