+S-Lab License 1.0 + +Copyright 2022 S-Lab + +Redistribution and use for non-commercial purpose in source and +binary forms, with or without modification, are permitted provided +that the following conditions are met: + +1. Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in + the documentation and/or other materials provided with the + distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS +"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT +LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR +A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT +HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, +SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT +LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY +THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT +(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +In the event that redistribution and/or use for commercial purpose in +source or binary forms, with or without modification is required, +please contact the contributor(s) of the work. ++ + +
+MIT License + +Copyright (c) 2021 victorca25 + +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. ++ +
+BSD 3-Clause License + +Copyright (c) 2021, Xintao Wang +All rights reserved. + +Redistribution and use in source and binary forms, with or without +modification, are permitted provided that the following conditions are met: + +1. Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +2. Redistributions in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + +3. Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from + this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ++ +
+MIT License + +Copyright (c) 2022 InvokeAI Team + +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. ++ +
+MIT License + +Copyright (c) 2022 Machine Vision and Learning Group, LMU Munich + +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. ++ +
+MIT License + +Copyright (c) 2022 pharmapsychotic + +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. ++ +
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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [2021] [SwinIR Authors] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. ++ diff --git a/javascript/ui.js b/javascript/ui.js index 587dd7825a3..34406f3f509 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -19,7 +19,7 @@ function selected_gallery_index(){ function extract_image_from_gallery(gallery){ if(gallery.length == 1){ - return gallery[0] + return [gallery[0]] } index = selected_gallery_index() @@ -28,7 +28,7 @@ function extract_image_from_gallery(gallery){ return [null] } - return gallery[index]; + return [gallery[index]]; } function args_to_array(args){ @@ -188,6 +188,17 @@ onUiUpdate(function(){ img2img_textarea = gradioApp().querySelector("#img2img_prompt > label > textarea"); img2img_textarea?.addEventListener("input", () => update_token_counter("img2img_token_button")); } + + show_all_pages = gradioApp().getElementById('settings_show_all_pages') + settings_tabs = gradioApp().querySelector('#settings div') + if(show_all_pages && settings_tabs){ + settings_tabs.appendChild(show_all_pages) + show_all_pages.onclick = function(){ + gradioApp().querySelectorAll('#settings > div').forEach(function(elem){ + elem.style.display = "block"; + }) + } + } }) let txt2img_textarea, img2img_textarea = undefined; diff --git a/modules/api/api.py b/modules/api/api.py index 1ceba75d7d4..9c670f006ba 100644 --- a/modules/api/api.py +++ b/modules/api/api.py @@ -100,6 +100,7 @@ def __init__(self, app: FastAPI, queue_lock: Lock): self.add_api_route("/sdapi/v1/prompt-styles", self.get_prompt_styles, methods=["GET"], response_model=List[PromptStyleItem]) self.add_api_route("/sdapi/v1/artist-categories", self.get_artists_categories, methods=["GET"], response_model=List[str]) self.add_api_route("/sdapi/v1/artists", self.get_artists, methods=["GET"], response_model=List[ArtistItem]) + self.add_api_route("/sdapi/v1/embeddings", self.get_embeddings, methods=["GET"], response_model=EmbeddingsResponse) self.add_api_route("/sdapi/v1/refresh-checkpoints", self.refresh_checkpoints, methods=["POST"]) self.add_api_route("/sdapi/v1/create/embedding", self.create_embedding, methods=["POST"], response_model=CreateResponse) self.add_api_route("/sdapi/v1/create/hypernetwork", self.create_hypernetwork, methods=["POST"], response_model=CreateResponse) @@ -121,7 +122,6 @@ def auth(self, credentials: HTTPBasicCredentials = Depends(HTTPBasic())): def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): populate = txt2imgreq.copy(update={ # Override __init__ params - "sd_model": shared.sd_model, "sampler_name": validate_sampler_name(txt2imgreq.sampler_name or txt2imgreq.sampler_index), "do_not_save_samples": True, "do_not_save_grid": True @@ -129,15 +129,14 @@ def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI): ) if populate.sampler_name: populate.sampler_index = None # prevent a warning later on - p = StableDiffusionProcessingTxt2Img(**vars(populate)) - # Override object param - - shared.state.begin() with self.queue_lock: + p = StableDiffusionProcessingTxt2Img(sd_model=shared.sd_model, **vars(populate)) + + shared.state.begin() processed = process_images(p) + shared.state.end() - shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) @@ -153,7 +152,6 @@ def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): mask = decode_base64_to_image(mask) populate = img2imgreq.copy(update={ # Override __init__ params - "sd_model": shared.sd_model, "sampler_name": validate_sampler_name(img2imgreq.sampler_name or img2imgreq.sampler_index), "do_not_save_samples": True, "do_not_save_grid": True, @@ -165,16 +163,14 @@ def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI): args = vars(populate) args.pop('include_init_images', None) # this is meant to be done by "exclude": True in model, but it's for a reason that I cannot determine. - p = StableDiffusionProcessingImg2Img(**args) - - p.init_images = [decode_base64_to_image(x) for x in init_images] - - shared.state.begin() with self.queue_lock: - processed = process_images(p) + p = StableDiffusionProcessingImg2Img(sd_model=shared.sd_model, **args) + p.init_images = [decode_base64_to_image(x) for x in init_images] - shared.state.end() + shared.state.begin() + processed = process_images(p) + shared.state.end() b64images = list(map(encode_pil_to_base64, processed.images)) @@ -332,6 +328,26 @@ def get_artists_categories(self): def get_artists(self): return [{"name":x[0], "score":x[1], "category":x[2]} for x in shared.artist_db.artists] + def get_embeddings(self): + db = sd_hijack.model_hijack.embedding_db + + def convert_embedding(embedding): + return { + "step": embedding.step, + "sd_checkpoint": embedding.sd_checkpoint, + "sd_checkpoint_name": embedding.sd_checkpoint_name, + "shape": embedding.shape, + "vectors": embedding.vectors, + } + + def convert_embeddings(embeddings): + return {embedding.name: convert_embedding(embedding) for embedding in embeddings.values()} + + return { + "loaded": convert_embeddings(db.word_embeddings), + "skipped": convert_embeddings(db.skipped_embeddings), + } + def refresh_checkpoints(self): shared.refresh_checkpoints() diff --git a/modules/api/models.py b/modules/api/models.py index c446ce7a6c8..4a632c68591 100644 --- a/modules/api/models.py +++ b/modules/api/models.py @@ -249,3 +249,13 @@ class ArtistItem(BaseModel): score: float = Field(title="Score") category: str = Field(title="Category") +class EmbeddingItem(BaseModel): + step: Optional[int] = Field(title="Step", description="The number of steps that were used to train this embedding, if available") + sd_checkpoint: Optional[str] = Field(title="SD Checkpoint", description="The hash of the checkpoint this embedding was trained on, if available") + sd_checkpoint_name: Optional[str] = Field(title="SD Checkpoint Name", description="The name of the checkpoint this embedding was trained on, if available. Note that this is the name that was used by the trainer; for a stable identifier, use `sd_checkpoint` instead") + shape: int = Field(title="Shape", description="The length of each individual vector in the embedding") + vectors: int = Field(title="Vectors", description="The number of vectors in the embedding") + +class EmbeddingsResponse(BaseModel): + loaded: Dict[str, EmbeddingItem] = Field(title="Loaded", description="Embeddings loaded for the current model") + skipped: Dict[str, EmbeddingItem] = Field(title="Skipped", description="Embeddings skipped for the current model (likely due to architecture incompatibility)") \ No newline at end of file diff --git a/modules/extras.py b/modules/extras.py index 68939dea8dd..5e2702508af 100644 --- a/modules/extras.py +++ b/modules/extras.py @@ -303,6 +303,8 @@ def add_difference(theta0, theta1_2_diff, alpha): theta_0[key][:, 0:4, :, :] = theta_func2(a[:, 0:4, :, :], b, multiplier) result_is_inpainting_model = True else: + assert a.shape == b.shape, f'Incompatible shapes for layer {key}: A is {a.shape}, and B is {b.shape}' + theta_0[key] = theta_func2(a, b, multiplier) if save_as_half: diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index fbd91300550..4baf4d9ae3a 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -1,12 +1,13 @@ import base64 import io +import math import os import re from pathlib import Path import gradio as gr from modules.shared import script_path -from modules import shared +from modules import shared, ui_tempdir import tempfile from PIL import Image @@ -36,9 +37,12 @@ def quote(text): def image_from_url_text(filedata): - if type(filedata) == dict and filedata["is_file"]: + if type(filedata) == list and len(filedata) > 0 and type(filedata[0]) == dict and filedata[0].get("is_file", False): + filedata = filedata[0] + + if type(filedata) == dict and filedata.get("is_file", False): filename = filedata["name"] - is_in_right_dir = any(Path(temp_dir).resolve() in Path(filename).resolve().parents for temp_dir in shared.demo.temp_dirs) + is_in_right_dir = ui_tempdir.check_tmp_file(shared.demo, filename) assert is_in_right_dir, 'trying to open image file outside of allowed directories' return Image.open(filename) @@ -93,7 +97,7 @@ def integrate_settings_paste_fields(component_dict): def create_buttons(tabs_list): buttons = {} for tab in tabs_list: - buttons[tab] = gr.Button(f"Send to {tab}") + buttons[tab] = gr.Button(f"Send to {tab}", elem_id=f"{tab}_tab") return buttons @@ -102,35 +106,57 @@ def bind_buttons(buttons, send_image, send_generate_info): bind_list.append([buttons, send_image, send_generate_info]) +def send_image_and_dimensions(x): + if isinstance(x, Image.Image): + img = x + else: + img = image_from_url_text(x) + + if shared.opts.send_size and isinstance(img, Image.Image): + w = img.width + h = img.height + else: + w = gr.update() + h = gr.update() + + return img, w, h + + def run_bind(): - for buttons, send_image, send_generate_info in bind_list: + for buttons, source_image_component, send_generate_info in bind_list: for tab in buttons: button = buttons[tab] - if send_image and paste_fields[tab]["init_img"]: - if type(send_image) == gr.Gallery: - button.click( - fn=lambda x: image_from_url_text(x), - _js="extract_image_from_gallery", - inputs=[send_image], - outputs=[paste_fields[tab]["init_img"]], - ) + destination_image_component = paste_fields[tab]["init_img"] + fields = paste_fields[tab]["fields"] + + destination_width_component = next(iter([field for field, name in fields if name == "Size-1"] if fields else []), None) + destination_height_component = next(iter([field for field, name in fields if name == "Size-2"] if fields else []), None) + + if source_image_component and destination_image_component: + if isinstance(source_image_component, gr.Gallery): + func = send_image_and_dimensions if destination_width_component else image_from_url_text + jsfunc = "extract_image_from_gallery" else: - button.click( - fn=lambda x: x, - inputs=[send_image], - outputs=[paste_fields[tab]["init_img"]], - ) + func = send_image_and_dimensions if destination_width_component else lambda x: x + jsfunc = None + + button.click( + fn=func, + _js=jsfunc, + inputs=[source_image_component], + outputs=[destination_image_component, destination_width_component, destination_height_component] if destination_width_component else [destination_image_component], + ) - if send_generate_info and paste_fields[tab]["fields"] is not None: + if send_generate_info and fields is not None: if send_generate_info in paste_fields: - paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (['Size-1', 'Size-2'] if shared.opts.send_size else []) + (["Seed"] if shared.opts.send_seed else []) + paste_field_names = ['Prompt', 'Negative prompt', 'Steps', 'Face restoration'] + (["Seed"] if shared.opts.send_seed else []) button.click( fn=lambda *x: x, inputs=[field for field, name in paste_fields[send_generate_info]["fields"] if name in paste_field_names], - outputs=[field for field, name in paste_fields[tab]["fields"] if name in paste_field_names], + outputs=[field for field, name in fields if name in paste_field_names], ) else: - connect_paste(button, paste_fields[tab]["fields"], send_generate_info) + connect_paste(button, fields, send_generate_info) button.click( fn=None, @@ -164,6 +190,35 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None): return None +def restore_old_hires_fix_params(res): + """for infotexts that specify old First pass size parameter, convert it into + width, height, and hr scale""" + + firstpass_width = res.get('First pass size-1', None) + firstpass_height = res.get('First pass size-2', None) + + if firstpass_width is None or firstpass_height is None: + return + + firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height) + width = int(res.get("Size-1", 512)) + height = int(res.get("Size-2", 512)) + + if firstpass_width == 0 or firstpass_height == 0: + # old algorithm for auto-calculating first pass size + desired_pixel_count = 512 * 512 + actual_pixel_count = width * height + scale = math.sqrt(desired_pixel_count / actual_pixel_count) + firstpass_width = math.ceil(scale * width / 64) * 64 + firstpass_height = math.ceil(scale * height / 64) * 64 + + hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height + + res['Size-1'] = firstpass_width + res['Size-2'] = firstpass_height + res['Hires upscale'] = hr_scale + + def parse_generation_parameters(x: str): """parses generation parameters string, the one you see in text field under the picture in UI: ``` @@ -221,6 +276,8 @@ def parse_generation_parameters(x: str): hypernet_hash = res.get("Hypernet hash", None) res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash) + restore_old_hires_fix_params(res) + return res diff --git a/modules/images.py b/modules/images.py index 31d4528dc71..c3a5fc8bc6a 100644 --- a/modules/images.py +++ b/modules/images.py @@ -39,11 +39,14 @@ def image_grid(imgs, batch_size=1, rows=None): cols = math.ceil(len(imgs) / rows) + params = script_callbacks.ImageGridLoopParams(imgs, cols, rows) + script_callbacks.image_grid_callback(params) + w, h = imgs[0].size - grid = Image.new('RGB', size=(cols * w, rows * h), color='black') + grid = Image.new('RGB', size=(params.cols * w, params.rows * h), color='black') - for i, img in enumerate(imgs): - grid.paste(img, box=(i % cols * w, i // cols * h)) + for i, img in enumerate(params.imgs): + grid.paste(img, box=(i % params.cols * w, i // params.cols * h)) return grid @@ -227,16 +230,32 @@ def draw_prompt_matrix(im, width, height, all_prompts): return draw_grid_annotations(im, width, height, hor_texts, ver_texts) -def resize_image(resize_mode, im, width, height): +def resize_image(resize_mode, im, width, height, upscaler_name=None): + """ + Resizes an image with the specified resize_mode, width, and height. + + Args: + resize_mode: The mode to use when resizing the image. + 0: Resize the image to the specified width and height. + 1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess. + 2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image. + im: The image to resize. + width: The width to resize the image to. + height: The height to resize the image to. + upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img. + """ + + upscaler_name = upscaler_name or opts.upscaler_for_img2img + def resize(im, w, h): - if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L': + if upscaler_name is None or upscaler_name == "None" or im.mode == 'L': return im.resize((w, h), resample=LANCZOS) scale = max(w / im.width, h / im.height) if scale > 1.0: - upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img] - assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}" + upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name] + assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}" upscaler = upscalers[0] im = upscaler.scaler.upscale(im, scale, upscaler.data_path) @@ -525,6 +544,9 @@ def _atomically_save_image(image_to_save, filename_without_extension, extension) image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality, pnginfo=pnginfo_data) elif extension.lower() in (".jpg", ".jpeg", ".webp"): + if image_to_save.mode == 'RGBA': + image_to_save = image_to_save.convert("RGB") + image_to_save.save(temp_file_path, format=image_format, quality=opts.jpeg_quality) if opts.enable_pnginfo and info is not None: diff --git a/modules/img2img.py b/modules/img2img.py index 81da4b1312d..ca58b5d87b3 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -162,4 +162,4 @@ def img2img(mode: int, prompt: str, negative_prompt: str, prompt_style: str, pro if opts.do_not_show_images: processed.images = [] - return processed.images, generation_info_js, plaintext_to_html(processed.info) + return processed.images, generation_info_js, plaintext_to_html(processed.info), plaintext_to_html(processed.comments) diff --git a/modules/interrogate.py b/modules/interrogate.py index 469352105d1..6f761c5a504 100644 --- a/modules/interrogate.py +++ b/modules/interrogate.py @@ -135,7 +135,7 @@ def generate_caption(self, pil_image): return caption[0] def interrogate(self, pil_image): - res = None + res = "" try: diff --git a/modules/memmon.py b/modules/memmon.py index 9fb9b687caf..a7060f58523 100644 --- a/modules/memmon.py +++ b/modules/memmon.py @@ -71,10 +71,13 @@ def monitor(self): def read(self): if not self.disabled: free, total = torch.cuda.mem_get_info() + self.data["free"] = free self.data["total"] = total torch_stats = torch.cuda.memory_stats(self.device) + self.data["active"] = torch_stats["active.all.current"] self.data["active_peak"] = torch_stats["active_bytes.all.peak"] + self.data["reserved"] = torch_stats["reserved_bytes.all.current"] self.data["reserved_peak"] = torch_stats["reserved_bytes.all.peak"] self.data["system_peak"] = total - self.data["min_free"] diff --git a/modules/modelloader.py b/modules/modelloader.py index e647f6fa7cf..6a1a7ac8326 100644 --- a/modules/modelloader.py +++ b/modules/modelloader.py @@ -123,6 +123,23 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None): pass +builtin_upscaler_classes = [] +forbidden_upscaler_classes = set() + + +def list_builtin_upscalers(): + load_upscalers() + + builtin_upscaler_classes.clear() + builtin_upscaler_classes.extend(Upscaler.__subclasses__()) + + +def forbid_loaded_nonbuiltin_upscalers(): + for cls in Upscaler.__subclasses__(): + if cls not in builtin_upscaler_classes: + forbidden_upscaler_classes.add(cls) + + def load_upscalers(): # We can only do this 'magic' method to dynamically load upscalers if they are referenced, # so we'll try to import any _model.py files before looking in __subclasses__ @@ -139,6 +156,9 @@ def load_upscalers(): datas = [] commandline_options = vars(shared.cmd_opts) for cls in Upscaler.__subclasses__(): + if cls in forbidden_upscaler_classes: + continue + name = cls.__name__ cmd_name = f"{name.lower().replace('upscaler', '')}_models_path" scaler = cls(commandline_options.get(cmd_name, None)) diff --git a/modules/processing.py b/modules/processing.py index 0a9a8f95cb4..a172af0baae 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -239,7 +239,7 @@ def close(self): class Processed: - def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None): + def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""): self.images = images_list self.prompt = p.prompt self.negative_prompt = p.negative_prompt @@ -247,6 +247,7 @@ def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", self.subseed = subseed self.subseed_strength = p.subseed_strength self.info = info + self.comments = comments self.width = p.width self.height = p.height self.sampler_name = p.sampler_name @@ -646,7 +647,7 @@ def infotext(iteration=0, position_in_batch=0): devices.torch_gc() - res = Processed(p, output_images, p.all_seeds[0], infotext() + "".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts) + res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts) if p.scripts is not None: p.scripts.postprocess(p, res) @@ -657,14 +658,18 @@ def infotext(iteration=0, position_in_batch=0): class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): sampler = None - def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs): + def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, **kwargs): super().__init__(**kwargs) self.enable_hr = enable_hr self.denoising_strength = denoising_strength - self.firstphase_width = firstphase_width - self.firstphase_height = firstphase_height - self.truncate_x = 0 - self.truncate_y = 0 + self.hr_scale = hr_scale + self.hr_upscaler = hr_upscaler + + if firstphase_width != 0 or firstphase_height != 0: + print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr) + self.hr_scale = self.width / firstphase_width + self.width = firstphase_width + self.height = firstphase_height def init(self, all_prompts, all_seeds, all_subseeds): if self.enable_hr: @@ -673,47 +678,29 @@ def init(self, all_prompts, all_seeds, all_subseeds): else: state.job_count = state.job_count * 2 - self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}" - - if self.firstphase_width == 0 or self.firstphase_height == 0: - desired_pixel_count = 512 * 512 - actual_pixel_count = self.width * self.height - scale = math.sqrt(desired_pixel_count / actual_pixel_count) - self.firstphase_width = math.ceil(scale * self.width / 64) * 64 - self.firstphase_height = math.ceil(scale * self.height / 64) * 64 - firstphase_width_truncated = int(scale * self.width) - firstphase_height_truncated = int(scale * self.height) - - else: - - width_ratio = self.width / self.firstphase_width - height_ratio = self.height / self.firstphase_height - - if width_ratio > height_ratio: - firstphase_width_truncated = self.firstphase_width - firstphase_height_truncated = self.firstphase_width * self.height / self.width - else: - firstphase_width_truncated = self.firstphase_height * self.width / self.height - firstphase_height_truncated = self.firstphase_height - - self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f - self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f + self.extra_generation_params["Hires upscale"] = self.hr_scale + if self.hr_upscaler is not None: + self.extra_generation_params["Hires upscaler"] = self.hr_upscaler def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) + latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") + if self.enable_hr and latent_scale_mode is None: + assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}" + + x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) + if not self.enable_hr: - x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x)) return samples - x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) - samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height)) - - samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2] + target_width = int(self.width * self.hr_scale) + target_height = int(self.height * self.hr_scale) - """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images""" def save_intermediate(image, index): + """saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images""" + if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix: return @@ -722,11 +709,11 @@ def save_intermediate(image, index): images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix") - if opts.use_scale_latent_for_hires_fix: + if latent_scale_mode