stable-diffusion-3.5-large-lora
Maintainer: lucataco - Last updated 12/8/2024
Model overview
The stable-diffusion-3.5-large-lora
model is an implementation of the Stability AI's Stable Diffusion 3.5 Large model with Low-Rank Adaptation (LoRA) technology, created by the developer lucataco. This model allows for efficient fine-tuning and inference on Stable Diffusion 3.5-Large, a powerful text-to-image generation model. It is similar to other LoRA-based models developed by lucataco, such as FLUX.1-Dev Multi LoRA Explorer, FLUX.1-Dev LoRA Explorer, and FLUX.1-Schnell LoRA Explorer.
Model inputs and outputs
This model takes in a text prompt and optional image as input, and generates an image as output. The key inputs include the prompt, aspect ratio, guidance scale, number of inference steps, and optional image for img2img mode. The output is a generated image in the specified format (WEBP, JPG, or PNG).
Inputs
- Prompt: The text prompt that describes the image you want to generate.
- Aspect Ratio: The aspect ratio of the generated image.
- Guidance Scale: The scale for classifier-free guidance, which controls the balance between the input prompt and the model's inherent biases.
- Num Inference Steps: The number of denoising steps performed during image generation, typically between 28-50.
- Image: An optional input image for img2img mode, where the model generates a new image based on the input image and the prompt.
Outputs
- Generated Image: The AI-generated image based on the input prompt and optional image.
Capabilities
The stable-diffusion-3.5-large-lora
model is capable of generating high-quality, photorealistic images from text prompts. It can create a wide variety of images, from realistic scenes to fantastical and imaginative compositions. The addition of LoRA technology allows for efficient fine-tuning and customization of the model, enabling users to create images tailored to their specific needs.
What can I use it for?
This model can be used for a variety of applications, such as:
- Automated content creation: Generate images for use in marketing, social media, or other digital content.
- Creative exploration: Experiment with different prompts and ideas to spark inspiration and generate unique artwork.
- Prototype visualization: Create visual concepts and prototypes to communicate ideas more effectively.
When running this model on Replicate, the generated images and their outputs can be used commercially, as per the STABILITY AI COMMUNITY
License.
Things to try
Some interesting things to try with this model include:
- Experimenting with different prompts and aspect ratios to see the range of images it can generate.
- Combining the model with other LoRA-based models, such as the FLUX.1-Dev Multi LoRA Explorer, to explore the potential for even more customized and specialized image generation.
- Exploring the use of this model in various creative and commercial applications, such as product visualization, digital art, and more.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
3
Related Models
2.3K
flux-dev-lora
lucataco
The flux-dev-lora model is a FLUX.1-Dev LoRA explorer created by replicate/lucataco. This model is an implementation of the black-forest-labs/FLUX.1-schnell model as a Cog model. The flux-dev-lora model shares similarities with other LoRA-based models like ssd-lora-inference, fad_v0_lora, open-dalle-1.1-lora, and lora, all of which focus on leveraging LoRA technology for improved inference performance. Model inputs and outputs The flux-dev-lora model takes in several inputs, including a prompt, seed, LoRA weights, LoRA scale, number of outputs, aspect ratio, output format, guidance scale, output quality, number of inference steps, and an option to disable the safety checker. These inputs allow for customized image generation based on the user's preferences. Inputs Prompt**: The text prompt that describes the desired image to be generated. Seed**: The random seed to use for reproducible generation. Hf Lora**: The Hugging Face path or URL to the LoRA weights. Lora Scale**: The scale to apply to the LoRA weights. Num Outputs**: The number of images to generate. Aspect Ratio**: The aspect ratio for the generated image. Output Format**: The format of the output images. Guidance Scale**: The guidance scale for the diffusion process. Output Quality**: The quality of the output images, from 0 to 100. Num Inference Steps**: The number of inference steps to perform. Disable Safety Checker**: An option to disable the safety checker for the generated images. Outputs A set of generated images in the specified format (e.g., WebP). Capabilities The flux-dev-lora model is capable of generating images from text prompts using a FLUX.1-based architecture and LoRA technology. This allows for efficient and customizable image generation, with the ability to control various parameters like the number of outputs, aspect ratio, and quality. What can I use it for? The flux-dev-lora model can be useful for a variety of applications, such as generating concept art, product visualizations, or even personalized content for marketing or social media. The ability to fine-tune the model with LoRA weights can also enable specialized use cases, like improving the model's performance on specific domains or styles. Things to try Some interesting things to try with the flux-dev-lora model include experimenting with different LoRA weights to see how they affect the generated images, testing the model's performance on a variety of prompts, and exploring the use of the safety checker toggle to generate potentially more creative or unusual content.
