Batouresearch

Models by this creator

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magic-image-refiner

batouresearch

Total Score

733

magic-image-refiner is a powerful AI model developed by batouresearch that serves as a better alternative to SDXL refiners. It provides remarkable quality and detail, and can also be used for inpainting or upscaling. While similar to models like gfpgan, multidiffusion-upscaler, sdxl-lightning-4step, animagine-xl-3.1, and supir, magic-image-refiner offers unique capabilities and a distinct approach to image refinement. Model inputs and outputs magic-image-refiner is a versatile model that accepts a variety of inputs to produce high-quality refined images. Users can provide an image, a mask to refine specific sections, and various parameters to control the refinement process, such as steps, creativity, resemblance, and guidance scale. Inputs Image**: The image to be refined Mask**: An optional mask to refine specific sections of the image Prompt**: A text prompt to guide the refinement process Seed**: A seed value for reproducibility Steps**: The number of steps to perform during refinement Scheduler**: The scheduler algorithm to use Creativity**: The denoising strength, where 1 means total destruction of the original image Resemblance**: The conditioning scale for the ControlNet Guidance Scale**: The scale for classifier-free guidance Guess Mode**: Whether to enable a mode where the ControlNet encoder tries to recognize the content of the input image Outputs Refined image**: The output of the refinement process, which can be an improved version of the input image, or a new image generated based on the provided inputs. Capabilities magic-image-refiner is capable of producing high-quality, detailed images by refining the input. It can be used to improve the quality of old photos, AI-generated faces, or other images that may benefit from additional refinement. The model's ability to perform inpainting and upscaling makes it a versatile tool for various image manipulation and enhancement tasks. What can I use it for? magic-image-refiner can be a valuable tool for a wide range of applications, such as photo restoration, image enhancement, and creative content generation. It could be used by batouresearch to offer image refinement services, or by individuals or businesses looking to improve the quality and visual appeal of their images. Things to try One interesting aspect of magic-image-refiner is its ability to work with masks, allowing users to refine specific sections of an image. This can be useful for tasks like object removal, background replacement, or selective enhancement. Additionally, experimenting with the various input parameters, such as creativity, resemblance, and guidance scale, can yield different results and enable users to fine-tune the refinement process to their specific needs.

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Updated 6/21/2024

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sdxl-controlnet-lora

batouresearch

Total Score

469

The sdxl-controlnet-lora model is an implementation of Stability AI's SDXL text-to-image model with support for ControlNet and Replicate's LoRA technology. This model is developed and maintained by batouresearch, and is similar to other SDXL-based models like instant-id-multicontrolnet and sdxl-lightning-4step. The key difference is the addition of ControlNet, which allows the model to generate images based on a provided control image, such as a Canny edge map. Model inputs and outputs The sdxl-controlnet-lora model takes a text prompt, an optional input image, and various settings as inputs. It outputs one or more generated images based on the provided prompt and settings. Inputs Prompt**: The text prompt describing the image to generate. Image**: An optional input image to use as a control or base image for the generation process. Seed**: A random seed value to use for generation. Img2Img**: A flag to enable the img2img generation pipeline, which uses the input image as both the control and base image. Strength**: The strength of the img2img denoising process, ranging from 0 to 1. Negative Prompt**: An optional negative prompt to guide the generation away from certain undesired elements. Num Inference Steps**: The number of denoising steps to take during the generation process. Guidance Scale**: The scale for classifier-free guidance, which controls the influence of the text prompt on the generated image. Scheduler**: The scheduler algorithm to use for the generation process. LoRA Scale**: The additive scale for the LoRA weights, which can be used to fine-tune the model's behavior. LoRA Weights**: The URL of the Replicate LoRA weights to use for the generation. Outputs Generated Images**: One or more images generated based on the provided inputs. Capabilities The sdxl-controlnet-lora model is capable of generating high-quality, photorealistic images based on text prompts. The addition of ControlNet support allows the model to generate images based on a provided control image, such as a Canny edge map, enabling more precise control over the generated output. The LoRA technology further enhances the model's flexibility by allowing for easy fine-tuning and customization. What can I use it for? The sdxl-controlnet-lora model can be used for a variety of image generation tasks, such as creating concept art, product visualizations, or custom illustrations. The ability to use a control image can be particularly useful for tasks like image inpainting, where the model can generate content to fill in missing or damaged areas of an image. Additionally, the fine-tuning capabilities enabled by LoRA can make the model well-suited for specialized applications or personalized use cases. Things to try One interesting thing to try with the sdxl-controlnet-lora model is experimenting with different control images and LoRA weight sets to see how they affect the generated output. You could, for example, try using a Canny edge map, a depth map, or a segmentation mask as the control image, and see how the model's interpretation of the prompt changes. Additionally, you could explore using LoRA to fine-tune the model for specific styles or subject matter, and see how that impacts the generated images.

