Vendor in the single module used from taming_transformers; remove taming_transformers dependency
(and fix the two ruff complaints)
This commit is contained in:
Aarni Koskela
parent
b957dcfece
commit
5fcdaa6a7f
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@ -10,7 +10,7 @@ from contextlib import contextmanager
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from torch.optim.lr_scheduler import LambdaLR
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from ldm.modules.ema import LitEma
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from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
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from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
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from ldm.modules.diffusionmodules.model import Encoder, Decoder
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from ldm.util import instantiate_from_config
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@ -0,0 +1,147 @@
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# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
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# where the license is as follows:
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#
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# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
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# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
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# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
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# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
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# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
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# OR OTHER DEALINGS IN THE SOFTWARE./
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import torch
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import torch.nn as nn
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import numpy as np
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from einops import rearrange
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class VectorQuantizer2(nn.Module):
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"""
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Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
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avoids costly matrix multiplications and allows for post-hoc remapping of indices.
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"""
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# NOTE: due to a bug the beta term was applied to the wrong term. for
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# backwards compatibility we use the buggy version by default, but you can
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# specify legacy=False to fix it.
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def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
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sane_index_shape=False, legacy=True):
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super().__init__()
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self.n_e = n_e
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self.e_dim = e_dim
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self.beta = beta
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self.legacy = legacy
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self.embedding = nn.Embedding(self.n_e, self.e_dim)
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self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
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self.remap = remap
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if self.remap is not None:
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self.register_buffer("used", torch.tensor(np.load(self.remap)))
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self.re_embed = self.used.shape[0]
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self.unknown_index = unknown_index # "random" or "extra" or integer
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if self.unknown_index == "extra":
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self.unknown_index = self.re_embed
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self.re_embed = self.re_embed + 1
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print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
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f"Using {self.unknown_index} for unknown indices.")
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else:
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self.re_embed = n_e
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self.sane_index_shape = sane_index_shape
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def remap_to_used(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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match = (inds[:, :, None] == used[None, None, ...]).long()
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new = match.argmax(-1)
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unknown = match.sum(2) < 1
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if self.unknown_index == "random":
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new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
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else:
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new[unknown] = self.unknown_index
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return new.reshape(ishape)
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def unmap_to_all(self, inds):
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ishape = inds.shape
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assert len(ishape) > 1
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inds = inds.reshape(ishape[0], -1)
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used = self.used.to(inds)
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if self.re_embed > self.used.shape[0]: # extra token
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inds[inds >= self.used.shape[0]] = 0 # simply set to zero
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back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
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return back.reshape(ishape)
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def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
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assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
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assert rescale_logits is False, "Only for interface compatible with Gumbel"
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assert return_logits is False, "Only for interface compatible with Gumbel"
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# reshape z -> (batch, height, width, channel) and flatten
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z = rearrange(z, 'b c h w -> b h w c').contiguous()
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z_flattened = z.view(-1, self.e_dim)
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# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
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d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
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torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
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torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
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min_encoding_indices = torch.argmin(d, dim=1)
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z_q = self.embedding(min_encoding_indices).view(z.shape)
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perplexity = None
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min_encodings = None
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# compute loss for embedding
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if not self.legacy:
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loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
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torch.mean((z_q - z.detach()) ** 2)
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else:
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loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
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torch.mean((z_q - z.detach()) ** 2)
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# preserve gradients
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z_q = z + (z_q - z).detach()
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# reshape back to match original input shape
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z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
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if self.remap is not None:
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min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
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min_encoding_indices = self.remap_to_used(min_encoding_indices)
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min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
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if self.sane_index_shape:
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min_encoding_indices = min_encoding_indices.reshape(
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z_q.shape[0], z_q.shape[2], z_q.shape[3])
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return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
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def get_codebook_entry(self, indices, shape):
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# shape specifying (batch, height, width, channel)
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if self.remap is not None:
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indices = indices.reshape(shape[0], -1) # add batch axis
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indices = self.unmap_to_all(indices)
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indices = indices.reshape(-1) # flatten again
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# get quantized latent vectors
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z_q = self.embedding(indices)
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if shape is not None:
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z_q = z_q.view(shape)
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# reshape back to match original input shape
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z_q = z_q.permute(0, 3, 1, 2).contiguous()
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return z_q
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@ -229,13 +229,11 @@ def prepare_environment():
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openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
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stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
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taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
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k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
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codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
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blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
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stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
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taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
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k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
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codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
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blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
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@ -286,7 +284,6 @@ def prepare_environment():
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os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
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git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
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git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
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git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
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git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
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git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
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@ -20,7 +20,6 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl
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path_dirs = [
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(sd_path, 'ldm', 'Stable Diffusion', []),
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(os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
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(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
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(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
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(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
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@ -36,7 +36,6 @@
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# Fixed git commits
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#export STABLE_DIFFUSION_COMMIT_HASH=""
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#export TAMING_TRANSFORMERS_COMMIT_HASH=""
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#export CODEFORMER_COMMIT_HASH=""
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#export BLIP_COMMIT_HASH=""
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