readme: ---
  license: apache-2.0
  ---
  
   GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
  
  **Paper**: [GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models](https://arxiv.org/abs/2112.10741)
  
  **Abstract**:
  
  *Diffusion models have recently been shown to generate high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.*
  
  ## Usage
  
  ```python
  # !pip install diffusers
  import torch
  from diffusers import DiffusionPipeline
  import PIL.Image
  
  model_id = "fusing/glide-base"
  
  # load model and scheduler
  pipeline = DiffusionPipeline.from_pretrained(model_id)
  
  # run inference (text-conditioned denoising + upscaling)
  img = pipeline("a crayon drawing of a corgi")
  
  # process image to PIL
  img = img.squeeze(0)
  img = ((img + 1)*127.5).round().clamp(0, 255).to(torch.uint8).cpu().numpy()
  image_pil = PIL.Image.fromarray(img)
  
  # save image
  image_pil.save("test.png")
  ```
  
  ## Samples
  
  1. ![sample_1](https://huggingface.co/datasets/anton-l/images/resolve/main/glide1.png)
  2. ![sample_2](https://huggingface.co/datasets/anton-l/images/resolve/main/glide2.png)
  3. ![sample_3](https://huggingface.co/datasets/anton-l/images/resolve/main/glide3.png)
  