Merge branch 'dev' into sync-req

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AUTOMATIC1111 2023-05-31 19:31:19 +03:00 committed by GitHub
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37 changed files with 800 additions and 186 deletions

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@ -43,8 +43,8 @@ body:
- type: input
id: commit
attributes:
label: Commit where the problem happens
description: Which commit are you running ? (Do not write *Latest version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Commit** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)
label: Version or Commit where the problem happens
description: "Which webui version or commit are you running ? (Do not write *Latest Version/repo/commit*, as this means nothing and will have changed by the time we read your issue. Rather, copy the **Version: v1.2.3** link at the bottom of the UI, or from the cmd/terminal if you can't launch it.)"
validations:
required: true
- type: dropdown

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@ -1,9 +1,8 @@
import os
import sys
import traceback
from basicsr.utils.download_util import load_file_from_url
from modules.errors import print_error
from modules.upscaler import Upscaler, UpscalerData
from ldsr_model_arch import LDSR
from modules import shared, script_callbacks
@ -51,10 +50,8 @@ class UpscalerLDSR(Upscaler):
try:
return LDSR(model, yaml)
except Exception:
print("Error importing LDSR:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error importing LDSR", exc_info=True)
return None
def do_upscale(self, img, path):

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@ -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

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@ -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

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@ -1,6 +1,5 @@
import os.path
import sys
import traceback
import PIL.Image
import numpy as np
@ -12,6 +11,8 @@ from basicsr.utils.download_util import load_file_from_url
import modules.upscaler
from modules import devices, modelloader, script_callbacks
from scunet_model_arch import SCUNet as net
from modules.errors import print_error
from modules.shared import opts
@ -38,8 +39,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
scaler_data = modules.upscaler.UpscalerData(name, file, self, 4)
scalers.append(scaler_data)
except Exception:
print(f"Error loading ScuNET model: {file}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error loading ScuNET model: {file}", exc_info=True)
if add_model2:
scaler_data2 = modules.upscaler.UpscalerData(self.model_name2, self.model_url2, self)
scalers.append(scaler_data2)

