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README.md

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+---
+name: Stable Diffusion Model
+description: Text-to-image generative model using PyTorch and Hugging Face Diffusers
+version: 1.0.0
+license: apache-2.0
+authors:
+  - name: Maneesh Singh
+    url: https://github.com/Maneesh-Singh123
+tags:
+  - text-to-image
+  - generative-model
+  - stable-diffusion
+  - pytorch
+  - hugging-face
+  - diffusers
+model-type: latent-diffusion
+task: image-generation
+dataset: various
+metrics:
+  - psnr
+  - ssim
+  - frechet-inception-distance
+parameters:
+  - learning-rate: 5e-5
+  - batch-size: 8
+  - num-epochs: 10
+  - num-inference-steps: 50
+---
+
+
+# Stable Diffusion Model - PyTorch & Hugging Face Diffusers
+
+This repository contains the implementation of the **Stable Diffusion** model using **PyTorch** and **Hugging Face Diffusers**. Stable Diffusion is a text-to-image generative model that leverages a diffusion process to generate high-quality, detailed images from textual descriptions.
+
+## Table of Contents
+- [Installation](#installation)
+- [Usage](#usage)
+- [Model Overview](#model-overview)
+- [Training](#training)
+- [Inference](#inference)
+- [Examples](#examples)
+- [Acknowledgments](#acknowledgments)
+
+## Installation
+
+To get started, you'll need to clone this repository and install the required dependencies. We recommend using a virtual environment to avoid conflicts.
+
+```bash
+git clone https://github.com/the-antique-piece/stable_diffusion.git
+cd stable_diffusion
+
+# Create and activate a virtual environment (optional)
+python -m venv venv
+source venv/bin/activate  # On Windows use `venv\Scripts\activate`
+
+# Install required dependencies
+pip install -r requirements.txt
+```
+
+### Requirements
+
+- Python 3.8+
+- PyTorch
+- Hugging Face Diffusers
+- Transformers
+- Datasets
+- PIL
+
+To install all dependencies manually, you can run:
+
+```bash
+pip install torch diffusers transformers datasets pillow flax
+```
+
+## Model Overview
+
+Stable Diffusion is a **latent diffusion model** that is trained to denoise a latent representation of the image, conditioned on a text prompt. It operates by gradually reversing a noise process applied to the data during training, allowing it to generate images starting from pure noise.
+
+This repository implements the following features:
+- **Text-to-image generation**: Generate images based on a text prompt.
+- **Fine-tuning**: Customize the model for specific datasets.
+- **Inference**: Run the model on pre-trained weights for fast image generation.
+
+### Model Architecture
+
+The Stable Diffusion model consists of:
+1. **Variational Autoencoder (VAE)** - Encodes images into latent space.
+2. **U-Net** - A denoising network that learns to reverse the noise process.
+3. **Text Encoder** - Encodes text prompts into latent space to guide image generation.
+
+## Usage
+
+### Text-to-Image Generation
+
+Once the environment is set up, you can generate images from text prompts as follows:
+
+```python
+from diffusers import DiffusionPipeline
+import torch
+
+# Remove torch_dtype=torch.float16
+pipeline = DiffusionPipeline.from_pretrained("stable-diffusion/stable-diffusion-v1")
+
+# Use a Nvidia GPU if available, or else cpu
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+pipeline.to(device)
+
+pipeline("An image of futuristic city where everyting is perfect").images[0]
+```
+### To get more control over image generation create seperate python file and paste this code and run it from virtual environment using 'python python_script.py'
+**If You don't running this model on nvidia GPU change torch_type=torch.float32**
+
+```python 
+from diffusers import DiffusionPipeline
+import torch
+# Provide a path to directory where the model_index.json is placed
+weights_path = "directory_path_to_model_index.json"
+pipeline = DiffusionPipeline.from_pretrained(weights_path, torch_dtype=torch.float16)
+# You can change prompt to get different photos, increase inference_steps's value to get high quality images
+prompt = 'a cat sitting on a windowsill, looking at the sunset'
+height, width = 512, 512
+num_inference_steps = 50
+
+image = pipeline(prompt, height=height, width=width, num_inference_steps=num_inference_steps).images[0]
+image.save("myimage.png")
+```
+### Custom Model Weights
+
+If you have custom model weights, load them into the pipeline:
+
+```python
+pipe = StableDiffusionPipeline.from_pretrained("path/to/your/model").to("cuda")
+```
+
+## Training
+
+This repository also supports fine-tuning the Stable Diffusion model on your own dataset. To prepare for training:
+
+1. **Prepare Dataset**: Ensure that your dataset is in a format compatible with Hugging Face's `datasets` library.
+2. **Configure Training Parameters**: Adjust hyperparameters such as learning rate, batch size, and number of epochs.
+
+### Fine-tuning Example
+
+```bash
+python train.py --dataset_path /path/to/dataset --output_dir /path/to/output --batch_size 8 --learning_rate 5e-5 --num_epochs 10
+```
+
+Training can be done with the `train.py` script, which supports distributed training for large datasets and multiple GPUs.
+
+## Inference
+
+To run inference on a trained model, use the `inference.py` script:
+
+```bash
+python inference.py --model_path /path/to/trained/model --prompt "a futuristic city skyline at sunset"
+```
+
+## Examples
+
+Here are some example prompts and the corresponding generated images:
+
+- **Prompt**: "a cat sitting on a windowsill, looking at the sunset"
+  ![Example Image 1](generated_images/image8.png)
+
+- **Prompt**: "a futuristic cityscape with flying cars"
+  ![Example Image 2](generated_images/image7.png)
+
+## Acknowledgments
+
+This implementation is based on the **Stable Diffusion** model by [Huggingface](https://github.com/huggingface/diffusers.git) and utilizes the Huggingface **Diffusers** library.
+
+## License
+
+This project is licensed under the terms of the [Apache 2.0 License](LICENSE).

model_index.json

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+{
+  "_class_name": "StableDiffusionPipeline",
+  "_diffusers_version": "0.6.0",
+  "feature_extractor": ["transformers", "CLIPImageProcessor"],
+  "safety_checker": ["stable_diffusion", "StableDiffusionSafetyChecker"],
+  "scheduler": ["diffusers", "PNDMScheduler"],
+  "text_encoder": ["transformers", "CLIPTextModel"],
+  "tokenizer": ["transformers", "CLIPTokenizer"],
+  "unet": ["diffusers", "UNet2DConditionModel"],
+  "vae": ["diffusers", "AutoencoderKL"]
+}