import gradio as gr import torch import argparse from omegaconf import OmegaConf from gligen.task_grounded_generation import grounded_generation_box, load_ckpt import json import numpy as np from PIL import Image, ImageDraw, ImageFont from functools import partial import math from gradio import processing_utils from typing import Optional from huggingface_hub import hf_hub_download hf_hub_download = partial(hf_hub_download, library_name="gligen_demo") arg_bool = lambda x: x.lower() == 'true' def parse_option(): parser = argparse.ArgumentParser('GLIGen Demo', add_help=False) parser.add_argument("--folder", type=str, default="create_samples", help="path to OUTPUT") parser.add_argument("--official_ckpt", type=str, default='ckpts/sd-v1-4.ckpt', help="") parser.add_argument("--guidance_scale", type=float, default=5, help="") parser.add_argument("--alpha_scale", type=float, default=1, help="scale tanh(alpha). If 0, the behaviour is same as original model") parser.add_argument("--load-text-box-generation", type=arg_bool, default=True, help="Load text-box generation pipeline.") parser.add_argument("--load-text-box-inpainting", type=arg_bool, default=False, help="Load text-box inpainting pipeline.") parser.add_argument("--load-text-image-box-generation", type=arg_bool, default=False, help="Load text-image-box generation pipeline.") args = parser.parse_args() return args args = parse_option() def load_from_hf(repo_id, filename='diffusion_pytorch_model.bin'): cache_file = hf_hub_download(repo_id=repo_id, filename=filename) return torch.load(cache_file, map_location='cpu') def load_ckpt_config_from_hf(modality): ckpt = load_from_hf(f'gligen/{modality}') config = load_from_hf('gligen/demo_config_legacy', filename=f'{modality}.pth') return ckpt, config if args.load_text_box_generation: pretrained_ckpt_gligen, config = load_ckpt_config_from_hf('gligen-generation-text-box') config = OmegaConf.create( config["_content"] ) # config used in training config.update( vars(args) ) config.model['params']['is_inpaint'] = False config.model['params']['is_style'] = False loaded_model_list = load_ckpt(config, pretrained_ckpt_gligen) if args.load_text_box_inpainting: pretrained_ckpt_gligen_inpaint, config = load_ckpt_config_from_hf('gligen-inpainting-text-box') config = OmegaConf.create( config["_content"] ) # config used in training config.update( vars(args) ) config.model['params']['is_inpaint'] = True config.model['params']['is_style'] = False loaded_model_list_inpaint = load_ckpt(config, pretrained_ckpt_gligen_inpaint) if args.load_text_image_box_generation: pretrained_ckpt_gligen_style, config = load_ckpt_config_from_hf('gligen-generation-text-image-box') config = OmegaConf.create( config["_content"] ) # config used in training config.update( vars(args) ) config.model['params']['is_inpaint'] = False config.model['params']['is_style'] = True loaded_model_list_style = load_ckpt(config, pretrained_ckpt_gligen_style) def load_clip_model(): from transformers import CLIPProcessor, CLIPModel version = "openai/clip-vit-large-patch14" model = CLIPModel.from_pretrained(version).cuda() processor = CLIPProcessor.from_pretrained(version) return { 'version': version, 'model': model, 'processor': processor, } clip_model = load_clip_model() class ImageMask(gr.components.Image): """ Sets: source="canvas", tool="sketch" """ is_template = True def __init__(self, **kwargs): super().__init__(source="upload", tool="sketch", interactive=True, **kwargs) def preprocess(self, x): if x is None: return x if self.tool == "sketch" and self.source in ["upload", "webcam"] and type(x) != dict: decode_image = processing_utils.decode_base64_to_image(x) width, height = decode_image.size mask = np.zeros((height, width, 4), dtype=np.uint8) mask[..., -1] = 255 mask = self.postprocess(mask) x = {'image': x, 'mask': mask} return super().preprocess(x) class Blocks(gr.Blocks): def __init__( self, theme: str = "default", analytics_enabled: Optional[bool] = None, mode: str = "blocks", title: str = "Gradio", css: Optional[str] = None, **kwargs, ): self.extra_configs = { 'thumbnail': kwargs.pop('thumbnail', ''), 'url': kwargs.pop('url', 'https://gradio.app/'), 'creator': kwargs.pop('creator', '@teamGradio'), } super(Blocks, self).