AI/diffusers
1
0
Fork 0
mirror of https://github.com/huggingface/diffusers.git synced 2026-01-29 07:22:12 +03:00
Code Activity

remove the output step

Browse Source
This commit is contained in:
yiyixuxu
2025-05-13 22:15:54 +02:00
parent a0deefb606
commit a7fb2d2a22
2 changed files with 5 additions and 52 deletions
Download Patch File Download Diff File Expand all files Collapse all files

56
src/diffusers/modular_pipelines/stable_diffusion_xl/after_denoise.py
View File

@@ -41,7 +41,7 @@ logger = logging.get_logger(__name__) # pylint: disable=invalid-name
class StableDiffusionXLDecodeLatentsStep(PipelineBlock):
class StableDiffusionXLDecodeStep(PipelineBlock):
model_name = "stable-diffusion-xl"
@@ -187,63 +187,17 @@ class StableDiffusionXLInpaintOverlayMaskStep(PipelineBlock):
return components, state
# YiYi TODO: remove this, we don't need this in modular
class StableDiffusionXLOutputStep(PipelineBlock):
model_name = "stable-diffusion-xl"
@property
def description(self) -> str:
return "final step to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or a plain tuple."
@property
def inputs(self) -> List[Tuple[str, Any]]:
return [InputParam("return_dict", default=True)]
@property
def intermediates_inputs(self) -> List[str]:
return [InputParam("images", required=True, type_hint=Union[List[PIL.Image.Image], List[torch.Tensor], List[np.array]], description="The generated images from the decode step.")]
@property
def intermediates_outputs(self) -> List[str]:
return [OutputParam("images", description="The final images output, can be a tuple or a `StableDiffusionXLPipelineOutput`")]
@torch.no_grad()
def __call__(self, components, state: PipelineState) -> PipelineState:
block_state = self.get_block_state(state)
if not block_state.return_dict:
block_state.images = (block_state.images,)
else:
block_state.images = StableDiffusionXLPipelineOutput(images=block_state.images)
self.add_block_state(state, block_state)
return components, state
# After denoise
class StableDiffusionXLDecodeStep(SequentialPipelineBlocks):
block_classes = [StableDiffusionXLDecodeLatentsStep, StableDiffusionXLOutputStep]
block_names = ["decode", "output"]
@property
def description(self):
return """Decode step that decode the denoised latents into images outputs.
This is a sequential pipeline blocks:
- `StableDiffusionXLDecodeLatentsStep` is used to decode the denoised latents into images
- `StableDiffusionXLOutputStep` is used to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or a plain tuple."""
class StableDiffusionXLInpaintDecodeStep(SequentialPipelineBlocks):
block_classes = [StableDiffusionXLDecodeLatentsStep, StableDiffusionXLInpaintOverlayMaskStep, StableDiffusionXLOutputStep]
block_names = ["decode", "mask_overlay", "output"]
block_classes = [StableDiffusionXLDecodeStep, StableDiffusionXLInpaintOverlayMaskStep]
block_names = ["decode", "mask_overlay"]
@property
def description(self):
return "Inpaint decode step that decode the denoised latents into images outputs.\n" + \
"This is a sequential pipeline blocks:\n" + \
" - `StableDiffusionXLDecodeLatentsStep` is used to decode the denoised latents into images\n" + \
" - `StableDiffusionXLInpaintOverlayMaskStep` is used to overlay the mask on the image\n" + \
" - `StableDiffusionXLOutputStep` is used to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or a plain tuple."
" - `StableDiffusionXLDecodeStep` is used to decode the denoised latents into images\n" + \
" - `StableDiffusionXLInpaintOverlayMaskStep` is used to overlay the mask on the image"
class StableDiffusionXLAutoDecodeStep(AutoPipelineBlocks):

1
src/diffusers/modular_pipelines/stable_diffusion_xl/modular_loader.py
View File

@@ -107,7 +107,6 @@ SDXL_INPUTS_SCHEMA = {
"negative_aesthetic_score": InputParam("negative_aesthetic_score", type_hint=float, default=2.0, description="Simulates negative aesthetic score"),
"eta": InputParam("eta", type_hint=float, default=0.0, description="Parameter η in the DDIM paper"),
"output_type": InputParam("output_type", type_hint=str, default="pil", description="Output format (pil/tensor/np.array)"),
"return_dict": InputParam("return_dict", type_hint=bool, default=True, description="Whether to return a StableDiffusionXLPipelineOutput"),
"ip_adapter_image": InputParam("ip_adapter_image", type_hint=PipelineImageInput, required=True, description="Image(s) to be used as IP adapter"),
"control_image": InputParam("control_image", type_hint=PipelineImageInput, required=True, description="ControlNet input condition"),
"control_guidance_start": InputParam("control_guidance_start", type_hint=Union[float, List[float]], default=0.0, description="When ControlNet starts applying"),