Update app.py

app.py

@@ -1,120 +1,335 @@
-import qrcode
-from PIL import Image
+import os
 import gradio as gr
-import io
+import json
+import logging
+import torch
+from PIL import Image
+import spaces
+from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
+from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
+from diffusers.utils import load_image
+from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
+import copy
+import random
+import time
 import base64
-import numpy as np
-import cv2
 import tempfile
 
+# Load LoRAs from JSON file
+with open('loras.json', 'r') as f:
+    loras = json.load(f)
 
-# Function to generate a QR code and return Base64 and PNG file
-def generate_qr(data):
-    qr = qrcode.QRCode(
-        version=1,
-        error_correction=qrcode.constants.ERROR_CORRECT_L,
-        box_size=10,
-        border=4,
-    )
-    qr.add_data(data)
-    qr.make(fit=True)
-    img = qr.make_image(fill="black", back_color="white")
+# Initialize the base model
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
+base_model = "black-forest-labs/FLUX.1-dev"
 
-    # Encode the image as a base64 string
-    buffered = io.BytesIO()
-    img.save(buffered, format="PNG")
-    img_base64 = base64.b64encode(buffered.getvalue()).decode("utf-8")
+taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
+good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype, vae=taef1).to(device)
+pipe_i2i = AutoPipelineForImage2Image.from_pretrained(base_model,
+                                                      vae=good_vae,
+                                                      transformer=pipe.transformer,
+                                                      text_encoder=pipe.text_encoder,
+                                                      tokenizer=pipe.tokenizer,
+                                                      text_encoder_2=pipe.text_encoder_2,
+                                                      tokenizer_2=pipe.tokenizer_2,
+                                                      torch_dtype=dtype
+                                                     )
 
-    # Save the image temporarily as a PNG file
-    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
-    img.save(temp_file.name, format="PNG")
-    temp_file.close()
+MAX_SEED = 2**32-1
 
-    return f"data:image/png;base64,{img_base64}", temp_file.name, img_base64
+pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
 
+class calculateDuration:
+    def __init__(self, activity_name=""):
+        self.activity_name = activity_name
 
-# Function to decode a QR code from an uploaded image
-def decode_qr(img):
-    if img is None:
-        return "No image uploaded."
+    def __enter__(self):
+        self.start_time = time.time()
+        return self
     
-    # Convert PIL image to a NumPy array
-    img_array = np.array(img)
+    def __exit__(self, exc_type, exc_value, traceback):
+        self.end_time = time.time()
+        self.elapsed_time = self.end_time - self.start_time
+        if self.activity_name:
+            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
+        else:
+            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
 
-    # Convert RGB to BGR as OpenCV expects
-    if img_array.ndim == 3:
-        img_array = cv2.cvtColor(img_array, cv2.COLOR_RGB2BGR)
+def update_selection(evt: gr.SelectData, width, height):
+    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}) ✨"
+    if "aspect" in selected_lora:
+        if selected_lora["aspect"] == "portrait":
+            width = 768
+            height = 1024
+        elif selected_lora["aspect"] == "landscape":
+            width = 1024
+            height = 768
+        else:
+            width = 1024
+            height = 1024
+    return (
+        gr.update(placeholder=new_placeholder),
+        updated_text,
+        evt.index,
+        width,
+        height,
+    )
 
-    # Initialize OpenCV QR code detector
-    detector = cv2.QRCodeDetector()
-    data, _, _ = detector.detectAndDecode(img_array)
-    return data if data else "No QR code found."
+@spaces.GPU(duration=30)
+def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, lora_scale, progress):
+    pipe.to("cuda")
+    generator = torch.Generator(device="cuda").manual_seed(seed)
+    with calculateDuration("Generating image"):
+        # Generate image
+        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt_mash,
+            num_inference_steps=steps,
+            guidance_scale=cfg_scale,
+            width=width,
+            height=height,
+            generator=generator,
+            joint_attention_kwargs={"scale": lora_scale},
+            output_type="pil",
+            good_vae=good_vae,
+        ):
+            yield img
 
+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]
 
