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--- |
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language: |
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- en |
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base_model: |
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- black-forest-labs/FLUX.1-Kontext-dev |
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- HighCWu/FLUX.1-Kontext-dev-bnb-hqq-4bit |
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pipeline_tag: image-to-image |
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library_name: diffusers |
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tags: |
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- Style |
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- lora |
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- Jojo |
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- FluxKontext |
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- Image-to-Image |
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datasets: |
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- HighCWu/OmniConsistencyJojo |
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--- |
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# Jojo Style LoRA V2 Trained with FLUX.1 Kontext dev 4bit on 16GB VRAM |
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This repository provides the **Jojo** style LoRA adapter for the [FLUX.1 Kontext Model](https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev). |
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It was trained by [a fork version of ostris/ai-toolkit](https://github.com/HighCWu/ai-toolkit/tree/vram-16gb) with the config file => [train_lora_flux_kontext_16gb_jojo_v2.yaml](./train_lora_flux_kontext_16gb_jojo_v2.yaml) |
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## Style Showcase |
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Here are some examples of images generated using this style LoRA: |
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## Inference Example |
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```python |
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from diffusers import FluxKontextPipeline |
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from diffusers.utils import load_image, peft_utils |
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import torch |
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try: # A temp hack for some version diffusers lora loading problem |
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from diffusers.utils.peft_utils import _derive_exclude_modules |
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def new_derive_exclude_modules(*args, **kwargs): |
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exclude_modules = _derive_exclude_modules(*args, **kwargs) |
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if exclude_modules is not None: |
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exclude_modules = [n for n in exclude_modules if "proj_out" not in n] |
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return exclude_modules |
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peft_utils._derive_exclude_modules = new_derive_exclude_modules |
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except: |
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pass |
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pipe = FluxKontextPipeline.from_pretrained("HighCWu/FLUX.1-Kontext-dev-bnb-hqq-4bit", torch_dtype=torch.bfloat16) |
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pipe.load_lora_weights("HighCWu/Jojo_lora_4bit_training_v2", weight_name="flux_kontext_jojo_style_lora_v2.safetensors", adapter_name="jojo") |
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pipe.set_adapters(["jojo"], [1.0]) |
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pipe.to("cuda") |
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# Load a source image (you can use any image) |
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image = load_image("https://huggingface.co/datasets/black-forest-labs/kontext-bench/resolve/main/test/images/0003.jpg").resize((1024, 1024)) |
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# Prepare the prompt |
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# The style_name is used in the prompt and for the output filename. |
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style_name = "JojoV2" |
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prompt = f"Turn this image into the style of JoJo's Bizarre Adventure. " |
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# Run inference |
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result_image = pipe( |
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image=image, |
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prompt=prompt, |
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height=1024, |
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width=1024, |
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guidance_scale=4.0, |
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num_inference_steps=28, |
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).images[0] |
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# Save the result |
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output_filename = f"{style_name.replace(' ', '_')}.png" |
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result_image.save(output_filename) |
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print(f"Image saved as {output_filename}") |
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``` |
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Feel free to open an issue or contact us for feedback or collaboration! |
