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| # demo inspired by https://huggingface.co/spaces/lambdalabs/image-mixer-demo | |
| import argparse | |
| import copy | |
| import os | |
| import shlex | |
| import subprocess | |
| from functools import partial | |
| from itertools import chain | |
| import cv2 | |
| import gradio as gr | |
| import torch | |
| from basicsr.utils import tensor2img | |
| from huggingface_hub import hf_hub_url | |
| from pytorch_lightning import seed_everything | |
| from torch import autocast | |
| from ldm.inference_base import (DEFAULT_NEGATIVE_PROMPT, diffusion_inference, | |
| get_adapters, get_sd_models) | |
| from ldm.modules.extra_condition import api | |
| from ldm.modules.extra_condition.api import (ExtraCondition, | |
| get_adapter_feature, | |
| get_cond_model) | |
| torch.set_grad_enabled(False) | |
| supported_cond = ['style', 'color', 'canny', 'sketch', 'openpose', 'depth'] | |
| # download the checkpoints | |
| urls = { | |
| 'TencentARC/T2I-Adapter': [ | |
| 'models/t2iadapter_keypose_sd14v1.pth', 'models/t2iadapter_color_sd14v1.pth', | |
| 'models/t2iadapter_openpose_sd14v1.pth', 'models/t2iadapter_seg_sd14v1.pth', | |
| 'models/t2iadapter_sketch_sd14v1.pth', 'models/t2iadapter_depth_sd14v1.pth', | |
| 'third-party-models/body_pose_model.pth', "models/t2iadapter_style_sd14v1.pth", | |
| "models/t2iadapter_canny_sd14v1.pth", "third-party-models/table5_pidinet.pth" | |
| ], | |
| 'runwayml/stable-diffusion-v1-5': ['v1-5-pruned-emaonly.ckpt'], | |
| 'andite/anything-v4.0': ['anything-v4.0-pruned.ckpt', 'anything-v4.0.vae.pt'], | |
| } | |
| if os.path.exists('models') == False: | |
| os.mkdir('models') | |
| for repo in urls: | |
| files = urls[repo] | |
| for file in files: | |
| url = hf_hub_url(repo, file) | |
| name_ckp = url.split('/')[-1] | |
| save_path = os.path.join('models', name_ckp) | |
| if os.path.exists(save_path) == False: | |
| subprocess.run(shlex.split(f'wget {url} -O {save_path}')) | |
| # config | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument( | |
| '--sd_ckpt', | |
| type=str, | |
| default='models/v1-5-pruned-emaonly.ckpt', | |
| help='path to checkpoint of stable diffusion model, both .ckpt and .safetensor are supported', | |
| ) | |
| parser.add_argument( | |
| '--vae_ckpt', | |
| type=str, | |
| default=None, | |
| help='vae checkpoint, anime SD models usually have seperate vae ckpt that need to be loaded', | |
| ) | |
| global_opt = parser.parse_args() | |
| global_opt.config = 'configs/stable-diffusion/sd-v1-inference.yaml' | |
| for cond_name in supported_cond: | |
| setattr(global_opt, f'{cond_name}_adapter_ckpt', f'models/t2iadapter_{cond_name}_sd14v1.pth') | |
| global_opt.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") | |
| global_opt.max_resolution = 512 * 512 | |
| global_opt.sampler = 'ddim' | |
| global_opt.cond_weight = 1.0 | |
| global_opt.C = 4 | |
| global_opt.f = 8 | |
| # stable-diffusion model | |
| sd_model, sampler = get_sd_models(global_opt) | |
| # adapters and models to processing condition inputs | |
| adapters = {} | |
| cond_models = {} | |
| torch.cuda.empty_cache() | |
| def run(*args): | |
| with torch.inference_mode(), \ | |
| sd_model.ema_scope(), \ | |
| autocast('cuda'): | |
| inps = [] | |
| for i in range(0, len(args) - 8, len(supported_cond)): | |
| inps.append(args[i:i + len(supported_cond)]) | |
| opt = copy.deepcopy(global_opt) | |
| opt.prompt, opt.neg_prompt, opt.scale, opt.n_samples, opt.seed, opt.steps, opt.resize_short_edge, opt.cond_tau \ | |
| = args[-8:] | |
| conds = [] | |
