# Copyright (c) 2024 Jaerin Lee # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # The above copyright notice and this permission notice shall be included in all # copies or substantial portions of the Software. # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # SOFTWARE. import sys sys.path.append('../../src') import argparse import random import time import json import os import glob import pathlib from functools import partial from pprint import pprint import numpy as np from PIL import Image import torch import gradio as gr from huggingface_hub import snapshot_download from model import StableMultiDiffusionSDXLPipeline from util import seed_everything from prompt_util import preprocess_prompts, _quality_dict, _style_dict from share_btn import community_icon_html, loading_icon_html, share_js ### Utils def log_state(state): pprint(vars(opt)) if isinstance(state, gr.State): state = state.value pprint(vars(state)) def is_empty_image(im: Image.Image) -> bool: if im is None: return True im = np.array(im) has_alpha = (im.shape[2] == 4) if not has_alpha: return False elif im.sum() == 0: return True else: return False ### Argument passing parser = argparse.ArgumentParser(description='Semantic Palette demo powered by StreamMultiDiffusion with SDXL support.') parser.add_argument('-H', '--height', type=int, default=1024) parser.add_argument('-W', '--width', type=int, default=2560) parser.add_argument('--model', type=str, default=None, help='Hugging face model repository or local path for a SD1.5 model checkpoint to run.') parser.add_argument('--bootstrap_steps', type=int, default=1) parser.add_argument('--seed', type=int, default=-1) parser.add_argument('--device', type=int, default=0) parser.add_argument('--port', type=int, default=8000) opt = parser.parse_args() ### Global variables and data structures device = f'cuda:{opt.device}' if opt.device >= 0 else 'cpu' if opt.model is None: model_dict = { 'Animagine XL 3.1': 'cagliostrolab/animagine-xl-3.1', } else: if opt.model.endswith('.safetensors'): opt.model = os.path.abspath(os.path.join('checkpoints', opt.model)) model_dict = {os.path.splitext(os.path.basename(opt.model))[0]: opt.model} models = { k: StableMultiDiffusionSDXLPipeline(device, hf_key=v, has_i2t=False) for k, v in model_dict.items() } prompt_suggestions = [ '1girl, souryuu asuka langley, neon genesis evangelion, solo, upper body, v, smile, looking at viewer', '1boy, solo, portrait, looking at viewer, white t-shirt, brown hair', '1girl, arima kana, oshi no ko, solo, upper body, from behind', ] opt.max_palettes = 5 opt.default_prompt_strength = 1.0 opt.default_mask_strength = 1.0 opt.default_mask_std = 0.0 opt.default_negative_prompt = ( 'nsfw, worst quality, bad quality, normal quality, cropped, framed' ) opt.verbose = True opt.colors = [ '#000000', '#2692F3', '#F89E12', '#16C232', '#F92F6C', '#AC6AEB', # '#92C62C', # '#92C6EC', # '#FECAC0', ] ### Event handlers def add_palette(state): old_actives = state.active_palettes state.active_palettes = min(state.active_palettes + 1, opt.max_palettes) if opt.verbose: log_state(state) if state.active_palettes != old_actives: return [state] + [ gr.update() if state.active_palettes != opt.max_palettes else gr.update(visible=False) ] + [ gr.update() if i != state.active_palettes - 1 else gr.update(value=state.prompt_names[i + 1], visible=True) for i in range(opt.max_palettes) ] else: return [state] + [gr.update() for i in range(opt.max_palettes + 1)] def select_palette(state, button, idx): if idx < 0 or idx > opt.max_palettes: idx = 0 old_idx = state.current_palette if old_idx == idx: return [state] + [gr.update() for _ in range(opt.max_palettes + 7)] state.current_palette = idx if opt.verbose: log_state(state) updates = [state] + [ gr.update() if i not in (idx, old_idx) else gr.update(variant='secondary') if i == old_idx else gr.update(variant='primary') for i in range(opt.max_palettes + 1) ] label = 'Background' if idx == 0 else f'Palette {idx}' updates.extend([ gr.update(value=button, interactive=(idx > 0)), gr.update(value=state.prompts[idx], label=f'Edit Prompt for {label}'), gr.update(value=state.neg_prompts[idx], label=f'Edit