{"id":20649640,"url":"https://github.com/rsn601kri/imagegenerationtool","last_synced_at":"2026-04-17T13:05:38.962Z","repository":{"id":246792485,"uuid":"822198180","full_name":"RSN601KRI/ImageGenerationTool","owner":"RSN601KRI","description":"Welcome to the Image Generation Tool, leveraging the power of diffusion models to create high-quality, realistic images. Diffusion models have emerged as a groundbreaking approach in the field of generative models, often surpassing the performance of traditional Generative Adversarial Networks (GANs).","archived":false,"fork":false,"pushed_at":"2024-06-30T15:15:03.000Z","size":363,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-17T10:12:51.504Z","etag":null,"topics":["googlecolab","jyputer-notebook","python"],"latest_commit_sha":null,"homepage":"https://colab.research.google.com/drive/1KpU1BZ3Rj6uiITQiJbXezkIkxBqAWE_p#scrollTo=Vi2HzE9ipLf1","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RSN601KRI.png","metadata":{"files":{"readme":"Readme.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-06-30T14:56:33.000Z","updated_at":"2024-06-30T15:17:29.000Z","dependencies_parsed_at":null,"dependency_job_id":"a9c783c7-8c53-4f39-add5-0871ad9c921d","html_url":"https://github.com/RSN601KRI/ImageGenerationTool","commit_stats":null,"previous_names":["rsn601kri/imagegenerationtool"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RSN601KRI%2FImageGenerationTool","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RSN601KRI%2FImageGenerationTool/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RSN601KRI%2FImageGenerationTool/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RSN601KRI%2FImageGenerationTool/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RSN601KRI","download_url":"https://codeload.github.com/RSN601KRI/ImageGenerationTool/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":242737094,"owners_count":20177105,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["googlecolab","jyputer-notebook","python"],"created_at":"2024-11-16T17:15:39.142Z","updated_at":"2026-04-17T13:05:38.932Z","avatar_url":"https://github.com/RSN601KRI.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Image Generation Tool using 🎨 Diffusion Models\n\nWelcome to the Image Generation Tool, leveraging the power of diffusion models to create high-quality, realistic images. Diffusion models have emerged as a groundbreaking approach in the field of generative models, often surpassing the performance of traditional Generative Adversarial Networks (GANs).\n\n## Table of Contents\n- [Introduction](#introduction)\n- [How Diffusion Models Work](#how-diffusion-models-work)\n- [Using Hugging Face for Diffusion Models](#using-hugging-face-for-diffusion-models)\n- [Generating Images with Dream-like Diffusion](#generating-images-with-dream-like-diffusion)\n- [Features](#features)\n- [Tech Stack](#tech-stack)\n- [Usage](#usage)\n- [Examples](#examples)\n- [Contributing](#contributing)\n- [License](#license)\n\n## Introduction\n\nDiffusion models generate images through a process of iterative noise addition and removal. By training on this process, these models learn to produce highly realistic images. Our tool utilizes pre-trained diffusion models from Hugging Face, specifically the Dream-like Diffusion 1.0 model, to simplify and enhance the image generation experience.\n\n## How Diffusion Models Work\n\nDiffusion models operate by:\n1. **Adding Noise:** Starting with a clear image, noise is gradually added to it.\n2. **Training to Reverse Noise:** The model learns to reverse the process, predicting the original clear image from the noisy one.\n3. **Iterative Process:** This iterative process of adding and removing noise enables the generation of new, high-quality images.\n\n## Using Hugging Face for Diffusion Models\n\nHugging Face is a leading machine-learning community that offers a wide range of pre-trained models, including diffusion models. The Hugging Face Diffusers library provides an easy-to-use interface for these models, allowing for seamless integration and image generation.\n\n## Generating Images with Dream-like Diffusion\n\nThe Dream-like Diffusion 1.0 model from Hugging Face enables the generation of realistic images based on text prompts. Key parameters that can be adjusted include:\n- **Number of Inference Steps:** Higher steps improve quality but increase computation time.\n- **Negative Prompting:** Helps refine the output by guiding the model on what not to include.\n- **Image Dimensions:** Customize the height and width of the generated images.\n- **Batch Generation:** Specify the number of images to generate per prompt.\n\n## Features\n\n- **High-Quality Image Generation:** Leveraging the strengths of diffusion models for superior image quality.\n- **Customizable Parameters:** Fine-tune the image generation process with adjustable parameters.\n- **User-Friendly Interface:** Intuitive and easy-to-use, even for those new to diffusion models.\n- **Pre-Trained Models:** Utilize robust, pre-trained models from Hugging Face for efficient image generation.\n\n## Tech Stack\n\n- **Languages:** Python\n- **Libraries:** \n  - Hugging Face Diffusers\n  - Transformers\n  - PyTorch\n- **APIs:** Hugging Face Hub\n\n## Usage\n\nTo generate images using the Dream-like Diffusion model:\n\n1. **Install the required libraries:**\n    ```bash\n    pip install diffusers transformers torch\n    ```\n\n2. **Load the pre-trained model:**\n    ```python\n    from diffusers import DiffusionPipeline\n\n    pipe = DiffusionPipeline.from_pretrained(\"huggingface/dreamlike-diffusion-1.0\")\n    ```\n\n3. **Generate an image from a text prompt:**\n    ```python\n    prompt = \"A serene landscape with mountains and a river\"\n    images = pipe(prompt, num_inference_steps=50, height=512, width=512, num_images_per_prompt=1)\n    ```\n\n4. **Save or display the generated image:**\n    ```python\n    images[0].save(\"generated_image.png\")\n    ```\n\n## Examples\n\nHere are a few example prompts and their generated images:\n\n- **Prompt:** \"A futuristic city skyline at sunset\"\n\n  \n  ![image1](https://github.com/RSN601KRI/ImageGenerationTool/assets/106860359/6efb0ffe-9fca-42c1-b670-671c969684c6)\n\n\n- **Prompt:** \"A vibrant forest in autumn\"\n\n  \n  ![image2](https://github.com/RSN601KRI/ImageGenerationTool/assets/106860359/80905ff8-d692-4fec-83e4-17b73044d327)\n  \n\n## License\n\nThis project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.\n\n---\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsn601kri%2Fimagegenerationtool","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frsn601kri%2Fimagegenerationtool","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frsn601kri%2Fimagegenerationtool/lists"}