colab运行stable diffusion以及本地部署方法

!nvidia-smi !pip install diffusers==0.2.4 ​ !pip install transformers scipy ftfy ​ !pip install “ipywidgets>=7,<8” from google.colab import output ​ output.enable_custom_widget_manager()

from huggingface_hub import notebook_login ​ notebook_login()

import torch from diffusers import StableDiffusionPipeline ​ # make sure youre logged in with `huggingface-cli login` pipe = StableDiffusionPipeline.from_pretrained(“CompVis/stable-diffusion-v1-4”, revision=“fp16”, torch_dtype=torch.float16, use_auth_token=True) pipe = pipe.to(“cuda”)

from torch import autocast ​ #下一行修改 prompt = “a photograph of an astronaut riding a horse” with autocast(“cuda”): image = pipe(prompt)[“sample”][0] #图片保存 image.save(f“astronaut_rides_horse.png”) ​ #或者你可以直接在这个notebook里显示查看. image

import torch ​ generator = torch.Generator(“cuda”).manual_seed(1024) ​ with autocast(“cuda”): image = pipe(prompt, num_inference_steps=15, generator=generator)[“sample”][0] #显示 image

conda install git

cd YOUR_PATH conda env create -f environment.yaml

git clone -q https://github.com/CompVis/taming-transformers.git YOUR_PATH\src\taming-transformers ​ git clone -q https://github.com/openai/CLIP.git YOUR_PATH\src\clip

conda activate ldm python setup.py install

conda install pytorch torchvision -c pytorch pip install transformers==4.19.2 diffusers invisible-watermark pip install -e .

mkdir -p models/ldm/stable-diffusion-v1/ ln -s <path/to/model.ckpt> models/ldm/stable-diffusion-v1/model.ckpt

conda env update -n ldm –file environment.yaml

python scripts/txt2img.py –prompt “a painting of Mads Mikkelsen smoking”

python scripts/txt2img.py –prompt “a painting of Mads Mikkelsen smoking” –plms –outdir “output/” –ckpt –ddim_steps 100 –H 512 –W 512 –seed 8

python scripts/img2img.py –prompt “a painting of Mads Mikkelsen smoking” –init-img ./input/yourpic.jpg –strength 0.8 –outdir ./SD/output –ckpt ./SD/models/ldm/sd-v1-4.ckpt –ddim_steps 100

usage: txt2img.py [-h] [–prompt [PROMPT]] [–outdir [OUTDIR]] [–skip_grid] [–skip_save] [–ddim_steps DDIM_STEPS] [–plms] [–laion400m] [–fixed_code] [–ddim_eta DDIM_ETA] [–n_iter N_ITER] [–H H] [–W W] [–C C] [–f F] [–n_samples N_SAMPLES] [–n_rows N_ROWS] [–scale SCALE] [–from-file FROM_FILE] [–config CONFIG] [–ckpt CKPT] [–seed SEED] [–precision {full,autocast}] optional arguments: -h, –help show this help message and exit –prompt [PROMPT] the prompt to render –outdir [OUTDIR] dir to write results to –skip_grid do not save a grid, only individual samples. Helpful when evaluating lots of samples –skip_save do not save individual samples. For speed measurements. –ddim_steps DDIM_STEPS number of ddim sampling steps –plms use plms sampling –laion400m uses the LAION400M model –fixed_code if enabled, uses the same starting code across samples –ddim_eta DDIM_ETA ddim eta (eta=0.0 corresponds to deterministic sampling –n_iter N_ITER sample this often –H H image height, in pixel space –W W image width, in pixel space –C C latent channels –f F downsampling factor –n_samples N_SAMPLES how many samples to produce for each given prompt. A.k.a. batch size –n_rows N_ROWS rows in the grid (default: n_samples) –scale SCALE unconditional guidance scale: eps = eps(x, empty) + scale * (eps(x, cond) – eps(x, empty)) –from-file FROM_FILE if specified, load prompts from this file –config CONFIG path to config which constructs model –ckpt CKPT path to checkpoint of model –seed SEED the seed (for reproducible sampling) –precision {full,autocast} evaluate at this precision