Update app.py

app.py

@@ -10,16 +10,25 @@ import spaces
 import torch
 from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
 
+def save_image(img):
+    unique_name = str(uuid.uuid4()) + ".png"
+    img.save(unique_name)
+    return unique_name
+
+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>"
 
 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", "4096"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
 
-device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
 
 if torch.cuda.is_available():
     pipe = StableDiffusionXLPipeline.from_pretrained(
@@ -29,23 +38,14 @@ if torch.cuda.is_available():
         add_watermarker=False
     )
     pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    
+    
     pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
     pipe.set_adapters("dalle")
 
     pipe.to("cuda")
 
-
-def save_image(img):
-    unique_name = str(uuid.uuid4()) + ".png"
-    img.save(unique_name)
-    return unique_name
-
-def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    return seed
-
-@spaces.GPU(duration=30, queue=False)
+@spaces.GPU(enable_queue=True)
 def generate(
     prompt: str,
     negative_prompt: str = "",
@@ -54,32 +54,30 @@ def generate(
     width: int = 1024,
     height: int = 1024,
     guidance_scale: float = 3,
-    num_inference_steps: int = 10,
     randomize_seed: bool = False,
-    use_resolution_binning: bool = True,
     progress=gr.Progress(track_tqdm=True),
 ):
-    pipe.to(device)
-    seed = int(randomize_seed_fn(seed, randomize_seed))
-    generator = torch.Generator().manual_seed(seed)   
-
-    options = {
-        "prompt":prompt,
-        "negative_prompt":negative_prompt,
-        "width":width,
-        "height":height,
-        "guidance_scale":guidance_scale,
-        "num_inference_steps":num_inference_steps,
-        "generator":generator,
-        "use_resolution_binning":use_resolution_binning,
-        "output_type":"pil",
-
-    }
     
-    images = pipe(**options).images
+    seed = int(randomize_seed_fn(seed, randomize_seed))
 
+    if not use_negative_prompt:
+        negative_prompt = ""  # type: ignore
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=25,
+        num_images_per_prompt=1,
+        cross_attention_kwargs={"scale": 0.65},
+        output_type="pil",
+    ).images
     image_paths = [save_image(img) for img in images]
+    print(image_paths)
     return image_paths, seed
+ 
 
 
 examples = [
@@ -112,55 +110,48 @@ with gr.Blocks(css=css) as demo:
                 container=False,
             )
             run_button = gr.Button("Run", scale=0)
-        result = gr.Gallery(label="Result", columns=1)
+        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
     with gr.Accordion("Advanced options", open=False):
-        with gr.Row():
-            use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=5,
-                lines=4,
-                placeholder="Enter a negative prompt",
-                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",
-                visible=True,
-            )
+        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=MAX_IMAGE_SIZE,
-                step=64,
+                maximum=2048,
+                step=8,
                 value=1024,
             )
             height = gr.Slider(
                 label="Height",
                 minimum=512,
-                maximum=MAX_IMAGE_SIZE,
-                step=64,
+                maximum=2048,
+                step=8,
                 value=1024,
             )
         with gr.Row():
             guidance_scale = gr.Slider(
                 label="Guidance Scale",
                 minimum=0.1,
-                maximum=6,
+                maximum=20.0,
                 step=0.1,
-                value=3.0,
-            )
-            num_inference_steps = gr.Slider(
-                label="Number of inference steps",
-                minimum=1,
-                maximum=15,
-                step=1,
-                value=8,
+                value=6,
             )
 
     gr.Examples(
@@ -168,7 +159,7 @@ with gr.Blocks(css=css) as demo:
         inputs=prompt,
         outputs=[result, seed],
         fn=generate,
-        cache_examples=CACHE_EXAMPLES,
+        cache_examples=False,
     )
 
     use_negative_prompt.change(
@@ -193,12 +184,11 @@ with gr.Blocks(css=css) as demo:
             width,
             height,
             guidance_scale,
-            num_inference_steps,
             randomize_seed,
         ],
         outputs=[result, seed],
-        api_name="run",
+        api_name="run"
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+    demo.queue(max_size=20).launch(show_api=False, debug=False)