# 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 spaces import gradio as gr from huggingface_hub import snapshot_download # from model import StreamMultiDiffusionSDXL from model import StreamMultiDiffusion from util import seed_everything from prompt_util import preprocess_prompts, _quality_dict, _style_dict ### 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=1024) parser = argparse.ArgumentParser(description='Semantic Palette demo powered by StreamMultiDiffusion.') parser.add_argument('-H', '--height', type=int, default=768) parser.add_argument('-W', '--width', type=int, default=768) 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('--guidance_scale', type=float, default=0) # 1.2 parser.add_argument('--run_time', type=float, default=60) 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: # opt.model = 'cagliostrolab/animagine-xl-3.1' # opt.model = 'ironjr/BlazingDriveV11m' opt.model = 'KBlueLeaf/kohaku-v2.1' else: if opt.model.endswith('.safetensors'): opt.model = os.path.abspath(os.path.join('checkpoints', opt.model)) # model = StreamMultiDiffusionSDXL( model = StreamMultiDiffusion( device, hf_key=opt.model, height=opt.height, width=opt.width, cfg_type="full", autoflush=True, use_tiny_vae=True, mask_type='continuous', bootstrap_steps=opt.bootstrap_steps, bootstrap_mix_steps=opt.bootstrap_steps, guidance_scale=opt.guidance_scale, seed=opt.seed, ).cuda() print(f'[INFO] Parameters prepared!') 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 = 3 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_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_dict = json.loads(json_text) for k in ('inpainting_mode', 'is_runing', 'active_palettes', 'current_palette'): if k in state_dict: del state_dict[k] state = argparse.Namespace(**state_dict) state.active_palettes = opt.max_palettes return [state] + [ gr.update(value=v, visible=True) for v in state.prompt_names ] + [ # 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(): return model() def register(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 if not inpainting_mode: background = Image.new(size=(opt.width, opt.height), mode='RGB', color=(255, 255, 255)) 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] has_masks = list(range(opt.max_palettes)) 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) model.update_background( background.convert('RGB'), prompt=None, negative_prompt=None, ) state.prompts[0] = model.background.prompt state.neg_prompts[0] = model.background.negative_prompt model.update_layers( prompts=prompts, negative_prompts=negative_prompts, masks=masks.to(device), mask_strengths=mask_strengths, mask_stds=mask_stds, prompt_strengths=prompt_strengths, ) state.inpainting_mode = inpainting_mode return state @spaces.GPU def run(state, drawpad): model.device = torch.device('cuda') model.reset_seed(model.generator, opt.seed) model.reset_latent() model.prepare() state = register(state, drawpad) state.is_running = True tic = time.time() while True: yield [state, generate()] toc = time.time() tdelta = toc - tic if tdelta > opt.run_time: state.is_running = False return [state, generate()] def hide_element(): return gr.update(visible=False) def show_element(): return gr.update(visible=True) def draw(state, drawpad): if not state.is_running: return 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] has_masks = list(range(opt.max_palettes)) print('Has mask: ', has_masks) masks = masks * foreground_mask masks = masks[has_masks] # if state.inpainting_mode: mask_strengths = [state.mask_strengths[v] for v in has_masks] mask_stds = [state.mask_stds[v] for v in has_masks] # else: # masks = torch.cat([torch.ones_like(foreground_mask), masks], dim=0) # mask_strengths = [1] + [state.mask_strengths[v] for v in has_masks] # mask_stds = [0] + [state.mask_stds[v] for v in has_masks] for i in range(len(has_masks)): model.update_single_layer( idx=i, mask=masks[i], mask_strength=mask_strengths[i], mask_std=mask_stds[i], ) ### 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: 18pt; 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; }} #run-anim {{ padding: 40px 45px; }} #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; }} """ css = css + f""" .mask-red {{ left: 0; width: 0; color: #BE002A; -webkit-animation: text-red {opt.run_time:.1f}s ease infinite; animation: text-red {opt.run_time:.1f}s ease infinite; z-index: 2; background: transparent; }} .mask-white {{ right: 0; }} /* Flames */ #red-flame {{ opacity: 0; -webkit-animation: show-flames {opt.run_time:.1f}s ease infinite, red-flame 120ms ease infinite; animation: show-flames {opt.run_time:.1f}s ease infinite, red-flame 120ms ease infinite; transform-origin: center bottom; }} #yellow-flame {{ opacity: 0; -webkit-animation: show-flames {opt.run_time:.1f}s ease infinite, yellow-flame 120ms ease infinite; animation: show-flames {opt.run_time:.1f}s ease infinite, yellow-flame 120ms ease infinite; transform-origin: center bottom; }} #white-flame {{ opacity: 0; -webkit-animation: show-flames {opt.run_time:.1f}s ease infinite, red-flame 100ms ease infinite; animation: show-flames {opt.run_time:.1f}s ease infinite, red-flame 100ms ease infinite; transform-origin: center bottom; }} """ with open(os.path.join(root, 'timer', 'style.css')) as f: added_css = ''.join(f.readlines()) css = css + added_css # js = '' # with open(os.path.join(root, 'timer', 'script.js')) as f: # added_js = ''.join(f.readlines()) # js = js + added_js head = f""" """ with gr.Blocks(theme=gr.themes.Soft(), css=css, head=head) as demo: iface = argparse.Namespace() def _define_state(): state = argparse.Namespace() # Cursor. state.is_running = False state.inpainting_mode = False state.current_palette = 0 # 0: Background; 1,2,3,...: Layers state.model_id = opt.model state.style_name = '(None)' state.quality_name = 'Standard v3.1' # State variables (one-hot). state.active_palettes = 5 # Front-end initialized to the default values. # prompt_props_ = prompt_props() state.prompt_names = [ '🌄 Background', '👧 Girl', '🐶 Dog', '💐 Garden', ] + ['🎨 New Palette' for _ in range(opt.max_palettes - 3)] state.prompts = [ '', 'A girl smiling at viewer', 'Doggy body part', 'Flower garden', ] + ['' for _ in range(opt.max_palettes - 3)] 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( """
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!