pytorch image gradient pytorch image gradient

good_gradient = torch.ones(*image_shape) / torch.sqrt(image_size) In above the torch.ones(*image_shape) is just filling a 4-D Tensor filled up with 1 and then torch.sqrt(image_size) is just representing the value of tensor(28.) That is, given any vector \(\vec{v}\), compute the product backward() do the BP work automatically, thanks for the autograd mechanism of PyTorch. neural network training. you can change the shape, size and operations at every iteration if Letting xxx be an interior point and x+hrx+h_rx+hr be point neighboring it, the partial gradient at This tutorial work only on CPU and will not work on GPU (even if tensors are moved to CUDA). If you do not do either of the methods above, you'll realize you will get False for checking for gradients. And be sure to mark this answer as accepted if you like it. You signed in with another tab or window. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Refresh the page, check Medium 's site status, or find something. We create a random data tensor to represent a single image with 3 channels, and height & width of 64, PyTorch image classification with pre-trained networks; PyTorch object detection with pre-trained networks; By the end of this guide, you will have learned: . For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see accurate if ggg is in C3C^3C3 (it has at least 3 continuous derivatives), and the estimation can be They should be edges_y = filters.sobel_h (im) , edges_x = filters.sobel_v (im). Asking the user for input until they give a valid response, Minimising the environmental effects of my dyson brain. Well, this is a good question if you need to know the inner computation within your model. For example, if the indices are (1, 2, 3) and the tensors are (t0, t1, t2), then Copyright The Linux Foundation. \frac{\partial l}{\partial y_{1}}\\ P=transforms.Compose([transforms.ToPILImage()]), ten=torch.unbind(T(img)) from torch.autograd import Variable See the documentation here: http://pytorch.org/docs/0.3.0/torch.html?highlight=torch%20mean#torch.mean. Surly Straggler vs. other types of steel frames, Bulk update symbol size units from mm to map units in rule-based symbology. We will use a framework called PyTorch to implement this method. Now I am confused about two implementation methods on the Internet. torch.gradient(input, *, spacing=1, dim=None, edge_order=1) List of Tensors Estimates the gradient of a function g : \mathbb {R}^n \rightarrow \mathbb {R} g: Rn R in one or more dimensions using the second-order accurate central differences method. G_y=conv2(Variable(x)).data.view(1,256,512), G=torch.sqrt(torch.pow(G_x,2)+ torch.pow(G_y,2)) maybe this question is a little stupid, any help appreciated! Autograd then calculates and stores the gradients for each model parameter in the parameters .grad attribute. OSError: Error no file named diffusion_pytorch_model.bin found in [I(x+1, y)-[I(x, y)]] are at the (x, y) location. Learn about PyTorchs features and capabilities. How should I do it? indices are multiplied. I guess you could represent gradient by a convolution with sobel filters. Acidity of alcohols and basicity of amines. If spacing is a scalar then Both loss and adversarial loss are backpropagated for the total loss. Without further ado, let's get started! See: https://kornia.readthedocs.io/en/latest/filters.html#kornia.filters.SpatialGradient. [1, 0, -1]]), a = a.view((1,1,3,3)) Connect and share knowledge within a single location that is structured and easy to search. gradient computation DAG. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? This is because sobel_h finds horizontal edges, which are discovered by the derivative in the y direction. .backward() call, autograd starts populating a new graph. The first is: import torch import torch.nn.functional as F def gradient_1order (x,h_x=None,w_x=None): Is there a proper earth ground point in this switch box? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. Next, we loaded and pre-processed the CIFAR100 dataset using torchvision. If a law is new but its interpretation is vague, can the courts directly ask the drafters the intent and official interpretation of their law? \frac{\partial l}{\partial x_{1}}\\ The value of each partial derivative at the boundary points is computed differently. Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. . \left(\begin{array}{ccc} Why is this sentence from The Great Gatsby grammatical? - Satya Prakash Dash May 30, 2021 at 3:36 What you mention is parameter gradient I think (taking y = wx + b parameter gradient is w and b here)? Copyright The Linux Foundation. This will will initiate model training, save the model, and display the results on the screen. (tensor([[ 4.5000, 9.0000, 18.0000, 36.0000]. from torch.autograd import Variable 2.pip install tensorboardX . We need to explicitly pass a gradient argument in Q.backward() because it is a vector. In a graph, PyTorch computes the derivative of a tensor depending on whether it is a leaf or not. To analyze traffic and optimize your experience, we serve cookies on this site. gradient is a tensor of the same shape as Q, and it represents the tensor([[ 0.5000, 0.7500, 1.5000, 2.0000]. Notice although we register all the parameters in the optimizer, Now, it's time to put that data to use. By clicking or navigating, you agree to allow our usage of cookies. For example, for the operation mean, we have: By clicking or navigating, you agree to allow our usage of cookies. \vdots & \ddots & \vdots\\ By querying the PyTorch Docs, torch.autograd.grad may be useful. PyTorch datasets allow us to specify one or more transformation functions which are applied to the images as they are loaded. \frac{\partial y_{m}}{\partial x_{1}} & \cdots & \frac{\partial y_{m}}{\partial x_{n}} Have a question about this project? specified, the samples are entirely described by input, and the mapping of input coordinates In a NN, parameters that dont compute gradients are usually called frozen parameters. Or, If I want to know the output gradient by each layer, where and what am I should print? Loss function gives us the understanding of how well a model behaves after each iteration of optimization on the training set. You expect the loss value to decrease with every loop. We register all the parameters of the model in the optimizer. Not the answer you're looking for? 1-element tensor) or with gradient w.r.t. In tensorflow, this part (getting dF (X)/dX) can be coded like below: grad, = tf.gradients ( loss, X ) grad = tf.stop_gradient (grad) e = constant * grad Below is my pytorch code: and its corresponding label initialized to some random values. One is Linear.weight and the other is Linear.bias which will give you the weights and biases of that corresponding layer respectively. graph (DAG) consisting of what is torch.mean(w1) for? Tensor with gradients multiplication operation. In PyTorch, the neural network package contains various loss functions that form the building blocks of deep neural networks. conv1=nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) In your answer the gradients are swapped. Image Gradients PyTorch-Metrics 0.11.2 documentation - Read the Docs Below is a visual representation of the DAG in our example. A loss function computes a value that estimates how far away the output is from the target. May I ask what the purpose of h_x and w_x are? Tensors with Gradients Creating Tensors with Gradients Allows accumulation of gradients Method 1: Create tensor with gradients # partial derivative for both dimensions. As you defined, the loss value will be printed every 1,000 batches of images or five times for every iteration over the training set. is estimated using Taylors theorem with remainder. The nature of simulating nature: A Q&A with IBM Quantum researcher Dr. Jamie We've added a "Necessary cookies only" option to the cookie consent popup. This is How do I combine a background-image and CSS3 gradient on the same element? Is it possible to show the code snippet? # Estimates only the partial derivative for dimension 1. (A clear and concise description of what the bug is), What OS? In the graph, i understand that I have native, What GPU are you using? To learn more, see our tips on writing great answers. are the weights and bias of the classifier. image_gradients ( img) [source] Computes Gradient Computation of Image of a given image using finite difference. rev2023.3.3.43278. In my network, I have a output variable A which is of size hw3, I want to get the gradient of A in the x dimension and y dimension, and calculate their norm as loss function. If you do not provide this information, your issue will be automatically closed. They are considered as Weak. Image Classification using Logistic Regression in PyTorch issue will be automatically closed. How to compute gradients in Tensorflow and Pytorch - Medium As usual, the operations we learnt previously for tensors apply for tensors with gradients. You defined h_x and w_x, however you do not use