__init__(theme, analytics_enabled, mode, title, css, **kwargs) def get_config_file(self): config = super(Blocks, self).get_config_file() for k, v in self.extra_configs.items(): config[k] = v return config ''' inference model ''' @torch.no_grad() def inference(task, language_instruction, grounding_instruction, inpainting_boxes_nodrop, image, alpha_sample, guidance_scale, batch_size, fix_seed, rand_seed, actual_mask, style_image, *args, **kwargs): grounding_instruction = json.loads(grounding_instruction) phrase_list, location_list = [], [] for k, v in grounding_instruction.items(): phrase_list.append(k) location_list.append(v) placeholder_image = Image.open('images/teddy.jpg').convert("RGB") image_list = [placeholder_image] * len(phrase_list) # placeholder input for visual prompt, which is disabled batch_size = int(batch_size) if not 1 <= batch_size <= 2: batch_size = 2 if style_image == None: has_text_mask = 1 has_image_mask = 0 # then we hack above 'image_list' else: valid_phrase_len = len(phrase_list) phrase_list += ['placeholder'] has_text_mask = [1]*valid_phrase_len + [0] image_list = [placeholder_image]*valid_phrase_len + [style_image] has_image_mask = [0]*valid_phrase_len + [1] location_list += [ [0.0, 0.0, 1, 0.01] ] # style image grounding location if task == 'Grounded Inpainting': alpha_sample = 1.0 instruction = dict( prompt = language_instruction, phrases = phrase_list, images = image_list, locations = location_list, alpha_type = [alpha_sample, 0, 1.0 - alpha_sample], has_text_mask = has_text_mask, has_image_mask = has_image_mask, save_folder_name = language_instruction, guidance_scale = guidance_scale, batch_size = batch_size, fix_seed = bool(fix_seed), rand_seed = int(rand_seed), actual_mask = actual_mask, inpainting_boxes_nodrop = inpainting_boxes_nodrop, ) with torch.autocast(device_type='cuda', dtype=torch.float16): if task == 'Grounded Generation': if style_image == None: return grounded_generation_box(loaded_model_list, instruction, *args, **kwargs) else: return grounded_generation_box(loaded_model_list_style, instruction, *args, **kwargs) elif task == 'Grounded Inpainting': assert image is not None instruction['input_image'] = image.convert("RGB") return grounded_generation_box(loaded_model_list_inpaint, instruction, *args, **kwargs) def draw_box(boxes=[], texts=[], img=None): if len(boxes) == 0 and img is None: return None if img is None: img = Image.new('RGB', (512, 512), (255, 255, 255)) colors = ["red", "olive", "blue", "green", "orange", "brown", "cyan", "purple"] draw = ImageDraw.Draw(img) font = ImageFont.truetype("DejaVuSansMono.ttf", size=18) for bid, box in enumerate(boxes): draw.rectangle([box[0], box[1], box[2], box[3]], outline=colors[bid % len(colors)], width=4) anno_text = texts[bid] draw.rectangle([box[0], box[3] - int(font.size * 1.2), box[0] + int((len(anno_text) + 0.8) * font.size * 0.6), box[3]], outline=colors[bid % len(colors)], fill=colors[bid % len(colors)], width=4) draw.text([box[0] + int(font.size * 0.2), box[3] - int(font.size*1.2)], anno_text, font=font, fill=(255,255,255)) return img def get_concat(ims): if len(ims) == 1: n_col = 1 else: n_col = 2 n_row = math.ceil(len(ims) / 2) dst = Image.new('RGB', (ims[0].width * n_col, ims[0].height * n_row), color="white") for i, im in enumerate(ims): row_id = i // n_col col_id = i % n_col dst.paste(im, (im.width * col_id, im.height * row_id)) return dst def auto_append_grounding(language_instruction, grounding_texts): for grounding_text in grounding_texts: if grounding_text not in language_instruction and grounding_text != 'auto': language_instruction += "; " + grounding_text print(language_instruction) return