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| ################################################################################ | |
| # Copyright (C) 2023 Xingqian Xu - All Rights Reserved # | |
| # # | |
| # Please visit Prompt-Free-Diffusion's arXiv paper for more details, link at # | |
| # arxiv.org/abs/2305.16223 # | |
| # # | |
| ################################################################################ | |
| import gradio as gr | |
| import os.path as osp | |
| from PIL import Image | |
| import numpy as np | |
| import time | |
| import torch | |
| import torchvision.transforms as tvtrans | |
| from lib.cfg_helper import model_cfg_bank | |
| from lib.model_zoo import get_model | |
| from collections import OrderedDict | |
| from lib.model_zoo.ddim import DDIMSampler | |
| from huggingface_hub import hf_hub_download | |
| n_sample_image = 1 | |
| # controlnet_path = OrderedDict([ | |
| # ['canny' , ('canny' , 'pretrained/controlnet/control_sd15_canny_slimmed.safetensors')], | |
| # ['canny_v11p' , ('canny' , 'pretrained/controlnet/control_v11p_sd15_canny_slimmed.safetensors')], | |
| # ['depth' , ('depth' , 'pretrained/controlnet/control_sd15_depth_slimmed.safetensors')], | |
| # ['hed' , ('hed' , 'pretrained/controlnet/control_sd15_hed_slimmed.safetensors')], | |
| # ['softedge_v11p' , ('hed' , 'pretrained/controlnet/control_v11p_sd15_softedge_slimmed.safetensors')], | |
| # ['mlsd' , ('mlsd' , 'pretrained/controlnet/control_sd15_mlsd_slimmed.safetensors')], | |
| # # ['mlsd_v11p' , ('mlsd' , 'pretrained/controlnet/control_v11p_sd15_mlsd_slimmed.safetensors')], | |
| # # ['normal' , ('normal' , 'pretrained/controlnet/control_sd15_normal_slimmed.safetensors')], | |
| # ['openpose' , ('openpose', 'pretrained/controlnet/control_sd15_openpose_slimmed.safetensors')], | |
| # ['openpose_v11p' , ('openpose', 'pretrained/controlnet/control_v11p_sd15_openpose_slimmed.safetensors')], | |
| # ['scribble' , ('scribble', 'pretrained/controlnet/control_sd15_scribble_slimmed.safetensors')], | |
| # ['seg' , ('none' , 'pretrained/controlnet/control_sd15_seg_slimmed.safetensors')], | |
| # ['lineart_v11p' , ('none' , 'pretrained/controlnet/control_v11p_sd15_lineart_slimmed.safetensors')], | |
| # ['lineart_anime_v11p', ('none' , 'pretrained/controlnet/control_v11p_sd15s2_lineart_anime_slimmed.safetensors')], | |
| # ]) | |
| controlnet_path = OrderedDict([ | |
| ['canny' , ('canny' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_canny_slimmed.safetensors'))], | |
| # ['canny_v11p' , ('canny' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15_canny_slimmed.safetensors'))], | |
| ['depth' , ('depth' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_depth_slimmed.safetensors'))], | |
| ['hed' , ('hed' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_hed_slimmed.safetensors'))], | |
| ['mlsd' , ('mlsd' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_mlsd_slimmed.safetensors'))], | |
| # ['mlsd_v11p' , ('mlsd' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15_mlsd_slimmed.safetensors'))], | |
| # ['normal' , ('normal' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_normal_slimmed.safetensors'))], | |
| # ['openpose' , ('openpose', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_openpose_slimmed.safetensors'))], | |
| ['openpose_v11p' , ('openpose', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15_openpose_slimmed.safetensors'))], | |
