Xlabs-ai
Models by this creator
🏅
flux-RealismLora
568
The flux-RealismLora model, developed by XLabs-AI, is a checkpoint with trained LoRA photorealism for the FLUX.1-dev model by Black Forest Labs. This model aims to enhance the photorealistic capabilities of the FLUX.1-dev model through fine-tuning. Similar models include the flux-lora-collection and flux-controlnet-canny by XLabs-AI, as well as the flux-dev-realism model by fofr, which also focus on improving the realism of the FLUX.1-dev model. Model inputs and outputs The flux-RealismLora model takes text prompts as input and generates photorealistic images as output. The model has been fine-tuned on a dataset of images with corresponding text captions to improve its ability to generate realistic imagery based on textual descriptions. Inputs Text prompt**: A textual description of the desired image, such as "handsome girl in a suit covered with bold tattoos and holding a pistol. Animatrix illustration style, fantasy style, natural photo cinematic". Outputs Image**: A photorealistic image generated based on the input text prompt. Capabilities The flux-RealismLora model excels at generating high-quality, photorealistic images based on detailed textual descriptions. The fine-tuning process has enhanced the model's ability to capture intricate visual details, realistic lighting and shading, and a natural, life-like appearance. Examples of the model's capabilities include generating images of people, animals, buildings, and scenes with a high level of realism and attention to detail. What can I use it for? The flux-RealismLora model can be particularly useful for applications that require photorealistic image generation, such as: Concept art and visualization for product design, architecture, and entertainment industries Augmented reality and virtual reality applications that require realistic digital assets Generating personalized, high-quality images for marketing, advertising, and e-commerce Enhancing the visual quality of AI-generated content for various applications Things to try One interesting aspect of the flux-RealismLora model is its ability to generate images with a specific artistic style, such as "Animatrix illustration style" or "fantasy style", in addition to the photorealistic quality. Users can experiment with different stylistic prompts to see how the model translates textual descriptions into unique and visually compelling imagery. Additionally, combining the flux-RealismLora model with other AI-powered tools, such as ControlNet, can open up new possibilities for image generation and manipulation, allowing users to further refine and iterate on the photorealistic output.
Updated 9/11/2024
⛏️
flux-lora-collection
343
The flux-lora-collection is a repository provided by XLabs-AI that offers trained LoRA (Lightweight Rank Adaptation) models for the FLUX.1-dev model developed by Black Forest Labs. LoRA is a technique used to fine-tune large language models with a smaller number of parameters, making them more efficient for specific tasks. This collection includes LoRA models for various styles and themes, such as a furry_lora model that can generate images of anthropomorphic animal characters. The repository also contains training details, dataset information, and example inference scripts to demonstrate the capabilities of these LoRA models. Model Inputs and Outputs Inputs Text prompts that describe the desired image content, such as "Female furry Pixie with text 'hello world'" LoRA model name and repository ID to specify the desired LoRA model to use Outputs Generated images based on the provided text prompts, utilizing the fine-tuned LoRA models Capabilities The flux-lora-collection models demonstrate the ability to generate high-quality, diverse images of anthropomorphic animal characters and other themes. The furry_lora model, for example, can produce vibrant and detailed images of furry characters, as shown in the example outputs. What Can I Use It For? The flux-lora-collection models can be useful for artists, content creators, and enthusiasts who are interested in generating images of anthropomorphic characters or exploring other thematic styles. These models can be integrated into text-to-image generation pipelines, allowing users to create unique and imaginative artwork with relative ease. Things to Try One interesting aspect of the flux-lora-collection models is the ability to fine-tune the level of detail in the generated images. By adjusting the LoRA scale slider, users can create images ranging from highly detailed to more abstract representations of the same prompt. Experimenting with this feature can lead to a wide variety of artistic expressions within the same thematic domain. Additionally, combining the flux-lora-collection models with other techniques, such as ControlNet or advanced prompting strategies, could unlock even more creative possibilities for users.
