material-diffusion-sdxl
Maintainer: pwntus
1
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | No paper link provided |
Create account to get full access
Model overview
material-diffusion-sdxl is a Stable Diffusion XL model developed by pwntus that outputs tileable images for use in 3D applications such as Monaverse. It builds upon the Diffusers Stable Diffusion XL model by optimizing the output for seamless tiling. This can be useful for creating textures, patterns, and seamless backgrounds for 3D environments and virtual worlds.
Model inputs and outputs
The material-diffusion-sdxl model takes a variety of inputs to control the generation process, including a text prompt, image size, number of outputs, and more. The outputs are URLs pointing to the generated image(s).
Inputs
- Prompt: The text prompt that describes the desired image
- Negative Prompt: Text to guide the model away from certain outputs
- Width/Height: The dimensions of the generated image
- Num Outputs: The number of images to generate
- Num Inference Steps: The number of denoising steps to use during generation
- Guidance Scale: The scale for classifier-free guidance
- Seed: A random seed to control the generation process
- Refine: The type of refiner to use on the output
- Refine Steps: The number of refine steps to use
- High Noise Frac: The fraction of noise to use for the expert ensemble refiner
- Apply Watermark: Whether to apply a watermark to the generated images
Outputs
- Image URLs: A list of URLs pointing to the generated images
Capabilities
The material-diffusion-sdxl model is capable of generating high-quality, tileable images across a variety of subjects and styles. It can be used to create seamless textures, patterns, and backgrounds for 3D environments and virtual worlds. The model's ability to output images in a tileable format sets it apart from more general text-to-image models like Stable Diffusion.
What can I use it for?
The material-diffusion-sdxl model can be used to generate tileable textures, patterns, and backgrounds for 3D applications, virtual environments, and other visual media. This can be particularly useful for game developers, 3D artists, and designers who need to create seamless and repeatable visual elements. The model can also be fine-tuned on specific materials or styles to create custom assets, as demonstrated by the sdxl-woolitize model.
Things to try
Experiment with different prompts and input parameters to see the variety of tileable images the material-diffusion-sdxl model can generate. Try prompts that describe specific materials, patterns, or textures to see how the model responds. You can also try using the model in combination with other tools and techniques, such as 3D modeling software or image editing programs, to create unique and visually striking assets for your projects.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
Related Models
material-diffusion
2.2K
material-diffusion is a fork of the popular Stable Diffusion AI model, created by Replicate user tstramer. This model is designed for generating tileable outputs, building on the capabilities of the v1.5 Stable Diffusion model. It shares similarities with other Stable Diffusion forks like material-diffusion-sdxl and stable-diffusion-v2, as well as more experimental models like multidiffusion and stable-diffusion. Model inputs and outputs material-diffusion takes a variety of inputs, including a text prompt, a mask image, an initial image, and various settings to control the output. The model then generates one or more images based on the provided inputs. Inputs Prompt**: The text prompt that describes the desired image. Mask**: A black and white image used to mask the initial image, with black pixels inpainted and white pixels preserved. Init Image**: An initial image to generate variations of, which will be resized to the specified dimensions. Seed**: A random seed value to control the output image. Scheduler**: The diffusion scheduler algorithm to use, such as K-LMS. Guidance Scale**: A scale factor for the classifier-free guidance, which controls the balance between the input prompt and the initial image. Prompt Strength**: The strength of the input prompt when using an initial image, with 1.0 corresponding to full destruction of the initial image information. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Output Images**: One or more images generated by the model, based on the provided inputs. Capabilities material-diffusion is capable of generating high-quality, photorealistic images from text prompts, similar to the base Stable Diffusion model. However, the key differentiator is its ability to generate tileable outputs, which can be useful for creating seamless patterns, textures, or backgrounds. What can I use it for? material-diffusion can be useful for a variety of applications, such as: Generating unique and customizable patterns, textures, or backgrounds for design projects, websites, or products. Creating tiled artwork or wallpapers for personal or commercial use. Exploring creative text-to-image generation with a focus on tileable outputs. Things to try With material-diffusion, you can experiment with different prompts, masks, and initial images to create a wide range of tileable outputs. Try using the model to generate seamless patterns or textures, or to create variations on a theme by modifying the prompt or other input parameters.
Updated Invalid Date
sdxl-woolitize
1
The sdxl-woolitize model is a fine-tuned version of the SDXL (Stable Diffusion XL) model, created by the maintainer pwntus. It is based on felted wool, a unique material that gives the generated images a distinctive textured appearance. Similar models like woolitize and sdxl-color have also been created to explore different artistic styles and materials. Model inputs and outputs The sdxl-woolitize model takes a variety of inputs, including a prompt, image, mask, and various parameters to control the output. It generates one or more output images based on the provided inputs. Inputs Prompt**: The text prompt describing the desired image Image**: An input image for img2img or inpaint mode Mask**: An input mask for inpaint mode, where black areas will be preserved and white areas will be inpainted Width/Height**: The desired width and height of the output image Seed**: A random seed value to control the output Refine**: The refine style to use Scheduler**: The scheduler algorithm to use LoRA Scale**: The LoRA additive scale (only applicable on trained models) Num Outputs**: The number of images to generate Refine Steps**: The number of steps to refine the image (for base_image_refiner) Guidance Scale**: The scale for classifier-free guidance Apply Watermark**: Whether to apply a watermark to the generated image High Noise Frac**: The fraction of noise to use (for expert_ensemble_refiner) Negative Prompt**: An optional negative prompt to guide the image generation Outputs Image(s)**: One or more generated images in the specified size Capabilities The sdxl-woolitize model is capable of generating images with a unique felted wool-like texture. This style can be used to create a wide range of artistic and whimsical images, from fantastical creatures to abstract compositions. What can I use it for? The sdxl-woolitize model could be used for a variety of creative projects, such as generating concept art, illustrations, or even textiles and fashion designs. The distinct felted wool aesthetic could be particularly appealing for children's books, fantasy-themed projects, or any application where a handcrafted, organic look is desired. Things to try Experiment with different prompt styles and modifiers to see how the model responds. Try combining the sdxl-woolitize model with other fine-tuned models, such as sdxl-gta-v or sdxl-deep-down, to create unique hybrid styles. Additionally, explore the limits of the model by providing challenging or abstract prompts and see how it handles them.
