akba08/ultra-realistic-cinematic-photography

Ultra Realistic Cinematic Photography Dataset

📘 Dataset Card: ultra-realistic-cinematic-photography

🏷️ Dataset Summary

ultra-realistic-cinematic-photography is a high-quality image dataset curated for training and fine-tuning generative models on ultra-realistic, cinematic-style photography. The dataset contains a diverse collection of images across multiple categories—wildlife, domestic animals, food, flowers, landscapes, nature scenes, and artistic compositions—all rendered in a consistent cinematic, photorealistic, shallow-depth-of-field visual style.

This dataset is ideal for:

• High-fidelity image generation
• Cinematic photography style transfer
• Photorealistic diffusion model training
• Aesthetic model conditioning
• Benchmarking fine-tuned generative models

All images demonstrate a strong focus on:

• Cinematic lighting
• High dynamic range
• Crisp details and textures
• Shallow DOF / bokeh
• Professional DSLR-like composition
• Ultra-realistic rendering of subjects

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🖼️ Dataset Contents

This dataset includes 165+ high-resolution images, spanning:

Wildlife & Animals

• Lions, lionesses
• White tigers
• Snow leopards
• Triceratops (fantasy realism)
• Flamingos
• Birds / seagulls
• Cats & kittens
• Dogs (indoor + beach scenes)

Nature & Landscapes

• Mountain rivers
• Coastal sunsets
• Beach scenes
• Flower fields
• Tropical canyons

Food Photography

• Gourmet dishes
• Steaks, lamb chops
• Pasta, soups, casseroles
• Oven-baked dishes
• Styled table settings All photographed in warm, cinematic, rustic lighting.

Floral Macro Photography

• Lilies, daisies, blossoms
• Dew-covered petals
• Macro shallow-focus closeups

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🧭 Intended Use

This dataset is designed for:

• Training diffusion models, LDMs, GANs, or transformer-based vision models

• Fine-tuning Stable Diffusion, SDXL, Flux, Sana, LlamaGen, and similar

• Building cinematic aesthetic models

• Conditioning models to generate:

  • Ultra-realistic animals
  • Photorealistic food
  • Cinematic landscapes
  • High-end macro floral photography

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🔍 Keywords (Search Optimization)

photorealistic, realistic photography, cinematic photography, ultra-realistic images,
high-quality dataset, bokeh, shallow depth of field, wildlife photography, food photography,
flower photos, nature photography, cinematic lighting, HDR images, DSLR look, high-detail,
diffusion model dataset, generative AI dataset, realistic animals, macro flowers,
ultra-detailed photos, aesthetic dataset, image generation, huggingface dataset

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📂 Dataset Structure

Format

• JSONL annotations
• Optimized for generative tasks

Image Resolutions

Common resolutions:

• 768×1024
• 1024×1024
• 1152×768
• 1408×704 (variations depending on aspect ratio)

Images are high quality and consistent in style.

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🔧 How to Use

Load with Hugging Face Datasets

from datasets import load_dataset

dataset = load_dataset("your-username/ultra-realistic-cinematic-photography")

Example (Stable Diffusion Fine-tuning with Diffusers)

from diffusers import StableDiffusionPipeline
import torch

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipe = pipe.to("cuda")

prompt = "a majestic lion resting on a rock at golden hour, ultra-realistic cinematic photography"
image = pipe(prompt).images[0]
image.show()

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📜 Dataset Creation

Source and Curation

• Images are curated and selected for consistent cinematic aesthetics.
• Scenes were chosen to represent a broad range of natural textures, subjects, and lighting conditions.
• The dataset focuses on ultra-realistic rendering with visually rich detail useful for generative modeling.

Ethical Considerations

• No identifiable humans are present.
• All content is safe for academic use.
• Users should avoid generating harmful or misleading content.

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📊 Statistics

Category	Approx. Count
Wildlife Animals	~40
Domestic Animals	~30
Food Photography	~30
Flowers & Macro	~25
Landscapes & Nature	~25
Artistic Fantasy (e.g., triceratops)	~5
Total	165+

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📎 Citation

Coming Soon

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📮 Contact / Issues

For questions, issues, improvements, or extended versions of the dataset, please open a GitHub issue or contact the dataset maintainer.