Read moreUpdated 12/8/2024
7
stable-diffusion-x4-upscaler
lucataco
The stable-diffusion-x4-upscaler is an AI model developed by Stability AI and maintained by lucataco. It is an implementation of the Stable Diffusion x4 upscaler model, which can be used to enhance the resolution of images. This model is similar to other Stable Diffusion-based models like stable-diffusion-inpainting, dreamshaper-xl-lightning, and pasd-magnify in its use of the Stable Diffusion framework. Model inputs and outputs The stable-diffusion-x4-upscaler model takes in a grayscale input image and a text prompt, and outputs an upscaled image. The input image can be scaled by a factor of up to 4, and the text prompt can be used to guide the upscaling process. Inputs Image**: A grayscale input image Scale**: The factor to scale the image by, with a default of 4 Prompt**: A text prompt to guide the upscaling process, with a default of "A white cat" Outputs Output**: The upscaled image Capabilities The stable-diffusion-x4-upscaler model can be used to enhance the resolution of images while preserving the content and style of the original image. It can be particularly useful for tasks like enlarging low-resolution images or generating high-quality images from sketches or low-quality source material. What can I use it for? The stable-diffusion-x4-upscaler model can be used for a variety of image-related tasks, such as creating high-quality images for marketing materials, enhancing the resolution of family photos, or generating concept art for games and animations. The model's ability to preserve the content and style of the original image makes it a versatile tool for creative projects. Additionally, the model's maintainer, lucataco, has developed other Stable Diffusion-based models like dreamshaper-xl-lightning and pasd-magnify that may be of interest for similar use cases. Things to try One interesting aspect of the stable-diffusion-x4-upscaler model is its ability to generate high-quality images from low-resolution input. This can be particularly useful for tasks like restoring old photographs or creating high-quality images from sketches or low-quality source material. Additionally, experimenting with different text prompts can result in unique and creative upscaled images, allowing users to explore the model's capabilities in generating content-aware image enhancements.
Read moreUpdated 12/8/2024
454
flux-schnell-lora
lucataco
The flux-schnell-lora is an AI model developed by lucataco and is an implementation of the black-forest-labs/FLUX.1-schnell model as a Cog model. This model is an explorer for the FLUX.1-Schnell LoRA, allowing users to experiment with different LoRA weights. Model inputs and outputs The flux-schnell-lora model takes a variety of inputs, including a prompt, a random seed, the number of outputs, the aspect ratio, the output format and quality, the number of inference steps, and the option to disable the safety checker. The model outputs one or more generated images based on the provided inputs. Inputs Prompt**: The text prompt that describes the image you want to generate. Seed**: A random seed to ensure reproducible generation. Num Outputs**: The number of images to generate. Aspect Ratio**: The aspect ratio of the generated images. Output Format**: The file format of the output images (e.g. WEBP, PNG). Output Quality**: The quality of the output images, ranging from 0 to 100. Num Inference Steps**: The number of inference steps to use during image generation. Disable Safety Checker**: An option to disable the safety checker for the generated images. Outputs Generated Images**: The model outputs one or more generated images based on the provided inputs. Capabilities The flux-schnell-lora model is capable of generating images based on text prompts, with the ability to explore different LoRA weights to influence the generation process. This can be useful for creative projects or exploring the capabilities of the underlying FLUX.1-Schnell model. What can I use it for? You can use the flux-schnell-lora model to generate images for a variety of creative projects, such as illustrations, concept art, or product visualizations. The ability to explore different LoRA weights can be particularly useful for experimenting with different artistic styles or visual effects. Things to try Some ideas for things to try with the flux-schnell-lora model include: Experimenting with different prompts to see how the model responds. Trying different LoRA weights to see how they affect the generated images. Comparing the output of the flux-schnell-lora model to other similar models, such as flux-dev-multi-lora, flux-dev-lora, or open-dalle-1.1-lora. Exploring the use of the flux-schnell-lora model in various creative or commercial applications.
Read moreUpdated 12/8/2024
125
lora
cloneofsimo
The lora model is a LoRA (Low-Rank Adaptation) inference model developed by Replicate creator cloneofsimo. It is designed to work with the Stable Diffusion text-to-image diffusion model, allowing users to fine-tune and apply LoRA models to generate images. The model can be deployed and used with various Stable Diffusion-based models, such as the fad_v0_lora, ssd-lora-inference, sdxl-outpainting-lora, and photorealistic-fx-lora models. Model inputs and outputs The lora model takes in a variety of inputs, including a prompt, image, and various parameters to control the generation process. The model can output multiple images based on the provided inputs. Inputs Prompt**: The input prompt used to generate the images, which can include special tags like `` to specify LoRA concepts. Image**: An initial image to generate variations of, if using Img2Img mode. Width and Height**: The size of the output images, up to a maximum of 1024x768 or 768x1024. Number of Outputs**: The number of images to generate, up to a maximum of 4. LoRA URLs and Scales**: URLs and scales for LoRA models to apply during generation. Scheduler**: The denoising scheduler to use for the generation process. Prompt Strength**: The strength of the prompt when using Img2Img mode. Guidance Scale**: The scale for classifier-free guidance, which controls the balance between the prompt and the input image. Adapter Type**: The type of adapter to use for additional conditioning (e.g., sketch). Adapter Condition Image**: An additional image to use for conditioning when using the T2I-adapter. Outputs Generated Images**: The model outputs one or more images based on the provided inputs. Capabilities The lora model allows users to fine-tune and apply LoRA models to the Stable Diffusion text-to-image diffusion model, enabling them to generate images with specific styles, objects, or other characteristics. This can be useful for a variety of applications, such as creating custom avatars, generating illustrations, or enhancing existing images. What can I use it for? The lora model can be used to generate a wide range of images, from portraits and landscapes to abstract art and fantasy scenes. By applying LoRA models, users can create images with unique styles, textures, and other characteristics that may not be achievable with the base Stable Diffusion model alone. This can be particularly useful for creative professionals, such as designers, artists, and content creators, who are looking to incorporate custom elements into their work. Things to try One interesting aspect of the lora model is its ability to apply multiple LoRA models simultaneously, allowing users to combine different styles, concepts, or characteristics in a single image. This can lead to unexpected and serendipitous results, making it a fun and experimental tool for creativity and exploration.
Read moreUpdated 12/8/2024