mirror of
https://github.com/huggingface/diffusers.git
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448 lines
20 KiB
Python
448 lines
20 KiB
Python
# coding=utf-8
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# Copyright 2026 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import re
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import numpy as np
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import PIL.Image
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import pytest
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import torch
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from diffusers.image_processor import VaeImageProcessor
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from diffusers.utils.constants import (
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DECODE_ENDPOINT_FLUX,
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DECODE_ENDPOINT_HUNYUAN_VIDEO,
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DECODE_ENDPOINT_SD_V1,
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DECODE_ENDPOINT_SD_XL,
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)
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from diffusers.utils.remote_utils import (
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remote_decode,
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)
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from diffusers.video_processor import VideoProcessor
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from ..testing_utils import (
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enable_full_determinism,
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slow,
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torch_all_close,
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torch_device,
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)
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enable_full_determinism()
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class RemoteAutoencoderKLMixin:
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shape: tuple[int, ...] = None
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out_hw: tuple[int, int] = None
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endpoint: str = None
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dtype: torch.dtype = None
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scaling_factor: float = None
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shift_factor: float = None
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processor_cls: VaeImageProcessor | VideoProcessor = None
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output_pil_slice: torch.Tensor = None
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output_pt_slice: torch.Tensor = None
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partial_postprocess_return_pt_slice: torch.Tensor = None
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return_pt_slice: torch.Tensor = None
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width: int = None
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height: int = None
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def get_dummy_inputs(self):
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# The tensor is serialized and decoded remotely, so the local device has no bearing on the result.
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# Seed it on CPU and move it afterwards, otherwise `torch.randn` yields different latents on a CUDA
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# runner than on a CPU one and the reference slices below only match on whichever recorded them.
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inputs = {
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"endpoint": self.endpoint,
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"tensor": torch.randn(
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self.shape,
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device="cpu",
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dtype=self.dtype,
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generator=torch.Generator("cpu").manual_seed(13),
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).to(torch_device),
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"scaling_factor": self.scaling_factor,
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"shift_factor": self.shift_factor,
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"height": self.height,
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"width": self.width,
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}
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return inputs
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def test_no_scaling(self):
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inputs = self.get_dummy_inputs()
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if inputs["scaling_factor"] is not None:
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inputs["tensor"] = inputs["tensor"] / inputs["scaling_factor"]
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inputs["scaling_factor"] = None
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if inputs["shift_factor"] is not None:
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inputs["tensor"] = inputs["tensor"] + inputs["shift_factor"]
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inputs["shift_factor"] = None
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processor = self.processor_cls()
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output = remote_decode(
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output_type="pt",
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# required for now, will be removed in next update
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do_scaling=False,
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processor=processor,
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**inputs,
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)
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assert isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}"
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assert output.height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}"
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assert output.width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}"
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output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten())
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# Increased tolerance for Flux Packed diff [1, 0, 1, 0, 0, 0, 0, 0, 0]
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1)
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def test_output_type_pt(self):
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inputs = self.get_dummy_inputs()
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processor = self.processor_cls()
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output = remote_decode(output_type="pt", processor=processor, **inputs)
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assert isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}"
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assert output.height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}"
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assert output.width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}"
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output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2)
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# output is visually the same, slice is flaky?
