Update README.md

README.md

@@ -33,9 +33,7 @@ The GLIGEN model was created by researchers and engineers from [University of Wi
 The [`StableDiffusionGLIGENPipeline`] can generate photorealistic images conditioned on grounding inputs.
 
 Along with text and bounding boxes, if input images are given, this pipeline can insert objects described by text at the region defined by bounding boxes.
-Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes.
-
-It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.
+Otherwise, it'll generate an image described by the caption/prompt and insert objects described by text at the region defined by bounding boxes. It's trained on COCO2014D and COCO2014CD datasets, and the model uses a frozen CLIP ViT-L/14 text encoder to condition itself on grounding inputs.
 
 This weights here are intended to be used with the 🧨 Diffusers library. If you want to use one of the official checkpoints for a task, explore the [gligen](https://huggingface.co/gligen) Hub organizations!
 
@@ -77,7 +75,6 @@ from diffusers.utils import load_image
 model_id = "masterful/gligen-1-4-inpainting-text-box"
 device = "cuda"
 
-
 pipe = StableDiffusionGLIGENPipeline.from_pretrained(model_id, variant="fp16", torch_dtype=torch.float16)
 pipe = pipe.to(device)