is not None: for i in range(samples.shape[0]): save_intermediate(samples, i) - samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode) # Avoid making the inpainting conditioning unless necessary as # this does need some extra compute to decode / encode the image again. @@ -746,7 +733,7 @@ def save_intermediate(image, index): save_intermediate(image, i) - image = images.resize_image(0, image, self.width, self.height) + image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler) image = np.array(image).astype(np.float32) / 255.0 image = np.moveaxis(image, 2, 0) batch_images.append(image) @@ -763,7 +750,7 @@ def save_intermediate(image, index): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) - noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self) + noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self) # GC now before running the next img2img to prevent running out of memory x = None diff --git a/modules/script_callbacks.py b/modules/script_callbacks.py index 8e22f875564..de69fd9f493 100644 --- a/modules/script_callbacks.py +++ b/modules/script_callbacks.py @@ -51,6 +51,13 @@ def __init__(self, txt2img_preview_params): self.txt2img_preview_params = txt2img_preview_params +class ImageGridLoopParams: + def __init__(self, imgs, cols, rows): + self.imgs = imgs + self.cols = cols + self.rows = rows + + ScriptCallback = namedtuple("ScriptCallback", ["script", "callback"]) callback_map = dict( callbacks_app_started=[], @@ -63,6 +70,7 @@ def __init__(self, txt2img_preview_params): callbacks_cfg_denoiser=[], callbacks_before_component=[], callbacks_after_component=[], + callbacks_image_grid=[], ) @@ -155,6 +163,14 @@ def after_component_callback(component, **kwargs): report_exception(c, 'after_component_callback') +def image_grid_callback(params: ImageGridLoopParams): + for c in callback_map['callbacks_image_grid']: + try: + c.callback(params) + except Exception: + report_exception(c, 'image_grid') + + def add_callback(callbacks, fun): stack = [x for x in inspect.stack() if x.filename != __file__] filename = stack[0].filename if len(stack) > 0 else 'unknown file' @@ -255,3 +271,11 @@ def on_before_component(callback): def on_after_component(callback): """register a function to be called after a component is created. See on_before_component for more.""" add_callback(callback_map['callbacks_after_component'], callback) + + +def on_image_grid(callback): + """register a function to be called before making an image grid. + The callback is called with one argument: + - params: ImageGridLoopParams - parameters to be used for grid creation. Can be modified. + """ + add_callback(callback_map['callbacks_image_grid'], callback) diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py index 019a6f3f276..55a684cc28d 100644 --- a/modules/sd_hijack.py +++ b/modules/sd_hijack.py @@ -5,7 +5,7 @@ from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint from modules.hypernetworks import hypernetwork from modules.shared import cmd_opts -from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet +from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr import ldm.modules.attention import ldm.modules.diffusionmodules.model @@ -65,6 +65,7 @@ def fix_checkpoint(): ldm.modules.diffusionmodules.openaimodel.ResBlock.forward = sd_hijack_checkpoint.ResBlock_forward ldm.modules.diffusionmodules.openaimodel.AttentionBlock.forward = sd_hijack_checkpoint.AttentionBlock_forward + class StableDiffusionModelHijack: fixes = None comments = [] @@ -75,17 +76,25 @@ class StableDiffusionModelHijack: embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir) def hijack(self, m): - if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder: + + if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: + model_embeddings = m.cond_stage_model.roberta.embeddings + model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self) + m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self) + + elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder: model_embeddings = m.cond_stage_model.transformer.text_model.embeddings model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self) m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) + elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder: m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self) m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self) - self.clip = m.cond_stage_model - apply_optimizations() + + self.clip = m.cond_stage_model + fix_checkpoint() def flatten(el): @@ -98,7 +107,11 @@ def flatten(el): self.layers = flatten(m) def undo_hijack(self, m): - if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords: + + if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation: + m.cond_stage_model = m.cond_stage_model.wrapped + + elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords: m.cond_stage_model = m.cond_stage_model.wrapped model_embeddings = m.cond_stage_model.transformer.text_model.embeddings @@ -126,8 +139,8 @@ def clear_comments(self): def tokenize(self, text): _, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text]) - return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count) + return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count) class EmbeddingsWithFixes(torch.nn.Module): diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py index b451d1cfe2f..ca92b142cbb 100644 --- a/modules/sd_hijack_clip.py +++ b/modules/sd_hijack_clip.py @@ -5,7 +5,6 @@ from modules import prompt_parser, devices from modules.shared import opts - def get_target_prompt_token_count(token_count): return math.ceil(max(token_count, 1) / 75) * 75 @@ -254,10 +253,13 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase): def __init__(self, wrapped, hijack): super().