We present SDXL, a latent diffusion model for text-to-image synthesis. It bundles Stable Diffusion along with commonly-used features (like SDXL, ControlNet, LoRA, Embeddings, GFPGAN, RealESRGAN, k-samplers, custom VAE etc). text_to_image( "Iron Man making breakfast") We first import the StabelDiffusion class from Keras and then create an instance of it, model. Load safetensors. These models are designed for image enhancement, generative tasks, and probabilistic modeling, offering a versatile set of tools for working with image data and text embeddings. Currently, there are two models that have been released: stable-video-diffusion-img2vid; stable-video-diffusion-img2vid-xt; The first model, stable-video-diffusion-img2vid, generates up to 14frames from a given input Stable Diffusion pipelines. 7 with starting control step 0 and ending control step 0. safetensors is a safe and fast file format for storing and loading tensors. You can also use the image-to-image pipeline to make text guided image to image generations. Stable Diffusion Tutorial Part 2: Using Textual Inversion Embeddings to gain substantial control over your generated images. Set the desired prompt strings for positive and negative prompts in the autoCreateImg. Previously we saw how to implement the Stable Diffusion text-to-image model using the Python Diffusers library, which is a library for state-of-the-art pre-trained diffusion models. transform_imgs(imgs) return imgs. 1: Stable Diffusion Model Architecture during model inference. These special words can then be used within text prompts to achieve very fine StableDiffusion, a Swift package that developers can add to their Xcode projects as a dependency to deploy image generation capabilities in their apps. Latent diffusion applies the diffusion process over a lower dimensional latent space to reduce memory and compute complexity. Not Found. 9): 0. Technically, a positive prompt steers the diffusion toward the images associated with it, while a negative prompt steers the diffusion away from it. The response contains three entries; images, parameters, and info, and I have to find some way to get the information from these entries. In this post, I will go through the workflow step-by-step. In this tutorial, you will discover how to train and load word embedding models for natural […] After the backend does its thing, the API sends the response back in a variable that was assigned above: response. The StableDiffusionPipeline is capable of generating photorealistic images given any text input. In this example, we have a prompt that describes a scene: “nice cars on speed. This method takes the raw output by the VAE and converts it to the PIL image format: def transform_image (self, image): """convert image from pytorch tensor to PIL format""" image = self. Dec 23, 2022 · We have a text prompt i. Note that you can omit the filename extension so these two are equivalent: embedding:SDA768. Jun 15, 2023 · Among these pioneering technologies, one open-source image generation model stands out - "Stable Diffusion. Figure from Rombach et al (2021) Stable Diffusion XL. Text-to-image. Below is a figure from the paper by Rombach et al. g. 1, Hugging Face) at 768x768 resolution, based on SD2. Manas Gupta · 16 min read · Updated apr 2023 · Machine Learning · Computer Vision · Natural Language Processing Textual Inversion. It integrates local information about the similarity of each point to its 3 days ago · Stable Diffusion 3. Prompt: oil painting of zwx in style of van gogh. はじめに. It's trained on 512x512 images from a subset of the LAION-5B database. moe/e2ui6r. The input to the module is a list of indices, and the output is the corresponding word embeddings. t (int, optional, default: 'auto') – power to which the diffusion operator is powered. Mar 17, 2024 · stable diffusionのインストール方法を紹介しているサイトでは、このような状況の方に対して、「別バージョンのpythonがインストールされている方は、削除して、改めてver3. Word embeddings are a modern approach for representing text in natural language processing. This guide will show you how to boost its capabilities with Refiners, using iconic adapters the framework supports out-of-the-box, i. Oct 30, 2023 · はじめに Stable Diffusion web UIのクラウド版画像生成サービス「Akuma. Guide to finetuning a Stable Diffusion model on your own dataset. Obtain the Model: Download Stable Diffusion: Access the model from a reputable source or platform offering the pre-trained Stable Diffusion model. pt embedding in the previous picture. This module is often used to store word embeddings and retrieve them using indices. 