Update app.py

app.py

@@ -55,11 +55,30 @@ class calculateDuration:
         else:
             print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
-def update_selection(evt: gr.SelectData, width, height):
+def update_selection(evt: gr.SelectData, width, height, selected_lora1, selected_lora2):
     selected_lora = loras[evt.index]
     new_placeholder = f"Type a prompt for {selected_lora['title']}"
-    lora_repo = selected_lora["repo"]
-    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨"
+
+    # Initialize outputs
+    outputs = []
+
+    if selected_lora1 is None:
+        selected_lora1 = selected_lora
+        selected_lora1_info = f"### LoRA 1 Selected: [{selected_lora1['title']}](https://huggingface.co/{selected_lora1['repo']}) ✨"
+        lora_scale1_visible = True
+        remove_lora1_visible = True
+    elif selected_lora2 is None:
+        selected_lora2 = selected_lora
+        selected_lora2_info = f"### LoRA 2 Selected: [{selected_lora2['title']}](https://huggingface.co/{selected_lora2['repo']}) ✨"
+        lora_scale2_visible = True
+        remove_lora2_visible = True
+    else:
+        raise gr.Error("You can only select up to two LoRAs. Please remove one before selecting another.")
+
+    # Update placeholder
+    placeholder_update = gr.update(placeholder=new_placeholder)
+
+    # For width and height adjustment
     if "aspect" in selected_lora:
         if selected_lora["aspect"] == "portrait":
             width = 768
@@ -70,16 +89,21 @@ def update_selection(evt: gr.SelectData, width, height):
         else:
             width = 1024
             height = 1024
-    return (
-        gr.update(placeholder=new_placeholder),
-        updated_text,
-        evt.index,
-        width,
-        height,
-    )
+
+    return placeholder_update, selected_lora1, selected_lora2, selected_lora1_info, selected_lora2_info, gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True), width, height
+
+def remove_selected_lora1(selected_lora1, selected_lora1_info):
+    selected_lora1 = None
+    selected_lora1_info = ""
+    return selected_lora1, selected_lora1_info, gr.update(visible=False), gr.update(visible=False)
+
+def remove_selected_lora2(selected_lora2, selected_lora2_info):
+    selected_lora2 = None
+    selected_lora2_info = ""
+    return selected_lora2, selected_lora2_info, gr.update(visible=False), gr.update(visible=False)
 
 @spaces.GPU(duration=70)
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
     pipe.to("cuda")
     generator = torch.Generator(device="cuda").manual_seed(seed)
     with calculateDuration("Generating image"):
@@ -91,14 +115,13 @@ def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scal
             width=width,
             height=height,
             generator=generator,
-            joint_attention_kwargs={"scale": lora_scale},
             output_type="pil",
             good_vae=good_vae,
         ):
             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):
+def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
     generator = torch.Generator(device="cuda").manual_seed(seed)
     pipe_i2i.to("cuda")
     image_input = load_image(image_input_path)
@@ -111,93 +134,99 @@ def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps
         width=width,
         height=height,
         generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
         output_type="pil",
     ).images[0]
     return final_image 
+
+def run_lora(prompt, image_input, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, selected_lora1, selected_lora2, lora_scale1, lora_scale2, progress=gr.Progress(track_tqdm=True)):
+    if selected_lora1 is None and selected_lora2 is None:
+        raise gr.Error("You must select at least one LoRA before proceeding.")
     
-def run_lora(prompt, image_input, image_strength, 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_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}"
+    # Build the prompt mash
+    prompt_mash = prompt
+
+    # Handle trigger words and positions
+    trigger_words = []
+    if selected_lora1 is not None:
+        trigger_word1 = selected_lora1.get("trigger_word", "")
+        if trigger_word1:
+            if selected_lora1.get("trigger_position") == "prepend":
+                trigger_words.insert(0, trigger_word1)
             else:
-                prompt_mash = f"{prompt} {trigger_word}"
-        else:
-            prompt_mash = f"{trigger_word} {prompt}"
-    else:
-        prompt_mash = prompt
+                trigger_words.append(trigger_word1)
+    if selected_lora2 is not None:
+        trigger_word2 = selected_lora2.get("trigger_word", "")
+        if trigger_word2:
+            if selected_lora2.get("trigger_position") == "prepend":
+                trigger_words.insert(0, trigger_word2)
+            else:
+                trigger_words.append(trigger_word2)
+    # Combine trigger words with the prompt
+    if trigger_words:
+        prompt_mash = f"{' '.join(trigger_words)} {prompt}"
 
