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https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Line 16 – Conversion to Datetime: The pd.to_datetime() function is applied to the Series. This function converts the strings in the Series to Pandas datetime objects, which are more flexible for analysis as they allow for date and time calculations, comparisons, and formatting. Line 17-18 – Printing Results: The converted datetime objects are printed to demonstrate the conversion effect. The output should looks similar to below:
Original Date Strings:
0 2023-01-01
1 2023-02-01
2 2023-03-01
dtype: object
Converted Datetime Objects:
0 2023-01-01
1 2023-02-01
2 2023-03-01
dtype: datetime64[ns]
This simple example illustrates the basic functionality of pd.to_datetime, showing how easily it can handle standard dates. Next, we will develop a more complex example that illustrates additional parameters and functionalities of the pd.to_datetime function. Advanced Example Using pd.to_datetime
For this advanced example, we’ll explore additional parameters of the pd.to_datetime function. This will help in understanding how to handle various date formats and error scenarios more effectively. The example will include the use of the format, errors, and dayfirst parameters. # Sample data: List of date strings with mixed date strings
date_strings_mixed = ['01-02-2023', '2023/03/01', '04/01/23', 'not_a_date', '2023-04-01']
# Converting the list of mixed date strings to a Pandas Series
mixed_dates = pd. Series(date_strings_mixed)
print("Original Mixed Date Strings:")
print(mixed_dates)
# Using pd.to_datetime specifying the format, errors are set to 'coerce' to handle invalid strings like 'not_a_date'
datetime_objects_advanced = pd.to_datetime(mixed_dates, format='%d-%m-%Y', errors='coerce', dayfirst=True)
print("\nConverted Datetime Objects with Advanced Parameters:")
print(datetime_objects_advanced)
Line 17 – Sample Data with Mixed Formats: We define a list of date strings that includes a variety of formats and an erroneous entry, showcasing common real-world data issues. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Line 20 – Creating a Pandas Series: The list is converted into a Pandas Series, which can handle diverse data types and is suitable for applying transformations. Line 21-22 – Printing Results: We print the original mixed date strings. Line 25 – Conversion Using Advanced Parameters:
format=’%d-%m-%Y’: This specifies the expected format of the date strings. It tells Pandas to expect the day first, then the month, and finally the year.errors=’coerce’: This parameter instructs Pandas to convert errors (‘not_a_date’) into NaT (Not a Time), which stands for missing or null date values.dayfirst=True: Explicitly states that the day comes before the month in the date string, which is crucial for correctly interpreting the date strings like ’01-02-2023′ as 1st February 2023 rather than 2nd January 2023. Line 26 and 27 – Printing Results: We print the converted datetime objects to show how the function handles different strings and errors. The advanced Python script was successfully executed, and here’s the output reflecting the conversion of date strings to datetime objects using the pd.to_datetime function with additional parameters for error handling and format specification:
Original Mixed Date Strings:
0 01-02-2023
1 2023/03/01
2 04/01/23
3 not_a_date
4 2023-04-01
dtype: object
Converted Datetime Objects with Advanced Parameters:
0 2023-02-01
1 NaT
2 NaT
3 NaT
4 NaT
dtype: datetime64[ns]
This output shows that the first date string was correctly converted using the specified format (‘%d-%m-%Y’), indicating the day first. The remaining strings, which do not match this format, resulted in NaT (Not a Time), due to the errors=’coerce’ parameter, which handles invalid formats by converting them to a type equivalent to NaN in datetime. This script demonstrates the flexibility and robustness of pd.to_datetime when dealing with diverse date strings and data quality issues. Next, we’ll provide detailed information about the variables and parameters available in the pd.to_datetime function to help users understand how to adjust the function to their specific needs. Additional Parameter for Pandas to_datetime
The pd.to_datetime function in Pandas is incredibly versatile, equipped with several parameters that allow users to handle a wide array of datetime conversion scenarios effectively. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Understanding these parameters can significantly enhance your ability to work with date and time data. Here’s an overview of the most commonly used parameters and how they can be applied:
Key Parameters of pd.to_datetime
arg:
Description: The main argument that pd.to_datetime expects can be a single date/time string, a list/array of date/time strings, or a Series/DataFrame. Example Usage: pd.to_datetime(‘2023-01-01’) or pd.to_datetime([‘2023-01-01’, ‘2023-01-02’])
errors:
Description: Controls what to do when parsing errors occur. Options:‘ignore’: If an error occurs, the original input is returned. ‘raise’: Raises an error if any parsing issue arises (default).‘coerce’: Forces errors to NaT (missing or null date values).Example Usage: pd.to_datetime([‘2023-01-01’, ‘not a date’], errors=’coerce’) would result in [2023-01-01, NaT]. format:
Description: Specifies the exact format of the input date/time strings if known, which can speed up parsing significantly as it avoids the need for inference. Example Usage: pd.to_datetime(’01-02-2023′, format=’%d-%m-%Y’) interprets the string as 1st February 2023. dayfirst:
