Update app.py

app.py

@@ -1,146 +1,58 @@
-import gradio as gr
-import numpy as np
-import random
-from diffusers import DiffusionPipeline
-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.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
+!pip install gradio
+!pip install requests
 
-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)
+import gradio as gr
+import requests
+import base64
+from io import BytesIO
+from PIL import Image
+
+def generate_image(api_key, prompt, size):
+    sizes = {
+        "512x512": (512, 512),
+        "1024x1024": (1024, 1024),
+        "512x624": (512, 624),
+        "624x512": (624, 512)
+    }
     
-    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] 
+    height, width = sizes[size]
     
-    return image
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css="""
-#col-container {
-    margin: 0 auto;
-    max-width: 520px;
-}
-"""
-
-if torch.cuda.is_available():
-    power_device = "GPU"
-else:
-    power_device = "CPU"
-
-with gr.Blocks(css=css) as demo:
+    url = 'https://api.stability.ai/v1/generation/stable-diffusion-v1-6/text-to-image'
     
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""
-        # Text-to-Image Gradio Template
-        Currently running on {power_device}.
-        """)
-        
-        with gr.Row():
-            
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-            
-            run_button = gr.Button("Run", 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,
-                step=1,
-                value=0,
-            )
-            
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-            
-            with gr.Row():
-                
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-                
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=512,
-                )
-            
-            with gr.Row():
-                
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,
-                )
-                
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=12,
-                    step=1,
-                    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],
-        outputs = [result]
-    )
-
-demo.queue().launch()
+    headers = {
+        'Authorization': f'Bearer {api_key}',
+        'Content-Type': 'application/json'
+    }
+    
+    data = {
+        'text_prompts': [{'text': prompt}],
+        'cfg_scale': 7,
+        'height': height,
+        'width': width,
+        'samples': 1,
+        'steps': 30
+    }
+    
+    response = requests.post(url, headers=headers, json=data)
+    
+    if response.status_code == 200:
+        response_data = response.json()
+        image_base64 = response_data['artifacts'][0]['base64']
+        image = Image.open(BytesIO(base64.b64decode(image_base64)))
+        return image
+    else:
+        return f"Error: {response.json()['error']}"
+
+api_key_input = gr.Textbox(label="API Key", type="password")
+prompt_input = gr.Textbox(label="Prompt")
+size_input = gr.Dropdown(choices=["512x512", "1024x1024", "512x624", "624x512"], label="Image Size")
+
+iface = gr.Interface(
+    fn=generate_image,
+    inputs=[api_key_input, prompt_input, size_input],
+    outputs=gr.Image(type="pil"),
+    title="Stable Diffusion Image Generator",
+    description="Enter your Stability AI API key, a prompt, and select the image size to generate an image using Stable Diffusion 1.6."
+)
+
+iface.launch()