Negative Prompt for {label}'), ( gr.update(value=state.mask_strengths[idx - 1], interactive=True) if idx > 0 else gr.update(value=opt.default_mask_strength, interactive=False) ), ( gr.update(value=state.prompt_strengths[idx - 1], interactive=True) if idx > 0 else gr.update(value=opt.default_prompt_strength, interactive=False) ), ( gr.update(value=state.mask_stds[idx - 1], interactive=True) if idx > 0 else gr.update(value=opt.default_mask_std, interactive=False) ), ]) return updates def change_prompt_strength(state, strength): if state.current_palette == 0: return state state.prompt_strengths[state.current_palette - 1] = strength if opt.verbose: log_state(state) return state def change_std(state, std): if state.current_palette == 0: return state state.mask_stds[state.current_palette - 1] = std if opt.verbose: log_state(state) return state def change_mask_strength(state, strength): if state.current_palette == 0: return state state.mask_strengths[state.current_palette - 1] = strength if opt.verbose: log_state(state) return state def reset_seed(state, seed): state.seed = seed if opt.verbose: log_state(state) return state def rename_prompt(state, name): state.prompt_names[state.current_palette] = name if opt.verbose: log_state(state) return [state] + [ gr.update() if i != state.current_palette else gr.update(value=name) for i in range(opt.max_palettes + 1) ] def change_prompt(state, prompt): state.prompts[state.current_palette] = prompt if opt.verbose: log_state(state) return state def change_neg_prompt(state, neg_prompt): state.neg_prompts[state.current_palette] = neg_prompt if opt.verbose: log_state(state) return state def select_model(state, model_id): state.model_id = model_id if opt.verbose: log_state(state) return state def select_style(state, style_name): state.style_name = style_name if opt.verbose: log_state(state) return state def select_quality(state, quality_name): state.quality_name = quality_name if opt.verbose: log_state(state) return state def import_state(state, json_text): current_palette = state.current_palette # active_palettes = state.active_palettes state = argparse.Namespace(**json.loads(json_text)) state.active_palettes = opt.max_palettes return [state] + [ gr.update(value=v, visible=True) for v in state.prompt_names ] + [ state.model_id, state.style_name, state.quality_name, state.prompts[current_palette], state.prompt_names[current_palette], state.neg_prompts[current_palette], state.prompt_strengths[current_palette - 1], state.mask_strengths[current_palette - 1], state.mask_stds[current_palette - 1], state.seed, ] ### Main worker def generate(state, *args, **kwargs): return models[state.model_id](*args, **kwargs) def run(state, drawpad): seed_everything(state.seed if state.seed >=0 else np.random.randint(2147483647)) print('Generate!') background = drawpad['background'].convert('RGBA') inpainting_mode = np.asarray(background).sum() != 0 print('Inpainting mode: ', inpainting_mode) user_input = np.asarray(drawpad['layers'][0]) # (H, W, 4) foreground_mask = torch.tensor(user_input[..., -1])[None, None] # (1, 1, H, W) user_input = torch.tensor(user_input[..., :-1]) # (H, W, 3) palette = torch.tensor([ tuple(int(s[i+1:i+3], 16) for i in (0, 2, 4)) for s in opt.colors[1:] ]) # (N, 3) masks = (palette[:, None, None, :] == user_input[None]).all(dim=-1)[:, None, ...] # (N, 1, H, W) has_masks = [i for i, m in enumerate(masks.sum(dim=(1, 2, 3)) == 0) if not m] print('Has mask: ', has_masks) masks = masks * foreground_mask masks = masks[has_masks] if inpainting_mode: prompts = [state.prompts[v + 1] for v in has_masks] negative_prompts = [state.neg_prompts[v + 1] for v in has_masks] mask_strengths = [state.mask_strengths[v] for v in has_masks] mask_stds = [state.mask_stds[v] for v in has_masks] prompt_strengths = [state.prompt_strengths[v] for v in has_masks] else: masks = torch.cat([torch.ones_like(foreground_mask), masks], dim=0) prompts = [state.prompts[0]] + [state.prompts[v + 1] for v in has_masks] negative_prompts = [state.neg_prompts[0]] + [state.neg_prompts[v + 1] for v in has_masks] mask_strengths = [1] + [state.mask_strengths[v] for v in has_masks] mask_stds = [0] + [state.mask_stds[v] for v in has_masks] prompt_strengths = [1] + [state.prompt_strengths[v] for v in has_masks] prompts, negative_prompts = preprocess_prompts( prompts, negative_prompts, style_name=state.style_name, quality_name=state.quality_name) return generate( state, prompts, negative_prompts, masks=masks, mask_strengths=mask_strengths, mask_stds=mask_stds, prompt_strengths=prompt_strengths, background=background.convert('RGB'), background_prompt=state.prompts[0], background_negative_prompt=state.neg_prompts[0], height=opt.height, width=opt.width, bootstrap_steps=2, guidance_scale=0, ) ### Load examples root = pathlib.Path(__file__).parent print(root) example_root = os.path.join(root, 'examples') example_images = glob.glob(os.path.join(example_root, '*.png')) example_images = [Image.open(i) for i in example_images] with open(os.path.join(example_root, 'prompt_background_advanced.txt')) as f: prompts_background = [l.strip() for l in f.readlines() if l.strip() != ''] with open(os.path.join(example_root, 'prompt_girl.txt')) as f: prompts_girl = [l.strip() for l in f.readlines() if l.strip() != ''] with open(os.path.join(example_root, 'prompt_boy.txt')) as f: prompts_boy = [l.strip() for l in f.readlines() if l.strip() != ''] with open(os.path.join(example_root, 'prompt_props.txt')) as f: prompts_props = [l.strip() for l in f.readlines() if l.strip() != ''] prompts_props = {l.split(',')[0].strip(): ','.join(l.split(',')[1:]).strip() for l in prompts_props} prompt_background = lambda: random.choice(prompts_background) prompt_girl = lambda: random.choice(prompts_girl) prompt_boy = lambda: random.choice(prompts_boy) prompt_props = lambda: np.random.choice(list(prompts_props.keys()), size=(opt.max_palettes - 2), replace=False).tolist() ### Main application css = f""" #run-button {{ font-size: 30pt; background-image: linear-gradient(to right, #4338ca 0%, #26a0da 51%, #4338ca 100%); margin: 0; padding: 15px 45px; text-align: center; text-transform: uppercase; transition: 0.5s; background-size: 200% auto; color: white; box-shadow: 0 0 20px #eee; border-radius: 10px; display: block; background-position: right center; }} #run-button:hover {{ background-position: left center; color: #fff; text-decoration: none; }} #semantic-palette {{ border-style: solid; border-width: 0.2em; border-color: #eee; }} #semantic-palette:hover {{ box-shadow: 0 0 20px #eee; }} #output-screen {{ width: 100%; aspect-ratio: {opt.width} / {opt.height}; }} .layer-wrap {{ display: none; }} """ for i in range(opt.max_palettes + 1): css = css + f""" .secondary#semantic-palette-{i} {{ background-image: linear-gradient(to right, #374151 0%, #374151 71%, {opt.colors[i]} 100%); color: white; }} .primary#semantic-palette-{i} {{ background-image: linear-gradient(to right, #4338ca 0%, #4338ca 71%, {opt.colors[i]} 100%); color: white; }} """ with gr.Blocks(theme=gr.themes.Soft(), css=css) as demo: iface = argparse.Namespace() def _define_state(): state = argparse.Namespace() # Cursor. state.current_palette = 0 # 0: Background; 1,2,3,...: Layers state.model_id = list(model_dict.keys())[0] state.style_name = '(None)' state.quality_name = 'Standard v3.1' # State variables (one-hot). state.active_palettes = 1 # Front-end initialized to the default values. prompt_props_ = prompt_props() state.prompt_names = [ '🌄 Background', '👧 Girl', '👦 Boy', ] + prompt_props_ + ['🎨 New Palette' for _ in range(opt.max_palettes - 5)] state.prompts = [ prompt_background(), prompt_girl(), prompt_boy(), ] + [prompts_props[k] for k in prompt_props_] + ['' for _ in range(opt.max_palettes - 5)] state.neg_prompts = [ opt.default_negative_prompt + (', humans, humans, humans' if i == 0 else '') for i in range(opt.max_palettes + 1) ] state.prompt_strengths = [opt.default_prompt_strength for _ in range(opt.max_palettes)] state.mask_strengths = [opt.default_mask_strength for _ in range(opt.max_palettes)] state.mask_stds = [opt.default_mask_std for _ in range(opt.max_palettes)] state.seed = opt.seed return state state = gr.State(value=_define_state) ### Demo user interface gr.HTML( """