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Updated 6/21/2024

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high-resolution-controlnet-tile

batouresearch

Total Score

405

The high-resolution-controlnet-tile is an open-source implementation of the ControlNet 1.1 model, developed by batouresearch. This model is designed to provide efficient and high-quality upscaling capabilities, with a focus on encouraging creative hallucination. It can be seen as a counterpart to the magic-image-refiner model, which aims to provide a better alternative to SDXL refiners. Additionally, the sdxl-controlnet-lora model, which supports img2img, and the GFPGAN face restoration model, can also be considered related to this implementation. Model inputs and outputs The high-resolution-controlnet-tile model takes a variety of inputs, including an image, a prompt, and various parameters such as the number of steps, the resemblance, creativity, and guidance scale. These inputs allow users to fine-tune the model's behavior and output, enabling them to achieve their desired results. Inputs Image**: The control image for the scribble controlnet. Prompt**: The text prompt that guides the model's generation process. Steps**: The number of steps to be used in the sampling process. Scheduler**: The scheduler to be used, with options like DDIM. Creativity**: The denoising strength, with 1 meaning total destruction of the original image. Resemblance**: The conditioning scale for the controlnet. Guidance Scale**: The scale for classifier-free guidance. Negative Prompt**: The negative prompt to be used during generation. Outputs The generated image(s) as a list of URIs. Capabilities The high-resolution-controlnet-tile model is capable of producing high-quality upscaled images while encouraging creative hallucination. By leveraging the ControlNet 1.1 architecture, the model can generate images that are both visually appealing and aligned with the provided prompts and control images. What can I use it for? The high-resolution-controlnet-tile model can be used for a variety of creative and artistic applications, such as generating illustrations, concept art, or even photorealistic images. Its ability to upscale images while maintaining visual quality and introducing creative elements makes it a valuable tool for designers, artists, and content creators. Additionally, the model's flexibility in terms of input parameters allows users to fine-tune the output to their specific needs and preferences. Things to try One interesting aspect of the high-resolution-controlnet-tile model is its ability to handle the trade-off between maintaining the original image and introducing creative hallucination. By adjusting the "creativity" and "resemblance" parameters, users can experiment with different levels of deviation from the input image, allowing them to explore a wide range of creative possibilities.

