Reposer = Consistent Stable Diffusion Generated Characters in ANY pose from 1 image!

Nerdy Rodent
12 Oct 202311:34

TLDRThe video introduces a new workflow called 'reposer' that combines an IP adapter face model with an open pose control net, allowing users to create a consistent, posable character from a single face image. The presenter demonstrates how the system adapts to various styles and poses, emphasizing the ease and speed of the process. The video also guides viewers on setting up the workflow in Comfy UI, organizing models, and using the system to generate images with optional prompts for added consistency.


  • 🎨 A new UI workflow called 'reposer' is introduced for creating consistent, posable characters from a single face image.
  • 👤 The workflow combines an IP adapter face model with an open pose control net for versatile character generation.
  • 🖼️ Users can input various types of images, including partial faces, full bodies, and even images without faces, to experiment with the character creation.
  • 💡 The character's face remains consistent even when the background or other elements of the image change.
  • 🔧 The setup for the workflow is straightforward, requiring only the loading of models and minimal fine-tuning.
  • 📂 Organizing models into subdirectories can help users manage and find specific models more efficiently within Comfy UI.
  • 🔍 The Comfy UI interface allows for filtering models by typing keywords into the search bar.
  • 🏗️ Users should ensure that the models they use match their desired output style, such as choosing a cartoon-oriented model for generating cartoon characters.
  • 🔄 The workflow includes options for upscaling the generated images, which is optional and can be bypassed if not needed.
  • 🎭 Prompts can be used to maintain consistency in character traits, such as specifying a name or clothing, which will influence the generated image.
  • 📹 The video provides a guide on how to use the reposer workflow and includes links to additional resources and tutorials in the video description.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is the introduction of a new workflow called 'reposer' in the Comfy UI environment, which combines the IP adapter face model with an open pose control net to create a consistent, posable character from a single face image.

  • How does the reposer workflow work?

    -The reposer workflow works by using a single input image of a face to generate a character that can be posed in any desired way. The user can guide the generation process with prompt control to maintain consistency in the character's appearance.

  • What kind of character is used as an example in the video?

    -The example character used in the video is a 1970s style detective, with the image mostly containing the face and some elements of clothing, such as a large, collared leather jacket.

  • How does changing the background color in the image affect the character?

    -Changing the background color in the image affects the entire aesthetic of the character, as everything else in the image changes to match the new color scheme, while the character's face and overall appearance remain consistent.

  • What are the requirements for the input image in the reposer workflow?

    -The input image can be any image, including just a part of a face looking left or right, a full body, half body painting, anime photo, or even an image with no face at all. Users are encouraged to experiment with different images.

  • What is the role of the stable diffusion 1.5 checkpoint loader in the workflow?

    -The stable diffusion 1.5 checkpoint loader is used to load checkpoint models from the models checkpoints directory of the Comfy UI installation. The choice of model can influence the image generation, so users should select a model that matches their desired output style.

  • Why is it recommended to use a control Laura model in the workflow?

    -Using a control Laura model is recommended because it should be faster and use fewer resources compared to other models, making the image generation process more efficient.

  • How does the clip Vision IP adapter stable diffusion 1.5 image encoder work in the workflow?

    -The clip Vision IP adapter stable diffusion 1.5 image encoder loads clip Vision models from the models clip vision directory. It is particularly useful for processing images and is an essential part of the reposer workflow.

  • What is the purpose of the upscaling models in the workflow?

    -The upscaling models are used for enlarging the generated image. This is an optional step, and users can choose to bypass this process if they do not wish to upscale their images.

  • How can users adjust the influence of the input image on the generated character?

    -Users can adjust the influence of the input image on the generated character by modifying the prompt strength and image strength settings. This allows for control over how much the input image affects the final output.

  • What additional options are available in the pose pre-processor for further customization?

    -In the pose pre-processor, users can disable or enable hand, body, and face detection based on their preferences. This provides more control over the generation process and allows for greater customization of the character.



🎨 Introducing the Reposer Workflow for Character Consistency

This paragraph introduces the viewer to the Reposer workflow, a UI designed to create a consistently posable character using the IP adapter face model combined with an open pose control net. The workflow allows for character generation in various poses with prompt control for guidance. The video demonstrates the versatility of the workflow by showing how a single face can be used to create different character styles, such as a 1970s style detective, and how changing the background color can alter the entire image while maintaining character consistency. The paragraph emphasizes the ease and speed of the process, requiring only a single input image and minimal setup.


🔍 Understanding Model Selection and Organization

The second paragraph delves into the specifics of model selection and organization within the Reposer workflow. It explains the influence of the model used on image generation, suggesting the choice of a cartoon-oriented model for creating cartoon characters. The paragraph also discusses the importance of using an open pose model and provides tips for organizing models into subdirectories for easier searching. It mentions the use of the control net and the stable diffusion models, emphasizing the need to match the model to the desired output style. The paragraph concludes with a brief overview of the upscaling models and their optional use in the workflow.


