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README.md

@@ -1,12 +1,12 @@
 ---
-title: ShopGenV2
-emoji: 🏃
-colorFrom: purple
-colorTo: pink
+title: SD-XL + Control LoRas
+emoji: 🦀
+colorFrom: green
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.7.1
+sdk_version: 3.44.4
 app_file: app.py
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,461 @@
+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:
+        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>
+                  """)
+
+        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):
+                  # 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=True)
+    
+    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)

requirements.txt

@@ -0,0 +1,8 @@
+torch==2.0.1
+torchvision==0.15.2
+invisible_watermark
+accelerate
+transformers
+safetensors
+opencv-python
+git+https://github.com/huggingface/diffusers.git