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.gitattributes

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FLUX.1-Krea-dev/FLUX_1_Krea_dev.py

@@ -0,0 +1,120 @@
+import torch
+from diffusers import FluxPipeline
+from PIL import Image
+import io
+import base64
+import os
+from dotenv import load_dotenv
+
+# Load environment variables
+load_dotenv()
+
+class ImageGenerator:
+    def __init__(self):
+        self.image_url = ""
+        self.status = "Ready"
+        self.model_loaded = False
+        self.pipe = None
+
+    def load_model(self):
+        """Lazy load the model when first needed"""
+        if not self.model_loaded:
+            try:
+                self.status = "Loading model (first time may take a minute)..."
+
+                # Configure for Apple Silicon
+                if torch.backends.mps.is_available():
+                    torch.mps.set_per_process_memory_fraction(0.9)
+
+                # Assign to the state variable
+                self.pipe = FluxPipeline.from_pretrained(
+                    "./models/FLUX.1-Krea-dev",
+                    torch_dtype=torch.float32,
+                    local_files_only=True
+                ).to("mps")
+
+                self.model_loaded = True
+                self.status = "Model loaded successfully!"
+                return True
+            except Exception as e:
+                self.status = f"Model loading failed: {str(e)}"
+                raise
+        return True
+
+    def generate_image(self, prompt):
+        if not prompt.strip():
+            self.status = "Please enter a prompt"
+            return False
+
+        try:
+            # Load model if needed
+            if not self.load_model():
+                return False
+            
+            self.status = "Generating image..."
+            
+            # Clear memory before generation
+            torch.mps.empty_cache()
+            
+            # Generate image with optimized settings
+            image = self.pipe(
+                prompt,
+                height=2048,
+                width=2048,
+                guidance_scale=7.0,
+                num_inference_steps=28,
+            ).images[0]
+
+            # Convert to base64 for display/saving
+            buffered = io.BytesIO()
+            image.save(buffered, format="PNG")
+            self.image_url = f"data:image/png;base64,{base64.b64encode(buffered.getvalue()).decode()}"
+            self.status = "Generation complete!"
+            return True
+
+        except Exception as e:
+            self.status = f"Error: {str(e)}"
+            return False
+
+def main():
+    generator = ImageGenerator()
+    
+    while True:
+        print("\nCurrent status:", generator.status)
+        if generator.image_url:
+            print("Image generated! You can save it or view it using the base64 data.")
+            print("Base64 data length:", len(generator.image_url))
+        
+        print("\nOptions:")
+        print("1. Generate image")
+        print("2. Save image to file")
+        print("3. Exit")
+        
+        choice = input("Enter your choice (1-3): ")
+        
+        if choice == "1":
+            prompt = input("Enter your prompt: ")
+            generator.generate_image(prompt)
+        elif choice == "2":
+            if not generator.image_url:
+                print("No image to save. Generate an image first.")
+                continue
+            
+            try:
+                # Extract base64 data
+                img_data = base64.b64decode(generator.image_url.split(",")[1])
+                filename = input("Enter filename to save (without extension): ") + ".png"
+                
+                with open(filename, "wb") as f:
+                    f.write(img_data)
+                print(f"Image saved as {filename}")
+            except Exception as e:
+                print(f"Error saving image: {str(e)}")
+        elif choice == "3":
+            print("Exiting...")
+            break
+        else:
+            print("Invalid choice. Please try again.")
+
+if __name__ == "__main__":
+    main()

