Spaces:
Sleeping
Sleeping
| import torch | |
| from torchvision.utils import make_grid | |
| import math | |
| from PIL import Image | |
| from diffusion import create_diffusion | |
| from diffusers.models import AutoencoderKL | |
| import gradio as gr | |
| from imagenet_class_data import IMAGENET_1K_CLASSES | |
| from models import MDT_XL_2 | |
| import os | |
| from huggingface_hub import snapshot_download | |
| def load_model(image_size=256): | |
| assert image_size in [256] | |
| latent_size = image_size // 8 | |
| model = MDT_XL_2(input_size=latent_size, decode_layer=2).to(device) | |
| models_path = snapshot_download("shgao/MDT-XL2") | |
| ckpt_model_path = os.path.join(models_path, "mdt_xl2_v1_ckpt.pt") | |
| state_dict = torch.load( | |
| ckpt_model_path, map_location=lambda storage, loc: storage) | |
| model.load_state_dict(state_dict) | |
| model.eval() | |
| return model | |
| torch.set_grad_enabled(False) | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model = load_model(image_size=256) | |
| vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse").to(device) | |
| current_image_size = 256 | |
| current_vae_model = "stabilityai/sd-vae-ft-mse" | |
| def generate(image_size, vae_model, class_label, cfg_scale, pow_scale, num_sampling_steps, seed): | |
| n = 1 | |
| image_size = int(image_size.split("x")[0]) | |
| global current_image_size | |
| if image_size != current_image_size: | |
| global model | |
| model = model.to("cpu") | |
| del model | |
| if device == "cuda": | |
| torch.cuda.empty_cache() | |
| model = load_model(image_size=image_size) | |
| current_image_size = image_size | |
| global current_vae_model | |
| if vae_model != current_vae_model: | |
| global vae | |
| if device == "cuda": | |
| vae.to("cpu") | |
| del vae | |
| vae = AutoencoderKL.from_pretrained(vae_model).to(device) | |
| # Seed PyTorch: | |
| torch.manual_seed(seed) | |
| # Setup diffusion | |
| diffusion = create_diffusion(str(num_sampling_steps)) | |
| # Create sampling noise: | |
| latent_size = image_size // 8 | |
| z = torch.randn(n, 4, latent_size, latent_size, device=device) | |
| y = torch.tensor([class_label] * n, device=device) | |
| # Setup classifier-free guidance: | |
| z = torch.cat([z, z], 0) | |
| y_null = torch.tensor([1000] * n, device=device) | |
| y = torch.cat([y, y_null], 0) | |
| model_kwargs = dict(y=y, cfg_scale=cfg_scale, scale_pow=pow_scale) | |
| # Sample images: | |
| samples = diffusion.p_sample_loop( | |
| model.forward_with_cfg, z.shape, z, clip_denoised=False, model_kwargs=model_kwargs, progress=True, device=device | |
| ) | |
| samples, _ = samples.chunk(2, dim=0) # Remove null class samples | |
| samples = vae.decode(samples / 0.18215).sample | |
| # Convert to PIL.Image format: | |
| samples = samples.mul(127.5).add_(128.0).clamp_( | |
| 0, 255).permute(0, 2, 3, 1).to("cpu", torch.uint8).numpy() | |
| samples = [Image.fromarray(sample) for sample in samples] | |
| return samples | |
| description = '''This is a demo of our MDT image generation models. MDT is a class-conditional model trained on ImageNet-1K.''' | |
| duplicate = '''Skip the queue by duplicating this space and upgrading to GPU in settings | |
| <a href="https://huggingface.co/spaces/wpeebles/DiT?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>''' | |
| more_info = ''' | |
| # Masked Diffusion Transformer | |
| [](https://paperswithcode.com/sota/image-generation-on-imagenet-256x256?p=masked-diffusion-transformer-is-a-strong) | |
| The official codebase for [Masked Diffusion Transformer is a Strong Image Synthesizer](https://arxiv.org/abs/2303.14389). | |
| ## Introduction | |
| Despite its success in image synthesis, we observe that diffusion probabilistic models (DPMs) often lack contextual reasoning ability to learn the relations among object parts in an image, leading to a slow learning process. | |
