
 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)

{"license": "apache-2.0"}
arxiv:2112.10741 license:apache-2.0 region:us
TAGS
#arxiv-2112.10741 #license-apache-2.0 #region-us 


 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

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



## Samples

1. !sample_1
2. !sample_2
3. !sample_3

## Usage ## Samples

1. !sample_1
2. !sample_2
3. !sample_3
TAGS
#arxiv-2112.10741 #license-apache-2.0 #region-us 
 ## Usage ## Samples

1. !sample_1
2. !sample_2
3. !sample_3
passage: TAGS
#arxiv-2112.10741 #license-apache-2.0 #region-us 
## Usage## Samples

1. !sample_1
2. !sample_2
3. !sample_3