🖌️ Customizing the Character with Prompts and Poses

This paragraph focuses on the customization of characters using the Reposer workflow. It explains how to use the workflow by dragging an image onto the face box and a pose into the pose box, then generating the character with a click. The paragraph highlights the use of optional prompts to maintain consistency, such as naming the character or specifying clothing. It also discusses the ability to switch between different face styles and how the character's appearance will adapt accordingly. The paragraph concludes with a mention of additional controls for prompt strength and the option to blend multiple images using the IP adapter.



💡Comfy UI

Comfy UI refers to a user interface that is designed for ease of use and comfort. In the context of the video, it is a platform that allows users to create and manipulate images with various models. It is mentioned as a tool that simplifies the process of setting up and using the workflow described in the video, making it accessible for users to generate images without needing complex technical knowledge.

💡IP Adapter Face Model

The IP Adapter Face Model is a specific type of model used within the Comfy UI workflow for generating facial features in images. It is integrated with the open pose control net to create a consistent character across different poses. This model is crucial for maintaining the character's identity while altering other aspects of the image, such as pose and background.

💡Open Pose Control Net

Open Pose Control Net is a component of the workflow that manages the pose aspect of the image generation process. It works in tandem with the IP Adapter Face Model to ensure that the character's pose can be changed without affecting the consistency of the facial features. This allows for dynamic and varied image outputs while keeping the character recognizable.

💡Prompt Control

Prompt Control refers to the ability to guide the image generation process by providing specific instructions or descriptions to the Comfy UI system. This feature allows users to influence the output by adding details such as character names or clothing, which helps in maintaining consistency and achieving the desired look for the generated images.

💡Stable Diffusion 1.5

Stable Diffusion 1.5 is a version of the Stable Diffusion model, which is an AI-based image generation model. It is used within the Comfy UI workflow to generate images with a certain level of realism or stylization, depending on the chosen model. The model's choice can influence the overall aesthetic of the generated images, such as photorealism or cartoon style.

💡Control Auras

Control Auras in the context of the video refers to a set of models that are used to manage specific aspects of the image generation process, such as facial expressions or body posture. These models help in refining the details of the generated images to align with the user's input and desired outcome.

💡CLIP Vision

CLIP Vision is a model used in the Comfy UI workflow for image encoding. It is part of the process that allows the system to understand and process the input image, particularly the facial features, to generate new images with the same facial characteristics in different poses or settings.


Upscaling in the context of the video refers to the process of increasing the resolution or quality of the generated images. This is an optional step in the workflow that allows users to refine the output further, making the images more detailed and high-quality.

💡Pose Pre-processor

Pose Pre-processor is a component of the workflow that allows users to adjust settings related to the detection and processing of poses in the input image. It provides options to enable or disable hand, body, and face detection, which can be customized according to the user's needs.

💡Weight for Prompt

The weight for prompt is a parameter within the Comfy UI workflow that determines the influence of the input image on the final generated image. Adjusting this weight can help users control how much of the original image's characteristics are retained in the output, allowing for a balance between the input's influence and the model's creative freedom.

💡Image or Batch

Image or Batch refers to the mode of operation within the Comfy UI workflow that determines how multiple input images are processed. In 'Image' mode, only the single input image is used, while 'Batch' mode allows for the blending of characteristics from multiple images, creating a composite output that incorporates features from all the images in the batch.


The introduction of a new comfy UI workflow called 'reposer'.

The reposer workflow combines the IP adapter face model with an open pose control net.

This workflow allows for the creation of a consistent character in any pose with prompt control.

The character's face remains consistent even when the background and other elements change.

The workflow is efficient for generating characters quickly without the need for fine-tuning a model.

A single input image is sufficient to start the character generation process.

Comfy UI setup is straightforward, and a tutorial video guide is available for installation and basic usage.

The image loaded into Comfy UI serves as the workflow itself.

The reposer image can be found on the Comfy UI website, along with links to all necessary models.

Comfy UI updates the names in the loaders to match those on the user's personal computer.

Users can organize models into subdirectories to facilitate searching and management.

The control net pull down can be filtered by typing, making it easier to find specific models.

The choice of stable diffusion checkpoint model influences the image generation style.

Using a control Laura model is suggested for faster and more resource-efficient processing.

The IP adapter face model is crucial for achieving accurate face results in the generated images.

Upscaling is an optional step in the workflow, with models placed in a specific directory.

The prompt strength and image or batch mode can be adjusted for more control over the output.

Experimentation with different images is encouraged to understand the workflow's capabilities.

Additional information and model links can be found in the video description.