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
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
# !pip install diffusers
from diffusers import DiffusionPipeline
import PIL.Image
import numpy as np
model_id = "fusing/glide-base"
# load model and scheduler
ddpm = DiffusionPipeline.from_pretrained(model_id)
# run pipeline in inference (sample random noise and denoise)
image = ddpm(eta=0.0, num_inference_steps=50)
# process image to PIL
image_processed = image.cpu().permute(0, 2, 3, 1)
image_processed = (image_processed + 1.0) * 127.5
image_processed = image_processed.numpy().astype(np.uint8)
image_pil = PIL.Image.fromarray(image_processed[0])
# save image
image_pil.save("test.png")