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@ -0,0 +1,431 @@
// Main
// Helper functions
// Get active tab
function getActiveTab(elements, all = false) {
const tabs = elements.img2imgTabs.querySelectorAll("button");
if (all) return tabs;
for (let tab of tabs) {
if (tab.classList.contains("selected")) {
return tab;
}
}
}
onUiLoaded(async() => {
const hotkeysConfig = {
resetZoom: "KeyR",
fitToScreen: "KeyS",
moveKey: "KeyF",
overlap: "KeyO"
};
let isMoving = false;
let mouseX, mouseY;
const elementIDs = {
sketch: "#img2img_sketch",
inpaint: "#img2maskimg",
inpaintSketch: "#inpaint_sketch",
img2imgTabs: "#mode_img2img .tab-nav"
};
async function getElements() {
const elements = await Promise.all(
Object.values(elementIDs).map(id => document.querySelector(id))
);
return Object.fromEntries(
Object.keys(elementIDs).map((key, index) => [key, elements[index]])
);
}
const elements = await getElements();
function applyZoomAndPan(targetElement, elemId) {
targetElement.style.transformOrigin = "0 0";
let [zoomLevel, panX, panY] = [1, 0, 0];
let fullScreenMode = false;
// In the course of research, it was found that the tag img is very harmful when zooming and creates white canvases. This hack allows you to almost never think about this problem, it has no effect on webui.
function fixCanvas() {
const activeTab = getActiveTab(elements).textContent.trim();
if (activeTab !== "img2img") {
const img = targetElement.querySelector(`${elemId} img`);
if (img && img.style.display !== "none") {
img.style.display = "none";
img.style.visibility = "hidden";
}
}
}
// Reset the zoom level and pan position of the target element to their initial values
function resetZoom() {
zoomLevel = 1;
panX = 0;
panY = 0;
fixCanvas();
targetElement.style.transform = `scale(${zoomLevel}) translate(${panX}px, ${panY}px)`;
const canvas = gradioApp().querySelector(
`${elemId} canvas[key="interface"]`
);
toggleOverlap("off");
fullScreenMode = false;
if (
canvas &&
parseFloat(canvas.style.width) > 865 &&
parseFloat(targetElement.style.width) > 865
) {
fitToElement();
return;
}
targetElement.style.width = "";
if (canvas) {
targetElement.style.height = canvas.style.height;
}
}
// Toggle the zIndex of the target element between two values, allowing it to overlap or be overlapped by other elements
function toggleOverlap(forced = "") {
const zIndex1 = "0";
const zIndex2 = "998";
targetElement.style.zIndex =
targetElement.style.zIndex !== zIndex2 ? zIndex2 : zIndex1;
if (forced === "off") {
targetElement.style.zIndex = zIndex1;
} else if (forced === "on") {
targetElement.style.zIndex = zIndex2;
}
}
// Adjust the brush size based on the deltaY value from a mouse wheel event
function adjustBrushSize(
elemId,
deltaY,
withoutValue = false,
percentage = 5
) {
const input =
gradioApp().querySelector(
`${elemId} input[aria-label='Brush radius']`
) ||
gradioApp().querySelector(
`${elemId} button[aria-label="Use brush"]`
);
if (input) {
input.click();
if (!withoutValue) {
const maxValue =
parseFloat(input.getAttribute("max")) || 100;
const changeAmount = maxValue * (percentage / 100);
const newValue =
parseFloat(input.value) +
(deltaY > 0 ? -changeAmount : changeAmount);
input.value = Math.min(Math.max(newValue, 0), maxValue);
input.dispatchEvent(new Event("change"));
}
}
}
// Reset zoom when uploading a new image
const fileInput = gradioApp().querySelector(
`${elemId} input[type="file"][accept="image/*"].svelte-116rqfv`
);
fileInput.addEventListener("click", resetZoom);
// Update the zoom level and pan position of the target element based on the values of the zoomLevel, panX and panY variables
function updateZoom(newZoomLevel, mouseX, mouseY) {
newZoomLevel = Math.max(0.5, Math.min(newZoomLevel, 15));
panX += mouseX - (mouseX * newZoomLevel) / zoomLevel;
panY += mouseY - (mouseY * newZoomLevel) / zoomLevel;
targetElement.style.transformOrigin = "0 0";
targetElement.style.transform = `translate(${panX}px, ${panY}px) scale(${newZoomLevel})`;
toggleOverlap("on");
return newZoomLevel;
}
// Change the zoom level based on user interaction
function changeZoomLevel(operation, e) {
if (e.shiftKey) {
e.preventDefault();
let zoomPosX, zoomPosY;
let delta = 0.2;
if (zoomLevel > 7) {
delta = 0.9;
} else if (zoomLevel > 2) {
delta = 0.6;
}
zoomPosX = e.clientX;
zoomPosY = e.clientY;
fullScreenMode = false;
zoomLevel = updateZoom(
zoomLevel + (operation === "+" ? delta : -delta),
zoomPosX - targetElement.getBoundingClientRect().left,
zoomPosY - targetElement.getBoundingClientRect().top
);
}
}
/**
* This function fits the target element to the screen by calculating
* the required scale and offsets. It also updates the global variables
* zoomLevel, panX, and panY to reflect the new state.
*/
function fitToElement() {
//Reset Zoom
targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
// Get element and screen dimensions
const elementWidth = targetElement.offsetWidth;
const elementHeight = targetElement.offsetHeight;
const parentElement = targetElement.parentElement;
const screenWidth = parentElement.clientWidth;
const screenHeight = parentElement.clientHeight;
// Get element's coordinates relative to the parent element
const elementRect = targetElement.getBoundingClientRect();
const parentRect = parentElement.getBoundingClientRect();
const elementX = elementRect.x - parentRect.x;
// Calculate scale and offsets
const scaleX = screenWidth / elementWidth;
const scaleY = screenHeight / elementHeight;
const scale = Math.min(scaleX, scaleY);
const transformOrigin =
window.getComputedStyle(targetElement).transformOrigin;
const [originX, originY] = transformOrigin.split(" ");
const originXValue = parseFloat(originX);
const originYValue = parseFloat(originY);
const offsetX =
(screenWidth - elementWidth * scale) / 2 -
originXValue * (1 - scale);
const offsetY =
(screenHeight - elementHeight * scale) / 2.5 -
originYValue * (1 - scale);
// Apply scale and offsets to the element
targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
// Update global variables
zoomLevel = scale;
panX = offsetX;
panY = offsetY;
fullScreenMode = false;
toggleOverlap("off");
}
/**
* This function fits the target element to the screen by calculating
* the required scale and offsets. It also updates the global variables
* zoomLevel, panX, and panY to reflect the new state.
*/
// Fullscreen mode
function fitToScreen() {
const canvas = gradioApp().querySelector(
`${elemId} canvas[key="interface"]`
);
if (!canvas) return;
if (canvas.offsetWidth > 862) {
targetElement.style.width = canvas.offsetWidth + "px";
}
if (fullScreenMode) {
resetZoom();
fullScreenMode = false;
return;
}
//Reset Zoom
targetElement.style.transform = `translate(${0}px, ${0}px) scale(${1})`;
// Get scrollbar width to right-align the image
const scrollbarWidth = window.innerWidth - document.documentElement.clientWidth;
// Get element and screen dimensions
const elementWidth = targetElement.offsetWidth;
const elementHeight = targetElement.offsetHeight;
const screenWidth = window.innerWidth - scrollbarWidth;
const screenHeight = window.innerHeight;
// Get element's coordinates relative to the page
const elementRect = targetElement.getBoundingClientRect();
const elementY = elementRect.y;
const elementX = elementRect.x;
// Calculate scale and offsets
const scaleX = screenWidth / elementWidth;
const scaleY = screenHeight / elementHeight;
const scale = Math.min(scaleX, scaleY);
// Get the current transformOrigin
const computedStyle = window.getComputedStyle(targetElement);
const transformOrigin = computedStyle.transformOrigin;
const [originX, originY] = transformOrigin.split(" ");
const originXValue = parseFloat(originX);
const originYValue = parseFloat(originY);
// Calculate offsets with respect to the transformOrigin
const offsetX =
(screenWidth - elementWidth * scale) / 2 -
elementX -
originXValue * (1 - scale);
const offsetY =
(screenHeight - elementHeight * scale) / 2 -
elementY -
originYValue * (1 - scale);
// Apply scale and offsets to the element
targetElement.style.transform = `translate(${offsetX}px, ${offsetY}px) scale(${scale})`;
// Update global variables
zoomLevel = scale;
panX = offsetX;
panY = offsetY;
fullScreenMode = true;
toggleOverlap("on");
}
// Handle keydown events
function handleKeyDown(event) {
const hotkeyActions = {
[hotkeysConfig.resetZoom]: resetZoom,
[hotkeysConfig.overlap]: toggleOverlap,
[hotkeysConfig.fitToScreen]: fitToScreen
// [hotkeysConfig.moveKey] : moveCanvas,
};
const action = hotkeyActions[event.code];
if (action) {
event.preventDefault();
action(event);
}
}
// Get Mouse position
function getMousePosition(e) {
mouseX = e.offsetX;
mouseY = e.offsetY;
}
targetElement.addEventListener("mousemove", getMousePosition);
// Handle events only inside the targetElement
let isKeyDownHandlerAttached = false;
function handleMouseMove() {
if (!isKeyDownHandlerAttached) {
document.addEventListener("keydown", handleKeyDown);
isKeyDownHandlerAttached = true;
}
}
function handleMouseLeave() {
if (isKeyDownHandlerAttached) {
document.removeEventListener("keydown", handleKeyDown);
isKeyDownHandlerAttached = false;
}
}
// Add mouse event handlers
targetElement.addEventListener("mousemove", handleMouseMove);
targetElement.addEventListener("mouseleave", handleMouseLeave);
// Reset zoom when click on another tab
elements.img2imgTabs.addEventListener("click", resetZoom);
elements.img2imgTabs.addEventListener("click", () => {
// targetElement.style.width = "";
if (parseInt(targetElement.style.width) > 865) {
setTimeout(fitToElement, 0);
}
});
targetElement.addEventListener("wheel", e => {
// change zoom level
const operation = e.deltaY > 0 ? "-" : "+";
changeZoomLevel(operation, e);
// Handle brush size adjustment with ctrl key pressed
if (e.ctrlKey || e.metaKey) {
e.preventDefault();
// Increase or decrease brush size based on scroll direction
adjustBrushSize(elemId, e.deltaY);
}
});
/**
* Handle the move event for pan functionality. Updates the panX and panY variables and applies the new transform to the target element.
* @param {MouseEvent} e - The mouse event.
*/
function handleMoveKeyDown(e) {
if (e.code === hotkeysConfig.moveKey) {
if (!e.ctrlKey && !e.metaKey) {
isMoving = true;
}
}
}
function handleMoveKeyUp(e) {
if (e.code === hotkeysConfig.moveKey) {
isMoving = false;
}
}
document.addEventListener("keydown", handleMoveKeyDown);
document.addEventListener("keyup", handleMoveKeyUp);
// Detect zoom level and update the pan speed.
function updatePanPosition(movementX, movementY) {
let panSpeed = 1.5;
if (zoomLevel > 8) {
panSpeed = 2.5;
}
panX = panX + movementX * panSpeed;
panY = panY + movementY * panSpeed;
targetElement.style.transform = `translate(${panX}px, ${panY}px) scale(${zoomLevel})`;
toggleOverlap("on");
}
function handleMoveByKey(e) {
if (isMoving) {
updatePanPosition(e.movementX, e.movementY);
targetElement.style.pointerEvents = "none";
} else {
targetElement.style.pointerEvents = "auto";
}
}
gradioApp().addEventListener("mousemove", handleMoveByKey);
}
applyZoomAndPan(elements.sketch, elementIDs.sketch);
applyZoomAndPan(elements.inpaint, elementIDs.inpaint);
applyZoomAndPan(elements.inpaintSketch, elementIDs.inpaintSketch);
});

