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README.md

@@ -1,13 +1,13 @@
----
-title: Ttuu
-emoji: 📚
-colorFrom: blue
-colorTo: blue
-sdk: gradio
-sdk_version: 5.20.1
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Tile Upscaler
+emoji: 🚀
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 4.37.2
+app_file: app.py
+pinned: true
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,233 @@
+import spaces
+
+import os
+import requests
+import time
+
+import torch
+
+from diffusers import StableDiffusionControlNetImg2ImgPipeline, ControlNetModel, DDIMScheduler
+from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
+from diffusers.models import AutoencoderKL
+from diffusers.models.attention_processor import AttnProcessor2_0
+
+from PIL import Image
+import cv2
+import numpy as np
+
+from RealESRGAN import RealESRGAN
+
+import gradio as gr
+from gradio_imageslider import ImageSlider
+
+from huggingface_hub import hf_hub_download
+
+USE_TORCH_COMPILE = False
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1"
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+def download_models():
+    models = {
+        "MODEL": ("dantea1118/juggernaut_reborn", "juggernaut_reborn.safetensors", "models/models/Stable-diffusion"),
+        "UPSCALER_X2": ("ai-forever/Real-ESRGAN", "RealESRGAN_x2.pth", "models/upscalers/"),
+        "UPSCALER_X4": ("ai-forever/Real-ESRGAN", "RealESRGAN_x4.pth", "models/upscalers/"),
+        "NEGATIVE_1": ("philz1337x/embeddings", "verybadimagenegative_v1.3.pt", "models/embeddings"),
+        "NEGATIVE_2": ("philz1337x/embeddings", "JuggernautNegative-neg.pt", "models/embeddings"),
+        "LORA_1": ("philz1337x/loras", "SDXLrender_v2.0.safetensors", "models/Lora"),
+        "LORA_2": ("philz1337x/loras", "more_details.safetensors", "models/Lora"),
+        "CONTROLNET": ("lllyasviel/ControlNet-v1-1", "control_v11f1e_sd15_tile.pth", "models/ControlNet"),
+        "VAE": ("stabilityai/sd-vae-ft-mse-original", "vae-ft-mse-840000-ema-pruned.safetensors", "models/VAE"),
+    }
+
+    for model, (repo_id, filename, local_dir) in models.items():
+        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=local_dir)
+
+download_models()
+
+def timer_func(func):
+    def wrapper(*args, **kwargs):
+        start_time = time.time()
+        result = func(*args, **kwargs)
+        end_time = time.time()
+        print(f"{func.__name__} took {end_time - start_time:.2f} seconds")
+        return result
+    return wrapper
+
+class LazyLoadPipeline:
+    def __init__(self):
+        self.pipe = None
+
+    @timer_func
+    def load(self):
+        if self.pipe is None:
+            print("Starting to load the pipeline...")
+            self.pipe = self.setup_pipeline()
+            print(f"Moving pipeline to device: {device}")
+            self.pipe.to(device)
+            if USE_TORCH_COMPILE:
+                print("Compiling the model...")
+                self.pipe.unet = torch.compile(self.pipe.unet, mode="reduce-overhead", fullgraph=True)
+
+    @timer_func
+    def setup_pipeline(self):
+        print("Setting up the pipeline...")
+        controlnet = ControlNetModel.from_single_file(
+            "models/ControlNet/control_v11f1e_sd15_tile.pth", torch_dtype=torch.float16
+        )
+        safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
+        model_path = "models/models/Stable-diffusion/juggernaut_reborn.safetensors"
+        pipe = StableDiffusionControlNetImg2ImgPipeline.from_single_file(
+            model_path,
+            controlnet=controlnet,
+            torch_dtype=torch.float16,
+            use_safetensors=True,
+            safety_checker=safety_checker
+        )
+        vae = AutoencoderKL.from_single_file(
+            "models/VAE/vae-ft-mse-840000-ema-pruned.safetensors",
+            torch_dtype=torch.float16
+        )
+        pipe.vae = vae
+        pipe.load_textual_inversion("models/embeddings/verybadimagenegative_v1.3.pt")
+        pipe.load_textual_inversion("models/embeddings/JuggernautNegative-neg.pt")
+        pipe.load_lora_weights("models/Lora/SDXLrender_v2.0.safetensors")
+        pipe.fuse_lora(lora_scale=0.5)
+        pipe.load_lora_weights("models/Lora/more_details.safetensors")
+        pipe.fuse_lora(lora_scale=1.)
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.enable_freeu(s1=0.9, s2=0.2, b1=1.3, b2=1.4)
+        return pipe
+
+    def __call__(self, *args, **kwargs):
+        return self.pipe(*args, **kwargs)
+
+class LazyRealESRGAN:
+    def __init__(self, device, scale):
+        self.device = device
+        self.scale = scale
+        self.model = None
+
+    def load_model(self):
+        if self.model is None:
+            self.model = RealESRGAN(self.device, scale=self.scale)
+            self.model.load_weights(f'models/upscalers/RealESRGAN_x{self.scale}.pth', download=False)
+    def predict(self, img):
+        self.load_model()
+        return self.model.predict(img)
+
