# https://huggingface.co/DragGan/DragGan-Models # https://arxiv.org/abs/2305.10973 import os import os.path as osp from argparse import ArgumentParser from functools import partial from pathlib import Path import time import psutil import gradio as gr import numpy as np import torch from PIL import Image import dnnlib from gradio_utils import (ImageMask, draw_mask_on_image, draw_points_on_image, get_latest_points_pair, get_valid_mask, on_change_single_global_state) from viz.renderer import Renderer, add_watermark_np # download models from Hugging Face hub from huggingface_hub import snapshot_download model_dir = Path('./checkpoints') snapshot_download('DragGan/DragGan-Models', repo_type='model', local_dir=model_dir) cache_dir = model_dir device = 'cuda' IS_SPACE = "DragGan/DragGan" in os.environ.get('SPACE_ID', '') TIMEOUT = 80 def reverse_point_pairs(points): new_points = [] for p in points: new_points.append([p[1], p[0]]) return new_points def clear_state(global_state, target=None): """Clear target history state from global_state If target is not defined, points and mask will be both removed. 1. set global_state['points'] as empty dict 2. set global_state['mask'] as full-one mask. """ if target is None: target = ['point', 'mask'] if not isinstance(target, list): target = [target] if 'point' in target: global_state['points'] = dict() print('Clear Points State!') if 'mask' in target: image_raw = global_state["images"]["image_raw"] global_state['mask'] = np.ones((image_raw.size[1], image_raw.size[0]), dtype=np.uint8) print('Clear mask State!') return global_state def init_images(global_state): """This function is called only ones with Gradio App is started. 0. pre-process global_state, unpack value from global_state of need 1. Re-init renderer 2. run `renderer._render_drag_impl` with `is_drag=False` to generate new image 3. Assign images to global state and re-generate mask """ if isinstance(global_state, gr.State): state = global_state.value else: state = global_state state['renderer'].init_network( state['generator_params'], # res valid_checkpoints_dict[state['pretrained_weight']], # pkl state['params']['seed'], # w0_seed, None, # w_load state['params']['latent_space'] == 'w+', # w_plus 'const', state['params']['trunc_psi'], # trunc_psi, state['params']['trunc_cutoff'], # trunc_cutoff, None, # input_transform state['params']['lr'] # lr, ) state['renderer']._render_drag_impl(state['generator_params'], is_drag=False, to_pil=True) init_image = state['generator_params'].image state['images']['image_orig'] = init_image state['images']['image_raw'] = init_image state['images']['image_show'] = Image.fromarray( add_watermark_np(np.array(init_image))) state['mask'] = np.ones((init_image.size[1], init_image.size[0]), dtype=np.uint8) return global_state def update_image_draw(image, points, mask, show_mask, global_state=None): image_draw = draw_points_on_image(image, points) if show_mask and mask is not None and not (mask == 0).all() and not ( mask == 1).all(): image_draw = draw_mask_on_image(image_draw, mask) image_draw = Image.fromarray(add_watermark_np(np.array(image_draw))) if global_state is not None: global_state['images']['image_show'] = image_draw return image_draw def preprocess_mask_info(global_state, image): """Function to handle mask information. 1. last_mask is None: Do not need to change mask, return mask 2. last_mask is not None: 2.1 global_state is remove_mask: 2.2 global_state is add_mask: """ if isinstance(image, dict): last_mask = get_valid_mask(image['mask']) else: last_mask = None mask = global_state['mask'] # mask in global state is a placeholder with all 1. if (mask == 1).all(): mask = last_mask # last_mask = global_state['last_mask'] editing_mode = global_state['editing_state'] if last_mask is None: return global_state if editing_mode == 'remove_mask': updated_mask = np.clip(mask - last_mask, 0, 1) print(f'Last editing_state is {editing_mode}, do remove.') elif editing_mode == 'add_mask': updated_mask = np.clip(mask + last_mask, 0, 1) print(f'Last editing_state is {editing_mode}, do add.') else: updated_mask = mask print(f'Last editing_state is {editing_mode}, ' 'do nothing to mask.') global_state['mask'] = updated_mask # global_state['last_mask'] = None # clear buffer return global_state def print_memory_usage(): # Print system memory usage print(f"System memory usage: {psutil.virtual_memory().percent}%") # Print GPU memory usage if torch.cuda.is_available(): device = torch.device("cuda") print(f"GPU memory usage: {torch.cuda.memory_allocated() / 1e9} GB") print( f"Max GPU memory usage: {torch.cuda.max_memory_allocated() / 1e9} GB") device_properties = torch.cuda.get_device_properties(device) available_memory = device_properties.total_memory - \ torch.cuda.max_memory_allocated() print(f"Available GPU memory: {available_memory / 1e9} GB") else: print("No GPU available") # filter large models running on SPACES allowed_checkpoints = [] # all checkpoints if IS_SPACE: allowed_checkpoints = ["stylegan_human_v2_512.pkl", "stylegan2_dogs_1024_pytorch.pkl"] valid_checkpoints_dict = { f.name.split('.')[0]: str(f) for f in Path(cache_dir).glob('*.pkl') if f.name in allowed_checkpoints or not IS_SPACE } print('Valid checkpoint file:') print(valid_checkpoints_dict) init_pkl = 'stylegan_human_v2_512' with gr.Blocks() as app: gr.Markdown(""" # DragGAN - Drag Your GAN ## Interactive Point-based Manipulation on the Generative Image Manifold ### Unofficial Gradio Demo **Due to high demand, only one model can be run at a time, or you can duplicate the space and run your own copy.** Duplicate Space for no queue on your own hardware.

