Update app.py

app.py

@@ -1,6 +1,6 @@
-import base64
-import re
-import os
+import requests
+import random
+from typing import Tuple
 import random
 import requests
 import time
@@ -12,6 +12,8 @@ import torch
 from flask import Flask, request, jsonify
 app = Flask(__name__)
 
+
+
 style_list = [
     {
         "name": "(No style)",
@@ -20,7 +22,7 @@ style_list = [
     },
     {
         "name": "Cinematic",
-        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh,realistic,textured, cinemascope, moody, epic, gorgeous, film grain, grainy",
         "negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
     },
     {
@@ -63,33 +65,62 @@ style_list = [
         "prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
         "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
     },
+    {
+        "name":"Portrait",
+        "prompt":"Portrait Art {prompt}.soft light, sharp, exposure blend, medium shot, bokeh, (hdr:1.4), high contrast, (cinematic, teal and orange:0.85), (muted colors, dim colors, soothing tones:1.3), low saturation, (hyperdetailed:1.2), (noir:0.4), (natural skin texture, hyperrealism, soft light, sharp:1.2)",
+        "negative_prompt":""
+    }
 ]
 
-def infer(prompt, negative="low_quality", style_name=None, guidance_scale=None):
+styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+STYLE_NAMES = list(styles.keys())
+DEFAULT_STYLE_NAME = "(No style)"
+
+def apply_style(style_name: str, positive: str, negative: str = "") -> Tuple[str, str]:
+    p, n = styles.get(style_name, styles[DEFAULT_STYLE_NAME])
+    return p.replace("{prompt}", positive), n + negative
 
 
-    seed = random.randint(0,4294967295)
-    prompt, negative = apply_style(style_name, prompt, negative)
-    print(prompt)
+def text_to_image(prompt,guidance,style_name):
+    url = "https://api.getimg.ai/v1/stable-diffusion-xl/text-to-image"
+    #seed = random.randint(0,4294967295)
+    prompts, negative = apply_style(style_name, prompt)
+    print(prompts)
     print(negative)
 
-    # Load the Stable Diffusion model
-    pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True,variant="fp16")
-    pipe.to("cuda")
+    payload = {
+        "model": "stable-diffusion-xl-v1-0",
+        "prompt": prompts,
+        "width": 1024,
+        "height": 1024,
+        "steps": 90,
+        "guidance": guidance,
+        "output_format": "jpeg",
+        "response_format": "url",
+        "negative_prompt": negative,
+    
+        
+    }
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+        "authorization": "Bearer key-3ryTG9YlTHzB0hkjgFHoL7yhsOQkWUbodn1sOU7WwOkhDXNSWVL8heMXWwNqCliAbdpm9xNtrITY3WInbRdC98bZ5QVdfOoi"
+    }
+
+    response = requests.post(url, json=payload, headers=headers)
+    #image_url =response.json()['url']
+    # Fetch the image data directly from the URL
+    #image_datas = requests.get(image_url).content
+
+    # Save the image to a local file (e.g., 'my_image.jpg')
+    #with open('my_image.jpg', 'wb') as f:
+    # f.write(image_datas)
+
 
-    # Generate the images
-    images = pipe(prompt=prompt, negative_prompt=negative, guidance_scale=guidance_scale, seed=seed).images
 
-    # Convert the images to base64-encoded strings
-    image_urls = []
-    for i, image in enumerate(images):
-        buffered = BytesIO()
-        image.save(buffered, format="JPEG")
-        image_b64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        #image_url = f"data:image/jpeg;base64,{image_b64}"
-        #image_urls.append(image_url)
+    #print(response.text)
+    return response.text
 
-    return image_b64
 
 
 @app.route('/', methods=['POST'])
@@ -100,15 +131,19 @@ def generate_image():
         style_name = data['style_name']
         guidance_scale = data['guidance_scale']
         
-        image_urls = infer(prompt, style_name=style_name, guidance_scale=guidance_scale)
+        image_urls = text_to_image(prompt=prompt, style_name=style_name, guidance=guidance_scale)
         
         # Convert the first generated image to base64
-        image_b64 = image_urls[0].split(",")[1]
+        #image_b64 = image_urls[0].split(",")[1]
         
-        return jsonify({'image_base64': image_b64})
+        #return jsonify({'image_base64': image_b64})
+        return jsonify({'image_urls':image_urls})
     else:
         return jsonify({'error': 'Missing required parameters'}), 400
   
 
+
+  
+
 if __name__ == '__main__':
     app.run(host='0.0.0.0',port=7860,debug=True)