Update app.py

app.py

@@ -1,43 +1,164 @@
+import os
+import random
+import uuid
+
 import gradio as gr
+import numpy as np
+from PIL import Image
 import torch
-from transformers import DiffusionModel, DiffusionImageProcessor, AutoTokenizer
-from threading import Thread
+from transformers import DiffusionModel, DiffusionImageProcessor
 
-print("Starting to load the model to memory")
-# Load the diffusion model and image processor
-model = DiffusionModel.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
-processor = DiffusionImageProcessor.from_model(model)
-tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0")
+DESCRIPTION = """
+# Image Generator
+"""
 
-print("Successfully loaded the model to memory")
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
 
-def generate_image(text):
-    # Generate an image from the given text prompt
-    inputs = tokenizer(text, return_tensors="pt")
-    # Run generation on GPU if available
-    inputs.to(torch.device("cuda" if torch.cuda.is_available() else "cpu"))
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
 
-    # Run diffusion model for image generation
+def generate_image(text):
+    model = DiffusionModel.from_pretrained("stability/stable-diffusion-xl-base-1.0")
+    processor = DiffusionImageProcessor.from_model(model)
+    inputs = processor(text, return_tensors="pt")
     with torch.no_grad():
         result = processor.generate(**inputs)
-
-    # Return the generated image
     return result[0]
 
-# Define a function to handle user input and generate images
-def image_generator(text):
-    generated_image = generate_image(text)
-    return generated_image
-
-# Create a Gradio interface for the image generation
-interface = gr.Interface(
-    fn=image_generator,
-    inputs="text",
-    outputs="image",
-    title="Image Generation from Text",
-    description="Enter a text prompt to generate an image.",
-    examples=["a cat sitting on a couch"]
-)
-
-# Launch the interface
-interface.launch(share=True)
+@gr.output_hook
+def post_process_images(paths):
+    return [gr.outputs.Image(path) for path in paths]
+
+@gr.output_hook
+def post_process_seed(seed):
+    return seed
+
+examples = [
+    "Newton and Einstein sitting together and thinking about gravity and space",
+    "an astronaut riding a horse in space",
+    "a cartoon of a boy playing with a tiger",
+    "neon holography crystal cat",
+    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone",
+    "a cute robot artist painting on an easel,concept art",
+    "a cat eating a piece of cheese",
+]
+
+css = '''
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+footer {
+    visibility: hidden
+}
+'''
+with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=False,
+    )
+
+    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, preview=True, show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            lines=4,
+            max_lines=6,
+            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
+            placeholder="Enter a negative prompt",
+            visible=True,
+        )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            visible=True
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=2048,
+                step=8,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=2048,
+                step=8,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=20.0,
+                step=0.1,
+                value=6,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate_image,
+        cache_examples=False,
+        output_hooks=[post_process_images, post_process_seed]
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+    
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate_image,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+        ],
+        outputs=[result, seed],
+        api_name="run",
+    )
+    
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(show_api=False, debug=False)