-# Gradio Interface
-def create_gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("## QR Code Generator and Decoder")
+    # Save the image as a downloadable PNG file
+    temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    final_image.save(temp_file.name, "PNG")
 
-        # Tab for generating QR codes
-        with gr.Tab("Generate QR Code"):
-            with gr.Row():
-                data_input = gr.Textbox(placeholder="Enter text or URL to encode", label="Input Data")
-                generate_button = gr.Button("Generate QR Code")
+    # Convert the image to a base64 string
+    buffered = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+    final_image.save(buffered.name, format="PNG")
+    with open(buffered.name, "rb") as f:
+        img_base64 = base64.b64encode(f.read()).decode("utf-8")
 
-            qr_code_html = gr.HTML(label="Generated QR Code (Base64 Embedded)")
-            qr_png_file = gr.File(label="Download QR Code (PNG)")
-            qr_base64_file = gr.File(label="Download Base64 (TXT)")
+    return final_image, temp_file.name, f"data:image/png;base64,{img_base64}"
 
-            def generate_qr_interface(data):
-                if not data.strip():
-                    raise ValueError("Input text cannot be empty!")
-                img_base64, png_path, base64_str = generate_qr(data)
-                
-                # Save Base64 string as a .txt file
-                base64_txt_path = tempfile.NamedTemporaryFile(delete=False, suffix=".txt")
-                with open(base64_txt_path.name, "w") as f:
-                    f.write(base64_str)
-
-                # Wrap the base64 string in an <img> tag for display
-                html_content = f'<img src="{img_base64}" alt="QR Code" style="max-width:300px;">'
-                return html_content, png_path, base64_txt_path.name
-
-            generate_button.click(
-                generate_qr_interface,
-                inputs=data_input,
-                outputs=[qr_code_html, qr_png_file, qr_base64_file],
-            )
+@spaces.GPU(duration=30)
+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}"
+            else:
+                prompt_mash = f"{prompt} {trigger_word}"
+        else:
+            prompt_mash = f"{trigger_word} {prompt}"
+    else:
+        prompt_mash = prompt
 
-        # Tab for decoding QR codes
-        with gr.Tab("Decode QR Code"):
-            with gr.Row():
-                image_input = gr.Image(type="pil", label="Upload QR Code Image")
-                decode_button = gr.Button("Decode QR Code")
+    with calculateDuration("Unloading LoRA"):
+        pipe.unload_lora_weights()
+        pipe_i2i.unload_lora_weights()
+        
+    # Load LoRA weights
+    with calculateDuration(f"Loading LoRA weights for {selected_lora['title']}"):
+        pipe_to_use = pipe_i2i if image_input is not None else pipe
+        weight_name = selected_lora.get("weights", None)
+        
+        pipe_to_use.load_lora_weights(
+            lora_path, 
+            weight_name=weight_name, 
+            low_cpu_mem_usage=True
+        )
+                
+    # 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, file_path, base64_str = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, lora_scale, seed)
+        yield final_image, seed, file_path, base64_str, gr.update(visible=False)
+    else:
+        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
+        for image in image_generator:
+            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, None, None, gr.update(value=progress_bar, visible=True)
+            
+        # Save the final image and encode to Base64
+        temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
+        final_image.save(temp_file.name, "PNG")
+        with open(temp_file.name, "rb") as f:
+            img_base64 = base64.b64encode(f.read()).decode("utf-8")
 
-            decoded_text = gr.Textbox(label="Decoded Text", interactive=True)
+        yield final_image, seed, temp_file.name, f"data:image/png;base64,{img_base64}", 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
 
-            decode_button.click(
-                decode_qr,
-                inputs=image_input,
-                outputs=decoded_text,
-            )
+def check_custom_model(link):
+    if(link.startswith("https://")):
+        if(link.startswith("https://huggingface.co") or link.startswith("https://www.huggingface.co")):
+            link_split = link.split("huggingface.co/")
+            return get_huggingface_safetensors(link_split[1])
+    else: 
+        return get_huggingface_safetensors(link)
 
