license: apache-2.0 language:

• en metrics:
• accuracy
• character base_model:
• black-forest-labs/FLUX.1-dev
• Jovie/Midjourney pipeline_tag: text-to-image library_name: diffusers tags:
• text-to-image
• image-generation
• floral
• High-Dynamic-Range
• Large-Image-Generation-Model
• custom_code
• bf16
• text-to-image
• diffusion
• AI
• art
• photorealistic
• image
• powerful
• future datasets:
• future-technologies/Universal-Transformers-Dataset

Floral High Dynamic Range (LIGM):

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A Large Image Generation Model (LIGM) celebrated for its exceptional accuracy in generating high-quality, highly detailed scenes like never seen before! Derived from the groundbreaking Floral AI Model—renowned for its use in film generation—this model marks a milestone in image synthesis technology.

Created by: Future Technologies Limited

Model Description

Floral High Dynamic Range (LIGM) is a state-of-the-art Large Image Generation Model (LIGM) that excels in generating images with stunning clarity, precision, and intricate detail. Known for its high accuracy in producing hyper-realistic and aesthetically rich images, this model sets a new standard in image synthesis. Whether it's landscapes, objects, or scenes, Floral HDR brings to life visuals that are vivid, lifelike, and unmatched in quality.

Originally derived from the Floral AI Model, which has been successfully applied in film generation, Floral HDR integrates advanced techniques to handle complex lighting, dynamic ranges, and detailed scene compositions. This makes it ideal for applications where high-resolution imagery and realistic scene generation are critical.

Designed and developed by Future Technologies Limited, Floral HDR is a breakthrough achievement in AI-driven image generation, marking a significant leap in creative industries such as digital art, film, and immersive media. With the power to create images that push the boundaries of realism and artistic innovation, this model is a testament to Future Technologies Limited's commitment to shaping the future of AI.

• Developed by: Future Technologies Limited (Lambda Go Technologies Limited)
• Model type: Large Image Generation Model
• Language(s) (NLP): English
• License: apache-2.0

Crucial Notice:

This model is based on black-forest-labs/FLUX.1-dev but has been significantly modified and improved by Future Technologies Limited with our own dataset and enhancements, making it a unique and original model. Although it uses FluxPipeline, the improvements make it distinct.

• License: This model is released under Future Technologies License , and is not subject to black-forest-labs's non-commercial restrictions. You can use, modify, and distribute it freely under Future Technologies License.

• Disclaimer: While black-forest-labs/FLUX.1-dev is non-commercial, we’ve made enough changes to our model to make it an independent work. Please review our license before use.

For any licensing questions, contact us at lambda.go.company@gmail.com

Uses

Film and Animation Studios

• Intended Users: Directors, animators, visual effects artists, and film production teams.
• Impact: This model empowers filmmakers to generate realistic scenes and environments with reduced reliance on traditional CGI and manual artistry. It provides faster production timelines and cost-effective solutions for creating complex visuals.

Game Developers

• Intended Users: Game designers, developers, and 3D artists.
• Impact: Floral HDR helps create highly detailed game worlds, characters, and assets. It allows developers to save time and resources, focusing on interactive elements while the model handles the visual richness of the environments. This can enhance game immersion and the overall player experience.

Virtual Reality (VR) and Augmented Reality (AR) Creators

• Intended Users: VR/AR developers, interactive media creators, and immersive experience designers.
• Impact: Users can quickly generate lifelike virtual environments, helping VR and AR applications appear more realistic and convincing. This is crucial for applications ranging from training simulations to entertainment.

Artists and Digital Designers

• Intended Users: Digital artists, illustrators, and graphic designers.
• Impact: Artists can use the model to generate high-quality visual elements, scenes, and concepts, pushing their creative boundaries. The model aids in visualizing complex artistic ideas in a faster, more efficient manner.

