Updates

app.py

@@ -13,8 +13,6 @@ import copy
 import random
 import time
 
-selected_lora_index = None
-
 # Load LoRAs from JSON file
 with open('loras.json', 'r') as f:
     loras = json.load(f)
@@ -59,8 +57,6 @@ class calculateDuration:
             print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
 def update_selection(evt: gr.SelectData, width, height):
-    global selected_lora_index
-    selected_lora_index = evt.index
     selected_lora = loras[evt.index]
     new_placeholder = f"{selected_lora['trigger_word']} {prompt.value}"
     lora_repo = selected_lora["repo"]
@@ -78,31 +74,12 @@ def update_selection(evt: gr.SelectData, width, height):
     return (
         gr.update(value=new_placeholder),
         updated_text,
+        evt.index,
         width,
         height,
         gr.update(interactive=True)  # Enable the Generate button
     )
 
-def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-    global selected_lora_index
-    if selected_lora_index is None:
-        raise gr.Error("You must select a LoRA before proceeding.")
-    
-    selected_lora = loras[selected_lora_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
-
 @spaces.GPU(duration=70)
 def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
     pipe.to("cuda")
@@ -122,31 +99,11 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
         ):
             yield img
 
-@spaces.GPU(duration=70)
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, lora_scale, seed):
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    pipe_i2i.to("cuda")
-    image_input = load_image(image_input_path)
-    final_image = pipe_i2i(
-        prompt=prompt_mash,
-        image=image_input,
-        strength=image_strength,
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-        output_type="pil",
-    ).images[0]
-    return final_image
-
-def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
-    global selected_lora_index
-    if selected_lora_index is None:
+def run_lora(prompt, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    if selected_index is None:
         raise gr.Error("You must select a LoRA before proceeding.")
     
-    selected_lora = loras[selected_lora_index]
+    selected_lora = loras[selected_index]
     lora_path = selected_lora["repo"]
     trigger_word = selected_lora["trigger_word"]
     
@@ -176,11 +133,7 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
         if randomize_seed:
             seed = random.randint(0, MAX_SEED)
 
-    if image_input is not None:
-        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
-        yield final_image, seed, gr.update(visible=False)
-    else:
-        image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
+    image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress)
     # Consume the generator to get the final image
     final_image = None
     step_counter = 0
@@ -192,8 +145,6 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
     
     yield final_image, seed, gr.update(visible=False)
 
-# ...
-
 # Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Awaken Ones' Lora Previews")
@@ -245,9 +196,9 @@ with gr.Blocks() as demo:
     progress_bar = gr.Markdown(visible=False)
 
     # Event handlers
-    gallery.select(update_selection, [width, height], [prompt, selected_lora, width, height, generate])
+    gallery.select(update_selection, [width, height], [prompt, selected_lora, gr.State(), width, height, generate])
     randomize_seed.change(lambda x: gr.update(visible=not x), randomize_seed, seed_input)
-    generate_event = generate.click(run_lora, inputs=[prompt, cfg_scale, steps, randomize_seed, seed_input, width, height, lora_scale], outputs=[result, seed_output, progress_bar])
+    generate_event = generate.click(run_lora, inputs=[prompt, cfg_scale, steps, gr.State(), randomize_seed, seed_input, width, height, lora_scale], outputs=[result, seed_output, progress_bar])
     cancel.click(lambda: None, None, None, cancels=[generate_event])
 
 demo.queue().launch()