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_xl import RegionDiffusionXL 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 from share_btn import community_icon_html, loading_icon_html, share_js, css 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. If you use font color and get completely corrupted results, you may consider decrease the color weight lambda. 3. Consider using a different seed. """ canvas_html = """""" get_js_data = """ async (text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, height, width, steps, guidance_weights) => { const richEl = document.getElementById("rich-text-root"); const data = richEl? richEl.contentDocument.body._data : {}; return [text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, JSON.stringify(data), height, width, steps, guidance_weights]; } """ set_js_data = """ async (text_input) => { const richEl = document.getElementById("rich-text-root"); const data = text_input ? JSON.parse(text_input) : null; if (richEl && data) richEl.contentDocument.body.setQuillContents(data); } """ get_window_url_params = """ async (url_params) => { const params = new URLSearchParams(window.location.search); url_params = Object.fromEntries(params); return [url_params]; } """ def load_url_params(url_params): if 'prompt' in url_params: return gr.update(visible=True), url_params else: return gr.update(visible=False), url_params def main(): device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') model = RegionDiffusionXL() def generate( text_input: str, negative_text: str, num_segments: int, segment_threshold: float, inject_interval: float, inject_background: float, seed: int, color_guidance_weight: float, rich_text_input: str, height: int, width: int, steps: int, guidance_weight: float, ): run_dir = 'results/' os.makedirs(run_dir, exist_ok=True) # Load region diffusion model. height = int(height) if height else 1024 width = int(width) if width else 1024 steps = 41 if not steps else steps guidance_weight = 8.5 if not guidance_weight else guidance_weight text_input = rich_text_input if rich_text_input != '' and rich_text_input != None else text_input print('text_input', text_input, width, height, steps, guidance_weight, num_segments, segment_threshold, inject_interval, inject_background, color_guidance_weight, negative_text) if (text_input == '' or rich_text_input == ''): raise gr.Error("Please enter some text.") # 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, color_guidance_weight=color_guidance_weight) seed_everything(seed) # get token maps from plain text to image generation. begin_time = time.time() if model.selfattn_maps is None and model.crossattn_maps is None: model.remove_tokenmap_hooks() model.register_tokenmap_hooks() else: model.reset_attention_maps() model.remove_tokenmap_hooks() plain_img = model.sample([base_text_prompt], negative_prompt=[negative_text], height=height, width=width, num_inference_steps=steps, guidance_scale=guidance_weight, run_rich_text=False) print('time lapses to get attention maps: %.4f' % (time.time()-begin_time)) seed_everything(seed) color_obj_masks, segments_vis, token_maps = get_token_maps(model.selfattn_maps, model.crossattn_maps, model.n_maps, run_dir, 1024//8, 1024//8, color_target_token_ids[:-1], seed, base_tokens, segment_threshold=segment_threshold, num_segments=num_segments, return_vis=True) seed_everything(seed) model.masks, segments_vis, token_maps = get_token_maps(model.selfattn_maps, model.crossattn_maps, model.n_maps, run_dir, 1024//8, 1024//8, region_target_token_ids[:-1], seed, base_tokens, segment_threshold=segment_threshold, num_segments=num_segments, return_vis=True) color_obj_atten_all = torch.zeros_like(color_obj_masks[-1]) for obj_mask in color_obj_masks[:-1]: color_obj_atten_all += obj_mask 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 text_format_dict['color_obj_atten_all'] = color_obj_atten_all model.remove_tokenmap_hooks() # generate image from rich text begin_time = time.time() seed_everything(seed) rich_img = model.sample(region_text_prompts, negative_prompt=[negative_text], height=height, width=width, num_inference_steps=steps, guidance_scale=guidance_weight, use_guidance=use_grad_guidance, text_format_dict=text_format_dict, inject_selfattn=inject_interval, inject_background=inject_background, run_rich_text=True) print('time lapses to generate image from rich text: %.4f' % (time.time()-begin_time)) return [plain_img.images[0], rich_img.images[0], segments_vis, token_maps] with gr.Blocks(css=css) as demo: url_params = gr.JSON({}, visible=False, label="URL Params") gr.HTML("""

