FillandClaude Opus 4.5
37aa6cf9ce
- 4x video upscaling with temporal coherence using Stream-DiffVSR - Model Loader node with precision/device options and xformers support - Upscaler node with chunked processing for memory efficiency - Automatic model download from HuggingFace - Text-guided upscaling support 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
139 lines
4.2 KiB
Python
139 lines
4.2 KiB
Python
# Copyright 2024 The HuggingFace Team. All rights reserved.
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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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from dataclasses import dataclass
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from typing import Optional, Tuple
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import torch
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import torch.nn as nn
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from diffusers.utils import BaseOutput
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from diffusers.models.activations import get_activation
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from .models.unets.unet_2d_blocks import TemporalAutoencoderTinyBlock
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@dataclass
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class DecoderOutput(BaseOutput):
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"""
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Output of decoding method.
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Args:
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sample (`torch.Tensor` of shape `(batch_size, num_channels, height, width)`):
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The decoded output sample from the last layer of the model.
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"""
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sample: torch.Tensor
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commit_loss: Optional[torch.FloatTensor] = None
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class EncoderTiny(nn.Module):
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"""
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The `EncoderTiny` layer is a simpler version of the `Encoder` layer.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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num_blocks: Tuple[int, ...],
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block_out_channels: Tuple[int, ...],
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act_fn: str,
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):
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super().__init__()
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layers = []
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for i, num_block in enumerate(num_blocks):
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num_channels = block_out_channels[i]
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if i == 0:
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layers.append(nn.Conv2d(in_channels, num_channels, kernel_size=3, padding=1))
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else:
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layers.append(
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nn.Conv2d(
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num_channels,
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num_channels,
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kernel_size=3,
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padding=1,
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stride=2,
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bias=False,
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)
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)
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for _ in range(num_block):
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layers.append(TemporalAutoencoderTinyBlock(num_channels, num_channels, act_fn))
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layers.append(nn.Conv2d(block_out_channels[-1], out_channels, kernel_size=3, padding=1))
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self.layers = nn.Sequential(*layers)
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self.gradient_checkpointing = False
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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x = self.layers(x.add(1).div(2))
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return x
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class TemporalDecoderTiny(nn.Module):
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"""
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The `TemporalDecoderTiny` layer is a simpler version of the `Decoder` layer with temporal processing.
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"""
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def __init__(
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self,
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in_channels: int,
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out_channels: int,
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num_blocks: Tuple[int, ...],
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block_out_channels: Tuple[int, ...],
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upsampling_scaling_factor: int,
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act_fn: str,
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upsample_fn: str,
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):
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super().__init__()
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layers = [
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nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, padding=1),
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get_activation(act_fn),
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]
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for i, num_block in enumerate(num_blocks):
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is_final_block = i == (len(num_blocks) - 1)
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num_channels = block_out_channels[i]
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for _ in range(num_block):
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block = TemporalAutoencoderTinyBlock(num_channels, num_channels, act_fn)
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layers.append(block)
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if not is_final_block:
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layers.append(nn.Upsample(scale_factor=upsampling_scaling_factor, mode=upsample_fn))
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conv_out_channel = num_channels if not is_final_block else out_channels
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layers.append(
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nn.Conv2d(
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num_channels,
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conv_out_channel,
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kernel_size=3,
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padding=1,
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bias=is_final_block,
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)
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)
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self.layers = nn.Sequential(*layers)
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self.gradient_checkpointing = False
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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# Clamp
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x = torch.tanh(x / 3) * 3
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x = self.layers(x)
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# scale image from [0, 1] to [-1, 1] to match diffusers convention
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return x.mul(2).sub(1)
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