Instructions to use stillerman/poke-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use stillerman/poke-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("stillerman/poke-lora") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| # Copyright 2023 The HuggingFace Team. All rights reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| from ..utils import is_flax_available, is_torch_available | |
| if is_torch_available(): | |
| from .adapter import MultiAdapter, T2IAdapter | |
| from .autoencoder_asym_kl import AsymmetricAutoencoderKL | |
| from .autoencoder_kl import AutoencoderKL | |
| from .autoencoder_tiny import AutoencoderTiny | |
| from .controlnet import ControlNetModel | |
| from .dual_transformer_2d import DualTransformer2DModel | |
| from .modeling_utils import ModelMixin | |
| from .prior_transformer import PriorTransformer | |
| from .t5_film_transformer import T5FilmDecoder | |
| from .transformer_2d import Transformer2DModel | |
| from .unet_1d import UNet1DModel | |
| from .unet_2d import UNet2DModel | |
| from .unet_2d_condition import UNet2DConditionModel | |
| from .unet_3d_condition import UNet3DConditionModel | |
| from .vq_model import VQModel | |
| if is_flax_available(): | |
| from .controlnet_flax import FlaxControlNetModel | |
| from .unet_2d_condition_flax import FlaxUNet2DConditionModel | |
| from .vae_flax import FlaxAutoencoderKL | |