import os import yaml import torch import sys sys.path.append(os.path.abspath('./')) from inference.utils import * from train import WurstCoreB from gdf import DDPMSampler from train import WurstCore_t2i as WurstCoreC import numpy as np import random import argparse import gradio as gr import spaces from huggingface_hub import hf_hub_url import subprocess def parse_args(): parser = argparse.ArgumentParser() parser.add_argument( '--height', type=int, default=2560, help='image height') parser.add_argument('--width', type=int, default=5120, help='image width') parser.add_argument('--seed', type=int, default=123, help='random seed') parser.add_argument('--dtype', type=str, default='bf16', help=' if bf16 does not work, change it to float32 ') parser.add_argument('--config_c', type=str, default='configs/training/t2i.yaml' ,help='config file for stage c, latent generation') parser.add_argument('--config_b', type=str, default='configs/inference/stage_b_1b.yaml' ,help='config file for stage b, latent decoding') parser.add_argument( '--prompt', type=str, default='A photo-realistic image of a west highland white terrier in the garden, high quality, detail rich, 8K', help='text prompt') parser.add_argument( '--num_image', type=int, default=1, help='how many images generated') parser.add_argument( '--output_dir', type=str, default='figures/output_results/', help='output directory for generated image') parser.add_argument( '--stage_a_tiled', action='store_true', help='whther or nor to use tiled decoding for stage a to save memory') parser.add_argument( '--pretrained_path', type=str, default='models/ultrapixel_t2i.safetensors', help='pretrained path of newly added paramter of UltraPixel') args = parser.parse_args() return args def clear_image(): return None def load_message(height, width, seed, prompt, args, stage_a_tiled): args.height = height args.width = width args.seed = seed args.prompt = prompt + ' rich detail, 4k, high quality' args.stage_a_tiled = stage_a_tiled return args @spaces.GPU(duration=120) def get_image(height, width, seed, prompt, cfg, timesteps, stage_a_tiled): global args args = load_message(height, width, seed, prompt, args, stage_a_tiled) torch.manual_seed(args.seed) random.seed(args.seed) np.random.seed(args.seed) dtype = torch.bfloat16 if args.dtype == 'bf16' else torch.float captions = [args.prompt] * args.num_image height, width = args.height, args.width batch_size=1 height_lr, width_lr = get_target_lr_size(height / width, std_size=32) stage_c_latent_shape, stage_b_latent_shape = calculate_latent_sizes(height, width, batch_size=batch_size) stage_c_latent_shape_lr, stage_b_latent_shape_lr = calculate_latent_sizes(height_lr, width_lr, batch_size=batch_size) # Stage C Parameters extras.sampling_configs['cfg'] = 4 extras.sampling_configs['shift'] = 1 extras.sampling_configs['timesteps'] = 20 extras.sampling_configs['t_start'] = 1.0 extras.sampling_configs['sampler'] = DDPMSampler(extras.gdf) # Stage B Parameters extras_b.sampling_configs['cfg'] = 1.1 extras_b.sampling_configs['shift'] = 1 extras_b.sampling_configs['timesteps'] = 10 extras_b.sampling_configs['t_start'] = 1.0 for _, caption in enumerate(captions): batch = {'captions': [caption] * batch_size} #conditions = core.get_conditions(batch, models, extras, is_eval=True, is_unconditional=False, eval_image_embeds=False) #unconditions = core.get_conditions(batch, models, extras, is_eval=True, is_unconditional=True, eval_image_embeds=False) conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False) unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True) with torch.no_grad(): models.generator.cuda() print('STAGE C GENERATION***************************') with torch.cuda.amp.autocast(dtype=dtype): sampled_c = generation_c(batch, models, extras, core, stage_c_latent_shape, stage_c_latent_shape_lr, device) models.generator.cpu() torch.cuda.empty_cache() conditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=False) unconditions_b = core_b.get_conditions(batch, models_b, extras_b, is_eval=True, is_unconditional=True) conditions_b['effnet'] = sampled_c unconditions_b['effnet'] = torch.zeros_like(sampled_c) print('STAGE B + A DECODING***************************') with torch.cuda.amp.autocast(dtype=dtype): sampled = decode_b(conditions_b, unconditions_b, models_b, stage_b_latent_shape, extras_b, device, stage_a_tiled=args.stage_a_tiled) torch.cuda.empty_cache() imgs = show_images(sampled) #for idx, img in enumerate(imgs): #print(os.path.join(save_dir, args.prompt[:20]+'_' + str(cnt).zfill(5) + '.jpg'), idx) #img.save(os.path.join(save_dir, args.prompt[:20]+'_' + str(cnt).zfill(5) + '.jpg')) return imgs[0] #print('finished! Results ') with gr.Blocks() as demo: with gr.Column(elem_id="col-container"): gr.Markdown("

UltraPixel: Advancing Ultra-High-Resolution Image Synthesis to New Peaks