language_instruction def generate(task, language_instruction, grounding_texts, sketch_pad, alpha_sample, guidance_scale, batch_size, fix_seed, rand_seed, use_actual_mask, append_grounding, style_cond_image, state): if 'boxes' not in state: state['boxes'] = [] boxes = state['boxes'] grounding_texts = [x.strip() for x in grounding_texts.split(';')] assert len(boxes) == len(grounding_texts) boxes = (np.asarray(boxes) / 512).tolist() grounding_instruction = json.dumps({obj: box for obj,box in zip(grounding_texts, boxes)}) image = None actual_mask = None if task == 'Grounded Inpainting': image = state.get('original_image', sketch_pad['image']).copy() image = center_crop(image) image = Image.fromarray(image) if use_actual_mask: actual_mask = sketch_pad['mask'].copy() if actual_mask.ndim == 3: actual_mask = actual_mask[..., 0] actual_mask = center_crop(actual_mask, tgt_size=(64, 64)) actual_mask = torch.from_numpy(actual_mask == 0).float() if state.get('inpaint_hw', None): boxes = np.asarray(boxes) * 0.9 + 0.05 boxes = boxes.tolist() grounding_instruction = json.dumps({obj: box for obj,box in zip(grounding_texts, boxes) if obj != 'auto'}) if append_grounding: language_instruction = auto_append_grounding(language_instruction, grounding_texts) gen_images, gen_overlays = inference( task, language_instruction, grounding_instruction, boxes, image, alpha_sample, guidance_scale, batch_size, fix_seed, rand_seed, actual_mask, style_cond_image, clip_model=clip_model, ) for idx, gen_image in enumerate(gen_images): if task == 'Grounded Inpainting' and state.get('inpaint_hw', None): hw = min(*state['original_image'].shape[:2]) gen_image = sized_center_fill(state['original_image'].copy(), np.array(gen_image.resize((hw, hw))), hw, hw) gen_image = Image.fromarray(gen_image) gen_images[idx] = gen_image blank_samples = batch_size % 2 if batch_size > 1 else 0 gen_images = [gr.Image.update(value=x, visible=True) for i,x in enumerate(gen_images)] \ + [gr.Image.update(value=None, visible=True) for _ in range(blank_samples)] \ + [gr.Image.update(value=None, visible=False) for _ in range(4 - batch_size - blank_samples)] return gen_images + [state] def binarize(x): return (x != 0).astype('uint8') * 255 def sized_center_crop(img, cropx, cropy): y, x = img.shape[:2] startx = x // 2 - (cropx // 2) starty = y // 2 - (cropy // 2) return img[starty:starty+cropy, startx:startx+cropx] def sized_center_fill(img, fill, cropx, cropy): y, x = img.shape[:2] startx = x // 2 - (cropx // 2) starty = y // 2 - (cropy // 2) img[starty:starty+cropy, startx:startx+cropx] = fill return img def sized_center_mask(img, cropx, cropy): y, x = img.shape[:2] startx = x // 2 - (cropx // 2) starty = y // 2 - (cropy // 2) center_region = img[starty:starty+cropy, startx:startx+cropx].copy() img = (img * 0.2).astype('uint8') img[starty:starty+cropy, startx:startx+cropx] = center_region return img def center_crop(img, HW=None, tgt_size=(512, 512)): if HW is None: H, W = img.shape[:2] HW = min(H, W) img = sized_center_crop(img, HW, HW) img = Image.fromarray(img) img = img.resize(tgt_size) return np.array(img) def draw(task, input, grounding_texts, new_image_trigger, state): if type(input) == dict: image = input['image'] mask = input['mask'] else: mask = input if mask.ndim == 3: mask = mask[..., 0] image_scale = 1.0 # resize trigger if task == "Grounded Inpainting": mask_cond = mask.sum() == 0 # size_cond = mask.shape != (512, 512) if mask_cond and 'original_image' not in state: image = Image.fromarray(image) width, height = image.size scale = 600 / min(width, height) image = image.resize((int(width * scale), int(height * scale))) state['original_image'] = np.array(image).copy() image_scale = float(height / width) return [None, new_image_trigger + 1, image_scale, state] else: original_image = state['original_image'] H, W = original_image.shape[:2] image_scale = float(H / W) mask = binarize(mask) if mask.shape != (512, 512): # assert