Update README.md on using the model

README.md

@@ -102,25 +102,31 @@ Adding the vanilla refiner sharpens edges and cleans up the background, but stil
 </p>
 
 > Note: All images shown here are fan-made generations produced by LoRA checkpoints in this repo. They are provided for illustrative, non-commercial purposes only and are not official Pokémon artwork.
+
+## Use this model
+
+This repo contains a ready-to-run inference script: `poke_sdxl_lora_inference.py`. After downloading / cloning this model repo and editing the config block at the top:   
+
+```bash
+python poke_sdxl_lora_inference.py
+```
 ---
 
 ## Limitations
 
-- Uneven generation qualities across different Pokemon
+- Uneven generation qualities across different Pokemon   
   Generation quality tends to degrade for less popular Pokemon. Popular pokemon have far more high-quality fanart available in both the fanart community and the training data, so they’re usually rendered more cleanly and consistently. Less well-known Pokémon may look off, have simplified designs, or show more style drift.
 
-- Not meant for official, pixel-perfect designs 
+- Not meant for official, pixel-perfect designs    
   This LoRA is not trained to exactly match the official Pokémon Company designs, art-style, or exact Pokedex poses. It is domain-adapted to *fanart*, so the output reflects the creativity, style, and tastes of the Pokemon fanart community. As a result, appearance, anatomy, markings, or colors are not guaranteed to be 100% faithful to official designs.
 
-- Probabilistic outputs: multiple generations may be required 
+- Probabilistic outputs: multiple generations may be required    
   SDXL is a probabilistic diffusion model: even with the same prompt and inference settings, different random seeds can produce very different images. To get a result you’re happy with, you may need to:
   - Try multiple `torch.Generator` seeds, or  
   - Generate a small batch and cherry-pick your favorite sample.
 
-- Struggles with complex compositional prompts  
-  Like vanilla SDXL, this LoRA can struggle with prompts that require precise spatial relationships or multi-character interactions. For example, a prompt like  
-  *“Sylveon standing on Umbreon’s back while Glaceon waves in the background”*  
-  may produce incorrect positions, merged characters, or missing elements. The model is best suited for single-character or simple multi-character scenes rather than intricate, layout-sensitive compositions.
+- Struggles with complex compositional prompts     
+  Like vanilla SDXL, this LoRA can struggle with prompts that require precise spatial relationships or multi-character interactions. For example, a prompt like *“Sylveon standing on Umbreon’s back while Glaceon waves in the background”* may produce incorrect positions, merged characters, or missing elements. The model is best suited for single-character or simple multi-character scenes rather than intricate, layout-sensitive compositions.
 
 ---