Update app.py

app.py

@@ -18,10 +18,10 @@ from typing import Tuple
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import paramiko
 
-os.system("chmod +x ./cusparselt.sh")
-os.system("./cusparselt.sh")
-os.system("chmod +x ./cudnn.sh")
-os.system("./cudnn.sh")
+#os.system("chmod +x ./cusparselt.sh")
+#os.system("./cusparselt.sh")
+#os.system("chmod +x ./cudnn.sh")
+#os.system("./cudnn.sh")
 
 torch.backends.cuda.matmul.allow_tf32 = False
 torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = False
@@ -109,14 +109,12 @@ def load_and_prepare_model(model_id):
         "ford442/RealVisXL_V5.0_BF16": torch.bfloat16,
     }
     dtype = model_dtypes.get(model_id, torch.bfloat16)  # Default to float32 if not found
-    vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16,safety_checker=None).to('cuda')
+    vae = AutoencoderKL.from_pretrained("ford442/sdxl-vae-bf16", torch_dtype=torch.bfloat16,safety_checker=None).to(torch.bfloat16).to('cuda')
     pipe = StableDiffusionXLPipeline.from_pretrained(
         model_id,
         torch_dtype=torch.bfloat16,
-        use_safetensors=True,
         add_watermarker=False,
         vae=vae,
-        safety_checker=None,
     )
     pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
     pipe.to(torch.bfloat16)
@@ -161,8 +159,8 @@ def generate(
     seed: int = 1,
     width: int = 768,
     height: int = 768,
-    guidance_scale: float = 5,
-    num_inference_steps: int = 300,
+    guidance_scale: float = 4.2,
+    num_inference_steps: int = 250,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
@@ -212,7 +210,7 @@ def generate_cpu(
     width: int = 768,
     height: int = 768,
     guidance_scale: float = 5,
-    num_inference_steps: int = 225,
+    num_inference_steps: int = 250,
     randomize_seed: bool = False,
     use_resolution_binning: bool = True, 
     num_images: int = 1,  
@@ -359,14 +357,14 @@ with gr.Blocks(css=css, theme="bethecloud/storj_theme") as demo:
                 minimum=0.1,
                 maximum=6,
                 step=0.1,
-                value=5.0,
+                value=4.2,
             )
             num_inference_steps = gr.Slider(
                 label="Number of inference steps",
                 minimum=10,
                 maximum=1000,
                 step=10,
-                value=300,
+                value=250,
             )
 
     gr.Examples(