False, "should not receive any non- 512x512 masks." if 'original_image' in state and state['original_image'].shape[:2] == mask.shape: mask = center_crop(mask, state['inpaint_hw']) image = center_crop(state['original_image'], state['inpaint_hw']) else: mask = np.zeros((512, 512), dtype=np.uint8) # mask = center_crop(mask) mask = binarize(mask) if type(mask) != np.ndarray: mask = np.array(mask) if mask.sum() == 0 and task != "Grounded Inpainting": state = {} if task != 'Grounded Inpainting': image = None else: image = Image.fromarray(image) if 'boxes' not in state: state['boxes'] = [] if 'masks' not in state or len(state['masks']) == 0: state['masks'] = [] last_mask = np.zeros_like(mask) else: last_mask = state['masks'][-1] if type(mask) == np.ndarray and mask.size > 1: diff_mask = mask - last_mask else: diff_mask = np.zeros([]) if diff_mask.sum() > 0: x1x2 = np.where(diff_mask.max(0) != 0)[0] y1y2 = np.where(diff_mask.max(1) != 0)[0] y1, y2 = y1y2.min(), y1y2.max() x1, x2 = x1x2.min(), x1x2.max() if (x2 - x1 > 5) and (y2 - y1 > 5): state['masks'].append(mask.copy()) state['boxes'].append((x1, y1, x2, y2)) grounding_texts = [x.strip() for x in grounding_texts.split(';')] grounding_texts = [x for x in grounding_texts if len(x) > 0] if len(grounding_texts) < len(state['boxes']): grounding_texts += [f'Obj. {bid+1}' for bid in range(len(grounding_texts), len(state['boxes']))] box_image = draw_box(state['boxes'], grounding_texts, image) if box_image is not None and state.get('inpaint_hw', None): inpaint_hw = state['inpaint_hw'] box_image_resize = np.array(box_image.resize((inpaint_hw, inpaint_hw))) original_image = state['original_image'].copy() box_image = sized_center_fill(original_image, box_image_resize, inpaint_hw, inpaint_hw) return [box_image, new_image_trigger, image_scale, state] def clear(task, sketch_pad_trigger, batch_size, state, switch_task=False): if task != 'Grounded Inpainting': sketch_pad_trigger = sketch_pad_trigger + 1 blank_samples = batch_size % 2 if batch_size > 1 else 0 out_images = [gr.Image.update(value=None, visible=True) for i in range(batch_size)] \ + [gr.Image.update(value=None, visible=True) for _ in range(blank_samples)] \ + [gr.Image.update(value=None, visible=False) for _ in range(4 - batch_size - blank_samples)] state = {} return [None, sketch_pad_trigger, None, 1.0] + out_images + [state] css = """ #generate-btn { --tw-border-opacity: 1; border-color: rgb(255 216 180 / var(--tw-border-opacity)); --tw-gradient-from: rgb(255 216 180 / .7); --tw-gradient-to: rgb(255 216 180 / 0); --tw-gradient-stops: var(--tw-gradient-from), var(--tw-gradient-to); --tw-gradient-to: rgb(255 176 102 / .8); --tw-text-opacity: 1; color: rgb(238 116 0 / var(--tw-text-opacity)); } #img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img { height: var(--height) !important; max-height: var(--height) !important; min-height: var(--height) !important; } #mirrors a:hover { cursor:pointer; } #paper-info a { color:#008AD7; } #paper-info a:hover { cursor: pointer; } """ rescale_js = """ function(x) { const root = document.querySelector('gradio-app').shadowRoot || document.querySelector('gradio-app'); let image_scale = parseFloat(root.querySelector('#image_scale input').value) || 1.0; const image_width = root.querySelector('#img2img_image').clientWidth; const target_height = parseInt(image_width * image_scale); document.body.style.setProperty('--height', `${target_height}px`); root.querySelectorAll('button.justify-center.rounded')[0].style.display='none'; root.querySelectorAll('button.justify-center.rounded')[1].style.display='none'; return x; } """ mirror_js = """ function () { const root = document.querySelector('gradio-app').shadowRoot || document.querySelector('gradio-app'); const mirrors_div = root.querySelector('#mirrors'); const current_url = window.location.href; const mirrors = [ 'https://dev.hliu.cc/gligen_mirror1/', 'https://dev.hliu.cc/gligen_mirror2/', ]; let mirror_html = ''; mirror_html += '[Project Page]'; mirror_html += '[Paper]'; mirror_html += '[GitHub Repo]'; mirror_html += '   |   '; mirror_html += 'Mirrors: '; mirrors.forEach((e, index) => { let cur_index = index + 1; if (current_url.includes(e)) { mirror_html += `[Mirror ${cur_index}] `; } else { mirror_html += `[Mirror ${cur_index}] `; } }); mirror_html = `

