Update app.py

app.py

@@ -38,7 +38,7 @@ def get_lora_sd_pipeline(
     
     return pipe
 
-def process_prompt(prompt, tokenizer, text_encoder, max_length=77):
+def long_prompt_encoder(prompt, tokenizer, text_encoder, max_length=77):
     tokens = tokenizer(prompt, truncation=False, return_tensors="pt")["input_ids"]
     part_s = [tokens[:, i:i + max_length] for i in range(0, tokens.shape[1], max_length)]
     
@@ -79,13 +79,13 @@ def infer(
     
     if model_id != model_id_default:
         pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype).to(device)
-        prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
-        negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+        prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
+        negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
         prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
     else:
         pipe = pipe_default
-        prompt_embeds = process_prompt(prompt, pipe.tokenizer, pipe.text_encoder)
-        negative_prompt_embeds = process_prompt(negative_prompt, pipe.tokenizer, pipe.text_encoder)
+        prompt_embeds = long_prompt_encoder(prompt, pipe.tokenizer, pipe.text_encoder)
+        negative_prompt_embeds = long_prompt_encoder(negative_prompt, pipe.tokenizer, pipe.text_encoder)
         prompt_embeds, negative_prompt_embeds = align_embeddings(prompt_embeds, negative_prompt_embeds)
         print(f"LoRA adapter loaded: {pipe.unet.active_adapters}")
         print(f"LoRA scale applied: {lora_scale}")
@@ -241,4 +241,3 @@ with gr.Blocks(css=css) as demo:
 
 if __name__ == "__main__":
     demo.launch()
-