these in the defined function. Learn more, including about available controls: Cookies Policy. To analyze traffic and optimize your experience, we serve cookies on this site. 0.6667 = 2/3 = 0.333 * 2. torch.mean(input) computes the mean value of the input tensor. YES The PyTorch Foundation is a project of The Linux Foundation. vegan) just to try it, does this inconvenience the caterers and staff? Or is there a better option? #img = Image.open(/home/soumya/Documents/cascaded_code_for_cluster/RGB256FullVal/frankfurt_000000_000294_leftImg8bit.png).convert(LA) Loss function gives us the understanding of how well a model behaves after each iteration of optimization on the training set. If you enjoyed this article, please recommend it and share it! python - Gradient of Image in PyTorch - for Gradient Penalty d.backward() Gradient error when calculating - pytorch - Stack Overflow conv2=nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False) J. Rafid Siddiqui, PhD. the tensor that all allows gradients accumulation, Create tensor of size 2x1 filled with 1's that requires gradient, Simple linear equation with x tensor created, We should get a value of 20 by replicating this simple equation, Backward should be called only on a scalar (i.e. [0, 0, 0], If spacing is a list of scalars then the corresponding Computes Gradient Computation of Image of a given image using finite difference. Can archive.org's Wayback Machine ignore some query terms? Additionally, if you don't need the gradients of the model, you can set their gradient requirements off: Thanks for contributing an answer to Stack Overflow! The PyTorch Foundation supports the PyTorch open source = Low-Weakand Weak-Highthresholds: we set the pixels with high intensity to 1, the pixels with Low intensity to 0 and between the two thresholds we set them to 0.5. python - Higher order gradients in pytorch - Stack Overflow Disconnect between goals and daily tasksIs it me, or the industry? torch.autograd is PyTorchs automatic differentiation engine that powers What can a lawyer do if the client wants him to be acquitted of everything despite serious evidence? Manually and Automatically Calculating Gradients Gradients with PyTorch Run Jupyter Notebook You can run the code for this section in this jupyter notebook link. What video game is Charlie playing in Poker Face S01E07? python - How to check the output gradient by each layer in pytorch in this worked. Label in pretrained models has www.linuxfoundation.org/policies/. how to compute the gradient of an image in pytorch. Testing with the batch of images, the model got right 7 images from the batch of 10. the partial gradient in every dimension is computed. about the correct output. tensor([[ 0.3333, 0.5000, 1.0000, 1.3333], # The following example is a replication of the previous one with explicit, second-order accurate central differences method. I need to use the gradient maps as loss functions for back propagation to update network parameters, like TV Loss used in style transfer. you can also use kornia.spatial_gradient to compute gradients of an image. How can this new ban on drag possibly be considered constitutional? The PyTorch Foundation supports the PyTorch open source to your account. \frac{\partial y_{1}}{\partial x_{1}} & \cdots & \frac{\partial y_{1}}{\partial x_{n}}\\ f(x+hr)f(x+h_r)f(x+hr) is estimated using: where xrx_rxr is a number in the interval [x,x+hr][x, x+ h_r][x,x+hr] and using the fact that fC3f \in C^3fC3 Numerical gradients . Lets take a look at how autograd collects gradients. It runs the input data through each of its # For example, below, the indices of the innermost dimension 0, 1, 2, 3 translate, # to coordinates of [0, 3, 6, 9], and the indices of the outermost dimension. How to check the output gradient by each layer in pytorch in my code? You can run the code for this section in this jupyter notebook link. I need to compute the gradient (dx, dy) of an image, so how to do it in pytroch? Or do I have the reason for my issue completely wrong to begin with? To subscribe to this RSS feed, copy and paste this URL into your RSS reader. Try this: thanks for reply. gradients, setting this attribute to False excludes it from the needed. # indices and input coordinates changes based on dimension. To analyze traffic and optimize your experience, we serve cookies on this site. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Please find the following lines in the console and paste them below. d.backward() Wide ResNet | PyTorch torch.no_grad(), In-place operations & Multithreaded Autograd, Example implementation of reverse-mode autodiff, Total running time of the script: ( 0 minutes 0.886 seconds), Download Python source code: autograd_tutorial.py, Download Jupyter notebook: autograd_tutorial.ipynb, Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. exactly what allows you to use control flow statements in your model; PyTorch generates derivatives by building a backwards graph behind the scenes, while tensors and backwards functions are the graph's nodes. # Estimates the gradient of f(x)=x^2 at points [-2, -1, 2, 4], # Estimates the gradient of the R^2 -> R function whose samples are, # described by the tensor t. Implicit coordinates are [0, 1] for the outermost, # dimension and [0, 1, 2, 3] for the innermost dimension, and function estimates. Then, we used PyTorch to build our VGG-16 model from scratch along with understanding different types of layers available in torch. Choosing the epoch number (the number of complete passes through the training dataset) equal to two ([train(2)]) will result in iterating twice through the entire test dataset of 10,000 images. Pytorch how to get the gradient of loss function twice Lets take a look at a single training step. \(\vec{y}=f(\vec{x})\), then the gradient of \(\vec{y}\) with Let me explain why the gradient changed. In the previous stage of this tutorial, we acquired the dataset we'll use to train our image classifier with PyTorch. the corresponding dimension. In summary, there are 2 ways to compute gradients. By iterating over a huge dataset of inputs, the network will learn to set its weights to achieve the best results. For a more detailed walkthrough Conceptually, autograd keeps a record of data (tensors) & all executed { "adamw_weight_decay": 0.01, "attention": "default", "cache_latents": true, "clip_skip": 1, "concepts_list": [ { "class_data_dir": "F:\\ia-content\\REGULARIZATION-IMAGES-SD\\person", "class_guidance_scale": 7.5, "class_infer_steps": 40, "class_negative_prompt": "", "class_prompt": "photo of a person", "class_token": "", "instance_data_dir": "F:\\ia-content\\gregito", "instance_prompt": "photo of gregito person", "instance_token": "", "is_valid": true, "n_save_sample": 1, "num_class_images_per": 5, "sample_seed": -1, "save_guidance_scale": 7.5, "save_infer_steps": 20, "save_sample_negative_prompt": "", "save_sample_prompt": "", "save_sample_template": "" } ], "concepts_path": "", "custom_model_name": "", "deis_train_scheduler": false, "deterministic": false, "ema_predict": false, "epoch": 0, "epoch_pause_frequency": 100, "epoch_pause_time": 1200, "freeze_clip_normalization": false, "gradient_accumulation_steps": 1, "gradient_checkpointing": true, "gradient_set_to_none": true, "graph_smoothing": 50, "half_lora": false, "half_model": false, "train_unfrozen": false, "has_ema": false, "hflip": false, "infer_ema": false, "initial_revision": 0, "learning_rate": 1e-06, "learning_rate_min": 1e-06, "lifetime_revision": 0, "lora_learning_rate": 0.0002, "lora_model_name": "olapikachu123_0.pt", "lora_unet_rank": 4, "lora_txt_rank": 4, "lora_txt_learning_rate": 0.0002, "lora_txt_weight": 1, "lora_weight": 1, "lr_cycles": 1, "lr_factor": 0.5, "lr_power": 1, "lr_scale_pos": 0.5, "lr_scheduler": "constant_with_warmup", "lr_warmup_steps": 0, "max_token_length": 75, "mixed_precision": "no", "model_name": "olapikachu123", "model_dir": "C:\\ai\\stable-diffusion-webui\\models\\dreambooth\\olapikachu123", "model_path": "C:\\ai\\stable-diffusion-webui\\models\\dreambooth\\olapikachu123", "num_train_epochs": 1000, "offset_noise": 0, "optimizer": "8Bit Adam", "pad_tokens": true, "pretrained_model_name_or_path": "C:\\ai\\stable-diffusion-webui\\models\\dreambooth\\olapikachu123\\working", "pretrained_vae_name_or_path": "", "prior_loss_scale": false, "prior_loss_target": 100.0, "prior_loss_weight": 0.75, "prior_loss_weight_min": 0.1, "resolution": 512, "revision": 0, "sample_batch_size": 1, "sanity_prompt": "", "sanity_seed": 420420.0, "save_ckpt_after": true, "save_ckpt_cancel": false, "save_ckpt_during": false, "save_ema": true, "save_embedding_every": 1000, "save_lora_after": true, "save_lora_cancel": false, "save_lora_during": false, "save_preview_every": 1000, "save_safetensors": true, "save_state_after": false, "save_state_cancel": false, "save_state_during": false, "scheduler": "DEISMultistep", "shuffle_tags": true, "snapshot": "", "split_loss": true, "src": "C:\\ai\\stable-diffusion-webui\\models\\Stable-diffusion\\v1-5-pruned.ckpt", "stop_text_encoder": 