CACHE_EXAMPLES

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 import random
 import numpy as np
 import spaces
-
+import os
 
 if torch.cuda.is_available():
     device = "cuda"
@@ -15,7 +15,7 @@ else:
 
 
 MAX_SEED = np.iinfo(np.int32).max
-
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
 
 # Initialize the pipeline and download the sdxl flash model
 pipe = StableDiffusionXLPipeline.from_pretrained("sd-community/sdxl-flash", torch_dtype=torch.float16)
@@ -96,28 +96,16 @@ with gr.Blocks(css=css) as demo:
 
     gr.Examples(
         examples=examples,
-        inputs=[prompt],
-        outputs=[result],
         fn=generate_image,
+        inputs=[prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
+        outputs=[result],
+        cache_examples=CACHE_EXAMPLES
     )
 
-    gr.on(
-        triggers=[
-            prompt.submit,
-            run_button.click,
-        ],
+    run_button.click(
         fn=generate_image,
-        inputs=[
-            prompt,
-            negative_prompt,
-            num_inference_steps,
-            width,
-            height,
-            guidance_scale,
-            seed,
-            num_images_per_prompt,
-        ],
+        inputs=[prompt, negative_prompt, num_inference_steps, height, width, guidance_scale, seed, num_images_per_prompt],
         outputs=[result],
     )
 
-demo.queue().launch(share = False)
+demo.queue().launch(share=False)