replicate demo

README.md

@@ -1,6 +1,6 @@
 ## Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
 
-> [[Paper](https://arxiv.org/abs/2401.13627)] &emsp; [[Project Page](http://supir.xpixel.group/)] &emsp; [Online Demo (Coming soon)] <br>
+> [[Paper](https://arxiv.org/abs/2401.13627)] &emsp; [[Project Page](http://supir.xpixel.group/)] &emsp; [[Replicate Demo](https://replicate.com/cjwbw/supir)] <br>
 > Fanghua, Yu, [Jinjin Gu](https://www.jasongt.com/), Zheyuan Li, Jinfan Hu, Xiangtao Kong, [Xintao Wang](https://xinntao.github.io/), [Jingwen He](https://scholar.google.com.hk/citations?user=GUxrycUAAAAJ), [Yu Qiao](https://scholar.google.com.hk/citations?user=gFtI-8QAAAAJ), [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ) <br>
 > Shenzhen Institute of Advanced Technology; Shanghai AI Laboratory; University of Sydney; The Hong Kong Polytechnic University; ARC Lab, Tencent PCG; The Chinese University of Hong Kong <br>
 

cog.yaml

@@ -0,0 +1,41 @@
+# Configuration for Cog ⚙️
+# Reference: https://github.com/replicate/cog/blob/main/docs/yaml.md
+
+build:
+  gpu: true
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+  python_version: "3.11"
+  python_packages:
+    - sentencepiece==0.1.98
+    - tokenizers==0.13.3
+    - torch>=2.1.0
+    - torchvision>=0.16.0
+    - uvicorn==0.21.1
+    - transformers==4.28.1
+    - accelerate==0.18.0
+    - scikit-learn==1.2.2
+    - sentencepiece==0.1.98
+    - einops==0.7.0
+    - einops-exts==0.0.4
+    - timm==0.9.8
+    - openai-clip==1.0.1
+    - kornia==0.6.9
+    - matplotlib==3.7.1
+    - ninja==1.11.1
+    - omegaconf==2.3.0
+    - open-clip-torch==2.17.1
+    - opencv-python==4.7.0.72
+    - pandas==2.0.1
+    - Pillow==9.4.0
+    - pytorch-lightning==2.1.2
+    - PyYAML==6.0
+    - scipy==1.12.0
+    - tqdm==4.65.0
+    - triton==2.1.0
+    - webdataset==0.2.48
+    - xformers>=0.0.20
+  run:
+    - curl -o /usr/local/bin/pget -L "https://github.com/replicate/pget/releases/download/v0.6.0/pget_linux_x86_64" && chmod +x /usr/local/bin/pget
+predict: "predict.py:Predictor"