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Updated 6/21/2024

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open-dalle-1.1-lora

batouresearch

Total Score

113

The open-dalle-1.1-lora model, created by batouresearch, is an improved text-to-image generation model that builds upon the capabilities of the original DALL-E model. This model is particularly adept at prompt adherence and generating high-quality images, surpassing the performance of the SDXL model in these areas. Similar models from batouresearch include the sdxl-controlnet-lora, sdxl-outpainting-lora, and magic-image-refiner. Model inputs and outputs The open-dalle-1.1-lora model accepts a variety of inputs, including an input prompt, image, and mask for inpainting tasks. Users can also specify parameters like image size, seed, and scheduler. The model outputs one or more generated images as image URIs. Inputs Prompt**: The input prompt describing the desired image Negative Prompt**: An optional prompt describing elements to exclude from the generated 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 Seed**: A random seed, which can be left blank to randomize Width/Height**: The desired dimensions of the output image Num Outputs**: The number of images to generate (up to 4) Scheduler**: The scheduler algorithm to use for image generation Guidance Scale**: The scale for classifier-free guidance Num Inference Steps**: The number of denoising steps to perform Outputs Output Images**: One or more generated images as image URIs Capabilities The open-dalle-1.1-lora model excels at generating high-quality images that closely adhere to the provided prompt. Compared to the SDXL model, it produces images with improved detail, coherence, and faithfulness to the input text. This model can be particularly useful for tasks like illustration, product visualization, and conceptual art generation. What can I use it for? The open-dalle-1.1-lora model can be used for a variety of creative and commercial applications. For example, you could use it to generate concept art for a new product, illustrate a children's book, or create unique digital art pieces. The model's strong prompt adherence and image quality make it a valuable tool for designers, artists, and content creators looking to quickly and easily generate high-quality visuals. Additionally, the sdxl-controlnet-lora and sdxl-outpainting-lora models from batouresearch offer additional capabilities for tasks like image-to-image translation and outpainting. Things to try One interesting aspect of the open-dalle-1.1-lora model is its ability to generate images that capture subtle details and nuances specified in the input prompt. For example, you could try using the model to create detailed, fantastical scenes that blend realistic elements with imaginative, whimsical components. Experimenting with different prompts and prompt engineering techniques can help you unlock the full potential of this powerful text-to-image generation tool.

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Updated 6/21/2024

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photorealistic-fx

batouresearch

Total Score

40

The photorealistic-fx model, developed by batouresearch, is a powerful AI model designed to generate photorealistic images. This model is part of the RunDiffusion FX series, which aims to create highly realistic and visually stunning outputs. It can be used to generate a wide range of photorealistic images, from fantastical scenes to hyper-realistic depictions of the natural world. When compared to similar models like photorealistic-fx-controlnet, photorealistic-fx-lora, stable-diffusion, and thinkdiffusionxl, the photorealistic-fx model stands out for its ability to generate exceptionally detailed and lifelike images, while also maintaining a high degree of flexibility and versatility. Model inputs and outputs The photorealistic-fx model accepts a variety of inputs, including a prompt, an optional initial image, and various parameters that allow for fine-tuning the output. The model's outputs are high-quality, photorealistic images that can be used for a wide range of applications, from art and design to visualization and simulation. Inputs Prompt**: The input prompt, which can be a short description or a more detailed description of the desired image. Image**: An optional initial image that the model can use as a starting point for generating variations. Width and Height**: The desired dimensions of the output image, with a maximum size of 1024x768 or 768x1024. Seed**: A random seed value, which can be used to ensure reproducible results. Scheduler**: The scheduler algorithm used to generate the output image. Num Outputs**: The number of images to generate, up to a maximum of 4. Guidance Scale**: The scale for classifier-free guidance, which influences the level of detail and realism in the output. Negative Prompt**: Text that specifies things the model should avoid including in the output. Prompt Strength**: The strength of the input prompt when using an initial image. Num Inference Steps**: The number of denoising steps used to generate the output image. Outputs The photorealistic-fx model generates high-quality, photorealistic images that can be saved and used for a variety of purposes. Capabilities The photorealistic-fx model is capable of generating a wide range of photorealistic images, from landscapes and cityscapes to portraits and product shots. It can handle a variety of subject matter and styles, and is particularly adept at creating highly detailed and lifelike outputs. What can I use it for? The photorealistic-fx model can be used for a variety of applications, including art and design, visualization and simulation, and product development. It could be used to create photo-realistic renderings of architectural designs, visualize scientific data, or generate high-quality product images for e-commerce. Additionally, the model's flexibility and versatility make it a valuable tool for creators and businesses looking to produce stunning, photorealistic imagery. Things to try One interesting thing to try with the photorealistic-fx model is to experiment with different input prompts and parameters to see how they affect the output. For example, you could try varying the guidance scale or the number of inference steps to see how that impacts the level of detail and realism in the generated images. You could also try using different initial images as a starting point for the model, or explore the effects of including or excluding certain elements in the negative prompt.