Updated 9/11/2024
🤷
flux-ip-adapter
263
flux-ip-adapter is an IP-Adapter checkpoint for the FLUX.1-dev model by Black Forest Labs. IP-Adapter is an effective and lightweight adapter that enables image prompt capabilities for pre-trained text-to-image diffusion models. Compared to finetuning the entire model, the flux-ip-adapter with only 22M parameters can achieve comparable or even better performance. It can be generalized to other custom models fine-tuned from the same base model, as well as used with existing controllable tools for multimodal image generation. Model inputs and outputs The flux-ip-adapter takes an image as input and generates an image as output. It can work with both 512x512 and 1024x1024 resolutions. The model is regularly updated with new checkpoint releases, so users should check for the latest version. Inputs Image at 512x512 or 1024x1024 resolution Outputs Image generated based on the input image, respecting the provided text prompt Capabilities The flux-ip-adapter allows users to leverage image prompts in addition to text prompts for more precise and controllable image generation. It can outperform finetuned models, while being more efficient and lightweight. Users can combine the image and text prompts to accomplish multimodal image generation. What can I use it for? The flux-ip-adapter can be used for a variety of creative applications that require precise image generation, such as art creation, concept design, and product visualization. Its ability to utilize both image and text prompts makes it a versatile tool for users looking to unlock new levels of control and creativity in their image generation workflows. Things to try Try combining the flux-ip-adapter with the Flux.1-dev model and the ComfyUI custom nodes to explore the full potential of this technology. Experiment with different image and text prompts to see how the model responds and generates unique and compelling visuals.
Updated 9/17/2024
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flux-controlnet-canny
262
The flux-controlnet-canny model is a checkpoint with a trained ControlNet Canny model for the FLUX.1-dev model by Black Forest Labs. ControlNet is a neural network structure that can control diffusion models by adding extra conditions, in this case Canny edge detection. It can be used in combination with Stable Diffusion models. Similar models include the sd-controlnet-canny checkpoint, which also uses Canny edge conditioning, as well as the controlnet-canny-sdxl-1.0 and controlnet-canny-sdxl-1.0 models, which use Canny conditioning with the larger Stable Diffusion XL base model. Model inputs and outputs Inputs Control image**: A Canny edge image used to guide the image generation process. Prompt**: A text description of the desired output image. Outputs Generated image**: An image created by the model based on the provided prompt and control image. Capabilities The flux-controlnet-canny model can generate high-quality images guided by Canny edge maps, allowing for precise control over the output. This can be useful for creating illustrations, concept art, and design assets where the edges and structure of the image are important. What can I use it for? The flux-controlnet-canny model can be used for a variety of image generation tasks, such as: Generating detailed illustrations and concept art Creating design assets and product visualizations Producing architectural renderings and technical diagrams Enhancing existing images by adding edge-based details Things to try One interesting thing to try with the flux-controlnet-canny model is to experiment with different types of control images. While the model was trained on Canny edge maps, you could try using other edge detection techniques or even hand-drawn sketches as the control image to see how the model responds. This could lead to unexpected and creative results. Another idea is to try combining the flux-controlnet-canny model with other AI-powered tools, such as 3D modeling software or animation tools, to create more complex and multi-faceted projects. The ability to precisely control the edges and structure of the generated images could be a valuable asset in these types of workflows.
