Update app.py

app.py

@@ -1,69 +1,32 @@
+import gradio as gr
 import torch
+import modin.pandas as pd
 import numpy as np
-from flask import Flask, request, jsonify
-from diffusers import DiffusionPipeline
-from PIL import Image
-import base64
-import io
-import gc
-import os
-
-app = Flask(__name__)
-
-# Device set to CPU and optimize threading
-device = "cpu"
-torch.set_num_threads(max(1, os.cpu_count() or 4))  # Use all available CPU threads
-torch.set_num_interop_threads(max(1, os.cpu_count() or 4))
-
-# Load model with accelerate for low memory usage
-pipe = DiffusionPipeline.from_pretrained(
-    "stabilityai/sdxl-turbo",
-    use_safetensors=True,
-    low_cpu_mem_usage=True  # Enabled with accelerate
-)
-pipe = pipe.to(device)
-pipe.unet.enable_model_cpu_offload()  # Offload U-Net to CPU memory
-
-def infer(prompt, steps=1, seed=0):  # 1 step for speed
-    if not prompt or len(prompt.split()) > 77:  # Enforce 77 token limit
-        return "Prompt missing or exceeds 77 tokens!", 0
+from diffusers import DiffusionPipeline 
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+if torch.cuda.is_available():
+    torch.cuda.max_memory_allocated(device=device)
+    torch.cuda.empty_cache()
+    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)
+    torch.cuda.empty_cache()
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = pipe.to(device)
     
-    # Set seed
-    generator = torch.Generator(device=device).manual_seed(seed) if seed != 0 else torch.Generator(device=device).manual_seed(np.random.randint(0, 2**32 - 1))
-
-    # Generate image on CPU
-    with torch.no_grad():  # Disable gradient computation
-        image = pipe(
-            prompt=prompt,
-            num_inference_steps=steps,
-            guidance_scale=0.0,
-            height=512,
-            width=512,
-            generator=generator,
-            output_type="pil",
-            num_images_per_prompt=1
-        ).images[0]
-
-    gc.collect()  # Free memory immediately
-    return image, seed
-
-@app.route('/generate', methods=['POST'])
-def generate():
-    prompt = request.form.get('prompt')
-    steps = int(request.form.get('steps', 1))  # Default to 1 step
-    seed = int(request.form.get('seed', 0))    # Default to 0 or random
-
-    result, seed_used = infer(prompt, steps, seed)
-
-    if isinstance(result, str):  # Error message
-        return jsonify({'error': result, 'seed': seed_used}), 400
-
-    # Convert image to base64
-    buffered = io.BytesIO()
-    result.save(buffered, format="PNG")
-    img_str = base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-    return jsonify({'image': img_str, 'seed': seed_used})
-
-if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8000)
+def genie (prompt, steps, seed):
+    generator = np.random.seed(0) if seed == 0 else torch.manual_seed(seed)
+    int_image = pipe(prompt=prompt, generator=generator, num_inference_steps=steps, guidance_scale=0.0).images[0]
+    return int_image
+    
+gr.Interface(fn=genie, inputs=[gr.Textbox(label='What you want the AI to generate. 77 Token Limit.'), 
+    gr.Slider(1, maximum=5, value=2, step=1, label='Number of Iterations'), 
+    gr.Slider(minimum=0, step=1, maximum=999999999999999999, randomize=True),
+    ],
+    outputs='image', 
+    title="Stable Diffusion Turbo CPU or GPU", 
+    description="SDXL Turbo CPU or GPU. Currently running on CPU. <br><br><b>WARNING: This model is capable of producing NSFW (Softcore) images.</b>", 
+    article = "If You Enjoyed this Demo and would like to Donate, you can send to any of these Wallets. <br>BTC: bc1qzdm9j73mj8ucwwtsjx4x4ylyfvr6kp7svzjn84 <br>3LWRoKYx6bCLnUrKEdnPo3FCSPQUSFDjFP <br>DOGE: DK6LRc4gfefdCTRk9xPD239N31jh9GjKez <br>SHIB (BEP20): 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>PayPal: https://www.paypal.me/ManjushriBodhisattva <br>ETH: 0xbE8f2f3B71DFEB84E5F7E3aae1909d60658aB891 <br>Code Monkey: <a href=\"https://huggingface.co/Manjushri\">Manjushri</a>").launch(debug=True, max_threads=80)