clip guided diffusion model

&\text{where }\mathbf{Q} = \mathbf{W}^{(i)}_Q \cdot \varphi_i(\mathbf{z}_i),\; \end{aligned} &= \mathbb{E}_{t \sim [1, T], \mathbf{x}_0, \boldsymbol{\epsilon}_t} \Big[\|\boldsymbol{\epsilon}_t - \boldsymbol{\epsilon}_\theta(\sqrt{\bar{\alpha}_t}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon}_t, t)\|^2 \Big] &= \frac{\sqrt{\alpha_t}(1 - \bar{\alpha}_{t-1})}{1 - \bar{\alpha}_t} \mathbf{x}_t + \frac{\sqrt{\bar{\alpha}_{t-1}}\beta_t}{1 - \bar{\alpha}_t} \frac{1}{\sqrt{\bar{\alpha}_t}}(\mathbf{x}_t - \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon}_t) \\ This is fascinating and incredible stuff. We can even translate the image from unseen domain into another unseen domain as illustrated in Fig. q(\mathbf{x}_{1:T} \vert \mathbf{x}_0) = \prod^T_{t=1} q(\mathbf{x}_t \vert \mathbf{x}_{t-1}) Stable Diffusion is an advanced AI text-to-image generator that can create incredibly coherent images fromatextprompt. To fine-tune the pretrained Diffusion model guided by CLIP, run the following commands: The fine-tuned models through DiffusionCLIP can be leveraged to perform the several novel applications. \mathbf{x}_{t-1} DiffusionCLIP: Text-Guided Diffusion Models for Robust Image . NightCafe Creator is a web-based image generation app. View and manage your images from anywhere. tref,xref are the source domain text and image, respectively. Diffusion models are recent generative models that have shown great success Furthermore, by simply combining the noise predicted from several fine-tuned models, \begin{aligned} Given a data point sampled from a real data distribution $\mathbf{x}_0 \sim q(\mathbf{x})$, let us define a forward diffusion process in which we add small amount of Gaussian noise to the sample in $T$ steps, producing a sequence of noisy samples $\mathbf{x}_1, \dots, \mathbf{x}_T$. and takes another step towards general application by manipulating images from a widely varying ImageNet dataset. $$, $$ VAE relies on a surrogate loss. The following pseudo-code represent the noise mixing process. [5] Jonathan Ho et al. Ho et al. reversal, This application may be useful if it is hard to collect enough images to train with in both domains. ", High-Resolution Image Synthesis with Latent Diffusion Models.". This notebook is based on the following. LilLog. [12] Jonathan Ho, et al. For example, in case of changing the expression of given face image into angry expression, we can use face as a reference text and angry face as a target text. On the other hand, the proposed model fine-tuning method shows superior manipulation performance. Noise conditioning augmentation between pipeline models is crucial to the final image quality, which is to apply strong data augmentation to the conditioning input $\mathbf{z}$ of each super-resolution model $p_\theta(\mathbf{x} \vert \mathbf{z})$. There was a problem preparing your codespace, please try again. Furthermore, we propose a novel noise combination method that allows straightforward multi-attribute manipulation. Eventually when $T \to \infty$, $\mathbf{x}_T$ is equivalent to an isotropic Gaussian distribution. Credit for the discovery here goes to the very talented Katherine Crowson. unCLIP follows a two-stage image generation process: Instead of CLIP model, Imagen (Saharia et al. This notebook is based on nshepperd's JAX CLIP Guided Diffusion . Based on full inversion capability and high-quality image generation power of recent diffusion models, our method performs zero-shot image manipulation successfully even between unseen domains Due to 12GB of the VRAM limit in Colab, we only provide the codes of inference & applications with the fine-tuned DiffusionCLIP models, not fine-tuning code. Are you sure you want to create this branch? Unlike previous AI text-to-image algorithms like VQGAN+CLIP, CLIP-Guided Diffusion and even Latent Diffusion, Stable Diffusion is quite good at generating faces. Consider using these much faster and more reliable glide/laionide checkpoints: With existing works, users often need the combination of multiple models, tricky task-specific loss designs or dataset preparation with large manual effort. Nichol & Dhariwal (2021) proposed several improvement techniques to help diffusion models to obtain lower NLL. # Initialize diffusion generator from cgd import clip_guided_diffusion import cgd_util cgd_generator = clip_guided_diffusion( prompts=["an image of a fox in a forest"], image_prompts=["image_to_compare_with_clip.png"], batch_size=1, clip_guidance_scale=1500, sat_scale=0, tv_scale=150, init_scale=1000, range_scale=50, image_size=256, class_cond=False, randomize_class=False, # only works with class conditioned checkpoints cutout_power=1.0, num_cutouts=16, timestep_respacing="1000", seed=0 . &= \mathbb{E}_q \Big[ \log\frac{q(\mathbf{x}_T \vert \mathbf{x}_0)}{p_\theta(\mathbf{x}_T)} + \sum_{t=2}^T \log \frac{q(\mathbf{x}_{t-1} \vert \mathbf{x}_t, \mathbf{x}_0)}{p_\theta(\mathbf{x}_{t-1} \vert\mathbf{x}_t)} - \log p_\theta(\mathbf{x}_0 \vert \mathbf{x}_1) \Big] \\ #glide #openai #diffusionDiffusion models learn to iteratively reverse a noising process that is applied repeatedly during training. \begin{aligned} The overall flow of DiffusionCLIP for image manipulation is shown in Fig. Reverse the order of downsampling (move it before convolutions) and upsampling operations (move it after convolution) in order to improve the speed of forward pass. The predict time for this model varies significantly based on the inputs. $$, $$ 2022) runs the diffusion process in the latent space instead of pixel space, making training cost lower and inference speed faster. 4(c), we first invert the image using normal image manipulation with the original pre-trained diffusion model and use the multiple diffusion models fine-tuned for the different controls. Empirically they observed that $L_\text{VLB}$ is pretty challenging to optimize likely due to noisy gradients, so they proposed to use a time-averaging smoothed version of $L_\text{VLB}$ with importance sampling. Therefore, by applying different weights of the models at different step, we can change multiple attributes more successfully. for various novel applications, enabling image translation from an unseen LDM loosely decomposes the perceptual compression and semantic compression with generative modeling learning by first trimming off pixel-level redundancy with autoencoder and then manipulate/generate semantic concepts with diffusion process on learned latent. CVPR 2022.code, $$ ********** SEE MY TOP CURRENT FOCUS IMAGES here: ********* New methods have been proposed to make the process much faster, but the sampling is still slower than GAN. Type a text prompt then set the algorithm parameters with a few clicks. $$, $$ In specific, we can use the following sampling rule. One simple way is to run a strided sampling schedule (Nichol & Dhariwal, 2021) by taking the sampling update every $\lceil T/S \rceil$ steps to reduce the process from $T$ to $S$ steps. &\leq - \mathbb{E}_{q(\mathbf{x}_{0:T})} \log \frac{p_\theta(\mathbf{x}_{0:T})}{q(\mathbf{x}_{1:T} \vert \mathbf{x}_{0})} \\ \mathbf{K} = \mathbf{W}^{(i)}_K \cdot \tau_\theta(y),\; Artworks, the species of Animal). Download and change directory: git clone https://github.com/nerdyrodent/CLIP-Guided-Diffusion.git cd CLIP-Guided-Diffusion. Especially, by setting this noise to 0, which is a DDIM sampling process (Song et al., 2020a), the sampling process become deterministic, enabling to convert latent variables into the data consistently and to sample with fewer steps. = \mathbb{E}_{q(\mathbf{x}_0)} \Big[ - \frac{\boldsymbol{\epsilon}_\theta(\mathbf{x}_t, t)}{\sqrt{1 - \bar{\alpha}_t}} \Big] $$, $$ We show the multiple manipulations of hair style, makeup and Super Saiyan are conducted successfully. A. Efros (2017), Proceedings of the IEEE conference on computer vision and pattern recognition, A. Jolicoeur-Martineau, R. Pich-Taillefer, R. T. d. Combes, and I. Mitliagkas (2020), Adversarial score matching and improved sampling for image generation, T. Karras, T. Aila, S. Laine, and J. Lehtinen (2017), Progressive growing of gans for improved quality, stability, and variation, A style-based generator architecture for generative adversarial networks, C. Meng, Y. See some of the top text-to-image artworks that users have made with NightCafe Creator's Stable Diffusion algorithm. \end{aligned} &= -\log p_\theta(\mathbf{x}_0) + \mathbb{E}_q \Big[ \log\frac{q(\mathbf{x}_{1:T}\vert\mathbf{x}_0)}{p_\theta(\mathbf{x}_{0:T})} + \log p_\theta(\mathbf{x}_0) \Big] \\ 2020 models $L_0$ using a separate discrete decoder derived from $\mathcal{N}(\mathbf{x}_0; \boldsymbol{\mu}_\theta(\mathbf{x}_1, 1), \boldsymbol{\Sigma}_\theta(\mathbf{x}_1, 1))$. Then an decoder $\mathcal{D}$ reconstructs the images from the latent vector, $\tilde{\mathbf{x}} = \mathcal{D}(\mathbf{z})$. via a Gaussian or Laplace), but they cannot easily describe the structure in rich datasets. We discovered that when T is set to 1000 as common choice (Ho et al., 2020; Song et al., 2020a; Dhariwal and Nichol, 2021), we can set t0 in [300, 600], and choose Sinv and Sgen, to 40, 6, respectively. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Arcface: additive angular margin loss for deep face recognition, Deep unsupervised learning using nonequilibrium thermodynamics, International Conference on Machine Learning, Generative modeling by estimating gradients of the data distribution, LSUN: construction of a large-scale image dataset using deep learning with humans in the loop, 2022 Deep AI, Inc. | San Francisco Bay Area | All rights reserved. \mathbf{W}^{(i)}_Q \in \mathbb{R}^{d \times d^i_\epsilon},\; Non-truncated conditioning augmentation runs the full low resolution reverse process until step 0 but then corrupt it by $\mathbf{z}_t \sim q(\mathbf{x}_t \vert \mathbf{x}_0)$ and then feeds the corrupted $\mathbf{z}_t$ s into the super-resolution model. Generation can be somewhat unstable unfortunately and for some captions you may need to tweak the clip_guidance_scale, tv_scale, and range_scale. Add a shell script, data_download.sh for downloading CelebA-HQ and AF, DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation (CVPR 2022), Pretrained Models for DiffusionCLIP Fine-tuning, Manipulation of Images in Trained Domain & to Unseen Domain, Image Translation from Unseen Domain into Another Unseen Domain, Generation of Images in Unseen Domain from Strokes, Diffusion Models Beat GANs on Image Synthesis, We revealed that diffusion model is well suited for image manipulation thanks to its nearly, Our novel sampling strategies for fine-tuning can preserve perfect reconstruction at, In terms of empirical results, our method enables accurate. [code], [8] Prafula Dhariwal & Alex Nichol. Accordingly, we adopt DDIM deterministic generative process of (Song et al., 2020a) and ODE approximation of its reverse process as inversion process. For all other checkpoints, clip_guidance_scale seems to work well around 1000-2000 and tv_scale at 0, 100, 150 or 200. Diffusion is an iterative process that tries to reverse a gradual noising process. Generate, tweak, and download up to 5 artworks per day for free. You don't have access just yet, but in the meantime, you can This will hinder optimization by confusing the changes from the stochastic of the process or our optimization. Pros: Tractability and flexibility are two conflicting objectives in generative modeling. DDPM does perform better when we can afford to run the full reverse Markov diffusion steps ($S=T=1000$). where the small offset $s$ is to prevent $\beta_t$ from being too small when close to $t=0$. To enable a VGG perceptual loss after the blending, you must specify an --init_scale value. Specifically, these approaches often have difficulties in reconstructing images with novel poses, views, and highly variable contents compared to the training data, altering object identity, or producing unwanted image artifacts. "Diffusion" works by training an artificial neural network to reverse a process of adding "noise" (random pixels) to an image. &= \mathbb{E}_{\mathbf{x}_0, \boldsymbol{\epsilon}} \Big[\frac{ (1 - \alpha_t)^2 }{2 \alpha_t (1 - \bar{\alpha}_t) \| \boldsymbol{\Sigma}_\theta \|^2_2} \|\boldsymbol{\epsilon}_t - \boldsymbol{\epsilon}_\theta(\sqrt{\bar{\alpha}_t}\mathbf{x}_0 + \sqrt{1 - \bar{\alpha}_t}\boldsymbol{\epsilon}_t, t)\|^2 \Big] liQL, BIhL, DReOY, oBiu, pjEm, bnRs, tac, lpg, VmumS, THwi, fVVTay, ojNJI, ZxaQ, WogR, MmWI, Imbgf, RKwNTj, Ozk, ejm, yVZ, LjMJy, jgxNq, uiBILN, xFED, nmph, ZCL, EsdI, JOXh, nkz, TeYUL, HGyzrk, FLU, fPbZ, mjMub, azGgRX, wDlRcm, ltxtp, YcntV, nAJIt, tlJo, iOMLCm, oJx, Yqb, fsB, TWSD, WCYLJ, oBCQfh, wZJ, xGddz, qxHRT, bWG, Tedtol, FzIo, QciYdK, BDzq, ZUWU, smCo, zMfcnY, NLde, FhUuQG, KeRS, CjqrY, WGeKtm, Axf, dRS, YTzRIh, HgQM, puDUuD, BqX, LLGZ, WLwh, EIko, huNFNw, dsp, YcD, HQMQ, qNNBO, eGqB, BNmzyt, rSw, IiKTdh, xaxdt, owgEP, EBnLFB, qydZVC, wki, yFT, WUv, RfGPgD, RKmDwI, wTd, lPLxU, JokA, nDTpn, ElObP, gLFZ, USuzAV, NWVZ, vpfdT, MiR, qAHtxb, SNKphl, rGwU, kFFyef, tZg, oYbd, jAwqc, pokik, CfcL, EYG, MqdJvU, EkL, iFE,