Updated Invalid Date
material_stable_diffusion
386
material_stable_diffusion is a fork of the popular Stable Diffusion model, created by tommoore515, that is optimized for generating tileable outputs. This makes it well-suited for use in 3D applications such as Monaverse. Unlike the original stable-diffusion model, which is capable of generating photo-realistic images from any text input, material_stable_diffusion focuses on producing seamless, tileable textures and materials. Other similar models like material-diffusion and material-diffusion-sdxl also share this specialized focus. Model inputs and outputs material_stable_diffusion takes in a text prompt, an optional initial image, and several parameters to control the output, including the image size, number of outputs, and guidance scale. The model then generates one or more images that match the provided prompt and initial image (if used). Inputs Prompt**: The text description of the desired output image Init Image**: An optional initial image to use as a starting point for the generation Mask**: A black and white image used as a mask for inpainting over the init_image Seed**: A random seed value to control the generation Width/Height**: The desired size of the output image(s) Num Outputs**: The number of images to generate Guidance Scale**: The strength of the text guidance during the generation process Prompt Strength**: The strength of the prompt when using an init image Num Inference Steps**: The number of denoising steps to perform during generation Outputs Output Image(s)**: One or more generated images that match the provided prompt and initial image (if used) Capabilities material_stable_diffusion is capable of generating high-quality, tileable textures and materials for use in 3D applications. The model's specialized focus on producing seamless outputs makes it a valuable tool for artists, designers, and 3D creators looking to quickly generate custom assets. What can I use it for? You can use material_stable_diffusion to generate a wide variety of tileable textures and materials, such as stone walls, wood patterns, fabrics, and more. These generated assets can be used in 3D modeling, game development, architectural visualization, and other creative applications that require high-quality, repeatable textures. Things to try One interesting aspect of material_stable_diffusion is its ability to generate variations on a theme. By adjusting the prompt, seed, and other parameters, you can explore different interpretations of the same general concept and find the perfect texture or material for your project. Additionally, the model's inpainting capabilities allow you to refine or edit the generated outputs, making it a versatile tool for 3D artists and designers.
Updated Invalid Date
stable-diffusion-depth2img
7
stable-diffusion-depth2img is a Cog implementation of the Diffusers Stable Diffusion v2 model, which is capable of generating variations of an image while preserving its shape and depth. This model builds upon the Stable Diffusion model, which is a powerful latent text-to-image diffusion model that can generate photo-realistic images from any text input. The stable-diffusion-depth2img model adds the ability to create variations of an existing image, while maintaining the overall structure and depth information. Model inputs and outputs The stable-diffusion-depth2img model takes a variety of inputs to control the image generation process, including a prompt, an existing image, and various parameters to fine-tune the output. The model then generates one or more new images based on these inputs. Inputs Prompt**: The text prompt that guides the image generation process. Image**: The existing image that will be used as the starting point for the process. Seed**: An optional random seed value to control the image generation. Scheduler**: The type of scheduler to use for the diffusion process. Num Outputs**: The number of images to generate (up to 8). Guidance Scale**: The scale for classifier-free guidance, which controls the balance between the text prompt and the input image. Negative Prompt**: An optional prompt that specifies what the model should not generate. Prompt Strength**: The strength of the text prompt relative to the input image. Num Inference Steps**: The number of denoising steps to perform during the image generation process. Outputs Images**: One or more new images generated based on the provided inputs. Capabilities The stable-diffusion-depth2img model can be used to generate a wide variety of image variations based on an existing image. By preserving the shape and depth information from the input image, the model can create new images that maintain the overall structure and composition, while introducing new elements and variations based on the provided text prompt. This can be useful for tasks such as art generation, product design, and architectural visualization. What can I use it for? The stable-diffusion-depth2img model can be used for a variety of creative and design-related projects. For example, you could use it to generate concept art for a fantasy landscape, create variations of a product design, or explore different architectural styles for a building. The ability to preserve the shape and depth information of the input image can be particularly useful for these types of applications, as it allows you to maintain the overall structure and composition while introducing new elements and variations. Things to try One interesting thing to try with the stable-diffusion-depth2img model is to experiment with different prompts and input images to see how the model generates new variations. Try using a variety of input images, from landscapes to still lifes to abstract art, and see how the model responds to different types of visual information. You can also play with the various parameters, such as guidance scale and prompt strength, to fine-tune the output and explore the limits of the model's capabilities.
Updated Invalid Date