Update app.py

app.py

@@ -1,46 +1,48 @@
 import gradio as gr
 import numpy as np
 import random
-from diffusers import DiffusionPipeline
+from diffusers import FluxPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
     torch.cuda.max_memory_allocated(device=device)
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
+    pipe = FluxPipeline.from_pretrained("enhanceaiteam/kalpana", torch_dtype=torch.bfloat16)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
+    pipe.enable_model_cpu_offload()
 else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = FluxPipeline.from_pretrained("enhanceaiteam/kalpana", torch_dtype=torch.bfloat16)
     pipe = pipe.to(device)
+    pipe.enable_model_cpu_offload()
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
-
+def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+    
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator().manual_seed(seed)
     
     image = pipe(
-        prompt = prompt, 
-        negative_prompt = negative_prompt,
-        guidance_scale = guidance_scale, 
-        num_inference_steps = num_inference_steps, 
-        width = width, 
-        height = height,
-        generator = generator
-    ).images[0] 
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        height=height,
+        width=width,
+        num_inference_steps=num_inference_steps,
+        max_sequence_length=256,
+        generator=generator,
+    ).images[0]
     
     return image
 
 examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "A cat holding a sign that says 'hello world'",
     "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+    "A futuristic cityscape at sunset",
 ]
 
 css="""
@@ -79,12 +81,6 @@ with gr.Blocks(css=css) as demo:
 
         with gr.Accordion("Advanced Settings", open=False):
             
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
             
             seed = gr.Slider(
                 label="Seed",
@@ -129,7 +125,7 @@ with gr.Blocks(css=css) as demo:
                     minimum=1,
                     maximum=12,
                     step=1,
-                    value=2,
+                    value=4,
                 )
         
         gr.Examples(
@@ -139,7 +135,7 @@ with gr.Blocks(css=css) as demo:
 
     run_button.click(
         fn = infer,
-        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
         outputs = [result]
     )