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Updated 6/21/2024

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sdxl-outpainting-lora

batouresearch

Total Score

32

The sdxl-outpainting-lora model is an improved version of Stability AI's SDXL outpainting model, which supports LoRA (Low-Rank Adaptation) for fine-tuning the model. This model uses PatchMatch, an algorithm that improves the quality of the generated mask, allowing for more seamless outpainting. The model is implemented as a Cog model, making it easy to use as a cloud API. Model inputs and outputs The sdxl-outpainting-lora model takes a variety of inputs, including a prompt, an input image, a seed, and various parameters to control the outpainting and generation process. The model outputs one or more generated images that extend the input image in the specified direction. Inputs Prompt**: The text prompt that describes the desired output image. Image**: The input image to be outpainted. Seed**: The random seed to use for generation, allowing for reproducible results. Scheduler**: The scheduler algorithm to use for the diffusion process. LoRA Scale**: The scale to apply to the LoRA weights, which can be used to fine-tune the model. Num Outputs**: The number of output images to generate. LoRA Weights**: The LoRA weights to use, which must be from the Replicate platform. Outpaint Size**: The size of the outpainted region, in pixels. Guidance Scale**: The scale to apply to the classifier-free guidance, which controls the balance between the prompt and the input image. Apply Watermark**: Whether to apply a watermark to the generated images. Condition Scale**: The scale to apply to the ControlNet guidance, which controls the influence of the input image. Negative Prompt**: An optional negative prompt to guide the generation away from certain outputs. Outpaint Direction**: The direction in which to outpaint the input image. Outputs Generated Images**: The one or more output images that extend the input image in the specified direction. Capabilities The sdxl-outpainting-lora model is capable of seamlessly outpainting input images in a variety of directions, using the PatchMatch algorithm to improve the quality of the generated mask. The model can be fine-tuned using LoRA, allowing for customization and adaptation to specific use cases. What can I use it for? The sdxl-outpainting-lora model can be used for a variety of applications, such as: Image Editing**: Extending the canvas of existing images to create new compositions or add additional context. Creative Expression**: Generating unique and imaginative outpainted images based on user prompts. Architectural Visualization**: Extending architectural renderings or product images to showcase more of the environment or surroundings. Things to try Some interesting things to try with the sdxl-outpainting-lora model include: Experimenting with different LoRA scales to see how it affects the output quality and fidelity. Trying out various prompts and input images to see the range of outputs the model can generate. Combining the outpainting capabilities with other AI models, such as GFPGAN for face restoration or stable-diffusion-inpainting for more advanced inpainting.

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Updated 6/21/2024

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sdxl-lcm-lora-controlnet

batouresearch

Total Score

11

The sdxl-lcm-lora-controlnet model is an all-in-one AI model developed by batouresearch that combines Stability AI's SDXL model with LCM LoRA for faster inference and ControlNet capabilities. This model builds upon similar models like the sdxl-controlnet-lora, open-dalle-1.1-lora, and sdxl-multi-controlnet-lora to provide an efficient and versatile all-in-one solution for text-to-image generation. Model inputs and outputs The sdxl-lcm-lora-controlnet model accepts a variety of inputs, including a text prompt, an optional input image, a seed value, and various settings to control the output, such as resolution, guidance scale, and LoRA scale. The model can generate one or more images based on the provided inputs. Inputs Prompt**: The text prompt that describes the desired output image. Image**: An optional input image that can be used for img2img or inpaint mode. Seed**: A random seed value that can be used to generate reproducible outputs. Resolution**: The desired width and height of the output image. Scheduler**: The scheduler to use for the diffusion process, with the default being LCM. Number of outputs**: The number of images to generate. LoRA scale**: The additive scale for the LoRA weights. ControlNet image**: An optional input image that will be converted to a Canny edge image and used as a conditioning input. Outputs Images**: One or more generated images that match the provided prompt. Capabilities The sdxl-lcm-lora-controlnet model is capable of generating high-quality images from text prompts, leveraging the power of the SDXL model combined with the efficiency of LCM LoRA and the control provided by ControlNet. This model excels at generating a wide range of image types, from realistic scenes to fantastical and imaginative creations. What can I use it for? The sdxl-lcm-lora-controlnet model can be used for a variety of applications, including: Creative content generation**: Produce unique, high-quality images for use in art, advertising, or entertainment. Prototyping and visualization**: Generate visual concepts and mockups to aid in the design and development process. Educational and research purposes**: Explore the capabilities of text-to-image AI models and experiment with different prompts and settings. Things to try With the sdxl-lcm-lora-controlnet model, you can explore the power of combining SDXL, LCM LoRA, and ControlNet by trying different prompts, input images, and settings. Experiment with the LoRA scale and condition scale to see how they affect the output, or use the ControlNet input to guide the generation process in specific ways.