2 days ago · The following is an example of what you can get: Word art generated by Stable Diffusion that text blended nicely into background. InstructPix2Pix Overview; Generating the Multi-modal Training Dataset. Jul 6, 2024 · Creating the Illustration. Pytorch implementation for our paper: A Recipe for Watermarking Diffusion Models. without the need for tedious prompt engineering. Those are the steps to follow to make this work: install the repo with conda env create -f environment. yaml, conda activate ldm and pip install -e . E. In this article we're going to optimize Stable Diffusion XL, both to use the least amount of memory possible and to obtain maximum performance and generate images faster. Interface(), gr. pt files, each with only one trained embedding, and the filename (without . ai Stable Diffusion import base64 import boto3 import json import os import random # Create a Bedrock Runtime client in the AWS Region of your choice. STAGE1: Autoencoder. Some commonly used blocks are Loading a Checkpoint Model, entering a prompt, specifying a sampler, etc. For example: Q-diffusion is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance (small FID change of at most 2. Dec 3, 2023 · When using a negative prompt, a diffusion step is a step towards the positive prompt and away from the negative prompt. Full coding of Stable Diffusion from scratch, with full explanation, including explanation of the mathematics. Preprocessing. Stable Diffusion During Inference Feb 23, 2024 · base_path: path/to/stable-diffusion-webui/ Replace path/to/stable-diffusion-webui/ to your actual path to it. One last thing you need to do before training your model is telling the Kohya GUI where the folders you created in the first step are located on your hard drive. The resolution has increased by 168%, from 768×768 pixels in v2 to 2048× Nov 10, 2022 · This reduces the memory and computational complexity compared to the pixel space diffusion. DreamBooth, a technique for generating personalized images of a subject given several input images of the subject. Jan 26, 2023 · LoRA fine-tuning. Once you have updated the path, save the file and run the script as usual. ckpt checkpoint was downloaded), run the following: Install necessary Python libraries, typically including torch (a deep learning framework), transformers, and other dependencies specified in the Stable Diffusion documentation. Typically, PyTorch model weights are saved or pickled into a . Aug 4, 2023 · Finetuning Your Own Custom Stable Diffusion Model with just 4 Images End-to-End Python Guide For Giving a Stable Diffusion Model Your Own Images for Training and Making Inferences from Text Feb 13 Fig. It is also possible to embed Python in a C++ program; precisely how this is done will depend on the details of the C++ system used; in general you will need to write the main program in C++, and use the C++ compiler to compile and link your program. model = StableDiffusion() img = model. May 13, 2024 · Step 4: Train Your LoRA Model. This enhances scalability, supporting models with up to 8 billion parameters and multi-modal inputs. However, pickle is not secure and pickled files may contain malicious code that can be executed. ← Text-to-image Image-to-video →. Note that the diffusion in Stable Diffusion happens in latent space, not images. The prompt text is converted into a Python list from which we get the prompt text embeddings using the methods we previously defined. We use the default nomic-ai v1. 2 days ago · Running Stable Diffusion with Python. to illustrate this workflow: Stable Diffusion architecture. This is the codebase for the article Personalizing Text-to-Image Generation via Aesthetic Gradients: This work proposes aesthetic gradients, a method to personalize a CLIP-conditioned diffusion model by guiding the generative process towards custom aesthetics defined by the user from a set of images. New stable diffusion finetune ( Stable unCLIP 2. pt ; it does not produce very good results, but it does work. Veți putea să experimentați cu diferite prompturi text și să vedeți rezultatele în Nov 2, 2022 · Step 1 - Create a new Embedding. The concept doesn't have to actually exist in the real world. 1, but replace the decoder with a temporally-aware deflickering decoder. If not or if you want a quick brush, check out our stable diffusion article before getting into this one. images [0] upscaled_image. Stable diffusion pipelines Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Adapting Stable Diffusion XL. ChatInterface() or in gr. 5 model in this example. Introduction #. 3. Forms an affinity matrix given by the specified function and applies spectral decomposition to the corresponding graph laplacian. Diffusion in latent space – AutoEncoderKL. Before training an embedding diffusion, it’s essential to preprocess the input data. Stable Diffusion is a latent diffusion model conditioned on the (non-pooled) text embeddings of a CLIP ViT-L/14 text encoder. image_processor. Compared to previous versions of Stable Diffusion, SDXL leverages a three times larger UNet backbone: The increase of model parameters is mainly due to more attention blocks and a larger cross-attention context as SDXL uses a second text encoder. We provide a reference script for sampling, but there also exists a diffusers integration, which we expect to see more active community development. 