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425 lines (383 loc) · 15.7 KB
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# coding=utf-8
# Copyright 2025 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import io
import json
from typing import List, Literal, Optional, Union, cast
import requests
from .deprecation_utils import deprecate
from .import_utils import is_safetensors_available, is_torch_available
if is_torch_available():
import torch
from ..image_processor import VaeImageProcessor
from ..video_processor import VideoProcessor
if is_safetensors_available():
import safetensors.torch
DTYPE_MAP = {
"float16": torch.float16,
"float32": torch.float32,
"bfloat16": torch.bfloat16,
"uint8": torch.uint8,
}
from PIL import Image
def detect_image_type(data: bytes) -> str:
if data.startswith(b"\xff\xd8"):
return "jpeg"
elif data.startswith(b"\x89PNG\r\n\x1a\n"):
return "png"
elif data.startswith(b"GIF87a") or data.startswith(b"GIF89a"):
return "gif"
elif data.startswith(b"BM"):
return "bmp"
return "unknown"
def check_inputs_decode(
endpoint: str,
tensor: "torch.Tensor",
processor: Optional[Union["VaeImageProcessor", "VideoProcessor"]] = None,
do_scaling: bool = True,
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
output_type: Literal["mp4", "pil", "pt"] = "pil",
return_type: Literal["mp4", "pil", "pt"] = "pil",
image_format: Literal["png", "jpg"] = "jpg",
partial_postprocess: bool = False,
input_tensor_type: Literal["binary"] = "binary",
output_tensor_type: Literal["binary"] = "binary",
height: Optional[int] = None,
width: Optional[int] = None,
):
if tensor.ndim == 3 and height is None and width is None:
raise ValueError("`height` and `width` required for packed latents.")
if (
output_type == "pt"
and return_type == "pil"
and not partial_postprocess
and not isinstance(processor, (VaeImageProcessor, VideoProcessor))
):
raise ValueError("`processor` is required.")
if do_scaling and scaling_factor is None:
deprecate(
"do_scaling",
"1.0.0",
"`do_scaling` is deprecated, pass `scaling_factor` and `shift_factor` if required.",
standard_warn=False,
)
def postprocess_decode(
response: requests.Response,
processor: Optional[Union["VaeImageProcessor", "VideoProcessor"]] = None,
output_type: Literal["mp4", "pil", "pt"] = "pil",
return_type: Literal["mp4", "pil", "pt"] = "pil",
partial_postprocess: bool = False,
):
if output_type == "pt" or (output_type == "pil" and processor is not None):
output_tensor = response.content
parameters = response.headers
shape = json.loads(parameters["shape"])
dtype = parameters["dtype"]
torch_dtype = DTYPE_MAP[dtype]
output_tensor = torch.frombuffer(bytearray(output_tensor), dtype=torch_dtype).reshape(shape)
if output_type == "pt":
if partial_postprocess:
if return_type == "pil":
output = [Image.fromarray(image.numpy()) for image in output_tensor]
if len(output) == 1:
output = output[0]
elif return_type == "pt":
output = output_tensor
else:
if processor is None or return_type == "pt":
output = output_tensor
else:
if isinstance(processor, VideoProcessor):
output = cast(
List[Image.Image],
processor.postprocess_video(output_tensor, output_type="pil")[0],
)
else:
output = cast(
Image.Image,
processor.postprocess(output_tensor, output_type="pil")[0],
)
elif output_type == "pil" and return_type == "pil" and processor is None:
output = Image.open(io.BytesIO(response.content)).convert("RGB")
detected_format = detect_image_type(response.content)
output.format = detected_format
elif output_type == "pil" and processor is not None:
if return_type == "pil":
output = [
Image.fromarray(image)
for image in (output_tensor.permute(0, 2, 3, 1).float().numpy() * 255).round().astype("uint8")
]
elif return_type == "pt":
output = output_tensor
elif output_type == "mp4" and return_type == "mp4":
output = response.content
return output
def prepare_decode(
tensor: "torch.Tensor",
processor: Optional[Union["VaeImageProcessor", "VideoProcessor"]] = None,
do_scaling: bool = True,
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
output_type: Literal["mp4", "pil", "pt"] = "pil",
image_format: Literal["png", "jpg"] = "jpg",
partial_postprocess: bool = False,
height: Optional[int] = None,
width: Optional[int] = None,
):
headers = {}
parameters = {
"image_format": image_format,