-    with calculateDuration("Unloading LoRA"):
+    with calculateDuration("Unloading LoRAs"):
         pipe.unload_lora_weights()
         pipe_i2i.unload_lora_weights()
         
-    # Load LoRA weights
-    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
-        if(image_input is not None):
-            if "weights" in selected_lora:
-                pipe_i2i.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
-            else:
-                pipe_i2i.load_lora_weights(lora_path)
+    # Load LoRA weights with respective scales
+    with calculateDuration("Loading LoRA weights"):
+        if image_input is not None:
+            if selected_lora1 is not None:
+                pipe_i2i.load_lora_weights(selected_lora1['repo'], weight_name=selected_lora1.get('weights'), scale=lora_scale1)
+            if selected_lora2 is not None:
+                pipe_i2i.load_lora_weights(selected_lora2['repo'], weight_name=selected_lora2.get('weights'), scale=lora_scale2)
         else:
-            if "weights" in selected_lora:
-                pipe.load_lora_weights(lora_path, weight_name=selected_lora["weights"])
-            else:
-                pipe.load_lora_weights(lora_path)
+            if selected_lora1 is not None:
+                pipe.load_lora_weights(selected_lora1['repo'], weight_name=selected_lora1.get('weights'), scale=lora_scale1)
+            if selected_lora2 is not None:
+                pipe.load_lora_weights(selected_lora2['repo'], weight_name=selected_lora2.get('weights'), scale=lora_scale2)
                 
     # Set random seed for reproducibility
     with calculateDuration("Randomizing seed"):
         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)
+    if image_input is not None:
+        final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, 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, progress)
         # Consume the generator to get the final image
         final_image = None
         step_counter = 0
         for image in image_generator:
-            step_counter+=1
+            step_counter += 1
             final_image = image
             progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
             yield image, seed, gr.update(value=progress_bar, visible=True)
-            
         yield final_image, seed, gr.update(value=progress_bar, visible=False)
         
 def get_huggingface_safetensors(link):
-  split_link = link.split("/")
-  if(len(split_link) == 2):
-            model_card = ModelCard.load(link)
-            base_model = model_card.data.get("base_model")
-            print(base_model)
-            if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
-                raise Exception("Not a FLUX LoRA!")
-            image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
-            fs = HfFileSystem()
-            try:
-                list_of_files = fs.ls(link, detail=False)
-                for file in list_of_files:
-                    if(file.endswith(".safetensors")):
-                        safetensors_name = file.split("/")[-1]
-                    if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
-                      image_elements = file.split("/")
-                      image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
-            except Exception as e:
-              print(e)
-              gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
-              raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
-            return split_link[1], link, safetensors_name, trigger_word, image_url
+    split_link = link.split("/")
+    if(len(split_link) == 2):
+        model_card = ModelCard.load(link)
+        base_model = model_card.data.get("base_model")
+        print(base_model)
+        if((base_model != "black-forest-labs/FLUX.1-dev") and (base_model != "black-forest-labs/FLUX.1-schnell")):
+            raise Exception("Not a FLUX LoRA!")
+        image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
+        trigger_word = model_card.data.get("instance_prompt", "")
+        image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
+        fs = HfFileSystem()
+        try:
+            list_of_files = fs.ls(link, detail=False)
+            for file in list_of_files:
+                if(file.endswith(".safetensors")):
+                    safetensors_name = file.split("/")[-1]
+                if (not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp"))):
+                    image_elements = file.split("/")
+                    image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
+        except Exception as e:
+            print(e)
+            gr.Warning(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+            raise Exception(f"You didn't include a link neither a valid Hugging Face repository with a *.safetensors LoRA")
+        return split_link[1], link, safetensors_name, trigger_word, image_url
 