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@ -1,7 +1,9 @@
let gamepads = [];
window.addEventListener('gamepadconnected', (e) => {
const index = e.gamepad.index;
let isWaiting = false;
setInterval(async() => {
gamepads[index] = setInterval(async() => {
if (!opts.js_modal_lightbox_gamepad || isWaiting) return;
const gamepad = navigator.getGamepads()[index];
const xValue = gamepad.axes[0];
@ -24,6 +26,10 @@ window.addEventListener('gamepadconnected', (e) => {
}, 10);
});
window.addEventListener('gamepaddisconnected', (e) => {
clearInterval(gamepads[e.gamepad.index]);
});
/*
Primarily for vr controller type pointer devices.
I use the wheel event because there's currently no way to do it properly with web xr.

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@ -16,6 +16,7 @@ from secrets import compare_digest
import modules.shared as shared
from modules import sd_samplers, deepbooru, sd_hijack, images, scripts, ui, postprocessing
from modules.api import models
from modules.errors import print_error
from modules.shared import opts
from modules.processing import StableDiffusionProcessingTxt2Img, StableDiffusionProcessingImg2Img, process_images
from modules.textual_inversion.textual_inversion import create_embedding, train_embedding
@ -23,6 +24,7 @@ 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
@ -108,7 +110,6 @@ def api_middleware(app: FastAPI):
from rich.console import Console
console = Console()
except Exception:
import traceback
rich_available = False
@app.middleware("http")
@ -139,11 +140,12 @@ def api_middleware(app: FastAPI):
"errors": str(e),
}
if not isinstance(e, HTTPException): # do not print backtrace on known httpexceptions
print(f"API error: {request.method}: {request.url} {err}")
message = f"API error: {request.method}: {request.url} {err}"
if rich_available:
print(message)
console.print_exception(show_locals=True, max_frames=2, extra_lines=1, suppress=[anyio, starlette], word_wrap=False, width=min([console.width, 200]))
else:
traceback.print_exc()
print_error(message, exc_info=True)
return JSONResponse(status_code=vars(e).get('status_code', 500), content=jsonable_encoder(err))
@app.middleware("http")
@ -189,6 +191,7 @@ 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 +544,9 @@ 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]

