Update

app.py

@@ -11,47 +11,29 @@ import torch
 from diffusers import AutoencoderKL, DiffusionPipeline
 
 DESCRIPTION = "# SDXL"
-if not torch.cuda.is_available():
-    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
-USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
-ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
 ENABLE_REFINER = os.getenv("ENABLE_REFINER", "1") == "1"
 
 device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-if torch.cuda.is_available():
-    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-    pipe = DiffusionPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+pipe = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    vae=vae,
+    torch_dtype=torch.float16,
+    use_safetensors=True,
+    variant="fp16",
+).to(device)
+if ENABLE_REFINER:
+    refiner = DiffusionPipeline.from_pretrained(
+        "stabilityai/stable-diffusion-xl-refiner-1.0",
         vae=vae,
         torch_dtype=torch.float16,
         use_safetensors=True,
         variant="fp16",
-    )
-    if ENABLE_REFINER:
-        refiner = DiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-xl-refiner-1.0",
-            vae=vae,
-            torch_dtype=torch.float16,
-            use_safetensors=True,
-            variant="fp16",
-        )
-
-    if ENABLE_CPU_OFFLOAD:
-        pipe.enable_model_cpu_offload()
-        if ENABLE_REFINER:
-            refiner.enable_model_cpu_offload()
-    else:
-        pipe.to(device)
-        if ENABLE_REFINER:
-            refiner.to(device)
-
-    if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-        if ENABLE_REFINER:
-            refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
+    ).to(device)
 
 
 def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
@@ -136,7 +118,7 @@ with gr.Blocks(css_paths="style.css") as demo:
 
     with gr.Group():
         with gr.Row():
-            prompt = gr.Text(
+            prompt = gr.Textbox(
                 label="Prompt",
                 show_label=False,
                 max_lines=1,
@@ -149,23 +131,26 @@ with gr.Blocks(css_paths="style.css") as demo:
             use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
             use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
             use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
-        negative_prompt = gr.Text(
+        negative_prompt = gr.Textbox(
             label="Negative prompt",
             max_lines=1,
             placeholder="Enter a negative prompt",
             visible=False,
+            value="",
         )
-        prompt_2 = gr.Text(
+        prompt_2 = gr.Textbox(
             label="Prompt 2",
             max_lines=1,
             placeholder="Enter your prompt",
             visible=False,
+            value="",
         )
-        negative_prompt_2 = gr.Text(
+        negative_prompt_2 = gr.Textbox(
             label="Negative prompt 2",
             max_lines=1,
             placeholder="Enter a negative prompt",
             visible=False,
+            value="",
         )
 
         seed = gr.Slider(
@@ -291,8 +276,8 @@ with gr.Blocks(css_paths="style.css") as demo:
             apply_refiner,
         ],
         outputs=result,
-        api_name="run",
+        api_name="predict",
     )
 
 if __name__ == "__main__":
-    demo.queue(max_size=20).launch()
+    demo.launch()

style.css

@@ -3,13 +3,6 @@ h1 {
   display: block;
 }
 
-#duplicate-button {
-  margin: auto;
-  color: #fff;
-  background: #1565c0;
-  border-radius: 100vh;
-}
-
 .contain {
   max-width: 730px;
   margin: auto;