File size: 5,298 Bytes
d44c760 42abcf4 d44c760 66216ad d44c760 66216ad 862921d 66216ad d44c760 66216ad b32e15e 66216ad b32e15e 66216ad b32e15e 66216ad b32e15e 66216ad |
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 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 |
---
language:
- en
tags:
- reco
- text-to-image
- layout-to-image
pipeline_tag: text-to-image
widget:
- text: "A box contains six donuts with varying types of glazes and toppings. <|endoftext|> <bin514> <bin575> <bin741> <bin765> <|startoftext|> chocolate donut. <|endoftext|> <bin237> <bin517> <bin520> <bin784> <|startoftext|> dark vanilla donut. <|endoftext|> <bin763> <bin575> <bin988> <bin745> <|startoftext|> donut with sprinkles. <|endoftext|> <bin234> <bin281> <bin524> <bin527> <|startoftext|> donut with powdered sugar. <|endoftext|> <bin515> <bin259> <bin767> <bin514> <|startoftext|> pink donut. <|endoftext|> <bin753> <bin289> <bin958> <bin506> <|startoftext|> brown donut. <|endoftext|>"
---
# Diffusers 🧨 port of [ReCo: Region-Controlled Text-to-Image Generation (CVPR 2023)](https://arxiv.org/abs/2211.15518)
- Original authors: Zhengyuan Yang, Jianfeng Wang, Zhe Gan, Linjie Li, Kevin Lin, Chenfei Wu, Nan Duan, Zicheng Liu, Ce Liu, Michael Zeng, Lijuan Wang
- Original github repo by authors: https://github.com/microsoft/ReCo
- Converted to Diffusers: Jaemin Cho
# LAION checkpoint
- original pytorch lightning checkpoint: https://unitab.blob.core.windows.net/data/reco/reco_laion_1232.ckpt
- original configuration yaml: https://github.com/microsoft/ReCo/blob/main/configs/reco/v1-finetune_laion.yaml
# Example Usage
```python
import torch
from diffusers import StableDiffusionPipeline
pipe = StableDiffusionPipeline.from_pretrained(
"j-min/reco_sd14_laion",
torch_dtype=torch.float16
)
pipe = pipe.to("cuda")
prompt = "A box contains six donuts with varying types of glazes and toppings. <|endoftext|> <bin514> <bin575> <bin741> <bin765> <|startoftext|> chocolate donut. <|endoftext|> <bin237> <bin517> <bin520> <bin784> <|startoftext|> dark vanilla donut. <|endoftext|> <bin763> <bin575> <bin988> <bin745> <|startoftext|> donut with sprinkles. <|endoftext|> <bin234> <bin281> <bin524> <bin527> <|startoftext|> donut with powdered sugar. <|endoftext|> <bin515> <bin259> <bin767> <bin514> <|startoftext|> pink donut. <|endoftext|> <bin753> <bin289> <bin958> <bin506> <|startoftext|> brown donut. <|endoftext|>"
generated_image = pipe(
prompt,
guidance_scale=4).images[0]
generated_image
```
## method to create ReCo prompts
```python
def create_reco_prompt(
caption: str = '',
phrases=[],
boxes=[],
normalize_boxes=True,
image_resolution=512,
num_bins=1000,
):
"""
method to create ReCo prompt
caption: global caption
phrases: list of regional captions
boxes: list of regional coordinates (unnormalized xyxy)
"""
SOS_token = '<|startoftext|>'
EOS_token = '<|endoftext|>'
box_captions_with_coords = []
box_captions_with_coords += [caption]
box_captions_with_coords += [EOS_token]
for phrase, box in zip(phrases, boxes):
if normalize_boxes:
box = [float(x) / image_resolution for x in box]
# quantize into bins
quant_x0 = int(round((box[0] * (num_bins - 1))))
quant_y0 = int(round((box[1] * (num_bins - 1))))
quant_x1 = int(round((box[2] * (num_bins - 1))))
quant_y1 = int(round((box[3] * (num_bins - 1))))
# ReCo format
# Add SOS/EOS before/after regional captions
box_captions_with_coords += [
f"<bin{str(quant_x0).zfill(3)}>",
f"<bin{str(quant_y0).zfill(3)}>",
f"<bin{str(quant_x1).zfill(3)}>",
f"<bin{str(quant_y1).zfill(3)}>",
SOS_token,
phrase,
EOS_token
]
text = " ".join(box_captions_with_coords)
return text
caption = "a photo of bus and boat; boat is left to bus."
phrases = ["a photo of a bus.", "a photo of a boat."]
boxes = [[0.702, 0.404, 0.927, 0.601], [0.154, 0.383, 0.311, 0.487]]
prompt = create_reco_prompt(caption, phrases, boxes, normalize_boxes=False)
prompt
>>> 'a photo of bus and boat; boat is left to bus. <|endoftext|> <bin701> <bin404> <bin926> <bin600> <|startoftext|> a photo of a bus. <|endoftext|> <bin154> <bin383> <bin311> <bin487> <|startoftext|> a photo of a boat. <|endoftext|>'
caption = "A box contains six donuts with varying types of glazes and toppings."
phrases = ["chocolate donut.", "dark vanilla donut.", "donut with sprinkles.", "donut with powdered sugar.", "pink donut.", "brown donut."]
boxes = [[263.68, 294.912, 380.544, 392.832], [121.344, 265.216, 267.392, 401.92], [391.168, 294.912, 506.368, 381.952], [120.064, 143.872, 268.8, 270.336], [264.192, 132.928, 393.216, 263.68], [386.048, 148.48, 490.688, 259.584]]
prompt = create_reco_prompt(caption, phrases, boxes)
prompt
>>> 'A box contains six donuts with varying types of glazes and toppings. <|endoftext|> <bin514> <bin575> <bin743> <bin766> <|startoftext|> chocolate donut. <|endoftext|> <bin237> <bin517> <bin522> <bin784> <|startoftext|> dark vanilla donut. <|endoftext|> <bin763> <bin575> <bin988> <bin745> <|startoftext|> donut with sprinkles. <|endoftext|> <bin234> <bin281> <bin524> <bin527> <|startoftext|> donut with powdered sugar. <|endoftext|> <bin515> <bin259> <bin767> <bin514> <|startoftext|> pink donut. <|endoftext|> <bin753> <bin290> <bin957> <bin506> <|startoftext|> brown donut. <|endoftext|>'
```
|