-        # Add the logo at the bottom center using gr.HTML
-        gr.HTML("""
-            <div style="
-                position: fixed;
-                bottom: 20px;
-                left: 50%;
-                transform: translateX(-50%);
-                z-index: 1000;
-            ">
-                <img src="file=space-logo.png" alt="Space Logo" style="width: 150px; height: auto;">
+def add_custom_lora(custom_lora):
+    global loras
+    if(custom_lora):
+        try:
+            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
+            print(f"Loaded custom LoRA: {repo}")
+            card = f'''
+            <div class="custom_lora_card">
+              <span>Loaded custom LoRA:</span>
+              <div class="card_internal">
+                <img src="{image}" />
+                <div>
+                    <h3>{title}</h3>
+                    <small>{"Using: <code><b"+trigger_word+"</code></b> as the trigger word" if trigger_word else "No trigger word found. If there's a trigger word, include it in your prompt"}<br></small>
+                </div>
+              </div>
             </div>
-        """)
+            '''
+            existing_item_index = next((index for (index, item) in enumerate(loras) if item['repo'] == repo), None)
+            if(not existing_item_index):
+                new_item = {
+                    "image": image,
+                    "title": title,
+                    "repo": repo,
+                    "weights": path,
+                    "trigger_word": trigger_word
+                }
+                print(new_item)
+                existing_item_index = len(loras)
+                loras.append(new_item)
+        
+            return gr.update(visible=True, value=card), gr.update(visible=True), gr.Gallery(selected_index=None), f"Custom: {path}", existing_item_index, trigger_word
+        except Exception as e:
+            gr.Warning(f"Invalid LoRA: either you entered an invalid link, or a non-FLUX LoRA")
+            return gr.update(visible=True, value=f"Invalid LoRA: either you entered an invalid link, a non-FLUX LoRA"), gr.update(visible=True), gr.update(), "", None, ""
+    else:
+        return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
-    demo.launch(share=True)
+def remove_custom_lora():
+    return gr.update(visible=False), gr.update(visible=False), gr.update(), "", None, ""
 
+run_lora.zerogpu = True
 
-# Run the Gradio interface
-create_gradio_interface()
+css = '''
+#gen_btn{height: 100%}
+#gen_column{align-self: stretch}
+#title{text-align: center}
+#title h1{font-size: 3em; display:inline-flex; align-items:center}
+#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}
+.styler{--form-gap-width: 0px !important}
+#progress{height:30px}
+#progress .generating{display:none}
+.progress-container {width: 100%;height: 30px;background-color: #f0f0f0;border-radius: 15px;overflow: hidden;margin-bottom: 20px}
+.progress-bar {height: 100%;background-color: #4f46e5;width: calc(var(--current) / var(--total) * 100%);transition: width 0.5s ease-in-out}
+'''
+font=[gr.themes.GoogleFont("Source Sans Pro"), "Arial", "sans-serif"]
+with gr.Blocks(theme=gr.themes.Soft(font=font), css=css, delete_cache=(60, 60)) 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>""",
+        elem_id="title",
+    )
+    selected_index = 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")
+        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",
+                allow_preview=False,
+                columns=3,
+                elem_id="gallery",
+                show_share_button=False
+            )
+            with gr.Group():
+                custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path", placeholder="multimodalart/vintage-ads-flux")
+                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)
+        with gr.Column():
+            progress_bar = gr.Markdown(elem_id="progress",visible=False)
+            result = gr.Image(label="Generated Image")
+            download_link = gr.File(label="Download Image")
+            base64_output = gr.Textbox(label="Base64 Encoded Image")
+
+    with gr.Row():
+        with gr.Accordion("Advanced Settings", open=False):
+            with gr.Row():
+                input_image = gr.Image(label="Input image", type="filepath")
+                image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
+            with gr.Column():
+                with gr.Row():
+                    cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
+                    steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
+                
+                with gr.Row():
+                    width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
+                    height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
+                
+                with gr.Row():
+                    randomize_seed = gr.Checkbox(True, label="Randomize seed")
+                    seed = gr.Slider(label="Seed", minimum=0, maximum