ComfyUI Node: OmniConsistency Generator

OmniConsistency Generator:

The Comfyui_OmniConsistency node is designed to generate consistent and high-quality images based on a given prompt and spatial image input. It leverages advanced AI models to produce images that adhere to specified styles and themes, making it a powerful tool for AI artists looking to create visually coherent and stylistically consistent artwork. The node's primary goal is to facilitate the creation of images that maintain a consistent aesthetic across different scenes or elements, which is particularly beneficial for projects requiring a uniform visual language. By utilizing the OmniConsistency Generator, you can achieve a seamless integration of various artistic elements, ensuring that the final output aligns with your creative vision.

OmniConsistency Generator Input Parameters:

prompt

The prompt parameter is a string input that serves as the textual description guiding the image generation process. It allows you to specify the style, theme, and content of the image, such as "3D Chibi style, Three individuals standing together in the office." This parameter is crucial as it directly influences the artistic direction and subject matter of the generated image. The prompt can be multiline, providing flexibility in describing complex scenes or concepts.

spatial_image

The spatial_image parameter is an image input that acts as a reference or base for the generated output. It helps in maintaining spatial consistency and can be used to guide the composition and layout of the final image. This parameter is essential for ensuring that the generated image aligns with specific spatial requirements or pre-existing visual elements.

height

The height parameter is an integer that defines the height of the generated image in pixels. It allows you to control the vertical dimension of the output, with a default value of 1024 pixels. The parameter accepts values ranging from 256 to 2048 pixels, in increments of 8, providing flexibility in determining the image size based on your project needs.

width

The width parameter is an integer that specifies the width of the generated image in pixels. Similar to the height parameter, it controls the horizontal dimension of the output, with a default value of 1024 pixels. The width can be set between 256 and 2048 pixels, in steps of 8, allowing you to tailor the image dimensions to suit your artistic requirements.

guidance_scale

The guidance_scale parameter is a float that influences the adherence of the generated image to the provided prompt. A higher value increases the emphasis on the prompt, potentially leading to more precise interpretations, while a lower value allows for more creative freedom. The default value is 3.5, with a range from 0.0 to 20.0, adjustable in increments of 0.1.

num_inference_steps

The num_inference_steps parameter is an integer that determines the number of steps the model takes during the image generation process. More steps can lead to higher quality and more detailed images, but may also increase computation time. The default is 25 steps, with a permissible range from 1 to 100.

seed

The seed parameter is an integer used to initialize the random number generator, ensuring reproducibility of the generated images. By setting a specific seed value, you can achieve consistent results across multiple runs. The default seed is 42, with a valid range from 0 to 2<sup>32-1.

base_model_path

The base_model_path parameter is a string that specifies the path to the base model used for image generation. It allows you to select different models to influence the style and quality of the output. The default path is "black-forest-labs/FLUX.1-dev," which points to a pre-trained model optimized for this node.

omni_model_path

The omni_model_path parameter is a string that indicates the path to the OmniConsistency model file. This model is crucial for achieving the desired consistency and style in the generated images. The default path is "/path/to/OmniConsistency.safetensors," which should be replaced with the actual path to your model file.

lora_path

The lora_path parameter is an optional string that allows you to load additional LoRA (Low-Rank Adaptation) models to further customize the image generation process. This parameter can enhance the flexibility and adaptability of the node, enabling you to incorporate specific stylistic elements. The default is an empty string, indicating no additional LoRA models are used.

OmniConsistency Generator Output Parameters:

image

The image output parameter is the final generated image, produced based on the input parameters and models. This image reflects the specified prompt, spatial image, and other settings, providing a visually consistent and stylistically coherent result. The output is crucial for AI artists as it represents the culmination of the node's processing, ready for use in creative projects or further refinement.

OmniConsistency Generator Usage Tips:

• Experiment with different prompt descriptions to explore a variety of artistic styles and themes, enhancing the creative potential of your projects.
• Adjust the guidance_scale to balance between strict adherence to the prompt and allowing for creative variations, depending on the desired outcome.
• Utilize the seed parameter to reproduce specific results, which is particularly useful for iterative design processes or when sharing settings with collaborators.

OmniConsistency Generator Common Errors and Solutions:

Error: "Model file not found at specified path"

• Explanation: This error occurs when the path provided in base_model_path or omni_model_path does not point to a valid model file.
• Solution: Verify that the paths are correct and that the model files exist at the specified locations. Ensure that the file extensions and names are accurate.

Error: "Invalid image dimensions"

• Explanation: This error arises when the height or width parameters are set outside the allowed range or not in the specified increments.
• Solution: Check that the dimensions are within the range of 256 to 2048 pixels and are multiples of 8. Adjust the values accordingly to meet these criteria.