Update app.py

app.py

@@ -49,6 +49,10 @@ with open("sdxl_loras.json", "r") as file:
         for item in data
     ]
 
+with open("defaults_data.json", "r") as file:
+    lora_defaults = json.load(file)
+    
+
 device = "cuda" 
 
 state_dicts = {}
@@ -131,49 +135,20 @@ button.addEventListener('click', function() {
     element.classList.add('selected');
 });
 '''
-def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
+def update_selection(selected_state: gr.SelectData, sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative, is_new=False):
     lora_repo = sdxl_loras[selected_state.index]["repo"]
-    instance_prompt = sdxl_loras[selected_state.index]["trigger_word"]
-    new_placeholder = "Type a prompt. This LoRA applies for all prompts, no need for a trigger word" if instance_prompt == "" else "Type a prompt to use your selected LoRA"
+    new_placeholder = "Type a prompt to use your selected LoRA"
     weight_name = sdxl_loras[selected_state.index]["weights"]
     updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo}) ✨ {'(non-commercial LoRA, `cc-by-nc`)' if sdxl_loras[selected_state.index]['is_nc'] else '' }"
-    is_compatible = sdxl_loras[selected_state.index]["is_compatible"]
-    is_pivotal = sdxl_loras[selected_state.index]["is_pivotal"]
-    
-    use_with_diffusers = f'''
-    ## Using [`{lora_repo}`](https://huggingface.co/{lora_repo})
-                        
-    ## Use it with diffusers:
-    '''
-    if is_compatible:
-        use_with_diffusers += f'''
-        from diffusers import StableDiffusionXLPipeline
-        import torch
-    
-        model_path = "stabilityai/stable-diffusion-xl-base-1.0"
-        pipe = StableDiffusionPipeline.from_pretrained(model_path, torch_dtype=torch.float16)
-        pipe.to("cuda")
-        pipe.load_lora_weights("{lora_repo}", weight_name="{weight_name}")
-    
-        prompt = "{instance_prompt}..."
-        lora_scale= 0.9
-        image = pipe(prompt, num_inference_steps=30, guidance_scale=7.5, cross_attention_kwargs={{"scale": lora_scale}}).images[0]
-        image.save("image.png")
-        '''
-    elif not is_pivotal:
-        use_with_diffusers += "This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with `bmaltais/kohya_ss` LoRA class, check out this [Google Colab](https://colab.research.google.com/drive/14aEJsKdEQ9_kyfsiV6JDok799kxPul0j )"
-    else:
-        use_with_diffusers += f"This LoRA is not compatible with diffusers natively yet. But you can still use it on diffusers with sdxl-cog `TokenEmbeddingsHandler` class, check out the [model repo](https://huggingface.co/{lora_repo}#inference-with-🧨-diffusers)"
-    use_with_uis = f'''
-    ## Use it with Comfy UI, Invoke AI, SD.Next, AUTO1111: 
 
-    ### Download the `*.safetensors` weights of [here](https://huggingface.co/{lora_repo}/resolve/main/{weight_name})
+    for lora_list in lora_defaults:
+        if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
+            face_strength = lora_list.get("face_strength", face_strength)
+            image_strength = lora_list.get("image_strength", image_strength)
+            weight = lora_list.get("weight", weight)
+            depth_control_scale = lora_list.get("depth_control_scale", depth_control_scale)
+            negative = lora_list.get("negative", negative)
     
-    - [ComfyUI guide](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
-    - [Invoke AI guide](https://invoke-ai.github.io/InvokeAI/features/CONCEPTS/?h=lora#using-loras)
-    - [SD.Next guide](https://github.com/vladmandic/automatic)
-    - [AUTOMATIC1111 guide](https://stable-diffusion-art.com/lora/)
-    '''
     if(is_new):
         if(selected_state.index == 0):
             selected_state.index = -9999
@@ -182,24 +157,23 @@ def update_selection(selected_state: gr.SelectData, sdxl_loras, is_new=False):
     
     return (
         updated_text,
-        instance_prompt,
         gr.update(placeholder=new_placeholder),
-        selected_state,
-        use_with_diffusers,
-        use_with_uis,
-        gr.Gallery(selected_index=None)
+        face_strength,
+        image_strength,
+        weight,
+        depth_control_scale,
+        negative,
+        selected_state
     )
 
 def center_crop_image_as_square(img):
-    square_size = min(img.size)  # Use the smaller dimension of the image
+    square_size = min(img.size)
     
