return to normal

app.py

@@ -3,25 +3,12 @@ import numpy as np
 import random
 import spaces
 import torch
-from diffusers import FluxPipeline
+from diffusers import DiffusionPipeline
 
-# Enable cuDNN benchmarking for potential performance improvement
-torch.backends.cudnn.benchmark = True
-
-# Set up device and data types
+dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
-DTYPE = torch.float16
-
-# Load the model
-pipe = FluxPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell",
-    torch_dtype=torch.bfloat16,
-)
 
-# Configure the pipeline
-pipe.enable_sequential_cpu_offload()
-pipe.vae.enable_tiling()
-pipe = pipe.to(DTYPE)
+pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
@@ -30,18 +17,15 @@ MAX_IMAGE_SIZE = 2048
 def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
+    generator = torch.Generator().manual_seed(seed)
     image = pipe(
-        prompt,
-        num_inference_steps=num_inference_steps,
-        num_images_per_prompt=1,
-        guidance_scale=0.0,
-        height=height,
-        width=width,
-        generator=generator,
-    ).images[0]
-    
+            prompt = prompt, 
+            width = width,
+            height = height,
+            num_inference_steps = num_inference_steps, 
+            generator = generator,
+            guidance_scale=0.0
+    ).images[0] 
     return image, seed
 
 # Gradio interface