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def test_output_type_pil(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pil", **inputs)
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assert isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}"
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assert output.height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}"
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assert output.width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}"
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def test_output_type_pil_image_format(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pil", image_format="png", **inputs)
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assert isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}"
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assert output.height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}"
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assert output.width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}"
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assert output.format == "png", f"Expected image format `png`, got {output.format}"
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output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2)
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def test_output_type_pt_partial_postprocess(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pt", partial_postprocess=True, **inputs)
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assert isinstance(output, PIL.Image.Image), f"Expected `PIL.Image.Image` output, got {type(output)}"
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assert output.height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.height}"
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assert output.width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.width}"
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output_slice = torch.from_numpy(np.array(output)[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1e-2)
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def test_output_type_pt_return_type_pt(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pt", return_type="pt", **inputs)
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assert isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}"
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assert output.shape[2] == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[2]}"
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assert output.shape[3] == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[3]}"
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output_slice = output[0, 0, -3:, -3:].flatten()
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assert torch_all_close(output_slice, self.return_pt_slice.to(output_slice.dtype), rtol=1e-3, atol=1e-3)
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def test_output_type_pt_partial_postprocess_return_type_pt(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pt", partial_postprocess=True, return_type="pt", **inputs)
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assert isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}"
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assert output.shape[1] == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[1]}"
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assert output.shape[2] == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[2]}"
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output_slice = output[0, -3:, -3:, 0].flatten().cpu()
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assert torch_all_close(
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output_slice, self.partial_postprocess_return_pt_slice.to(output_slice.dtype), rtol=1e-2
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)
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def test_do_scaling_deprecation(self):
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inputs = self.get_dummy_inputs()
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inputs.pop("scaling_factor", None)
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inputs.pop("shift_factor", None)
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expected = "`do_scaling` is deprecated, pass `scaling_factor` and `shift_factor` if required."
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with pytest.warns(FutureWarning, match=re.escape(expected)):
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_ = remote_decode(output_type="pt", partial_postprocess=True, **inputs)
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def test_input_tensor_type_base64_deprecation(self):
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inputs = self.get_dummy_inputs()
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expected = "input_tensor_type='base64' is deprecated. Using `binary`."
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with pytest.warns(FutureWarning, match=re.escape(expected)):
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_ = remote_decode(output_type="pt", input_tensor_type="base64", partial_postprocess=True, **inputs)
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def test_output_tensor_type_base64_deprecation(self):
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inputs = self.get_dummy_inputs()
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expected = "output_tensor_type='base64' is deprecated. Using `binary`."
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with pytest.warns(FutureWarning, match=re.escape(expected)):
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_ = remote_decode(output_type="pt", output_tensor_type="base64", partial_postprocess=True, **inputs)
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class RemoteAutoencoderKLHunyuanVideoMixin(RemoteAutoencoderKLMixin):
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def test_no_scaling(self):
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inputs = self.get_dummy_inputs()
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if inputs["scaling_factor"] is not None:
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inputs["tensor"] = inputs["tensor"] / inputs["scaling_factor"]
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inputs["scaling_factor"] = None
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if inputs["shift_factor"] is not None:
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inputs["tensor"] = inputs["tensor"] + inputs["shift_factor"]
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inputs["shift_factor"] = None
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processor = self.processor_cls()
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output = remote_decode(
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output_type="pt",
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# required for now, will be removed in next update
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do_scaling=False,
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processor=processor,
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**inputs,
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)
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assert isinstance(output, list) and isinstance(output[0], PIL.Image.Image), (
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f"Expected `List[PIL.Image.Image]` output, got {type(output)}"
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)
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assert output[0].height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}"
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assert output[0].width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}"
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output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1)
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def test_output_type_pt(self):
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inputs = self.get_dummy_inputs()
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processor = self.processor_cls()
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output = remote_decode(output_type="pt", processor=processor, **inputs)
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assert isinstance(output, list) and isinstance(output[0], PIL.Image.Image), (
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f"Expected `List[PIL.Image.Image]` output, got {type(output)}"
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)
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assert output[0].height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}"
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assert output[0].width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}"
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output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1)
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# output is visually the same, slice is flaky?