5. 📄️ FireworksEmbeddings. Word embedding algorithms like word2vec and GloVe are key to the state-of-the-art results achieved by neural network models on natural language processing problems like machine translation. It’s easy to overfit and run into issues like catastrophic forgetting. Examples(). 1 documentation. The Stable Diffusion model was created by researchers and engineers from CompVis, Stability AI, Runway, and LAION. They have allowed me to gain deeper insights into complex datasets and make more informed decisions. It’s where a lot of the performance gain over previous models is achieved. Let words modulate diffusion – Conditional Diffusion, Cross Attention. This picture is using a CFG scale of 5 on the “Euler a” sampler with Karras schedule on 25 steps. Now use this as a negative prompt: [the: (ear:1. În acest notebook, veți învăța cum să utilizați modelul de difuzie stabilă, un model avansat de generare de imagini din text, dezvoltat de CompVis, Stability AI și LAION. Stable Diffusion XL. This process ensures that the output images are not just random creations but are closely aligned with the themes, subjects, and styles described in the input text. Using the prompt. Algorithms for this task are based on the idea that the dimensionality of many data sets is only artificially high. Our final model demonstrates one strong example of The Stable Diffusion 2 repository implemented all the servers in gradio and streamlit model-type is the type of image modification demo to launch For example, to launch the streamlit version of the image upscaler on the model created in the original step (assuming the x4-upscaler-ema. Stable Diffusion XL (SDXL) is a very popular text-to-image open source foundation model. Loading Guides for how to load and configure all the components (pipelines, models, and schedulers) of the library, as well as how to use different schedulers. By default, these files are saved in the gradio_cached_examples/ subdirectory within your app's working May 8, 2023 · In the case of Stable Diffusion this term can be used for the reverse diffusion process. Textual Inversion is a technique for capturing novel concepts from a small number of example images in a way that can later be used to control text-to-image pipelines. With its 860M UNet and 123M text encoder, the Jan 6, 2024 · DiffusersライブラリでStable Diffusionの画像生成. models import StableDiffusion. We now define a method to post-process images for us. As one of the pineering works, we comprehensively investigate adding an "invisible watermark" to (multi-modal) diffusion model (DM) generated contents (e. This tutorial shows in detail how to train Textual Inversion for Stable Diffusion in a Gradient Notebook, and use it to generate samples that accurately represent the features of the training images using control over the prompt. Let’s upscale it! First, we will upscale using the SD Upscaler with a simple prompt: prompt = "an aesthetic kingfisher" upscaled_image = pipeline (prompt=prompt, image=low_res_img). postprocess (image, output_type='pil') return image. Positional embedding and the diffusion process # Needs improvement. Feb 16, 2023 · Click the Start button and type "miniconda3" into the Start Menu search bar, then click "Open" or hit Enter. For example, you might have seen many generated images whose negative prompt (np This article assumes you're familiar with how latent diffusion models work. In the System Properties window, click “Environment Variables. ComfyUI breaks down a workflow into rearrangeable elements so you can easily make your own. It works by defining a new keyword representing the desired concept and finding the corresponding embedding vector within the language model. Manifold learning is an approach to non-linear dimensionality reduction. Feb 18, 2024 · Use Embeddings & LoRA Models. Sep 7, 2022 · They must be . ”. client("bedrock-runtime", region_name="us-east-1") # Set the model ID, e. ai」を開発している福山です。 今回は、画像生成AI「Stable Diffusion」を使いこなす上で覚えておきたいEmbeddingの使い方を解説します。 Embeddingとは？ Embeddingは、Textual Inversionという追加学習の手法によって作られます。 LoRAと同様に Nov 1, 2023 · 「EasyNegative」に代表される「Embedding」の効果や導入方法、使用方法について解説しています。「細部の破綻」や「手の破綻」に対して、現在一番有効とされているのが「Embedding」を使用した修復です。「Embedding」を使うことで画像のクオリティーを上げることができます。 Sep 22, 2022 · delete the venv directory (wherever you cloned the stable-diffusion-webui, e. C:\Users\you\stable-diffusion-webui\venv) check the environment variables (click the Start button, then type “environment properties” into the search bar and hit Enter. 6を再インストールしてください」と説明している場合が多いです。 Dec 23, 2022 · sdkit (stable diffusion kit) is an easy-to-use library for using Stable Diffusion in your AI Art projects. jpg files in a folder your_folder. We use the standard image encoder from SD 2. Understanding prompts – Word as vectors, CLIP. A simple lookup table that stores embeddings of a fixed dictionary and size. Generating the Images FastEmbed from Qdrant is a lightweight, fast, Python library built for embedding generation. 