__init__(wrapped, hijack) self.tokenizer = wrapped.tokenizer - self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ','][0] + + vocab = self.tokenizer.get_vocab() + + self.comma_token = vocab.get(',', None) self.token_mults = {} - tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k] + tokens_with_parens = [(k, v) for k, v in vocab.items() if '(' in k or ')' in k or '[' in k or ']' in k] for text, ident in tokens_with_parens: mult = 1.0 for c in text: @@ -296,6 +298,6 @@ def encode_with_transformers(self, tokens): def encode_embedding_init_text(self, init_text, nvpt): embedding_layer = self.wrapped.transformer.text_model.embeddings ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"] - embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0) + embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0) return embedded diff --git a/modules/sd_hijack_inpainting.py b/modules/sd_hijack_inpainting.py index bb5499b3c28..3c214a35705 100644 --- a/modules/sd_hijack_inpainting.py +++ b/modules/sd_hijack_inpainting.py @@ -12,191 +12,6 @@ from ldm.models.diffusion.plms import PLMSSampler from ldm.models.diffusion.ddim import DDIMSampler, noise_like -# ================================================================================================= -# Monkey patch DDIMSampler methods from RunwayML repo directly. -# Adapted from: -# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddim.py -# ================================================================================================= -@torch.no_grad() -def sample_ddim(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - ctmp = conditioning[list(conditioning.keys())[0]] - while isinstance(ctmp, list): - ctmp = ctmp[0] - cbs = ctmp.shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for DDIM sampling is {size}, eta {eta}') - - samples, intermediates = self.ddim_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - ) - return samples, intermediates - -@torch.no_grad() -def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, - temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None, - unconditional_guidance_scale=1., unconditional_conditioning=None): - b, *_, device = *x.shape, x.device - - if unconditional_conditioning is None or unconditional_guidance_scale == 1.: - e_t = self.model.apply_model(x, t, c) - else: - x_in = torch.cat([x] * 2) - t_in = torch.cat([t] * 2) - if isinstance(c, dict): - assert isinstance(unconditional_conditioning, dict) - c_in = dict() - for k in c: - if isinstance(c[k], list): - c_in[k] = [ - torch.cat([unconditional_conditioning[k][i], c[k][i]]) - for i in range(len(c[k])) - ] - else: - c_in[k] = torch.cat([unconditional_conditioning[k], c[k]]) - else: - c_in = torch.cat([unconditional_conditioning, c]) - e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2) - e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond) - - if score_corrector is not None: - assert self.model.parameterization == "eps" - e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs) - - alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas - alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev - sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas - sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas - # select parameters corresponding to the currently considered timestep - a_t = torch.full((b, 1, 1, 1), alphas[index], device=device) - a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device) - sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device) - sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device) - - # current prediction for x_0 - pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt() - if quantize_denoised: - pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0) - # direction pointing to x_t - dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t - noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature - if noise_dropout > 0.: - noise = torch.nn.functional.dropout(noise, p=noise_dropout) - x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise - return x_prev, pred_x0 - - -# ================================================================================================= -# Monkey patch PLMSSampler methods. -# This one was not actually patched correctly in the RunwayML repo, but we can replicate the changes. -# Adapted from: -# https://github.com/CompVis/stable-diffusion/blob/main/ldm/models/diffusion/plms.py -# ================================================================================================= -@torch.no_grad() -def sample_plms(self, - S, - batch_size, - shape, - conditioning=None, - callback=None, - normals_sequence=None, - img_callback=None, - quantize_x0=False, - eta=0., - mask=None, - x0=None, - temperature=1., - noise_dropout=0., - score_corrector=None, - corrector_kwargs=None, - verbose=True, - x_T=None, - log_every_t=100, - unconditional_guidance_scale=1., - unconditional_conditioning=None, - # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ... - **kwargs - ): - if conditioning is not None: - if isinstance(conditioning, dict): - ctmp = conditioning[list(conditioning.keys())[0]] - while isinstance(ctmp, list): - ctmp = ctmp[0] - cbs = ctmp.shape[0] - if cbs != batch_size: - print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}") - else: - if conditioning.shape[0] != batch_size: - print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}") - - self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose) - # sampling - C, H, W = shape - size = (batch_size, C, H, W) - print(f'Data shape for PLMS sampling is {size}') - - samples, intermediates = self.plms_sampling(conditioning, size, - callback=callback, - img_callback=img_callback, - quantize_denoised=quantize_x0, - mask=mask, x0=x0, - ddim_use_original_steps=False, - noise_dropout=noise_dropout, - temperature=temperature, - score_corrector=score_corrector, - corrector_kwargs=corrector_kwargs, - x_T=x_T, - log_every_t=log_every_t, - unconditional_guidance_scale=unconditional_guidance_scale, - unconditional_conditioning=unconditional_conditioning, - ) - return samples, intermediates - @torch.no_grad() def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False, @@ -280,44 +95,6 @@ def get_x_prev_and_pred_x0(e_t, index): return x_prev, pred_x0, e_t -# ================================================================================================= -# Monkey