* Official Repo: [XingangPan](https://github.com/XingangPan/DragGAN) * Gradio Demo by: [LeoXing1996](https://github.com/LeoXing1996) © [OpenMMLab MMagic](https://github.com/open-mmlab/mmagic) """) # renderer = Renderer() global_state = gr.State({ "images": { # image_orig: the original image, change with seed/model is changed # image_raw: image with mask and points, change durning optimization # image_show: image showed on screen }, "temporal_params": { # stop }, 'mask': None, # mask for visualization, 1 for editing and 0 for unchange 'last_mask': None, # last edited mask 'show_mask': True, # add button "generator_params": dnnlib.EasyDict(), "params": { "seed": int(np.random.randint(0, 2**32 - 1)), "motion_lambda": 20, "r1_in_pixels": 3, "r2_in_pixels": 12, "magnitude_direction_in_pixels": 1.0, "latent_space": "w+", "trunc_psi": 0.7, "trunc_cutoff": None, "lr": 0.001, }, "device": device, "draw_interval": 1, "renderer": Renderer(disable_timing=True), "points": {}, "curr_point": None, "curr_type_point": "start", 'editing_state': 'add_points', 'pretrained_weight': init_pkl }) # init image global_state = init_images(global_state) with gr.Row(): with gr.Row(): # Left --> tools with gr.Column(scale=3): # Pickle with gr.Row(): with gr.Column(scale=1, min_width=10): gr.Markdown(value='Pickle', show_label=False) with gr.Column(scale=4, min_width=10): form_pretrained_dropdown = gr.Dropdown( choices=list(valid_checkpoints_dict.keys()), label="Pretrained Model", value=init_pkl, ) # Latent with gr.Row(): with gr.Column(scale=1, min_width=10): gr.Markdown(value='Latent', show_label=False) with gr.Column(scale=4, min_width=10): form_seed_number = gr.Slider( mininium=0, maximum=2**32-1, step=1, value=global_state.value['params']['seed'], interactive=True, # randomize=True, label="Seed", ) form_lr_number = gr.Number( value=global_state.value["params"]["lr"], interactive=True, label="Step Size") with gr.Row(): with gr.Column(scale=2, min_width=10): form_reset_image = gr.Button("Reset Image") with gr.Column(scale=3, min_width=10): form_latent_space = gr.Radio( ['w', 'w+'], value=global_state.value['params'] ['latent_space'], interactive=True, label='Latent space to optimize', show_label=False, ) # Drag with gr.Row(): with gr.Column(scale=1, min_width=10): gr.Markdown(value='Drag', show_label=False) with gr.Column(scale=4, min_width=10): with gr.Row(): with gr.Column(scale=1, min_width=10): enable_add_points = gr.Button('Add Points') with gr.Column(scale=1, min_width=10): undo_points = gr.Button('Reset Points') with gr.Row(): with gr.Column(scale=1, min_width=10): form_start_btn = gr.Button("Start") with gr.Column(scale=1, min_width=10): form_stop_btn = gr.Button("Stop") form_steps_number = gr.Number(value=0, label="Steps", interactive=False) # Mask with gr.Row(): with gr.Column(scale=1, min_width=10): gr.Markdown(value='Mask', show_label=False) with gr.Column(scale=4, min_width=10): enable_add_mask = gr.Button('Edit Flexible Area') with gr.Row(): with gr.Column(scale=1, min_width=10): form_reset_mask_btn = gr.Button("Reset mask") with gr.Column(scale=1, min_width=10): show_mask = gr.Checkbox( label='Show Mask', value=global_state.value['show_mask'], show_label=False) with gr.Row(): form_lambda_number = gr.Number( value=global_state.value["params"] ["motion_lambda"], interactive=True, label="Lambda", ) form_draw_interval_number = gr.Number( value=global_state.value["draw_interval"], label="Draw Interval (steps)", interactive=True, visible=False) # Right --> Image with gr.Column(scale=8): form_image = ImageMask( value=global_state.value['images']['image_show'], brush_radius=20).style( width=768, height=768) # NOTE: hard image size code here. gr.Markdown(""" ## Quick Start 1. Select desired `Pretrained Model` and adjust `Seed` to generate an initial image. 2. Click on image to add control points. 3. Click `Start` and enjoy it! ## Advance Usage 1. Change `Step Size` to adjust learning rate in drag optimization. 2. Select `w` or `w+` to change latent space to optimize: * Optimize on `w` space may cause greater influence to the image. * Optimize on `w+` space may work slower than `w`, but usually achieve better results. * Note that changing the latent space will reset the image, points and mask (this has the same effect as `Reset Image` button). 3. Click `Edit Flexible Area` to create a mask and constrain the unmasked region to remain unchanged. """) gr.HTML("""
Gradio demo supported by OpenMMLab MMagic