| ['scribble' , ('scribble', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_scribble_slimmed.safetensors'))], | |
| ['softedge_v11p' , ('scribble', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15_softedge_slimmed.safetensors'))], | |
| ['seg' , ('none' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_sd15_seg_slimmed.safetensors'))], | |
| ['lineart_v11p' , ('none' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15_lineart_slimmed.safetensors'))], | |
| ['lineart_anime_v11p', ('none' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/controlnet/control_v11p_sd15s2_lineart_anime_slimmed.safetensors'))], | |
| ]) | |
| preprocess_method = [ | |
| 'canny' , | |
| 'depth' , | |
| 'hed' , | |
| 'mlsd' , | |
| # 'normal' , | |
| 'openpose' , | |
| 'openpose_withface' , | |
| 'openpose_withfacehand', | |
| 'scribble' , | |
| 'none' , | |
| ] | |
| # diffuser_path = OrderedDict([ | |
| # ['SD-v1.5' , 'pretrained/pfd/diffuser/SD-v1-5.safetensors'], | |
| # ['OpenJouney-v4' , 'pretrained/pfd/diffuser/OpenJouney-v4.safetensors'], | |
| # ['Deliberate-v2.0' , 'pretrained/pfd/diffuser/Deliberate-v2-0.safetensors'], | |
| # ['RealisticVision-v2.0', 'pretrained/pfd/diffuser/RealisticVision-v2-0.safetensors'], | |
| # ['Anything-v4' , 'pretrained/pfd/diffuser/Anything-v4.safetensors'], | |
| # ['Oam-v3' , 'pretrained/pfd/diffuser/AbyssOrangeMix-v3.safetensors'], | |
| # ['Oam-v2' , 'pretrained/pfd/diffuser/AbyssOrangeMix-v2.safetensors'], | |
| # ]) | |
| # ctxencoder_path = OrderedDict([ | |
| # ['SeeCoder' , 'pretrained/pfd/seecoder/seecoder-v1-0.safetensors'], | |
| # ['SeeCoder-PA' , 'pretrained/pfd/seecoder/seecoder-pa-v1-0.safetensors'], | |
| # ['SeeCoder-Anime', 'pretrained/pfd/seecoder/seecoder-anime-v1-0.safetensors'], | |
| # ]) | |
| diffuser_path = OrderedDict([ | |
| ['SD-v1.5' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/SD-v1-5.safetensors')], | |
| ['OpenJouney-v4' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/OpenJouney-v4.safetensors')], | |
| ['Deliberate-v2.0' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/Deliberate-v2-0.safetensors')], | |
| ['RealisticVision-v2.0', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/RealisticVision-v2-0.safetensors')], | |
| ['Anything-v4' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/Anything-v4.safetensors')], | |
| ['Oam-v3' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/AbyssOrangeMix-v3.safetensors')], | |
| ['Oam-v2' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/diffuser/AbyssOrangeMix-v2.safetensors')], | |
| ]) | |
| ctxencoder_path = OrderedDict([ | |
| ['SeeCoder' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/seecoder/seecoder-v1-0.safetensors')], | |
| ['SeeCoder-PA' , hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/seecoder/seecoder-pa-v1-0.safetensors')], | |
| ['SeeCoder-Anime', hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/seecoder/seecoder-anime-v1-0.safetensors')], | |
| ]) | |
| ########## | |
| # helper # | |
| ########## | |
| def highlight_print(info): | |
| print('') | |
| print(''.join(['#']*(len(info)+4))) | |
| print('# '+info+' #') | |
| print(''.join(['#']*(len(info)+4))) | |
| print('') | |
| def load_sd_from_file(target): | |
| if osp.splitext(target)[-1] == '.ckpt': | |
| sd = torch.load(target, map_location='cpu')['state_dict'] | |
| elif osp.splitext(target)[-1] == '.pth': | |
| sd = torch.load(target, map_location='cpu') | |
| elif osp.splitext(target)[-1] == '.safetensors': | |
