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>
279 lines
11 KiB
Python
279 lines
11 KiB
Python
# Copyright 2024 Ollin Boer Bohan and 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, Union
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import torch
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.utils import BaseOutput
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from diffusers.utils.accelerate_utils import apply_forward_hook
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from diffusers.models.modeling_utils import ModelMixin
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from .vae import DecoderOutput, TemporalDecoderTiny, EncoderTiny
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from .models.unets.unet_2d_blocks import TemporalAutoencoderTinyBlock
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@dataclass
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class TemporalAutoencoderTinyOutput(BaseOutput):
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"""
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Output of TemporalAutoencoderTiny encoding method.
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Args:
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latents (`torch.Tensor`): Encoded outputs of the `Encoder`.
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"""
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latents: torch.Tensor
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class TemporalAutoencoderTiny(ModelMixin, ConfigMixin):
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"""
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A tiny distilled VAE model for encoding images into latents and decoding latent representations into images.
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"""
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_supports_gradient_checkpointing = True
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@register_to_config
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def __init__(
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self,
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in_channels: int = 3,
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out_channels: int = 3,
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encoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
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decoder_block_out_channels: Tuple[int, ...] = (64, 64, 64, 64),
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act_fn: str = "relu",
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upsample_fn: str = "nearest",
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latent_channels: int = 4,
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upsampling_scaling_factor: int = 2,
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num_encoder_blocks: Tuple[int, ...] = (1, 3, 3, 3),
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num_decoder_blocks: Tuple[int, ...] = (3, 3, 3, 1),
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latent_magnitude: int = 3,
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latent_shift: float = 0.5,
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force_upcast: bool = False,
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scaling_factor: float = 1.0,
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shift_factor: float = 0.0,
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block_out_channels: Tuple[int, ...] = None, # For compatibility with saved configs
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):
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super().__init__()
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if len(encoder_block_out_channels) != len(num_encoder_blocks):
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raise ValueError("`encoder_block_out_channels` should have the same length as `num_encoder_blocks`.")
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if len(decoder_block_out_channels) != len(num_decoder_blocks):
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raise ValueError("`decoder_block_out_channels` should have the same length as `num_decoder_blocks`.")
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self.encoder = EncoderTiny(
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in_channels=in_channels,
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out_channels=latent_channels,
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num_blocks=num_encoder_blocks,
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block_out_channels=encoder_block_out_channels,
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act_fn=act_fn,
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)
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self.encoder.requires_grad_(False)
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self.decoder = TemporalDecoderTiny(
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in_channels=latent_channels,
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out_channels=out_channels,
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num_blocks=num_decoder_blocks,
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block_out_channels=decoder_block_out_channels,
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upsampling_scaling_factor=upsampling_scaling_factor,
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act_fn=act_fn,
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upsample_fn=upsample_fn,
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)
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self.decoder.requires_grad_(False)
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for name, param in self.decoder.named_parameters():
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if "alpha" in name or "temporal_processor" in name:
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param.requires_grad_(True)
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self.latent_magnitude = latent_magnitude
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self.latent_shift = latent_shift
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self.scaling_factor = scaling_factor
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self.use_slicing = False
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self.use_tiling = False
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self.spatial_scale_factor = 2**out_channels
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self.tile_overlap_factor = 0.125
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self.tile_sample_min_size = 512
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self.tile_latent_min_size = self.tile_sample_min_size // self.spatial_scale_factor
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self.register_to_config(block_out_channels=decoder_block_out_channels)
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self.register_to_config(force_upcast=False)
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def reset_temporal_condition(self):
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"""reset temporal memory"""
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for module in self.encoder.layers:
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if isinstance(module, TemporalAutoencoderTinyBlock):
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module.reset_temporal()
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for module in self.decoder.layers:
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if isinstance(module, TemporalAutoencoderTinyBlock):
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module.reset_temporal()
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def _set_gradient_checkpointing(self, module, value: bool = False) -> None:
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if isinstance(module, (EncoderTiny, TemporalDecoderTiny)):
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module.gradient_checkpointing = value
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def scale_latents(self, x: torch.Tensor) -> torch.Tensor:
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"""raw latents -> [0, 1]"""
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return x.div(2 * self.latent_magnitude).add(self.latent_shift).clamp(0, 1)
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def unscale_latents(self, x: torch.Tensor) -> torch.Tensor:
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"""[0, 1] -> raw latents"""
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return x.sub(self.latent_shift).mul(2 * self.latent_magnitude)
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def enable_slicing(self) -> None:
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self.use_slicing = True
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def disable_slicing(self) -> None:
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self.use_slicing = False
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def enable_tiling(self, use_tiling: bool = True) -> None:
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self.use_tiling = use_tiling
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def disable_tiling(self) -> None:
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self.enable_tiling(False)
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def _tiled_encode(self, x: torch.Tensor) -> torch.Tensor:
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sf = self.spatial_scale_factor
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tile_size = self.tile_sample_min_size
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blend_size = int(tile_size * self.tile_overlap_factor)
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traverse_size = tile_size - blend_size
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ti = range(0, x.shape[-2], traverse_size)
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tj = range(0, x.shape[-1], traverse_size)
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blend_masks = torch.stack(
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torch.meshgrid([torch.arange(tile_size / sf) / (blend_size / sf - 1)] * 2, indexing="ij")
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)
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blend_masks = blend_masks.clamp(0, 1).to(x.device)
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out = torch.zeros(x.shape[0], 4, x.shape[-2] // sf, x.shape[-1] // sf, device=x.device)
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for i in ti:
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for j in tj:
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tile_in = x[..., i : i + tile_size, j : j + tile_size]
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tile_out = out[..., i // sf : (i + tile_size) // sf, j // sf : (j + tile_size) // sf]
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tile = self.encoder(tile_in)
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h, w = tile.shape[-2], tile.shape[-1]
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blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]
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blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]
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blend_mask = blend_mask_i * blend_mask_j
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tile, blend_mask = tile[..., :h, :w], blend_mask[..., :h, :w]
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tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)
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return out
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def _tiled_decode(self, x: torch.Tensor) -> torch.Tensor:
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sf = self.spatial_scale_factor
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tile_size = self.tile_latent_min_size
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blend_size = int(tile_size * self.tile_overlap_factor)
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traverse_size = tile_size - blend_size
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ti = range(0, x.shape[-2], traverse_size)
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tj = range(0, x.shape[-1], traverse_size)
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blend_masks = torch.stack(
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torch.meshgrid([torch.arange(tile_size * sf) / (blend_size * sf - 1)] * 2, indexing="ij")
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)
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blend_masks = blend_masks.clamp(0, 1).to(x.device)
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out = torch.zeros(x.shape[0], 3, x.shape[-2] * sf, x.shape[-1] * sf, device=x.device)
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for i in ti:
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for j in tj:
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tile_in = x[..., i : i + tile_size, j : j + tile_size]
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tile_out = out[..., i * sf : (i + tile_size) * sf, j * sf : (j + tile_size) * sf]
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tile = self.decoder(tile_in)
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h, w = tile.shape[-2], tile.shape[-1]
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blend_mask_i = torch.ones_like(blend_masks[0]) if i == 0 else blend_masks[0]
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blend_mask_j = torch.ones_like(blend_masks[1]) if j == 0 else blend_masks[1]
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blend_mask = (blend_mask_i * blend_mask_j)[..., :h, :w]
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tile_out.copy_(blend_mask * tile + (1 - blend_mask) * tile_out)
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return out
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@apply_forward_hook
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def encode(self, x: torch.Tensor, return_dict: bool = True, return_layers_features: bool = True, return_features_only: bool = False) -> Union[TemporalAutoencoderTinyOutput, Tuple[torch.Tensor]]:
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layer_features = [] if return_layers_features else None
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if self.use_slicing and x.shape[0] > 1:
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output = [
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self._tiled_encode(x_slice) if self.use_tiling else self.encoder(x_slice)
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for x_slice in x.split(1)
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]
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output = torch.cat(output)
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else:
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if self.use_tiling:
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output = self._tiled_encode(x)
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elif return_layers_features:
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current_features = x
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for module in self.encoder.layers:
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current_features = module(current_features)
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if isinstance(module, TemporalAutoencoderTinyBlock):
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layer_features.append(current_features)
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if return_features_only:
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return layer_features
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output = self.encoder(x)
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if not return_dict:
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return (output,), layer_features
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return TemporalAutoencoderTinyOutput(latents=output)
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@apply_forward_hook
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def decode(
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self, x: torch.Tensor, temporal_features=None, generator: Optional[torch.Generator] = None, return_dict: bool = True
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) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
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if self.use_slicing and x.shape[0] > 1:
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output = [
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self._tiled_decode(x_slice) if self.use_tiling else self.decoder(x_slice) for x_slice in x.split(1)
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]
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output = torch.cat(output)
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elif temporal_features is not None:
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block_idx = 0
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for module in self.decoder.layers:
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if isinstance(module, TemporalAutoencoderTinyBlock):
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module.prev_features = temporal_features[block_idx]
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block_idx += 1
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output = self.decoder(x)
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else:
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output = self._tiled_decode(x) if self.use_tiling else self.decoder(x)
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if not return_dict:
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return (output,)
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return DecoderOutput(sample=output)
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def forward(
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self,
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sample: torch.Tensor,
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previous_sample: Optional[torch.Tensor] = None,
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return_dict: bool = False,
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) -> Union[DecoderOutput, Tuple[torch.Tensor]]:
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layer_features = None
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if previous_sample is not None:
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prev_enc, layer_features = self.encode(previous_sample, return_dict=return_dict)
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if layer_features is not None:
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temporal_features = layer_features[::-1]
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else:
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temporal_features = None
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dec = self.decode(sample, temporal_features=temporal_features, return_dict=return_dict)[0]
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if not return_dict:
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return (dec,)
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return DecoderOutput(sample=dec)
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