Update app.py

app.py

@@ -1,18 +1,30 @@
 #!/usr/bin/env python
 
-import os
-import uuid
 import gradio as gr
-import spaces
 import torch
 from diffusers import DiffusionPipeline
+import spaces
+import os
+import random
+import numpy as np
+from PIL import Image
+import uuid
 
+# Description et Configuration
 DESCRIPTION = """# Playground v2.5"""
 if not torch.cuda.is_available():
     DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
 
+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"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+# Définir l'appareil
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
 
+# Charger le pipeline de diffusion
 pipe = DiffusionPipeline.from_pretrained(
     "playgroundai/playground-v2.5-1024px-aesthetic",
     torch_dtype=torch.float16,
@@ -21,12 +33,24 @@ pipe = DiffusionPipeline.from_pretrained(
     variant="fp16"
 )
 pipe.to(device)
+if ENABLE_CPU_OFFLOAD:
+    pipe.enable_model_cpu_offload()
+if USE_TORCH_COMPILE:
+    pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
 
+# Fonction pour enregistrer l'image
 def save_image(img):
     unique_name = str(uuid.uuid4()) + ".png"
     img.save(unique_name)
     return unique_name
 
+# Fonction pour randomiser le seed
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+# Fonction de génération avec ZeroGPU
 @spaces.GPU(enable_queue=True)
 def generate(
     prompt: str,
@@ -37,48 +61,142 @@ def generate(
     height: int = 1024,
     guidance_scale: float = 3,
     randomize_seed: bool = False,
+    use_resolution_binning: bool = True,
+    progress=gr.Progress(track_tqdm=True),
 ):
     pipe.to(device)
-    seed = random.randint(0, np.iinfo(np.int32).max) if randomize_seed else seed
+    seed = int(randomize_seed_fn(seed, randomize_seed))
     generator = torch.Generator().manual_seed(seed)
 
+    if not use_negative_prompt:
+        negative_prompt = None
+
     images = pipe(
         prompt=prompt,
-        negative_prompt=negative_prompt if use_negative_prompt else None,
+        negative_prompt=negative_prompt,
         width=width,
         height=height,
         guidance_scale=guidance_scale,
         num_inference_steps=25,
         generator=generator,
+        num_images_per_prompt=1,
+        use_resolution_binning=use_resolution_binning,
+        output_type="pil",
     ).images
 
     image_paths = [save_image(img) for img in images]
     return image_paths, seed
 
-with gr.Blocks() as demo:
+# Exemples de prompts
+examples = [
+    "neon holography crystal cat",
+    "a cat eating a piece of cheese",
+    "an astronaut riding a horse in space",
+    "a cartoon of a boy playing with a tiger",
+    "a cute robot artist painting on an easel, concept art",
+    "a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone"
+]
+
+# CSS et Interface Gradio
+css = '''
+.gradio-container{max-width: 560px !important}
+h1{text-align:center}
+'''
+with gr.Blocks(css=css) as demo:
     gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(
+        value="Duplicate Space for private use",
+        elem_id="duplicate-button",
+        visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1",
+    )
     with gr.Group():
         with gr.Row():
-            prompt = gr.Textbox(label="Prompt")
-            run_button = gr.Button("Run")
-        result = gr.Gallery(label="Result")
+            prompt = gr.Textbox(
+                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)
     with gr.Accordion("Advanced options", open=False):
-        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
-        negative_prompt = gr.Textbox(label="Negative prompt")
-        seed = gr.Slider(label="Seed", minimum=0, maximum=np.iinfo(np.int32).max, step=1, value=0)
+        with gr.Row():
+            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+            negative_prompt = gr.Textbox(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=True,
+            )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+        )
         randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=32, value=1024)
-        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=32, value=1024)
-        guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=20, step=0.1, value=3.0)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=256,
+                maximum=MAX_IMAGE_SIZE,
+                step=32,
+                value=1024,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=20,
+                step=0.1,
+                value=3.0,
+            )
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=CACHE_EXAMPLES,
+    )
+
+    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],
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
         fn=generate,
-        inputs=[prompt, negative_prompt, use_negative_prompt, seed, width, height, guidance_scale, randomize_seed],
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+        ],
         outputs=[result, seed],
         api_name="run",
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.queue(max_size=20).launch()