| from safetensors.torch import load_file as stload | |
| sd = OrderedDict(stload(target, device='cpu')) | |
| else: | |
| assert False, "File type must be .ckpt or .pth or .safetensors" | |
| return sd | |
| ######## | |
| # main # | |
| ######## | |
| class prompt_free_diffusion(object): | |
| def __init__(self, | |
| fp16=False, | |
| tag_ctx=None, | |
| tag_diffuser=None, | |
| tag_ctl=None,): | |
| self.tag_ctx = tag_ctx | |
| self.tag_diffuser = tag_diffuser | |
| self.tag_ctl = tag_ctl | |
| self.strict_sd = True | |
| cfgm = model_cfg_bank()('pfd_seecoder_with_controlnet') | |
| self.net = get_model()(cfgm) | |
| sdvae = hf_hub_download('shi-labs/prompt-free-diffusion', 'pretrained/pfd/vae/sd-v2-0-base-autokl.pth') | |
| sdvae = torch.load(sdvae) | |
| self.net.vae['image'].load_state_dict(sdvae) | |
| self.action_load_ctx(tag_ctx) | |
| self.action_load_diffuser(tag_diffuser) | |
| self.action_load_ctl(tag_ctl) | |
| if fp16: | |
| highlight_print('Running in FP16') | |
| self.net.ctx['image'].fp16 = True | |
| self.net = self.net.half() | |
| self.dtype = torch.float16 | |
| else: | |
| self.dtype = torch.float32 | |
| self.use_cuda = torch.cuda.is_available() | |
| if self.use_cuda: | |
| self.net.to('cuda') | |
| self.net.eval() | |
| self.sampler = DDIMSampler(self.net) | |
| self.n_sample_image = n_sample_image | |
| self.ddim_steps = 50 | |
| self.ddim_eta = 0.0 | |
| self.image_latent_dim = 4 | |
| def load_ctx(self, pretrained): | |
| sd = load_sd_from_file(pretrained) | |
| sd_extra = [(ki, vi) for ki, vi in self.net.state_dict().items() \ | |
| if ki.find('ctx.')!=0] | |
| sd.update(OrderedDict(sd_extra)) | |
| self.net.load_state_dict(sd, strict=True) | |
| print('Load context encoder from [{}] strict [{}].'.format(pretrained, True)) | |
| def load_diffuser(self, pretrained): | |
| sd = load_sd_from_file(pretrained) | |
| if len([ki for ki in sd.keys() if ki.find('diffuser.image.context_blocks.')==0]) == 0: | |
| sd = [( | |
| ki.replace('diffuser.text.context_blocks.', 'diffuser.image.context_blocks.'), vi) | |
| for ki, vi in sd.items()] | |
| sd = OrderedDict(sd) | |
| sd_extra = [(ki, vi) for ki, vi in self.net.state_dict().items() \ | |
| if ki.find('diffuser.')!=0] | |
| sd.update(OrderedDict(sd_extra)) | |
| self.net.load_state_dict(sd, strict=True) | |
| print('Load diffuser from [{}] strict [{}].'.format(pretrained, True)) | |
| def load_ctl(self, pretrained): | |
| sd = load_sd_from_file(pretrained) | |
| self.net.ctl.load_state_dict(sd, strict=True) | |
| print('Load controlnet from [{}] strict [{}].'.format(pretrained, True)) | |
| def action_load_ctx(self, tag): | |
| pretrained = ctxencoder_path[tag] | |
| if tag == 'SeeCoder-PA': | |
| from lib.model_zoo.seecoder import PPE_MLP | |
| pe_layer = \ | |
| PPE_MLP(freq_num=20, freq_max=None, out_channel=768, mlp_layer=3) | |
| if self.dtype == torch.float16: | |
| pe_layer = pe_layer.half() | |
| if self.use_cuda: | |
| pe_layer.to('cuda') | |
| pe_layer.eval() | |
| self.net.ctx['image'].qtransformer.pe_layer = pe_layer | |
| else: | |
| self.net.ctx['image'].qtransformer.pe_layer = None | |
| if pretrained is not None: | |
| self.load_ctx(pretrained) | |
| self.tag_ctx = tag | |
| return tag | |
| def action_load_diffuser(self, tag): | |
| pretrained = diffuser_path[tag] | |
| if pretrained is not None: | |
| self.load_diffuser(pretrained) | |
| self.tag_diffuser = tag | |
| return tag | |