${mirror_html}

`; mirrors_div.innerHTML = mirror_html; } """ with Blocks( css=css, analytics_enabled=False, title="GLIGen demo", ) as main: gr.Markdown('

GLIGen: Open-Set Grounded Text-to-Image Generation

') gr.Markdown("""

[Project Page] [Paper] [GitHub Repo]

""") # gr.HTML("", elem_id="mirrors") gr.Markdown("To ground concepts of interest with desired spatial specification, please (1) ⌨️ enter the concept names in Grounding Instruction, and (2) 🖱️ draw their corresponding bounding boxes one by one using Sketch Pad -- the parsed boxes will be displayed automatically.") with gr.Row(): with gr.Column(scale=4): sketch_pad_trigger = gr.Number(value=0, visible=False) sketch_pad_resize_trigger = gr.Number(value=0, visible=False) init_white_trigger = gr.Number(value=0, visible=False) image_scale = gr.Number(value=0, elem_id="image_scale", visible=False) new_image_trigger = gr.Number(value=0, visible=False) task = gr.Radio( choices=["Grounded Generation", 'Grounded Inpainting'], type="value", value="Grounded Generation", label="Task", ) language_instruction = gr.Textbox( label="Language instruction", ) grounding_instruction = gr.Textbox( label="Grounding instruction (Separated by semicolon)", ) with gr.Row(): sketch_pad = ImageMask(label="Sketch Pad", elem_id="img2img_image") out_imagebox = gr.Image(type="pil", label="Parsed Sketch Pad") with gr.Row(): clear_btn = gr.Button(value='Clear') gen_btn = gr.Button(value='Generate', elem_id="generate-btn") with gr.Accordion("Advanced Options", open=False): with gr.Column(): alpha_sample = gr.Slider(minimum=0, maximum=1.0, step=0.1, value=0.3, label="Scheduled Sampling (τ)") guidance_scale = gr.Slider(minimum=0, maximum=50, step=0.5, value=7.5, label="Guidance Scale") batch_size = gr.Slider(minimum=1, maximum=2, step=1, value=2, label="Number of Samples") append_grounding = gr.Checkbox(value=True, label="Append grounding instructions to the caption") use_actual_mask = gr.Checkbox(value=False, label="Use actual mask for inpainting", visible=False) with gr.Row(): fix_seed = gr.Checkbox(value=True, label="Fixed seed") rand_seed = gr.Slider(minimum=0, maximum=1000, step=1, value=0, label="Seed") with gr.Row(): use_style_cond = gr.Checkbox(value=False, label="Enable Style Condition") style_cond_image = gr.Image(type="pil", label="Style Condition", visible=False, interactive=True) with gr.Column(scale=4): gr.Markdown("### Generated Images") with gr.Row(): out_gen_1 = gr.Image(type="pil", visible=True, show_label=False) out_gen_2 = gr.Image(type="pil", visible=True, show_label=False) with gr.Row(): out_gen_3 = gr.Image(type="pil", visible=False, show_label=False) out_gen_4 = gr.Image(type="pil", visible=False, show_label=False) state = gr.State({}) class Controller: def __init__(self): self.calls = 0 self.tracks = 0 self.resizes = 0 self.scales = 0 def init_white(self, init_white_trigger): self.calls += 1 return np.ones((512, 512), dtype='uint8') * 255, 1.0, init_white_trigger+1 def change_n_samples(self, n_samples): blank_samples = n_samples % 2 if n_samples > 1 else 0 return [gr.Image.update(visible=True) for _ in range(n_samples + blank_samples)] \ + [gr.Image.update(visible=False) for _ in range(4 - n_samples - blank_samples)] def resize_centercrop(self, state): self.resizes += 1 image = state['original_image'].copy() inpaint_hw = int(0.9 * min(*image.shape[:2])) state['inpaint_hw'] = inpaint_hw image_cc = center_crop(image, inpaint_hw) # print(f'resize triggered {self.resizes}', image.shape, '->', image_cc.shape) return image_cc, state def resize_masked(self, state): self.resizes += 1 