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Updated 6/21/2024

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photorealistic-fx-lora

batouresearch

Total Score

5

The photorealistic-fx-lora model is a powerful AI model created by batouresearch that generates photorealistic images with stunning visual effects. This model builds upon the capabilities of the RunDiffusion and RealisticVision models, offering enhanced image quality and prompt adherence. It utilizes Latent Diffusion with LoRA integration, which allows for more precise control over the generated imagery. Model inputs and outputs The photorealistic-fx-lora model accepts a variety of inputs, including a prompt, image, and various settings to fine-tune the generation process. The model can output multiple images based on the provided inputs. Inputs Prompt**: A text description that guides the image generation process. Image**: An initial image to be used as a starting point for image variations. Seed**: A random seed value to control the generation process. Width and Height**: The desired dimensions of the output image. LoRA URLs and Scales**: URLs and scales for LoRA models to be used in the generation. Scheduler**: The scheduling algorithm to be used during the denoising process. Guidance Scale**: The scale factor for classifier-free guidance, which influences the balance between the prompt and the image. Negative Prompt**: A text description of elements to be avoided in the output image. Prompt Strength**: The strength of the prompt in the Img2Img process. Num Inference Steps**: The number of denoising steps to be performed during the generation process. Adapter Condition Image**: An additional image to be used as a conditioning factor in the generation process. Outputs Generated Images**: One or more images generated based on the provided inputs. Capabilities The photorealistic-fx-lora model excels at generating highly photorealistic images with impressive visual effects. It can produce stunning landscapes, portraits, and scenes that closely match the provided prompt. The model's LoRA integration allows for the incorporation of specialized visual styles and effects, expanding the range of possible outputs. What can I use it for? The photorealistic-fx-lora model can be a valuable tool for a wide range of applications, such as: Creative Visualization**: Generating concept art, illustrations, or promotional materials for creative projects. Product Visualization**: Creating photorealistic product mockups or renderings for e-commerce or marketing purposes. Visual Effects**: Generating realistic visual effects, such as explosions, weather phenomena, or supernatural elements, for use in film, TV, or video games. Architectural Visualization**: Producing photorealistic renderings of architectural designs or interior spaces. Things to try One interesting aspect of the photorealistic-fx-lora model is its ability to seamlessly blend LoRA models with the core diffusion model. By experimenting with different LoRA URLs and scales, users can explore a wide range of visual styles and effects, from hyperrealistic to stylized. Additionally, the model's Img2Img capabilities allow for the creation of variations on existing images, opening up possibilities for iterative design and creative exploration.