Expressive Text-to-Image Generation with Rich Text

Songwei Ge, Taesung Park, Jun-Yan Zhu, Jia-Bin Huang

UMD, Adobe, CMU

Duplicate Space | [Website] | [Code] | [Paper]

For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.""") 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', visible=False, 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"}]}\'', elem_id="text_input" ) negative_prompt = gr.Textbox( label='Negative Prompt', max_lines=1, placeholder='Example: poor quality, blurry, dark, low resolution, low quality, worst quality', elem_id="negative_prompt" ) segment_threshold = gr.Slider(label='Token map threshold', info='(See less area in token maps? Decrease this. See too much area? Increase this.)', minimum=0, maximum=1, step=0.01, value=0.25) inject_interval = gr.Slider(label='Detail preservation', info='(To preserve more structure from plain-text generation, increase this. To see more rich-text attributes, decrease this.)', minimum=0, maximum=1, step=0.01, value=0.) inject_background = gr.Slider(label='Unformatted token preservation', info='(To affect less the tokens without any rich-text attributes, increase this.)', minimum=0, maximum=1, step=0.01, value=0.3) color_guidance_weight = gr.Slider(label='Color weight', info='(To obtain more precise color, increase this, while too large value may cause artifacts.)', minimum=0, maximum=2, step=0.1, value=0.5) num_segments = gr.Slider(label='Number of segments', minimum=2, maximum=20, step=1, value=9) seed = gr.Slider(label='Seed', minimum=0, maximum=100000, step=1, value=6, elem_id="seed" ) 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=[1024], value=1024, label='Width', visible=True) height = gr.Dropdown(choices=[1024], value=1024, label='height', visible=True) with gr.Row(): with gr.Column(scale=1, min_width=100): generate_button = gr.Button("Generate") load_params_button = gr.Button( "Load from URL Params", visible=True) with gr.Column(): richtext_result = gr.Image( label='Rich-text', elem_id="rich-text-image") richtext_result.style(height=1024) with gr.Row(): plaintext_result = gr.Image( label='Plain-text', elem_id="plain-text-image") segments = gr.Image(label='Segmentation') with gr.Row(): token_map = gr.Image(label='Token Maps') with gr.Row(visible=False) as share_row: with gr.Group(elem_id="share-btn-container"): community_icon = gr.HTML(community_icon_html) loading_icon = gr.HTML(loading_icon_html) share_button = gr.Button( "Share to community", elem_id="share-btn") share_button.click(None, [], [], _js=share_js) with gr.Row(): gr.Markdown(help_text) with gr.Row(): footnote_examples = [ [ '{"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."}]}', '', 9, 0.3, 0.3, 0.5, 3, 0, None, ], [ '{"ops":[{"insert":"A cozy "},{"attributes":{"link":"A charming wooden cabin with Christmas decoration, warm light coming out from the windows."},"insert":"cabin"},{"insert":" nestled in a "},{"attributes":{"link":"Towering evergreen trees covered in a thick layer of pristine snow."},"insert":"snowy forest"},{"insert":", and a "},{"attributes":{"link":"A cute snowman wearing a carrot nose, coal eyes, and a colorful scarf, welcoming visitors with a cheerful vibe."},"insert":"snowman"},{"insert":" stands in the yard."}]}', '', 12, 0.4, 0.3, 0.5, 4, 0, None, ], [ '{"ops":[{"insert":"A "},{"attributes":{"link":"Happy Kung fu panda art, elder, asian art, volumetric lighting, dramatic scene, ultra detailed, realism, chinese"},"insert":"panda"},{"insert":" standing on a cliff by a waterfall, wildlife photography, photograph, high quality, wildlife, f 1.8, soft focus, 8k, national geographic, award - winning photograph by nick nichols"}]}', '', 5, 0.3, 0, 0.1, 4, 0, None, ], ] gr.Examples(examples=footnote_examples, label='Footnote examples', inputs=[ text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, ], outputs=[ plaintext_result, richtext_result, segments, token_map, ], fn=generate, cache_examples=True, examples_per_page=20) with