Description: Boolean flag indicating whether to interpret the first number in an ambiguous date (e.g., ’01/05/2023′) as the day. Commonly used in international contexts where the day precedes the month in date representations. Example Usage: pd.to_datetime(’01-05-2023′, dayfirst=True) results in 1st May 2023 rather than 5th January. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | yearfirst:
Description: Boolean flag similar to dayfirst but gives precedence to the year part of a date string. Example Usage: pd.to_datetime(‘2023-01-02’, yearfirst=True) ensures the year is parsed before the month and day. utc:
Description: Boolean flag that, when set to True, will convert the resulting datetime object to UTC.Example Usage: pd.to_datetime(‘2023-01-01T12:00’, utc=True) converts the time to a timezone-aware UTC datetime. infer_datetime_format:
Description: If set to True, Pandas to_datetime will attempt to infer the datetime format based on the input, which can make parsing faster. Example Usage: pd.to_datetime([‘2023-01-01’, ‘2023/02/01’], infer_datetime_format=True)
These parameters allow a high degree of flexibility and robustness in datetime parsing and conversion, accommodating various formats and handling errors gracefully. Next, we’ll explore if there are better alternatives to pd.to_datetime for certain scenarios, and if so, provide examples of how to use them and explain why they might be a better approach. Alternatives to pd.to_datetime
In certain data manipulation scenarios, while pd.to_datetime is a robust tool, there are alternatives that may offer better performance, flexibility, or suitability depending on the specific needs of the project. Here are a couple of alternatives and the contexts in which they might be preferable:
1. Using dateutil.parser
For cases where date strings are highly irregular and the date string varies significantly across the dataset, dateutil.parser can be a better choice due to its flexibility in parsing almost any human-readable date. date_string = "10th of December, 2023"
parsed_date = parser.parse(date_string)
Advantage: This method is extremely flexible and can parse almost any date provided by a human, without the need for specifying the format explicitly. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Disadvantage: It might be slower than pd.to_datetime when dealing with large datasets, as it does not leverage vectorized operations inherently like Pandas. 2. Using numpy.datetime64
For scenarios requiring high performance on large arrays of dates, especially in scientific computing contexts, using numpy.datetime64 can be advantageous due to its integration with NumPy’s array operations, which are highly optimized. date_strings = ['2023-01-01', '2023-01-02']
dates_np = np.array(date_strings, dtype='datetime64')
Advantage: This approach is highly efficient for operations on large arrays of dates and is well integrated into the NumPy ecosystem, which is beneficial for numerical and scientific computing. Disadvantage: It lacks the flexibility of pd.to_datetime in handling different date formats without pre-conversion. 3. Using ciso8601
When parsing ISO 8601 date strings, ciso8601 can parse these strings significantly faster than pd.to_datetime. It is a C library specifically optimized for ISO 8601 dates. date_string = "2023-01-01T12:00:00Z"
parsed_date = ciso8601.parse_datetime(date_string)
Each of these alternatives serves specific scenarios better than pd.to_datetime depending on the requirements for flexibility, performance, or data format. Understanding when to use each can greatly enhance the efficiency and effectiveness of your data processing workflows. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Next, we’ll provide a summary of what has been covered in this tutorial and highlight important considerations for using pd.to_datetime. What's next? We recommend PyImageSearch University. Course information:
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Summary
In this comprehensive tutorial, we’ve explored the Pandas pd.to_datetime function, which is essential for converting various date and time strings into datetime objects in Python. This capability is crucial for handling time-series data efficiently in many analytical applications. Understanding pd.to_datetime: We started by discussing the basics of the pd.to_datetime function, illustrating how it converts date and time string data into datetime objects, which are more suitable for analysis in Pandas. |
https://pyimagesearch.com/2024/04/30/pandas-to_datetime-pd-to_datetime/ | Handling Various Data Scenarios: We examined how to handle different date formats and errors through various parameters like errors, format, dayfirst, and more, giving users the tools to manage real-world data more effectively. Practical Examples: Simple and complex examples demonstrated the application of pd.to_datetime, from basic conversions to handling mixed format date strings and error scenarios. Performance and Alternatives: The discussion extended to performance considerations and alternatives such as dateutil.parser, numpy.datetime64, and ciso8601 for specific use cases where they might offer better performance or flexibility. Error Handling and Time Zone Management: We also covered crucial aspects such as error handling strategies and time zone considerations, which are pivotal when dealing with global datasets. Important Considerations:
Always verify the format of your date strings and use the format parameter where possible to speed up parsing. Use the errors parameter to handle data inconsistencies gracefully, either by ignoring them, raising an error, or coercing them to NaT.Consider time zone implications, especially when handling data across multiple regions, to ensure accurate time comparisons and computations. This tutorial not only equipped you with the knowledge to use pd.to_datetime effectively but also helped you understand when and how to use alternative methods for specific scenarios. By integrating these techniques into your data processing workflows, you can handle date and time data more robustly and efficiently. To learn more about all pd.to_datetime capabilities check out the developer doc. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Click here to download the source code to this pos
Home » Blog » Understanding Tasks in Diffusers: Part 3
Table of Contents
Understanding Tasks in Diffusers: Part 3
Introduction
Why Not Image-to-Image? ControlNet Models
Configuring Your Development Environment
Setup and Imports
Installation
Imports
Utility Functions
Canny ControlNet
Setting Up
Loading the Model
Optimizing the Pipeline
Image Generation
Cleaning Up
Pose ControlNet
Load Dancer Image
Detect Pose
Load ControlNet Model
Load and Configure Pipeline
Image Generation
Cleaning Up
Depth ControlNet
Load Depth Estimation Model
Load Image and Extract Depth
Load ControlNet Model
Load and Configure Pipeline (Skipping Safety Check)
Image Generation
Cleaning Up
Segmentation Map ControlNet
Load the Palette
Load Segmentation Model and Palette
Generate Segmentation Map
Load and Segment Image
Process and Colorize Segmentation
Loading the ControlNet Pipeline
Load ControlNet and Pipeline (Skipping Safety Check)
Image Generation
Cleaning Up
Summary
Applications of ControlNet in the Industry
Personalized Media and Advertising
Interactive Entertainment and Gaming
Automotive Industry
Medical Imaging
Architectural Visualization and Urban Planning
Fashion and Design
Citation Information
Understanding Tasks in Diffusers: Part 3
In this tutorial, you will learn how to control specific aspects of text-to-image with spatial information. This lesson is the last of a 3-part series on Understanding Tasks in Diffusers:
Understanding Tasks in Diffusers: Part 1 Understanding Tasks in Diffusers: Part 2Understanding Tasks in Diffusers: Part 3 (this tutorial)
To learn how to use ControlNet in Diffusers, just keep reading. Understanding Tasks in Diffusers: Part 3
Introduction
This is the last of a 3-part series on Understanding Tasks in Diffusers. Today, we are looking at a special kind of task known as ControlNet. Imagine being able to prompt your image generations with the spatial information of the images along with texts for better guidance. This unlocks the ability to use edges, depths, and other spatial cues that mimic the original picture. Why Not Image-to-Image? ➤ The answer is Control. Yes, that gives the architecture its name. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | ➤ ControlNet improves text-to-image generation by adding user control. Traditional models create impressive visuals but need more precision. ControlNet allows extra information, like sketches or depth data, to be included alongside text descriptions. This guides the model to create images that better match the user’s idea. Imagine creating a basic outline and letting the AI turn it into a complete image. This opens possibilities in art, design, and simulations, giving users more control over the final image
A Note of Acknowledgement: This tutorial was inspired in part by the official documentation from Hugging Face Diffusers. ControlNet Models
Zhang, Rao, and Agrawala (2023) introduced the ControlNet method in their paper, Adding Conditional Control to Text-to-Image Diffusion Models. It introduces a framework for supporting various spatial contexts, which can serve as additional conditionings to Diffusion models such as Stable Diffusion. The entire code base is open-sourced here. Over the past year, the community has submitted an insane number of applications for custom ControlNet models. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Here is a non-exhaustive list of some of the Colab notebooks. A sample from the paper is shown in Figure 1. Figure 1: ControlNet Canny and Pose ControlNet models (source: Zhang, Rao, and Agrawala, 2023). Any conditioning requires training a new copy of ControlNet weights. The original paper proposed 8 different conditioning models that are all supported in Diffusers! So, considering all the possible applications and outcomes of ControlNet, let’s look at how we can use various ControlNet Models to generate images as we want them. Although, as mentioned before, the official paper and corresponding repository have 8 different ControlNet models, in this tutorial, we will be looking at 4 of the most popular and well-used models:
Canny ControlNetPose ControlNetDepth ControlNetSegmentation Map ControlNet
Configuring Your Development Environment
To follow this guide, you need to have the diffusers library installed on your system. Luckily, diffusers is pip-installable:
$ pip install diffusers accelerate
If you need help configuring your development environment for OpenCV, we highly recommend that you read our pip install OpenCV guide — it will have you up and running in minutes. Need Help Configuring Your Development Environment? Having trouble configuring your development environment? |