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@ -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")

View File

@ -1,10 +1,9 @@
import html
import sys
import threading
import traceback
import time
from modules import shared, progress
from modules.errors import print_error
queue_lock = threading.Lock()
@ -56,16 +55,14 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
try:
res = list(func(*args, **kwargs))
except Exception as e:
# When printing out our debug argument list, do not print out more than a MB of text
max_debug_str_len = 131072 # (1024*1024)/8
print("Error completing request", file=sys.stderr)
argStr = f"Arguments: {args} {kwargs}"
print(argStr[:max_debug_str_len], file=sys.stderr)
if len(argStr) > max_debug_str_len:
print(f"(Argument list truncated at {max_debug_str_len}/{len(argStr)} characters)", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
# When printing out our debug argument list,
# do not print out more than a 100 KB of text
max_debug_str_len = 131072
message = "Error completing request"
arg_str = f"Arguments: {args} {kwargs}"[:max_debug_str_len]
if len(arg_str) > max_debug_str_len:
arg_str += f" (Argument list truncated at {max_debug_str_len}/{len(arg_str)} characters)"
print_error(f"{message}\n{arg_str}", exc_info=True)
shared.state.job = ""
shared.state.job_count = 0
@ -108,4 +105,3 @@ def wrap_gradio_call(func, extra_outputs=None, add_stats=False):
return tuple(res)
return f

View File

@ -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")

View File

@ -1,6 +1,4 @@
import os
import sys
import traceback
import cv2
import torch
@ -8,6 +6,7 @@ import torch
import modules.face_restoration
import modules.shared
from modules import shared, devices, modelloader
from modules.errors import print_error
from modules.paths import models_path
# codeformer people made a choice to include modified basicsr library to their project which makes
@ -105,8 +104,8 @@ def setup_model(dirname):
restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
del output
torch.cuda.empty_cache()
except Exception as error:
print(f'\tFailed inference for CodeFormer: {error}', file=sys.stderr)
except Exception:
print_error('Failed inference for CodeFormer', exc_info=True)
restored_face = tensor2img(cropped_face_t, rgb2bgr=True, min_max=(-1, 1))
restored_face = restored_face.astype('uint8')
@ -135,7 +134,6 @@ def setup_model(dirname):
shared.face_restorers.append(codeformer)
except Exception:
print("Error setting up CodeFormer:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error setting up CodeFormer", exc_info=True)
# sys.path = stored_sys_path

View File

@ -3,8 +3,6 @@ Supports saving and restoring webui and extensions from a known working set of c
"""
import os
import sys
import traceback
import json
import time
import tqdm
@ -14,6 +12,7 @@ from collections import OrderedDict
import git
from modules import shared, extensions
from modules.errors import print_error
from modules.paths_internal import script_path, config_states_dir
@ -53,8 +52,7 @@ def get_webui_config():
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error reading webui git info from {script_path}", exc_info=True)
webui_remote = None
webui_commit_hash = None
@ -134,8 +132,7 @@ def restore_webui_config(config):
if os.path.exists(os.path.join(script_path, ".git")):
webui_repo = git.Repo(script_path)
except Exception:
print(f"Error reading webui git info from {script_path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error reading webui git info from {script_path}", exc_info=True)
return
try:
@ -143,8 +140,7 @@ def restore_webui_config(config):
webui_repo.git.reset(webui_commit_hash, hard=True)
print(f"* Restored webui to commit {webui_commit_hash}.")
except Exception:
print(f"Error restoring webui to commit {webui_commit_hash}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error restoring webui to commit{webui_commit_hash}")
def restore_extension_config(config):

View File

@ -1,7 +1,23 @@
import sys
import textwrap
import traceback
def print_error(
message: str,
*,
exc_info: bool = False,
) -> None:
"""
Print an error message to stderr, with optional traceback.
"""
for line in message.splitlines():
print("***", line, file=sys.stderr)
if exc_info:
print(textwrap.indent(traceback.format_exc(), " "), file=sys.stderr)
print("---")
def print_error_explanation(message):
lines = message.strip().split("\n")
max_len = max([len(x) for x in lines])

View File

@ -1,11 +1,9 @@
import os
import sys
import threading
import traceback
import git
from modules import shared
from modules.errors import print_error
from modules.gitpython_hack import Repo
from modules.paths_internal import extensions_dir, extensions_builtin_dir, script_path # noqa: F401
extensions = []
@ -54,10 +52,9 @@ class Extension:
repo = None
try:
if os.path.exists(os.path.join(self.path, ".git")):
repo = git.Repo(self.path)
repo = Repo(self.path)
except Exception:
print(f"Error reading github repository info from {self.path}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error reading github repository info from {self.path}", exc_info=True)
if repo is None or repo.bare:
self.remote = None
@ -72,8 +69,8 @@ class Extension:
self.commit_hash = commit.hexsha
self.version = self.commit_hash[:8]
except Exception as ex:
print(f"Failed reading extension data from Git repository ({self.name}): {ex}", file=sys.stderr)
except Exception:
print_error(f"Failed reading extension data from Git repository ({self.name})", exc_info=True)
self.remote = None
self.have_info_from_repo = True
@ -94,7 +91,7 @@ class Extension:
return res
def check_updates(self):
repo = git.Repo(self.path)
repo = Repo(self.path)
for fetch in repo.remote().fetch(dry_run=True):
if fetch.flags != fetch.HEAD_UPTODATE:
self.can_update = True
@ -116,7 +113,7 @@ class Extension:
self.status = "latest"
def fetch_and_reset_hard(self, commit='origin'):
repo = git.Repo(self.path)
repo = Repo(self.path)
# Fix: `error: Your local changes to the following files would be overwritten by merge`,
# because WSL2 Docker set 755 file permissions instead of 644, this results to the error.
repo.git.fetch(all=True)