patch LatentInpaintDiffusion to load the checkpoint with a proper config. -# Adapted from: -# https://github.com/runwayml/stable-diffusion/blob/main/ldm/models/diffusion/ddpm.py -# ================================================================================================= - -@torch.no_grad() -def get_unconditional_conditioning(self, batch_size, null_label=None): - if null_label is not None: - xc = null_label - if isinstance(xc, ListConfig): - xc = list(xc) - if isinstance(xc, dict) or isinstance(xc, list): - c = self.get_learned_conditioning(xc) - else: - if hasattr(xc, "to"): - xc = xc.to(self.device) - c = self.get_learned_conditioning(xc) - else: - # todo: get null label from cond_stage_model - raise NotImplementedError() - c = repeat(c, "1 ... -> b ...", b=batch_size).to(self.device) - return c - - -class LatentInpaintDiffusion(LatentDiffusion): - def __init__( - self, - concat_keys=("mask", "masked_image"), - masked_image_key="masked_image", - *args, - **kwargs, - ): - super().__init__(*args, **kwargs) - self.masked_image_key = masked_image_key - assert self.masked_image_key in concat_keys - self.concat_keys = concat_keys - def should_hijack_inpainting(checkpoint_info): ckpt_basename = os.path.basename(checkpoint_info.filename).lower() @@ -326,15 +103,6 @@ def should_hijack_inpainting(checkpoint_info): def do_inpainting_hijack(): - # most of this stuff seems to no longer be needed because it is already included into SD2.0 # p_sample_plms is needed because PLMS can't work with dicts as conditionings - # this file should be cleaned up later if everything turns out to work fine - - # ldm.models.diffusion.ddpm.get_unconditional_conditioning = get_unconditional_conditioning - # ldm.models.diffusion.ddpm.LatentInpaintDiffusion = LatentInpaintDiffusion - - # ldm.models.diffusion.ddim.DDIMSampler.p_sample_ddim = p_sample_ddim - # ldm.models.diffusion.ddim.DDIMSampler.sample = sample_ddim ldm.models.diffusion.plms.PLMSSampler.p_sample_plms = p_sample_plms - # ldm.models.diffusion.plms.PLMSSampler.sample = sample_plms diff --git a/modules/sd_hijack_xlmr.py b/modules/sd_hijack_xlmr.py new file mode 100644 index 00000000000..4ac51c386fd --- /dev/null +++ b/modules/sd_hijack_xlmr.py @@ -0,0 +1,34 @@ +import open_clip.tokenizer +import torch + +from modules import sd_hijack_clip, devices +from modules.shared import opts + + +class FrozenXLMREmbedderWithCustomWords(sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords): + def __init__(self, wrapped, hijack): + super().__init__(wrapped, hijack) + + self.id_start = wrapped.config.bos_token_id + self.id_end = wrapped.config.eos_token_id + self.id_pad = wrapped.config.pad_token_id + + self.comma_token = self.tokenizer.get_vocab().get(',', None) # alt diffusion doesn't have bits for comma + + def encode_with_transformers(self, tokens): + # there's no CLIP Skip here because all hidden layers have size of 1024 and the last one uses a + # trained layer to transform those 1024 into 768 for unet; so you can't choose which transformer + # layer to work with - you have to use the last + + attention_mask = (tokens != self.id_pad).to(device=tokens.device, dtype=torch.int64) + features = self.wrapped(input_ids=tokens, attention_mask=attention_mask) + z = features['projection_state'] + + return z + + def encode_embedding_init_text(self, init_text, nvpt): + embedding_layer = self.wrapped.roberta.embeddings + ids = self.wrapped.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"] + embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0) + + return embedded diff --git a/modules/sd_models.py b/modules/sd_models.py index ecdd91c5084..b98b05fc2b8 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -228,6 +228,8 @@ def load_model_weights(model, checkpoint_info, vae_file="auto"): model.sd_model_checkpoint = checkpoint_file model.sd_checkpoint_info = checkpoint_info + model.logvar = model.logvar.to(devices.device) # fix for training + sd_vae.delete_base_vae() sd_vae.clear_loaded_vae() vae_file = sd_vae.resolve_vae(checkpoint_file, vae_file=vae_file) @@ -322,9 +324,12 @@ def load_model(checkpoint_info=None): sd_model.eval() shared.sd_model = sd_model + sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model + script_callbacks.model_loaded_callback(sd_model) print("Model loaded.") + return sd_model diff --git a/modules/sd_samplers.py b/modules/sd_samplers.py index 177b5338045..e904d86091f 100644 --- a/modules/sd_samplers.py +++ b/modules/sd_samplers.py @@ -465,7 +465,9 @@ def get_sigmas(self, p, steps): if p.sampler_noise_scheduler_override: sigmas = p.sampler_noise_scheduler_override(steps) elif self.config is not None and self.config.options.get('scheduler', None) == 'karras': - sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=0.1, sigma_max=10, device=shared.device) + sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item()) + + sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device) else: sigmas = self.model_wrap.get_sigmas(steps) diff --git a/modules/sd_vae.py b/modules/sd_vae.py index 3856418e9c6..ac71d62db73 100644 --- a/modules/sd_vae.py +++ b/modules/sd_vae.py @@ -1,5 +1,6 @@ import torch import os +import collections from collections import namedtuple from modules import shared, devices, script_callbacks from modules.paths import models_path @@ -30,6 +31,7 @@ loaded_vae_file = None checkpoint_info = None +checkpoints_loaded = collections.OrderedDict() def get_base_vae(model): if base_vae is not None and checkpoint_info == model.sd_checkpoint_info and model: @@ -149,13 +151,30 @@ def load_vae(model, vae_file=None): global first_load, vae_dict, vae_list, loaded_vae_file # save_settings = False + cache_enabled = shared.opts.sd_vae_checkpoint_cache > 0 + if vae_file: - assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}" - print(f"Loading VAE weights from: {vae_file}") - store_base_vae(model) - vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) - vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} - _load_vae_dict(model, vae_dict_1) + if cache_enabled and vae_file in