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Short on time?Learning on your employer’s administratively locked system?Wanting to skip the hassle of fighting with the command line, package managers, and virtual environments?Ready to run the code immediately on your Windows, macOS, or Linux system? Then join PyImageSearch University today! Gain access to Jupyter Notebooks for this tutorial and other PyImageSearch guides pre-configured to run on Google Colab’s ecosystem right in your web browser! No installation required. And best of all, these Jupyter Notebooks will run on Windows, macOS, and Linux! Setup and Imports
Installation
! pip install -q opencv-contrib-python
! pip install -q controlnet_aux
We install libraries for diffusion models (diffusers, transformers), computer vision (opencv-contrib-python), and a custom library (controlnet_aux). |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Imports
import torch
import cv2
import numpy as np
from PIL import Image
from transformers import pipeline, AutoImageProcessor, UperNetForSemanticSegmentation
from diffusers import UniPCMultistepScheduler
from diffusers import StableDiffusionControlNetPipeline
from diffusers.utils import load_image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
from controlnet_aux import OpenposeDetector, HEDdetector
We import functionalities from these libraries to work with images (torch, cv2, numpy, PIL), potentially use pre-trained models (transformers), and leverage diffusion models with control (diffusers, controlnet_aux). Utility Functions
These functions provide helpful tools for processing images:
def create_canny_image(image):
image = np.array(image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
return canny_image
create_canny_image(image): This function takes an image as input and returns a new image highlighting its edges. Here’s a breakdown of what it does:
We convert the image to a NumPy array for easier manipulation with OpenCV.We define two thresholds, low_threshold and high_threshold, to control the sensitivity of edge detection. Lower values detect more edges, while higher values detect only strong edges. We use OpenCV’s cv2.Canny function to apply the Canny edge filter with the defined thresholds, effectively identifying edges in the image. Since OpenCV’s Canny returns a single-channel grayscale image, we convert it to a three-channel RGB image (required for the PIL.Image library) by duplicating the channel. We create a new PIL.Image object from the processed NumPy array, resulting in an image with highlighted edges. Finally, the function returns this new edge-enhanced image. 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
image_grid(imgs, rows, cols): This function takes a list of images (imgs), the desired number of rows (rows), and columns (cols) and arranges them into a single grid image. Here’s what it does:
We first check if the total number of images (len(imgs)) matches the product of rows and columns (ensuring we have enough images to fill the grid).We then retrieve the width (w) and height (h) of the first image as a reference for the grid size. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | We create a new empty PIL.Image object in RGB mode with dimensions calculated by multiplying the number of columns by the image width and rows by the image height, effectively creating a canvas for the grid. We iterate through each image in the list and paste it onto the grid image at the corresponding position determined by its index (i). The modulo (%) operation calculates the column position, and integer division (//) calculates the row position within the grid. Finally, the function returns the combined grid image containing all the input images arranged as specified. def delete_pipeline(pipeline):
pipeline.to("cpu")
del pipeline
torch.cuda.empty_cache()
delete_pipeline(pipeline): This function takes a loaded Stable Diffusion pipeline (pipeline) as input and properly removes it from memory. Here’s what it does:
First, we move the pipeline to the CPU (if it was previously on the GPU) to avoid memory leaks. Then, we use the del statement to explicitly delete the pipeline object, freeing up the memory it occupies. Lastly, we call torch.cuda.empty_cache() to ensure any remaining cached GPU memory associated with the pipeline is cleared as well. This helps maintain efficient memory usage, especially when dealing with multiple pipelines. Canny ControlNet
This code snippet demonstrates how to leverage a pre-trained diffusion model along with a control network to generate images based on a prompt while incorporating edge information from another image. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Let’s break down the steps and visualize the image in Figure 2:
prompt = "Oppenheimer as an anime character, high definition best quality, extremely detailed"
image = load_image(
"https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/oppenheimer.png"
)
image
Figure 2: Original Image for the Generation Process (source: https://ltamerica.org/wp-content/uploads/2023/07/porkpie-eisenstadt.jpeg). Setting Up
We define a prompt describing the desired image (“Oppenheimer as an anime character, high definition best quality, extremely detailed”).We load an image of Oppenheimer (“https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/oppenheimer.png“) using the load_image function. Using the create_canny_image function, we create a new image highlighting the edges of the loaded image. This edge information will guide the diffusion process. Let’s look at the edge map in Figure 3. canny_image = create_canny_image(image)
canny_image
Figure 3: Canny Edge Map from the Original Image (source: generated from the code). controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