Updated 9/11/2024
📊
flux-controlnet-collections
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flux-controlnet-collections is a set of ControlNet models provided by XLabs-AI that can be used with the FLUX.1-dev model to enhance image generation capabilities. The collection includes ControlNet models for Canny edge detection, Holistically-Nested Edge Detection (HED), and Depth (Midas) processing. These ControlNet models are trained on 1024x1024 resolution and can be used directly in tools like ComfyUI. Model inputs and outputs The flux-controlnet-collections models take a source image as input and provide a processed version of that image as output. The processed outputs can then be used as conditioning inputs to guide the FLUX.1-dev model during image generation. Inputs Source images to be processed Outputs Canny edge map HED edge map Depth map Capabilities The flux-controlnet-collections models enable more precise and controllable image generation by providing additional guidance to the FLUX.1-dev model. By incorporating these ControlNet models, users can generate images that follow specific structural or depth characteristics, leading to more realistic and coherent outputs. What can I use it for? The flux-controlnet-collections models can be leveraged in various creative and practical applications, such as: Generating images with specific visual characteristics (e.g., architectural designs, product renderings) Enhancing image-to-image translation tasks (e.g., sketch-to-image, depth-to-image) Integrating with other AI-powered tools and workflows, such as the provided ComfyUI workflows Things to try Experiment with the different ControlNet models to explore how they can influence the output of the FLUX.1-dev model. Try combining multiple ControlNet inputs or adjusting the strength of the conditioning to achieve desired effects. Additionally, consider integrating the flux-controlnet-collections models into your own custom AI-powered projects or workflows.
Updated 9/14/2024
📊
flux-controlnet-collections
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flux-controlnet-collections is a set of ControlNet models provided by XLabs-AI that can be used with the FLUX.1-dev model to enhance image generation capabilities. The collection includes ControlNet models for Canny edge detection, Holistically-Nested Edge Detection (HED), and Depth (Midas) processing. These ControlNet models are trained on 1024x1024 resolution and can be used directly in tools like ComfyUI. Model inputs and outputs The flux-controlnet-collections models take a source image as input and provide a processed version of that image as output. The processed outputs can then be used as conditioning inputs to guide the FLUX.1-dev model during image generation. Inputs Source images to be processed Outputs Canny edge map HED edge map Depth map Capabilities The flux-controlnet-collections models enable more precise and controllable image generation by providing additional guidance to the FLUX.1-dev model. By incorporating these ControlNet models, users can generate images that follow specific structural or depth characteristics, leading to more realistic and coherent outputs. What can I use it for? The flux-controlnet-collections models can be leveraged in various creative and practical applications, such as: Generating images with specific visual characteristics (e.g., architectural designs, product renderings) Enhancing image-to-image translation tasks (e.g., sketch-to-image, depth-to-image) Integrating with other AI-powered tools and workflows, such as the provided ComfyUI workflows Things to try Experiment with the different ControlNet models to explore how they can influence the output of the FLUX.1-dev model. Try combining multiple ControlNet inputs or adjusting the strength of the conditioning to achieve desired effects. Additionally, consider integrating the flux-controlnet-collections models into your own custom AI-powered projects or workflows.
Updated 9/14/2024
👁️
flux-controlnet-canny-v3
82
The flux-controlnet-canny-v3 model is a Canny ControlNet checkpoint developed by XLabs-AI for the FLUX.1-dev model by Black Forest Labs. This model is part of a broader collection of ControlNet checkpoints released by XLabs-AI for the FLUX.1-dev model, which also includes Depth (Midas) and HED ControlNet versions. The flux-controlnet-canny-v3 model is a more advanced and realistic version of the Canny ControlNet compared to previous releases, and can be used directly in ComfyUI. Model inputs and outputs The flux-controlnet-canny-v3 model takes two main inputs: Inputs Prompt**: A text description of the desired image Control image**: A Canny edge map that provides additional guidance to the model during image generation Outputs Generated image**: The model outputs a 1024x1024 resolution image based on the provided prompt and Canny control image. Capabilities The flux-controlnet-canny-v3 model can generate high-quality images by leveraging the Canny edge map as an additional input. This allows the model to produce more defined and realistic-looking images compared to generation without the control input. The model has been trained on a wide range of subjects and styles, from portraits to landscapes and fantasy scenes. What can I use it for? The flux-controlnet-canny-v3 model can be a powerful tool for artists, designers, and content creators looking to generate unique and compelling images. By providing a Canny edge map as a control input, you can guide the model to produce images that closely match your creative vision. This could be useful for concept art, book covers, product renderings, and many other applications where high-quality, customized imagery is needed. Things to try One interesting thing to try with the flux-controlnet-canny-v3 model is to experiment with different levels of control image influence. By adjusting the controlnet_conditioning_scale parameter, you can find the sweet spot between the control image and the text prompt, allowing you to achieve the desired balance between realism and creative expression. Additionally, you can try using the model in conjunction with other ControlNet versions, such as Depth or HED, to see how the different control inputs interact and influence the final output.