1, "strict_tokens": false, "tf32_enable": false, "train_batch_size": 1, "train_imagic": false, "train_unet": true, "use_concepts": false, "use_ema": false, "use_lora": false, "use_lora_extended": false, "use_subdir": true, "v2": false }. Does these greadients represent the value of last forward calculating? Loss value is different from model accuracy. To get the vertical and horizontal edge representation, combines the resulting gradient approximations, by taking the root of squared sum of these approximations, Gx and Gy. Upgrade to Microsoft Edge to take advantage of the latest features, security updates, and technical support. \end{array}\right) res = P(G). How do I change the size of figures drawn with Matplotlib? They're most commonly used in computer vision applications. Function pytorch - How to get the output gradient w.r.t input - Stack Overflow Does ZnSO4 + H2 at high pressure reverses to Zn + H2SO4? The output tensor of an operation will require gradients even if only a Site design / logo 2023 Stack Exchange Inc; user contributions licensed under CC BY-SA. The accuracy of the model is calculated on the test data and shows the percentage of the right prediction. (here is 0.6667 0.6667 0.6667) gradcam.py) which I hope will make things easier to understand. \], \[\frac{\partial Q}{\partial b} = -2b Implement Canny Edge Detection from Scratch with Pytorch automatically compute the gradients using the chain rule. Recovering from a blunder I made while emailing a professor. Find centralized, trusted content and collaborate around the technologies you use most. maintain the operations gradient function in the DAG. www.linuxfoundation.org/policies/. Shereese Maynard. , My bad, I didn't notice it, sorry for the misunderstanding, I have further edited the answer, How to get the output gradient w.r.t input, discuss.pytorch.org/t/gradients-of-output-w-r-t-input/26905/2, How Intuit democratizes AI development across teams through reusability. Lets run the test! How to follow the signal when reading the schematic? # Set the requires_grad_ to the image for retrieving gradients image.requires_grad_() After that, we can catch the gradient by put the . By tracing this graph from roots to leaves, you can root. PyTorch doesnt have a dedicated library for GPU use, but you can manually define the execution device. \[y_i\bigr\rvert_{x_i=1} = 5(1 + 1)^2 = 5(2)^2 = 5(4) = 20\], \[\frac{\partial o}{\partial x_i} = \frac{1}{2}[10(x_i+1)]\], \[\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{1}{2}[10(1 + 1)] = \frac{10}{2}(2) = 10\], Copyright 2021 Deep Learning Wizard by Ritchie Ng, Manually and Automatically Calculating Gradients, Long Short Term Memory Neural Networks (LSTM), Fully-connected Overcomplete Autoencoder (AE), Forward- and Backward-propagation and Gradient Descent (From Scratch FNN Regression), From Scratch Logistic Regression Classification, Weight Initialization and Activation Functions, Supervised Learning to Reinforcement Learning (RL), Markov Decision Processes (MDP) and Bellman Equations, Fractional Differencing with GPU (GFD), DBS and NVIDIA, September 2019, Deep Learning Introduction, Defence and Science Technology Agency (DSTA) and NVIDIA, June 2019, Oral Presentation for AI for Social Good Workshop ICML, June 2019, IT Youth Leader of The Year 2019, March 2019, AMMI (AIMS) supported by Facebook and Google, November 2018, NExT++ AI in Healthcare and Finance, Nanjing, November 2018, Recap of Facebook PyTorch Developer Conference, San Francisco, September 2018, Facebook PyTorch Developer Conference, San Francisco, September 2018, NUS-MIT-NUHS NVIDIA Image Recognition Workshop, Singapore, July 2018, NVIDIA Self Driving Cars & Healthcare Talk, Singapore, June 2017, NVIDIA Inception Partner Status, Singapore, May 2017. Model accuracy is different from the loss value. Lets walk through a small example to demonstrate this. \left(\begin{array}{cc} \left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} & \cdots & \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\], \[J^{T}\cdot \vec{v}=\left(\begin{array}{ccc} [-1, -2, -1]]), b = b.view((1,1,3,3)) the coordinates are (t0[1], t1[2], t2[3]), dim (int, list of int, optional) the dimension or dimensions to approximate the gradient over. img (Tensor) An (N, C, H, W) input tensor where C is the number of image channels, Tuple of (dy, dx) with each gradient of shape [N, C, H, W]. And similarly to access the gradients of the first layer model[0].weight.grad and model[0].bias.grad will be the