ComfyUI 36 Inpainting with Differential Diffusion Node - Workflow Included -Stable Diffusion
TLDRThis tutorial showcases the power of the differential diffusion node in the latest ComfyUI 36 Stable Diffusion upgrade for inpainting tasks. The video demonstrates how to create a workflow for inpainting by masking areas in an image, such as hair, and using the node to refine details and avoid distortions. It also explores different selection methods for altering specific elements like a t-shirt or jeans, highlighting the significant improvement in image quality when using the differential diffusion feature.
Takeaways
- π Upgrading conv to the latest version enables the differential diffusion node by default, which is great for inpainting tasks.
- π¨ A new column is added between the loader and the sampler for loading the image and masking for inpainting.
- ποΈ The mask editor is used to create a mask for the inpainting process, capturing all the hair, though not needing to be very accurate.
- π A Gaussian Blur mask is added to the workflow, adjustable to fit different images, with 2020 being a suitable parameter for the given example.
- π The 'grow mask' feature can be used to enlarge the drawn mask slightly, but it's turned off for the hair in this example.
- π Initially, the inpainted image may look okay at first glance, but closer inspection reveals distortions in the eye, eyebrow, face, and hairline.
- π Turning on the differential diffusion significantly improves the image quality, reducing distortions and providing a much nicer result.
- π The process of changing the t-shirt color to purple involves adjusting the prompt and using the mask editor to clear and save the selection.
- π For changing the jeans color, a 'some detector' is used to select the t-shirt, with adjustments to confidence level to ensure accurate selection.
- 𧩠Using different selection methods, such as the 'clip' tool, helps in accurately targeting specific elements like jeans for color changes.
- π Comparing images with and without the differential diffusion node highlights the significant benefits of using the node for inpainting tasks.
Q & A
What is the differential diffusion node and how does it improve the inpainting process in the video?
-The differential diffusion node is a feature available in the latest version of ComfyUI 36 Stable Diffusion. It enhances the inpainting process by providing a more accurate and less distorted result when filling in missing or masked areas of an image.
How is the inpainting workflow created in the video?
-The inpainting workflow is created by adding a column between the loader and the sampler in ComfyUI 36. This column is used to load the image and apply the masking for the inpainting process.
What is the purpose of the mask editor in the video?
-The mask editor is used to create a mask that defines the area to be inpainted. It allows the user to draw a cursor of variable size to capture specific parts of the image, such as the hair in the example provided.
What is the role of the Gaussian Blur in the inpainting workflow?
-The Gaussian Blur is used to soften the edges of the mask, which helps to blend the inpainted areas more naturally with the rest of the image. Users can adjust the parameters to see what works best for a specific image.
How does the 'grow mask' function in the workflow?
-The 'grow mask' function allows users to enlarge the drawn mask slightly, which can be useful for ensuring that all the desired areas are included in the inpainting process.
What issues were observed in the initial inpainted image without using the differential diffusion node?
-Without using the differential diffusion node, the initial inpainted image showed distortions such as an odd eye and eyebrow, an unnatural hairline, and a distorted left side of the face.
What was the effect of using the differential diffusion node on the inpainted image?
-When the differential diffusion node was switched on, the resulting image showed a significant improvement in quality, with less distortion and a more natural appearance, especially in the hairline and facial features.
How does changing the prompt to 'Dark Purple T-shirt with a Logo' affect the inpainting process?
-Changing the prompt to 'Dark Purple T-shirt with a Logo' guides the inpainting process to generate a new image where the subject's clothing matches the description provided in the prompt.
What selection method is used to change the color of the t-shirt in the video?
-The 'some detector' selection method is used to identify and select the t-shirt area in the image, which is then inpainted with the new color as specified in the prompt.
What challenges were faced when trying to create white jeans instead of blue jeans in the video?
-The challenges included difficulties with the edges of the jeans, issues around the neck and arm, and problems with the hair integration. These issues were partly addressed by adjusting the mask and using the differential diffusion node.
How does the differential diffusion node help in creating a better image of white jeans?
-The differential diffusion node helps by smoothing out the edges and reducing the visual artifacts, resulting in a much more natural and better-looking image of white jeans.
Outlines
π¨ Differential Diffusion for In-Painting
This paragraph introduces the use of a differential diffusion node in a software update for in-painting tasks. It demonstrates the process of creating a mask in the mask editor to capture an image of a woman's hair, adjusting the parameters of a Gaussian Blur mask, and then comparing the results with and without the differential diffusion node. The node significantly improves the quality of the in-painted image, making it look more natural and fixing distortions in the face and hairline.
π Color and Object Selection Enhancements
The second paragraph discusses the use of different selection methods to modify specific parts of an image, such as changing a t-shirt color and selecting objects like blue jeans. It details the process of using a blur mask and a mask editor to refine selections, and then shows the impact of the differential diffusion node on the final image quality. The node is used to improve the edges and overall appearance of the selected objects, demonstrating its utility in enhancing image editing tasks.
Mindmap
Keywords
π‘Differential Diffusion Node
π‘Inpainting
π‘Masking
π‘Mask Editor
π‘Gaussian Blur
π‘Grow Mask
π‘Prompt
π‘Color Change
π‘Selection Method
π‘Blur Mask
π‘Differential Diffusion
Highlights
Upgrading conv to the latest version includes the differential diffusion node by default.
The differential diffusion node is fantastic for inpainting, making perfect in-picture.
Creating an inpainting workflow by adding a column between the loader and the sampler for image and mask loading.
Using the mask editor to create a mask for inpainting, capturing all the hair.
Applying a Gaussian Blur mask to see what works for a specific image.
Growing the mask to enlarge the drawn area for more inpainting control.
First inpainting result shows a blond girl, but with some distortions.
Using the differential diffuser to improve the inpainting result significantly.
Comparing the inpainting results with and without the differential diffusion node.
Changing the prompt to create a dark purple t-shirt with a logo.
Using a different method to select the t-shirt with the some detector.
Adjusting the confidence level for better t-shirt selection.
Second inpainting attempt shows improvement but still has edge issues.
Adding a bit of mask to fix the edges and other problem areas.
Third selection method using clip to create white jeans instead of blue.
First attempt at creating white jeans without differential diffusion shows issues.
Enabling differential diffusion significantly improves the white jeans result.
The differential diffusion node provides great benefits for inpainting tasks.