import math
import random
import os
import json
import time
import argparse
import torch
import numpy as np
from torchvision import transforms
from models.region_diffusion import RegionDiffusion
from utils.attention_utils import get_token_maps
from utils.richtext_utils import seed_everything, parse_json, get_region_diffusion_input,\
get_attention_control_input, get_gradient_guidance_input
import gradio as gr
from PIL import Image, ImageOps
help_text = """
If you are encountering an error or not achieving your desired outcome, here are some potential reasons and recommendations to consider:
1. If you format only a portion of a word rather than the complete word, an error may occur.
2. The token map may not always accurately capture the region of the formatted tokens. If you're experiencing this problem, experiment with selecting more or fewer tokens to expand or reduce the area covered by the token maps.
3. If you use font color and get completely corrupted results, you may consider decrease the color weight lambda.
4. Consider using a different seed.
"""
canvas_html = """<rich-text-editor id="rich-text-root"></rich-text-editor>"""
load_js = """
async () => {
const scripts = ["https://cdn.quilljs.com/1.3.6/quill.min.js","file=rich-text-to-json.js"]
scripts.forEach(src => {
const script = document.createElement('script');
script.src = src;
document.head.appendChild(script);
})
}
"""
get_js_data = """
async (text_input, negative_prompt, height, width, seed, steps, guidance_weight, color_guidance_weight, rich_text_input) => {
const richEl = document.getElementById("rich-text-root");
const data = richEl? richEl._data : {};
return [text_input, negative_prompt, height, width, seed, steps, guidance_weight, color_guidance_weight, JSON.stringify(data)];
}
"""
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RegionDiffusion(device)
def generate(
text_input: str,
negative_text: str,
height: int,
width: int,
seed: int,
steps: int,
guidance_weight: float,
color_guidance_weight: float,
rich_text_input: str
):
run_dir = 'results/'
# Load region diffusion model.
steps = 41 if not steps else steps
guidance_weight = 8.5 if not guidance_weight else guidance_weight
text_input = text_input if text_input != '' else rich_text_input
print('text_input', text_input)
# parse json to span attributes
base_text_prompt, style_text_prompts, footnote_text_prompts, footnote_target_tokens,\
color_text_prompts, color_names, color_rgbs, size_text_prompts_and_sizes, use_grad_guidance = parse_json(
json.loads(text_input), device)
# create control input for region diffusion
region_text_prompts, region_target_token_ids, base_tokens = get_region_diffusion_input(
model, base_text_prompt, style_text_prompts, footnote_text_prompts,
footnote_target_tokens, color_text_prompts, color_names)
# create control input for cross attention
text_format_dict = get_attention_control_input(
model, base_tokens, size_text_prompts_and_sizes)
# create control input for region guidance
text_format_dict, color_target_token_ids = get_gradient_guidance_input(
model, base_tokens, color_text_prompts, color_rgbs, text_format_dict, color_guidance_weight=color_guidance_weight)
seed_everything(seed)
# get token maps from plain text to image generation.
begin_time = time.time()
if model.attention_maps is None:
model.register_evaluation_hooks()
else:
model.reset_attention_maps()
plain_img = model.produce_attn_maps([base_text_prompt], [negative_text],
height=height, width=width, num_inference_steps=steps,
guidance_scale=guidance_weight)
print('time lapses to get attention maps: %.4f' % (time.time()-begin_time))
color_obj_masks, _ = get_token_maps(
model.attention_maps, run_dir, width//8, height//8, color_target_token_ids, seed)
model.masks, token_maps = get_token_maps(
model.attention_maps, run_dir, width//8, height//8, region_target_token_ids, seed, base_tokens)
color_obj_masks = [transforms.functional.resize(color_obj_mask, (height, width),
interpolation=transforms.InterpolationMode.BICUBIC,
antialias=True)
for color_obj_mask in color_obj_masks]
text_format_dict['color_obj_atten'] = color_obj_masks
model.remove_evaluation_hooks()
# generate image from rich text
begin_time = time.time()
seed_everything(seed)