| def action_load_ctl(self, tag): | |
| pretrained = controlnet_path[tag][1] | |
| if pretrained is not None: | |
| self.load_ctl(pretrained) | |
| self.tag_ctl = tag | |
| return tag | |
| def action_autoset_hw(self, imctl): | |
| if imctl is None: | |
| return 512, 512 | |
| w, h = imctl.size | |
| w = w//64 * 64 | |
| h = h//64 * 64 | |
| w = w if w >=512 else 512 | |
| w = w if w <=1024 else 1024 | |
| h = h if h >=512 else 512 | |
| h = h if h <=1024 else 1024 | |
| return h, w | |
| def action_autoset_method(self, tag): | |
| return controlnet_path[tag][0] | |
| def action_inference( | |
| self, im, imctl, ctl_method, do_preprocess, | |
| h, w, ugscale, seed, | |
| tag_ctx, tag_diffuser, tag_ctl,): | |
| if tag_ctx != self.tag_ctx: | |
| self.action_load_ctx(tag_ctx) | |
| if tag_diffuser != self.tag_diffuser: | |
| self.action_load_diffuser(tag_diffuser) | |
| if tag_ctl != self.tag_ctl: | |
| self.action_load_ctl(tag_ctl) | |
| n_samples = self.n_sample_image | |
| sampler = self.sampler | |
| device = self.net.device | |
| w = w//64 * 64 | |
| h = h//64 * 64 | |
| if imctl is not None: | |
| imctl = imctl.resize([w, h], Image.Resampling.BICUBIC) | |
| craw = tvtrans.ToTensor()(im)[None].to(device).to(self.dtype) | |
| c = self.net.ctx_encode(craw, which='image').repeat(n_samples, 1, 1) | |
| u = torch.zeros_like(c) | |
| if tag_ctx in ["SeeCoder-Anime"]: | |
| u = torch.load('assets/anime_ug.pth')[None].to(device).to(self.dtype) | |
| pad = c.size(1) - u.size(1) | |
| u = torch.cat([u, torch.zeros_like(u[:, 0:1].repeat(1, pad, 1))], axis=1) | |
| if tag_ctl != 'none': | |
| ccraw = tvtrans.ToTensor()(imctl)[None].to(device).to(self.dtype) | |
| if do_preprocess: | |
| cc = self.net.ctl.preprocess(ccraw, type=ctl_method, size=[h, w]) | |
| cc = cc.to(self.dtype) | |
| else: | |
| cc = ccraw | |
| else: | |
| cc = None | |
| shape = [n_samples, self.image_latent_dim, h//8, w//8] | |
| if seed < 0: | |
| np.random.seed(int(time.time())) | |
| torch.manual_seed(-seed + 100) | |
| else: | |
| np.random.seed(seed + 100) | |
| torch.manual_seed(seed) | |
| x, _ = sampler.sample( | |
| steps=self.ddim_steps, | |
| x_info={'type':'image',}, | |
| c_info={'type':'image', 'conditioning':c, 'unconditional_conditioning':u, | |
| 'unconditional_guidance_scale':ugscale, | |
| 'control':cc,}, | |
| shape=shape, | |
| verbose=False, | |
| eta=self.ddim_eta) | |
| ccout = [tvtrans.ToPILImage()(i) for i in cc] if cc is not None else [] | |
| imout = self.net.vae_decode(x, which='image') | |
| imout = [tvtrans.ToPILImage()(i) for i in imout] | |
| return imout + ccout | |
| pfd_inference = prompt_free_diffusion( | |
| fp16=True, tag_ctx = 'SeeCoder', tag_diffuser = 'Deliberate-v2.0', tag_ctl = 'canny',) | |
| ################# | |
| # sub interface # | |
| ################# | |
| cache_examples = True | |
| def get_example(): | |
| case = [ | |
| [ | |
| 'assets/examples/ghibli-input.jpg', | |
| 'assets/examples/ghibli-canny.png', | |
| 'canny', False, | |
| 768, 1024, 1.8, 23, | |
| 'SeeCoder', 'Deliberate-v2.0', 'canny', ], | |
| [ | |
| 'assets/examples/astronautridinghouse-input.jpg', | |
| 'assets/examples/astronautridinghouse-canny.png', | |
| 'canny', False, | |
| 512, 768, 2.0, 21, | |
| 'SeeCoder', 'Deliberate-v2.0', 'canny', ], | |
| [ | |
| 'assets/examples/grassland-input.jpg', | |
| 'assets/examples/grassland-scribble.png', | |
| 'scribble', False, | |
| 768, 512, 2.0, 41, | |
