README draft

README.md

@@ -12,8 +12,49 @@ inference: true
 
 # sdxl-wrong-lora
 
-A LoRA for SDXL 1.0 Base which increases output image quality after loading it and using `wrong` as a negative prompt.
+A LoRA for SDXL 1.0 Base which improves output image quality after loading it and using `wrong` as a negative prompt during inference.
+
+Benefits of using this LoRA:
+
+- Higher color saturation and vibrance
+- Higher detail in textures/fabrics
+- Higher sharpness for blurry/background objects
+- Less likely to have random artifacts
+- Appears to allow the model to follow the input prompt with a more expected behavior
+
+## Usage
+
+The LoRA can be loaded using `load_lora_weights` like any other LoRA in `diffusers`:
+
+```py
+import torch
+from diffusers import DiffusionPipeline, AutoencoderKL
+
+vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+base = DiffusionPipeline.from_pretrained(
+    "stabilityai/stable-diffusion-xl-base-1.0",
+    vae=vae,
+    torch_dtype=torch.float16,
+    variant="fp16",
+    use_safetensors=True
+)
+
+base.load_lora_weights("minimaxir/sdxl-wrong-lora")
+
+_ = base.to("cuda")
+```
+
+During inference, use `wrong` as the sole negative prompt.
+
+## Examples
+
+Left is the base model output (no LoRA) + refiner, right is base + LoRA and refiner. The generations use the same seed.
 
 ## Methodology
 
-The methodology for creating this LoRA is similar to the [wrong SD 2.0 textual inversion embedding](https://huggingface.co/minimaxir/wrong_embedding_sd_2_0), except trained as a LoRA since textual inversion on SDXL is complicated.
+The methodology for creating this LoRA is similar to my [wrong SD 2.0 textual inversion embedding](https://huggingface.co/minimaxir/wrong_embedding_sd_2_0), except trained as a LoRA since textual inversion on SDXL is complicated. The base images were generated from SDXL itself.
+
+## Notes
+
+- It's possible to use `not wrong` in the prompt itself but in testing it has no effect.
+- You can use other negative prompts in conjunction with the `wrong` prompt but you may want to weight them.