| activated_conds = [] | |
| ims1 = [] | |
| ims2 = [] | |
| for idx, (b, im1, im2, cond_weight) in enumerate(zip(*inps)): | |
| if idx > 1: | |
| if im1 is not None or im2 is not None: | |
| if im1 is not None: | |
| h, w, _ = im1.shape | |
| else: | |
| h, w, _ = im2.shape | |
| break | |
| # resize all the images to the same size | |
| for idx, (b, im1, im2, cond_weight) in enumerate(zip(*inps)): | |
| if idx == 0: | |
| ims1.append(im1) | |
| ims2.append(im2) | |
| continue | |
| if im1 is not None: | |
| im1 = cv2.resize(im1, (w, h), interpolation=cv2.INTER_CUBIC) | |
| if im2 is not None: | |
| im2 = cv2.resize(im2, (w, h), interpolation=cv2.INTER_CUBIC) | |
| ims1.append(im1) | |
| ims2.append(im2) | |
| for idx, (b, _, _, cond_weight) in enumerate(zip(*inps)): | |
| cond_name = supported_cond[idx] | |
| if b == 'Nothing': | |
| if cond_name in adapters: | |
| adapters[cond_name]['model'] = adapters[cond_name]['model'].cpu() | |
| else: | |
| activated_conds.append(cond_name) | |
| if cond_name in adapters: | |
| adapters[cond_name]['model'] = adapters[cond_name]['model'].to(opt.device) | |
| else: | |
| adapters[cond_name] = get_adapters(opt, getattr(ExtraCondition, cond_name)) | |
| adapters[cond_name]['cond_weight'] = cond_weight | |
| process_cond_module = getattr(api, f'get_cond_{cond_name}') | |
| if b == 'Image': | |
| if cond_name not in cond_models: | |
| cond_models[cond_name] = get_cond_model(opt, getattr(ExtraCondition, cond_name)) | |
| conds.append(process_cond_module(opt, ims1[idx], 'image', cond_models[cond_name])) | |
| else: | |
| conds.append(process_cond_module(opt, ims2[idx], cond_name, None)) | |
| adapter_features, append_to_context = get_adapter_feature( | |
| conds, [adapters[cond_name] for cond_name in activated_conds]) | |
| output_conds = [] | |
| for cond in conds: | |
| output_conds.append(tensor2img(cond, rgb2bgr=False)) | |
| ims = [] | |
| seed_everything(opt.seed) | |
| for _ in range(opt.n_samples): | |
| result = diffusion_inference(opt, sd_model, sampler, adapter_features, append_to_context) | |
| ims.append(tensor2img(result, rgb2bgr=False)) | |
| # Clear GPU memory cache so less likely to OOM | |
| torch.cuda.empty_cache() | |
| return ims, output_conds | |
| def change_visible(im1, im2, val): | |
| outputs = {} | |
| if val == "Image": | |
| outputs[im1] = gr.update(visible=True) | |
| outputs[im2] = gr.update(visible=False) | |
| elif val == "Nothing": | |
| outputs[im1] = gr.update(visible=False) | |
| outputs[im2] = gr.update(visible=False) | |
| else: | |
| outputs[im1] = gr.update(visible=False) | |
| outputs[im2] = gr.update(visible=True) | |
| return outputs | |
| DESCRIPTION = '# [Composable T2I-Adapter](https://github.com/TencentARC/T2I-Adapter)' | |
| DESCRIPTION += f'<p>Gradio demo for **T2I-Adapter**: [[GitHub]](https://github.com/TencentARC/T2I-Adapter), [[Paper]](https://arxiv.org/abs/2302.08453). If T2I-Adapter is helpful, please help to ⭐ the [Github Repo](https://github.com/TencentARC/T2I-Adapter) and recommend it to your friends 😊 </p>' | |
| DESCRIPTION += f'<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/Adapter/T2I-Adapter?duplicate=true"><img style="display: inline; margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space" /></a></p>' | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| btns = [] | |
| ims1 = [] | |
| ims2 = [] | |
| cond_weights = [] | |
| with gr.Row(): | |
| with gr.Column(scale=1.9): | |
| with gr.Box(): | |
| gr.Markdown("<h5><center>Style & Color</center></h5>") | |
| with gr.Row(): | |