| 'SeeCoder', 'Deliberate-v2.0', 'scribble', ], | |
| [ | |
| 'assets/examples/jeep-input.jpg', | |
| 'assets/examples/jeep-depth.png', | |
| 'depth', False, | |
| 512, 768, 2.0, 30, | |
| 'SeeCoder', 'Deliberate-v2.0', 'depth', ], | |
| [ | |
| 'assets/examples/bedroom-input.jpg', | |
| 'assets/examples/bedroom-mlsd.png', | |
| 'mlsd', False, | |
| 512, 512, 2.0, 31, | |
| 'SeeCoder', 'Deliberate-v2.0', 'mlsd', ], | |
| [ | |
| 'assets/examples/nightstreet-input.jpg', | |
| 'assets/examples/nightstreet-canny.png', | |
| 'canny', False, | |
| 768, 512, 2.3, 20, | |
| 'SeeCoder', 'Deliberate-v2.0', 'canny', ], | |
| [ | |
| 'assets/examples/woodcar-input.jpg', | |
| 'assets/examples/woodcar-depth.png', | |
| 'depth', False, | |
| 768, 512, 2.0, 20, | |
| 'SeeCoder', 'Deliberate-v2.0', 'depth', ], | |
| [ | |
| 'assets/examples-anime/miku.jpg', | |
| 'assets/examples-anime/miku-canny.png', | |
| 'canny', False, | |
| 768, 576, 1.5, 22, | |
| 'SeeCoder-Anime', 'Anything-v4', 'canny', ], | |
| [ | |
| 'assets/examples-anime/random1.jpg', | |
| 'assets/examples-anime/pose_small.png', | |
| 'openpose', False, | |
| 768, 1024, 2.5, 29, | |
| 'SeeCoder-Anime', 'Oam-v2', 'openpose_v11p', ], | |
| [ | |
| 'assets/examples-anime/camping.jpg', | |
| 'assets/examples-anime/pose_small.png', | |
| 'openpose', False, | |
| 768, 1024, 2.0, 38, | |
| 'SeeCoder-Anime', 'Anything-v4', 'openpose_v11p', ], | |
| [ | |
| 'assets/examples-anime/hanfu_girl.jpg', | |
| 'assets/examples-anime/pose_small.png', | |
| 'openpose', False, | |
| 768, 1024, 2.0, 20, | |
| 'SeeCoder-Anime', 'Anything-v4', 'openpose_v11p', ], | |
| ] | |
| return case | |
| def interface(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| img_input = gr.Image(label='Image Input', type='pil', elem_id='customized_imbox') | |
| with gr.Row(): | |
| out_width = gr.Slider(label="Width" , minimum=512, maximum=1024, value=512, step=64, visible=True) | |
| out_height = gr.Slider(label="Height", minimum=512, maximum=1024, value=512, step=64, visible=True) | |
| with gr.Row(): | |
| scl_lvl = gr.Slider(label="CFGScale", minimum=0, maximum=4, value=2, step=0.01, visible=True) | |
| seed = gr.Number(20, label="Seed", precision=0) | |
| with gr.Row(): | |
| tag_ctx = gr.Dropdown(label='Context Encoder', choices=[pi for pi in ctxencoder_path.keys()], value='SeeCoder') | |
| tag_diffuser = gr.Dropdown(label='Diffuser', choices=[pi for pi in diffuser_path.keys()], value='Deliberate-v2.0') | |
| button = gr.Button("Run") | |
| with gr.Column(): | |
| ctl_input = gr.Image(label='Control Input', type='pil', elem_id='customized_imbox') | |
| do_preprocess = gr.Checkbox(label='Preprocess', value=False) | |
| with gr.Row(): | |
| ctl_method = gr.Dropdown(label='Preprocess Type', choices=preprocess_method, value='canny') | |
| tag_ctl = gr.Dropdown(label='ControlNet', choices=[pi for pi in controlnet_path.keys()], value='canny') | |
| with gr.Column(): | |
| img_output = gr.Gallery(label="Image Result", elem_id='customized_imbox').style(grid=n_sample_image+1) | |
| tag_ctl.change( | |
| pfd_inference.action_autoset_method, | |
| inputs = [tag_ctl], | |
| outputs = [ctl_method],) | |
| ctl_input.change( | |
| pfd_inference.action_autoset_hw, | |
| inputs = [ctl_input], | |
| outputs = [out_height, out_width],) | |
| # tag_ctx.change( | |
| # pfd_inference.action_load_ctx, | |
| # inputs = [tag_ctx], | |
| # outputs = [tag_ctx],) | |
| # tag_diffuser.change( | |
