Instructions to use FangDai/Tiger-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use FangDai/Tiger-Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("FangDai/Tiger-Model", dtype=torch.bfloat16, device_map="cuda") 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
| # This file is autogenerated by the command `make fix-copies`, do not edit. | |
| from ..utils import DummyObject, requires_backends | |
| class FlaxControlNetModel(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxModelMixin(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxUNet2DConditionModel(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxAutoencoderKL(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxDiffusionPipeline(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxDDIMScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxDDPMScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxDPMSolverMultistepScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxKarrasVeScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxLMSDiscreteScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxPNDMScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxSchedulerMixin(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| class FlaxScoreSdeVeScheduler(metaclass=DummyObject): | |
| _backends = ["flax"] | |
| def __init__(self, *args, **kwargs): | |
| requires_backends(self, ["flax"]) | |
| def from_config(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |
| def from_pretrained(cls, *args, **kwargs): | |
| requires_backends(cls, ["flax"]) | |