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HomeArtificial Intelligence and Machine LearningInpaint images with Stable Diffusion using Amazon SageMaker JumpStart

Inpaint images with Stable Diffusion using Amazon SageMaker JumpStart

In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models using Amazon SageMaker JumpStart. Today, we are excited to introduce a new feature that enables users to inpaint images with Stable Diffusion models. Inpainting refers to the process of replacing a portion of an image with another image based on a textual prompt. By providing the original image, a mask image that outlines the portion to be replaced, and a textual prompt, the Stable Diffusion model can produce a new image that replaces the masked area with the object, subject, or environment described in the textual prompt.

You can use inpainting for restoring degraded images or creating new images with novel subjects or styles in certain sections. Within the realm of architectural design, Stable Diffusion inpainting can be applied to repair incomplete or damaged areas of building blueprints, providing precise information for construction crews. In the case of clinical MRI imaging, the patient’s head must be restrained, which may lead to subpar results due to the cropping artifact causing data loss or reduced diagnostic accuracy. Image inpainting can effectively help mitigate these suboptimal outcomes.

In this post, we present a comprehensive guide on deploying and running inference using the Stable Diffusion inpainting model in two methods: through JumpStart’s user interface (UI) in Amazon SageMaker Studio, and programmatically through JumpStart APIs available in the SageMaker Python SDK.

Solution overview

The following images are examples of inpainting. The original images are on the left, the mask image is in the center, and the inpainted image generated by the model is on the right. For the first example, the model was provided with the original image, a mask image, and the textual prompt “a white cat, blue eyes, wearing a sweater, lying in park,” as well as the negative prompt “poorly drawn feet.” For the second example, the textual prompt was “A female model gracefully showcases a casual long dress featuring a blend of pink and blue hues,”

Running large generative AI models like Stable Diffusion requires custom inference scripts. You have to run end-to-end tests to make sure that the script, the model, and the desired instance work together efficiently. JumpStart simplifies this process by providing ready-to-use scripts that have been robustly tested. You can access these scripts with one click through the Studio UI or with very few lines of code through the JumpStart APIs.

The following sections guide you through deploying the model and running inference using either the Studio UI or the JumpStart APIs.

Note that by using this model, you agree to the CreativeML Open RAIL++-M License.

Access JumpStart through the Studio UI

In this section, we illustrate the deployment of JumpStart models using the Studio UI. The accompanying video demonstrates locating the pre-trained Stable Diffusion inpainting model on JumpStart and deploying it. The model page offers essential details about the model and its usage. To perform inference, we employ the ml.p3.2xlarge instance type, which delivers the required GPU acceleration for low-latency inference at an affordable price. After the SageMaker hosting instance is configured, choose Deploy. The endpoint will be operational and prepared to handle inference requests within approximately 10 minutes.

JumpStart provides a sample notebook that can help accelerate the time it takes to run inference on the newly created endpoint. To access the notebook in Studio, choose Open Notebook in the Use Endpoint from Studio section of the model endpoint page.

Use JumpStart programmatically with the SageMaker SDK

Utilizing the JumpStart UI enables you to deploy a pre-trained model interactively with only a few clicks. Alternatively, you can employ JumpStart models programmatically by using APIs integrated within the SageMaker Python SDK.

In this section, we choose an appropriate pre-trained model in JumpStart, deploy this model to a SageMaker endpoint, and perform inference on the deployed endpoint, all using the SageMaker Python SDK. The following examples contain code snippets. To access the complete code with all the steps included in this demonstration, refer to the Introduction to JumpStart Image editing – Stable Diffusion Inpainting example notebook.

Deploy the pre-trained model

SageMaker utilizes Docker containers for various build and runtime tasks. JumpStart utilizes the SageMaker Deep Learning Containers (DLCs) that are framework-specific. We first fetch any additional packages, as well as scripts to handle training and inference for the selected task. Then the pre-trained model artifacts are separately fetched with model_uris, which provides flexibility to the platform. This allows multiple pre-trained models to be used with a single inference script. The following code illustrates this process:

model_id, model_version = “model-inpainting-stabilityai-stable-diffusion-2-inpainting-fp16”, “*”
# Retrieve the inference docker container uri
deploy_image_uri = image_uris.retrieve(
region=None,
framework=None, # automatically inferred from model_id
image_scope=”inference”,
model_id=model_id,
model_version=model_version,
instance_type=inference_instance_type,
)
# Retrieve the inference script uri
deploy_source_uri = script_uris.retrieve(model_id=model_id, model_version=model_version, script_scope=”inference”)

base_model_uri = model_uris.retrieve(model_id=model_id, model_version=model_version, model_scope=”inference”)