"output_type": output_type,
"partial_postprocess": partial_postprocess,
"shape": list(tensor.shape),
"dtype": str(tensor.dtype).split(".")[-1],
}
if do_scaling and scaling_factor is not None:
parameters["scaling_factor"] = scaling_factor
if do_scaling and shift_factor is not None:
parameters["shift_factor"] = shift_factor
if do_scaling and scaling_factor is None:
parameters["do_scaling"] = do_scaling
elif do_scaling and scaling_factor is None and shift_factor is None:
parameters["do_scaling"] = do_scaling
if height is not None and width is not None:
parameters["height"] = height
parameters["width"] = width
headers["Content-Type"] = "tensor/binary"
headers["Accept"] = "tensor/binary"
if output_type == "pil" and image_format == "jpg" and processor is None:
headers["Accept"] = "image/jpeg"
elif output_type == "pil" and image_format == "png" and processor is None:
headers["Accept"] = "image/png"
elif output_type == "mp4":
headers["Accept"] = "text/plain"
tensor_data = safetensors.torch._tobytes(tensor, "tensor")
return {"data": tensor_data, "params": parameters, "headers": headers}
def remote_decode(
endpoint: str,
tensor: "torch.Tensor",
processor: Optional[Union["VaeImageProcessor", "VideoProcessor"]] = None,
do_scaling: bool = True,
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
output_type: Literal["mp4", "pil", "pt"] = "pil",
return_type: Literal["mp4", "pil", "pt"] = "pil",
image_format: Literal["png", "jpg"] = "jpg",
partial_postprocess: bool = False,
input_tensor_type: Literal["binary"] = "binary",
output_tensor_type: Literal["binary"] = "binary",
height: Optional[int] = None,
width: Optional[int] = None,
) -> Union[Image.Image, List[Image.Image], bytes, "torch.Tensor"]:
"""
Hugging Face Hybrid Inference that allow running VAE decode remotely.
Args:
endpoint (`str`):
Endpoint for Remote Decode.
tensor (`torch.Tensor`):
Tensor to be decoded.
processor (`VaeImageProcessor` or `VideoProcessor`, *optional*):
Used with `return_type="pt"`, and `return_type="pil"` for Video models.
do_scaling (`bool`, default `True`, *optional*):
**DEPRECATED**. **pass `scaling_factor`/`shift_factor` instead.** **still set
do_scaling=None/do_scaling=False for no scaling until option is removed** When `True` scaling e.g. `latents
/ self.vae.config.scaling_factor` is applied remotely. If `False`, input must be passed with scaling
applied.
scaling_factor (`float`, *optional*):
Scaling is applied when passed e.g. [`latents /
self.vae.config.scaling_factor`](https://github.com/huggingface/diffusers/blob/7007febae5cff000d4df9059d9cf35133e8b2ca9/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L1083C37-L1083C77).
- SD v1: 0.18215
- SD XL: 0.13025
- Flux: 0.3611
If `None`, input must be passed with scaling applied.
shift_factor (`float`, *optional*):
Shift is applied when passed e.g. `latents + self.vae.config.shift_factor`.
- Flux: 0.1159
If `None`, input must be passed with scaling applied.
output_type (`"mp4"` or `"pil"` or `"pt", default `"pil"):
**Endpoint** output type. Subject to change. Report feedback on preferred type.
`"mp4": Supported by video models. Endpoint returns `bytes` of video. `"pil"`: Supported by image and video
models.
Image models: Endpoint returns `bytes` of an image in `image_format`. Video models: Endpoint returns
`torch.Tensor` with partial `postprocessing` applied.
Requires `processor` as a flag (any `None` value will work).
`"pt"`: Support by image and video models. Endpoint returns `torch.Tensor`.
With `partial_postprocess=True` the tensor is postprocessed `uint8` image tensor.
Recommendations:
`"pt"` with `partial_postprocess=True` is the smallest transfer for full quality. `"pt"` with
`partial_postprocess=False` is the most compatible with third party code. `"pil"` with
`image_format="jpg"` is the smallest transfer overall.
return_type (`"mp4"` or `"pil"` or `"pt", default `"pil"):
**Function** return type.
`"mp4": Function returns `bytes` of video. `"pil"`: Function returns `PIL.Image.Image`.
With `output_type="pil" no further processing is applied. With `output_type="pt" a `PIL.Image.Image` is
created.
`partial_postprocess=False` `processor` is required. `partial_postprocess=True` `processor` is
**not** required.
`"pt"`: Function returns `torch.Tensor`.