🧠 Semantic Paint X Animagine XL 3.1 🎨

powered by

StreamMultiDiffusion: Real-Time Interactive Generation with Region-Based Semantic Control

and

Animagine XL 3.1 by Cagliostro Research Lab

If you ❤️ our project, please visit our Github and give us a 🌟!

  Project Page            

""" ) with gr.Row(): iface.image_slot = gr.Image( interactive=False, show_label=False, show_download_button=True, type='pil', label='Generated Result', elem_id='output-screen', value=lambda: random.choice(example_images), ) with gr.Row(): with gr.Column(scale=1): with gr.Group(elem_id='semantic-palette'): gr.HTML( """

🧠 Semantic Palette 🎨


""" ) iface.btn_semantics = [gr.Button( value=state.value.prompt_names[0], variant='primary', elem_id='semantic-palette-0', )] for i in range(opt.max_palettes): iface.btn_semantics.append(gr.Button( value=state.value.prompt_names[i + 1], variant='secondary', visible=(i < state.value.active_palettes), elem_id=f'semantic-palette-{i + 1}' )) iface.btn_add_palette = gr.Button( value='Create New Semantic Brush', variant='primary', ) with gr.Accordion(label='Import/Export Semantic Palette', open=False): iface.tbox_state_import = gr.Textbox(label='Put Palette JSON Here To Import') iface.json_state_export = gr.JSON(label='Exported Palette') iface.btn_export_state = gr.Button("Export Palette ➡️ JSON", variant='primary') iface.btn_import_state = gr.Button("Import JSON ➡️ Palette", variant='secondary') gr.HTML( """

❓Usage❓


1-1. Type in the background prompt. Background is not required if you paint the whole drawpad.

1-2. (Optional: Inpainting mode) Uploading a background image will make the app into inpainting mode. Removing the image returns to the creation mode. In the inpainting mode, increasing the Mask Blur STD > 8 for every colored palette is recommended for smooth boundaries.

2. Select a semantic brush by clicking onto one in the Semantic Palette above. Edit prompt for the semantic brush.

2-1. If you are willing to draw more diverse images, try Create New Semantic Brush.

