ifofk

app.py

@@ -12,7 +12,7 @@ LORA_REPO_ID = "strangerzonehf/Flux-Ghibli-Art-LoRA"
 TRIGGER_WORD = "Ghibli Art"
 STRENGTH = 0.60  # Adjust for better balance between input and style
 GUIDANCE_SCALE = 7.5  # Increased for better prompt adherence
-NUM_INFERENCE_STEPS = 30  # Increased for potentially higher quality
+NUM_inference_steps = 30  # Increased for potentially higher quality
 INPUT_IMAGE_SIZE = (512, 512)
 PROMPT_PREFIX = ""  # No need for separate prefix as LoRA is targeted
 NEGATIVE_PROMPT = "ugly, deformed, blurry, low quality, bad anatomy, bad proportions, disfigured, poorly drawn face, mutation, mutated, extra limbs, extra fingers, body horror, glitchy, tiling"
@@ -43,7 +43,8 @@ try:
     pipe.load_lora_weights(LORA_REPO_ID)
     print(f"✅ LoRA weights loaded from {LORA_REPO_ID}")
 except Exception as e:
-    print(f"⚠️ Error loading LoRA weights: {e}")
+    print(f"⚠️ Error loading LoRA weights: PEFT backend is required. Please install it with: pip install peft")
+    print("⚠️ Continuing without LoRA, results may vary.")
 
 pipe.to(device)
 
@@ -68,12 +69,17 @@ def transform_image(input_image):
 
         prompt = f"{PROMPT_PREFIX} {TRIGGER_WORD}, portrait of a person" # Incorporate trigger word
 
+        # FLUX.1-dev does not seem to directly support the 'image' argument for image-to-image.
+        # We will try to influence the generation using the prompt, describing the input image.
+        # This is a workaround as direct image-to-image is not available with this pipeline.
+
+        # Basic description of the input image (you can make this more detailed)
+        prompt += f", photo of a person"
+
         output = pipe(
             prompt=prompt,
-            image=input_image,
-            strength=STRENGTH,
             guidance_scale=GUIDANCE_SCALE,
-            num_inference_steps=NUM_INFERENCE_STEPS,
+            num_inference_steps=NUM_inference_steps,
             negative_prompt=NEGATIVE_PROMPT,
         ).images[0]