Update README.md

README.md

@@ -35,21 +35,23 @@ This model is a fine-tuned version of the Stable Diffusion architecture, leverag
 ### Example Usages:
 
 
-```python
+```py
 
 import torch
 from diffusers import StableDiffusionPipeline,UNet2DConditionModel
 
-pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda")
+pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5").to("cuda")
 
-pipeline.load_lora_weights("phil329/face_lora_sd15", weight_name="pytorch_lora_weights.safetensors")
+pipeline.load_lora_weights("phil329/face_lora_sd15", weight_name="pytorch_lora_weights.safetensors")
 
-text = 'A young woman with smile, wearing a purple hat.'
-lora_image = pipeline(text).images[0]
+NEGATIVE_PROMPT = "worst quality, low quality, bad anatomy, watermark, text, blurry, cartoon, unreal"
+text = 'A young woman with smile, wearing a purple hat.'
+
+lora_image = pipeline(text,negative_prompt=NEGATIVE_PROMPT).images[0]
 
 display(lora_image)
 
-```
+```
 
 ### Results
 
@@ -59,6 +61,10 @@ We use four prompts as follows:
 - 'A girl with long blonde hair'
 - 'An young man with curry hair'
 
+The **negative prompt** are the same as the example codes. All the results are randomly generated and **not** cherry-picked. 
+
+If the generation effect is not good, try adding a negative prompt, or try different prompts and seeds.
+
 
 ![Result](./images/merge.png)