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""" Reuse components across pipelines You can also reuse the same components in multiple pipelines to avoid loading the weights into RAM twice. Use the components method to save the components: Copied from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline |
model_id = "runwayml/stable-diffusion-v1-5" |
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True) |
components = stable_diffusion_txt2img.components Then you can pass the components to another pipeline without reloading the weights into RAM: Copied stable_diffusion_img2img = StableDiffusionImg2ImgPipeline(**components) You can also pass the components individually to the pipeline if you want more flexibility over w... |
model_id = "runwayml/stable-diffusion-v1-5" |
stable_diffusion_txt2img = StableDiffusionPipeline.from_pretrained(model_id, use_safetensors=True) |
stable_diffusion_img2img = StableDiffusionImg2ImgPipeline( |
vae=stable_diffusion_txt2img.vae, |
text_encoder=stable_diffusion_txt2img.text_encoder, |
tokenizer=stable_diffusion_txt2img.tokenizer, |
unet=stable_diffusion_txt2img.unet, |
scheduler=stable_diffusion_txt2img.scheduler, |
safety_checker=None, |
feature_extractor=None, |
requires_safety_checker=False, |
) Checkpoint variants A checkpoint variant is usually a checkpoint whose weights are: Stored in a different floating point type for lower precision and lower storage, such as torch.float16, because it only requires half the bandwidth and storage to download. You can’t use this variant if you’re continuing training or ... |
import torch |
# load fp16 variant |
stable_diffusion = DiffusionPipeline.from_pretrained( |
"runwayml/stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True |
) |
# load non_ema variant |
stable_diffusion = DiffusionPipeline.from_pretrained( |
"runwayml/stable-diffusion-v1-5", variant="non_ema", use_safetensors=True |
) To save a checkpoint stored in a different floating-point type or as a non-EMA variant, use the DiffusionPipeline.save_pretrained() method and specify the variant argument. You should try and save a variant to the same folder as the original checkpoint, so you can load both from the same folder: Copied from diffuse... |
# save as fp16 variant |
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="fp16") |
# save as non-ema variant |
stable_diffusion.save_pretrained("runwayml/stable-diffusion-v1-5", variant="non_ema") If you don’t save the variant to an existing folder, you must specify the variant argument otherwise it’ll throw an Exception because it can’t find the original checkpoint: Copied # 👎 this won't work |
stable_diffusion = DiffusionPipeline.from_pretrained( |
"./stable-diffusion-v1-5", torch_dtype=torch.float16, use_safetensors=True |
) |
# 👍 this works |
stable_diffusion = DiffusionPipeline.from_pretrained( |
"./stable-diffusion-v1-5", variant="fp16", torch_dtype=torch.float16, use_safetensors=True |
) Models Models are loaded from the ModelMixin.from_pretrained() method, which downloads and caches the latest version of the model weights and configurations. If the latest files are available in the local cache, from_pretrained() reuses files in the cache instead of re-downloading them. Models can be loaded from a s... |
repo_id = "runwayml/stable-diffusion-v1-5" |
model = UNet2DConditionModel.from_pretrained(repo_id, subfolder="unet", use_safetensors=True) Or directly from a repository’s directory: Copied from diffusers import UNet2DModel |
repo_id = "google/ddpm-cifar10-32" |
model = UNet2DModel.from_pretrained(repo_id, use_safetensors=True) You can also load and save model variants by specifying the variant argument in ModelMixin.from_pretrained() and ModelMixin.save_pretrained(): Copied from diffusers import UNet2DConditionModel |
model = UNet2DConditionModel.from_pretrained( |
"runwayml/stable-diffusion-v1-5", subfolder="unet", variant="non_ema", use_safetensors=True |
) |
model.save_pretrained("./local-unet", variant="non_ema") Schedulers Schedulers are loaded from the SchedulerMixin.from_pretrained() method, and unlike models, schedulers are not parameterized or trained; they are defined by a configuration file. Loading schedulers does not consume any significant amount of memory and ... |
For example, the following schedulers are compatible with StableDiffusionPipeline, which means you can load the same scheduler configuration file in any of these classes: Copied from diffusers import StableDiffusionPipeline |
from diffusers import ( |
DDPMScheduler, |
DDIMScheduler, |
PNDMScheduler, |
LMSDiscreteScheduler, |
EulerAncestralDiscreteScheduler, |
EulerDiscreteScheduler, |
DPMSolverMultistepScheduler, |
) |
repo_id = "runwayml/stable-diffusion-v1-5" |
ddpm = DDPMScheduler.from_pretrained(repo_id, subfolder="scheduler") |
ddim = DDIMScheduler.from_pretrained(repo_id, subfolder="scheduler") |
pndm = PNDMScheduler.from_pretrained(repo_id, subfolder="scheduler") |
lms = LMSDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler") |
euler_anc = EulerAncestralDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler") |
euler = EulerDiscreteScheduler.from_pretrained(repo_id, subfolder="scheduler") |
dpm = DPMSolverMultistepScheduler.from_pretrained(repo_id, subfolder="scheduler") |
# replace `dpm` with any of `ddpm`, `ddim`, `pndm`, `lms`, `euler_anc`, `euler` |
pipeline = StableDiffusionPipeline.from_pretrained(repo_id, scheduler=dpm, use_safetensors=True) DiffusionPipeline explained As a class method, DiffusionPipeline.from_pretrained() is responsible for two things: Download the latest version of the folder structure required for inference and cache it. If the latest folde... |
repo_id = "runwayml/stable-diffusion-v1-5" |
pipeline = DiffusionPipeline.from_pretrained(repo_id, use_safetensors=True) |
print(pipeline) You’ll see pipeline is an instance of StableDiffusionPipeline, which consists of seven components: "feature_extractor": a CLIPImageProcessor from 🤗 Transformers. "safety_checker": a component for screening against harmful content. "scheduler": an instance of PNDMScheduler. "text_encoder": a CLIPTextMod... |
"feature_extractor": [ |
"transformers", |
"CLIPImageProcessor" |
], |
"safety_checker": [ |
"stable_diffusion", |
"StableDiffusionSafetyChecker" |
], |
"scheduler": [ |
"diffusers", |
"PNDMScheduler" |
], |
"text_encoder": [ |
"transformers", |
"CLIPTextModel" |
], |
"tokenizer": [ |
"transformers", |
"CLIPTokenizer" |
], |
"unet": [ |
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