+lazy_realesrgan_x2 = LazyRealESRGAN(device, scale=2)
+lazy_realesrgan_x4 = LazyRealESRGAN(device, scale=4)
+
+@timer_func
+def resize_and_upscale(input_image, resolution):
+    scale = 2 if resolution <= 2048 else 4
+    input_image = input_image.convert("RGB")
+    W, H = input_image.size
+    k = float(resolution) / min(H, W)
+    H = int(round(H * k / 64.0)) * 64
+    W = int(round(W * k / 64.0)) * 64
+    img = input_image.resize((W, H), resample=Image.LANCZOS)
+    if scale == 2:
+        img = lazy_realesrgan_x2.predict(img)
+    else:
+        img = lazy_realesrgan_x4.predict(img)
+    return img
+
+@timer_func
+def create_hdr_effect(original_image, hdr):
+    if hdr == 0:
+        return original_image
+    cv_original = cv2.cvtColor(np.array(original_image), cv2.COLOR_RGB2BGR)
+    factors = [1.0 - 0.9 * hdr, 1.0 - 0.7 * hdr, 1.0 - 0.45 * hdr,
+               1.0 - 0.25 * hdr, 1.0, 1.0 + 0.2 * hdr,
+               1.0 + 0.4 * hdr, 1.0 + 0.6 * hdr, 1.0 + 0.8 * hdr]
+    images = [cv2.convertScaleAbs(cv_original, alpha=factor) for factor in factors]
+    merge_mertens = cv2.createMergeMertens()
+    hdr_image = merge_mertens.process(images)
+    hdr_image_8bit = np.clip(hdr_image * 255, 0, 255).astype('uint8')
+    return Image.fromarray(cv2.cvtColor(hdr_image_8bit, cv2.COLOR_BGR2RGB))
+
+lazy_pipe = LazyLoadPipeline()
+lazy_pipe.load()
+
+def prepare_image(input_image, resolution, hdr):
+    condition_image = resize_and_upscale(input_image, resolution)
+    condition_image = create_hdr_effect(condition_image, hdr)
+    return condition_image
+
+@spaces.GPU
+@timer_func
+def gradio_process_image(input_image, resolution, num_inference_steps, strength, hdr, guidance_scale):
+    print("Starting image processing...")
+    torch.cuda.empty_cache()
+    
+    condition_image = prepare_image(input_image, resolution, hdr)
+    
+    prompt = "masterpiece, best quality, highres"
+    negative_prompt = "low quality, normal quality, ugly, blurry, blur, lowres, bad anatomy, bad hands, cropped, worst quality, verybadimagenegative_v1.3, JuggernautNegative-neg"
+    
+    options = {
+        "prompt": prompt,
+        "negative_prompt": negative_prompt,
+        "image": condition_image,
+        "control_image": condition_image,
+        "width": condition_image.size[0],
+        "height": condition_image.size[1],
+        "strength": strength,
+        "num_inference_steps": num_inference_steps,
+        "guidance_scale": guidance_scale,
+        "generator": torch.Generator(device=device).manual_seed(0),
+    }
+    
+    print("Running inference...")
+    result = lazy_pipe(**options).images[0]
+    print("Image processing completed successfully")
+    
+    # Convert input_image and result to numpy arrays
+    input_array = np.array(input_image)
+    result_array = np.array(result)
+    
+    return [input_array, result_array]
+
+title = """<h1 align="center">Image Upscaler with Tile Controlnet</h1>
+<p align="center">The main ideas come from</p>
+<p><center>
+<a href="https://github.com/philz1337x/clarity-upscaler" target="_blank">[philz1337x]</a>
+<a href="https://github.com/BatouResearch/controlnet-tile-upscale" target="_blank">[Pau-Lozano]</a>
+</center></p>
+"""
+
+with gr.Blocks() as demo:
+    gr.HTML(title)
+    with gr.Row():
+        with gr.Column():
+            input_image = gr.Image(type="pil", label="Input Image")
+            run_button = gr.Button("Enhance Image")
+        with gr.Column():
+            output_slider = ImageSlider(label="Before / After", type="numpy")
+    with gr.Accordion("Advanced Options", open=False):
+        resolution = gr.Slider(minimum=256, maximum=2048, value=512, step=256, label="Resolution")
+        num_inference_steps = gr.Slider(minimum=1, maximum=50, value=20, step=1, label="Number of Inference Steps")
+        strength = gr.Slider(minimum=0, maximum=1, value=0.4, step=0.01, label="Strength")
+        hdr = gr.Slider(minimum=0, maximum=1, value=0, step=0.1, label="HDR Effect")
+        guidance_scale = gr.Slider(minimum=0, maximum=20, value=3, step=0.5, label="Guidance Scale")
+
+    run_button.click(fn=gradio_process_image, 
+                     inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
+                     outputs=output_slider)
+
+    # Add examples with all required inputs
+    gr.Examples(
+        examples=[
+            ["image1.jpg", 512, 20, 0.4, 0, 3],
+            ["image2.png", 512, 20, 0.4, 0, 3],
+            ["image3.png", 512, 20, 0.4, 0, 3],
+        ],
+        inputs=[input_image, resolution, num_inference_steps, strength, hdr, guidance_scale],
+        outputs=output_slider,
+        fn=gradio_process_image,
+        cache_examples=True,
+    )
+
+demo.launch(share=True)

requirements.txt

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+git+https://github.com/doevent/Real-ESRGAN.git
+opencv-python
+spaces
+diffusers
+torch
+torchvision
+pipeline
+transformers
+accelerate
+safetensors
+spaces
+peft
+gradio
+pillow
+gradio-imageslider