t2i-adapter-sdxl-sketch
Maintainer: adirik
27
| Property | Value |
|---|---|
| Run this model | Run on Replicate |
| API spec | View on Replicate |
| Github link | View on Github |
| Paper link | View on Arxiv |
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Model overview
The t2i-adapter-sdxl-sketch model is a text-to-image diffusion model that allows users to modify images using sketches. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the diffuser team. This model is part of a family of similar models, including t2i-adapter-sdxl-lineart, t2i-adapter-sdxl-depth-midas, t2i-adapter-sdxl-canny, and t2i-adapter-sdxl-openpose, all created by adirik.
Model inputs and outputs
The t2i-adapter-sdxl-sketch model takes in an input image and a text prompt, and generates a modified image based on the provided prompt. The model can generate multiple samples, controlled by the num_samples parameter. The model also allows for fine-tuning of the generation process through parameters like guidance_scale, num_inference_steps, adapter_conditioning_scale, and adapter_conditioning_factor.
Inputs
- Image: The input image to be modified
- Prompt: The text prompt describing the desired modifications
- Scheduler: The scheduler to use for the diffusion process
- Num Samples: The number of output images to generate
- Random Seed: A seed for reproducibility
- Guidance Scale: The scale to match the prompt
- Negative Prompt: Specify things to not see in the output
- Num Inference Steps: The number of diffusion steps
- Adapter Conditioning Scale: The conditioning scale for the adapter
- Adapter Conditioning Factor: The factor to scale the image by
Outputs
- Output Images: The modified images generated by the model, based on the input prompt and image.
Capabilities
The t2i-adapter-sdxl-sketch model can be used to generate a wide range of modified images by leveraging the input sketch. This allows for more precise control over the image generation process, enabling users to create unique and personalized visual content.
What can I use it for?
The t2i-adapter-sdxl-sketch model can be used for a variety of applications, such as product visualization, concept art creation, and visual storytelling. By combining the power of text-to-image generation with the flexibility of sketch-based modification, users can explore their creative ideas and bring them to life in a highly customized way.
Things to try
Try experimenting with different input sketches and prompts to see how the model can transform the original image. You can also explore the various tuning parameters to fine-tune the generation process and achieve the desired results. The family of similar models, such as t2i-adapter-sdxl-lineart and t2i-adapter-sdxl-depth-midas, offer additional capabilities that you can leverage for your specific use cases.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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t2i-adapter-sdxl-sketch
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t2i-adapter-sdxl-sketch is a Cog model that allows you to modify images using sketches. It is an implementation of the T2I-Adapter-SDXL model, developed by TencentARC and the diffuser team. This model is similar to other T2I-Adapter-SDXL models, such as those for modifying images using line art, depth maps, canny edges, and human pose. Model inputs and outputs The t2i-adapter-sdxl-sketch model takes in an input image and a prompt, and generates a modified image based on the sketch. The model also allows you to customize the number of samples, guidance scale, inference steps, and other parameters. Inputs Image**: The input image to be modified Prompt**: The text prompt describing the desired image Scheduler**: The scheduler to use for the diffusion process Num Samples**: The number of output images to generate Random Seed**: The random seed for reproducibility Guidance Scale**: The scale to match the prompt Negative Prompt**: Things to not see in the output Num Inference Steps**: The number of diffusion steps Adapter Conditioning Scale**: The conditioning scale for the adapter Adapter Conditioning Factor**: The factor to scale the image by Outputs Output**: The modified image(s) based on the input prompt and sketch Capabilities The t2i-adapter-sdxl-sketch model allows you to generate images based on a prompt and a sketch of the desired image. This can be useful for creating concept art, illustrations, and other visual content where you have a specific idea in mind but need to refine the details. What can I use it for? You can use the t2i-adapter-sdxl-sketch model to create a wide range of images, from fantasy scenes to product designs. For example, you could use it to generate concept art for a new character in a video game, or to create product renderings for a new design. The model's ability to modify images based on sketches can also be useful for prototyping and early-stage design work. Things to try One interesting thing to try with the t2i-adapter-sdxl-sketch model is to experiment with different input sketches and prompts to see how the model responds. You could also try using the model in combination with other image editing tools or AI models, such as the masactrl-sdxl model, to create even more complex and refined images.
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t2i-adapter-sdxl-lineart
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The t2i-adapter-sdxl-lineart model is a text-to-image generation model developed by Tencent ARC that can modify images using line art. It is an implementation of the T2I-Adapter model, which provides additional conditioning to the Stable Diffusion model. The T2I-Adapter-SDXL lineart model is trained on the StableDiffusionXL checkpoint and can generate images based on a text prompt while using line art as a conditioning input. The T2I-Adapter-SDXL lineart model is part of a family of similar models developed by Tencent ARC, including the t2i-adapter-sdxl-sketch and t2i-adapter-sdxl-sketch models, which use sketches as conditioning, and the masactrl-sdxl model, which provides editable image generation capabilities. Model inputs and outputs Inputs Image**: The input image, which will be used as the line art conditioning for the generation process. Prompt**: The text prompt that describes the desired image to generate. Scheduler**: The scheduling algorithm to use for the diffusion process, with the default being the K_EULER_ANCESTRAL scheduler. Num Samples**: The number of output images to generate, up to a maximum of 4. Random Seed**: An optional random seed to ensure reproducibility of the generated output. Guidance Scale**: A scaling factor that determines how closely the generated image will match the input prompt. Negative Prompt**: A text prompt that specifies elements that should not be present in the generated image. Num Inference Steps**: The number of diffusion steps to perform during the generation process, up to a maximum of 100. Adapter Conditioning Scale**: A scaling factor that determines the influence of the line art conditioning on the generated image. Adapter Conditioning Factor**: A scaling factor that determines the overall size of the generated image. Outputs Output**: An array of generated images in the form of image URIs. Capabilities The T2I-Adapter-SDXL lineart model can generate images based on text prompts while using line art as a conditioning input. This allows for more fine-grained control over the generated images, enabling the creation of artistic or stylized outputs that incorporate the line art features. What can I use it for? The T2I-Adapter-SDXL lineart model can be used for a variety of creative and artistic applications, such as generating concept art, illustrations, or stylized images for use in design projects, games, or other creative endeavors. The ability to incorporate line art as a conditioning input can be especially useful for generating images with a distinct artistic or technical style, such as comic book-style illustrations or technical diagrams. Things to try One interesting application of the T2I-Adapter-SDXL lineart model could be to generate images for use in educational or instructional materials, where the line art conditioning could be used to create clear, technical-looking diagrams or illustrations to accompany written content. Additionally, the model's ability to generate images based on text prompts could be leveraged to create personalized or customized artwork, such as character designs or scene illustrations for stories or games.
