fix: improve SDXL negative prompt parsing

- Make parsing more robust to handle multi-line prompts
- Add fallback negative prompt if not provided
- Update SDXL template to be more explicit about format
- Add example to ensure Gemini returns both prompts

kikotools/tools/gemini_prompt/node.py

@@ -118,15 +118,44 @@ Install: pip install google-generativeai
             positive_prompt = ""
             negative_prompt = ""
 
-            for line in lines:
-                if line.startswith("Positive:"):
-                    positive_prompt = line.replace("Positive:", "").strip()
-                elif line.startswith("Negative:"):
-                    negative_prompt = line.replace("Negative:", "").strip()
+            # Look for positive prompt
+            for i, line in enumerate(lines):
+                if line.strip().startswith("Positive:"):
+                    # Extract everything after "Positive:"
+                    positive_prompt = line.split("Positive:", 1)[1].strip()
+                    # Check if it continues on next lines (without "Negative:")
+                    j = i + 1
+                    while j < len(lines) and not lines[j].strip().startswith(
+                        "Negative:"
+                    ):
+                        if lines[j].strip():
+                            positive_prompt += " " + lines[j].strip()
+                        j += 1
+                    break
+
+            # Look for negative prompt
+            for i, line in enumerate(lines):
+                if line.strip().startswith("Negative:"):
+                    # Extract everything after "Negative:"
+                    negative_prompt = line.split("Negative:", 1)[1].strip()
+                    # Check if it continues on next lines
+                    j = i + 1
+                    while (
+                        j < len(lines)
+                        and lines[j].strip()
+                        and not lines[j].strip().startswith("Positive:")
+                    ):
+                        negative_prompt += " " + lines[j].strip()
+                        j += 1
+                    break
 
             # If format not found, assume entire response is positive prompt
-            if not positive_prompt:
+            if not positive_prompt and not negative_prompt:
                 positive_prompt = prompt
+                negative_prompt = ""
+            elif not negative_prompt:
+                # If we found positive but no negative, provide a basic negative
+                negative_prompt = "ugly, blurry, low quality, distorted, deformed"
 
             return (positive_prompt, negative_prompt)
 

kikotools/tools/gemini_prompt/prompts.py

@@ -24,7 +24,7 @@ SDXL_PROMPT = """You are an expert SDXL prompt engineer. Analyze the image and g
 
 SDXL works best with natural language descriptions but also supports comma-separated keywords. Keep prompts concise but descriptive.
 
-Return your response in EXACTLY this format:
+You MUST return your response in EXACTLY this format (two lines only):
 Positive: [your positive prompt here]
 Negative: [your negative prompt here]
 
@@ -37,12 +37,18 @@ Guidelines for Positive prompt:
 - Keep it clear and specific but not overly long
 
 Guidelines for Negative prompt:
-- Keep it simple and minimal
+- Always include a negative prompt, even if minimal
 - Common negatives: ugly, blurry, low quality, distorted, deformed
 - Only add specifics you want to avoid (e.g., "cartoon" for photorealistic)
 - Don't overload with negative prompts - SDXL needs fewer than SD1.5
 
-IMPORTANT: Return ONLY the two lines starting with "Positive:" and "Negative:". No other text."""
+CRITICAL: Your response must be EXACTLY two lines:
+Line 1: Positive: [prompt]
+Line 2: Negative: [prompt]
+
+Example response:
+Positive: photograph of a young woman with flowing red hair, professional portrait, soft lighting, bokeh background, highly detailed, 8K
+Negative: ugly, blurry, low quality, distorted features, bad anatomy"""
 
 DANBOORU_PROMPT = """You are a Danbooru tagging expert specializing in anime-style image tagging. Analyze the image and generate ONLY Danbooru-style tags - no explanations or analysis.