app.py

app.py

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+import spaces
+import os
+import datetime
+import einops
+import gradio as gr
+from gradio_imageslider import ImageSlider
+import numpy as np
+import torch
+import random
+from PIL import Image
+from pathlib import Path
+from torchvision import transforms
+import torch.nn.functional as F
+from torchvision.models import resnet50, ResNet50_Weights
+
+from pytorch_lightning import seed_everything
+from transformers import CLIPTextModel, CLIPTokenizer, CLIPImageProcessor
+from diffusers import AutoencoderKL, DDIMScheduler, PNDMScheduler, DPMSolverMultistepScheduler, UniPCMultistepScheduler
+
+from pipelines.pipeline_pasd import StableDiffusionControlNetPipeline
+from myutils.misc import load_dreambooth_lora, rand_name
+from myutils.wavelet_color_fix import wavelet_color_fix
+from annotator.retinaface import RetinaFaceDetection
+
+use_pasd_light = False
+face_detector = RetinaFaceDetection()
+
+if use_pasd_light:
+    from models.pasd_light.unet_2d_condition import UNet2DConditionModel
+    from models.pasd_light.controlnet import ControlNetModel
+else:
+    from models.pasd.unet_2d_condition import UNet2DConditionModel
+    from models.pasd.controlnet import ControlNetModel
+
+pretrained_model_path = "checkpoints/stable-diffusion-v1-5"
+ckpt_path = "runs/pasd/checkpoint-100000"
+#dreambooth_lora_path = "checkpoints/personalized_models/toonyou_beta3.safetensors"
+dreambooth_lora_path = "checkpoints/personalized_models/majicmixRealistic_v6.safetensors"
+#dreambooth_lora_path = "checkpoints/personalized_models/Realistic_Vision_V5.1.safetensors"
+weight_dtype = torch.float16
+device = "cuda"
+
+scheduler = UniPCMultistepScheduler.from_pretrained(pretrained_model_path, subfolder="scheduler")
+text_encoder = CLIPTextModel.from_pretrained(pretrained_model_path, subfolder="text_encoder")
+tokenizer = CLIPTokenizer.from_pretrained(pretrained_model_path, subfolder="tokenizer")
+vae = AutoencoderKL.from_pretrained(pretrained_model_path, subfolder="vae")
+feature_extractor = CLIPImageProcessor.from_pretrained(f"{pretrained_model_path}/feature_extractor")
+unet = UNet2DConditionModel.from_pretrained(ckpt_path, subfolder="unet")
+controlnet = ControlNetModel.from_pretrained(ckpt_path, subfolder="controlnet")
+vae.requires_grad_(False)
+text_encoder.requires_grad_(False)
+unet.requires_grad_(False)
+controlnet.requires_grad_(False)
+
+unet, vae, text_encoder = load_dreambooth_lora(unet, vae, text_encoder, dreambooth_lora_path)
+
+text_encoder.to(device, dtype=weight_dtype)
+vae.to(device, dtype=weight_dtype)
+unet.to(device, dtype=weight_dtype)
+controlnet.to(device, dtype=weight_dtype)
+
+validation_pipeline = StableDiffusionControlNetPipeline(
+        vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, feature_extractor=feature_extractor, 
+        unet=unet, controlnet=controlnet, scheduler=scheduler, safety_checker=None, requires_safety_checker=False,
+    )
+#validation_pipeline.enable_vae_tiling()
+validation_pipeline._init_tiled_vae(decoder_tile_size=224)
+
+weights = ResNet50_Weights.DEFAULT
+preprocess = weights.transforms()
+resnet = resnet50(weights=weights)
+resnet.eval()
+
+def resize_image(image_path, target_height):
+    # Open the image file
+    with Image.open(image_path) as img:
+        # Calculate the ratio to resize the image to the target height
+        ratio = target_height / float(img.size[1])
+        # Calculate the new width based on the aspect ratio
+        new_width = int(float(img.size[0]) * ratio)
+        # Resize the image
+        resized_img = img.resize((new_width, target_height), Image.LANCZOS)
+        # Save the resized image
+        #resized_img.save(output_path)
+        return resized_img
+
+@spaces.GPU(enable_queue=True)
+def inference(input_image, prompt, a_prompt, n_prompt, denoise_steps, upscale, alpha, cfg, seed):
+    
+    #tempo fix for seed equals-1
+    if seed == -1:
+        seed = 0
+        
+    input_image = resize_image(input_image, 512)
+    process_size = 768
+    resize_preproc = transforms.Compose([
+        transforms.Resize(process_size, interpolation=transforms.InterpolationMode.BILINEAR),
+    ])
+    
+    # Get the current timestamp
+    timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
+
+    with torch.no_grad():
+        seed_everything(seed)
+        generator = torch.Generator(device=device)
+
+        input_image = input_image.convert('RGB')
+        batch = preprocess(input_image).unsqueeze(0)
+        prediction = resnet(batch).squeeze(0).softmax(0)
+        class_id = prediction.argmax().item()
+        score = prediction[class_id].item()
+        category_name = weights.meta["categories"][class_id]
+        if score >= 0.1:
+            prompt += f"{category_name}" if prompt=='' else f", {category_name}"