predict.py

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+# Prediction interface for Cog ⚙️
+# https://github.com/replicate/cog/blob/main/docs/python.md
+
+import os
+import subprocess
+import time
+from omegaconf import OmegaConf
+from PIL import Image
+from cog import BasePredictor, Input, Path
+
+from SUPIR.util import (
+    create_SUPIR_model,
+    PIL2Tensor,
+    Tensor2PIL,
+    convert_dtype,
+)
+from llava.llava_agent import LLavaAgent
+import CKPT_PTH
+
+SUPIR_v0Q_URL = "https://weights.replicate.delivery/default/SUPIR-v0Q.ckpt"
+SUPIR_v0F_URL = "https://weights.replicate.delivery/default/SUPIR-v0F.ckpt"
+LLAVA_URL = "https://weights.replicate.delivery/default/llava-v1.5-13b.tar"
+LLAVA_CLIP_URL = (
+    "https://weights.replicate.delivery/default/clip-vit-large-patch14-336.tar"
+)
+SDXL_URL = "https://weights.replicate.delivery/default/stable-diffusion-xl-base-1.0/sd_xl_base_1.0_0.9vae.safetensors"
+SDXL_CLIP1_URL = "https://weights.replicate.delivery/default/clip-vit-large-patch14.tar"
+SDXL_CLIP2_URL = (
+    "https://weights.replicate.delivery/default/CLIP-ViT-bigG-14-laion2B-39B-b160k.tar"
+)
+
+MODEL_CACHE = "/opt/data/private/AIGC_pretrain/"  # Follow the default in CKPT_PTH.py
+LLAVA_CLIP_PATH = CKPT_PTH.LLAVA_CLIP_PATH
+LLAVA_MODEL_PATH = CKPT_PTH.LLAVA_MODEL_PATH
+SDXL_CLIP1_PATH = CKPT_PTH.SDXL_CLIP1_PATH
+SDXL_CLIP2_CACHE = f"{MODEL_CACHE}/models--laion--CLIP-ViT-bigG-14-laion2B-39B-b160k"
+SDXL_CKPT = f"{MODEL_CACHE}/SDXL_cache/sd_xl_base_1.0_0.9vae.safetensors"
+SUPIR_CKPT_F = f"{MODEL_CACHE}/SUPIR_cache/SUsPIR-v0F.ckpt"
+SUPIR_CKPT_Q = f"{MODEL_CACHE}/SUPIR_cache/SUPIR-v0Q.ckpt"
+
+
+def download_weights(url, dest, extract=True):
+    start = time.time()
+    print("downloading url: ", url)
+    print("downloading to: ", dest)
+    args = ["pget"]
+    if extract:
+        args.append("-x")
+    subprocess.check_call(args + [url, dest], close_fds=False)
+    print("downloading took: ", time.time() - start)
+
+
+class Predictor(BasePredictor):
+    def setup(self) -> None:
+        """Load the model into memory to make running multiple predictions efficient"""
+        for model_dir in [
+            MODEL_CACHE,
+            f"{MODEL_CACHE}/SUPIR_cache",
+            f"{MODEL_CACHE}/SDXL_cache",
+        ]:
+            if not os.path.exists(model_dir):
+                os.makedirs(model_dir)
+        if not os.path.exists(SUPIR_CKPT_Q):
+            download_weights(SUPIR_v0Q_URL, SUPIR_CKPT_Q, extract=False)
+        if not os.path.exists(SUPIR_CKPT_F):
+            download_weights(SUPIR_v0F_URL, SUPIR_CKPT_F, extract=False)
+        if not os.path.exists(LLAVA_MODEL_PATH):
+            download_weights(LLAVA_URL, LLAVA_MODEL_PATH)
+        if not os.path.exists(LLAVA_CLIP_PATH):
+            download_weights(LLAVA_CLIP_URL, LLAVA_CLIP_PATH)
+        if not os.path.exists(SDXL_CLIP1_PATH):
+            download_weights(SDXL_CLIP1_URL, SDXL_CLIP1_PATH)
+        if not os.path.exists(SDXL_CKPT):
+            download_weights(SDXL_URL, SDXL_CKPT, extract=False)
+        if not os.path.exists(SDXL_CKPT):
+            download_weights(SDXL_CLIP2_URL, SDXL_CKPT)
+
+        self.supir_device = "cuda:0"
+        self.llava_device = "cuda:0"
+        ae_dtype = "bf16"  # Inference data type of AutoEncoder
+        diff_dtype = "bf16"  # Inference data type of Diffusion
+
+        self.models = {
+            k: create_SUPIR_model("options/SUPIR_v0.yaml", SUPIR_sign=k).to(
+                self.supir_device
+            )
+            for k in ["Q", "F"][1:]
+        }
+
+        for k in ["Q", "F"][1:]:
+            self.models[k].ae_dtype = convert_dtype(ae_dtype)
+            self.models[k].model.dtype = convert_dtype(diff_dtype)
+
+        # load LLaVA
+        self.llava_agent = LLavaAgent(LLAVA_MODEL_PATH, device=self.llava_device)
+
+    def predict(
+        self,
+        model_name: str = Input(
+            description="Choose a model. SUPIR-v0Q is the default training settings with paper. SUPIR-v0F is high generalization and high image quality in most cases. Training with light degradation settings. Stage1 encoder of SUPIR-v0F remains more details when facing light degradations.",
+            choices=["SUPIR-v0Q", "SUPIR-v0F"],
+            default="SUPIR-v0Q",
+        ),
+        image: Path = Input(description="Low quality input image."),
+        upscale: int = Input(
+            description="Upsampling ratio of given inputs.", default=1
