Update app.py

app.py

@@ -3,10 +3,9 @@ import torch
 from diffusers import StableDiffusionPipeline
 import os
 
-print("--- FINAL VERSION: CORRECT BASE MODEL + LORA ---")
+print("--- FINAL VERSION: ADJUSTING LORA SCALE ---")
 
 # --- Step 1: Define constants ---
-# We revert to the neutral v1.5 base model to let the LoRA style dominate.
 BASE_MODEL_ID = "runwayml/stable-diffusion-v1-5"
 LORA_FILENAME = "MyStickmanProject-10.safetensors"
 
@@ -22,10 +21,10 @@ pipe = StableDiffusionPipeline.from_pretrained(
 print("Base model loaded successfully.")
 
 # --- Step 3: Load and apply the LoRA weights ---
-print("Loading and fusing LoRA weights...")
+# This is the standard way to load LoRA weights.
+print("Loading LoRA weights...")
 pipe.load_lora_weights(LORA_FILENAME)
-pipe.fuse_lora()
-print("LoRA weights fused successfully.")
+print("LoRA weights loaded.")
 
 
 # --- Step 4: Move to CPU and optimize ---
@@ -36,26 +35,35 @@ pipe.enable_attention_slicing()
 print("--- MODEL SETUP COMPLETE ---")
 
 # --- Step 5: Define the core generation function ---
-def generate_image(prompt, negative_prompt, guidance_scale, num_steps):
+def generate_image(prompt, negative_prompt, guidance_scale, num_steps, lora_scale):
     print(f"Generating image with prompt: {prompt}")
+    
+    # This is the KEY CHANGE: We are now controlling the LoRA strength.
+    # lora_scale=1.0 means full strength. lora_scale=0.7 means 70% strength.
     image = pipe(
         prompt=prompt,
         negative_prompt=negative_prompt,
         num_inference_steps=int(num_steps),
         guidance_scale=float(guidance_scale),
+        cross_attention_kwargs={"scale": lora_scale}
     ).images[0]
+    
     print("Image generation complete.")
     return image
 
 # --- Step 6: Create the Gradio user interface ---
 with gr.Blocks() as iface:
-    gr.Markdown("# My Custom Explainer Video Style Demo")
-    gr.Markdown("Enter a prompt to generate an image in my unique webcomic style.")
+    gr.Markdown("# My Custom Stickman LoRA Demo")
+    gr.Markdown("Enter a prompt to generate an image in my unique style.")
     
     with gr.Row():
         with gr.Column(scale=70):
-            prompt_input = gr.Textbox(label="Prompt", placeholder="A stickman scientist looking at a test tube")
-            negative_prompt_input = gr.Textbox(label="Negative Prompt", value="(worst quality, low quality:1.4), blurry, noisy, grainy, jpeg artifacts, ugly, deformed, messy, 3d, realistic, photo")
+            prompt_input = gr.Textbox(label="Prompt", placeholder="A stickman wearing a fedora hat, holding a magnifying glass")
+            negative_prompt_input = gr.Textbox(label="Negative Prompt", value="(worst quality, low quality:1.4), blurry, noisy, grainy, 3d, realistic, photo")
+            
+            # We add a new slider to control the LoRA strength!
+            lora_slider = gr.Slider(minimum=0, maximum=1.5, step=0.05, value=0.75, label="LoRA Strength (Style Intensity)")
+
             with gr.Row():
                 guidance_scale_slider = gr.Slider(minimum=1, maximum=20, step=0.5, value=7.5, label="Guidance Scale")
                 steps_slider = gr.Slider(minimum=10, maximum=100, step=1, value=25, label="Inference Steps")
@@ -66,7 +74,7 @@ with gr.Blocks() as iface:
     submit_button = gr.Button("Generate", variant="primary")
     submit_button.click(
         fn=generate_image,
-        inputs=[prompt_input, negative_prompt_input, guidance_scale_slider, steps_slider],
+        inputs=[prompt_input, negative_prompt_input, guidance_scale_slider, steps_slider, lora_slider],
         outputs=image_output
     )