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README.md

@@ -30,40 +30,45 @@ Here are some examples of images generated using this style LoRA:
 ![Clay Toy Style Example](./example-4.png)
 ![Clay Toy Style Example](./example-5.png)
 ![Clay Toy Style Example](./example-6.png)
-![Clay Toy Style Example](./example-7.png)
 
 ## Inference Example
 ```python
-from huggingface_hub import hf_hub_download
 from diffusers import FluxKontextPipeline
 from diffusers.utils import load_image
 import torch
 
-# Define the style and model details
-STYLE_NAME = "Clay_Toy"
-LORA_FILENAME = "Clay_Toy_lora_weights.safetensors"
-REPO_ID = "Kontext-Style/Clay_Toy_lora"
+# Load the base pipeline
+pipeline = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", 
+    torch_dtype=torch.bfloat16
+).to('cuda')
 
-# Download the LoRA weights
-# Make sure you have created a folder named 'LoRAs' in your current directory
-hf_hub_download(repo_id=REPO_ID, filename=LORA_FILENAME, local_dir="./LoRAs")
+# Load the LoRA adapter for the Clay Toy style directly from the Hub
+pipeline.load_lora_weights("Kontext-Style/Clay_Toy_lora", weight_name="Clay_Toy_lora_weights.safetensors", adapter_name="lora")
+pipeline.set_adapters(["lora"], adapter_weights=[1])
 
-# Load an image
+# Load a source image (you can use any image)
 image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024))
 
-# Load the pipeline
-pipeline = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to('cuda')
-
-# Load and set the LoRA adapter
-pipeline.load_lora_weights(f"./LoRAs/{LORA_FILENAME}", adapter_name="lora")
-pipeline.set_adapters(["lora"], adapter_weights=[1])
+# Prepare the prompt
+# The style_name is used in the prompt and for the output filename.
+style_name = "Clay Toy"
+prompt = f"Turn this image into the Clay_Toy style."
 
 # Run inference
-prompt = f"Turn this image into the {STYLE_NAME.replace('_', ' ')} style."
-result_image = pipeline(image=image, prompt=prompt, height=1024, width=1024, num_inference_steps=24).images[0]
-result_image.save(f"{STYLE_NAME}.png")
+result_image = pipeline(
+    image=image, 
+    prompt=prompt, 
+    height=1024, 
+    width=1024, 
+    num_inference_steps=24
+).images[0]
+
+# Save the result
+output_filename = f"{style_name.replace(' ', '_')}.png"
+result_image.save(output_filename)
 
-print(f"Image saved as {STYLE_NAME}.png")
+print(f"Image saved as {output_filename}")
 ```
 
 Feel free to open an issue or contact us for feedback or collaboration!