Upscaler/512_to_2048_upscaler.py

@@ -0,0 +1,83 @@
+import os
+import torch
+from diffusers import FluxControlNetModel, FluxControlNetPipeline
+from PIL import Image
+from huggingface_hub import snapshot_download
+from tqdm import tqdm
+
+# Configuration
+INPUT_FOLDER = "input_images"  # Folder containing 512x512 images
+OUTPUT_FOLDER = "2048_2048_upscaled_output"  # Output folder
+UPSCALE_FACTOR = 4  # 512 -> 2048
+NUM_INFERENCE_STEPS = 28
+CONTROLNET_CONDITIONING_SCALE = 0.6
+SEED = 42
+
+# Device setup
+device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
+dtype = torch.float32 if device == "mps" else torch.bfloat16
+
+# Create output folder
+os.makedirs(OUTPUT_FOLDER, exist_ok=True)
+
+# Load model
+print("Loading model...")
+model_path = snapshot_download(
+    repo_id="black-forest-labs/FLUX.1-dev",
+    ignore_patterns=["*.md", "*..gitattributes"],
+    local_dir="FLUX.1-dev"
+)
+
+controlnet = FluxControlNetModel.from_pretrained(
+    "jasperai/Flux.1-dev-Controlnet-Upscaler", 
+    torch_dtype=dtype
+).to(device)
+
+pipe = FluxControlNetPipeline.from_pretrained(
+    model_path, 
+    controlnet=controlnet, 
+    torch_dtype=dtype
+).to(device)
+
+def process_image(input_path, output_path):
+    # Load image
+    input_image = Image.open(input_path).convert("RGB")
+    
+    # Resize to multiple of 8 (required by model)
+    w, h = input_image.size
+    w = w - w % 8
+    h = h - h % 8
+    input_image = input_image.resize((w, h))
+    
+    # Create control image
+    control_image = input_image.resize((w * UPSCALE_FACTOR, h * UPSCALE_FACTOR))
+    
+    # Generate upscaled image
+    generator = torch.Generator(device=device).manual_seed(SEED)
+    image = pipe(
+        prompt="",
+        control_image=control_image,
+        controlnet_conditioning_scale=CONTROLNET_CONDITIONING_SCALE,
+        num_inference_steps=NUM_INFERENCE_STEPS,
+        guidance_scale=3.5,
+        generator=generator,
+    ).images[0]
+    
+    # Save output
+    image.save(output_path)
+
+# Process all images in input folder
+print(f"Processing images in {INPUT_FOLDER}...")
+image_files = [f for f in os.listdir(INPUT_FOLDER) if f.lower().endswith(('.png', '.jpg', '.jpeg'))]
+
+for i, filename in enumerate(tqdm(image_files, desc="Upscaling images")):
+    input_path = os.path.join(INPUT_FOLDER, filename)
+    output_filename = f"Unknown.{i}.png" if i > 0 else "Unknown.png"
+    output_path = os.path.join(OUTPUT_FOLDER, output_filename)
+    
+    try:
+        process_image(input_path, output_path)
+    except Exception as e:
+        print(f"Error processing {filename}: {str(e)}")
+
+print(f"Done! Upscaled images saved in {OUTPUT_FOLDER}")