Updated 9/17/2024
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flux-controlnet-depth-v3
78
The flux-controlnet-depth-v3 model, developed by XLabs-AI, is a depth ControlNet checkpoint for the FLUX.1-dev model by Black Forest Labs. This model adds conditional control to the Stable Diffusion text-to-image generation process by using a depth map as an additional input. The depth map is generated from the input image and provides spatial information to guide the model's generation. This can result in more realistic and consistent depth-aware images compared to using Stable Diffusion alone. The model is part of a collection of ControlNet checkpoints for the FLUX.1-dev model, which also includes Canny, HED, and other conditioning types. These models can be used with the ComfyUI tool or through the provided command-line scripts and workflows. Model inputs and outputs Inputs Input Image**: The model takes an input image, which is used to generate the depth map that serves as the conditioning input. Text Prompt**: The text prompt describing the desired output image. Outputs Generated Image**: The model outputs a generated image based on the input text prompt and the depth map conditioning. Capabilities The flux-controlnet-depth-v3 model can generate images that are more spatially consistent and depth-aware compared to using Stable Diffusion alone. This can be particularly useful for creating realistic scenes with depth cues, such as landscapes, interiors, or portraits. The model has been trained on a large dataset of depth-image pairs and can handle a wide variety of input images and prompts. What can I use it for? The flux-controlnet-depth-v3 model can be used for a variety of image generation tasks, such as: Creating realistic landscape scenes with accurate depth perception Generating depth-aware interior scenes, like rooms or buildings Producing portraits and character illustrations with a strong sense of depth Enhancing the spatial consistency and realism of any image generation task Things to try One interesting aspect of the flux-controlnet-depth-v3 model is its ability to handle a wide range of input images and prompts. Try experimenting with different types of input images, such as photographs, sketches, or abstract compositions, and see how the depth conditioning affects the generated output. You can also try varying the text prompt to explore different styles, subjects, or scenes, and observe how the depth information is incorporated into the final image.
Updated 9/17/2024
🗣️
flux-controlnet-hed-v3
45
The flux-controlnet-hed-v3 model, created by XLabs-AI, is a Hierarchical Edge Detector (HED) ControlNet checkpoint for the FLUX.1-dev model by Black Forest Labs. This model is part of a collection of ControlNet checkpoints provided by XLabs-AI, including Canny and Depth (Midas) models. The HED ControlNet is trained on 1024x1024 resolution and can be used directly in ComfyUI workflows. Model inputs and outputs Inputs Image**: The input image that the model will use as a control signal for generation. Outputs Generated image**: The output image generated by the model, guided by the input control image. Capabilities The flux-controlnet-hed-v3 model can use the input HED control image to guide the generation of new images. This allows for fine-grained control over the structure and edges of the generated output, leading to more detailed and realistic results. The model can be used in combination with the FLUX.1-dev model to create high-quality, photorealistic images. What can I use it for? The flux-controlnet-hed-v3 model can be used for a variety of image generation tasks, such as creating concept art, illustrations, and detailed photographic scenes. By leveraging the HED control signal, users can generate images with specific structural elements and edges, making it useful for design, architecture, and other applications where precise control over the output is important. Things to try One interesting thing to try with the flux-controlnet-hed-v3 model is to experiment with different input control images and prompts to see how the generated output changes. For example, you could try using a hand-drawn sketch or a simple line drawing as the control image, and see how the model incorporates those elements into the final generated image. Additionally, you can explore the other ControlNet models provided by XLabs-AI, such as the Canny and Depth models, to see how they can be used in combination with the HED model to create even more varied and compelling results.
Updated 9/17/2024