Marketing and Advertising Agencies

• Intended Users: Creative directors, marketers, advertising professionals, and content creators.
• Impact: Floral HDR enables agencies to create striking visuals for advertisements, product launches, and promotional materials. This helps businesses stand out in competitive markets by delivering high-impact imagery for campaigns.

Environmental and Scientific Researchers

• Intended Users: Environmental scientists, researchers, and visual data analysts.
• Impact: The model can simulate realistic environments, aiding in research areas like climate studies, ecosystem modeling, and scientific visualizations. It provides an accessible tool for researchers to communicate complex concepts through imagery.

Content Creators and Social Media Influencers

• Intended Users: Influencers, social media managers, and visual content creators.
• Impact: Social media professionals can create stunning and engaging content for their platforms with minimal effort. The model enhances the visual quality of posts, helping users build a more captivating online presence.

Out-of-Scope Use

Generation of Misleading or Harmful Content

• Misuse: The model should not be used to create fake, misleading, or harmful images intended to deceive individuals or manipulate public opinion (e.g., deepfakes, fake news visuals, or malicious propaganda).
• Why It's Out-of-Scope: The model generates high-fidelity imagery, and when used irresponsibly, it could perpetuate misinformation or mislead viewers into believing manipulated content is authentic.

Creating Offensive, Discriminatory, or Inappropriate Images

• Misuse: Generating content that is offensive, harmful, discriminatory, or violates ethical norms (e.g., hate speech, explicit content, or violence).
• Why It's Out-of-Scope: Floral HDR is designed to create visually rich and realistic images, and any generation that involves harmful themes goes against its ethical use, potentially causing harm or perpetuating negativity.

Overly Sensitive or Personal Data Generation

• Misuse: Generating images that involve identifiable individuals, private data, or exploit sensitive personal situations.
• Why It's Out-of-Scope: Using the model to simulate or generate sensitive, private, or identifiable personal content without consent violates privacy rights and can lead to harmful consequences for individuals involved.

Incorporating in Systems for Autonomous Decision-Making

• Misuse: Using the model in automated decision-making systems that could impact individuals' lives (e.g., in high-stakes domains like criminal justice, finance, or healthcare) without proper human oversight.
• Why It's Out-of-Scope: While the model generates high-quality visuals, it is not designed or trained for tasks requiring logical, contextual decision-making or ethical judgment, and may lead to errors or harmful outcomes when used in these contexts.

Large-Scale Commercial Use Without Licensing

• Misuse: Utilizing the model to produce images for large-scale commercial purposes without adhering to licensing and ethical guidelines, including the redistribution or resale of generated images as standalone assets.
• Why It's Out-of-Scope: The model is not intended to replace artists or designers in creating commercial products at scale unless appropriate licensing and commercial usage policies are in place.

Generating Unethical or Inaccurate Scientific/Medical Content

• Misuse: Using the model to generate scientific, medical, or educational content that could lead to false or harmful interpretations of real-world data.
• Why It's Out-of-Scope: The model’s capabilities are focused on creative and artistic image generation, not on generating scientifically or medically accurate content, which requires domain-specific expertise.

Real-Time Interactivity in Live Environments

• Misuse: Using the model for real-time, interactive image generation in live environments (e.g., live-streaming or real-time gaming) where speed and consistency are critical, without proper optimization.
• Why It's Out-of-Scope: The model is designed for high-quality image generation but may not perform efficiently or effectively for live, real-time interactions, where real-time rendering and low latency are essential.

Bias, Risks, and Limitations

• Cultural Bias: The model may generate images that are more reflective of dominant cultures, potentially underrepresenting minority cultures, though it can still create diverse visual content when properly guided.
• Gender and Racial Bias: The model might produce stereotypical representations based on gender or race, but it is capable of generating diverse and inclusive imagery when trained with diverse datasets.
• Over-simplification: In certain cases, the model might oversimplify complex scenarios or settings, reducing intricate details that may be crucial in highly specialized fields, while still excelling in creative visual tasks.
• Unintended Interpretations: The model may generate images that are open to misinterpretation, but it can be adjusted and refined to ensure better alignment with user intent without losing its creative potential.
• Abstract and Conceptual Limitations: While the model is adept at generating realistic imagery, it may struggle to visualize abstract or conceptual ideas in the same way it handles realistic or tangible subjects. However, it can still generate impressive, visually appealing concepts.

