Hyper Stable Diffusion with Blender & any 3D software in real time - SD Experimental

Andrea Baioni
29 Apr 202424:33

TLDRThis video tutorial explores integrating Stable Diffusion into Blender or other 3D software in real-time for creative workflows. The host demonstrates two methods using SDXL Lightning and Hyper SDXL, highlighting the resource-intensive nature of live screen capture and the benefits of using Cycles for detailed lighting. Viewers learn to set up custom nodes and models for real-time image generation, offering a fast yet experimental approach to concept art and scene composition.

Takeaways

  • 😀 The video introduces a method to integrate Stable Diffusion into 3D software like Blender in real time.
  • 🔧 The process is described as 'experimental' due to the resource-intensive nature of the setup.
  • 💻 The tutorial covers two workflows: one with 'sdxl lightning' and the other with 'Hyper sdxl', which is a newer, faster model.
  • 🔍 The 'screen share' node is crucial for capturing live images from the 3D environment to use as a reference for image generation.
  • 🛠️ The video demonstrates how to install necessary nodes and models for the workflow, including mixlab nodes and sdxl models.
  • 📈 The use of 'control nets' like depth estimation is vital for maintaining the structure of the 3D scene in the generated images.
  • 🖼️ The video shows how to build the workflow from scratch, including setting up nodes for image resizing, encoding, and sampling.
  • 🎨 The importance of using Cycles render in Blender for better volumetric lighting in the source images is highlighted.
  • 📹 The video includes a comparison between using the fine-tuned 'sdxl lightning' model and the faster 'Hyper sdxl' model.
  • 🔄 The process allows for real-time adjustments in Blender to be reflected in the generated images, offering a dynamic workflow.
  • 🌐 The video concludes by emphasizing the potential of this method for concept artists and professionals needing quick composite results.

Q & A

  • What is the main topic of the video 'Hyper Stable Diffusion with Blender & any 3D software in real time - SD Experimental'?

    -The video focuses on integrating stable diffusion technology into 3D software like Blender in real time, showcasing two different workflows using sdxl lightning and Hyper sdxl.

  • Why is the setup referred to as 'experimental' in the video?

    -The setup is called experimental because it is not yet refined and could be considered 'janky,' meaning it may be unstable or have issues, but it is being explored for its potential in creative applications.

  • What is the purpose of the 'screen share' node used in the workflows?

    -The 'screen share' node is used to capture a live feed from the 3D software's viewport, which serves as a reference image for the generation process in the stable diffusion workflow.

  • What are the two different workflows presented in the video?

    -The two workflows presented are one using sdxl lightning, which requires multiple steps of inference, and the other using Hyper sdxl, which only requires a single step for image generation.

  • What is the significance of using Cycles instead of Eevee in Blender for this workflow?

    -Cycles is used instead of Eevee because it provides a more detailed and accurate representation of the scene, which is crucial for the screen share node to capture the necessary details for image generation.

  • How does the video script differentiate between the sdxl lightning model and the Hyper sdxl model?

    -The sdxl lightning model requires a fine-tuned approach with multiple inference steps, while the Hyper sdxl model is based on the base sdxl model fine-tuned for one-step image generation, making it faster but potentially less precise.

  • What is the role of control nets in the workflow presented in the video?

    -Control nets, such as depth estimators, are used to maintain the overall structure of the 3D scene within the composition, ensuring that the generated images align with the 3D environment's layout and elements.

  • What are the system requirements for implementing the workflows described in the video?

    -The workflows require a powerful system to handle the resource-intensive process of real-time screen capturing, 3D rendering, and image generation, with the video mentioning a 3080 TI as the hardware used for demonstration.

  • How does the video script address the issue of resource consumption during the workflow?

    -The script acknowledges that screen grabbing live consumes a lot of resources and suggests using a lightweight setup with minimal control nets to reduce the load on the system.

  • What is the final outcome of using the workflows described in the video?

