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api/index.py

@@ -64,16 +64,20 @@ def image_classifier(moodboard, prompt):
         
     # Call Stable Diffusion API with the response from OpenAI
     input = {
+        "width": 768,
+        "height": 768,
         "prompt": "high quality render of " + prompt + ", " + openai_response[20:],
-        "output_format": "jpg"
+        "negative_prompt": "worst quality, low quality, illustration, 2d, painting, cartoons, sketch",
+        "refine": "expert_ensemble_refiner",
+        "apply_watermark": False,
+        "num_inference_steps": 25,
+        "num_outputs": 3
     }
     
     output = replicate.run(
-        "stability-ai/stable-diffusion-3",
+        "stability-ai/sdxl:7762fd07cf82c948538e41f63f77d685e02b063e37e496e96eefd46c929f9bdc",
         input=input
     )
-
-    print(output)
     
     # Download the image from the URL
     image_url = output[0]
@@ -82,50 +86,19 @@ def image_classifier(moodboard, prompt):
     print(response)
     img1 = Image.open(io.BytesIO(response.content))
 
-    input = {
-        "prompt": "high quality render of " + prompt + ", " + openai_response[20:],
-        "aspect_ratio": "3:2",
-        "output_format": "jpg",
-        "cfg":6
-    }
-    
-    output = replicate.run(
-        "stability-ai/stable-diffusion-3",
-        input=input
-    )
-
-    print(output)
-    
-    # Download the image from the URL
-    image_url = output[0]
+    image_url = output[1]
     print(image_url)
     response = requests.get(image_url)
     print(response)
     img2 = Image.open(io.BytesIO(response.content))
 
-    input = {
-        "prompt": "high quality render of " + prompt + ", " + openai_response[20:],
-        "aspect_ratio": "4:5",
-        "output_format": "jpg",
-        "cfg":5.5,
-        "output_quality": 85
-    }
-    
-    output = replicate.run(
-        "stability-ai/stable-diffusion-3",
-        input=input
-    )
-
-    print(output)
-    
-    # Download the image from the URL
-    image_url = output[0]
+    image_url = output[2]
     print(image_url)
     response = requests.get(image_url)
     print(response)
     img3 = Image.open(io.BytesIO(response.content))
-
-    return [img1, img2, img3]
+    
+    return [img1, img2, img3]  # Return the image object
 
 
 # app = Flask(__name__)
@@ -135,4 +108,4 @@ def image_classifier(moodboard, prompt):
 # def index():
 
 demo = gr.Interface(fn=image_classifier, inputs=["image", "text"], outputs=["image", "image", "image"])
-demo.launch()
+demo.launch(share=True)