 def check_custom_model(link):
     if(link.startswith("https://")):
@@ -257,8 +286,8 @@ css = '''
 #title img{width: 100px; margin-right: 0.5em}
 #gallery .grid-wrap{height: 10vh}
 #lora_list{background: var(--block-background-fill);padding: 0 1em .3em; font-size: 90%}
-.card_internal{display: flex;height: 100px;margin-top: .5em}
-.card_internal img{margin-right: 1em}
+.custom_lora_card .card_internal{display: flex;height: 100px;margin-top: .5em}
+.custom_lora_card .card_internal img{margin-right: 1em}
 .styler{--form-gap-width: 0px !important}
 #progress{height:30px}
 #progress .generating{display:none}
@@ -267,18 +296,18 @@ css = '''
 '''
 with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
     title = gr.HTML(
-        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> FLUX LoRA the Explorer</h1>""",
+        """<h1><img src="https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer/resolve/main/flux_lora.png" alt="LoRA"> LoRA Lab</h1>""",
         elem_id="title",
     )
-    selected_index = gr.State(None)
+    selected_lora1 = gr.State(None)
+    selected_lora2 = gr.State(None)
     with gr.Row():
         with gr.Column(scale=3):
-            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
+            prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting LoRAs")
         with gr.Column(scale=1, elem_id="gen_column"):
             generate_button = gr.Button("Generate", variant="primary", elem_id="gen_btn")
     with gr.Row():
         with gr.Column():
-            selected_info = gr.Markdown("")
             gallery = gr.Gallery(
                 [(item["image"], item["title"]) for item in loras],
                 label="LoRA Gallery",
@@ -291,6 +320,17 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
                 gr.Markdown("[Check the list of FLUX LoRas](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
             custom_lora_info = gr.HTML(visible=False)
             custom_lora_button = gr.Button("Remove custom LoRA", visible=False)
+            # Selected LoRAs section
+            gr.Markdown("### Selected LoRAs")
+            with gr.Row():
+                with gr.Column():
+                    selected_lora1_info = gr.Markdown("", visible=False)
+                    lora_scale1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=0.95, visible=False)
+                    remove_lora1_button = gr.Button("Remove LoRA 1", visible=False)
+                with gr.Column():
+                    selected_lora2_info = gr.Markdown("", visible=False)
+                    lora_scale2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=0.95, visible=False)
+                    remove_lora2_button = gr.Button("Remove LoRA 2", visible=False)
         with gr.Column():
             progress_bar = gr.Markdown(elem_id="progress",visible=False)
             result = gr.Image(label="Generated Image")
@@ -312,28 +352,37 @@ with gr.Blocks(theme=gr.themes.Soft(), css=css, delete_cache=(60, 3600)) as app:
                 with gr.Row():
                     randomize_seed = gr.Checkbox(True, label="Randomize seed")
                     seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
-                    lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=3, step=0.01, value=0.95)
 
     gallery.select(
         update_selection,
-        inputs=[width, height],
-        outputs=[prompt, selected_info, selected_index, width, height]
+        inputs=[width, height, selected_lora1, selected_lora2],
+        outputs=[prompt, selected_lora1, selected_lora2, selected_lora1_info, selected_lora2_info, lora_scale1, remove_lora1_button, lora_scale2, remove_lora2_button, width, height]
+    )
+    remove_lora1_button.click(
+        remove_selected_lora1,
+        inputs=[selected_lora1, selected_lora1_info],
+        outputs=[selected_lora1, selected_lora1_info, lora_scale1, remove_lora1_button]
+    )
+    remove_lora2_button.click(
+        remove_selected_lora2,
+        inputs=[selected_lora2, selected_lora2_info],
+        outputs=[selected_lora2, selected_lora2_info, lora_scale2, remove_lora2_button]
     )
     custom_lora.input(
         add_custom_lora,
         inputs=[custom_lora],
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, prompt]
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_lora1_info, selected_lora2_info, prompt]
     )
     custom_lora_button.click(
         remove_custom_lora,
-        outputs=[custom_lora_info, custom_lora_button, gallery, selected_info, selected_index, custom_lora]
+        outputs=[custom_lora_info, custom_lora_button, gallery, selected_lora1_info, selected_lora2_info, custom_lora]
     )
     gr.on(
         triggers=[generate_button.click, prompt.submit],
         fn=run_lora,
-        inputs=[prompt, input_image, image_strength, cfg_scale, steps, selected_index, randomize_seed, seed, width, height, lora_scale],
+        inputs=[prompt, input_image, image_strength, cfg_scale, steps, randomize_seed, seed, width, height, selected_lora1, selected_lora2, lora_scale1, lora_scale2],
         outputs=[result, seed, progress_bar]
     )
 
 app.queue()
-app.launch()
+app.launch()