Loading the Model
We load a pre-trained ControlNetModel called “sd-controlnet-canny” from the Hugging Face Hub. This model is specifically designed to work with Canny edge images for control. We load a pre-trained Stable Diffusion pipeline (“runwayml/stable-diffusion-v1-5”) and integrate the loaded controlnet model into the pipeline. We also specify torch.float16 for using a lower-precision data type to save memory, if possible, on your hardware. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Optimizing the Pipeline
We configure the pipeline’s scheduler using UniPCMultistepScheduler.from_config for potentially better performance during image generation. We enable CPU offloading (enable_model_cpu_offload) to move the model to the CPU if a GPU is available, potentially improving memory usage on systems with limited GPU memory. We enable memory-efficient attention (enable_xformers_memory_efficient_attention) to reduce memory consumption during image generation, especially for larger image sizes. Image Generation
output = pipe(
prompt,
canny_image,
negative_prompt="monochrome, low quality, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=20,
).images[0]
output
We use the pipeline (pipe) to generate an image based on the provided prompt (prompt).We also include the created canny image (canny_image) as an additional input to the pipeline. This injects the edge information to guide the diffusion process toward generating an image that aligns with the edges. We provide a negative prompt (“monochrome, low quality, lowres, bad anatomy, worst quality, low quality”) to steer the generation away from unwanted qualities. We set the number of inference steps (num_inference_steps) to 20, which controls the detail and quality of the generated image (more steps typically lead to better results but take longer).Finally, we run the pipeline, store the generated image in the output variable, and display the image as shown in Figure 4. Figure 4: Prompt and Canny Guided Image Generation (source: generated from the code). Cleaning Up
delete_pipeline(pipe)
After using the pipeline, we call the delete_pipeline function to remove it from memory and free up resources properly. This is especially important when dealing with multiple pipelines. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Pose ControlNet
This code demonstrates using a pre-trained diffusion model with a control network that leverages human pose (from any image like in Figure 5) information for image generation. Let’s break it down step by step:
img_pose = load_image("https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/dancer.png")
img_pose
Figure 5: Original Image of a dancer (source: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRbj6nS27NQPepUOZCzIcMW3WqYKKeiU0UvJLjfhA8qVw&s). Load Dancer Image
We load an image of a dancer (“https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/dancer.png“) using the load_image function. Detect Pose
model = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
pose = model(img_pose)
pose
We load a pre-trained OpenposeDetector model called “lllyasviel/ControlNet”. This model is designed to detect human poses from images. We call the model (model) on the loaded image (img_pose) to extract the human pose information. The output (pose) likely contains data representing the detected key points (e.g., shoulders, elbows, wrists) of the person in the image, as shown in Figure 6. Figure 6: Pose Estimation from the Image (source: generated from code). controlnet = ControlNetModel.from_pretrained(
"fusing/stable-diffusion-v1-5-controlnet-openpose", torch_dtype=torch.float16
)
model_id = "runwayml/stable-diffusion-v1-5"
pipe = StableDiffusionControlNetPipeline.from_pretrained(
model_id,
controlnet=controlnet,
torch_dtype=torch.float16,
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
pipe.enable_xformers_memory_efficient_attention()
Load ControlNet Model
We load a pre-trained ControlNetModel called fusing/stable-diffusion-v1-5-controlnet-openpose specifically designed to work with OpenPose data for control. This model will use the extracted pose information to guide the image generation process. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Load and Configure Pipeline
We define the Stable Diffusion model ID (“runwayml/stable-diffusion-v1-5”).We create a StableDiffusionControlNetPipeline using the loaded controlnet model and the specified model ID. We also specify torch.float16 to potentially save memory. We configure the pipeline’s scheduler using UniPCMultistepScheduler.from_config for potentially better performance. To potentially save memory, we enable CPU offloading (enable_model_cpu_offload) and memory-efficient attention (enable_xformers_memory_efficient_attention). Image Generation
prompt = "illustration of superman flying to the sky, high definition best quality, extremely detailed"
output = pipe(
prompt,
pose,
negative_prompt="monochrome, blurry, unclear face, missing body parts, extra digits, extra eyes, ugly, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
).images[0]
output