image = state['original_image'].copy() inpaint_hw = int(0.9 * min(*image.shape[:2])) state['inpaint_hw'] = inpaint_hw image_mask = sized_center_mask(image, inpaint_hw, inpaint_hw) state['masked_image'] = image_mask.copy() # print(f'mask triggered {self.resizes}') return image_mask, state def switch_task_hide_cond(self, task): cond = False if task == "Grounded Generation": cond = True return gr.Checkbox.update(visible=cond, value=False), gr.Image.update(value=None, visible=False), gr.Slider.update(visible=cond), gr.Checkbox.update(visible=(not cond), value=False) controller = Controller() main.load( lambda x:x+1, inputs=sketch_pad_trigger, outputs=sketch_pad_trigger, queue=False) sketch_pad.edit( draw, inputs=[task, sketch_pad, grounding_instruction, sketch_pad_resize_trigger, state], outputs=[out_imagebox, sketch_pad_resize_trigger, image_scale, state], queue=False, ) grounding_instruction.change( draw, inputs=[task, sketch_pad, grounding_instruction, sketch_pad_resize_trigger, state], outputs=[out_imagebox, sketch_pad_resize_trigger, image_scale, state], queue=False, ) clear_btn.click( clear, inputs=[task, sketch_pad_trigger, batch_size, state], outputs=[sketch_pad, sketch_pad_trigger, out_imagebox, image_scale, out_gen_1, out_gen_2, out_gen_3, out_gen_4, state], queue=False) task.change( partial(clear, switch_task=True), inputs=[task, sketch_pad_trigger, batch_size, state], outputs=[sketch_pad, sketch_pad_trigger, out_imagebox, image_scale, out_gen_1, out_gen_2, out_gen_3, out_gen_4, state], queue=False) sketch_pad_trigger.change( controller.init_white, inputs=[init_white_trigger], outputs=[sketch_pad, image_scale, init_white_trigger], queue=False) sketch_pad_resize_trigger.change( controller.resize_masked, inputs=[state], outputs=[sketch_pad, state], queue=False) batch_size.change( controller.change_n_samples, inputs=[batch_size], outputs=[out_gen_1, out_gen_2, out_gen_3, out_gen_4], queue=False) gen_btn.click( generate, inputs=[ task, language_instruction, grounding_instruction, sketch_pad, alpha_sample, guidance_scale, batch_size, fix_seed, rand_seed, use_actual_mask, append_grounding, style_cond_image, state, ], outputs=[out_gen_1, out_gen_2, out_gen_3, out_gen_4, state], queue=True ) sketch_pad_resize_trigger.change( None, None, sketch_pad_resize_trigger, _js=rescale_js, queue=False) init_white_trigger.change( None, None, init_white_trigger, _js=rescale_js, queue=False) use_style_cond.change( lambda cond: gr.Image.update(visible=cond), use_style_cond, style_cond_image, queue=False) task.change( controller.switch_task_hide_cond, inputs=task, outputs=[use_style_cond, style_cond_image, alpha_sample, use_actual_mask], queue=False) with gr.Column(): gr.Examples( examples=[ [ "images/blank.png", "Grounded Generation", "a dog and an apple", "a dog;an apple", ], [ "images/blank.png", "Grounded Generation", "John Lennon is using a pc", "John Lennon;a pc", [ "images/blank.png", "Grounded Generation", "a painting of a fox sitting in a field at sunrise in the style of Claude Mone", "fox;sunrise", ], ], [ "images/blank.png", "Grounded Generation", "a beautiful painting of hot dog by studio ghibli, octane render, brilliantly coloured", "hot dog", ], [ "images/blank.png", "Grounded Generation", "a sport car, unreal engine, global illumination, ray tracing", "a sport car", ], [ "images/flower_beach.jpg", "Grounded Inpainting", "a squirrel and the space needle", "a squirrel;the space needle", ], [ "images/arg_corgis.jpeg", "Grounded Inpainting", "a dog and a birthday cake", "a dog; a birthday cake", ], [ "images/teddy.jpg", "Grounded Inpainting", "a teddy bear wearing a santa claus red shirt; holding a Christmas gift box on hand", "a santa claus shirt; a Christmas gift box", ], ], inputs=[sketch_pad, task, language_instruction, grounding_instruction], outputs=None, fn=None, cache_examples=False, ) main.queue(concurrency_count=1, api_open=False) main.launch(share=False, show_api=False)