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Nov 28, 2022
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Ensure Flax pipeline always returns numpy array #1435

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d7607ed
Ensure Flax pipeline always returns numpy array.
pcuenca Nov 26, 2022
45bcda3
Clarify documentation.
pcuenca Nov 26, 2022
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7 changes: 3 additions & 4 deletions src/diffusers/pipelines/stable_diffusion/__init__.py
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Expand Up @@ -63,15 +63,14 @@ class FlaxStableDiffusionPipelineOutput(BaseOutput):
Output class for Stable Diffusion pipelines.

Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
images (`np.ndarray`)
Array of shape `(batch_size, height, width, num_channels)` with images from the diffusion pipeline.
nsfw_content_detected (`List[bool]`)
List of flags denoting whether the corresponding generated image likely represents "not-safe-for-work"
(nsfw) content.
"""

images: Union[List[PIL.Image.Image], np.ndarray]
images: np.ndarray
nsfw_content_detected: List[bool]

from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
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4 changes: 1 addition & 3 deletions src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
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Expand Up @@ -317,9 +317,6 @@ def __call__(
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 will ge generated by sampling using the supplied random `generator`.
output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
jit (`bool`, defaults to `False`):
Whether to run `pmap` versions of the generation and safety scoring functions. NOTE: This argument
exists because `__call__` is not yet end-to-end pmap-able. It will be removed in a future release.
Expand Down Expand Up @@ -383,6 +380,7 @@ def __call__(

images = images.reshape(num_devices, batch_size, height, width, 3)
else:
images = np.asarray(images)
has_nsfw_concept = False

if not return_dict:
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