Update app.py

app.py

@@ -1,3 +1,4 @@
+
 import os
 # os.system('pip install pip==23.3.0')
 # os.system('pip uninstall spaces -y')
@@ -42,465 +43,14 @@ controlnet = ControlNetModel.from_pretrained(
     torch_dtype=torch.float16
 )
 
-def check_use_custom_or_no(value):
-    if value is True:
-        return gr.update(visible=True)
-    else:
-        return gr.update(visible=False)
-
-def get_files(file_paths):
-    last_files = {}  # Dictionary to store the last file for each path
-
-    for file_path in file_paths:
-        # Split the file path into directory and file components
-        directory, file_name = file_path.rsplit('/', 1)
-    
-        # Update the last file for the current path
-        last_files[directory] = file_name
-    
-    # Extract the last files from the dictionary
-    result = list(last_files.values())
-
-    return result
-
-def load_model(model_name):
-
-    if model_name == "":
-        gr.Warning("If you want to use a private model, you need to duplicate this space on your personal account.")
-        raise gr.Error("You forgot to define Model ID.")
-
-    # Get instance_prompt a.k.a trigger word
-    card = ModelCard.load(model_name)
-    repo_data = card.data.to_dict()
-    instance_prompt = repo_data.get("instance_prompt")
-
-    if instance_prompt is not None:
-        print(f"Trigger word: {instance_prompt}")
-    else:
-        instance_prompt = "no trigger word needed"
-        print(f"Trigger word: no trigger word needed")
-
-    # List all ".safetensors" files in repo
-    sfts_available_files = fs.glob(f"{model_name}/*safetensors")
-    sfts_available_files = get_files(sfts_available_files)
-
-    if sfts_available_files == []:
-        sfts_available_files = ["NO SAFETENSORS FILE"]
-
-    print(f"Safetensors available: {sfts_available_files}")
-
-    return model_name, "Model Ready", gr.update(choices=sfts_available_files, value=sfts_available_files[0], visible=True), gr.update(value=instance_prompt, visible=True)
-
-def custom_model_changed(model_name, previous_model):
-    if model_name == "" and previous_model == "" :
-        status_message = ""      
-    elif model_name != previous_model:
-        status_message = "model changed, please reload before any new run"
-    else:
-        status_message = "model ready"
-    return status_message
-
-def resize_image(input_path, output_path, target_height):
-    # Open the input image
-    img = Image.open(input_path)
-
-    # Calculate the aspect ratio of the original image
-    original_width, original_height = img.size
-    original_aspect_ratio = original_width / original_height
-
-    # Calculate the new width while maintaining the aspect ratio and the target height
-    new_width = int(target_height * original_aspect_ratio)
-
-    # Resize the image while maintaining the aspect ratio and fixing the height
-    img = img.resize((new_width, target_height), Image.LANCZOS)
-
-    # Save the resized image
-    img.save(output_path)
-
-    return output_path
-
-def predict(image):
-    inputs = feature_extractor(images=image, return_tensors="pt")
-    with torch.no_grad():
-        outputs = modeld(**inputs)
-        predicted_depth = outputs.predicted_depth
-        # interpolate to original size
-    prediction = torch.nn.functional.interpolate(
-        predicted_depth.unsqueeze(1),
-        size=image.size[::-1],
-        mode="bicubic",
-        align_corners=False,
-    )
-    # visualize the prediction
-    output = prediction.squeeze().cpu().numpy()
-    formatted = (output * 255 / np.max(output)).astype("uint8")
-    depth_image = Image.fromarray(formatted)
-    depth_image.save(f"depth.png")
-    return depth_image
-
-
-@spaces.GPU
-def infer(use_custom_model, model_name, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed, progress=gr.Progress(track_tqdm=True)):
-
-    pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
-        "stabilityai/stable-diffusion-xl-base-1.0",
-        controlnet=controlnet,
-        vae=vae,
-        torch_dtype=torch.float16, 
-        variant="fp16",
-        use_safetensors=True
-    )
-    
-    pipe.to(device)
-    
-    prompt = prompt
-    negative_prompt = negative_prompt
-
-    if seed < 0 :
-        seed = random.randint(0, 423538377342)
-    
-    generator = torch.Generator(device=device).manual_seed(seed)
-
-    if image_in == None:
-        raise gr.Error("You forgot to upload a source image.")
-    
-    image_in = resize_image(image_in, "resized_input.jpg", 1024)
-    
-    if preprocessor == "canny":
-
-        image = load_image(image_in)
-
-        image = np.array(image)
-        image = cv2.Canny(image, 100, 200)
-        image = image[:, :, None]
-        image = np.concatenate([image, image, image], axis=2)
-        image = Image.fromarray(image)
-    
-    if use_custom_model:
-        
-        if model_name == "":
-            raise gr.Error("you forgot to set a custom model name.")
-        
-        custom_model = model_name
-
-        # This is where you load your trained weights
-        if weight_name == "NO SAFETENSORS FILE": 
-            pipe.load_lora_weights(
-                custom_model,     
-                low_cpu_mem_usage = True,
-                use_auth_token = True
-            )
-    
-        else:
-            pipe.load_lora_weights(
-                custom_model,
-                weight_name = weight_name,        
-                low_cpu_mem_usage = True,
-                use_auth_token = True
-            )
-    
-        lora_scale=custom_lora_weight
-
-        images = pipe(
-            prompt, 
-            negative_prompt=negative_prompt, 
-            image=image, 
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            guidance_scale = float(guidance_scale),
-            num_inference_steps=inf_steps,
-            generator=generator,
-            cross_attention_kwargs={"scale": lora_scale}
-        ).images
-    else:
-        images = pipe(
-            prompt, 
-            negative_prompt=negative_prompt, 
-            image=image, 
-            controlnet_conditioning_scale=float(controlnet_conditioning_scale),
-            guidance_scale = float(guidance_scale),
-            num_inference_steps=inf_steps,
-            generator=generator,
-        ).images
-
-    images[0].save(f"result.png")
-    print("HELP")
-    predict(images[0])
-    # create_visual_demo(); 
-    return f"result.png", seed
-
-
-css="""
-.{
-  height: 20%;
-}
-#col-container{
-    margin: 0 auto;
-    max-width: 720px;
-    text-align: left;
-}
-div#warning-duplicate {
-    background-color: #ebf5ff;
-    padding: 0 10px 5px;
-    margin: 20px 0;
-}