| for cond_name in supported_cond[:2]: | |
| with gr.Box(): | |
| with gr.Column(): | |
| if cond_name == 'style': | |
| btn1 = gr.Radio( | |
| choices=["Image", "Nothing"], | |
| label=f"Input type for {cond_name}", | |
| interactive=True, | |
| value="Nothing", | |
| ) | |
| else: | |
| btn1 = gr.Radio( | |
| choices=["Image", cond_name, "Nothing"], | |
| label=f"Input type for {cond_name}", | |
| interactive=True, | |
| value="Nothing", | |
| ) | |
| im1 = gr.Image( | |
| source='upload', label="Image", interactive=True, visible=False, type="numpy") | |
| im2 = gr.Image( | |
| source='upload', label=cond_name, interactive=True, visible=False, type="numpy") | |
| cond_weight = gr.Slider( | |
| label="Condition weight", | |
| minimum=0, | |
| maximum=5, | |
| step=0.05, | |
| value=1, | |
| interactive=True) | |
| fn = partial(change_visible, im1, im2) | |
| btn1.change(fn=fn, inputs=[btn1], outputs=[im1, im2], queue=False) | |
| btns.append(btn1) | |
| ims1.append(im1) | |
| ims2.append(im2) | |
| cond_weights.append(cond_weight) | |
| with gr.Column(scale=4): | |
| with gr.Box(): | |
| gr.Markdown("<h5><center>Structure</center></h5>") | |
| with gr.Row(): | |
| for cond_name in supported_cond[2:6]: | |
| with gr.Box(): | |
| with gr.Column(): | |
| if cond_name == 'openpose': | |
| btn1 = gr.Radio( | |
| choices=["Image", 'pose', "Nothing"], | |
| label=f"Input type for {cond_name}", | |
| interactive=True, | |
| value="Nothing", | |
| ) | |
| else: | |
| btn1 = gr.Radio( | |
| choices=["Image", cond_name, "Nothing"], | |
| label=f"Input type for {cond_name}", | |
| interactive=True, | |
| value="Nothing", | |
| ) | |
| im1 = gr.Image( | |
| source='upload', label="Image", interactive=True, visible=False, type="numpy") | |
| im2 = gr.Image( | |
| source='upload', label=cond_name, interactive=True, visible=False, type="numpy") | |
| cond_weight = gr.Slider( | |
| label="Condition weight", | |
| minimum=0, | |
| maximum=5, | |
| step=0.05, | |
| value=1, | |
| interactive=True) | |
| fn = partial(change_visible, im1, im2) | |
| btn1.change(fn=fn, inputs=[btn1], outputs=[im1, im2], queue=False) | |
| btns.append(btn1) | |
| ims1.append(im1) | |
| ims2.append(im2) | |
| cond_weights.append(cond_weight) | |
| with gr.Column(): | |
| prompt = gr.Textbox(label="Prompt") | |
| with gr.Accordion('Advanced options', open=False): | |
| neg_prompt = gr.Textbox(label="Negative Prompt", value=DEFAULT_NEGATIVE_PROMPT) | |
| scale = gr.Slider( | |
| label="Guidance Scale (Classifier free guidance)", value=7.5, minimum=1, maximum=20, step=0.1) | |
| n_samples = gr.Slider(label="Num samples", value=1, minimum=1, maximum=8, step=1) | |
| seed = gr.Slider(label="Seed", value=42, minimum=0, maximum=10000, step=1) | |
| steps = gr.Slider(label="Steps", value=50, minimum=10, maximum=100, step=1) | |
| resize_short_edge = gr.Slider(label="Image resolution", value=512, minimum=320, maximum=1024, step=1) | |
| cond_tau = gr.Slider( | |
| label="timestamp parameter that determines until which step the adapter is applied", | |
| value=1.0, | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.05) | |
| with gr.Row(): | |
| submit = gr.Button("Generate") | |
| output = gr.Gallery().style(grid=2, height='auto') | |
| cond = gr.Gallery().style(grid=2, height='auto') | |
| inps = list(chain(btns, ims1, ims2, cond_weights)) | |
| inps.extend([prompt, neg_prompt, scale, n_samples, seed, steps, resize_short_edge, cond_tau]) | |
| submit.click(fn=run, inputs=inps, outputs=[output, cond]) | |
| demo.launch(server_name='0.0.0.0', share=False, server_port=47313) | |