Update app.py

app.py

@@ -1,48 +1,24 @@
 import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-import torch
+from gradio_client import Client
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
+client = Client("multimodalart/FLUX.1-merged")
 
-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.enable_xformers_memory_efficient_attention()
-    pipe = pipe.to(device)
-else: 
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
-    pipe = pipe.to(device)
-
-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):
-
-    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] 
+def infer(prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, api_name):
     
-    return image
+    result = client.predict(
+		prompt=prompt,
+		seed=seed,
+		randomize_seed=True,
+		width=width,
+		height=height,
+		guidance_scale=guidance_scale,
+		num_inference_steps=num_inference_steps,
+		api_name="/infer"
+    )
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
+    return result
 
+    
 css="""
 #col-container {
     margin: 0 auto;
@@ -50,17 +26,12 @@ css="""
 }
 """
 
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
+        #DiffusionLab Beta
         """)
         
         with gr.Row():
@@ -73,23 +44,16 @@ with gr.Blocks(css=css) as demo:
                 container=False,
             )
             
-            run_button = gr.Button("Run", scale=0)
+            run_button = gr.Button("Create", scale=0)
         
         result = gr.Image(label="Result", show_label=False)
 
         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",
                 minimum=0,
-                maximum=MAX_SEED,
+                maximum=999999,
                 step=1,
                 value=0,
             )
@@ -101,27 +65,27 @@ with gr.Blocks(css=css) as demo:
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
+                    maximum=2048,
                     step=32,
-                    value=512,
+                    value=1024,
                 )
                 
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
+                    maximum=2024,
                     step=32,
-                    value=512,
+                    value=1024,
                 )
             
             with gr.Row():
                 
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
-                    minimum=0.0,
+                    minimum=0.1,
                     maximum=10.0,
                     step=0.1,
-                    value=0.0,
+                    value=1.0,
                 )
                 
                 num_inference_steps = gr.Slider(
@@ -132,14 +96,10 @@ with gr.Blocks(css=css) as demo:
                     value=2,
                 )
         
-        gr.Examples(
-            examples = examples,
-            inputs = [prompt]
-        )
 
     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]
     )