Update README.md

README.md

@@ -14,35 +14,6 @@ configs:
   data_files:
   - split: train
     path: data/train-*
-  dataset_name: "sd-class-beer-32"
-  dataset_link: "https://huggingface.co/datasets/ffjefckds/sd-class-beer-32"
-  size: "25 images"
-  resolution: "1024x1024 px"
-  creator: "Crayon (DALL·E Mini)"
-  task: "Unconditional image generation"
-  license: "MIT"  
-  intended_use: "This dataset is intended for training diffusion models on beer-related images."
-  dataset_description: |
-    This synthetic dataset contains 25 images of beer in 1024x1024 px resolution, created using Crayon (DALL·E Mini). 
-    The images depict various beer-related objects and scenes, designed for use in training diffusion models for unconditional image generation.
-  features: 
-    - Images: The dataset consists of 25 images generated in the style of Crayon (DALL·E Mini). Each image is 1024x1024 px and portrays scenes related to beer.
-
-  usage_instructions: |
-    To use the dataset, you can load the images using standard image-processing libraries like PIL or OpenCV.
-    For model training, this dataset can be used with diffusion models like DDPMPipeline from Hugging Face's diffusers library.
-
-    Example usage:
-    ```python
-    from diffusers import DDPMPipeline
-
-    pipeline = DDPMPipeline.from_pretrained('{hub_model_id}')
-    image = pipeline().images[0]
-    image
-    ```
-  additional_info: |
-    The dataset is focused on the class of beer and can be used for training or fine-tuning image generation models.
-    Since it is synthetic, the data may not represent real-world beer in diverse settings but is intended for the development of general beer-related imagery generation.
 ---