Recommendations

• Awareness of Bias: Users should be mindful of the potential cultural, racial, and gender biases that may appear in generated content. It’s important to actively curate and diversify training datasets or input prompts to minimize such biases.

• Responsible Use: Users should ensure that the model is used in ways that promote positive, constructive, and inclusive imagery. For projects involving sensitive or personal content, human oversight is recommended to avoid misrepresentation or harm.

• Verification and Fact-Checking: Given the model’s inability to provide accurate domain-specific knowledge, users should verify the accuracy of the generated content in fields requiring high precision, such as scientific, medical, or historical images.

• Contextual Refinement: Since the model doesn’t inherently understand context, users should carefully refine prompts to avoid misaligned or inappropriate outputs, especially in creative fields where subtlety and nuance are critical.

• Ethical and Responsible Use: Users must ensure that the model is not exploited for harmful purposes such as generating misleading content, deepfakes, or offensive imagery. Ethical guidelines and responsible practices should be followed in all use cases.

How to Get Started with the Model

Prerequisites:

• Install necessary libraries:

pip install transformers diffusers torch Pillow huggingface_hub PIL io requests

• Code to Use the Model:

from transformers import AutoTokenizer #, AutoModelForImageGeneration
from diffusers import DiffusionPipeline, FluxPipeline
import torch
from PIL import Image
import requests
from io import BytesIO

# Your Hugging Face API token
API_TOKEN = "<retacted>"

# Load the model and tokenizer from Hugging Face
model_name = "future-technologies/Floral-High-Dynamic-Range"
#model_name = "black-forest-labs/FLUX.1-dev"

# Error handling for model loading
try:
    #model = AutoModelForImageGeneration.from_pretrained(model_name, use_auth_token=API_TOKEN)
    tokenizer = AutoTokenizer.from_pretrained(model_name, token=API_TOKEN)
except Exception as e:
    print(f"Error loading model: {e}")
    exit()

# Initialize the diffusion pipeline
try:
    pipe = FluxPipeline.from_pretrained(model_name)
    pipe.to("cuda" if torch.cuda.is_available() else "cpu")
except Exception as e:
    print(f"Error initializing pipeline: {e}")
    exit()

# Example prompt for image generation
prompt = "A futuristic city skyline with glowing skyscrapers during sunset, reflecting the light."

# Error handling for image generation
try:
    result = pipe(prompt)
    image = result.images[0]
except Exception as e:
    print(f"Error generating image: {e}")
    exit()

# Save or display the image
try:
    image.save("floral-hdr.png")
    image.show()
except Exception as e:
    print(f"Error saving or displaying image: {e}")
    exit()

print("Image generation and saving successful!")

Training Details

The Floral High Dynamic Range (LIGM) model has been trained on a diverse and extensive dataset containing over 1 billion high-quality images. This vast dataset encompasses a wide range of visual styles and content, enabling the model to generate highly detailed and accurate images. The training process focused on capturing intricate features, dynamic lighting, and complex scenes, which allows the model to produce images with stunning realism and creative potential.

Training Hyperparameters

• Training regime: bf16 non-mixed precision

Environmental Impact

• Hardware Type: Nvidia A100 GPU
• Hours used: 45k+
• Cloud Provider: Future Technologies Limited
• Compute Region: Rajasthan, India
• Carbon Emitted: 0 (Powered by clean Solar Energy with no harmful or polluting machines used. Environmentally sustainable and eco-friendly!)