File size: 1,919 Bytes
24558dc 1a72b9b 1d0d7c6 1a72b9b 1d0d7c6 1a72b9b 1d0d7c6 f096f47 1a72b9b 1d0d7c6 1a72b9b 1d0d7c6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
---
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. 
2. 
3. 
|