    -The final outcome is the generation of images that closely resemble the 3D scene setup in real-time, providing a quick way to visualize and iterate on creative ideas without the need for detailed 3D rendering.

  • How does the video script suggest using the generated images in a practical context?

    -The script suggests using the generated images for rough ideas, concept art, and image research, allowing creators to set the scene and quickly iterate on compositions without the need for detailed 3D materials and shaders.

Outlines

00:00

😲 Integrating Stable Diffusion with Blender in Real Time

The script introduces an experimental method to integrate Stable Diffusion, an AI model for generating images, with Blender, a 3D software, in real time. The presenter acknowledges the limitations of a previous attempt and introduces two new workflows using 'sdxl lightning' and 'Hyper sdxl'. The process relies on a 'screen share' node, which captures the viewport from Blender or any 3D application. The presenter also discusses the challenges of resource consumption when combining Stable Diffusion with Blender and other software like OBS for recording.

05:01

🤖 Setting Up Workflows with MixLab Nodes and Models

The script details the setup process for integrating Stable Diffusion with Blender using a suite of nodes called MixLab, specifically the real-time design nodes. It guides the user through installing MixLab, setting up the necessary dependencies, and choosing between 'sdxl lightning' and 'sdxl hyper' models. The 'lightning' model requires fine-tuning and multiple inference steps, while 'hyper' is a faster, one-step model. The process involves downloading specific model files and workflow JSON configurations, and installing any missing custom nodes within the Comfy instance.

10:01

🛠️ Building the Workflow for Real-Time Image Generation

The script explains how to construct the workflow for real-time image generation using the screen share node to capture the Blender viewport. It emphasizes the importance of using Cycles for rendering due to its volumetric lighting capabilities, which are better for the image capturing process. The presenter describes the workflow for using 'hyper sdxl' with a depth control net and a custom case sampler node, which is necessary for the one-step inference process. The workflow is designed to be lightweight to minimize resource consumption.

15:01

🔧 Adjusting the Workflow for SdxL Lightning and Testing

The script outlines the modifications required to switch the workflow from 'hyper sdxl' to 'sdxl lightning'. This involves removing certain nodes related to 'hyper sdxl' and adding a regular K-sampler node. The presenter also discusses the importance of scene settings in Blender, such as using Cycles for rendering and adjusting the viewport for better image detail. The testing process involves using a positive prompt to describe the scene and observing how changes in the Blender viewport are reflected in the generated images.

20:03

🖼️ Real-Time Image Generation and Workflow Comparison

The script demonstrates the real-time image generation process using both 'sdxl hyper' and 'sdxl lightning' workflows. It shows how the screen share node updates the image in the Comfy instance as the Blender viewport changes. The presenter also introduces a popup preview node for easier image assessment within Blender. The comparison highlights the trade-off between speed and quality between the two workflows, with 'sdxl lightning' providing more refined images at the cost of increased processing time.

🎨 Exploring Creative Potentials and Limitations

The script concludes by reflecting on the creative potential and limitations of using Blender and Stable Diffusion for real-time image generation. It discusses the practical applications for concept artists and architects, emphasizing the speed and ease of generating rough ideas. The presenter acknowledges that while this method may not replace traditional rendering techniques, it offers a valuable tool for preliminary work and client presentations. The video ends with a call to action for viewers to like, subscribe, and follow the presenter's social media for more content.

Mindmap

Keywords

💡Stable Diffusion

Stable Diffusion is a type of deep learning model used for generating images from textual descriptions. It is a significant concept in the video as it is the core technology being integrated with Blender for real-time image generation. The script discusses using Stable Diffusion in conjunction with Blender, highlighting its experimental nature and potential for professional creatives.

💡Blender

Blender is a free and open-source 3D computer graphics software used for creating animated films, visual effects, art, 3D printed models, motion graphics, and computer games. In the video, Blender is used as the 3D software platform with which Stable Diffusion is being integrated to create real-time visual effects and image generation.

