the best results are obtained from finetuning a pretrained model on a specific dataset. You can find many of these checkpoints on theHub, but if you can’t find one you like, you can always train your own!
‘–enable_bucket’, ‘–min_bucket_reso=256’, ‘–max_bucket_reso=2048’, ‘–pretrained_model_name_or_path=stable-diffusion-v1-5/stable-diffusion-v1-5’, ‘–train_data_dir=/content/drive/MyDrive/AI_PICS/training/Lexi001’, ‘–resolution=512,650’, ‘–output_dir=/content...
A notebook containing code to train your own Dreambooth model using Stable Diffusion. - GitHub - tryolabs/stable-diffusion-dreambooth: A notebook containing code to train your own Dreambooth model using Stable Diffusion.
打开一个终端 pip3 install 包名 示例 pip3 install wget # 或者 pip3 install pymysql 解决错误的方法 更新pip3 pip3 install --upgrade pip # 或者 python3 -m pip install -U pip 更换国内的pip 源 # 在用户主目录下创建一个文件夹 .pip mkdir ~/.pip #在 ~/.pip/文件夹下创建一个文件 'pip.c...
import os os.environ['MODEL_NAME']="CompVis/stable-diffusion-v1-4" os.environ['OUTPUT_DIR']="/content/stable_diffusion_weights/cat" os.environ['INSTANCE_DIR']="/content/data/cat" !accelerate launch train_custom_diffusion.py \ --pretrained_model_name_or_path=$MODEL_NAME \ --instance_...
# StableDiffusionControlNetPipeline, UNet2DConditionModel, UniPCMultistepScheduler, ) from diffusers.optimization import get_scheduler from diffusers.utils import check_min_version, is_wandb_available from diffusers.utils.hub_utils import load_or_create_model_card, populate_model_card ...
A mystifying aspect of diffusion model training—often hidden in opaque hyperparameter tables in appendices of research papers or default parameters in codebases—is the need to apply a very long average to get good results, often several percent of the entire length of the training. Using the...
- stable-diffusion-inpainting-diffusers - text-to-image - diffusers - realfill inference: true --- """ model_card = f""" # RealFill - {repo_id} This is a realfill model derived from {base_model}. The weights were trained using [RealFill](https://realfill.github.io/)...
Training your own ControlNet requires 3 steps: Planning your condition: ControlNet is flexible enough to tame Stable Diffusion towards many tasks. The pre-trained models showcase a wide-range of conditions, and the community has built others, such as conditioning onpixelated color palettes. ...
You can see that the model is a Pipeline Regressor and is about 10 KB. It takes in 3 features (just like we wanted) and output the final price! Conclusion In this tutorial, you saw how to create your own machine learning models using Apple’s newest framework Create ML! With just a ...