View File

@ -1,12 +1,11 @@
import os
import sys
import traceback
import facexlib
import gfpgan
import modules.face_restoration
from modules import paths, shared, devices, modelloader
from modules.errors import print_error
model_dir = "GFPGAN"
user_path = None
@ -112,5 +111,4 @@ def setup_model(dirname):
shared.face_restorers.append(FaceRestorerGFPGAN())
except Exception:
print("Error setting up GFPGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error setting up GFPGAN", exc_info=True)

42
modules/gitpython_hack.py Normal file
View File

@ -0,0 +1,42 @@
from __future__ import annotations
import io
import subprocess
import git
class Git(git.Git):
"""
Git subclassed to never use persistent processes.
"""
def _get_persistent_cmd(self, attr_name, cmd_name, *args, **kwargs):
raise NotImplementedError(f"Refusing to use persistent process: {attr_name} ({cmd_name} {args} {kwargs})")
def get_object_header(self, ref: str | bytes) -> tuple[str, str, int]:
ret = subprocess.check_output(
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch-check"],
input=self._prepare_ref(ref),
cwd=self._working_dir,
timeout=2,
)
return self._parse_object_header(ret)
def stream_object_data(self, ref: str) -> tuple[str, str, int, "Git.CatFileContentStream"]:
# Not really streaming, per se; this buffers the entire object in memory.
# Shouldn't be a problem for our use case, since we're only using this for
# object headers (commit objects).
ret = subprocess.check_output(
[self.GIT_PYTHON_GIT_EXECUTABLE, "cat-file", "--batch"],
input=self._prepare_ref(ref),
cwd=self._working_dir,
timeout=30,
)
bio = io.BytesIO(ret)
hexsha, typename, size = self._parse_object_header(bio.readline())
return (hexsha, typename, size, self.CatFileContentStream(size, bio))
class Repo(git.Repo):
GitCommandWrapperType = Git

View File

@ -2,8 +2,6 @@ import datetime
import glob
import html
import os
import sys
import traceback
import inspect
import modules.textual_inversion.dataset
@ -12,6 +10,7 @@ import tqdm
from einops import rearrange, repeat
from ldm.util import default
from modules import devices, processing, sd_models, shared, sd_samplers, hashes, sd_hijack_checkpoint
from modules.errors import print_error
from modules.textual_inversion import textual_inversion, logging
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from torch import einsum
@ -325,17 +324,14 @@ def load_hypernetwork(name):
if path is None:
return None
hypernetwork = Hypernetwork()
try:
hypernetwork = Hypernetwork()
hypernetwork.load(path)
return hypernetwork
except Exception:
print(f"Error loading hypernetwork {path}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error loading hypernetwork {path}", exc_info=True)
return None
return hypernetwork
def load_hypernetworks(names, multipliers=None):
already_loaded = {}
@ -770,7 +766,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
except Exception:
print(traceback.format_exc(), file=sys.stderr)
print_error("Exception in training hypernetwork", exc_info=True)
finally:
pbar.leave = False
pbar.close()

View File

@ -1,6 +1,4 @@
import datetime
import sys
import traceback
import pytz
import io
@ -18,6 +16,7 @@ import json
import hashlib
from modules import sd_samplers, shared, script_callbacks, errors
from modules.errors import print_error
from modules.paths_internal import roboto_ttf_file
from modules.shared import opts
@ -464,8 +463,7 @@ class FilenameGenerator:
replacement = fun(self, *pattern_args)
except Exception:
replacement = None
print(f"Error adding [{pattern}] to filename", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error adding [{pattern}] to filename", exc_info=True)
if replacement == NOTHING_AND_SKIP_PREVIOUS_TEXT:
continue
@ -511,9 +509,12 @@ def save_image_with_geninfo(image, geninfo, filename, extension=None, existing_p
existing_pnginfo['parameters'] = geninfo
if extension.lower() == '.png':
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
pnginfo_data.add_text(k, str(v))
if opts.enable_pnginfo:
pnginfo_data = PngImagePlugin.PngInfo()
for k, v in (existing_pnginfo or {}).items():
pnginfo_data.add_text(k, str(v))
else:
pnginfo_data = None
image.save(filename, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data)
@ -697,8 +698,7 @@ def read_info_from_image(image):
Negative prompt: {json_info["uc"]}
Steps: {json_info["steps"]}, Sampler: {sampler}, CFG scale: {json_info["scale"]}, Seed: {json_info["seed"]}, Size: {image.width}x{image.height}, Clip skip: 2, ENSD: 31337"""
except Exception:
print("Error parsing NovelAI image generation parameters:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error parsing NovelAI image generation parameters", exc_info=True)
return geninfo, items

View File

@ -1,6 +1,5 @@
import os
import sys
import traceback
from collections import namedtuple
from pathlib import Path
import re
@ -12,6 +11,7 @@ from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
from modules import devices, paths, shared, lowvram, modelloader, errors
from modules.errors import print_error
blip_image_eval_size = 384
clip_model_name = 'ViT-L/14'
@ -216,8 +216,7 @@ class InterrogateModels:
res += f", {match}"
except Exception:
print("Error interrogating", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error interrogating", exc_info=True)
res += "<error>"
self.unload()