""") # Network & latents tab listeners def on_change_pretrained_dropdown(pretrained_value, global_state): """Function to handle model change. 1. Set pretrained value to global_state 2. Re-init images and clear all states """ global_state['pretrained_weight'] = pretrained_value init_images(global_state) clear_state(global_state) return global_state, global_state["images"]['image_show'] form_pretrained_dropdown.change( on_change_pretrained_dropdown, inputs=[form_pretrained_dropdown, global_state], outputs=[global_state, form_image], queue=True, ) def on_click_reset_image(global_state): """Reset image to the original one and clear all states 1. Re-init images 2. Clear all states """ init_images(global_state) clear_state(global_state) return global_state, global_state['images']['image_show'] form_reset_image.click( on_click_reset_image, inputs=[global_state], outputs=[global_state, form_image], queue=False, ) # Update parameters def on_change_update_image_seed(seed, global_state): """Function to handle generation seed change. 1. Set seed to global_state 2. Re-init images and clear all states """ global_state["params"]["seed"] = int(seed) init_images(global_state) clear_state(global_state) return global_state, global_state['images']['image_show'] form_seed_number.change( on_change_update_image_seed, inputs=[form_seed_number, global_state], outputs=[global_state, form_image], ) def on_click_latent_space(latent_space, global_state): """Function to reset latent space to optimize. NOTE: this function we reset the image and all controls 1. Set latent-space to global_state 2. Re-init images and clear all state """ global_state['params']['latent_space'] = latent_space init_images(global_state) clear_state(global_state) return global_state, global_state['images']['image_show'] form_latent_space.change(on_click_latent_space, inputs=[form_latent_space, global_state], outputs=[global_state, form_image]) # ==== Params form_lambda_number.change( partial(on_change_single_global_state, ["params", "motion_lambda"]), inputs=[form_lambda_number, global_state], outputs=[global_state], ) def on_change_lr(lr, global_state): if lr == 0: print('lr is 0, do nothing.') return global_state else: global_state["params"]["lr"] = lr renderer = global_state['renderer'] renderer.update_lr(lr) print('New optimizer: ') print(renderer.w_optim) return global_state form_lr_number.change( on_change_lr, inputs=[form_lr_number, global_state], outputs=[global_state], queue=False, ) def on_click_start(global_state, image): p_in_pixels = [] t_in_pixels = [] valid_points = [] # handle of start drag in mask editing mode global_state = preprocess_mask_info(global_state, image) # Prepare the points for the inference if len(global_state["points"]) == 0: # yield on_click_start_wo_points(global_state, image) image_raw = global_state['images']['image_raw'] update_image_draw( image_raw, global_state['points'], global_state['mask'], global_state['show_mask'], global_state, ) yield ( global_state, 0, global_state['images']['image_show'], # gr.File.update(visible=False), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), # latent space gr.Radio.update(interactive=True), gr.Button.update(interactive=True), # NOTE: disable stop button gr.Button.update(interactive=False), # update other comps gr.Dropdown.update(interactive=True), gr.Number.update(interactive=True), gr.Number.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Checkbox.update(interactive=True), # gr.Number.update(interactive=True), gr.Number.update(interactive=True), ) else: # Transform the points into torch tensors for key_point, point in global_state["points"].items(): try: p_start = point.get("start_temp", point["start"]) p_end = point["target"] if p_start is None or p_end is None: continue except KeyError: continue p_in_pixels.append(p_start) t_in_pixels.append(p_end) valid_points.append(key_point) mask = torch.tensor(global_state['mask']).float() drag_mask = 1 - mask renderer: Renderer = global_state["renderer"] global_state['temporal_params']['stop'] = False global_state['editing_state'] = 'running' # reverse points order p_to_opt = reverse_point_pairs(p_in_pixels) t_to_opt = reverse_point_pairs(t_in_pixels) print('Running with:') print(f' Source: {p_in_pixels}') print(f' Target: {t_in_pixels}') step_idx = 0 last_time = time.time() while True: print_memory_usage() # add a TIMEOUT break print(f'Running time: {time.time() - last_time}') if IS_SPACE and time.time() - last_time > TIMEOUT: print('Timeout