Update app.py

app.py

@@ -1,16 +1,25 @@
 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
@@ -30,6 +39,7 @@ def download_models():
         "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)
 
@@ -54,8 +64,7 @@ class LazyLoadPipeline:
             print("Starting to load the pipeline...")
             self.pipe = self.setup_pipeline()
             print(f"Moving pipeline to device: {device}")
-            self.pipe.to(device=device, dtype=torch.float16)
-
+            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)
@@ -63,49 +72,89 @@ class LazyLoadPipeline:
     @timer_func
     def setup_pipeline(self):
         print("Setting up the pipeline...")
-
-        # Load ControlNet model correctly
         controlnet = ControlNetModel.from_single_file(
-            "models/ControlNet/control_v11f1e_sd15_tile.pth"
-        ).to(device=device, dtype=torch.float16)
-
+            "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=None
-        ).to(device=device, dtype=torch.float16)
-
-        # Load VAE
+            safety_checker=safety_checker
+        )
         vae = AutoencoderKL.from_single_file(
-            "models/VAE/vae-ft-mse-840000-ema-pruned.safetensors"
-        ).to(device=device, dtype=torch.float16)
+            "models/VAE/vae-ft-mse-840000-ema-pruned.safetensors",
+            torch_dtype=torch.float16
+        )
         pipe.vae = vae
-
-        # Load textual inversions and Lora
         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=1.0)
-
+        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):
-    input_image = input_image.convert("RGB")
-    return input_image
+    condition_image = resize_and_upscale(input_image, resolution)
+    condition_image = create_hdr_effect(condition_image, hdr)
+    return condition_image
 
 @spaces.GPU
 @timer_func
@@ -116,7 +165,7 @@ def gradio_process_image(input_image, resolution, num_inference_steps, strength,
     condition_image = prepare_image(input_image, resolution, hdr)
     
     prompt = "masterpiece, best quality, highres"
-    negative_prompt = "low quality, ugly, blurry, lowres, bad anatomy, bad hands, cropped, worst quality"
+    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,
@@ -135,9 +184,19 @@ def gradio_process_image(input_image, resolution, num_inference_steps, strength,
     result = lazy_pipe(**options).images[0]
     print("Image processing completed successfully")
     
-    return [np.array(input_image), np.array(result)]
+    # 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>"""
+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)
@@ -149,11 +208,26 @@ with gr.Blocks() as demo:
             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="Inference Steps")
+        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)
-
-demo.launch(share=True)
+    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)