-div#warning-duplicate > .gr-prose > h2, div#warning-duplicate > .gr-prose > p {
-    color: #0f4592!important;
-}
-div#warning-duplicate strong {
-    color: #0f4592;
-}
-p.actions {
-    display: flex;
-    align-items: center;
-    margin: 20px 0;
-}
-div#warning-duplicate .actions a {
-    display: inline-block;
-    margin-right: 10px;
-}
-button#load_model_btn{
-    height: 45px !important;
-    border: none;
-    background-color: #99F6E4; !important;
-    border-radius: 10px !important;
-    padding: 10px !important;
-    cursor: pointer;
-    display: block;
-    position: relative;
-    top: -20px;
-    z-index: 100;
-}
-#status_info{
-    font-size: 0.9em;
-}
-"""
-
-theme = gr.themes.Soft(
-    primary_hue="teal",
-    secondary_hue="gray",
-).set(
-    body_text_color_dark='*neutral_800',
-    background_fill_primary_dark='*neutral_50',
-    background_fill_secondary_dark='*neutral_50',
-    border_color_accent_dark='*primary_300',
-    border_color_primary_dark='*neutral_200',
-    color_accent_soft_dark='*neutral_50',
-    link_text_color_dark='*secondary_600',
-    link_text_color_active_dark='*secondary_600',
-    link_text_color_hover_dark='*secondary_700',
-    link_text_color_visited_dark='*secondary_500',
-    code_background_fill_dark='*neutral_100',
-    shadow_spread_dark='6px',
-    block_background_fill_dark='white',
-    block_label_background_fill_dark='*primary_100',
-    block_label_text_color_dark='*primary_500',
-    block_title_text_color_dark='*primary_500',
-    checkbox_background_color_dark='*background_fill_primary',
-    checkbox_background_color_selected_dark='*primary_600',
-    checkbox_border_color_dark='*neutral_100',
-    checkbox_border_color_focus_dark='*primary_500',
-    checkbox_border_color_hover_dark='*neutral_300',
-    checkbox_border_color_selected_dark='*primary_600',
-    checkbox_label_background_fill_selected_dark='*primary_500',
-    checkbox_label_text_color_selected_dark='white',
-    error_background_fill_dark='#fef2f2',
-    error_border_color_dark='#b91c1c',
-    error_text_color_dark='#b91c1c',
-    error_icon_color_dark='#b91c1c',
-    input_background_fill_dark='white',
-    input_background_fill_focus_dark='*secondary_500',
-    input_border_color_dark='*neutral_50',
-    input_border_color_focus_dark='*secondary_300',
-    input_placeholder_color_dark='*neutral_400',
-    slider_color_dark='*primary_500',
-    stat_background_fill_dark='*primary_300',
-    table_border_color_dark='*neutral_300',
-    table_even_background_fill_dark='white',
-    table_odd_background_fill_dark='*neutral_50',
-    button_primary_background_fill_dark='*primary_500',
-    button_primary_background_fill_hover_dark='*primary_400',
-    button_primary_border_color_dark='*primary_00',
-    button_secondary_background_fill_dark='whiite',
-    button_secondary_background_fill_hover_dark='*neutral_100',
-    button_secondary_border_color_dark='*neutral_200',
-    button_secondary_text_color_dark='*neutral_800'
-)
-
-#examples = [["examples/" + img] for img in os.listdir("examples/")]
-im = gr.Image(visible=False)
-
-with gr.Blocks(theme=theme, css=css) as demo:
-  with gr.Row():
-    with gr.Column(elem_id="col-container"):
-        
-        gr.HTML("""
-        <h2 style="text-align: left;">Choose a Style</h2>
-        <p style="text-align: left;">Our Pretrained Models can be found on Huggingface</p>
-                """)
-
-        use_custom_model = gr.Checkbox(label="Use a custom pre-trained LoRa model ? (optional)", visible = False, value=False, info="To use a private model, you'll need to duplicate the space with your own access token.")
-        
-        with gr.Blocks(visible=False) as custom_model_box:
-            with gr.Row():
-                with gr.Column():
-                    if not is_shared_ui:
-                        your_username = api.whoami()["name"]
-                        my_models = api.list_models(author=your_username, filter=["diffusers", "stable-diffusion-xl", 'lora'])
-                        model_names = [item.modelId for item in my_models]
-    
-                    if not is_shared_ui:
-                        custom_model = gr.Dropdown(
-                            label = "Your custom model ID",
-                            info="You can pick one of your private models",
-                            choices = model_names,
-                            allow_custom_value = True
-                            #placeholder = "username/model_id"
-                        )
-                    else:
-                        custom_model = gr.Textbox(
-                            label="Your custom model ID", 
-                            placeholder="your_username/your_trained_model_name", 
-                            info="Make sure your model is set to PUBLIC"
-                        )
-                    
-                    weight_name = gr.Dropdown(
-                        label="Safetensors file", 
-                        #value="pytorch_lora_weights.safetensors", 
-                        info="specify which one if model has several .safetensors files",
-                        allow_custom_value=True,
-                        visible = False
-                    )
-                with gr.Column():
-                    with gr.Group():
-                        # load_model_btn = gr.Button("Load my model", elem_id="load_model_btn")
-                        previous_model = gr.Textbox(
-                            visible = False
-                        )
-                        
-                        model_status = gr.Textbox(
-                            label = "model status",
-                            show_label = False,
-                            elem_id = "status_info"
-                        )
-                    trigger_word = gr.Textbox(label="Trigger word", interactive=False, visible=False)
-       
-        load_model_btn = gr.Button("Load my model", elem_id="load_model_btn")
-        image_in = gr.Image(sources="upload", type="filepath", value=( "shop1.jpg"))
-        # gr.Examples(
-        #   examples=[[os.path.join(os.path.dirname(__file__), "shop2.jpg")],[os.path.join(os.path.dirname(__file__), "shop3.jpg")]], inputs=im)
-            
-
-    with gr.Column(elem_id="col-container"):
-        gr.HTML("""
-          <h2 style="text-align: left;">Input a Prompt!</h2>
-          <p style="text-align: left;">Negative prompts and other settings can be found in advanced options</p>
-                  """)
+# for file naming
+counter_file_path = "counter.txt"
+if os.path.exists(counter_file_path):
+    with open(counter_file_path, "r") as file:
+        counter = int(file.read())
+else:
+    counter = 0
 