rich_img = model.prompt_to_img(region_text_prompts, [negative_text],
height=height, width=width, num_inference_steps=steps,
guidance_scale=guidance_weight, use_grad_guidance=use_grad_guidance,
text_format_dict=text_format_dict)
print('time lapses to generate image from rich text: %.4f' %
(time.time()-begin_time))
return [plain_img[0], rich_img[0], token_maps]
with gr.Blocks() as demo:
demo.load(None, None, None, _js=load_js)
gr.HTML("""<h1 style="font-weight: 900; margin-bottom: 7px;">Expressive Text-to-Image Generation with Rich Text</h1>
<p> Visit our <a href="https://rich-text-to-image.github.io/rich-text-to-json.html">rich-text-to-json interface</a> to generate rich-text JSON input.<p/>
<p> <a href="https://rich-text-to-image.github.io">[Website]</a> | <a href="https://github.com/SongweiGe/rich-text-to-image">[Code]</a> <p/> """)
with gr.Row():
with gr.Column():
rich_text_el = gr.HTML(canvas_html,elem_id="canvas_html")
rich_text_input = gr.Textbox(value="", visible=False)
text_input = gr.Textbox(
label='Rich-text JSON Input',
max_lines=1,
placeholder='Example: \'{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#b26b00"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background.\n"}]}\'')
negative_prompt = gr.Textbox(
label='Negative Prompt',
max_lines=1,
placeholder='')
seed = gr.Slider(label='Seed',
minimum=0,
maximum=100000,
step=1,
value=6)
color_guidance_weight = gr.Slider(label='Color weight lambda',
minimum=0,
maximum=2,
step=0.1,
value=0.5)
with gr.Accordion('Other Parameters', open=False):
steps = gr.Slider(label='Number of Steps',
minimum=0,
maximum=500,
step=1,
value=41)
guidance_weight = gr.Slider(label='CFG weight',
minimum=0,
maximum=50,
step=0.1,
value=8.5)
width = gr.Dropdown(choices=[512, 768, 896],
value=512,
label='Width',
visible=True)
height = gr.Dropdown(choices=[512, 768, 896],
value=512,
label='height',
visible=True)
with gr.Row():
with gr.Column(scale=1, min_width=100):
generate_button = gr.Button("Generate")
with gr.Column():
with gr.Row():
plaintext_result = gr.Image(label='Plain-text')
richtext_result = gr.Image(label='Rich-text')
token_map = gr.Image(label='Token Maps')
with gr.Row():
gr.Markdown(help_text)
with gr.Row():
examples = [
[
'{"ops":[{"insert":"a "},{"attributes":{"font":"slabo"},"insert":"night sky filled with stars"},{"insert":" above a "},{"attributes":{"font":"roboto"},"insert":"turbulent sea with giant waves"}]}',
'',
512,
512,
6,
1,
None
],
[
'{"ops":[{"attributes":{"link":"the awe-inspiring sky and ocean in the style of J.M.W. Turner"},"insert":"the awe-inspiring sky and sea"},{"insert":" by "},{"attributes":{"font":"mirza"},"insert":"a coast with flowers and grasses in spring"}]}',
'',
512,
512,
9,
1,
None
],
[
'{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#b26b00"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background."}]}',
'',
512,
512,
6,
1,
None
],
[
'{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "50px"}, "insert": "pineapples"}, {"insert": ", pepperonis, and mushrooms on the top, 4k, photorealistic"}]}',
'blurry, art, painting, rendering, drawing, sketch, ugly, duplicate, morbid, mutilated, mutated, deformed, disfigured low quality, worst quality',
768,
896,
6,
1,
None
],
[
'{"ops":[{"insert":"a "},{"attributes":{"font":"mirza"},"insert":"beautiful garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain in the background"},{"insert":""}]}',
'',
512,
512,
3,
1,
None
],
[
'{"ops":[{"insert":"A close-up 4k dslr photo of a "},{"attributes":{"link":"A cat wearing sunglasses and a bandana around its neck."},"insert":"cat"},{"insert":" riding a scooter. Palm trees in the background."}]}',
'',
512,
512,
6,
1,
None
],
]
gr.Examples(examples=examples,
inputs=[
text_input,
negative_prompt,
height,
width,
seed,
color_guidance_weight,
rich_text_input,
],
outputs=[
plaintext_result,
richtext_result,
token_map,
],
fn=generate,
# cache_examples=True,
examples_per_page=20)
generate_button.click(
fn=generate,
inputs=[
text_input,
negative_prompt,
height,
width,
seed,
steps,
guidance_weight,
color_guidance_weight,
rich_text_input
],
outputs=[plaintext_result, richtext_result, token_map],
_js=get_js_data
)
demo.queue(concurrency_count=1)
demo.launch(share=False)
if __name__ == "__main__":
main()