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Default values in SD3 pipelines when submodules are not loaded #10393

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Merged
merged 1 commit into from
Dec 27, 2024
Merged

Default values in SD3 pipelines when submodules are not loaded #10393

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17 changes: 13 additions & 4 deletions src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -226,12 +226,21 @@ def __init__(
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.vae_scale_factor = (
2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
)
latent_channels = self.vae.config.latent_channels if hasattr(self, "vae") and self.vae is not None else 16
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, vae_latent_channels=self.vae.config.latent_channels
vae_scale_factor=self.vae_scale_factor, vae_latent_channels=latent_channels
)
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = (
self.transformer.config.sample_size
if hasattr(self, "transformer") and self.transformer is not None
else 128
)
self.tokenizer_max_length = self.tokenizer.model_max_length
self.default_sample_size = self.transformer.config.sample_size
self.patch_size = (
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
)
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19 changes: 14 additions & 5 deletions src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -225,19 +225,28 @@ def __init__(
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.vae_scale_factor = (
2 ** (len(self.vae.config.block_out_channels) - 1) if hasattr(self, "vae") and self.vae is not None else 8
)
latent_channels = self.vae.config.latent_channels if hasattr(self, "vae") and self.vae is not None else 16
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, vae_latent_channels=self.vae.config.latent_channels
vae_scale_factor=self.vae_scale_factor, vae_latent_channels=latent_channels
)
self.mask_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor,
vae_latent_channels=self.vae.config.latent_channels,
vae_latent_channels=latent_channels,
do_normalize=False,
do_binarize=True,
do_convert_grayscale=True,
)
self.tokenizer_max_length = self.tokenizer.model_max_length
self.default_sample_size = self.transformer.config.sample_size
self.tokenizer_max_length = (
self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
)
self.default_sample_size = (
self.transformer.config.sample_size
if hasattr(self, "transformer") and self.transformer is not None
else 128
)
self.patch_size = (
self.transformer.config.patch_size if hasattr(self, "transformer") and self.transformer is not None else 2
)
Expand Down
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