import diffusers from transformers import CLIPTextModel, CLIPTokenizer, CLIPTextConfig, logging from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline # , UNet2DConditionModel 7 changes: 6 additions & 1 deletion 7 library/sdxl_lpw_stable_diffusion.py Original file line numberDiff...
Let's now create a AnimateDiff pipeline with an additional `MotionAdapter` module ```bash from diffusers import AnimateDiffPipeline, MotionAdapter, DDIMScheduler from diffusers.utils import export_to_gif adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_...
在DDPM和DDIM论文中定义diffusion model的数学知识; Classifier-Free Guidance; Text-To-Image; Image-to-Image; 前提知识 概率和统计的基础知识,例如多变量gaussian,conditional probability, marginal probability, likelihood, Bayers' rule; Pytorch和neural networks的基础知识; Attention mechanism的工作原理; Convolution...
import gradio as gr import os import cv2 import numpy as np import torch from PIL import Image from insightface.app import FaceAnalysis from diffusers import StableDiffusionPipeline, DDIMScheduler, AutoencoderKL from ip_adapter.ip_adapter_faceid import IPAdapterFaceID # Function to list models in ...
from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", use_auth_token=True ).to("cuda") prompt = "a photo of an astronaut riding a horse on mars" with autocast("cuda"): image = pipe(prompt)["sample"][0] image...
Hi, I'm a bit confused about how from_config and from_pretrained work in the Scheduler, especially for the DDIM Scheduler. When I load a scheduler from a pre-trained model like Stable Diffusion, are these two methods basically doing the ...
from diffusers import StableDiffusionInpaintPipeline pipe = StableDiffusionInpaintPipeline.from_pretrained("Uminosachi/dreamshaper_5-inpainting") exit() ``` * If there's a specific model you'd like to use, you can cache it in advance using the following Python commands (`venv/bin/python` fo...
Diffusers Integration A simple way to download and sample Stable Diffusion is by using the diffusers library: # make sure you're logged in with `huggingface-cli login` from torch import autocast from diffusers import StableDiffusionPipeline pipe = StableDiffusionPipeline.from_pretrained( "CompVis/st...
│ 31 │ from .pipeline_utils import DiffusionPipeline │ │ ❱ 32 │ from .pipelines import ( │ │ 33 │ │ DanceDiffusionPipeline, │ │ 34 │ │ DDIMPipeline, │ │ 35 │ │ DDPMPipeline, │ │ │ │ /usr/local/lib/python3.7/dist-packages/diffusers/pipelines/init.py:18 in │...
import cv2 import numpy as np import torch # 初始化模型 import torchvision from diffusers import AutoencoderKL, DDIMScheduler from diffusers.pipelines.stable_diffusion import StableDiffusionPipeline from einops import rearrange, repeat from omegaconf import OmegaConf from PIL import Image from torchvision ...