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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 = """
Instructions placeholder.
"""
def main():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = RegionDiffusion(device)
def generate(
json.loads(text_input): str,
negative_text: str,
height: int,
width: int,
seed: int,
steps: int,
guidance_weight: float,
):
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
# 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))
# 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)
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))
cat_img = np.concatenate([plain_img[0], rich_img[0]], 1)
return [cat_img, token_maps]
with gr.Blocks() as demo:
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/>""")
with gr.Row():
with gr.Column():
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)
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():
result = gr.Image(label='Result')
token_map = gr.Image(label='TokenMap')
with gr.Row():
examples = [
[
'{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#b26b00"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background.\n"}]}',
'',
512,
512,
6,
],
[
'{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "50px"}, "insert": "pineapples"}, {"insert": ", pepperonis, and mushrooms on the top, 4k, photorealistic\n"}]}',
'blurry, art, painting, rendering, drawing, sketch, ugly, duplicate, morbid, mutilated, mutated, deformed, disfigured low quality, worst quality',
768,
896,
6,
],
[
'{"ops":[{"insert":"a "},{"attributes":{"font":"mirza"},"insert":"beautiful garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain in the background"},{"insert":"\n"}]}',
'',
512,
512,
3,
],
[
'{"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.\n"}]}',
'',
512,
512,
6,
],
]
gr.Examples(examples=examples,
inputs=[
text_input,
negative_prompt,
height,
width,
seed,
],
outputs=[
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,
],
outputs=[result, token_map],
)
demo.queue(concurrency_count=1)
demo.launch(share=False)
if __name__ == "__main__":
main() |