Update app.py

app.py

@@ -58,14 +58,14 @@ def update_selection(evt: gr.SelectData, width, height):
  )
 
 @spaces.GPU(duration=70)
-def generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress):
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
  pipe.to("cuda")
  generator = torch.Generator(device="cuda").manual_seed(seed)
  
  with calculateDuration("Generating image"):
  # Generate image
  image = pipe(
- prompt=f"{prompt} {trigger_word}",
+ prompt=prompt_mash,
  num_inference_steps=steps,
  guidance_scale=cfg_scale,
  width=width,
@@ -82,7 +82,16 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
  selected_lora = loras[selected_index]
  lora_path = selected_lora["repo"]
  trigger_word = selected_lora["trigger_word"]
-
+ if(trigger_word):
+ if "trigger_position" in selected_lora:
+ if selected_lora["trigger_position"] == "prepend":
+ prompt_mash = f"{trigger_word} {prompt}"
+ else:
+ prompt_mash = f"{prompt} {trigger_word}"
+ else:
+ prompt_mash = f"{trigger_word} {prompt}"
+ else:
+ prompt_mash = prompt
  # Load LoRA weights
  with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
  if "weights" in selected_lora:
@@ -96,7 +105,7 @@ def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, wid
  if randomize_seed:
  seed = random.randint(0, MAX_SEED)
  
- image = generate_image(prompt, trigger_word, steps, seed, cfg_scale, width, height, lora_scale, progress)
+ image = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
  pipe.to("cpu")
  #pipe.unfuse_lora()
  pipe.unload_lora_weights()