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| from cldm.ddim_hacked import DDIMSampler | |
| import math | |
| from omegaconf import OmegaConf | |
| from scripts.rendertext_tool import Render_Text, load_model_from_config, load_model_ckpt | |
| import gradio as gr | |
| import os | |
| import torch | |
| import time | |
| def process_multi_wrapper(rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3, | |
| shared_prompt, | |
| width_0, width_1, width_2, width_3, | |
| ratio_0, ratio_1, ratio_2, ratio_3, | |
| top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3, | |
| top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3, | |
| yaw_0, yaw_1, yaw_2, yaw_3, | |
| num_rows_0, num_rows_1, num_rows_2, num_rows_3, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt, ): | |
| rendered_txt_values = [rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3] | |
| width_values = [width_0, width_1, width_2, width_3] | |
| ratio_values = [ratio_0, ratio_1, ratio_2, ratio_3] | |
| top_left_x_values = [top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3] | |
| top_left_y_values = [top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3] | |
| yaw_values = [yaw_0, yaw_1, yaw_2, yaw_3] | |
| num_rows_values = [num_rows_0, num_rows_1, num_rows_2, num_rows_3] | |
| return render_tool.process_multi(rendered_txt_values, shared_prompt, | |
| width_values, ratio_values, | |
| top_left_x_values, top_left_y_values, | |
| yaw_values, num_rows_values, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt | |
| ) | |
| def process_multi_wrapper_only_show_rendered(rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3, | |
| shared_prompt, | |
| width_0, width_1, width_2, width_3, | |
| ratio_0, ratio_1, ratio_2, ratio_3, | |
| top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3, | |
| top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3, | |
| yaw_0, yaw_1, yaw_2, yaw_3, | |
| num_rows_0, num_rows_1, num_rows_2, num_rows_3, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt): | |
| rendered_txt_values = [rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3] | |
| width_values = [width_0, width_1, width_2, width_3] | |
| ratio_values = [ratio_0, ratio_1, ratio_2, ratio_3] | |
| top_left_x_values = [top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3] | |
| top_left_y_values = [top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3] | |
| yaw_values = [yaw_0, yaw_1, yaw_2, yaw_3] | |
| num_rows_values = [num_rows_0, num_rows_1, num_rows_2, num_rows_3] | |
| return render_tool.process_multi(rendered_txt_values, shared_prompt, | |
| width_values, ratio_values, | |
| top_left_x_values, top_left_y_values, | |
| yaw_values, num_rows_values, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt, | |
| only_show_rendered_image=True) | |
| def load_ckpt(model_ckpt = "LAION-Glyph-10M"): | |
| global render_tool, model | |
| if torch.cuda.is_available(): | |
| for i in range(5): | |
| torch.cuda.empty_cache() | |
| time.sleep(2) | |
| print("empty the cuda cache") | |
| if model_ckpt == "LAION-Glyph-1M": | |
| model = load_model_ckpt(model, "laion1M_model_wo_ema.ckpt") | |
| elif model_ckpt == "LAION-Glyph-10M": | |
| model = load_model_ckpt(model, "model_wo_ema.ckpt") | |
| render_tool = Render_Text(model) | |
| output_str = f"already change the model checkpoint to {model_ckpt}" | |
| print(output_str) | |
| if torch.cuda.is_available(): | |
| for i in range(5): | |
| torch.cuda.empty_cache() | |
| time.sleep(2) | |
| print("empty the cuda cache") | |
| cfg = OmegaConf.load("config.yaml") | |
| model = load_model_from_config(cfg, "model_wo_ema.ckpt", verbose=True) | |
| # model = load_model_from_config(cfg, "model_states.pt", verbose=True) | |
| # model = load_model_from_config(cfg, "model.ckpt", verbose=True) | |
| # ddim_sampler = DDIMSampler(model) | |
| render_tool = Render_Text(model) | |
| description = """ | |
| ## Control Stable Diffusion with Glyph Images | |
| """ | |
| SPACE_ID = os.getenv('SPACE_ID') | |
| if SPACE_ID is not None: | |
| # description += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. < a href=" ">< img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></ a></p >' | |
| description += f'\n<p>For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings. <a href="https://huggingface.co/spaces/{SPACE_ID}?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| block = gr.Blocks().queue() | |
| with block: | |
| with gr.Row(): | |
| gr.Markdown(description) | |
| only_show_rendered_image = gr.Number(value=1, visible=False) | |