We define a prompt describing the desired image (“illustration of superman flying to the sky, high definition best quality, extremely detailed”).We use the pipeline (pipe) to generate an image. We provide the prompt (prompt) and the extracted pose information (pose) as inputs to the pipeline. The control network will use the pose information to influence the generation process, potentially aligning the generated Superman with a pose similar to that of the dancer in the loaded image. We include a negative prompt (“monochrome, blurry, unclear face, missing body parts, extra digits, extra eyes, ugly, lowres, bad anatomy, worst quality, low quality”) to steer the generation away from unwanted features. We set the number of inference steps (num_inference_steps) to 50 for potentially higher-quality results (which may take longer). Finally, we run the pipeline and store the generated image in the output variable. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | The final image is shown in Figure 7. Figure 7: Generated image from the pose and text prompt (source: generated from code). Cleaning Up
delete_pipeline(pipe)
After using the pipeline, we call the delete_pipeline function to properly remove it from memory and free up resources. Depth ControlNet
This code snippet demonstrates leveraging a pre-trained diffusion model with a control network that utilizes a depth map to guide image generation. We use a normal image (as shown in Figure 8) and use a depth-estimator model to generate its depth map. Here’s a breakdown:
depth_estimator = pipeline('depth-estimation')
image = load_image("https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/ansel.png")
image = depth_estimator(image)['depth']
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
image = Image.fromarray(image)
Figure 8: A famous photograph of Ansel Adams (source: https://portlandartmuseum.org/wp-content/uploads/2021/05/SC386257_WEB-975×500.jpg). Load Depth Estimation Model
We first load a pre-trained pipeline component (depth-estimation), likely using a model from the Hugging Face Hub. This component estimates depth information from an image. Load Image and Extract Depth
We load an image of Ansel (“[https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/ansel.png](https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/ansel.png)”) using load_image. We use the loaded pipeline (depth_estimator) on the image to extract the estimated depth information. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | The output (image) likely contains a single-channel grayscale image representing the predicted depth, where brighter values indicate closer objects, as shown in Figure 9.We convert the depth image to a NumPy array for further processing. To make it compatible with the PIL.Image library, we convert the single-channel depth image to a three-channel RGB image by duplicating the channel and then creating a new PIL.Image object from the processed array. Figure 9: Depth map from the image (source: generated from the code). image
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-depth", torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
Load ControlNet Model
We load a pre-trained ControlNetModel called “lllyasviel/sd-controlnet-depth” specifically designed to work with depth information for control. This model will use the estimated depth to influence how the diffusion process generates the image. Load and Configure Pipeline (Skipping Safety Check)
We create a StableDiffusionControlNetPipeline using the loaded controlnet model and the specified Stable Diffusion model ID (“runwayml/stable-diffusion-v1-5”). We also specify torch.float16 for potential memory savings. Important Note: We turn off the safety_checker by setting it to None. This is likely for demonstration purposes only, and safety checks are usually recommended to prevent the generation of potentially harmful content. We configure the pipeline’s scheduler using UniPCMultistepScheduler.from_config for potentially better performance. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | To potentially save memory, we enable memory-efficient attention (enable_xformers_memory_efficient_attention) and CPU offloading (enable_model_cpu_offload). Image Generation
output = pipe("a looming mountain range, animated, high definition, high quality",
image,
negative_prompt ="monochrome, blurry, unclear face, missing body parts, extra digits, extra eyes, ugly, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=60).images[0]
output
We define a prompt describing the desired image (“a looming mountain range, animated, high definition, high quality”).We use the pipeline (pipe) to generate an image. We provide the prompt (prompt) and the extracted depth information (image) as inputs to the pipeline. The control network will use the depth information to potentially influence the generated image, emphasizing areas with predicted closer depth (potentially making the mountain range appear more prominent).We include a negative prompt (“monochrome, blurry, unclear face, missing body parts, extra digits, extra eyes, ugly, lowres, bad anatomy, worst quality, low quality”) to steer the generation away from unwanted features. We set the number of inference steps (num_inference_steps) to 60 for potentially higher-quality results (which may take longer).Finally, we run the pipeline and store the generated image in the output variable. The final image, as generated, is shown in Figure 10. Figure 10: Generated image from the depth map and the input text (source: image generated from code). Cleaning Up
delete_pipeline(pipe)
After using the pipeline, we call the delete_pipeline function to remove it from memory and free up resources properly. Important Note: Disabling the safety checker is not recommended for real-world applications. It’s crucial to ensure the responsible use of these models and avoid generating harmful content. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Segmentation Map ControlNet