+        ),
+        min_size: float = Input(
+            description="Minimum resolution of output images.", default=1024
+        ),
+        edm_steps: int = Input(
+            description="Number of steps for EDM Sampling Schedule.",
+            ge=1,
+            le=500,
+            default=50,
+        ),
+        use_llava: bool = Input(
+            description="Use LLaVA model to get captions.", default=True
+        ),
+        a_prompt: str = Input(
+            description="Additive positive prompt for the inputs.",
+            default="Cinematic, High Contrast, highly detailed, taken using a Canon EOS R camera, hyper detailed photo - realistic maximum detail, 32k, Color Grading, ultra HD, extreme meticulous detailing, skin pore detailing, hyper sharpness, perfect without deformations.",
+        ),
+        n_prompt: str = Input(
+            description="Negative prompt for the inputs.",
+            default="painting, oil painting, illustration, drawing, art, sketch, oil painting, cartoon, CG Style, 3D render, unreal engine, blurring, dirty, messy, worst quality, low quality, frames, watermark, signature, jpeg artifacts, deformed, lowres, over-smooth",
+        ),
+        color_fix_type: str = Input(
+            description="Color Fixing Type..",
+            choices=["None", "AdaIn", "Wavelet"],
+            default="Wavelet",
+        ),
+        s_stage1: int = Input(
+            description="Control Strength of Stage1 (negative means invalid).",
+            default=-1,
+        ),
+        s_churn: float = Input(
+            description="Original churn hy-param of EDM.", default=5
+        ),
+        s_noise: float = Input(
+            description="Original noise hy-param of EDM.", default=1.003
+        ),
+        s_cfg: float = Input(
+            description=" Classifier-free guidance scale for prompts.",
+            ge=1,
+            le=20,
+            default=7.5,
+        ),
+        s_stage2: float = Input(description="Control Strength of Stage2.", default=1.0),
+        linear_CFG: bool = Input(
+            description="Linearly (with sigma) increase CFG from 'spt_linear_CFG' to s_cfg.",
+            default=False,
+        ),
+        linear_s_stage2: bool = Input(
+            description="Linearly (with sigma) increase s_stage2 from 'spt_linear_s_stage2' to s_stage2.",
+            default=False,
+        ),
+        spt_linear_CFG: float = Input(
+            description="Start point of linearly increasing CFG.", default=1.0
+        ),
+        spt_linear_s_stage2: float = Input(
+            description="Start point of linearly increasing s_stage2.", default=0.0
+        ),
+        seed: int = Input(
+            description="Random seed. Leave blank to randomize the seed", default=None
+        ),
+    ) -> Path:
+        """Run a single prediction on the model"""
+
+        if seed is None:
+            seed = int.from_bytes(os.urandom(2), "big")
+        print(f"Using seed: {seed}")
+
+        model = self.models["Q"]  # if model_name == "SUPIR-v0Q" else self.models["F"]
+
+        lq_img = Image.open(str(image))
+        lq_img, h0, w0 = PIL2Tensor(lq_img, upsacle=upscale, min_size=min_size)
+        lq_img = lq_img.unsqueeze(0).to(self.supir_device)[:, :3, :, :]
+
+        # step 1: Pre-denoise for LLaVA)
+        clean_imgs = model.batchify_denoise(lq_img)
+        clean_PIL_img = Tensor2PIL(clean_imgs[0], h0, w0)
+
+        # step 2: LLaVA
+        captions = [""]
+        if use_llava:
+            captions = self.llava_agent.gen_image_caption([clean_PIL_img])
+            print(f"Captions from LLaVA: {captions}")
+
+        # step 3: Diffusion Process
+        samples = model.batchify_sample(
+            lq_img,
+            captions,
+            num_steps=edm_steps,
+            restoration_scale=s_stage1,
+            s_churn=s_churn,
+            s_noise=s_noise,
+            cfg_scale=s_cfg,
+            control_scale=s_stage2,
+            seed=seed,
+            num_samples=1,
+            p_p=a_prompt,
+            n_p=n_prompt,
+            color_fix_type=color_fix_type,
+            use_linear_CFG=linear_CFG,
+            use_linear_control_scale=linear_s_stage2,
+            cfg_scale_start=spt_linear_CFG,
+            control_scale_start=spt_linear_s_stage2,
+        )
+
+        out_path = "/tmp/out.png"
+        Tensor2PIL(samples[0], h0, w0).save(out_path)
+        return Path(out_path)