3. client = boto3. Secondly, the description is not optimized for use as a prompt. It also includes a model-downloader with a database of commonly used models, and May 20, 2023 · Textual inversion: Teach the base model new vocabulary about a particular concept with a couple of images reflecting that concept. Stable Diffusion v3 introduces a significant upgrade from v2 by shifting from a U-Net architecture to an advanced diffusion transformer architecture. Upscale the image. oil painting of zwx in style of van gogh. Stable UnCLIP 2. Copy and paste the code block below into the Miniconda3 window, then press Enter. 📄️ GigaChat Here is an example for how to use Textual Inversion/Embeddings. most prominently used in diffusion model benchmarking. model_id Apr 8, 2024 · Diffusion models are a family of neural network models that consider embedding to be a hint to restore a picture from random pixels. If you haven't already, you should start by reading the Stable Diffusion Tutorial. Mar 18, 2024 · We are releasing Stable Video Diffusion, an image-to-video model, for research purposes: SVD: This model was trained to generate 14 frames at resolution 576x1024 given a context frame of the same size. This GitHub repository contains a collection of Python code for implementing various probabilistic generative models and embedding techniques. py script shows how to fine-tune the stable diffusion model on your own dataset. The IS and KID capture similar sentiments of distribution distance, and we refer the reader to the citations for further explana-tion. Spectral embedding for non-linear dimensionality reduction. Manifold learning — scikit-learn 1. This model uses a frozen CLIP ViT-L/14 text We would like to show you a description here but the site won’t allow us. 6. The Swift package relies on the Core ML model files generated by python_coreml_stable_diffusion. These are files that are created by Gradio as part of caching examples for faster runtimes, if you set cache_examples=True or cache_examples="lazy" in gr. This is essentially using one image as a Diffusion maps are a non-linear dimensionality reduction technique that embeds the coordinates of each frame onto a lower-dimensional space, such that the distance between each frame in the lower-dimensional space represents their “diffusion distance”, or similarity. Textual Inversion is a training technique for personalizing image generation models with just a few example images of what you want it to learn. Let’s download the SDXL textual inversion embeddings and have a closer look at it’s Replace /path/to/stable_diffusion with the actual path to the stable_diffusion directory on your system. Unsupervised learning. It does so by learning new ‘words’ in the embedding space of the pipeline’s text encoder. 1. Stable Diffusion XL (SDXL) is a powerful text-to-image generation model that iterates on the previous Stable Diffusion models in three key ways: the UNet is 3x larger and SDXL combines a second text encoder (OpenCLIP ViT-bigG/14) with the original text encoder to significantly increase the number of parameters. This sets the level of diffusion. To start, we import KerasCV and load up a Stable Diffusion model using the optimizations discussed in the tutorial Generate images with Stable Diffusion. Stable Diffusion XL (SDXL) can also use textual inversion vectors for inference. But there are some problems. e. Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. It is hosted by huggingface. For example, the autoencoder used in Stable Diffusion has a reduction factor of 8. Sep 7, 2022 · In addition to the optimized version by basujindal, the additional tags following the prompt allows the model to run properly on a machine with NVIDIA or AMD 8+GB GPU. Learn how you can generate similar images with depth estimation (depth2img) using stable diffusion with huggingface diffusers and transformers libraries in Python. It’s trained on 512x512 images from a subset of the LAION-5B dataset. There’s no requirement that you must use a The train_text_to_image. put your . We're going to create a folder named "stable-diffusion" using the command line. The first step is to generate a 512x512 pixel image full of random noise, an image without any meaning. with my newly trained model, I am happy with what I got: Images from dreambooth model. Jan 17, 2024 · Step 4: Testing the model (optional) You can also use the second cell of the notebook to test using the model. Sep 11, 2023 · To generate an image, we need a textual prompt that describes what we want in the image. Go to stable-diffusion-webui folder. You can construct an image generation workflow by chaining different blocks (called nodes) together. This specific type of diffusion model was proposed in This guide assumes the reader has a high-level understanding of Stable Diffusion. 