download all files in specific folder from hugging face model

 refer to code:

.

# Download all files from the IP-Adapter/sdxl_models folder

from huggingface_hub import snapshot_download

# Download the sdxl_models folder and its contents

snapshot_download(

repo_id="h94/IP-Adapter",

repo_type="model",

local_dir="./IP-Adapter_sdxl_models",

allow_patterns=["sdxl_models/*"]

)

..

It download all files under the sdxl_models folder.

Thank you.

Download specific model from hugging face

 refer to code

.

import os

import shutil

from huggingface_hub import hf_hub_download

# Repository name

repo_id = "h94/IP-Adapter"

# Directory to save the downloaded files

local_directory = "./models/image_encoder"

# Ensure the local directory exists

os.makedirs(local_directory, exist_ok=True)

# List of files to download

files_to_download = [

"models/image_encoder/config.json",

"models/image_encoder/model.safetensors",

"models/image_encoder/pytorch_model.bin"

]

# Download each file and move it to the desired directory

for file in files_to_download:

file_path = hf_hub_download(repo_id=repo_id, filename=file)

# Construct the destination path

dest_path = os.path.join(local_directory, os.path.basename(file))

# Move the file to the destination path

shutil.move(file_path, dest_path)

print(f"Downloaded and moved to {dest_path}")

..

Thank you.

other option is

file_path = hf_hub_download(repo_id=repo_id, filename=file, cache_dir=local_directory, force_download=True)

draw image as grid on notebook

simply check below code and example result.

.

def image_grid(imgs, rows, cols):

assert len(imgs) == rows*cols

w, h = imgs[0].size

grid = Image.new('RGB', size=(cols*w, rows*h))

grid_w, grid_h = grid.size

for i, img in enumerate(imgs):

grid.paste(img, box=(i%cols*w, i//cols*h))

return grid

# read image prompt

image = Image.open("assets/images/statue.png")

depth_map = Image.open("assets/structure_controls/depth.png")

image_grid([image.resize((256, 256)), depth_map.resize((256, 256))], 1, 2)

..

Thank you.

Unknown parameter in retrievalConfiguration.vectorSearchConfiguration: "overrideSearchType", must be one of: numberOfResults

 Error in AWS bedrock like:

Unknown parameter in retrievalConfiguration.vectorSearchConfiguration: "overrideSearchType", must be one of: numberOfResults

Solution 

Update boto3 sdk as latest one.

The parameters changed on 2024-03-27.

refer to here: https://awsapichanges.com/archive/changes/cd42c1-bedrock-agent-runtime.html

Thank you!

Stop window update process in force

Open powershell by admin 

and put this on terminal

> net stop wuauserv #stop service update
> net stop bits #stop bits
> Remove-Item -Path "C:\Windows\SoftwareDistribution\Download\*" -Recurse -Force
> net start wuauserv #restart 
> net start bits #restart

You can do something else after stop bits, service update.

Thank you.

Embedding invisible water mark on image

 Firstly, install opencv

pip install opencv-python numpy

code for invisible water mark embedding 

import cv2

import numpy as np

def embed_watermark(image_path, watermark, output_path):

# Load the image

img = cv2.imread(image_path)

# Ensure the image is in 3 channels RGB

if img.shape[2] != 3:

print("Image needs to be RGB")

return

# Prepare the watermark

# For simplicity, the watermark is repeated to match the image size

watermark = (watermark * (img.size // len(watermark) + 1))[:img.size]

watermark = np.array(list(watermark), dtype=np.uint8).reshape(img.shape)

# Embed watermark by altering the least significant bit

img_encoded = img & ~1 | (watermark & 1)

# Save the watermarked image

cv2.imwrite(output_path, img_encoded)

print("Watermarked image saved to", output_path)

# Usage

embed_watermark('path_to_your_image.jpg', 'your_watermark_text', 'watermarked_image.jpg')

retrive code

def extract_watermark(watermarked_image_path, original_image_path, output_path):

# Load the watermarked and the original image

img_encoded = cv2.imread(watermarked_image_path)

img_original = cv2.imread(original_image_path)

# Extract the watermark by comparing the least significant bits

watermark = img_encoded & 1 ^ img_original & 1

watermark = (watermark * 255).astype(np.uint8) # Scale to 0-255 for visibility

# Save or display the extracted watermark

cv2.imwrite(output_path, watermark)

print("Extracted watermark saved to", output_path)

# Usage

extract_watermark('watermarked_image.jpg', 'path_to_your_image.jpg', 'extracted_watermark.jpg')

Error: "No data found. 0 train images with repeating" in LoRA Training with kohya-ss/sd-scripts.

 This is my cmd for training lora with kohya-ss/sd-scripts

python -m accelerate.commands.launch --num_cpu_threads_per_process=8 \

...

--enable_bucket \

...

--train_data_dir="/images/" \

--output_dir="models/loras" \

--logging_dir="./logs" \

--log_prefix=silostyle \

--resolution=512,512 \

--network_module=networks.lora \

...

And I met this error

> No data found.

   ...

   0 train images with repeating

   ..

One thing to check is folder name.

Your images and caption txt file has sub folder and the folder name should have "number_title"

correct :  /images/1_files, /images/2_files

wrong : /images/files, /images/0_files 

I am figuring out why kohya has such a rule..

Thank you.