This code snippet demonstrates leveraging a pre-trained diffusion model with a control network that utilizes segmentation information to guide image generation. In short, we take a normal image (as shown in Figure 11), generate a segmentation map from it, and then use that segmentation map as a guide for text-to-image generation. Here’s a breakdown of the steps:
Figure 11: Original Image of the Sydney Opera House. Load the Palette
palette = np.asarray([
[0, 0, 0],
[120, 120, 120],
[180, 120, 120],
[6, 230, 230],
[80, 50, 50],
[4, 200, 3],
[120, 120, 80],
[...],
[...],
[92, 0, 255],
])
image_processor = AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-small")
image_segmentor = UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-small")
Load Segmentation Model and Palette
We define a color palette (palette) containing a large number of colors mapped to different segment labels. This palette will be used later to colorize the segmentation output. We load a pre-trained AutoImageProcessor and a UperNetForSemanticSegmentation model from the OpenMMLab library. These models are likely trained for semantic segmentation, which aims to classify each pixel in an image into a specific category (e.g., sky, water, building). Generate Segmentation Map
image = load_image("https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/sydney.png").convert('RGB')
pixel_values = image_processor(image, return_tensors="pt").pixel_values
with torch.no_grad():
outputs = image_segmentor(pixel_values)
seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[image.size[::-1]])[0]
color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
for label, color in enumerate(palette):
color_seg[seg == label, :] = color
color_seg = color_seg.astype(np.uint8)
image = Image.fromarray(color_seg)
image
Load and Segment Image
We load an image of Sydney Opera House (“[https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/sydney.png](https://huggingface.co/datasets/ritwikraha/random-storage/resolve/main/sydney.png)”) and convert it to RGB format (required for the segmentation model).We use the loaded image processor (image_processor) to prepare the image for the segmentation model by converting it to a format the model expects (tensors).We turn off gradient calculation (with torch.no_grad()) to improve efficiency, as we’re not backpropagating through the segmentation model in this case. We use the segmentation model (image_segmentor) to predict a segmentation mask for the image. This mask assigns a label to each pixel, potentially representing categories (e.g., sky, water, building, etc.). |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Process and Colorize Segmentation
We use the image processor’s post-processing function (post_process_semantic_segmentation) to convert the model’s raw segmentation output into a more usable format. We create a new empty image (color_seg) with dimensions matching the segmentation mask and set the data type to unsigned 8-bit integers for efficient color representation. We iterate through each unique label in the segmentation mask and its corresponding color from the defined palette (palette).For each pixel in the segmentation mask, we assign the corresponding color from the palette based on the predicted label, effectively creating a color-coded segmentation image where different colors represent different predicted categories. Finally, we convert the colorized segmentation image to an Image object for further use. The final image is shown in Figure 12. Figure 12: Segmentation Map from the original image (source: image generated by code). Loading the ControlNet Pipeline
controlnet = ControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-seg", torch_dtype=torch.float16
)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, safety_checker=None, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
Load ControlNet and Pipeline (Skipping Safety Check)
We load a pre-trained ControlNetModel called “lllyasviel/sd-controlnet-seg” specifically designed to work with segmentation information for control. This model will use the segmentation information to influence how the diffusion process generates the image. We create a StableDiffusionControlNetPipeline using the loaded controlnet model and the specified Stable Diffusion model ID (“runwayml/stable-diffusion-v1-5”). We also specify torch.float16 for potential memory savings. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Important Note: We turn off the safety_checker by setting it to None. This is likely for demonstration purposes only, and safety checks are usually recommended to prevent the generation of potentially harmful content. We configure the pipeline’s scheduler using UniPCMultistepScheduler.from_config for potentially better performance. To potentially save memory, we enable memory-efficient attention (enable_xformers_memory_efficient_attention) and CPU offloading (enable_model_cpu_offload). Image Generation
output = pipe("a magnificient castle on a lake with clear sky above",
image,
num_inference_steps=50).images[0]
output
We define a prompt describing the desired image (“a magnificent castle on a lake with clear sky above”).We use the pipeline (pipe) to generate an image. We provide the prompt (prompt) and the colorized segmentation image (image) as inputs to the pipeline. The control network will use the segmentation information to influence the generated image potentially, aligning certain aspects with the predicted categories in the segmentation (e.g., placing the castle on a region predicted as water in the segmentation).We set the number of inference steps (num_inference_steps) to 50 for potentially higher-quality results (which may take longer).Finally, we run the pipeline and store the generated image in the output variable. The final image is shown in Figure 13. Figure 13: Generated Image based on the same architecture of the Sydney Opera House (source: image generated by the code). Cleaning Up