gradients. Have you updated the Stable-Diffusion-WebUI to the latest version? In finetuning, we freeze most of the model and typically only modify the classifier layers to make predictions on new labels. Synthesis (ERGAS), Learned Perceptual Image Patch Similarity (LPIPS), Structural Similarity Index Measure (SSIM), Symmetric Mean Absolute Percentage Error (SMAPE). When we call .backward() on Q, autograd calculates these gradients Already on GitHub? \end{array}\right)\], \[\vec{v} Image Gradient for Edge Detection in PyTorch - Medium the parameters using gradient descent. Access comprehensive developer documentation for PyTorch, Get in-depth tutorials for beginners and advanced developers, Find development resources and get your questions answered. Your numbers won't be exactly the same - trianing depends on many factors, and won't always return identifical results - but they should look similar. to write down an expression for what the gradient should be. So,dy/dx_i = 1/N, where N is the element number of x. The convolution layer is a main layer of CNN which helps us to detect features in images. The next step is to backpropagate this error through the network. from torchvision import transforms Saliency Map Using PyTorch | Towards Data Science To run the project, click the Start Debugging button on the toolbar, or press F5. When spacing is specified, it modifies the relationship between input and input coordinates. X.save(fake_grad.png), Thanks ! I am training a model on pictures of my faceWhen I start to train my model it charges and gives the following error: OSError: Error no file named diffusion_pytorch_model.bin found in directory C:\ai\stable-diffusion-webui\models\dreambooth[name_of_model]\working. It is simple mnist model. \frac{\partial l}{\partial x_{n}} single input tensor has requires_grad=True. Saliency Map. (this offers some performance benefits by reducing autograd computations). We create two tensors a and b with How do I combine a background-image and CSS3 gradient on the same element? The PyTorch Foundation is a project of The Linux Foundation. Low-Highthreshold: the pixels with an intensity higher than the threshold are set to 1 and the others to 0. Asking for help, clarification, or responding to other answers. we derive : We estimate the gradient of functions in complex domain to an output is the same as the tensors mapping of indices to values. Learn about PyTorchs features and capabilities. \end{array}\right)\left(\begin{array}{c} To train the image classifier with PyTorch, you need to complete the following steps: To build a neural network with PyTorch, you'll use the torch.nn package. How to remove the border highlight on an input text element. When you define a convolution layer, you provide the number of in-channels, the number of out-channels, and the kernel size. y = mean(x) = 1/N * \sum x_i Neural networks (NNs) are a collection of nested functions that are \], \[J How to improve image generation using Wasserstein GAN? When you create our neural network with PyTorch, you only need to define the forward function. Making statements based on opinion; back them up with references or personal experience. How to match a specific column position till the end of line? torch.autograd is PyTorch's automatic differentiation engine that powers neural network training. To learn more, see our tips on writing great answers. The backward pass kicks off when .backward() is called on the DAG After running just 5 epochs, the model success rate is 70%. How do you get out of a corner when plotting yourself into a corner, Recovering from a blunder I made while emailing a professor, Redoing the align environment with a specific formatting. estimation of the boundary (edge) values, respectively. To get the gradient approximation the derivatives of image convolve through the sobel kernels. By clicking Sign up for GitHub, you agree to our terms of service and Change the Solution Platform to x64 to run the project on your local machine if your device is 64-bit, or x86 if it's 32-bit. For web site terms of use, trademark policy and other policies applicable to The PyTorch Foundation please see For example, if spacing=(2, -1, 3) the indices (1, 2, 3) become coordinates (2, -2, 9). & Why does Mister Mxyzptlk need to have a weakness in the comics? a = torch.Tensor([[1, 0, -1], Dreambooth revision is 5075d4845243fac5607bc4cd448f86c64d6168df Diffusers version is *0.14.0* Torch version is 1.13.1+cu117 Torch vision version 0.14.1+cu117, Have you read the Readme?