gr.Row(): color_examples = [ [ '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#04a704"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}', 'lowres, had anatomy, bad hands, cropped, worst quality', 11, 0.5, 0.3, 0.3, 6, 0.5, None, ], [ '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#ff5df1"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}', 'lowres, had anatomy, bad hands, cropped, worst quality', 11, 0.5, 0.3, 0.3, 6, 0.5, None, ], [ '{"ops":[{"insert":"a beautifule girl with big eye, skin, and long "},{"attributes":{"color":"#999999"},"insert":"hair"},{"insert":", t-shirt, bursting with vivid color, intricate, elegant, highly detailed, photorealistic, digital painting, artstation, illustration, concept art."}]}', 'lowres, had anatomy, bad hands, cropped, worst quality', 11, 0.5, 0.3, 0.3, 6, 0.5, None, ], [ '{"ops":[{"insert":"a Gothic "},{"attributes":{"color":"#FD6C9E"},"insert":"church"},{"insert":" in a the sunset with a beautiful landscape in the background."}]}', '', 10, 0.5, 0.5, 0.3, 7, 0.5, None, ], ] gr.Examples(examples=color_examples, label='Font color examples', inputs=[ text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, ], outputs=[ plaintext_result, richtext_result, segments, token_map, ], fn=generate, cache_examples=True, examples_per_page=20) with gr.Row(): style_examples = [ [ '{"ops":[{"insert":"a beautiful"},{"attributes":{"font":"mirza"},"insert":" garden"},{"insert":" with a "},{"attributes":{"font":"roboto"},"insert":"snow mountain"},{"insert":" in the background"}]}', '', 10, 0.6, 0, 0.4, 5, 0, None, ], [ '{"ops":[{"insert":"a night"},{"attributes":{"font":"slabo"},"insert":" sky"},{"insert":" filled with stars above a turbulent"},{"attributes":{"font":"roboto"},"insert":" sea"},{"insert":" with giant waves"}]}', '', 2, 0.6, 0, 0, 6, 0.5, None, ], ] gr.Examples(examples=style_examples, label='Font style examples', inputs=[ text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, ], outputs=[ plaintext_result, richtext_result, segments, token_map, ], fn=generate, cache_examples=True, examples_per_page=20) with gr.Row(): size_examples = [ [ '{"ops": [{"insert": "A pizza with "}, {"attributes": {"size": "60px"}, "insert": "pineapple"}, {"insert": " pepperoni, and mushroom on the top"}]}', '', 5, 0.3, 0, 0, 3, 1, None, ], [ '{"ops": [{"insert": "A pizza with pineapple, "}, {"attributes": {"size": "60px"}, "insert": "pepperoni"}, {"insert": ", and mushroom on the top"}]}', '', 5, 0.3, 0, 0, 3, 1, None, ], [ '{"ops": [{"insert": "A pizza with pineapple, pepperoni, and "}, {"attributes": {"size": "60px"}, "insert": "mushroom"}, {"insert": " on the top"}]}', '', 5, 0.3, 0, 0, 3, 1, None, ], ] gr.Examples(examples=size_examples, label='Font size examples', inputs=[ text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, ], outputs=[ plaintext_result, richtext_result, segments, token_map, ], fn=generate, cache_examples=True, examples_per_page=20) generate_button.click(fn=lambda: gr.update(visible=False), inputs=None, outputs=share_row, queue=False).then( fn=generate, inputs=[ text_input, negative_prompt, num_segments, segment_threshold, inject_interval, inject_background, seed, color_guidance_weight, rich_text_input, height, width, steps, guidance_weight, ], outputs=[plaintext_result, richtext_result, segments, token_map], _js=get_js_data ).then( fn=lambda: gr.update(visible=True), inputs=None, outputs=share_row, queue=False) text_input.change( fn=None, inputs=[text_input], outputs=None, _js=set_js_data, queue=False) # load url param prompt to textinput load_params_button.click(fn=lambda x: x['prompt'], inputs=[ url_params], outputs=[text_input], queue=False) demo.load( fn=load_url_params, inputs=[url_params], outputs=[load_params_button, url_params], _js=get_window_url_params ) demo.queue(concurrency_count=1) demo.launch(share=False) if __name__ == "__main__": main()