-    # Calculate the coordinates of the crop box
     left = (img.width - square_size) / 2
     top = (img.height - square_size) / 2
     right = (img.width + square_size) / 2
     bottom = (img.height + square_size) / 2
     
-    # Perform the crop
     img_cropped = img.crop((left, top, right, bottom))
     return img_cropped
     
@@ -230,13 +204,21 @@ def merge_incompatible_lora(full_path_lora, lora_scale):
                 del lora_model
                 gc.collect()
 
-def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, sdxl_loras_new, progress=gr.Progress(track_tqdm=True)):
+def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, sdxl_loras, progress=gr.Progress(track_tqdm=True)):
     global last_lora, last_merged, last_fused, pipe
     face_info = app.get(cv2.cvtColor(np.array(face_image), cv2.COLOR_RGB2BGR))
     face_info = sorted(face_info, key=lambda x:(x['bbox'][2]-x['bbox'][0])*x['bbox'][3]-x['bbox'][1])[-1] # only use the maximum face
     face_emb = face_info['embedding']
     face_kps = draw_kps(face_image, face_info['kps'])
     
+    for lora_list in lora_defaults:
+        if lora_list["model"] == sdxl_loras[selected_state.index]["repo"]:
+            prompt_full = lora_list["model"].get("prompt", None)
+            if(prompt_full):
+                prompt = prompt_full.replace("<subject>", prompt)
+
+    
+    print("Prompt:", prompt)    
     #prepare face zoe
     with torch.no_grad():
         image_zoe = zoe(face_image)
@@ -245,12 +227,12 @@ def run_lora(face_image, prompt, negative, lora_scale, selected_state, face_stre
     images = [face_kps, image_zoe.resize((height, width))]
     
