Usage example (#1)

- Usage example (408f754095bcc387fa989d12d3c55ef26a3e8da4)

README.md

@@ -11,10 +11,33 @@ inference: true
 ---
     
 # LoRA text2image fine-tuning - https://huggingface.co/pcuenq/pokemon-lora
-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. 
+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. 
 
 ![img_0](./image_0.png)
 ![img_1](./image_1.png)
 ![img_2](./image_2.png)
 ![img_3](./image_3.png)
 
+## How to Use
+
+The script below loads the base model, then applies the LoRA weights and performs inference:
+
+```Python
+import torch
+from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+from huggingface_hub import model_info
+
+# LoRA weights ~3 MB
+model_path = "pcuenq/pokemon-lora"
+
+info = model_info(model_path)
+model_base = info.cardData["base_model"]
+pipe = StableDiffusionPipeline.from_pretrained(model_base, torch_dtype=torch.float16)
+pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
+
+pipe.unet.load_attn_procs(model_path)
+pipe.to("cuda")
+
+image = pipe("Green pokemon with menacing face", num_inference_steps=25).images[0]
+image.save("green_pokemon.png")
+```