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

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SD3 pipelines hasattr
This commit is contained in:
hlky
2024-12-27 17:58:49 +00:00
committed by GitHub
parent 83da817f73
commit 55ac1dbdf2
2 changed files with 28 additions and 10 deletions
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19
src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
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@@ -226,12 +226,21 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
transformer=transformer,
scheduler=scheduler,
)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.vae_scale_factor, vae_latent_channels=self.vae.config.latent_channels
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=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
)

19
src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
View File

@@ -225,19 +225,28 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
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
)