| default_width = [0.3, 0.3, 0.3, 0.3] | |
| default_top_left_x = [0.35, 0.15, 0.15, 0.5] | |
| default_top_left_y = [0.5, 0.25, 0.75, 0.75] | |
| with gr.Column(): | |
| with gr.Row(): | |
| for i in range(4): | |
| with gr.Column(): | |
| exec(f"""rendered_txt_{i} = gr.Textbox(label=f"Render Text {i+1}")""") | |
| with gr.Accordion(f"Advanced options {i+1}", open=False): | |
| exec(f"""width_{i} = gr.Slider(label="Bbox Width", minimum=0., maximum=1, value={default_width[i]}, step=0.01) """) | |
| exec(f"""ratio_{i} = gr.Slider(label="Bbox_width_height_ratio", minimum=0., maximum=5, value=0., step=0.02, visible=False) """) | |
| # exec(f"""top_left_x_{i} = gr.Slider(label="Bbox Top Left x", minimum=0., maximum=1, value={0.35 - 0.25 * math.cos(math.pi * i)}, step=0.01) """) | |
| # exec(f"""top_left_y_{i} = gr.Slider(label="Bbox Top Left y", minimum=0., maximum=1, value={0.1 if i < 2 else 0.6}, step=0.01) """) | |
| exec(f"""top_left_x_{i} = gr.Slider(label="Bbox Top Left x", minimum=0., maximum=1, value={default_top_left_x[i]}, step=0.01) """) | |
| exec(f"""top_left_y_{i} = gr.Slider(label="Bbox Top Left y", minimum=0., maximum=1, value={default_top_left_y[i]}, step=0.01) """) | |
| exec(f"""yaw_{i} = gr.Slider(label="Bbox Yaw", minimum=-20, maximum=20, value=0, step=5) """) | |
| # exec(f"""num_rows_{i} = gr.Slider(label="num_rows", minimum=1, maximum=4, value=1, step=1, visible=False) """) | |
| exec(f"""num_rows_{i} = gr.Slider(label="num_rows", minimum=1, maximum=4, value=1, step=1) """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| shared_prompt = gr.Textbox(label="Shared Prompt") | |
| with gr.Row(): | |
| show_render_button = gr.Button(value="Only Rendered") | |
| run_button = gr.Button(value="Run") | |
| with gr.Accordion("Model Options", open=False): | |
| with gr.Row(): | |
| # model_ckpt = gr.inputs.Dropdown(["LAION-Glyph-10M", "Textcaps5K-10"], label="Checkpoint", default = "LAION-Glyph-10M") | |
| model_ckpt = gr.inputs.Dropdown(["LAION-Glyph-10M", "LAION-Glyph-1M"], label="Checkpoint", default = "LAION-Glyph-10M") | |
| load_button = gr.Button(value = "Load Checkpoint") | |
| with gr.Accordion("Shared Advanced Options", open=False): | |
| with gr.Row(): | |
| shared_num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1) | |
| shared_image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64, visible=False) | |
| shared_strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01, visible=False) | |
| shared_guess_mode = gr.Checkbox(label='Guess Mode', value=False, visible=False) | |
| shared_seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True) | |
| with gr.Row(): | |
| shared_scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=9.0, step=0.1) | |
| shared_ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1) | |
| shared_eta = gr.Number(label="eta (DDIM)", value=0.0, visible=False) | |
| with gr.Row(): | |
| shared_a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed') | |
| shared_n_prompt = gr.Textbox(label="Negative Prompt", | |
| value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality') | |
| with gr.Row(): | |
| result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto') | |
| run_button.click(fn=process_multi_wrapper, | |
| inputs=[rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3, | |
| shared_prompt, | |
| width_0, width_1, width_2, width_3, | |
| ratio_0, ratio_1, ratio_2, ratio_3, | |
| top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3, | |
| top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3, | |
| yaw_0, yaw_1, yaw_2, yaw_3, | |
| num_rows_0, num_rows_1, num_rows_2, num_rows_3, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt], | |
| outputs=[result_gallery]) | |
| show_render_button.click(fn=process_multi_wrapper_only_show_rendered, | |
| inputs=[rendered_txt_0, rendered_txt_1, rendered_txt_2, rendered_txt_3, | |
| shared_prompt, | |
| width_0, width_1, width_2, width_3, | |
| ratio_0, ratio_1, ratio_2, ratio_3, | |
| top_left_x_0, top_left_x_1, top_left_x_2, top_left_x_3, | |
| top_left_y_0, top_left_y_1, top_left_y_2, top_left_y_3, | |
| yaw_0, yaw_1, yaw_2, yaw_3, | |
| num_rows_0, num_rows_1, num_rows_2, num_rows_3, | |
| shared_num_samples, shared_image_resolution, | |
| shared_ddim_steps, shared_guess_mode, | |
| shared_strength, shared_scale, shared_seed, | |
| shared_eta, shared_a_prompt, shared_n_prompt], | |
| outputs=[result_gallery]) | |
| load_button.click(fn = load_ckpt, | |
| inputs = [model_ckpt], | |
| outputs = [result_gallery] | |
| ) | |
| block.launch() |