text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> pipe = VersatileDiffusionPipeline.from_pretrained(
... "shi-labs/versatile-diffusion", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> generator... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Returns:
[`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the generated image... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
guidance_scale=guidance_scale,
negative_prompt=negative_prompt,
num_images_per_prompt=num_images_per_prompt,
eta=eta,
generator=generator,
latents=latents,
output_type=output_type,
return_dict=return_dict,
callback=callback,... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
return output
@torch.no_grad()
def dual_guided(
self,
prompt: Union[PIL.Image.Image, List[PIL.Image.Image]],
image: Union[str, List[str]],
text_to_image_strength: float = 0.5,
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps:... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.image_unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *opti... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`,... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
outp... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step. | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
Examples:
```py
>>> from diffusers import VersatileDiffusionPipeline
>>> import torch
>>> import requests
>>> from io import BytesIO
>>> from PIL import Image
>>> # let's download an initial image
>>> url = "https://huggingface.co/datasets/diffusers/imag... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
>>> image = pipe.dual_guided(
... prompt=text, image=image, text_to_image_strength=text_to_image_strength, generator=generator
... ).images[0]
>>> image.save("./car_variation.png")
```
Returns:
[`~pipelines.ImagePipelineOutput`] or `tuple`:
If `re... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
expected_components = inspect.signature(VersatileDiffusionDualGuidedPipeline.__init__).parameters.keys()
components = {name: component for name, component in self.components.items() if name in expected_components}
temp_pipeline = VersatileDiffusionDualGuidedPipeline(**components)
output = temp_p... | 415 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/deprecated/versatile_diffusion/pipeline_versatile_diffusion.py |
class StableDiffusionKDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "k_diffusion"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "k_diffusion"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backe... | 416 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py |
class StableDiffusionXLKDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "k_diffusion"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "k_diffusion"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_bac... | 417 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_k_diffusion_objects.py |
class OnnxRuntimeModel(metaclass=DummyObject):
_backends = ["onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["onnx"])
@classmethod
def from_pretrained(cls, *args, **kwargs... | 418 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_onnx_objects.py |
class EmptyTqdm:
"""Dummy tqdm which doesn't do anything."""
def __init__(self, *args, **kwargs): # pylint: disable=unused-argument
self._iterator = args[0] if args else None
def __iter__(self):
return iter(self._iterator)
def __getattr__(self, _):
"""Return empty function.""... | 419 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/logging.py |
class _tqdm_cls:
def __call__(self, *args, **kwargs):
if _tqdm_active:
return tqdm_lib.tqdm(*args, **kwargs)
else:
return EmptyTqdm(*args, **kwargs)
def set_lock(self, *args, **kwargs):
self._lock = None
if _tqdm_active:
return tqdm_lib.tqdm.s... | 420 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/logging.py |
class OnnxStableDiffusionImg2ImgPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["to... | 421 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class OnnxStableDiffusionInpaintPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["to... | 422 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class OnnxStableDiffusionInpaintPipelineLegacy(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls... | 423 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class OnnxStableDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "... | 424 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class OnnxStableDiffusionUpscalePipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["to... | 425 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class StableDiffusionOnnxPipeline(metaclass=DummyObject):
_backends = ["torch", "transformers", "onnx"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "transformers", "onnx"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "... | 426 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_transformers_and_onnx_objects.py |
class AudioDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch", "librosa"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "librosa"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "librosa"])
@classmethod
... | 427 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py |
class Mel(metaclass=DummyObject):
_backends = ["torch", "librosa"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "librosa"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "librosa"])
@classmethod
def from_pretrain... | 428 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_librosa_objects.py |
class CosineDPMSolverMultistepScheduler(metaclass=DummyObject):
_backends = ["torch", "torchsde"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "torchsde"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "torchsde"])
@... | 429 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_torchsde_objects.py |
class DPMSolverSDEScheduler(metaclass=DummyObject):
_backends = ["torch", "torchsde"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch", "torchsde"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch", "torchsde"])
@classmethod
... | 430 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_torch_and_torchsde_objects.py |
class AllegroTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *ar... | 431 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AsymmetricAutoencoderKL(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args... | 432 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AuraFlowTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *a... | 433 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderDC(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs... | 434 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKL(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs... | 435 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLAllegro(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 436 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLCogVideoX(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args,... | 437 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLHunyuanVideo(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *ar... | 438 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLLTXVideo(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 439 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLMochi(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **k... | 440 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderKLTemporalDecoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, ... | 441 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderOobleck(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **k... | 442 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoencoderTiny(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwar... | 443 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class CogVideoXTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *... | 444 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class CogView3PlusTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls... | 445 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ConsisIDTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *a... | 446 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ConsistencyDecoderVAE(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 447 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwar... | 448 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ControlNetUnionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 449 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ControlNetXSAdapter(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 450 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DiTTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 451 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class FluxControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 452 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class FluxMultiControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *arg... | 453 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class FluxTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args,... | 454 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class HunyuanDiT2DControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *... | 455 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class HunyuanDiT2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kw... | 456 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class HunyuanDiT2DMultiControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(c... | 457 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class HunyuanVideoTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls... | 458 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class I2VGenXLUNet(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs)... | 459 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class Kandinsky3UNet(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwarg... | 460 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class LatteTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args... | 461 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class LTXVideoTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *a... | 462 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class LuminaNextDiT2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 463 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class MochiTransformer3DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args... | 464 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ModelMixin(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
... | 465 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class MotionAdapter(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs... | 466 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class MultiAdapter(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs)... | 467 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class MultiControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 468 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class PixArtTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *arg... | 469 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class PriorTransformer(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwa... | 470 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class SanaTransformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args,... | 471 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class SD3ControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **k... | 472 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class SD3MultiControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args... | 473 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class SD3Transformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 474 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class SparseControlNetModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 475 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class StableAudioDiTModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 476 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class T2IAdapter(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
... | 477 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class T5FilmDecoder(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs... | 478 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class Transformer2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **k... | 479 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNet1DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):... | 480 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNet2DConditionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 481 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNet2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):... | 482 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNet3DConditionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, *... | 483 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNetControlNetXSModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 484 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNetMotionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwar... | 485 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UNetSpatioTemporalConditionModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(c... | 486 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class UVit2DModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):... | 487 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class VQModel(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
... | 488 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AudioPipelineOutput(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 489 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoPipelineForImage2Image(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *a... | 490 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoPipelineForInpainting(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *ar... | 491 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class AutoPipelineForText2Image(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *ar... | 492 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class BlipDiffusionControlNetPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cl... | 493 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class BlipDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, ... | 494 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class CLIPImageProjection(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 495 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ConsistencyModelPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *arg... | 496 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DanceDiffusionPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args,... | 497 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DDIMPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs)... | 498 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DDPMPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs)... | 499 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DiffusionPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kw... | 500 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class DiTPipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):... | 501 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class ImagePipelineOutput(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **... | 502 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
class KarrasVePipeline(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwa... | 503 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/utils/dummy_pt_objects.py |
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