View File

@ -8,6 +8,7 @@ import json
from functools import lru_cache
from modules import cmd_args
from modules.errors import print_error
from modules.paths_internal import script_path, extensions_dir
args, _ = cmd_args.parser.parse_known_args()
@ -188,7 +189,7 @@ def run_extension_installer(extension_dir):
print(run(f'"{python}" "{path_installer}"', errdesc=f"Error running install.py for extension {extension_dir}", custom_env=env))
except Exception as e:
print(e, file=sys.stderr)
print_error(str(e))
def list_extensions(settings_file):
@ -198,8 +199,8 @@ def list_extensions(settings_file):
if os.path.isfile(settings_file):
with open(settings_file, "r", encoding="utf8") as file:
settings = json.load(file)
except Exception as e:
print(e, file=sys.stderr)
except Exception:
print_error("Could not load settings", exc_info=True)
disabled_extensions = set(settings.get('disabled_extensions', []))
disable_all_extensions = settings.get('disable_all_extensions', 'none')
@ -229,13 +230,11 @@ 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 +285,6 @@ 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)

View File

@ -1,8 +1,7 @@
import json
import os
import sys
import traceback
from modules.errors import print_error
localizations = {}
@ -31,7 +30,6 @@ def localization_js(current_localization_name: str) -> str:
with open(fn, "r", encoding="utf8") as file:
data = json.load(file)
except Exception:
print(f"Error loading localization from {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error loading localization from {fn}", exc_info=True)
return f"window.localization = {json.dumps(data)}"

View File

@ -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"]),

View File

@ -1,4 +1,5 @@
import json
import logging
import math
import os
import sys
@ -23,7 +24,6 @@ import modules.images as images
import modules.styles
import modules.sd_models as sd_models
import modules.sd_vae as sd_vae
import logging
from ldm.data.util import AddMiDaS
from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
@ -321,14 +321,13 @@ class StableDiffusionProcessing:
have been used before. The second element is where the previously
computed result is stored.
"""
if cache[0] is not None and (required_prompts, steps) == cache[0]:
if cache[0] is not None and (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info) == cache[0]:
return cache[1]
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps)
cache[0] = (required_prompts, steps)
cache[0] = (required_prompts, steps, opts.CLIP_stop_at_last_layers, shared.sd_model.sd_checkpoint_info)
return cache[1]
def setup_conds(self):

View File

@ -1,12 +1,11 @@
import os
import sys
import traceback
import numpy as np
from PIL import Image
from basicsr.utils.download_util import load_file_from_url
from realesrgan import RealESRGANer
from modules.errors import print_error
from modules.upscaler import Upscaler, UpscalerData
from modules.shared import cmd_opts, opts
from modules import modelloader
@ -36,8 +35,7 @@ class UpscalerRealESRGAN(Upscaler):
self.scalers.append(scaler)
except Exception:
print("Error importing Real-ESRGAN:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error importing Real-ESRGAN", exc_info=True)
self.enable = False
self.scalers = []
@ -76,9 +74,8 @@ class UpscalerRealESRGAN(Upscaler):
info.local_data_path = load_file_from_url(url=info.data_path, model_dir=self.model_download_path, progress=True)
return info
except Exception as e:
print(f"Error making Real-ESRGAN models list: {e}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
except Exception:
print_error("Error making Real-ESRGAN models list", exc_info=True)
return None
def load_models(self, _):
@ -135,5 +132,4 @@ def get_realesrgan_models(scaler):
]
return models
except Exception:
print("Error making Real-ESRGAN models list:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error making Real-ESRGAN models list", exc_info=True)

View File

@ -2,8 +2,6 @@
import pickle
import collections
import sys
import traceback
import torch
import numpy
@ -11,6 +9,8 @@ import _codecs
import zipfile
import re
from modules.errors import print_error
# PyTorch 1.13 and later have _TypedStorage renamed to TypedStorage
TypedStorage = torch.storage.TypedStorage if hasattr(torch.storage, 'TypedStorage') else torch.storage._TypedStorage
@ -136,17 +136,20 @@ def load_with_extra(filename, extra_handler=None, *args, **kwargs):
check_pt(filename, extra_handler)
except pickle.UnpicklingError:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print("-----> !!!! The file is most likely corrupted !!!! <-----", file=sys.stderr)
print("You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n", file=sys.stderr)
print_error(
f"Error verifying pickled file from {filename}\n"
"-----> !!!! The file is most likely corrupted !!!! <-----\n"
"You can skip this check with --disable-safe-unpickle commandline argument, but that is not going to help you.\n\n",
exc_info=True,
)
return None
except Exception:
print(f"Error verifying pickled file from {filename}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print("\nThe file may be malicious, so the program is not going to read it.", file=sys.stderr)
print("You can skip this check with --disable-safe-unpickle commandline argument.\n\n", file=sys.stderr)
print_error(
f"Error verifying pickled file from {filename}\n"
f"The file may be malicious, so the program is not going to read it.\n"
f"You can skip this check with --disable-safe-unpickle commandline argument.\n\n",
exc_info=True,
)
return None
return unsafe_torch_load(filename, *args, **kwargs)
@ -190,4 +193,3 @@ with safe.Extra(handler):
unsafe_torch_load = torch.load
torch.load = load
global_extra_handler = None