+
+        prompt = a_prompt if prompt=='' else f"{prompt}, {a_prompt}"
+
+        ori_width, ori_height = input_image.size
+        resize_flag = False
+
+        rscale = upscale
+        input_image = input_image.resize((input_image.size[0]*rscale, input_image.size[1]*rscale))
+        
+        #if min(validation_image.size) < process_size:
+        #    validation_image = resize_preproc(validation_image)
+
+        input_image = input_image.resize((input_image.size[0]//8*8, input_image.size[1]//8*8))
+        width, height = input_image.size
+        resize_flag = True #
+
+        try:
+            image = validation_pipeline(
+                    None, prompt, input_image, num_inference_steps=denoise_steps, generator=generator, height=height, width=width, guidance_scale=cfg, 
+                    negative_prompt=n_prompt, conditioning_scale=alpha, eta=0.0,
+                ).images[0]
+            
+            if True: #alpha<1.0:
+                image = wavelet_color_fix(image, input_image)
+        
+            if resize_flag: 
+                image = image.resize((ori_width*rscale, ori_height*rscale))
+        except Exception as e:
+            print(e)
+            image = Image.new(mode="RGB", size=(512, 512))
+    
+    # Convert and save the image as JPEG
+    image.save(f'result_{timestamp}.jpg', 'JPEG')
+
+    # Convert and save the image as JPEG
+    input_image.save(f'input_{timestamp}.jpg', 'JPEG')
+    
+    return (f"input_{timestamp}.jpg", f"result_{timestamp}.jpg"), f"result_{timestamp}.jpg"
+
+title = "Pixel-Aware Stable Diffusion for Real-ISR"
+description = "Gradio Demo for PASD Real-ISR. To use it, simply upload your image, or click one of the examples to load them."
+article = "<a href='https://github.com/yangxy/PASD' target='_blank'>Github Repo Pytorch</a>"
+#examples=[['samples/27d38eeb2dbbe7c9.png'],['samples/629e4da70703193b.png']]
+
+css = """
+#col-container{
+    margin: 0 auto;
+    max-width: 720px;
+}
+#project-links{
+    margin: 0 0 12px !important;
+    column-gap: 8px;
+    display: flex;
+    justify-content: center;
+    flex-wrap: nowrap;
+    flex-direction: row;
+    align-items: center;
+}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.HTML(f"""
+        <h2 style="text-align: center;">
+            PASD Magnify
+        </h2>
+        <p style="text-align: center;">
+            Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
+        </p>
+        <p id="project-links" align="center">
+            <a href='https://github.com/yangxy/PASD'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://huggingface.co/papers/2308.14469'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
+        </p> 
+        <p style="margin:12px auto;display: flex;justify-content: center;">
+            <a href="https://huggingface.co/spaces/fffiloni/PASD?duplicate=true"><img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-lg.svg" alt="Duplicate this Space"></a>
+        </p>
+        
+        """)
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image(type="filepath", sources=["upload"], value="samples/frog.png")
+                prompt_in = gr.Textbox(label="Prompt", value="Frog")
+                with gr.Accordion(label="Advanced settings", open=False):
+                    added_prompt = gr.Textbox(label="Added Prompt", value='clean, high-resolution, 8k, best quality, masterpiece')
+                    neg_prompt = gr.Textbox(label="Negative Prompt",value='dotted, noise, blur, lowres, oversmooth, longbody, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
+                    denoise_steps = gr.Slider(label="Denoise Steps", minimum=10, maximum=50, value=20, step=1)
+                    upsample_scale = gr.Slider(label="Upsample Scale", minimum=1, maximum=4, value=2, step=1)
+                    condition_scale = gr.Slider(label="Conditioning Scale", minimum=0.5, maximum=1.5, value=1.1, step=0.1)
+                    classifier_free_guidance = gr.Slider(label="Classier-free Guidance", minimum=0.1, maximum=10.0, value=7.5, step=0.1)
+                    seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+                submit_btn = gr.Button("Submit")
+            with gr.Column():
+                b_a_slider = ImageSlider(label="B/A result", position=0.5)
+                file_output = gr.File(label="Downloadable image result")
+                
+    submit_btn.click(
+        fn = inference,
+        inputs = [
+            input_image, prompt_in,
+            added_prompt, neg_prompt,
+            denoise_steps,
+            upsample_scale, condition_scale,
+            classifier_free_guidance, seed
+        ],
+        outputs = [
+            b_a_slider,
+            file_output
+        ]
+    )
+demo.queue(max_size=20).launch()