-        with gr.Row():
-            
-            with gr.Column():
-                # with gr.Group():
-                prompt = gr.Textbox(label="Prompt", placeholder="Add your trigger word here + prompt")
-                
-                with gr.Accordion(label="Advanced Options", open=False, visible=False):
-                  # with gr.Group():
-                    negative_prompt = gr.Textbox(label="Negative prompt", value="extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured")
-                    guidance_scale = gr.Slider(label="Guidance Scale", minimum=1.0, maximum=10.0, step=0.1, value=8.8)
-                    inf_steps = gr.Slider(label="Inference Steps", minimum="25", maximum="50", step=1, value=25)
-                    custom_lora_weight = gr.Slider(label="Custom model weights", minimum=0.1, maximum=0.9, step=0.1, value=0.7)
-                    preprocessor = gr.Dropdown(label="Preprocessor", choices=["canny"], value="canny", interactive=False, info="For the moment, only canny is available")  
-                    controlnet_conditioning_scale = gr.Slider(label="Controlnet conditioning Scale", minimum=0.1, maximum=0.9, step=0.01, value=0.3)
-                    seed = gr.Slider(
-                        label="Seed",
-                        info = "-1 denotes a random seed",
-                        minimum=-1,
-                        maximum=423538377342,
-                        step=1,
-                        value=-1
-                    )
-                    last_used_seed = gr.Number(
-                        label = "Last used seed",
-                        info = "the seed used in the last generation",
-                    )
-            
-        submit_btn = gr.Button("Submit")
-
-        # label = gr.Label(label="Loader") 
-        # submit_btn.click(infer, outputs=[label])
-        
-        result = gr.Image(label="Result", visible=False)
-    
-    use_custom_model.change(
-        fn = check_use_custom_or_no,
-        inputs =[use_custom_model],
-        outputs = [custom_model_box],
-        queue = False
-    )
-    custom_model.blur(
-        fn=custom_model_changed,
-        inputs = [custom_model, previous_model],
-        outputs = [model_status],
-        queue = False
-    )
-    load_model_btn.click(
-        fn = load_model,
-        inputs=[custom_model],
-        outputs = [previous_model, model_status, weight_name, trigger_word],
-        queue = False
-    )
-    submit_btn.click(
-        fn = infer,
-        inputs = [use_custom_model,custom_model, weight_name, custom_lora_weight, image_in, prompt, negative_prompt, preprocessor, controlnet_conditioning_scale, guidance_scale, inf_steps, seed],
-        outputs = [result, last_used_seed]
-    )
-    
-
-  # return demo 
-  
-
-demo.queue().launch(share=True)import os
-# os.system('pip install pip==23.3.0')
-# os.system('pip uninstall spaces -y')
-# os.system('pip install spaces==0.18.0')
-# os.system('pip install gradio==4.0.2')
-
-
-import gradio as gr
-from huggingface_hub import login, HfFileSystem, HfApi, ModelCard
-import os
-import spaces
-import random
-import torch
-
-from transformers import GLPNFeatureExtractor, GLPNForDepthEstimation
-from transformers import AutoFeatureExtractor, AutoModelForDepthEstimation
-feature_extractor = AutoFeatureExtractor.from_pretrained("Intel/dpt-large")
-modeld = AutoModelForDepthEstimation.from_pretrained("Intel/dpt-large")
-
-# from depthGAN.app import create_visual_demo 
-
-is_shared_ui = False
-hf_token = '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'
-login(token=hf_token)
-
-fs = HfFileSystem(token=hf_token)
-api = HfApi()
-
-device="cuda" if torch.cuda.is_available() else "cpu"
-
-from diffusers import ControlNetModel, StableDiffusionXLControlNetPipeline, AutoencoderKL
-from diffusers.utils import load_image
-from PIL import Image
-import torch
-import numpy as np
-import cv2
-
-vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
-
-controlnet = ControlNetModel.from_pretrained(
-    "diffusers/controlnet-canny-sdxl-1.0",
-    torch_dtype=torch.float16
-)
 