app.py

@@ -0,0 +1,195 @@
+import gradio as gr
+from PIL import Image
+import os
+import glob
+import numpy as np
+from datetime import datetime
+
+def get_valid_image_paths():
+    """Return all valid image paths with error handling"""
+    image_paths = []
+    for pattern in ["output/Unknown*.png", "output/terminal_*.png", "output2/Unknown*.png", "output2/terminal_*"]:
+        try:
+            paths = sorted(glob.glob(pattern))
+            for path in paths:
+                try:
+                    with Image.open(path) as img:
+                        img.verify()  # Verify it's a valid image
+                    image_paths.append(path)
+                except (IOError, SyntaxError, Exception) as e:
+                    print(f"Skipping invalid image {path}: {str(e)}")
+        except Exception as e:
+            print(f"Error searching for {pattern}: {str(e)}")
+    return image_paths
+
+def load_and_convert_image(image_path):
+    """Load image and convert to RGB if needed"""
+    try:
+        with Image.open(image_path) as img:
+            if img.mode != 'RGB':
+                img = img.convert('RGB')
+            return np.array(img)
+    except Exception as e:
+        print(f"Error loading {image_path}: {str(e)}")
+        return None
+
+def display_image(image_number, image_paths):
+    """Display image with robust error handling"""
+    if not image_paths:
+        return None, "No valid images found"
+    
+    try:
+        image_number = int(image_number)
+        if image_number < 0 or image_number >= len(image_paths):
+            return None, "Image number out of range"
+            
+        image_path = image_paths[image_number]
+        img_array = load_and_convert_image(image_path)
+        if img_array is not None:
+            return img_array, os.path.basename(image_path)
+        return None, f"Could not load {os.path.basename(image_path)}"
+    except Exception as e:
+        return None, f"Error: {str(e)}"
+
+def update_selection(evt: gr.SelectData):
+    """Enhanced info display with beautiful Markdown formatting"""
+    image_paths = get_valid_image_paths()
+    if not image_paths or evt.index >= len(image_paths):
+        return ["No file selected", "", "### Image Information\nNo image selected"]
+    
+    path = image_paths[evt.index]
+    filename = os.path.basename(path)
+    
+    try:
+        with Image.open(path) as img:
+            ctime = os.path.getctime(path)
+            size_kb = os.path.getsize(path)/1024
+            
+            info_md = f"""
+            ### Image Information
+            **Dimensions:** {img.size[0]}×{img.size[1]} px  
+            **Color Mode:** {img.mode}  
+            **Format:** {img.format or 'Unknown'}  
+            **Created:** {datetime.fromtimestamp(ctime).strftime('%Y-%m-%d %H:%M:%S')}  
+            **Size:** {size_kb:.2f} KB  
+            **Location:** `{path}`
+            """
+            return [filename, path, info_md]
+            
+    except Exception as e:
+        print(f"⚠️ Error loading image info: {str(e)}")
+        return [filename, path, "### Image Information\n⚠️ Could not load image details"]
+
+# Create the Gradio interface
+with gr.Blocks(title="FLUX.1-Krea-dev 512|FLUX.1-dev-Controlnet-Upscaler 2048 Image Viewer", theme="soft") as demo:
+    image_paths = gr.State(get_valid_image_paths())
+    
+    gr.Markdown("""
+    # FLUX.1-Krea-dev | Flux.1-dev-Controlnet-Upscaler Image Viewer
+    Browse all output images including Unknown series and Terminal series
+    """)
+    
+    with gr.Row():
+        with gr.Column(scale=1):
+            image_number = gr.Slider(
+                minimum=0,
+                maximum=max(0, len(get_valid_image_paths())-1),
+                step=1,
+                value=0,
+                label="Image Number",
+                interactive=True
+            )
+            current_file = gr.Textbox(label="Current File")
+            file_path = gr.Textbox(label="Full Path", visible=False)
+            with gr.Row():
+                prev_btn = gr.Button("Previous")
+                next_btn = gr.Button("Next")
+            with gr.Row():
+                gr.Button("Show Unknown").click(
+                    lambda: [p for p in get_valid_image_paths() if "Unknown" in p],
+                    outputs=[image_paths]
+                )
+                gr.Button("Show Terminal").click(
+                    lambda: [p for p in get_valid_image_paths() if "terminal_" in p],
+                    outputs=[image_paths]
+                )
+                gr.Button("Show All").click(
+                    lambda: get_valid_image_paths(),
+                    outputs=[image_paths]
+                )
+            image_info = gr.Markdown("### Image Information\nSelect an image to view details")
+        with gr.Column(scale=4):
+            image_output = gr.Image(
+                label="Output Image",
+                interactive=False
+            )
+    
+    with gr.Row():
+        gallery = gr.Gallery(
+            value=[load_and_convert_image(p) for p in get_valid_image_paths()],
+            label="All Images",
+            columns=5,
+            height="auto",
+            object_fit="contain"
+        )
+    
+    def update_slider_max(paths):
+        return gr.Slider(maximum=max(0, len(paths)-1))
+    
+    def navigate(step, current_num, paths):
+        new_num = current_num + step
+        if new_num < 0:
+            new_num = len(paths) - 1
+        elif new_num >= len(paths):
+            new_num = 0
+        return new_num, *display_image(new_num, paths)
+    
+    image_paths.change(
+        fn=update_slider_max,
+        inputs=image_paths,
+        outputs=image_number
+    )
+    
+    image_number.change(
+        fn=display_image,
+        inputs=[image_number, image_paths],
+        outputs=[image_output, current_file]
+    )
+    
+    prev_btn.click(
+        fn=lambda n, p: navigate(-1, n, p),
+        inputs=[image_number, image_paths],
+        outputs=[image_number, image_output, current_file]
+    )
+    
+    next_btn.click(
+        fn=lambda n, p: navigate(1, n, p),
+        inputs=[image_number, image_paths],
+        outputs=[image_number, image_output, current_file]
+    )
+    
+    def handle_gallery_select(evt: gr.SelectData):
+        if evt is None:
+            return [0, None, "No file selected"]
+        image_paths = get_valid_image_paths()
+        if not image_paths or evt.index >= len(image_paths):
+            return [0, None, "No file selected"]
+        return [evt.index, *display_image(evt.index, image_paths)]
+    
+    gallery.select(
+        fn=handle_gallery_select,
+        outputs=[image_number, image_output, current_file]
+    )
+    
+    gallery.select(
+        fn=update_selection,
+        outputs=[current_file, file_path, image_info]
+    )
+    
+    demo.load(
+        fn=lambda: display_image(0, get_valid_image_paths()),
+        outputs=[image_output, current_file]
+    )
+
+if __name__ == "__main__":
+    demo.launch()