View File

@ -1,16 +1,15 @@
import sys
import traceback
from collections import namedtuple
import inspect
from collections import namedtuple
from typing import Optional, Dict, Any
from fastapi import FastAPI
from gradio import Blocks
from modules.errors import print_error
def report_exception(c, job):
print(f"Error executing callback {job} for {c.script}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error executing callback {job} for {c.script}", exc_info=True)
class ImageSaveParams:

View File

@ -1,8 +1,8 @@
import os
import sys
import traceback
import importlib.util
from modules.errors import print_error
def load_module(path):
module_spec = importlib.util.spec_from_file_location(os.path.basename(path), path)
@ -27,5 +27,4 @@ def preload_extensions(extensions_dir, parser):
module.preload(parser)
except Exception:
print(f"Error running preload() for {preload_script}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running preload() for {preload_script}", exc_info=True)

View File

@ -1,12 +1,12 @@
import os
import re
import sys
import traceback
from collections import namedtuple
import gradio as gr
from modules import shared, paths, script_callbacks, extensions, script_loading, scripts_postprocessing
from modules.errors import print_error
AlwaysVisible = object()
@ -264,8 +264,7 @@ def load_scripts():
register_scripts_from_module(script_module)
except Exception:
print(f"Error loading script: {scriptfile.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error loading script: {scriptfile.filename}", exc_info=True)
finally:
sys.path = syspath
@ -280,11 +279,9 @@ def load_scripts():
def wrap_call(func, filename, funcname, *args, default=None, **kwargs):
try:
res = func(*args, **kwargs)
return res
return func(*args, **kwargs)
except Exception:
print(f"Error calling: {filename}/{funcname}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error calling: {filename}/{funcname}", exc_info=True)
return default
@ -450,8 +447,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.process(p, *script_args)
except Exception:
print(f"Error running process: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running process: {script.filename}", exc_info=True)
def before_process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@ -459,8 +455,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.before_process_batch(p, *script_args, **kwargs)
except Exception:
print(f"Error running before_process_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running before_process_batch: {script.filename}", exc_info=True)
def process_batch(self, p, **kwargs):
for script in self.alwayson_scripts:
@ -468,8 +463,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.process_batch(p, *script_args, **kwargs)
except Exception:
print(f"Error running process_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running process_batch: {script.filename}", exc_info=True)
def postprocess(self, p, processed):
for script in self.alwayson_scripts:
@ -477,8 +471,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess(p, processed, *script_args)
except Exception:
print(f"Error running postprocess: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running postprocess: {script.filename}", exc_info=True)
def postprocess_batch(self, p, images, **kwargs):
for script in self.alwayson_scripts:
@ -486,8 +479,7 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_batch(p, *script_args, images=images, **kwargs)
except Exception:
print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running postprocess_batch: {script.filename}", exc_info=True)
def postprocess_image(self, p, pp: PostprocessImageArgs):
for script in self.alwayson_scripts:
@ -495,24 +487,21 @@ class ScriptRunner:
script_args = p.script_args[script.args_from:script.args_to]
script.postprocess_image(p, pp, *script_args)
except Exception:
print(f"Error running postprocess_batch: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running postprocess_image: {script.filename}", exc_info=True)
def before_component(self, component, **kwargs):
for script in self.scripts:
try:
script.before_component(component, **kwargs)
except Exception:
print(f"Error running before_component: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running before_component: {script.filename}", exc_info=True)
def after_component(self, component, **kwargs):
for script in self.scripts:
try:
script.after_component(component, **kwargs)
except Exception:
print(f"Error running after_component: {script.filename}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error running after_component: {script.filename}", exc_info=True)
def reload_sources(self, cache):
for si, script in list(enumerate(self.scripts)):

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@ -1,7 +1,5 @@
from __future__ import annotations
import math
import sys
import traceback
import psutil
import torch
@ -11,6 +9,7 @@ from ldm.util import default
from einops import rearrange
from modules import shared, errors, devices, sub_quadratic_attention
from modules.errors import print_error
from modules.hypernetworks import hypernetwork
import ldm.modules.attention
@ -140,8 +139,7 @@ if shared.cmd_opts.xformers or shared.cmd_opts.force_enable_xformers:
import xformers.ops
shared.xformers_available = True
except Exception:
print("Cannot import xformers", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Cannot import xformers", exc_info=True)
def get_available_vram():

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@ -416,12 +416,12 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
"comma_padding_backtrack": OptionInfo(20, "Prompt word wrap length limit", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1}).info("in tokens - for texts shorter than specified, if they don't fit into 75 token limit, move them to the next 75 token chunk"),
"CLIP_stop_at_last_layers": OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}).link("wiki", "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Features#clip-skip").info("ignore last layers of CLIP nrtwork; 1 ignores none, 2 ignores one layer"),
"upcast_attn": OptionInfo(False, "Upcast cross attention layer to float32"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different vidocard vendors"),
"randn_source": OptionInfo("GPU", "Random number generator source.", gr.Radio, {"choices": ["GPU", "CPU"]}).info("changes seeds drastically; use CPU to produce the same picture across different videocard vendors"),
}))
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"),

