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README.md

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+---
+language:
+- en
+base_model:
+- black-forest-labs/FLUX.1-Kontext-dev
+pipeline_tag: image-to-image
+library_name: diffusers
+tags:
+- Style
+- Vector
+- FluxKontext
+- Image-to-Image
+---
+
+# Vector Style LoRA for FLUX.1 Kontext Model
+This repository provides the **Vector** style LoRA adapter for the [FLUX.1 Kontext Model](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev).
+This LoRA is part of a collection of 20+ style LoRAs trained on high-quality paired data generated by GPT-4o from the [OmniConsistency](https://huggingface.co/datasets/showlab/OmniConsistency) dataset.
+
+![Comparison02](./style_comparison_02.jpg)
+![Comparison01](./style_comparison_01.jpg)
+
+Contributor: Tian YE & Song FEI, HKUST Guangzhou.
+
+## Style Showcase
+Here are some examples of images generated using this style LoRA:
+
+![Vector Style Example](./example-1.png)
+![Vector Style Example](./example-2.png)
+![Vector Style Example](./example-3.png)
+![Vector Style Example](./example-4.png)
+![Vector Style Example](./example-5.png)
+![Vector Style Example](./example-6.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 = "Vector"
+LORA_FILENAME = "Vector_lora_weights.safetensors"
+REPO_ID = "Kontext-Style/Vector_lora"
+
+# 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 an 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])
+
+# 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")
+
+print(f"Image saved as {STYLE_NAME}.png")
+```
+
+Feel free to open an issue or contact us for feedback or collaboration!