Merge branch 'dev' into sync-req

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -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

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -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):

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
 

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

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -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)

extensions-builtin/canvas-zoom-and-pan/javascript/zoom.js

@@ -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);
+});

javascript/imageviewerGamepad.js

@@ -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.

modules/api/api.py

@@ -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]
 

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")

modules/call_queue.py

@@ -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
-

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")

modules/codeformer_model.py

@@ -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

modules/config_states.py

@@ -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):

modules/errors.py

@@ -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])

modules/extensions.py

@@ -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)

modules/gfpgan_model.py

@@ -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)

modules/gitpython_hack.py

@@ -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

modules/hypernetworks/hypernetwork.py

@@ -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()

modules/images.py

@@ -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
 

modules/interrogate.py

@@ -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()

modules/launch_utils.py

@@ -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)

modules/localization.py

@@ -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)}"

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"]),

modules/processing.py

@@ -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):

modules/realesrgan_model.py

@@ -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)

modules/safe.py

@@ -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
-

modules/script_callbacks.py

@@ -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:

modules/script_loading.py

@@ -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)

modules/scripts.py

@@ -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)):

modules/sd_hijack_optimizations.py

@@ -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():

modules/shared.py

@@ -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"),

modules/textual_inversion/textual_inversion.py

@@ -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()

modules/ui.py

@@ -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

modules/ui_extensions.py

@@ -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():

modules/upscaler.py

@@ -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)

scripts/prompts_from_file.py

@@ -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}

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=""