Stable Cascade

저는 보통 cache_dir다음 세 가지 방법으로 사용자 정의하고 수정합니다. 1. 전역 환경 변수를 설정합니다. export TRANSFORMERS_CACHE=/path/to/my/cache/directory 2. 코드에서 환경 변수 설정 import os from transformers import AutoModel # Set the new cache directory os.environ["TRANSFORMERS_CACHE"] = "/path/to/my/cache/directory" # This directory will now be used as the cache model = AutoModel.from_pretrained("bert-base-uncased") 이 방법은 현재 실행 중인 Python 스크립트에만 영향을 미칩니다. 3. cache_dir매개변수로 전달하기 from transformers import AutoModel, AutoConfig # Set the cache directory cache_dir = "/path/to/my/cache/directory" # Load the model using the specified cache directory config = AutoConfig.from_pretrained("bert-base-uncased", cache_dir=cache_dir) model = AutoModel.from_pretrained("bert-base-uncased", config=config, cache_dir=cache_dir)

import torch from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline device = "cuda" num_images_per_prompt = 2 # hf cache 다른 디렉토리 설정 # https://stackoverflow.com/questions/63312859/how-to-change-huggingface-transformers-default-cache-directory cache_dir = "E:\\ai\\hf_cache" prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16, cache_dir=cache_dir).to(device) decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade", torch_dtype=torch.float16, cache_dir=cache_dir).to(device) prompt = "a beautiful portrait of girl, Ghibli style" negative_prompt = "" prior_output = prior( prompt=prompt, height=2048, width=2048, negative_prompt=negative_prompt, guidance_scale=4.0, num_images_per_prompt=num_images_per_prompt, num_inference_steps=20 ) decoder_output = decoder( image_embeddings=prior_output.image_embeddings.half(), prompt=prompt, negative_prompt=negative_prompt, guidance_scale=0.0, output_type="pil", num_inference_steps=10 ).images #Now decoder_output is a list with your PIL images # save image in local directory for i, img in enumerate(decoder_output): img.save(f"output_{i}.png") # display image import matplotlib.pyplot as plt import matplotlib.image as mpimg img = mpimg.imread('output_0.png') imgplot = plt.imshow(img) plt.show()

# create conda environment # 아래에서 my_venv는 본인이 원하는 이름으로 자유롭게 수정할 수 있습니다. conda create --name my_venv --clone base # activate new environment conda activate my_venv # execute .sh file ./requirements.sh # 만약 requiremenst가 .txt파일 형태로 정리돼 있다면 # pip install -r requirements.txt

#현재 연결되어 있는 jupyter notebook kernel을 먼저 확인하자. jupyter kernelspec list #원하는 kernel이 없다면 아래와 같이 추가한다. # 환경 진입 conda activate <MY_CONDA_ENV_NAME> # conda kernel package 설치 conda install nb_conda_kernels # kernel 연결 확인 jupyter kernelspec list #만약 kernel이 활성화 되어 있는 anaconda env와 연결되지 않았다면 해당 ENV에 jupyter kernel package가 설치되지 않았기 때문일 수 있다. 아래와 같이 설치하자. # kernel package 설치 pip install ipykernel # kernel 등록 python -m ipykernel install --user --name <MY_KERNEL_NAME> --display-name "<DISPLAY_NAME>"

#!/bin/bash # Check if at least two arguments were provided (excluding the optional first one) if [ $# -lt 2 ]; then echo "Insufficient arguments provided. At least two arguments are required." exit 1 fi # Check for the optional "essential" argument and download the essential models if present if [ "$1" == "essential" ]; then echo "Downloading Essential Models (EfficientNet, Stage A, Previewer)" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_a.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/previewer.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/effnet_encoder.safetensors -P . -q --show-progress shift # Move the arguments, $2 becomes $1, $3 becomes $2, etc. fi # Now, $1 is the second argument due to the potential shift above second_argument="$1" binary_decision="${2:-bfloat16}" # Use default or specific binary value if provided case $second_argument in big-big) if [ "$binary_decision" == "bfloat16" ]; then echo "Downloading Large Stage B & Large Stage C" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_bf16.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_bf16.safetensors -P . -q --show-progress else wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c.safetensors -P . -q --show-progress fi ;; big-small) if [ "$binary_decision" == "bfloat16" ]; then echo "Downloading Large Stage B & Small Stage C (BFloat16)" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_bf16.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_lite_bf16.safetensors -P . -q --show-progress else echo "Downloading Large Stage B & Small Stage C" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_lite.safetensors -P . -q --show-progress fi ;; small-big) if [ "$binary_decision" == "bfloat16" ]; then echo "Downloading Small Stage B & Large Stage C (BFloat16)" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite_bf16.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_bf16.safetensors -P . -q --show-progress else echo "Downloading Small Stage B & Large Stage C" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c.safetensors -P . -q --show-progress fi ;; small-small) if [ "$binary_decision" == "bfloat16" ]; then echo "Downloading Small Stage B & Small Stage C (BFloat16)" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite_bf16.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_lite_bf16.safetensors -P . -q --show-progress else echo "Downloading Small Stage B & Small Stage C" wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_b_lite.safetensors -P . -q --show-progress wget https://huggingface.co/stabilityai/StableWurst/resolve/main/stage_c_lite.safetensors -P . -q --show-progress fi ;; *) echo "Invalid second argument. Please provide a valid argument: big-big, big-small, small-big, or small-small." exit 2 ;; esac

# GLOBAL STUFF model_version: 3.6B dtype: bfloat16 # LoRA specific module_filters: ['.attn'] rank: 4 train_tokens: # - ['^snail', null] # token starts with "snail" -> "snail" & "snails", don't need to be reinitialized - ['[fernando]', '^dog</w>'] # custom token [snail], initialize as avg of snail & snails effnet_checkpoint_path: models/effnet_encoder.safetensors previewer_checkpoint_path: models/previewer.safetensors generator_checkpoint_path: models/stage_c_bf16.safetensors lora_checkpoint_path: models/lora_fernando_10k.safetensors