0 from Stability AI; Text-to-Text Model: Falcon 40B Instruct from TII; Text-to-Embedding Model: All MiniLM L6 v2 from Hugging Face; Vector Database: Pinecone # Use the native inference API to create an image with Stability. Google Colab este o platformă online care vă permite să executați cod Python și să creați notebook-uri colaborative. Table of contents: Introduction. Using Embeddings or LoRA models is another great way to fix eyes in Stable Diffusion. As a data scientist, I have personally witnessed the power of stable diffusion embeddings in action. to get started. Here are some examples of what you can learn: Textual Inversion, an algorithm that teaches a model a specific visual concept and integrates it into the generated image. Manifold learning #. In contrast to Stable Diffusion 1 and 2, SDXL has two text encoders so you’ll need two textual inversion embeddings - one for each text encoder model. We first encode the image from the pixel to the latent embedding space. The steps in this workflow are: Build a base prompt. Restart ComfyUI completely. Simply, you can open up the Stable Diffusion Web UI and enter the story as the positive prompt and generate. 1をインストールしている？. Nov 2, 2022 · The image generator goes through two stages: 1- Image information creator. cd C:/mkdir stable-diffusioncd stable-diffusion. Once your images are captioned, your settings are input and tweaked, now comes the time for the final step. . " Stable Diffusion quickly gained traction due to its impressive capabilities and openness, inspiring a new generation of models. We’re on a journey to advance and democratize artificial intelligence through open source and open science. In essence, it is a program in which you can provide input (such as a text prompt) and get back a tensor that represents an array of pixels, which, in turn, you can save as an image file. This model uses a frozen CLIP ViT-L/14 text encoder to condition the model on text prompts. With LoRA, it is much easier to fine-tune a model on a custom dataset. This image of the Kingfisher bird looks quite detailed! Mar 31, 2024 · In bioinformatics, stable diffusion embeddings can aid in the analysis of gene expression data and protein-protein interaction networks. Firstly, the story may have different scenes, and you need only one in an illustration. The resulting transformation is given by the value of the eigenvectors for each data point. Explore thousands of high-quality Stable Diffusion models, share your AI-generated art, and engage with a vibrant community of creators Stable Diffusion with Aesthetic Gradients 🎨. We pass these embeddings to the get_img_latents_similar() method. This technique works by learning and updating the text embeddings (the new embeddings are tied to a special word you must use in the prompt) to match the example images you provide. The name must be unique enough so that the textual inversion process will not confuse your personal embedding with something else. To generate this noise-filled image we can also modify a parameter known as seed, whose default value is -1 (random). The text-to-image fine-tuning script is experimental. , images in computer vision tasks), and their properties: imgs = self. Full model fine-tuning of Stable Diffusion used to be slow and difficult, and that's part of the reason why lighter-weight methods such as Dreambooth or Textual Inversion have become so popular. We will be able to generate images with SDXL using only 4 GB of memory, so it will be possible to use a low-end graphics card. base_path: C:\Users\USERNAME\stable-diffusion-webui. This model allows for image variations and mixing operations as described in Hierarchical Text-Conditional Image Generation with CLIP Latents, and, thanks to its modularity, can be combined with other models such as KARLO. This component runs for multiple steps to generate image information. num_embeddings ( int) – size of the dictionary of embeddings. exe from there (you can type "cmd" into the address bar of Explorer, or Shift+RightClick inside the folder and choose CMD/PowerShell; if you do get a blue powershell window – type cmd + Enter there!) Sep 26, 2023 · Text-to-Image Model: Stable Diffusion XL 1. ControlNet has weight of 0. realbenny-t1 for 1 token and realbenny-t2 for 2 tokens embeddings. Stable Diffusion is a deep learning model that can generate pictures. Faster examples with accelerated inference. Generating Instructions and Paired Captions This image is pretty small. To use an embedding put the file in the models/embeddings folder then use it in your prompt like I used the SDA768. This notebook explains how to use Fireworks Embeddings, which is included in the langchain_fireworks package, to embed texts in langchain. We'll follow a step by step approach Collaborate on models, datasets and Spaces. 