💡Workflow

A workflow in the context of the video refers to a sequence of steps or processes involved in achieving a particular outcome, such as integrating Stable Diffusion with Blender. The script presents two different workflows using distinct models of Stable Diffusion, illustrating the process of setting up the environment for real-time image generation.

💡SDXL

SDXL stands for Stable Diffusion eXtreme Large, which is a version of the Stable Diffusion model. The script mentions two types of SDXL models: 'sdxl lightning' and 'hyper sdxl', which differ in the number of inference steps required for image generation, impacting the speed and quality of the results.

💡Screen Share Node

The screen share node is a component in the workflow that allows for real-time capturing of the screen, specifically the Blender viewport in this case. It is crucial for integrating the live 3D environment with the image generation process, as it provides the visual input for Stable Diffusion to generate images based on the current scene setup.

💡Viewport

In 3D graphics, the viewport is the area of the screen that displays the 3D scene. The script discusses using the viewport from Blender as the source for the screen share node, which then feeds the live scene into the Stable Diffusion model for real-time image generation.

💡Inference

In the context of machine learning, inference refers to the process of making predictions or decisions based on a trained model. The script mentions that 'sdxl lightning' requires multiple steps of inference, while 'hyper sdxl' only needs one, affecting the efficiency and output of the image generation.

💡Control Nets

Control Nets are additional neural networks used to guide the image generation process by imposing certain constraints or conditions on the output. The script discusses using different control nets like depth estimation to maintain the structure and composition of the 3D scene within the generated images.

💡Real-time Design

Real-time design refers to the ability to create or modify designs instantaneously as changes are made, without significant delays. The video's main theme revolves around setting up a system for real-time image generation by integrating Stable Diffusion with Blender, allowing for immediate visual feedback and adjustments.

💡JSON

JSON, or JavaScript Object Notation, is a lightweight data interchange format that is used to transmit data objects. In the script, JSON is used to describe the workflow for the image generation process, which can be downloaded or manually created to set up the nodes and parameters for the Stable Diffusion integration.

💡3D Environment

A 3D environment refers to a virtual space created using 3D modeling techniques, which can be manipulated and rendered in software like Blender. The script emphasizes the use of a 3D environment as a dynamic source for generating images with Stable Diffusion, allowing for the creation of images that reflect changes made within the environment.

Highlights

Integrating stable diffusion into Blender or any 3D software in real time.

Using two different workflows: one with sdxl lightning and the other with Hyper sdxl.

Hyper sdxl is a new model that generates images with just one step, without fine-tune models.

The reliance on a 'screen share' node to capture the viewport from Blender or other 3D apps.

Building a node structure to integrate Comfy with Blender, sourcing images in real time from the 3D environment.

Installing custom nodes from mixlab for real-time design, including a screen share node.

Using the screen share node to capture live images from the Blender workspace.

The resource-intensive nature of live screen capturing when combined with Blender, Comfy, and OBS.

Differences between the fine-tuned sdxl lightning model and the base model for Hyper sdxl.

Building workflows from scratch using mixlab nodes and custom nodes.

Downloading and installing sdxl lightning or Hyper sdxl models from Hugging Face.

Using a custom case sampler node for Hyper sdxl, which requires a different scheduler and sigma node.

Encoding the resized image from the screen share node into a latent image for generation.

Using control nets like depth and open pose to maintain the overall structure of the composition in the 3D program.

The importance of using Cycles in Blender for better source images with volumetric lighting.

Real-time updates in Comfy as the Blender viewport changes, reflecting in the generated images.

Adding a popup preview node to display generated images in real time within the Blender environment.

The trade-off between precision and speed in using sdxl lightning versus Hyper sdxl.

Using basic shapes and materials in Blender to create complex environments for image generation.

The potential of this method for concept artists and image researchers to quickly set scenes and generate ideas.

The limitations of this method for architects who need to show clients actual materials and details.