| To solve this issue, we propose a Masked Diffusion Transformer (MDT) that introduces a mask latent modeling scheme to explicitly enhance the DPMs’ ability of contextual relation learning among object semantic parts in an image. During training, MDT operates on the latent space to mask certain tokens. Then, an asymmetric masking diffusion transformer is designed to predict masked tokens from unmasked ones while maintaining the diffusion generation process. Our MDT can reconstruct the full information of an image from its incomplete contextual input, thus enabling it to learn the associated relations among image tokens. | |
| Experimental results show that MDT achieves superior image synthesis performance, e.g. a new SoTA FID score on the ImageNet dataset, and has about 3× faster learning speed than the previous SoTA DiT. | |
| ## Citation | |
| ``` | |
| @misc{gao2023masked, | |
| title={Masked Diffusion Transformer is a Strong Image Synthesizer}, | |
| author={Shanghua Gao and Pan Zhou and Ming-Ming Cheng and Shuicheng Yan}, | |
| year={2023}, | |
| eprint={2303.14389}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CV} | |
| } | |
| ``` | |
| ## Acknowledgement | |
| This demo is built based on the [DiT](https://github.com/facebookresearch/dit). Thanks! | |
| ''' | |
| project_links = ''' | |
| <p style="text-align: center"> | |
| <a href="https://arxiv.org/abs/2303.14389">Paper</a> · | |
| <a href="https://github.com/sail-sg/MDT">GitHub</a></p>''' | |
| examples = [ | |
| ["256x256", "stabilityai/sd-vae-ft-mse", | |
| "Welsh springer spaniel", 5.0, 0.01, 300, 30, 3000], | |
| ["256x256", "stabilityai/sd-vae-ft-mse", | |
| "golden retriever", 5.0, 0.01, 300, 30, 3000], | |
| ["256x256", "stabilityai/sd-vae-ft-mse", | |
| "sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita", 5.0, 0.01, 300, 30, 1], | |
| ["256x256", "stabilityai/sd-vae-ft-mse", | |
| "cheeseburger", 5.0, 0.01, 300, 30, 2], | |
| ["256x256", "stabilityai/sd-vae-ft-mse", "macaw", 5.0, 0.01, 300, 30, 1], | |
| ] | |
| with gr.Blocks() as demo: | |
| gr.Markdown( | |
| "<h1 style='text-align: center'>Masked Diffusion Transformer (MDT)</h1>") | |
| gr.Markdown(project_links) | |
| gr.Markdown(description) | |
| gr.Markdown(duplicate) | |
| with gr.Tabs(): | |
| with gr.TabItem('Generate'): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| image_size = gr.inputs.Radio( | |
| choices=["256x256"], default="256x256", label='MDT Model Resolution') | |
| vae_model = gr.inputs.Radio(choices=["stabilityai/sd-vae-ft-mse", "stabilityai/sd-vae-ft-ema"], | |
| default="stabilityai/sd-vae-ft-mse", label='VAE Decoder') | |
| with gr.Row(): | |
| i1k_class = gr.inputs.Dropdown( | |
| list(IMAGENET_1K_CLASSES.values()), | |
| default='Welsh springer spaniel', | |
| type="index", label='ImageNet-1K Class' | |
| ) | |
| cfg_scale = gr.inputs.Slider( | |
| minimum=0, maximum=25, step=0.1, default=5.0, label='Classifier-free Guidance Scale') | |
| pow_scale = gr.inputs.Slider( | |
| minimum=0, maximum=25, step=0.1, default=0.01, label='Classifier-free Guidance Weight Scaling') | |
| steps = gr.inputs.Slider( | |
| minimum=4, maximum=1000, step=1, default=300, label='Sampling Steps') | |
| n = gr.inputs.Slider( | |
| minimum=1, maximum=16, step=1, default=1, label='Number of Samples') | |
| seed = gr.inputs.Number(default=30, label='Seed') | |
| button = gr.Button("Generate", variant="primary") | |
| with gr.Column(): | |
| output = gr.Gallery(label='Generated Images').style( | |
| grid=[2], height="auto") | |
| button.click(generate, inputs=[ | |
| image_size, vae_model, i1k_class, cfg_scale, pow_scale, steps, seed], outputs=[output]) | |
| with gr.Row(): | |
| ex = gr.Examples(examples=examples, fn=generate, | |
| inputs=[image_size, vae_model, i1k_class, | |
| cfg_scale, pow_scale, steps, seed], | |
| outputs=[output], | |
| cache_examples=True) | |
| gr.Markdown(more_info) | |
| demo.queue() | |
| demo.launch() | |