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Unverified Commit bdbaea8f authored by Bios's avatar Bios Committed by GitHub
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update StableDiffusion3Img2ImgPipeline.add image size validation (#10166) 



* update StableDiffusion3Img2ImgPipeline.add image size validation

---------
Co-authored-by: default avatarhlky <hlky@hlky.ac>
parent e8b65bff
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Showing with 55 additions and 2 deletions
src/diffusers/pipelines/pag/pipeline_pag_sd_3_img2img.py
View file @ bdbaea8f
......@@ -549,6 +549,8 @@ class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin,
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=None,
negative_prompt_2=None,
......@@ -560,6 +562,15 @@ class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin,
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
):
if (
height % (self.vae_scale_factor * self.patch_size) != 0
or width % (self.vae_scale_factor * self.patch_size) != 0
):
raise ValueError(
f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
)
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
......@@ -730,6 +741,8 @@ class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin,
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_3: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
image: PipelineImageInput = None,
strength: float = 0.6,
num_inference_steps: int = 50,
......@@ -860,11 +873,15 @@ class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin,
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.
"""
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
......@@ -933,7 +950,7 @@ class StableDiffusion3PAGImg2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin,
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
# 3. Preprocess image
image = self.image_processor.preprocess(image)
image = self.image_processor.preprocess(image, height=height, width=width)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
......
src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_img2img.py
View file @ bdbaea8f
......@@ -218,6 +218,9 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
)
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
)
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
......@@ -531,6 +534,8 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=None,
negative_prompt_2=None,
......@@ -542,6 +547,15 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
):
if (
height % (self.vae_scale_factor * self.patch_size) != 0
or width % (self.vae_scale_factor * self.patch_size) != 0
):
raise ValueError(
f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
)
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
......@@ -710,6 +724,8 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
prompt: Union[str, List[str]] = None,
prompt_2: Optional[Union[str, List[str]]] = None,
prompt_3: Optional[Union[str, List[str]]] = None,
height: Optional[int] = None,
width: Optional[int] = None,
image: PipelineImageInput = None,
strength: float = 0.6,
num_inference_steps: int = 50,
......@@ -824,12 +840,16 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
[`~pipelines.stable_diffusion_3.StableDiffusion3PipelineOutput`] if `return_dict` is True, otherwise a
`tuple`. When returning a tuple, the first element is a list with the generated images.
"""
height = height or self.default_sample_size * self.vae_scale_factor
width = width or self.default_sample_size * self.vae_scale_factor
# 1. Check inputs. Raise error if not correct
self.check_inputs(
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
......@@ -890,7 +910,7 @@ class StableDiffusion3Img2ImgPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
pooled_prompt_embeds = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
# 3. Preprocess image
image = self.image_processor.preprocess(image)
image = self.image_processor.preprocess(image, height=height, width=width)
# 4. Prepare timesteps
timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, sigmas=sigmas)
......
src/diffusers/pipelines/stable_diffusion_3/pipeline_stable_diffusion_3_inpaint.py
View file @ bdbaea8f
......@@ -224,6 +224,9 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
)
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
)
# Copied from diffusers.pipelines.stable_diffusion_3.pipeline_stable_diffusion_3.StableDiffusion3Pipeline._get_t5_prompt_embeds
def _get_t5_prompt_embeds(
......@@ -538,6 +541,8 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=None,
negative_prompt_2=None,
......@@ -549,6 +554,15 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
callback_on_step_end_tensor_inputs=None,
max_sequence_length=None,
):
if (
height % (self.vae_scale_factor * self.patch_size) != 0
or width % (self.vae_scale_factor * self.patch_size) != 0
):
raise ValueError(
f"`height` and `width` have to be divisible by {self.vae_scale_factor * self.patch_size} but are {height} and {width}."
f"You can use height {height - height % (self.vae_scale_factor * self.patch_size)} and width {width - width % (self.vae_scale_factor * self.patch_size)}."
)
if strength < 0 or strength > 1:
raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")
......@@ -953,6 +967,8 @@ class StableDiffusion3InpaintPipeline(DiffusionPipeline, SD3LoraLoaderMixin, Fro
prompt,
prompt_2,
prompt_3,
height,
width,
strength,
negative_prompt=negative_prompt,
negative_prompt_2=negative_prompt_2,
......
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