remove multi decoder

app.py

@@ -13,16 +13,13 @@ import uuid
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype = torch.float16
 
-MULTI_DECODER = (
-    torch.cuda.get_device_properties(0).total_memory < 18 * 1024 * 1024 * 1024
-)
-LOW_MEMORY = os.getenv("LOW_MEMORY", not MULTI_DECODER) == "1"
+LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"
 
 print(f"device: {device}")
 print(f"dtype: {dtype}")
-print(f"multi decoder: {MULTI_DECODER}")
 print(f"low memory: {LOW_MEMORY}")
 
+
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=dtype)
 pipe = DiffusionPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0",
@@ -33,6 +30,7 @@ pipe = DiffusionPipeline.from_pretrained(
     use_safetensors=True,
     vae=vae,
 )
+
 pipe = pipe.to(device)
 
 
@@ -96,7 +94,7 @@ def predict(
         cosine_scale_2=1,
         cosine_scale_3=1,
         sigma=0.8,
-        multi_decoder=MULTI_DECODER,
+        multi_decoder=False,
         show_image=False,
         lowvram=LOW_MEMORY,
     )
@@ -127,7 +125,7 @@ with gr.Blocks(css=css) as demo:
 [DemoFusion](https://ruoyidu.github.io/demofusion/demofusion.html) enables higher-resolution image generation.  
 You can upload an initial image and prompt to generate an enhanced version. 
 [Duplicate Space](https://huggingface.co/spaces/radames/Enhance-This-DemoFusion-SDXL?duplicate=true) to avoid the queue.  
-GPU Time Comparison: T4: - A10G: ~175s A100: RTX 4090: ~48.1s
+GPU Time Comparison: T4: ~276s - A10G: ~175s A100: ~43.5s RTX 4090: ~48.1s
 
 <small>
 <b>Notes</b> The author advises against the term "super resolution" because it's more like image-to-image generation than enhancement, but it's still a lot of fun!