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t2i-adapter-sdxl-lineart
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The t2i-adapter-sdxl-lineart model is a powerful tool for modifying images using line art. It is an implementation of the T2I-Adapter-SDXL model developed by TencentARC and the diffuser team. This model allows users to generate line art-based images from text prompts, making it a versatile tool for artists, designers, and creators. Similar models like masactrl-sdxl, stylemc, and pixart-xl-2 offer related capabilities for image generation and editing. Model inputs and outputs The t2i-adapter-sdxl-lineart model takes a text prompt as input and generates line art-based images as output. Users can specify various parameters, such as the number of samples, guidance scale, and random seed, to fine-tune the output. Inputs Image**: An input image to be modified Prompt**: The text prompt describing the desired image Scheduler**: The type of scheduler to use for the diffusion process Num Samples**: The number of output images to generate Random Seed**: A random seed for reproducibility Guidance Scale**: The scale to match the prompt Negative Prompt**: Specify things to not see in the output Num Inference Steps**: The number of diffusion steps Adapter Conditioning Scale**: The conditioning scale for the adapter Adapter Conditioning Factor**: The factor to scale the image by Outputs Array of output images**: The generated line art-based images Capabilities The t2i-adapter-sdxl-lineart model can be used to create unique and visually striking line art-based images from text prompts. This can be particularly useful for illustrators, graphic designers, and artists who want to explore new styles and techniques. The model's ability to generate multiple outputs from a single prompt also makes it a valuable tool for ideation and experimentation. What can I use it for? The t2i-adapter-sdxl-lineart model can be used for a variety of creative projects, such as: Generating unique cover art or illustrations for books, magazines, or album covers Designing eye-catching graphics or visuals for websites, social media, or marketing materials Producing concept art or study pieces for animation, film, or game development Exploring new artistic styles and techniques through experimentation with text prompts By leveraging the power of AI-driven image generation, users can unlock new possibilities for their creative work and push the boundaries of what's possible with line art. Things to try One interesting aspect of the t2i-adapter-sdxl-lineart model is its ability to generate line art-based images with a range of visual styles and aesthetics. Users can experiment with different prompts, varying the level of detail, abstraction, or realism, to see how the model responds. Additionally, playing with the various input parameters, such as the guidance scale or number of inference steps, can produce vastly different results, allowing for a high degree of creative exploration and customization.
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t2i-adapter-sdxl-canny
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The t2i-adapter-sdxl-canny model is a text-to-image diffusion model that allows users to modify images using canny edge detection. It is an implementation of the T2I-Adapter-SDXL model developed by TencentARC and the diffuser team. The model is maintained by adirik and is available on Replicate. Similar models maintained by adirik include t2i-adapter-sdxl-sketch, t2i-adapter-sdxl-lineart, and t2i-adapter-sdxl-depth-midas, which allow users to modify images using sketches, line art, and depth maps, respectively. Another similar model, t2i-adapter-sdxl-sketch, is maintained by alaradirik. Model inputs and outputs The t2i-adapter-sdxl-canny model takes an input image and a text prompt, and generates a modified image based on the prompt and the canny edge representation of the input image. The model also allows users to customize various parameters, such as the number of samples, the guidance scale, and the number of inference steps. Inputs Image**: The input image to be modified. Prompt**: The text prompt describing the desired output image. Scheduler**: The scheduler to use for the diffusion process. Num Samples**: The number of output images to generate. Random Seed**: A random seed for reproducibility. Guidance Scale**: The scale to match the prompt. Negative Prompt**: Specify things to not see in the output. Num Inference Steps**: The number of diffusion steps. Adapter Conditioning Scale**: The conditioning scale for the adapter. Adapter Conditioning Factor**: The factor to scale the image by. Outputs An array of generated image URIs. Capabilities The t2i-adapter-sdxl-canny model can be used to modify input images in various ways, such as adding or removing elements, changing the style or composition, or applying artistic effects. The model leverages the canny edge representation of the input image to guide the generation process, allowing for more precise and controllable modifications. What can I use it for? The t2i-adapter-sdxl-canny model can be used for a variety of creative and artistic applications, such as photo editing, digital art, and image generation. It could be particularly useful for tasks that involve modifying or enhancing existing images, such as product visualization, architectural rendering, or character design. Things to try One interesting thing to try with the t2i-adapter-sdxl-canny model is to experiment with different combinations of the input parameters, such as the guidance scale, the number of inference steps, and the adapter conditioning scale. This can help you find the optimal settings for your specific use case and achieve more compelling results.
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