") with gr.Row(): prompt = gr.Textbox( label="Text Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False ) polish_button = gr.Button("Submit!", scale=0) output_img = gr.Image(label="Output Image", show_label=False) with gr.Accordion("Advanced Settings", open=False): seed = gr.Number( label="Random Seed", value=123, step=1, minimum=0, #maximum=MAX_SEED ) #randomize_seed = gr.Checkbox(label="Randomize seed", value=False) with gr.Row(): width = gr.Slider( label="Width", minimum=1536, maximum=5120, step=32, value=4096 ) height = gr.Slider( label="Height", minimum=1536, maximum=4096, step=32, value=2304 ) with gr.Row(): cfg = gr.Slider( label="CFG", minimum=3, maximum=10, step=0.1, value=4 ) timesteps = gr.Slider( label="Timesteps", minimum=10, maximum=50, step=1, value=20 ) stage_a_tiled = gr.Checkbox(label="Stage_a_tiled", value=False) clear_button = gr.Button("Clear!") gr.Examples( examples=[ "A detailed view of a blooming magnolia tree, with large, white flowers and dark green leaves, set against a clear blue sky.", "A close-up portrait of a young woman with flawless skin, vibrant red lipstick, and wavy brown hair, wearing a vintage floral dress and standing in front of a blooming garden.", "The image features a snow-covered mountain range with a large, snow-covered mountain in the background. The mountain is surrounded by a forest of trees, and the sky is filled with clouds. The scene is set during the winter season, with snow covering the ground and the trees.", "Crocodile in a sweater.", "A vibrant anime scene of a young girl with long, flowing pink hair, big sparkling blue eyes, and a school uniform, standing under a cherry blossom tree with petals falling around her. The background shows a traditional Japanese school with cherry blossoms in full bloom.", "A playful Labrador retriever puppy with a shiny, golden coat, chasing a red ball in a spacious backyard, with green grass and a wooden fence.", "A cozy, rustic log cabin nestled in a snow-covered forest, with smoke rising from the stone chimney, warm lights glowing from the windows, and a path of footprints leading to the front door.", "A highly detailed, high-quality image of the Banff National Park in Canada. The turquoise waters of Lake Louise are surrounded by snow-capped mountains and dense pine forests. A wooden canoe is docked at the edge of the lake. The sky is a clear, bright blue, and the air is crisp and fresh.", "A highly detailed, high-quality image of a Shih Tzu receiving a bath in a home bathroom. The dog is standing in a tub, covered in suds, with a slightly wet and adorable look. The background includes bathroom fixtures, towels, and a clean, tiled floor.", ], inputs=[prompt], outputs=[output_img], examples_per_page=5 ) polish_button.click(get_image, inputs=[height, width, seed, prompt, cfg, timesteps, stage_a_tiled], outputs=output_img) polish_button.click(clear_image, inputs=[], outputs=output_img) def download_with_wget(url, save_path): try: subprocess.run(['wget', url, '-O', save_path], check=True) print(f"Downloaded to {save_path}") except subprocess.CalledProcessError as e: print(f"Error downloading file: {e}") def download_model(): urls = [ 'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_a.safetensors', 'https://huggingface.co/stabilityai/StableWurst/resolve/main/previewer.safetensors', 'https://huggingface.co/stabilityai/StableWurst/resolve/main/effnet_encoder.safetensors', 'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite_bf16.safetensors', 'https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_bf16.safetensors', 'https://huggingface.co/roubaofeipi/UltraPixel/blob/main/ultrapixel_t2i.safetensors' ] for file_url in urls: download_with_wget(file_url, os.path.join('models', file_url.split('/')[-1])) if __name__ == "__main__": args = parse_args() device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") download_model() config_file = args.config_c with open(config_file, "r", encoding="utf-8") as file: loaded_config = yaml.safe_load(file) core = WurstCoreC(config_dict=loaded_config, device=device, training=False) # SETUP STAGE B config_file_b = args.config_b with open(config_file_b, "r", encoding="utf-8") as file: config_file_b = yaml.safe_load(file) core_b = WurstCoreB(config_dict=config_file_b, device=device, training=False) extras = core.setup_extras_pre() models = core.setup_models(extras) models.generator.eval().requires_grad_(False) print("STAGE C READY") extras_b = core_b.setup_extras_pre() models_b = core_b.setup_models(extras_b, skip_clip=True) models_b = WurstCoreB.Models( **{**models_b.to_dict(), 'tokenizer': models.tokenizer, 'text_model': models.text_model} ) models_b.generator.bfloat16().eval().requires_grad_(False) print("STAGE B READY") pretrained_path = args.pretrained_path sdd = torch.load(pretrained_path, map_location='cpu') collect_sd = {} for k, v in sdd.items(): collect_sd[k[7:]] = v models.train_norm.load_state_dict(collect_sd) models.generator.eval() models.train_norm.eval() demo.launch( debug=True, share=True, )