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@ -1,6 +1,4 @@
import os
import sys
import traceback
from collections import namedtuple
import torch
@ -16,6 +14,7 @@ from torch.utils.tensorboard import SummaryWriter
from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint
import modules.textual_inversion.dataset
from modules.errors import print_error
from modules.textual_inversion.learn_schedule import LearnRateScheduler
from modules.textual_inversion.image_embedding import embedding_to_b64, embedding_from_b64, insert_image_data_embed, extract_image_data_embed, caption_image_overlay
@ -120,16 +119,29 @@ class EmbeddingDatabase:
self.embedding_dirs.clear()
def register_embedding(self, embedding, model):
self.word_embeddings[embedding.name] = embedding
ids = model.cond_stage_model.tokenize([embedding.name])[0]
return self.register_embedding_by_name(embedding, model, embedding.name)
def register_embedding_by_name(self, embedding, model, name):
ids = model.cond_stage_model.tokenize([name])[0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)
if name in self.word_embeddings:
# remove old one from the lookup list
lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]
else:
lookup = self.ids_lookup[first_id]
if embedding is not None:
lookup += [(ids, embedding)]
self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)
if embedding is None:
# unregister embedding with specified name
if name in self.word_embeddings:
del self.word_embeddings[name]
if len(self.ids_lookup[first_id])==0:
del self.ids_lookup[first_id]
return None
self.word_embeddings[name] = embedding
return embedding
def get_expected_shape(self):
@ -207,8 +219,7 @@ class EmbeddingDatabase:
self.load_from_file(fullfn, fn)
except Exception:
print(f"Error loading embedding {fn}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error loading embedding {fn}", exc_info=True)
continue
def load_textual_inversion_embeddings(self, force_reload=False):
@ -632,8 +643,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
except Exception:
print(traceback.format_exc(), file=sys.stderr)
pass
print_error("Error training embedding", exc_info=True)
finally:
pbar.leave = False
pbar.close()

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@ -2,7 +2,6 @@ import json
import mimetypes
import os
import sys
import traceback
from functools import reduce
import warnings
@ -14,6 +13,7 @@ from PIL import Image, PngImagePlugin # noqa: F401
from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call
from modules import sd_hijack, sd_models, localization, script_callbacks, ui_extensions, deepbooru, sd_vae, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave
from modules.errors import print_error
from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML
from modules.paths import script_path, data_path
@ -231,9 +231,8 @@ def connect_reuse_seed(seed: gr.Number, reuse_seed: gr.Button, generation_info:
res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
except json.decoder.JSONDecodeError:
if gen_info_string != '':
print("Error parsing JSON generation info:", file=sys.stderr)
print(gen_info_string, file=sys.stderr)
if gen_info_string:
print_error(f"Error parsing JSON generation info: {gen_info_string}")
return [res, gr_show(False)]
@ -505,10 +504,10 @@ 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:
@ -1753,8 +1752,7 @@ def create_ui():
try:
results = modules.extras.run_modelmerger(*args)
except Exception as e:
print("Error loading/saving model file:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error("Error loading/saving model file", exc_info=True)
modules.sd_models.list_models() # to remove the potentially missing models from the list
return [*[gr.Dropdown.update(choices=modules.sd_models.checkpoint_tiles()) for _ in range(4)], f"Error merging checkpoints: {e}"]
return results

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@ -1,10 +1,8 @@
import json
import os.path
import sys
import threading
import time
from datetime import datetime
import traceback
import git
@ -14,6 +12,7 @@ import shutil
import errno
from modules import extensions, shared, paths, config_states
from modules.errors import print_error
from modules.paths_internal import config_states_dir
from modules.call_queue import wrap_gradio_gpu_call
@ -46,8 +45,7 @@ def apply_and_restart(disable_list, update_list, disable_all):
try:
ext.fetch_and_reset_hard()
except Exception:
print(f"Error getting updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error getting updates for {ext.name}", exc_info=True)
shared.opts.disabled_extensions = disabled
shared.opts.disable_all_extensions = disable_all
@ -113,8 +111,7 @@ def check_updates(id_task, disable_list):
if 'FETCH_HEAD' not in str(e):
raise
except Exception:
print(f"Error checking updates for {ext.name}:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error checking updates for {ext.name}", exc_info=True)
shared.state.nextjob()
@ -490,8 +487,14 @@ def refresh_available_extensions_from_data(hide_tags, sort_column, filter_text="
def preload_extensions_git_metadata():
t0 = time.time()
for extension in extensions.extensions:
extension.read_info_from_repo()
print(
f"preload_extensions_git_metadata for "
f"{len(extensions.extensions)} extensions took "
f"{time.time() - t0:.2f}s"
)
def create_ui():

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@ -53,8 +53,8 @@ class Upscaler:
def upscale(self, img: PIL.Image, scale, selected_model: str = None):
self.scale = scale
dest_w = int(img.width * scale)
dest_h = int(img.height * scale)
dest_w = round((img.width * scale - 4) / 8) * 8
dest_h = round((img.height * scale - 4) / 8) * 8
for _ in range(3):
shape = (img.width, img.height)

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@ -1,13 +1,12 @@
import copy
import random
import sys
import traceback
import shlex
import modules.scripts as scripts
import gradio as gr
from modules import sd_samplers
from modules.errors import print_error
from modules.processing import Processed, process_images
from modules.shared import state
@ -136,8 +135,7 @@ class Script(scripts.Script):
try:
args = cmdargs(line)
except Exception:
print(f"Error parsing line {line} as commandline:", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
print_error(f"Error parsing line {line} as commandline", exc_info=True)
args = {"prompt": line}
else:
args = {"prompt": line}

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@ -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=""