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def test_output_type_pil(self):
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inputs = self.get_dummy_inputs()
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processor = self.processor_cls()
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output = remote_decode(output_type="pil", processor=processor, **inputs)
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assert isinstance(output, list) and isinstance(output[0], PIL.Image.Image), (
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f"Expected `List[PIL.Image.Image]` output, got {type(output)}"
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)
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assert output[0].height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}"
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assert output[0].width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}"
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def test_output_type_pil_image_format(self):
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inputs = self.get_dummy_inputs()
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processor = self.processor_cls()
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output = remote_decode(output_type="pil", processor=processor, image_format="png", **inputs)
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assert isinstance(output, list) and isinstance(output[0], PIL.Image.Image), (
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f"Expected `List[PIL.Image.Image]` output, got {type(output)}"
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)
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assert output[0].height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}"
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assert output[0].width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}"
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output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1)
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def test_output_type_pt_partial_postprocess(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pt", partial_postprocess=True, **inputs)
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assert isinstance(output, list) and isinstance(output[0], PIL.Image.Image), (
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f"Expected `List[PIL.Image.Image]` output, got {type(output)}"
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)
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assert output[0].height == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output[0].height}"
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assert output[0].width == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output[0].width}"
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output_slice = torch.from_numpy(np.array(output[0])[0, -3:, -3:].flatten())
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assert torch_all_close(output_slice, self.output_pt_slice.to(output_slice.dtype), rtol=1, atol=1)
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def test_output_type_pt_return_type_pt(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="pt", return_type="pt", **inputs)
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assert isinstance(output, torch.Tensor), f"Expected `torch.Tensor` output, got {type(output)}"
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assert output.shape[3] == self.out_hw[0], f"Expected image height {self.out_hw[0]}, got {output.shape[3]}"
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assert output.shape[4] == self.out_hw[1], f"Expected image width {self.out_hw[0]}, got {output.shape[4]}"
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output_slice = output[0, 0, 0, -3:, -3:].flatten()
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assert torch_all_close(output_slice, self.return_pt_slice.to(output_slice.dtype), rtol=1e-3, atol=1e-3)
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def test_output_type_mp4(self):
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inputs = self.get_dummy_inputs()
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output = remote_decode(output_type="mp4", return_type="mp4", **inputs)
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assert isinstance(output, bytes), f"Expected `bytes` output, got {type(output)}"
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# The tests below hit live HF Inference Endpoints, which are not part of the fast CI contract, so they are
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# gated behind `RUN_SLOW` instead of running on every push. The reference slices are recorded against the
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# deployed `DECODE_ENDPOINT_*` models and drift if those are redeployed.
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@slow
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class TestRemoteAutoencoderKLSDv1(RemoteAutoencoderKLMixin):
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shape = (
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1,
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4,
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64,
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64,
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)
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out_hw = (
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512,
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512,
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)
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endpoint = DECODE_ENDPOINT_SD_V1
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dtype = torch.float16
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scaling_factor = 0.18215
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shift_factor = None
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processor_cls = VaeImageProcessor
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output_pt_slice = torch.tensor([162, 131, 123, 158, 131, 124, 148, 128, 115], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[133, 146, 130, 161, 141, 132, 148, 134, 135], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.0400, 0.1448, 0.0217, 0.2649, 0.1049, 0.0376, 0.1622, 0.0552, 0.0574])
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@slow
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class TestRemoteAutoencoderKLSDXL(RemoteAutoencoderKLMixin):
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shape = (
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1,
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4,
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128,
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128,
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)
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out_hw = (
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1024,
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1024,
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)
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endpoint = DECODE_ENDPOINT_SD_XL
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dtype = torch.float16
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scaling_factor = 0.13025
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shift_factor = None
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processor_cls = VaeImageProcessor
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output_pt_slice = torch.tensor([133, 182, 166, 131, 179, 163, 122, 173, 151], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[172, 160, 147, 149, 136, 126, 118, 112, 107], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.3511, 0.2539, 0.1550, 0.1720, 0.0688, -0.0117, -0.0730, -0.1215, -0.1581])
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@slow
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class TestRemoteAutoencoderKLFlux(RemoteAutoencoderKLMixin):
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shape = (
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1,
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16,
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128,
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128,
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)
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out_hw = (
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1024,
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1024,
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)
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endpoint = DECODE_ENDPOINT_FLUX
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dtype = torch.bfloat16
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scaling_factor = 0.3611
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shift_factor = 0.1159
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processor_cls = VaeImageProcessor
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output_pt_slice = torch.tensor([236, 222, 171, 226, 208, 160, 210, 193, 154], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[173, 174, 173, 188, 182, 178, 183, 175, 167], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.3538, 0.3613, 0.3582, 0.4749, 0.4294, 0.3977, 0.4336, 0.3738, 0.3098])
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@slow
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class TestRemoteAutoencoderKLFluxPacked(RemoteAutoencoderKLMixin):
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shape = (
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1,
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4096,
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64,
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)
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out_hw = (
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1024,
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1024,
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)
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height = 1024
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width = 1024
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endpoint = DECODE_ENDPOINT_FLUX
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dtype = torch.bfloat16
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scaling_factor = 0.3611
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shift_factor = 0.1159
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processor_cls = VaeImageProcessor
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# slices are different due to randn on different shape. we can pack the latent instead if we want the same
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output_pt_slice = torch.tensor([203, 188, 127, 163, 140, 89, 113, 86, 50], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor(
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[194, 173, 131, 192, 168, 126, 187, 158, 129], dtype=torch.uint8
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)
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return_pt_slice = torch.tensor([0.5259, 0.3564, 0.0272, 0.5093, 0.3171, -0.0111, 0.4639, 0.2371, 0.0083])
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@slow
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class TestRemoteAutoencoderKLHunyuanVideo(RemoteAutoencoderKLHunyuanVideoMixin):
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shape = (
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1,
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16,
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3,
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40,
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64,
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)
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out_hw = (
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320,
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512,
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)
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endpoint = DECODE_ENDPOINT_HUNYUAN_VIDEO
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dtype = torch.float16
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scaling_factor = 0.476986
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processor_cls = VideoProcessor
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output_pt_slice = torch.tensor([126, 129, 195, 128, 130, 196, 129, 130, 196], dtype=torch.uint8)
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partial_postprocess_return_pt_slice = torch.tensor([19, 16, 13, 30, 25, 21, 39, 35, 31], dtype=torch.uint8)
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return_pt_slice = torch.tensor([-0.8501, -0.8750, -0.8979, -0.7681, -0.8027, -0.8389, -0.6958, -0.7222, -0.7593])
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|
|
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class RemoteAutoencoderKLSlowTestMixin:
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channels: int = 4
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endpoint: str = None
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dtype: torch.dtype = None
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scaling_factor: float = None
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shift_factor: float = None
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width: int = None
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height: int = None
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|
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def get_dummy_inputs(self):
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inputs = {
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"endpoint": self.endpoint,
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"scaling_factor": self.scaling_factor,
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"shift_factor": self.shift_factor,
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"height": self.height,
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"width": self.width,
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}
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return inputs
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|
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def test_multi_res(self, tmp_path):
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inputs = self.get_dummy_inputs()
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for height in {320, 512, 640, 704, 896, 1024, 1208, 1384, 1536, 1608, 1864, 2048}:
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for width in {320, 512, 640, 704, 896, 1024, 1208, 1384, 1536, 1608, 1864, 2048}:
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inputs["tensor"] = torch.randn(
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(1, self.channels, height // 8, width // 8),
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device="cpu",
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|
dtype=self.dtype,
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|
generator=torch.Generator("cpu").manual_seed(13),
|
|
).to(torch_device)
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inputs["height"] = height
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|
inputs["width"] = width
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output = remote_decode(output_type="pt", partial_postprocess=True, **inputs)
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|
output.save(tmp_path / f"test_multi_res_{height}_{width}.png")
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|
|
|
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|
@slow
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|
class TestRemoteAutoencoderKLSDv1Slow(RemoteAutoencoderKLSlowTestMixin):
|
|
endpoint = DECODE_ENDPOINT_SD_V1
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|
dtype = torch.float16
|
|
scaling_factor = 0.18215
|
|
shift_factor = None
|
|
|
|
|
|
@slow
|
|
class TestRemoteAutoencoderKLSDXLSlow(RemoteAutoencoderKLSlowTestMixin):
|
|
endpoint = DECODE_ENDPOINT_SD_XL
|
|
dtype = torch.float16
|
|
scaling_factor = 0.13025
|
|
shift_factor = None
|
|
|
|
|
|
@slow
|
|
class TestRemoteAutoencoderKLFluxSlow(RemoteAutoencoderKLSlowTestMixin):
|
|
channels = 16
|
|
endpoint = DECODE_ENDPOINT_FLUX
|
|
dtype = torch.bfloat16
|
|
scaling_factor = 0.3611
|
|
shift_factor = 0.1159
|