| # pfd_inference.action_load_diffuser, | |
| # inputs = [tag_diffuser], | |
| # outputs = [tag_diffuser],) | |
| # tag_ctl.change( | |
| # pfd_inference.action_load_ctl, | |
| # inputs = [tag_ctl], | |
| # outputs = [tag_ctl],) | |
| button.click( | |
| pfd_inference.action_inference, | |
| inputs=[img_input, ctl_input, ctl_method, do_preprocess, | |
| out_height, out_width, scl_lvl, seed, | |
| tag_ctx, tag_diffuser, tag_ctl, ], | |
| outputs=[img_output]) | |
| gr.Examples( | |
| label='Examples', | |
| examples=get_example(), | |
| fn=pfd_inference.action_inference, | |
| inputs=[img_input, ctl_input, ctl_method, do_preprocess, | |
| out_height, out_width, scl_lvl, seed, | |
| tag_ctx, tag_diffuser, tag_ctl, ], | |
| outputs=[img_output], | |
| cache_examples=cache_examples,) | |
| ############# | |
| # Interface # | |
| ############# | |
| css = """ | |
| #customized_imbox { | |
| min-height: 450px; | |
| } | |
| #customized_imbox>div[data-testid="image"] { | |
| min-height: 450px; | |
| } | |
| #customized_imbox>div[data-testid="image"]>div { | |
| min-height: 450px; | |
| } | |
| #customized_imbox>div[data-testid="image"]>iframe { | |
| min-height: 450px; | |
| } | |
| #customized_imbox>div.unpadded_box { | |
| min-height: 450px; | |
| } | |
| #myinst { | |
| font-size: 0.8rem; | |
| margin: 0rem; | |
| color: #6B7280; | |
| } | |
| #maskinst { | |
| text-align: justify; | |
| min-width: 1200px; | |
| } | |
| #maskinst>img { | |
| min-width:399px; | |
| max-width:450px; | |
| vertical-align: top; | |
| display: inline-block; | |
| } | |
| #maskinst:after { | |
| content: ""; | |
| width: 100%; | |
| display: inline-block; | |
| } | |
| """ | |
| if True: | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| """ | |
| <div style="text-align: center; max-width: 1200px; margin: 20px auto;"> | |
| <h1 style="font-weight: 900; font-size: 3rem; margin: 0rem"> | |
| Prompt-Free Diffusion | |
| </h1> | |
| <p style="font-size: 1rem; margin: 0rem"> | |
| Xingqian Xu<sup>1,5</sup>, Jiayi Guo<sup>1,2</sup>, Zhangyang Wang<sup>3,5</sup>, Gao Huang<sup>2</sup>, Irfan Essa<sup>4</sup>, and Humphrey Shi<sup>1,5</sup> | |
| </p> | |
| <p style="font-size: 0.8rem; margin: 0rem; line-height: 1em"> | |
| <sup>1</sup>SHI Labs @ UIUC & Oregon, <sup>2</sup>Tsinghua University, <sup>3</sup>UT Austin, <sup>4</sup>Georgia Tech, <sup>5</sup>Picsart AI Research (PAIR) | |
| </p> | |
| <p style="font-size: 0.9rem; margin: 0rem; line-height: 1.2em; margin-top:1em"> | |
| The performance of Text2Image is largely dependent on text prompts. | |
| In Prompt-Free Diffusion, no prompt is needed, just a reference images! | |
| At the core of Prompt-Free Diffusion is an image-only semantic context encoder (SeeCoder). | |
| SeeCoder is reusable to most CLIP-based T2I models: just drop in and replace CLIP, then you will create your own prompt-free diffusion. | |
| <a href="https://github.com/SHI-Labs/Prompt-Free-Diffusion">[Github]</a> <a href="https://arxiv.org/abs/2305.16223">[arXiv]</a> | |
| </p> | |
| </div> | |
| """) | |
| interface() | |
| # gr.HTML( | |
| # """ | |
| # <div style="text-align: justify; max-width: 1200px; margin: 20px auto;"> | |
| # <h3 style="font-weight: 450; font-size: 0.8rem; margin: 0rem"> | |
| # <b>Version</b>: {} | |
| # </h3> | |
| # </div> | |
| # """.format(' '+str(pfd_inference.pretrained))) | |
| # demo.launch(server_name="0.0.0.0", server_port=7992) | |
| # demo.launch() | |
| demo.launch(debug=True) | |