Update app.py

app.py

@@ -40,30 +40,21 @@ def gen_fn(model_str, prompt, negative_prompt=None, noise=None, cfg_scale=None,
         # ネガティブプロンプトとその他のパラメータをログに出力
         print(f"Prompt: {full_prompt}, Negative Prompt: {negative_prompt}, CFG Scale: {cfg_scale}, Steps: {num_inference_steps}, Sampler: {sampler}")
 
-        try:
-            # Adjust the inputs depending on model capabilities
-            if negative_prompt:
-                inputs = {
-                    'prompt': full_prompt,
-                    'negative_prompt': negative_prompt,
-                    'cfg_scale': cfg_scale,
-                    'num_inference_steps': num_inference_steps,
-                    'sampler': sampler
-                }
-            else:
-                inputs = {
-                    'prompt': full_prompt,
-                    'cfg_scale': cfg_scale,
-                    'num_inference_steps': num_inference_steps,
-                    'sampler': sampler
-                }
-
-            # Filter out None values
-            inputs = {k: v for k, v in inputs.items() if v is not None}
+        # Construct the function call parameters dynamically
+        inputs = [full_prompt]
+        if negative_prompt:
+            inputs.append(negative_prompt)
+        if cfg_scale is not None:
+            inputs.append(cfg_scale)
+        if num_inference_steps is not None:
+            inputs.append(num_inference_steps)
+        if sampler:
+            inputs.append(sampler)
 
+        try:
             # モデル呼び出し
-            result = models_load[model_str](**inputs)
-
+            result = models_load[model_str](*inputs)
+            
             # Debugging result type
             print(f"Result type: {type(result)}, Result: {result}")