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replicate demo

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README.md
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## 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>

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cog.yaml Normal file
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# 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"

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predict.py Normal file
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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)