stable-diffusion

Optimized Stable Diffusion This repo is a modified version of the Stable Diffusion repo, optimized to use less VRAM than the original by sacrificing inference speed. To reduce the VRAM usage, the following opimizations are used: the stable diffusion model is fragmented into four parts which are sent to the GPU only when needed. After the calculation is done, they are moved back to the CPU. The attention calculation is done in parts. Installation All the modified files are in the optimizedSD folder, so if you have already cloned the original repository you can just download and copy this folder into the original instead of cloning the entire repo. You can also clone this repo and follow the same installation steps as the original (mainly creating the conda environment and placing the weights at the specified location). Alternatively, if you prefer to use Docker, you can do the following: Install Docker, Docker Compose plugin, and NVIDIA Container Toolkit Clone this repo to, e.g., ~/stable-diffusion Put your downloaded model.ckpt file into ~/sd-data (it's a relative path, you can change it in docker-compose.yml) cd into ~/stable-diffusion and execute docker compose up --build This will launch gradio on port 7860 with txt2img. You can also use docker compose run to execute other Python scripts. Usage img2img img2img can generate 512x512 images from a prior image and prompt using under 2.4GB VRAM in under 20 seconds per image on an RTX 2060. The maximum size that can fit on 6GB GPU (RTX 2060) is around 1152x1088. For example, the following command will generate 10 512x512 images: python optimizedSD/optimized_img2img.py --prompt "Austrian alps" --init-img ~/sketch-mountains-input.jpg --strength 0.8 --n_iter 2 --n_samples 5 --H 512 --W 512 txt2img txt2img can generate 512x512 images from a prompt using under 2.4GB GPU VRAM in under 24 seconds per image on an RTX 2060. For example, the following command will generate 10 512x512 images: python optimizedSD/optimized_txt2img.py --prompt "Cyberpunk style image of a Tesla car reflection in rain" --H 512 --W 512 --seed 27 --n_iter 2 --n_samples 5 --ddim_steps 50 inpainting inpaint_gradio.py can fill masked parts of an image based on a given prompt. It can inpaint 512x512 images while using under 2.5GB of VRAM. To launch the gradio interface for inpainting, run python optimizedSD/inpaint_gradio.py. The mask for the image can be drawn on the selected image using the brush tool. The results are not yet perfect but can be improved by using a combination of prompt weighting, prompt engineering and testing out multiple values of the --strength argument. Suggestions to improve the inpainting algorithm are most welcome. Using the Gradio GUI You can also use the built-in gradio interface for img2img, txt2img & inpainting instead of the command line interface. Activate the conda environment and install the latest version of gradio using pip install gradio, Run img2img using python optimizedSD/img2img_gradio.py, txt2img using python optimizedSD/txt2img_gradio.py and inpainting using python optimizedSD/inpaint_gradio.py. img2img_gradio.py has a feature to crop input images. Look for the pen symbol in the image box after selecting the image. Arguments --seed Seed for image generation, can be used to reproduce previously generated images. Defaults to a random seed if unspecified. The code will give the seed number along with each generated image. To generate the same image again, just specify the seed using --seed argument. Images are saved with its seed number as its name by default. For example if the seed number for an image is 1234 and it's the 55th image in the folder, the image name will be named seed_1234_00055.png. --n_samples Batch size/amount of images to generate at once. To get the lowest inference time per image, use the maximum batch size --n_samples that can fit on the GPU. Inference time per image will reduce on increasing the batch size, but the required VRAM will increase. If you get a CUDA out of memory error, try reducing the batch size --n_samples. If it doesn't work, the other option is to reduce the image width --W or height --H or both. --n_iter Run x amount of times Equivalent to running the script n_iter number of times. Only difference is that the model is loaded only once per n_iter iterations. Unlike n_samples, reducing it doesn't have an effect on VRAM required or inference time. --H & --W Height & width of the generated image. Both height and width should be a multiple of 64. --turbo Increases inference speed at the cost of extra VRAM usage. Using this argument increases the inference speed by using around 700MB of extra GPU VRAM. It is especially effective when generating a small batch of images (~ 1 to 4) images. It takes under 20 seconds for txt2img and 15 seconds for img2img (on an RTX 2060, excluding the time to load the model). Use it on larger batch sizes if GPU VRAM available. --precision autocast or --precision full Whether to use full or mixed precision Mixed Precision is enabled by default. If you don't have a GPU with tensor cores (any GTX 10 series card), you may not be able use mixed precision. Use the --precision full argument to disable it. --format png or --format jpg Output image format The default output format is png. While png is lossless, it takes up a lot of space (unless large portions of the image happen to be a single colour). Use lossy jpg to get smaller image file sizes. --unet_bs Batch size for the unet model Takes up a lot of extra RAM for very little improvement in inference time. unet_bs > 1 is not recommended! Should generally be a multiple of 2x(n_samples) Weighted Prompts Prompts can also be weighted to put relative emphasis on certain words. eg. --prompt tabby cat:0.25 white duck:0.75 hybrid. The number followed by the colon represents the weight given to the words before the colon. The weights can