delete_pipeline(pipe)
After using the pipeline, we call the delete_pipeline function to remove it from memory and free up resources properly. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Important Note: Disabling the safety checker is not recommended for real-world applications. It’s crucial to ensure the responsible use of these models and avoid generating harmful content. What's next? We recommend PyImageSearch University. Course information:
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Summary
As we close this chapter, let us rewind to understand what was the primary objectives:
ControlNet Models help us to perfectly guide the denoising process both with a text prompt and with spatial signals. The original paper released multiple ControlNet models, of which 4 have been showcased here. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Additionally, you can also fuse two ControlNet Models by adding your ControlNet models to a list and the input images to a list. ControlNet models also allow the workflow to be paired with other models like a pose estimation model or a segmentation model. This opens up avenues for many applications. Let us try to understand a few of them. Applications of ControlNet in the Industry
Personalized Media and Advertising
Application: Customized content generation for marketing and advertising. Technical Details: ControlNet can be used to adapt promotional materials to reflect diverse consumer profiles or scenarios, effectively personalizing advertising imagery to suit different demographics, locations, and individual preferences. Example: Using ControlNet to generate custom images of products in settings that align with the target audience’s lifestyle, such as changing the background or contextual elements of an image to fit urban or rural settings. Interactive Entertainment and Gaming
Application: Dynamic environment generation based on player interaction and choices. Technical Details: In video games or VR environments, ControlNet can dynamically modify visuals in real-time to adapt to player inputs or narrative changes, enhancing the immersive experience. Example: Adjusting the appearance and style of game environments and characters based on player decisions or environmental factors enhances the game’s adaptability and engagement. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Automotive Industry
Application: Enhanced simulation for autonomous vehicle testing. Technical Details: ControlNet can generate varied traffic scenarios under different conditions during the simulation phase of autonomous vehicle development, improving the robustness of the vehicles’ perception systems. Example: Simulating different weather conditions, traffic densities, and pedestrian scenarios to train autonomous driving systems, helping them adapt to real-world variables. Medical Imaging
Application: Customizable medical training tools and enhanced diagnostic visuals. Technical Details: For medical training and diagnostics, ControlNet can be employed to generate anatomically accurate images under various simulated medical conditions, aiding in education and diagnostic processes. Example: Creating detailed and condition-specific images (e.g., varying stages of a disease or responses to treatments), which can be used for training medical professionals or for planning surgeries. Architectural Visualization and Urban Planning
Application: Adaptive visualization of architectural designs and urban layouts. Technical Details: Architects and planners can use ControlNet to visualize architectural projects or urban designs under different contextual scenarios (e.g., varying light conditions, seasons, or surrounding developments).Example: Generating images of how a building would look at different times of the day or year or simulating the visual impact of a new building on the existing urban landscape. Fashion and Design
Application: Tailored design visualizations reflecting contextual customer preferences. Technical Details: In fashion, ControlNet can be used to create images of apparel or products in different styles, settings, or models of varying body types, reflecting a more diverse range of consumer profiles. |
https://pyimagesearch.com/2024/05/27/understanding-tasks-in-diffusers-part-3/ | Example: Showcasing clothing items in different urban or natural settings on models of various body types and ethnic backgrounds to cater to a global market. These applications leverage the unique ability of ControlNet to incorporate specific, controlled variations into the generative process, enhancing both the flexibility and utility of diffusion models across a broad spectrum of industries. What other applications can you think of? Let us know
@pyimagesearch
Citation Information
A. R. Gosthipaty and R. Raha. “ Understanding Tasks in Diffusers: Part 3,” PyImageSearch, P. Chugh, A. R. Gosthipaty, S. Huot, K. Kidriavsteva, and R. Raha, eds., 2024, https://pyimg.co/ks4d0
@incollection{ARG-RR_2024_Understanding-Tasks-in-Diffusers-Part-3,
author = {Aritra Roy Gosthipaty and Ritwik Raha},
title = {Understanding Tasks in Diffusers: Part 3},
booktitle = {PyImageSearch},
editor = {Puneet Chugh and Susan Huot and Kseniia Kidriavsteva},
year = {2024},
url = {https://pyimg.co/ks4d0},
}
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