Next, we provide those resources to a SageMaker model instance and deploy an endpoint:

# Create the SageMaker model instance
# Create the SageMaker model instance
model = Model(
image_uri=deploy_image_uri,
source_dir=deploy_source_uri,
model_data=base_model_uri,
entry_point=”inference.py”, # entry point file in source_dir and present in deploy_source_uri
role=aws_role,
predictor_cls=Predictor,
name=endpoint_name,
)

# deploy the Model – note that we need to pass the Predictor class when we deploy the model through the Model class,
# in order to run inference through the SageMaker API
base_model_predictor = model.deploy(
initial_instance_count=1,
instance_type=inference_instance_type,
predictor_cls=Predictor,
endpoint_name=endpoint_name,
)

After the model is deployed, we can obtain real-time predictions from it!

Input

The input is the base image, a mask image, and the prompt describing the subject, object, or environment to be substituted in the masked-out portion. Creating the perfect mask image for in-painting effects involves several best practices. Start with a specific prompt, and don’t hesitate to experiment with various Stable Diffusion settings to achieve desired outcomes. Utilize a mask image that closely resembles the image you aim to inpaint. This approach aids the inpainting algorithm in completing the missing sections of the image, resulting in a more natural appearance. High-quality images generally yield better results, so make sure your base and mask images are of good quality and resemble each other. Additionally, opt for a large and smooth mask image to preserve detail and minimize artifacts.

The endpoint accepts the base image and mask as raw RGB values or a base64 encoded image. The inference handler decodes the image based on content_type:

For content_type = “application/json”, the input payload must be a JSON dictionary with the raw RGB values, textual prompt, and other optional parameters
For content_type = “application/json;jpeg”, the input payload must be a JSON dictionary with the base64 encoded image, a textual prompt, and other optional parameters

Output

The endpoint can generate two types of output: a Base64-encoded RGB image or a JSON dictionary of the generated images. You can specify which output format you want by setting the accept header to “application/json” or “application/json;jpeg” for a JPEG image or base64, respectively.

For accept = “application/json”, the endpoint returns the a JSON dictionary with RGB values for the image
For accept = “application/json;jpeg”, the endpoint returns a JSON dictionary with the JPEG image as bytes encoded with base64.b64 encoding

Note that sending or receiving the payload with the raw RGB values may hit default limits for the input payload and the response size. Therefore, we recommend using the base64 encoded image by setting content_type = “application/json;jpeg” and accept = “application/json;jpeg”.

The following code is an example inference request:

content_type = “application/json;jpeg”

with open(input_img_file_name, “rb”) as f:
input_img_image_bytes = f.read()
with open(input_img_mask_file_name, “rb”) as f:
input_img_mask_image_bytes = f.read()

encoded_input_image = base64.b64encode(bytearray(input_img_image_bytes)).decode()
encoded_mask = base64.b64encode(bytearray(input_img_mask_image_bytes)).decode()

payload = {
“prompt”: “a white cat, blue eyes, wearing a sweater, lying in park”,
“image”: encoded_input_image,
“mask_image”: encoded_mask,
“num_inference_steps”: 50,
“guidance_scale”: 7.5,
“seed”: 0,
“negative_prompt”: “poorly drawn feet”,
}

accept = “application/json;jpeg”

def query(model_predictor, payload, content_type, accept):
“””Query the model predictor.”””

query_response = model_predictor.predict(
payload,
{
“ContentType”: content_type,
“Accept”: accept,
},
)
return query_response

query_response = query(model_predictor, json.dumps(payload).encode(“utf-8”), content_type, accept)
generated_images = parse_response(query_response)

Supported parameters

Stable Diffusion inpainting models support many parameters for image generation:

image – The original image.
mask – An image where the blacked-out portion remains unchanged during image generation and the white portion is replaced.
prompt – A prompt to guide the image generation. It can be a string or a list of strings.
num_inference_steps (optional) – The number of denoising steps during image generation. More steps lead to higher quality image. If specified, it must be a positive integer. Note that more inference steps will lead to a longer response time.
guidance_scale (optional) – A higher guidance scale results in an image more closely related to the prompt, at the expense of image quality. If specified, it must be a float. guidance_scale<=1 is ignored.
negative_prompt (optional) – This guides the image generation against this prompt. If specified, it must be a string or a list of strings and used with guidance_scale. If guidance_scale is disabled, this is also disabled. Moreover, if the prompt is a list of strings, then the negative_prompt must also be a list of strings.
seed (optional) – This fixes the randomized state for reproducibility. If specified, it must be an integer. Whenever you use the same prompt with the same seed, the resulting image will always be the same.
batch_size (optional) – The number of images to generate in a single forward pass. If using a smaller instance or generating many images, reduce batch_size to be a small number (1–2). The number of images = number of prompts*num_images_per_prompt.