break!') break if global_state["temporal_params"]["stop"] or global_state['generator_params']["stop"]: break # do drage here! renderer._render_drag_impl( global_state['generator_params'], p_to_opt, # point t_to_opt, # target drag_mask, # mask, global_state['params']['motion_lambda'], # lambda_mask reg=0, feature_idx=5, # NOTE: do not support change for now r1=global_state['params']['r1_in_pixels'], # r1 r2=global_state['params']['r2_in_pixels'], # r2 # random_seed = 0, # noise_mode = 'const', trunc_psi=global_state['params']['trunc_psi'], # force_fp32 = False, # layer_name = None, # sel_channels = 3, # base_channel = 0, # img_scale_db = 0, # img_normalize = False, # untransform = False, is_drag=True, to_pil=True) if step_idx % global_state['draw_interval'] == 0: print('Current Source:') for key_point, p_i, t_i in zip(valid_points, p_to_opt, t_to_opt): global_state["points"][key_point]["start_temp"] = [ p_i[1], p_i[0], ] global_state["points"][key_point]["target"] = [ t_i[1], t_i[0], ] start_temp = global_state["points"][key_point][ "start_temp"] print(f' {start_temp}') image_result = global_state['generator_params']['image'] image_draw = update_image_draw( image_result, global_state['points'], global_state['mask'], global_state['show_mask'], global_state, ) global_state['images']['image_raw'] = image_result yield ( global_state, step_idx, global_state['images']['image_show'], # gr.File.update(visible=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), # latent space gr.Radio.update(interactive=False), gr.Button.update(interactive=False), # enable stop button in loop gr.Button.update(interactive=True), # update other comps gr.Dropdown.update(interactive=False), gr.Number.update(interactive=False), gr.Number.update(interactive=False), gr.Button.update(interactive=False), gr.Button.update(interactive=False), gr.Checkbox.update(interactive=False), # gr.Number.update(interactive=False), gr.Number.update(interactive=False), ) # increate step step_idx += 1 image_result = global_state['generator_params']['image'] global_state['images']['image_raw'] = image_result image_draw = update_image_draw(image_result, global_state['points'], global_state['mask'], global_state['show_mask'], global_state) # fp = NamedTemporaryFile(suffix=".png", delete=False) # image_result.save(fp, "PNG") global_state['editing_state'] = 'add_points' yield ( global_state, 0, # reset step to 0 after stop. global_state['images']['image_show'], # gr.File.update(visible=True, value=fp.name), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), gr.Button.update(interactive=True), # latent space gr.Radio.update(interactive=True), gr.Button.update(interactive=True), # NOTE: disable stop button with loop finish gr.Button.update(interactive=False), # update other comps gr.Dropdown.update(interactive=True), gr.Number.update(interactive=True), gr.Number.update(interactive=True), gr.Checkbox.update(interactive=True), gr.Number.update(interactive=True), ) form_start_btn.click( on_click_start, inputs=[global_state, form_image], outputs=[ global_state, form_steps_number, form_image, # form_download_result_file, # >>> buttons form_reset_image, enable_add_points, enable_add_mask, undo_points, form_reset_mask_btn, form_latent_space, form_start_btn, form_stop_btn, # <<< buttonm # >>> inputs comps form_pretrained_dropdown, form_seed_number, form_lr_number, show_mask, form_lambda_number, ], ) def on_click_stop(global_state): """Function to handle stop button is clicked. 1. send a stop signal by set global_state["temporal_params"]["stop"] as True 2. Disable Stop button """ global_state["temporal_params"]["stop"] = True return global_state, gr.Button.update(interactive=False) form_stop_btn.click(on_click_stop, inputs=[global_state], outputs=[global_state, form_stop_btn], queue=False) form_draw_interval_number.change( partial( on_change_single_global_state, "draw_interval", map_transform=lambda x: int(x), ), inputs=[form_draw_interval_number, global_state], outputs=[global_state], queue=False, ) def on_click_remove_point(global_state): choice = global_state["curr_point"] del global_state["points"][choice] choices = list(global_state["points"].keys()) if len(choices) > 0: global_state["curr_point"] = choices[0] return ( gr.Dropdown.update(choices=choices, value=choices[0]), global_state, ) # Mask def on_click_reset_mask(global_state): global_state['mask'] = np.ones( ( global_state["images"]["image_raw"].size[1], global_state["images"]["image_raw"].size[0], ), dtype=np.uint8, ) image_draw = update_image_draw(global_state['images']['image_raw'], global_state['points'], global_state['mask'], global_state['show_mask'], global_state) return global_state, image_draw form_reset_mask_btn.click( on_click_reset_mask, inputs=[global_state], outputs=[global_state, form_image], ) # Image def on_click_enable_draw(global_state, image): """Function to start add mask mode. 1. Preprocess mask info from last state 2. Change editing state to add_mask 3. Set curr image with points and mask """ global_state = preprocess_mask_info(global_state, image) global_state['editing_state'] = 'add_mask' image_raw = global_state['images']['image_raw'] image_draw = update_image_draw(image_raw, global_state['points'], global_state['mask'], True, global_state) return (global_state, gr.Image.update(value=image_draw, interactive=True)) def on_click_remove_draw(global_state, image): """Function to start remove mask mode. 1. Preprocess mask info from last state 2. Change editing state to remove_mask 3. Set curr image with points and mask """ global_state = preprocess_mask_info(global_state, image) global_state['edinting_state'] = 'remove_mask' image_raw = global_state['images']['image_raw'] image_draw = update_image_draw(image_raw, global_state['points'], global_state['mask'], True, global_state) return (global_state, gr.Image.update(value=image_draw, interactive=True)) enable_add_mask.click(on_click_enable_draw, inputs=[global_state, form_image], outputs=[ global_state, form_image, ], queue=False) def on_click_add_point(global_state, image: dict): """Function switch from add mask mode to add points mode. 1. Updaste mask buffer if need 2. Change global_state['editing_state'] to 'add_points' 3. Set current image with mask """ global_state = preprocess_mask_info(global_state, image) global_state['editing_state'] = 'add_points' mask = global_state['mask'] image_raw = global_state['images']['image_raw'] image_draw = update_image_draw(image_raw, global_state['points'], mask, global_state['show_mask'], global_state) return (global_state, gr.Image.update(value=image_draw, interactive=False)) enable_add_points.click(on_click_add_point, inputs=[global_state, form_image], outputs=[global_state, form_image], queue=False) def on_click_image(global_state, evt: gr.SelectData): """This function only support click for point selection """ xy = evt.index if global_state['editing_state'] != 'add_points': print(f'In {global_state["editing_state"]} state. ' 'Do not add points.') return global_state, global_state['images']['image_show'] points = global_state["points"] point_idx = get_latest_points_pair(points) if point_idx is None: points[0] = {'start': xy, 'target': None} print(f'Click Image - Start - {xy}') elif points[point_idx].get('target', None) is None: points[point_idx]['target'] = xy print(f'Click Image - Target - {xy}') else: points[point_idx + 1] = {'start': xy, 'target': None} print(f'Click Image - Start - {xy}') image_raw = global_state['images']['image_raw'] image_draw = update_image_draw( image_raw, global_state['points'], global_state['mask'], global_state['show_mask'], global_state, ) return global_state, image_draw form_image.select( on_click_image, inputs=[global_state], outputs=[global_state, form_image], queue=False, ) def on_click_clear_points(global_state): """Function to handle clear all control points 1. clear global_state['points'] (clear_state) 2. re-init network 2. re-draw image """ clear_state(global_state, target='point') renderer: Renderer = global_state["renderer"] renderer.feat_refs = None image_raw = global_state['images']['image_raw'] image_draw = update_image_draw(image_raw, {}, global_state['mask'], global_state['show_mask'], global_state) return global_state, image_draw undo_points.click(on_click_clear_points, inputs=[global_state], outputs=[global_state, form_image], queue=False) def on_click_show_mask(global_state, show_mask): """Function to control whether show mask on image.""" global_state['show_mask'] = show_mask image_raw = global_state['images']['image_raw'] image_draw = update_image_draw( image_raw, global_state['points'], global_state['mask'], global_state['show_mask'], global_state, ) return global_state, image_draw show_mask.change( on_click_show_mask, inputs=[global_state, show_mask], outputs=[global_state, form_image], queue=False, ) gr.close_all() app.queue(concurrency_count=1, max_size=200, api_open=False) app.launch(show_api=False, share=True)