3. Start drawing in the Semantic Drawpad tab. The brush color is directly linked to the semantic brushes.

4. Click [GENERATE!] button to create your (large-scale) artwork!

""" ) gr.HTML( """
... or run in your own 🤗 space!
""" ) gr.DuplicateButton() with gr.Column(scale=4): with gr.Row(): with gr.Column(scale=3): iface.ctrl_semantic = gr.ImageEditor( image_mode='RGBA', sources=['upload', 'clipboard', 'webcam'], transforms=['crop'], crop_size=(opt.width, opt.height), brush=gr.Brush( colors=opt.colors[1:], color_mode="fixed", ), type='pil', label='Semantic Drawpad', elem_id='drawpad', ) with gr.Column(scale=1): iface.btn_generate = gr.Button( value='Generate!', variant='primary', # scale=1, elem_id='run-button' ) with gr.Group(elem_id="share-btn-container"): gr.HTML(community_icon_html) gr.HTML(loading_icon_html) iface.btn_share = gr.Button("Share with Community", elem_id="share-btn") iface.model_select = gr.Radio( list(model_dict.keys()), label='Stable Diffusion Checkpoint', info='Choose your favorite style.', value=state.value.model_id, ) with gr.Accordion(label='Prompt Engineering', open=True): iface.quality_select = gr.Dropdown( label='Quality Presets', interactive=True, choices=list(_quality_dict.keys()), value='Standard v3.1', ) iface.style_select = gr.Radio( label='Style Preset', container=True, interactive=True, choices=list(_style_dict.keys()), value='(None)', ) with gr.Group(elem_id='control-panel'): with gr.Row(): iface.tbox_prompt = gr.Textbox( label='Edit Prompt for Background', info='What do you want to draw?', value=state.value.prompts[0], placeholder=lambda: random.choice(prompt_suggestions), scale=2, ) iface.tbox_name = gr.Textbox( label='Edit Brush Name', info='Just for your convenience.', value=state.value.prompt_names[0], placeholder='🌄 Background', scale=1, ) with gr.Row(): iface.tbox_neg_prompt = gr.Textbox( label='Edit Negative Prompt for Background', info='Add unwanted objects for this semantic brush.', value=opt.default_negative_prompt, scale=2, ) iface.slider_strength = gr.Slider( label='Prompt Strength', info='Blends fg & bg in the prompt level, >0.8 Preferred.', minimum=0.5, maximum=1.0, value=opt.default_prompt_strength, scale=1, ) with gr.Row(): iface.slider_alpha = gr.Slider( label='Mask Alpha', info='Factor multiplied to the mask before quantization. Extremely sensitive, >0.98 Preferred.', minimum=0.5, maximum=1.0, value=opt.default_mask_strength, ) iface.slider_std = gr.Slider( label='Mask Blur STD', info='Blends fg & bg in the latent level, 0 for generation, 8-32 for inpainting.', minimum=0.0001, maximum=100.0, value=opt.default_mask_std, ) iface.slider_seed = gr.Slider( label='Seed', info='The global seed.', minimum=-1, maximum=2147483647, step=1, value=opt.seed, ) ### Attach event handlers for idx, btn in enumerate(iface.btn_semantics): btn.click( fn=partial(select_palette, idx=idx), inputs=[state, btn], outputs=[state] + iface.btn_semantics + [ iface.tbox_name, iface.tbox_prompt, iface.tbox_neg_prompt, iface.slider_alpha, iface.slider_strength, iface.slider_std, ], api_name=f'select_palette_{idx}', ) iface.btn_add_palette.click( fn=add_palette, inputs=state, outputs=[state, iface.btn_add_palette] + iface.btn_semantics[1:], api_name='create_new', ) iface.btn_generate.click( fn=run, inputs=[state, iface.ctrl_semantic], outputs=iface.image_slot, api_name='run', ) iface.slider_alpha.input( fn=change_mask_strength, inputs=[state, iface.slider_alpha], outputs=state, api_name='change_alpha', ) iface.slider_std.input( fn=change_std, inputs=[state, iface.slider_std], outputs=state, api_name='change_std', ) iface.slider_strength.input( fn=change_prompt_strength, inputs=[state, iface.slider_strength], outputs=state, api_name='change_strength', ) iface.slider_seed.input( fn=reset_seed, inputs=[state, iface.slider_seed], outputs=state, api_name='reset_seed', ) iface.tbox_name.input( fn=rename_prompt, inputs=[state, iface.tbox_name], outputs=[state] + iface.btn_semantics, api_name='prompt_rename', ) iface.tbox_prompt.input( fn=change_prompt, inputs=[state, iface.tbox_prompt], outputs=state, api_name='prompt_edit', ) iface.tbox_neg_prompt.input( fn=change_neg_prompt, inputs=[state, iface.tbox_neg_prompt], outputs=state, api_name='neg_prompt_edit', ) iface.model_select.change( fn=select_model, inputs=[state, iface.model_select], outputs=state, api_name='model_select', ) iface.style_select.change( fn=select_style, inputs=[state, iface.style_select], outputs=state, api_name='style_select', ) iface.quality_select.change( fn=select_quality, inputs=[state, iface.quality_select], outputs=state, api_name='quality_select', ) iface.btn_share.click(None, [], [], _js=share_js) iface.btn_export_state.click(lambda x: vars(x), state, iface.json_state_export) iface.btn_import_state.click(import_state, [state, iface.tbox_state_import], [ state, *iface.btn_semantics, iface.model_select, iface.style_select, iface.quality_select, iface.tbox_prompt, iface.tbox_name, iface.tbox_neg_prompt, iface.slider_strength, iface.slider_alpha, iface.slider_std, iface.slider_seed, ]) gr.HTML( """ """ ) if __name__ == '__main__': demo.launch(server_port=opt.port)