     
-    if(selected_state.index < 0):
-        if(selected_state.index == -9999):
-            selected_state.index = 0
-        else:
-             selected_state.index *= -1
-        sdxl_loras = sdxl_loras_new
+    #if(selected_state.index < 0):
+    #    if(selected_state.index == -9999):
+    #        selected_state.index = 0
+    #    else:
+    #         selected_state.index *= -1
+        #sdxl_loras = sdxl_loras_new
     print("Selected State: ", selected_state.index)
     print(sdxl_loras[selected_state.index]["repo"])
     if negative == "":
@@ -342,7 +324,11 @@ with gr.Blocks(css="custom.css") as demo:
             photo = gr.Image(label="Upload a picture of yourself", interactive=True, type="pil")
             selected_loras = gr.Gallery(label="Selected LoRAs", height=80, show_share_button=False, visible=False, elem_id="gallery_selected", )
             order_gallery = gr.Radio(choices=["random", "likes"], value="random", label="Order by", elem_id="order_radio")
-            new_gallery = gr.Gallery(label="New LoRAs", elem_id="gallery_new", columns=3, value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False)
+            #new_gallery = gr.Gallery(
+            #    label="New LoRAs",
+            #    elem_id="gallery_new",
+            #    columns=3,
+            #    value=[(item["image"], item["title"]) for item in sdxl_loras_raw_new], allow_preview=False, show_share_button=False)
             gallery = gr.Gallery(
                 #value=[(item["image"], item["title"]) for item in sdxl_loras],
                 label="SDXL LoRA Gallery",
@@ -359,7 +345,7 @@ with gr.Blocks(css="custom.css") as demo:
                 elem_id="selected_lora",
             )
             with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="Type a prompt after selecting a LoRA", elem_id="prompt")
+                prompt = gr.Textbox(label="Prompt", show_label=False, lines=1, max_lines=1, placeholder="A person",, elem_id="prompt")
                 button = gr.Button("Run", elem_id="run_button")
             with gr.Group(elem_id="share-btn-container", visible=False) as share_group:
                 community_icon = gr.HTML(community_icon_html)
@@ -375,43 +361,7 @@ with gr.Blocks(css="custom.css") as demo:
                 weight = gr.Slider(0, 10, value=0.9, step=0.1, label="LoRA weight")
                 guidance_scale = gr.Slider(0, 50, value=7, step=0.1, label="Guidance Scale")
                 depth_control_scale = gr.Slider(0, 1, value=0.8, step=0.01, label="Zoe Depth ControlNet strenght")
-    with gr.Column(elem_id="extra_info"):
-        with gr.Accordion(
-            "Use it with: 🧨  diffusers, ComfyUI, Invoke AI, SD.Next, AUTO1111",
-            open=False,
-            elem_id="accordion",
-        ):
-            with gr.Row():
-                use_diffusers = gr.Markdown("""## Select a LoRA first 🤗""")
-                use_uis = gr.Markdown()
-        with gr.Accordion("Submit a LoRA! 📥", open=False):
-            submit_title = gr.Markdown(
-                "### Streamlined submission coming soon! Until then [suggest your LoRA in the community tab](https://huggingface.co/spaces/multimodalart/LoraTheExplorer/discussions) 🤗"
-            )
-            with gr.Group(elem_id="soon"):
-                submit_source = gr.Radio(
-                    ["Hugging Face", "CivitAI"],
-                    label="LoRA source",
-                    value="Hugging Face",
-                )
-                with gr.Row():
-                    submit_source_hf = gr.Textbox(
-                        label="Hugging Face Model Repo",
-                        info="In the format `username/model_id`",
-                    )
-                    submit_safetensors_hf = gr.Textbox(
-                        label="Safetensors filename",
-                        info="The filename `*.safetensors` in the model repo",
-                    )
-                with gr.Row():
-                    submit_trigger_word_hf = gr.Textbox(label="Trigger word")
-                    submit_image = gr.Image(
-                        label="Example image (optional if the repo already contains images)"
-                    )
-                submit_button = gr.Button("Submit!")
-                submit_disclaimer = gr.Markdown(
-                    "This is a curated gallery by me, [apolinário (multimodal.art)](https://twitter.com/multimodalart). I'll try to include as many cool LoRAs as they are submitted! You can [duplicate this Space](https://huggingface.co/spaces/multimodalart/LoraTheExplorer?duplicate=true) to use it privately, and add your own LoRAs by editing `sdxl_loras.json` in the Files tab of your private space."
-                )
+                
     order_gallery.change(
         fn=swap_gallery,
         inputs=[order_gallery, gr_sdxl_loras],
@@ -420,18 +370,18 @@ with gr.Blocks(css="custom.css") as demo:
     )
     gallery.select(
         fn=update_selection,
-        inputs=[gr_sdxl_loras],
-        outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, new_gallery],
-        queue=False,
-        show_progress=False
-    )
-    new_gallery.select(
-        fn=update_selection,
-        inputs=[gr_sdxl_loras_new, gr.State(True)],
-        outputs=[prompt_title, prompt, prompt, selected_state, use_diffusers, use_uis, gallery],
+        inputs=[gr_sdxl_loras, face_strength, image_strength, weight, depth_control_scale, negative],
+        outputs=[prompt_title, prompt, face_strength, image_strength, weight, depth_control_scale, negative, selected_state],
         queue=False,
         show_progress=False
     )
+    #new_gallery.select(
+    #    fn=update_selection,
+    #    inputs=[gr_sdxl_loras_new, gr.State(True)],
+    #    outputs=[prompt_title, prompt, prompt, selected_state, gallery],
+    #    queue=False,
+    #    show_progress=False
+    #)
     prompt.submit(
         fn=check_selected,
         inputs=[selected_state],
@@ -445,7 +395,7 @@ with gr.Blocks(css="custom.css") as demo:
         show_progress=False,
     ).success(
         fn=run_lora,
-        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras, gr_sdxl_loras_new],
+        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
         outputs=[result, share_group],
     )
     button.click(
@@ -461,7 +411,7 @@ with gr.Blocks(css="custom.css") as demo:
         show_progress=False,
     ).success(
         fn=run_lora,
-        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras, gr_sdxl_loras_new],
+        inputs=[photo, prompt, negative, weight, selected_state, face_strength, image_strength, guidance_scale, depth_control_scale, gr_sdxl_loras],
         outputs=[result, share_group],
     )
     share_button.click(None, [], [], js=share_js)