Limitations and biases

Even though Stable Diffusion has impressive performance in inpainting, it suffers from several limitations and biases. These include but are not limited to:

The model may not generate accurate faces or limbs because the training data doesn’t include sufficient images with these features.
The model was trained on the LAION-5B dataset, which has adult content and may not be fit for product use without further considerations.
The model may not work well with non-English languages because the model was trained on English language text.
The model can’t generate good text within images.
Stable Diffusion inpainting typically works best with images of lower resolutions, such as 256×256 or 512×512 pixels. When working with high-resolution images (768×768 or higher), the method might struggle to maintain the desired level of quality and detail.
Although the use of a seed can help control reproducibility, Stable Diffusion inpainting may still produce varied results with slight alterations to the input or parameters. This might make it challenging to fine-tune the output for specific requirements.
The method might struggle with generating intricate textures and patterns, especially when they span large areas within the image or are essential for maintaining the overall coherence and quality of the inpainted region.

For more information on limitations and bias, refer to the Stable Diffusion Inpainting model card.

Inpainting solution with mask generated via a prompt

CLIPSeq is an advanced deep learning technique that utilizes the power of pre-trained CLIP (Contrastive Language-Image Pretraining) models to generate masks from input images. This approach provides an efficient way to create masks for tasks such as image segmentation, inpainting, and manipulation. CLIPSeq uses CLIP to generate a text description of the input image. The text description is then used to generate a mask that identifies the pixels in the image that are relevant to the text description. The mask can then be used to isolate the relevant parts of the image for further processing.

CLIPSeq has several advantages over other methods for generating masks from input images. First, it’s a more efficient method, because it doesn’t require the image to be processed by a separate image segmentation algorithm. Second, it’s more accurate, because it can generate masks that are more closely aligned with the text description of the image. Third, it’s more versatile, because you can use it to generate masks from a wide variety of images.

However, CLIPSeq also has some disadvantages. First, the technique may have limitations in terms of subject matter, because it relies on pre-trained CLIP models that may not encompass specific domains or areas of expertise. Second, it can be a sensitive method, because it’s susceptible to errors in the text description of the image.

For more information, refer to Virtual fashion styling with generative AI using Amazon SageMaker.

Clean up

After you’re done running the notebook, make sure to delete all resources created in the process to ensure that the billing is stopped. The code to clean up the endpoint is available in the associated notebook.

Conclusion

In this post, we showed how to deploy a pre-trained Stable Diffusion inpainting model using JumpStart. We showed code snippets in this post—the full code with all of the steps in this demo is available in the Introduction to JumpStart – Enhance image quality guided by prompt example notebook. Try out the solution on your own and send us your comments.

To learn more about the model and how it works, see the following resources:

High-Resolution Image Synthesis with Latent Diffusion Models
Stable Diffusion Launch Announcement
Stable Diffusion 2.0 Release
Stable Diffusion Inpainting model card

To learn more about JumpStart, check out the following posts:

Zero-shot prompting for the Flan-T5 foundation model in Amazon SageMaker JumpStart
Generate images from text with the stable diffusion model on Amazon SageMaker JumpStart
Upscale images with Stable Diffusion in Amazon SageMaker JumpStart
AlexaTM 20B is now available in Amazon SageMaker JumpStart
Run text generation with Bloom and GPT models on Amazon SageMaker JumpStart
Run image segmentation with Amazon SageMaker JumpStart
Run text classification with Amazon SageMaker JumpStart using TensorFlow Hub and Hugging Face models
Amazon SageMaker JumpStart models and algorithms now available via API
Incremental training with Amazon SageMaker JumpStart
Transfer learning for TensorFlow object detection models in Amazon SageMaker
Transfer learning for TensorFlow text classification models in Amazon SageMaker
Transfer learning for TensorFlow image classification models in Amazon SageMaker

About the Authors

Dr. Vivek Madan is an Applied Scientist with the Amazon SageMaker JumpStart team. He got his PhD from University of Illinois at Urbana-Champaign and was a Post Doctoral Researcher at Georgia Tech. He is an active researcher in machine learning and algorithm design and has published papers in EMNLP, ICLR, COLT, FOCS, and SODA conferences.

Alfred Shen is a Senior AI/ML Specialist at AWS. He has been working in Silicon Valley, holding technical and managerial positions in diverse sectors including healthcare, finance, and high-tech. He is a dedicated applied AI/ML researcher, concentrating on CV, NLP, and multimodality. His work has been showcased in publications such as EMNLP, ICLR, and Public Health.

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