Update app.py

app.py

@@ -15,7 +15,7 @@ headers = {"Authorization": f"Bearer {API_TOKEN}"}
 timeout = 50000  # タイムアウトを300秒に設定
 
 # Function to query the API and return the generated image
-def query(prompt, negative_prompt="", steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024):
+def query(prompt, negative_prompt="", steps=35, cfg_scale=7, sampler="DPM++ 2M Karras", seed=-1, strength=0.7, width=1024, height=1024, num_inference_steps=30, guidance_scale=7.5, num_return_sequences=1, temperature=1.0, top_k=50, top_p=0.9, eta=0.1):
     if not prompt:
         return None, "Prompt is required"
 
@@ -39,7 +39,14 @@ def query(prompt, negative_prompt="", steps=35, cfg_scale=7, sampler="DPM++ 2M K
         "negative_prompt": negative_prompt,
         "parameters": {
             "width": width,
-            "height": height
+            "height": height,
+            "num_inference_steps": num_inference_steps,
+            "guidance_scale": guidance_scale,
+            "num_return_sequences": num_return_sequences,
+            "temperature": temperature,
+            "top_k": top_k,
+            "top_p": top_p,
+            "eta": eta
         }
     }
 
@@ -99,8 +106,15 @@ def generate_image():
     seed = int(request.args.get("seed", -1))
     width = int(request.args.get("width", 1024))
     height = int(request.args.get("height", 1024))
-
-    image, error = query(prompt, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height)
+    num_inference_steps = int(request.args.get("num_inference_steps", 30))
+    guidance_scale = float(request.args.get("guidance_scale", 7.5))
+    num_return_sequences = int(request.args.get("num_return_sequences", 1))
+    temperature = float(request.args.get("temperature", 1.0))
+    top_k = int(request.args.get("top_k", 50))
+    top_p = float(request.args.get("top_p", 0.9))
+    eta = float(request.args.get("eta", 0.1))
+
+    image, error = query(prompt, negative_prompt, steps, cfg_scale, sampler, seed, strength, width, height, num_inference_steps, guidance_scale, num_return_sequences, temperature, top_k, top_p, eta)
 
     if error:
         return jsonify({"error": error}), 400