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Updated 6/21/2024

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magic-style-transfer

batouresearch

Total Score

3

The magic-style-transfer model is a powerful tool for restyling images with the style of another image. Developed by batouresearch, this model is a great alternative to other style transfer models like style-transfer and style-transfer. It can also be used in conjunction with the magic-image-refiner model to further enhance the quality and detail of the results. Model inputs and outputs The magic-style-transfer model takes several inputs, including an input image, a prompt, and optional parameters like seed, IP image, and LoRA weights. The model then generates one or more output images that have the style of the input image applied to them. Inputs Image**: The input image to be restyled Prompt**: A text prompt describing the desired output Seed**: A random seed to control the output IP Image**: An additional input image for img2img or inpaint mode IP Scale**: The strength of the IP Adapter Strength**: The denoising strength when img2img is active Scheduler**: The scheduler to use LoRA Scale**: The LoRA additive scale Num Outputs**: The number of images to generate LoRA Weights**: The Replicate LoRA weights to use Guidance Scale**: The scale for classifier-free guidance Resizing Scale**: The scale of the solid margin Apply Watermark**: Whether to apply a watermark to the output Negative Prompt**: A negative prompt to guide the output Background Color**: The color to replace the alpha channel with Num Inference Steps**: The number of denoising steps Condition Canny Scale**: The scale for the Canny edge condition Condition Depth Scale**: The scale for the depth condition Outputs Output Images**: One or more images with the input image's style applied Capabilities The magic-style-transfer model can effectively apply the style of one image to another, creating unique and visually striking results. It can handle a wide range of input images and prompts, and the ability to fine-tune the model with LoRA weights adds an extra level of customization. What can I use it for? The magic-style-transfer model is a great tool for creative projects, such as generating art, designing album covers, or creating unique visual content for social media. By combining the style of one image with the content of another, you can produce highly compelling and original imagery. The model can also be used in commercial applications, such as product visualizations or marketing materials, where a distinctive visual style is desired. Things to try One interesting aspect of the magic-style-transfer model is its ability to handle a variety of input types, from natural images to more abstract or stylized artwork. Try experimenting with different input images and prompts to see how the model responds, and don't be afraid to push the boundaries of what it can do. You might be surprised by the unique and unexpected results you can achieve.

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Updated 6/21/2024

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instant-paint

batouresearch

Total Score

2

The instant-paint model is a very fast img2img AI model developed by batouresearch for real-time AI collaboration. It is similar to other AI art models like gfpgan, magic-style-transfer, magic-image-refiner, open-dalle-1.1-lora, and sdxl-outpainting-lora which are also focused on various image generation and enhancement tasks. Model inputs and outputs The instant-paint model takes in an input image, a text prompt, and various optional parameters to control the output. It then generates a new image based on the provided prompt and input image. The outputs are an array of image URLs. Inputs Prompt**: The text prompt that describes the desired output image. Image**: The input image to use for the img2img process. Num Outputs**: The number of images to generate, up to 4. Seed**: A random seed value to control the image generation. Scheduler**: The type of scheduler to use for the image generation. Guidance Scale**: The scale for classifier-free guidance. Num Inference Steps**: The number of denoising steps to perform. Prompt Strength**: The strength of the prompt when using img2img or inpainting. Lora Scale**: The additive scale for LoRA, if applicable. Lora Weights**: The LoRA weights to use, if any. Replicate Weights**: The Replicate weights to use, if any. Batched Prompt**: Whether to split the prompt by newlines and generate images for each line. Apply Watermark**: Whether to apply a watermark to the generated images. Condition Scale**: The scale for the ControlNet condition. Negative Prompt**: The negative prompt to use for the image generation. Disable Safety Checker**: Whether to disable the safety checker for the generated images. Outputs Image URLs**: An array of URLs for the generated images. Capabilities The instant-paint model is a powerful img2img AI that can quickly generate new images based on an input image and text prompt. It is capable of producing high-quality, visually striking images that adhere closely to the provided prompt. The model can be used for a variety of creative and artistic applications, such as concept art, illustration, and digital painting. What can I use it for? The instant-paint model can be used for various image generation and editing tasks, such as: Collaborating with AI in real-time on art projects Quickly generating new images based on an existing image and a text prompt Experimenting with different styles, effects, and compositions Prototyping and ideation for creative projects Enhancing existing images with additional details or effects Things to try With the instant-paint model, you can experiment with different prompts, input images, and parameter settings to explore the breadth of its capabilities. Try using the model to generate images in various styles, genres, and subjects, and see how the output changes based on the input. You can also try combining the instant-paint model with other AI tools or models, such as the magic-style-transfer model, to create even more interesting and unique images.

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Updated 6/21/2024