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Usage example

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@@ -11,10 +11,33 @@ inference: true
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  # LoRA text2image fine-tuning - https://huggingface.co/pcuenq/pokemon-lora
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- These are LoRA adaption weights for https://huggingface.co/pcuenq/pokemon-lora. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
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  ![img_0](./image_0.png)
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  ![img_1](./image_1.png)
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  ![img_2](./image_2.png)
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  ![img_3](./image_3.png)
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  # LoRA text2image fine-tuning - https://huggingface.co/pcuenq/pokemon-lora
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+ These are LoRA adaption weights trained on base model https://huggingface.co/runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
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  ![img_0](./image_0.png)
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  ![img_1](./image_1.png)
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  ![img_2](./image_2.png)
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  ![img_3](./image_3.png)
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+ ## How to Use
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+ The script below loads the base model, then applies the LoRA weights and performs inference:
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+ ```Python
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+ import torch
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+ from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
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+ from huggingface_hub import model_info
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+
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+ # LoRA weights ~3 MB
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+ model_path = "pcuenq/pokemon-lora"
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+
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+ info = model_info(model_path)
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+ model_base = info.cardData["base_model"]
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+ pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16)
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+ pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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+
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+ pipe.unet.load_attn_procs(model_path)
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+ pipe.to("cuda")
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+
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+ image = pipe("Green pokemon with menacing face", num_inference_steps=25).images[0]
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+ image.save("green_pokemon.png")
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+ ```