Update README.md

README.md

@@ -54,9 +54,6 @@ for i,j in zip(models,adapters):
     pipeline.load_lora_weights(i, weight_name="pytorch_lora_weights.safetensors",adapter_name=j) 
 
 
-prof='doctor'
-    
-
 pipeline.set_adapters(random.choice(adapters))
 
 
@@ -66,7 +63,7 @@ compel = Compel(tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2] ,
                     requires_pooled=[False, True],truncate_long_prompts=False)
 
     
-conditioning, pooled = compel("a photo of a {}, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus".format(prof)) 
+conditioning, pooled = compel("a photo of a doctor, looking at the camera, closeup headshot facing forward, ultra quality, sharp focus") 
 
 negative_conditioning, negative_pooled = compel(negative_prompt)
 [conditioning, negative_conditioning] = compel.pad_conditioning_tensors_to_same_length([conditioning, negative_conditioning])
@@ -96,6 +93,8 @@ image.save('/../../x.jpg')
 
 NYUAD-ComNets/Middle_Eastern_Female_Profession dataset was used to fine-tune stabilityai/stable-diffusion-xl-base-1.0
 
+profession list =['pilot','doctor','nurse','pharmacist','dietitian','professor','teacher','mathematics scientist','computer engineer','programmer','tailor','cleaner',
+'soldier','security guard','lawyer','manager','accountant','secretary','singer','journalist','youtuber','tiktoker','fashion model','chef','sushi chef']
 
 
 # Configurations