500. safetensors is a secure alternative to pickle Stable Diffusion is cool! Build Stable Diffusion “from Scratch”. You will get the same image as if you didn’t put anything. 1-768. bin file with Python’s pickle utility. pt) will be the term you'd use in prompt to get that embedding. Jun 5, 2024 · Key Steps to Training a Stable Embedding Diffusion. If you run into issues during installation or runtime, please refer to the FAQ section. This component is the secret sauce of Stable Diffusion. catbox. We recommend to explore different hyperparameters to get the best results on your dataset. Give it a name - this name is also what you will use in your prompts, e. The beauty of using these models is that you can either use them during image generation or use them during inpainting to fix a badly generated eye. Visual explanation of text-to-image, image-to- Nov 30, 2022 · We can use Stable Diffusion in just three lines of code: from keras_cv. With its ability to generate a wide variety of styles from short, human-readable prompts, Stable Diffusion SpectralEmbedding #. Note that if you are Text-to-Image with Stable Diffusion. Training on your own dataset can be beneficial to get better tokens and hence better images for your domain. , Stable Diffusion XL 1. Nov 15, 2023 · You can verify its uselessness by putting it in the negative prompt. 3 Update 2 をインストールしたけれども、Stable Diffusion web UI が 12. The noise variant of the image is generated by mixing the image with the noise. Diffusers now provides a LoRA fine-tuning script that can run Aug 16, 2023 · Stable Diffusion Textual Inversion is a technique that allows you to add new styles or objects to your text-to-image models without modifying the underlying model. 3 days ago · In a real application, the methods will expose an API of the application to Python. Feb 22, 2024 · Introduction. 1. If ‘auto’, t is selected according to the knee point in the Von Neumann Entropy of the diffusion operator; gamma (float, optional, default: 1) – Informational distance constant between -1 and 1. Below is a summary of the Text conditioning in Stable Diffusion involves embedding the text prompt into a format that the model can understand and use to guide image generation. This includes tasks such as tokenization, normalization, and stop-word removal. an astronaut riding a horse this text is then encoded and then we pass this textual embedding to a diffusion model, which is a model that adds random noise to images, with the aim to be able to train a text / image encoder with this information. と A basic crash course for learning how to use the library's most important features like using models and schedulers to build your own diffusion system, and training your own diffusion model. Windows 11で確認。. Jul 6, 2024 · ComfyUI is a node-based GUI for Stable Diffusion. 34 compared to >100 for traditional PTQ) in a training-free manner. There are plenty of Negative Embedding (or Textual Inversion) models that will Jun 30, 2023 · Aside from this normalization can help to make the training (at the start) more stable and it might converge faster. Open cmd. 9) in steps 11-20. 2. The concept can be: a pose, an artistic style, a texture, etc. The encoder then goes through a iteration stage, adding more noise across Dec 22, 2023 · The Stable Video Diffusion (SVD) Image-to-Video is a latent diffusion model trained to generate short video clips from an image. It is fast, feature-packed, and memory-efficient. Principle of Diffusion models (sampling, learning) Diffusion for Images – UNet architecture. NVIDIAのDeveloperのIDを無料作成して、CUDA Toolkit 12. As an example, I trained one for about 5000 steps: https://files. Choose a model. py script. 10. CUDAインストール. Fix defects with inpainting. Parameters. 5] Since, I am using 20 sampling steps, what this means is using the as the negative prompt in steps 1 – 10, and (ear:1. embedding:SDA768. If the configuration is correct, you should see the full list of your model by clicking the ckpt_name field in the Load Checkpoint node. pt. Switch between documentation themes. Sep 27, 2023 · The workflow is a multiple-step process. From my experience on MNIST it does not matter much if you normalize it or not. Embedding Python in C++¶. Preprocessing helps to remove noise and reduce the dimensionality of the dataset, making it easier to train a Stable Diffusion is a text-to-image latent diffusion model created by the researchers and engineers from CompVis, Stability AI and LAION. Final adjustment with photo-editing software. Our approach can also be plugged into text-guided image generation, where we run stable diffusion in 4-bit weights Cached examples created by Gradio. まだ手探り状態。. This means that an image of a shape (3, 512, 512 ) becomes (4, 64, 64 ) in latent space, which requires 64 times less memory. Refinement prompt and generate image with good composition. 2. zq kz sw dv ql pz sj xo br mo