magicalapi/YouTube_Thumbnail_Suggestion

Youtube Thumbnail Suggestion Model Card

Presenting our groundbreaking generative model, specifically engineered for YouTube content. This innovative model, generates visually captivating and realistic thumbnails based on input prompts. Designed to elevate content aesthetics and viewer engagement, this tool represents a significant advancement in custom thumbnail creation for YouTube videos.

Table of Contents

• Model Details
  • Model Description
• Uses
  • Downstream Use
  • Out-of-Scope Use
• Bias, Risks, and Limitations
• Training Details, Evaluation, and Technical Specifications
• How to Get Started with the Model
• Model Card Authors

Model Details

Model Description

The model is meticulously crafted for YouTube content creators. This innovative model, tailored for thumbnail generation, ingeniously suggests visually striking thumbnails based on user-input prompts. With a focus on creativity and customization, this tool empowers users to enhance their video presence by effortlessly generating eye-catching and contextually relevant thumbnails, optimizing visual appeal and audience engagement on the YouTube.

This iteration is the LORA, Dream-Booth meticulously fine-tuned using SD-XL 1.0. It represents the Non-Commercial version of our refined model, characterized by a reduced training set and training time. For accessing to the professional version, please refer to the contact details provided below.

• Developed by: MagicalAPI Co.
• Model type: Diffusion-based text-to-image generative model
• Language(s): en
• License: openrail++
• Base Model: Base model is enhanced from SD-XL 1.0
• Resources for more information: You can find out more details in our Github (comming soon)

Uses

This model is mainly designed for those creating videos and contents on YouTube, for better experiencing, describe your intended thumbnail by pointing on necessary elements or situation, separating by comma; for example: "The office is modern and vibrant, filled with young, diverse professionals (various descents and genders) engaged in creative work. There are brainstorming sessions happening.

Downstream Use

The base model is designed for generating Youtube thumbnails in various usecases and categories; specifically, you can also finetune the model with the particular images and captions needed for your own channel.

Out-of-Scope Use

The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.

It is crucial to emphasize that this tool should never be employed for the creation, promotion, or endorsement of abusive, violent, or pornographic materials, as its purpose is to contribute to a constructive and enriching online environment.

Bias, Risks, and Limitations

Extensive scholarly inquiry has delved into the pervasive challenges related to bias and fairness inherent in language models, (as exemplified by seminal works such as those conducted by Sheng et al. (2021) and Bender et al. (2021)). The outcomes of model predictions have the potential to perpetuate troubling and detrimental stereotypes within various domains, spanning protected classes, individual identity attributes, as well as social, and occupational groups. This underscores the critical need for ongoing vigilance and ethical considerations in the development and deployment of such models to ensure responsible, unbiased, and equitable outcomes across diverse contexts.

Training Details, Evaluation, and Technical Specifications

For a comprehensive insight into the intricacies of the model's training protocols, evaluation methodologies, and other specificities, I invite you to explore the model's GitHub repository and the associated page on Hugging Face. These repositories provide detailed documentation and resources, offering a nuanced understanding of the model's development processes and features. Delving into these platforms will enhance your comprehension of the model's architecture and its application, facilitating a deeper grasp of its functionalities and capabilities.

How to Get Started with the Model

Download model

Weights for this model are available in Safetensors format.

Download them in the Files & versions tab.

Trigger words

You should use naturalistic, realistic to trigger the image generation.

Use it with the 🧨 Diffusers library.

from diffusers import DiffusionPipeline
import torch

base = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16, use_safetensors=True).to('cuda')
refiner = DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-xl-refiner-1.0', text_encoder_2=base.text_encoder_2, vae=base.vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16").to("cuda")

lora_name = "Thumbnail Suggestion"
base.load_lora_weights('magicalapi/YouTube_Thumbnail_Suggestion', weight_name='pytorch_lora_weights.safetensors', adapter_name=lora_name)
impactness = 0.85
base.set_adapters([lora_name], adapter_weights=[impactness])

prompt = "Enter your input prompt here."

prompt = "naturalistic, realistic, " + prompt
image = base(prompt=prompt, guess_mode=True, num_inference_steps=50, output_type="latent").images
generated_images = refiner(prompt=prompt, guess_mode=True, num_inference_steps=50, image=image).images
generated_images[0].save("test.jpg")

Model Card Authors

MagicalAPI Co.

Empower your applications with cutting-edge intelligence using our AI APIs, transforming possibilities into realities.

Contact: info@magicalapi.com