Create app.py

app.py

@@ -0,0 +1,87 @@
+import os
+import random
+import uuid
+
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+from diffusers import DiffusionPipeline
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536"))
+
+device = torch.device("cuda:0")
+
+pipe = DiffusionPipeline.from_pretrained(
+    "playgroundai/playground-v2.5-1024px-aesthetic",
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    add_watermarker=False,
+    variant="fp16"
+)
+pipe.to(device)
+print("Loaded on Device!")
+
+
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+
+@spaces.GPU(enable_queue=True)
+def generate(
+        prompt: str,
+        progress=gr.Progress(track_tqdm=True),
+):
+    seed = random.randint(0, 2147483647)
+    pipe.to(device)
+    generator = torch.Generator().manual_seed(seed)
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=None,
+        width=1024,
+        height=1024,
+        guidance_scale=3,
+        num_inference_steps=25,
+        generator=generator,
+        num_images_per_prompt=1,
+        use_resolution_binning=True,
+        output_type="pil",
+    ).images
+
+    image_paths = [save_image(img) for img in images]
+    return image_paths
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Blossom Playground v2.5")
+    with gr.Group():
+        with gr.Row():
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            run_button = gr.Button("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, show_label=False)
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+        ],
+        outputs=[result],
+        api_name="run",
+    )
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch()