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-rw-r--r--extensions-builtin/LDSR/sd_hijack_autoencoder.py2
-rw-r--r--extensions-builtin/LDSR/vqvae_quantize.py147
-rw-r--r--modules/api/api.py5
-rw-r--r--modules/api/models.py4
-rw-r--r--modules/cmd_args.py2
-rw-r--r--modules/launch_utils.py3
-rw-r--r--modules/paths.py1
-rw-r--r--modules/shared.py2
-rw-r--r--modules/ui.py4
-rw-r--r--webui-user.sh1
10 files changed, 161 insertions, 10 deletions
diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
index 81c5101b..27a86e13 100644
--- a/extensions-builtin/LDSR/sd_hijack_autoencoder.py
+++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
@@ -10,7 +10,7 @@ from contextlib import contextmanager
from torch.optim.lr_scheduler import LambdaLR
from ldm.modules.ema import LitEma
-from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
+from vqvae_quantize import VectorQuantizer2 as VectorQuantizer
from ldm.modules.diffusionmodules.model import Encoder, Decoder
from ldm.util import instantiate_from_config
diff --git a/extensions-builtin/LDSR/vqvae_quantize.py b/extensions-builtin/LDSR/vqvae_quantize.py
new file mode 100644
index 00000000..dd14b8fd
--- /dev/null
+++ b/extensions-builtin/LDSR/vqvae_quantize.py
@@ -0,0 +1,147 @@
+# Vendored from https://raw.githubusercontent.com/CompVis/taming-transformers/24268930bf1dce879235a7fddd0b2355b84d7ea6/taming/modules/vqvae/quantize.py,
+# where the license is as follows:
+#
+# Copyright (c) 2020 Patrick Esser and Robin Rombach and Björn Ommer
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+# IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
+# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
+# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE
+# OR OTHER DEALINGS IN THE SOFTWARE./
+
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+
+
+class VectorQuantizer2(nn.Module):
+ """
+ Improved version over VectorQuantizer, can be used as a drop-in replacement. Mostly
+ avoids costly matrix multiplications and allows for post-hoc remapping of indices.
+ """
+
+ # NOTE: due to a bug the beta term was applied to the wrong term. for
+ # backwards compatibility we use the buggy version by default, but you can
+ # specify legacy=False to fix it.
+ def __init__(self, n_e, e_dim, beta, remap=None, unknown_index="random",
+ sane_index_shape=False, legacy=True):
+ super().__init__()
+ self.n_e = n_e
+ self.e_dim = e_dim
+ self.beta = beta
+ self.legacy = legacy
+
+ self.embedding = nn.Embedding(self.n_e, self.e_dim)
+ self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
+
+ self.remap = remap
+ if self.remap is not None:
+ self.register_buffer("used", torch.tensor(np.load(self.remap)))
+ self.re_embed = self.used.shape[0]
+ self.unknown_index = unknown_index # "random" or "extra" or integer
+ if self.unknown_index == "extra":
+ self.unknown_index = self.re_embed
+ self.re_embed = self.re_embed + 1
+ print(f"Remapping {self.n_e} indices to {self.re_embed} indices. "
+ f"Using {self.unknown_index} for unknown indices.")
+ else:
+ self.re_embed = n_e
+
+ self.sane_index_shape = sane_index_shape
+
+ def remap_to_used(self, inds):
+ ishape = inds.shape
+ assert len(ishape) > 1
+ inds = inds.reshape(ishape[0], -1)
+ used = self.used.to(inds)
+ match = (inds[:, :, None] == used[None, None, ...]).long()
+ new = match.argmax(-1)
+ unknown = match.sum(2) < 1
+ if self.unknown_index == "random":
+ new[unknown] = torch.randint(0, self.re_embed, size=new[unknown].shape).to(device=new.device)
+ else:
+ new[unknown] = self.unknown_index
+ return new.reshape(ishape)
+
+ def unmap_to_all(self, inds):
+ ishape = inds.shape
+ assert len(ishape) > 1
+ inds = inds.reshape(ishape[0], -1)
+ used = self.used.to(inds)
+ if self.re_embed > self.used.shape[0]: # extra token
+ inds[inds >= self.used.shape[0]] = 0 # simply set to zero
+ back = torch.gather(used[None, :][inds.shape[0] * [0], :], 1, inds)
+ return back.reshape(ishape)
+
+ def forward(self, z, temp=None, rescale_logits=False, return_logits=False):
+ assert temp is None or temp == 1.0, "Only for interface compatible with Gumbel"
+ assert rescale_logits is False, "Only for interface compatible with Gumbel"
+ assert return_logits is False, "Only for interface compatible with Gumbel"
+ # reshape z -> (batch, height, width, channel) and flatten
+ z = rearrange(z, 'b c h w -> b h w c').contiguous()
+ z_flattened = z.view(-1, self.e_dim)
+ # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
+
+ d = torch.sum(z_flattened ** 2, dim=1, keepdim=True) + \
+ torch.sum(self.embedding.weight ** 2, dim=1) - 2 * \
+ torch.einsum('bd,dn->bn', z_flattened, rearrange(self.embedding.weight, 'n d -> d n'))
+
+ min_encoding_indices = torch.argmin(d, dim=1)
+ z_q = self.embedding(min_encoding_indices).view(z.shape)
+ perplexity = None
+ min_encodings = None
+
+ # compute loss for embedding
+ if not self.legacy:
+ loss = self.beta * torch.mean((z_q.detach() - z) ** 2) + \
+ torch.mean((z_q - z.detach()) ** 2)
+ else:
+ loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * \
+ torch.mean((z_q - z.detach()) ** 2)
+
+ # preserve gradients
+ z_q = z + (z_q - z).detach()
+
+ # reshape back to match original input shape
+ z_q = rearrange(z_q, 'b h w c -> b c h w').contiguous()
+
+ if self.remap is not None:
+ min_encoding_indices = min_encoding_indices.reshape(z.shape[0], -1) # add batch axis
+ min_encoding_indices = self.remap_to_used(min_encoding_indices)
+ min_encoding_indices = min_encoding_indices.reshape(-1, 1) # flatten
+
+ if self.sane_index_shape:
+ min_encoding_indices = min_encoding_indices.reshape(
+ z_q.shape[0], z_q.shape[2], z_q.shape[3])
+
+ return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
+
+ def get_codebook_entry(self, indices, shape):
+ # shape specifying (batch, height, width, channel)
+ if self.remap is not None:
+ indices = indices.reshape(shape[0], -1) # add batch axis
+ indices = self.unmap_to_all(indices)
+ indices = indices.reshape(-1) # flatten again
+
+ # get quantized latent vectors
+ z_q = self.embedding(indices)
+
+ if shape is not None:
+ z_q = z_q.view(shape)
+ # reshape back to match original input shape
+ z_q = z_q.permute(0, 3, 1, 2).contiguous()
+
+ return z_q
diff --git a/modules/api/api.py b/modules/api/api.py
index 6a456861..be28c59a 100644
--- a/modules/api/api.py
+++ b/modules/api/api.py
@@ -23,6 +23,7 @@ from modules.textual_inversion.preprocess import preprocess
from modules.hypernetworks.hypernetwork import create_hypernetwork, train_hypernetwork
from PIL import PngImagePlugin,Image
from modules.sd_models import checkpoints_list, unload_model_weights, reload_model_weights
+from modules.sd_vae import vae_dict
from modules.sd_models_config import find_checkpoint_config_near_filename
from modules.realesrgan_model import get_realesrgan_models
from modules import devices
@@ -189,6 +190,7 @@ class Api:
self.add_api_route("/sdapi/v1/samplers", self.get_samplers, methods=["GET"], response_model=List[models.SamplerItem])
self.add_api_route("/sdapi/v1/upscalers", self.get_upscalers, methods=["GET"], response_model=List[models.UpscalerItem])
self.add_api_route("/sdapi/v1/sd-models", self.get_sd_models, methods=["GET"], response_model=List[models.SDModelItem])
+ self.add_api_route("/sdapi/v1/sd-vae", self.get_sd_vaes, methods=["GET"], response_model=List[models.SDVaeItem])
self.add_api_route("/sdapi/v1/hypernetworks", self.get_hypernetworks, methods=["GET"], response_model=List[models.HypernetworkItem])
self.add_api_route("/sdapi/v1/face-restorers", self.get_face_restorers, methods=["GET"], response_model=List[models.FaceRestorerItem])
self.add_api_route("/sdapi/v1/realesrgan-models", self.get_realesrgan_models, methods=["GET"], response_model=List[models.RealesrganItem])
@@ -541,6 +543,9 @@ class Api:
def get_sd_models(self):
return [{"title": x.title, "model_name": x.model_name, "hash": x.shorthash, "sha256": x.sha256, "filename": x.filename, "config": find_checkpoint_config_near_filename(x)} for x in checkpoints_list.values()]
+ def get_sd_vaes(self):
+ return [{"model_name": x, "filename": vae_dict[x]} for x in vae_dict.keys()]
+
def get_hypernetworks(self):
return [{"name": name, "path": shared.hypernetworks[name]} for name in shared.hypernetworks]
diff --git a/modules/api/models.py b/modules/api/models.py
index 1ff2fb33..47fdede2 100644
--- a/modules/api/models.py
+++ b/modules/api/models.py
@@ -249,6 +249,10 @@ class SDModelItem(BaseModel):
filename: str = Field(title="Filename")
config: Optional[str] = Field(title="Config file")
+class SDVaeItem(BaseModel):
+ model_name: str = Field(title="Model Name")
+ filename: str = Field(title="Filename")
+
class HypernetworkItem(BaseModel):
name: str = Field(title="Name")
path: Optional[str] = Field(title="Path")
diff --git a/modules/cmd_args.py b/modules/cmd_args.py
index 3eeb84d5..0974056d 100644
--- a/modules/cmd_args.py
+++ b/modules/cmd_args.py
@@ -11,7 +11,7 @@ parser.add_argument("--skip-python-version-check", action='store_true', help="la
parser.add_argument("--skip-torch-cuda-test", action='store_true', help="launch.py argument: do not check if CUDA is able to work properly")
parser.add_argument("--reinstall-xformers", action='store_true', help="launch.py argument: install the appropriate version of xformers even if you have some version already installed")
parser.add_argument("--reinstall-torch", action='store_true', help="launch.py argument: install the appropriate version of torch even if you have some version already installed")
-parser.add_argument("--update-check", action='store_true', help="launch.py argument: chck for updates at startup")
+parser.add_argument("--update-check", action='store_true', help="launch.py argument: check for updates at startup")
parser.add_argument("--test-server", action='store_true', help="launch.py argument: configure server for testing")
parser.add_argument("--skip-prepare-environment", action='store_true', help="launch.py argument: skip all environment preparation")
parser.add_argument("--skip-install", action='store_true', help="launch.py argument: skip installation of packages")
diff --git a/modules/launch_utils.py b/modules/launch_utils.py
index 35a52310..ca089674 100644
--- a/modules/launch_utils.py
+++ b/modules/launch_utils.py
@@ -229,13 +229,11 @@ def prepare_environment():
openclip_package = os.environ.get('OPENCLIP_PACKAGE', "https://github.com/mlfoundations/open_clip/archive/bb6e834e9c70d9c27d0dc3ecedeebeaeb1ffad6b.zip")
stable_diffusion_repo = os.environ.get('STABLE_DIFFUSION_REPO', "https://github.com/Stability-AI/stablediffusion.git")
- taming_transformers_repo = os.environ.get('TAMING_TRANSFORMERS_REPO', "https://github.com/CompVis/taming-transformers.git")
k_diffusion_repo = os.environ.get('K_DIFFUSION_REPO', 'https://github.com/crowsonkb/k-diffusion.git')
codeformer_repo = os.environ.get('CODEFORMER_REPO', 'https://github.com/sczhou/CodeFormer.git')
blip_repo = os.environ.get('BLIP_REPO', 'https://github.com/salesforce/BLIP.git')
stable_diffusion_commit_hash = os.environ.get('STABLE_DIFFUSION_COMMIT_HASH', "cf1d67a6fd5ea1aa600c4df58e5b47da45f6bdbf")
- taming_transformers_commit_hash = os.environ.get('TAMING_TRANSFORMERS_COMMIT_HASH', "24268930bf1dce879235a7fddd0b2355b84d7ea6")
k_diffusion_commit_hash = os.environ.get('K_DIFFUSION_COMMIT_HASH', "c9fe758757e022f05ca5a53fa8fac28889e4f1cf")
codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af")
blip_commit_hash = os.environ.get('BLIP_COMMIT_HASH', "48211a1594f1321b00f14c9f7a5b4813144b2fb9")
@@ -286,7 +284,6 @@ def prepare_environment():
os.makedirs(os.path.join(script_path, dir_repos), exist_ok=True)
git_clone(stable_diffusion_repo, repo_dir('stable-diffusion-stability-ai'), "Stable Diffusion", stable_diffusion_commit_hash)
- git_clone(taming_transformers_repo, repo_dir('taming-transformers'), "Taming Transformers", taming_transformers_commit_hash)
git_clone(k_diffusion_repo, repo_dir('k-diffusion'), "K-diffusion", k_diffusion_commit_hash)
git_clone(codeformer_repo, repo_dir('CodeFormer'), "CodeFormer", codeformer_commit_hash)
git_clone(blip_repo, repo_dir('BLIP'), "BLIP", blip_commit_hash)
diff --git a/modules/paths.py b/modules/paths.py
index 5f6474c0..5171df4f 100644
--- a/modules/paths.py
+++ b/modules/paths.py
@@ -20,7 +20,6 @@ assert sd_path is not None, f"Couldn't find Stable Diffusion in any of: {possibl
path_dirs = [
(sd_path, 'ldm', 'Stable Diffusion', []),
- (os.path.join(sd_path, '../taming-transformers'), 'taming', 'Taming Transformers', []),
(os.path.join(sd_path, '../CodeFormer'), 'inference_codeformer.py', 'CodeFormer', []),
(os.path.join(sd_path, '../BLIP'), 'models/blip.py', 'BLIP', []),
(os.path.join(sd_path, '../k-diffusion'), 'k_diffusion/sampling.py', 'k_diffusion', ["atstart"]),
diff --git a/modules/shared.py b/modules/shared.py
index 4d59fbf1..f1d8c24b 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -421,7 +421,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
options_templates.update(options_section(('optimizations', "Optimizations"), {
"cross_attention_optimization": OptionInfo("Automatic", "Cross attention optimization", gr.Dropdown, lambda: {"choices": shared_items.cross_attention_optimizations()}),
- "s_min_uncond": OptionInfo(0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
+ "s_min_uncond": OptionInfo(0.0, "Negative Guidance minimum sigma", gr.Slider, {"minimum": 0.0, "maximum": 4.0, "step": 0.01}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9177").info("skip negative prompt for some steps when the image is almost ready; 0=disable, higher=faster"),
"token_merging_ratio": OptionInfo(0.0, "Token merging ratio", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).link("PR", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/9256").info("0=disable, higher=faster"),
"token_merging_ratio_img2img": OptionInfo(0.0, "Token merging ratio for img2img", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
"token_merging_ratio_hr": OptionInfo(0.0, "Token merging ratio for high-res pass", gr.Slider, {"minimum": 0.0, "maximum": 0.9, "step": 0.1}).info("only applies if non-zero and overrides above"),
diff --git a/modules/ui.py b/modules/ui.py
index 001b9792..6189ceeb 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -505,10 +505,10 @@ def create_ui():
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=opts.hires_fix_show_prompts) as hr_prompts_container:
with gr.Column(scale=80):
with gr.Row():
- hr_prompt = gr.Textbox(label="Prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
+ hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
with gr.Column(scale=80):
with gr.Row():
- hr_negative_prompt = gr.Textbox(label="Negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
+ hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
elif category == "batch":
if not opts.dimensions_and_batch_together:
diff --git a/webui-user.sh b/webui-user.sh
index 49a426ff..70306c60 100644
--- a/webui-user.sh
+++ b/webui-user.sh
@@ -36,7 +36,6 @@
# Fixed git commits
#export STABLE_DIFFUSION_COMMIT_HASH=""
-#export TAMING_TRANSFORMERS_COMMIT_HASH=""
#export CODEFORMER_COMMIT_HASH=""
#export BLIP_COMMIT_HASH=""
generated by cgit v1.2.1 (git 2.18.0) at 2024-09-20 13:15:18 +0000