`processor` is **not** required. `partial_postprocess=False` tensor is `float16` or `bfloat16`, without
denormalization. `partial_postprocess=True` tensor is `uint8`, denormalized.
image_format (`"png"` or `"jpg"`, default `jpg`):
Used with `output_type="pil"`. Endpoint returns `jpg` or `png`.
partial_postprocess (`bool`, default `False`):
Used with `output_type="pt"`. `partial_postprocess=False` tensor is `float16` or `bfloat16`, without
denormalization. `partial_postprocess=True` tensor is `uint8`, denormalized.
input_tensor_type (`"binary"`, default `"binary"`):
Tensor transfer type.
output_tensor_type (`"binary"`, default `"binary"`):
Tensor transfer type.
height (`int`, **optional**):
Required for `"packed"` latents.
width (`int`, **optional**):
Required for `"packed"` latents.
Returns:
output (`Image.Image` or `List[Image.Image]` or `bytes` or `torch.Tensor`).
"""
if input_tensor_type == "base64":
deprecate(
"input_tensor_type='base64'",
"1.0.0",
"input_tensor_type='base64' is deprecated. Using `binary`.",
standard_warn=False,
)
input_tensor_type = "binary"
if output_tensor_type == "base64":
deprecate(
"output_tensor_type='base64'",
"1.0.0",
"output_tensor_type='base64' is deprecated. Using `binary`.",
standard_warn=False,
)
output_tensor_type = "binary"
check_inputs_decode(
endpoint,
tensor,
processor,
do_scaling,
scaling_factor,
shift_factor,
output_type,
return_type,
image_format,
partial_postprocess,
input_tensor_type,
output_tensor_type,
height,
width,
)
kwargs = prepare_decode(
tensor=tensor,
processor=processor,
do_scaling=do_scaling,
scaling_factor=scaling_factor,
shift_factor=shift_factor,
output_type=output_type,
image_format=image_format,
partial_postprocess=partial_postprocess,
height=height,
width=width,
)
response = requests.post(endpoint, **kwargs)
if not response.ok:
raise RuntimeError(response.json())
output = postprocess_decode(
response=response,
processor=processor,
output_type=output_type,
return_type=return_type,
partial_postprocess=partial_postprocess,
)
return output
def check_inputs_encode(
endpoint: str,
image: Union["torch.Tensor", Image.Image],
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
):
pass
def postprocess_encode(
response: requests.Response,
):
output_tensor = response.content
parameters = response.headers
shape = json.loads(parameters["shape"])
dtype = parameters["dtype"]
torch_dtype = DTYPE_MAP[dtype]
output_tensor = torch.frombuffer(bytearray(output_tensor), dtype=torch_dtype).reshape(shape)
return output_tensor
def prepare_encode(
image: Union["torch.Tensor", Image.Image],
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
):
headers = {}
parameters = {}
if scaling_factor is not None:
parameters["scaling_factor"] = scaling_factor
if shift_factor is not None:
parameters["shift_factor"] = shift_factor
if isinstance(image, torch.Tensor):
data = safetensors.torch._tobytes(image.contiguous(), "tensor")
parameters["shape"] = list(image.shape)
parameters["dtype"] = str(image.dtype).split(".")[-1]
else:
buffer = io.BytesIO()
image.save(buffer, format="PNG")
data = buffer.getvalue()
return {"data": data, "params": parameters, "headers": headers}
def remote_encode(
endpoint: str,
image: Union["torch.Tensor", Image.Image],
scaling_factor: Optional[float] = None,
shift_factor: Optional[float] = None,
) -> "torch.Tensor":
"""
Hugging Face Hybrid Inference that allow running VAE encode remotely.
Args:
endpoint (`str`):
Endpoint for Remote Decode.
image (`torch.Tensor` or `PIL.Image.Image`):
Image to be encoded.
scaling_factor (`float`, *optional*):
Scaling is applied when passed e.g. [`latents * self.vae.config.scaling_factor`].
- SD v1: 0.18215
- SD XL: 0.13025
- Flux: 0.3611
If `None`, input must be passed with scaling applied.
shift_factor (`float`, *optional*):
Shift is applied when passed e.g. `latents - self.vae.config.shift_factor`.
- Flux: 0.1159
If `None`, input must be passed with scaling applied.
Returns:
output (`torch.Tensor`).
"""
check_inputs_encode(
endpoint,
image,
scaling_factor,
shift_factor,
)
kwargs = prepare_encode(
image=image,
scaling_factor=scaling_factor,
shift_factor=shift_factor,
)
response = requests.post(endpoint, **kwargs)
if not response.ok:
raise RuntimeError(response.json())
output = postprocess_encode(
response=response,
)
return output
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