 def check_use_custom_or_no(value):
     if value is True:
@@ -579,7 +129,7 @@ def resize_image(input_path, output_path, target_height):
 
     return output_path
 
-def predict(image):
+def predict(image, counter):
     inputs = feature_extractor(images=image, return_tensors="pt")
     with torch.no_grad():
         outputs = modeld(**inputs)
@@ -595,7 +145,7 @@ def predict(image):
     output = prediction.squeeze().cpu().numpy()
     formatted = (output * 255 / np.max(output)).astype("uint8")
     depth_image = Image.fromarray(formatted)
-    depth_image.save(f"depth.png")
+    depth_image.save(f"viteGradio/images/depth{counter}.png")
     return depth_image
 
 
@@ -682,11 +232,15 @@ def infer(use_custom_model, model_name, weight_name, custom_lora_weight, image_i
             generator=generator,
         ).images
 
-    images[0].save(f"result.png")
+    global counter
+    images[0].save(f"viteGradio/images/result{counter}.png")
     print("HELP")
-    predict(images[0])
+    predict(images[0], counter)
+    counter+=1
+    with open(counter_file_path, "w") as file:
+      file.write(str(counter))
     # create_visual_demo(); 
-    return f"result.png", seed
+    return f"viteGradio/images/result{counter-1}.png", seed
 
 
 css="""
@@ -888,7 +442,7 @@ with gr.Blocks(theme=theme, css=css) as demo:
         # label = gr.Label(label="Loader") 
         # submit_btn.click(infer, outputs=[label])
         
-        result = gr.Image(label="Result", visible=False)
+        result = gr.Image(label="Result", visible=True)
     
     use_custom_model.change(
         fn = check_use_custom_or_no,