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Generate stereo images from image and depth map, creating 3D effects for immersive visual projects.
The StereoImageNode
is designed to generate stereo images from a given image and its corresponding depth map. This node is particularly useful for creating 3D visual effects by simulating the perception of depth, which can be viewed using stereoscopic methods. The node leverages depth information to create two slightly different images, one for each eye, which when viewed together, give the illusion of depth. This process involves adjusting the divergence and separation of the images based on the depth map, allowing for a variety of stereo effects such as left-right, top-bottom, and anaglyph modes. The node is capable of handling different stereo modes and can apply various techniques to fill gaps and smooth transitions, ensuring high-quality stereo images. By using this node, you can enhance your visual projects with immersive 3D effects, making it a valuable tool for AI artists looking to explore the realm of stereoscopic imagery.
The image
parameter is the primary input image that will be used to generate the stereo effect. It should be a high-quality image that you want to convert into a stereo image. The quality and resolution of this image will directly impact the final stereo output.
The depth_map
parameter provides the depth information for the corresponding input image. It is crucial for determining how the stereo effect will be applied, as it dictates the perceived distance of objects within the image. A well-defined depth map will result in a more accurate and visually appealing stereo effect.
The divergence
parameter controls the horizontal shift between the left and right images, affecting the perceived depth. A higher divergence value increases the depth effect, while a lower value results in a subtler effect. This parameter should be adjusted based on the desired intensity of the stereo effect.
The separation
parameter defines the distance between the left and right images. It works in conjunction with divergence to enhance the 3D effect. Adjusting this parameter allows you to fine-tune the stereo effect to achieve the desired level of depth perception.
The modes
parameter specifies the stereo mode to be used, such as left-right
, top-bottom
, or red-cyan-anaglyph
. Each mode offers a different way of presenting the stereo effect, allowing for flexibility in how the final image is viewed. Selecting the appropriate mode is essential for achieving the desired visual outcome.
The stereo_balance
parameter adjusts the balance between the left and right images. It can be used to emphasize one image over the other, which can be useful for creating specific visual effects or correcting imbalances in the stereo image.
The stereo_offset_exponent
parameter influences the mapping of depth values to pixel offsets, affecting how depth is perceived in the stereo image. This parameter allows for fine-tuning of the depth effect, providing control over the intensity and distribution of depth across the image.
The fill_technique
parameter determines the method used to fill gaps in the stereo image. Options include none_post
, inverse_post
, and hybrid_edge
, each offering different approaches to handling areas where depth information may be sparse or inconsistent. Choosing the right fill technique is important for maintaining image quality and ensuring a seamless stereo effect.
The depth_blur_edge_threshold
parameter sets the threshold for applying blur to the depth map edges. This helps in smoothing transitions and reducing artifacts in the stereo image, contributing to a more natural and visually pleasing result.
The depth_map_blur
parameter controls the amount of blur applied to the depth map. Blurring the depth map can help in reducing noise and creating a smoother stereo effect, especially in areas with abrupt depth changes.
The results_final
parameter provides the final stereo images generated by the node. These images are ready for viewing using stereoscopic methods and represent the culmination of the stereo generation process, incorporating all adjustments and effects applied through the input parameters.
The modified_depthmap_final_left
parameter outputs the modified depth map for the left image. This depth map reflects any changes made during the stereo generation process, such as blurring or edge adjustments, and is used to create the left stereo image.
The modified_depthmap_final_right
parameter outputs the modified depth map for the right image. Similar to the left depth map, it includes any modifications applied during processing and is essential for generating the right stereo image.
The mask_final
parameter provides a mask that highlights areas of the stereo image where depth information was adjusted or filled. This mask can be useful for further processing or analysis, offering insights into how the stereo effect was applied.
modes
to find the best stereo effect for your project, as each mode offers a unique viewing experience.divergence
and separation
parameters incrementally to achieve the desired depth effect without causing discomfort or distortion.fill_technique
parameter to address any gaps or inconsistencies in the depth map, ensuring a smooth and continuous stereo image.depth_map_blur
to reduce noise and enhance the overall quality of the stereo effect, especially in complex scenes.modes
parameter.left-right
, top-bottom
, or red-cyan-anaglyph
, and correct any typos.RunComfy is the premier ComfyUI platform, offering ComfyUI online environment and services, along with ComfyUI workflows featuring stunning visuals. RunComfy also provides AI Playground, enabling artists to harness the latest AI tools to create incredible art.