checkpoints_loaded: + # use vae checkpoint cache + print(f"Loading VAE weights [{get_filename(vae_file)}] from cache") + store_base_vae(model) + _load_vae_dict(model, checkpoints_loaded[vae_file]) + else: + assert os.path.isfile(vae_file), f"VAE file doesn't exist: {vae_file}" + print(f"Loading VAE weights from: {vae_file}") + store_base_vae(model) + vae_ckpt = torch.load(vae_file, map_location=shared.weight_load_location) + vae_dict_1 = {k: v for k, v in vae_ckpt["state_dict"].items() if k[0:4] != "loss" and k not in vae_ignore_keys} + _load_vae_dict(model, vae_dict_1) + + if cache_enabled: + # cache newly loaded vae + checkpoints_loaded[vae_file] = vae_dict_1.copy() + + # clean up cache if limit is reached + if cache_enabled: + while len(checkpoints_loaded) > shared.opts.sd_vae_checkpoint_cache + 1: # we need to count the current model + checkpoints_loaded.popitem(last=False) # LRU # If vae used is not in dict, update it # It will be removed on refresh though diff --git a/modules/shared.py b/modules/shared.py index 487a7792f16..373551da594 100644 --- a/modules/shared.py +++ b/modules/shared.py @@ -23,7 +23,7 @@ sd_model_file = os.path.join(script_path, 'model.ckpt') default_sd_model_file = sd_model_file parser = argparse.ArgumentParser() -parser.add_argument("--config", type=str, default=os.path.join(script_path, "v1-inference.yaml"), help="path to config which constructs model",) +parser.add_argument("--config", type=str, default=os.path.join(script_path, "configs/v1-inference.yaml"), help="path to config which constructs model",) parser.add_argument("--ckpt", type=str, default=sd_model_file, help="path to checkpoint of stable diffusion model; if specified, this checkpoint will be added to the list of checkpoints and loaded",) parser.add_argument("--ckpt-dir", type=str, default=None, help="Path to directory with stable diffusion checkpoints") parser.add_argument("--gfpgan-dir", type=str, help="GFPGAN directory", default=('./src/gfpgan' if os.path.exists('./src/gfpgan') else './GFPGAN')) @@ -113,6 +113,17 @@ "outdir_save", } +ui_reorder_categories = [ + "sampler", + "dimensions", + "cfg", + "seed", + "checkboxes", + "hires_fix", + "batch", + "scripts", +] + cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access devices.device, devices.device_interrogate, devices.device_gfpgan, devices.device_esrgan, devices.device_codeformer = \ @@ -172,7 +183,7 @@ def nextjob(self): def dict(self): obj = { "skipped": self.skipped, - "interrupted": self.skipped, + "interrupted": self.interrupted, "job": self.job, "job_count": self.job_count, "job_no": self.job_no, @@ -331,7 +342,6 @@ def list_samplers(): "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}), "realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}), "upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}), - "use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"), })) options_templates.update(options_section(('face-restoration', "Face restoration"), { @@ -360,6 +370,7 @@ def list_samplers(): options_templates.update(options_section(('sd', "Stable Diffusion"), { "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": list_checkpoint_tiles()}, refresh=refresh_checkpoints), "sd_checkpoint_cache": OptionInfo(0, "Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), + "sd_vae_checkpoint_cache": OptionInfo(0, "VAE Checkpoints to cache in RAM", gr.Slider, {"minimum": 0, "maximum": 10, "step": 1}), "sd_vae": OptionInfo("auto", "SD VAE", gr.Dropdown, lambda: {"choices": sd_vae.vae_list}, refresh=sd_vae.refresh_vae_list), "sd_vae_as_default": OptionInfo(False, "Ignore selected VAE for stable diffusion checkpoints that have their own .vae.pt next to them"), "sd_hypernetwork": OptionInfo("None", "Hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks), @@ -371,13 +382,17 @@ def list_samplers(): "img2img_background_color": OptionInfo("#ffffff", "With img2img, fill image's transparent parts with this color.", gr.ColorPicker, {}), "enable_quantization": OptionInfo(False, "Enable quantization in K samplers for sharper and cleaner results. This may change existing seeds. Requires restart to apply."), "enable_emphasis": OptionInfo(True, "Emphasis: use (text) to make model pay more attention to text and [text] to make it pay less attention"), - "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), "enable_batch_seeds": OptionInfo(True, "Make K-diffusion samplers produce same images in a batch as when making a single image"), "comma_padding_backtrack": OptionInfo(20, "Increase coherency by padding from the last comma within n tokens when using more than 75 tokens", gr.Slider, {"minimum": 0, "maximum": 74, "step": 1 }), 'CLIP_stop_at_last_layers': OptionInfo(1, "Clip skip", gr.Slider, {"minimum": 1, "maximum": 12, "step": 1}), "random_artist_categories": OptionInfo([], "Allowed categories for random artists selection when using the Roll button", gr.CheckboxGroup, {"choices": artist_db.categories()}), })) +options_templates.update(options_section(('compatibility', "Compatibility"), { + "use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."), + "use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."), +})) + options_templates.update(options_section(('interrogate', "Interrogate Options"), { "interrogate_keep_models_in_memory": OptionInfo(False, "Interrogate: keep models in VRAM"), "interrogate_use_builtin_artists": OptionInfo(True, "Interrogate: use artists from artists.csv"), @@ -409,7 +424,10 @@ def list_samplers(): "js_modal_lightbox": OptionInfo(True, "Enable full page image viewer"), "js_modal_lightbox_initially_zoomed": OptionInfo(True, "Show images zoomed in by default in full page image viewer"), "show_progress_in_title": OptionInfo(True, "Show generation progress in window title."), + "samplers_in_dropdown": OptionInfo(True, "Use dropdown for sampler selection instead of radio group"), + "dimensions_and_batch_together": OptionInfo(True, "Show Witdth/Height and Batch sliders in same row"), 'quicksettings': OptionInfo("sd_model_checkpoint", "Quicksettings list"), + 'ui_reorder': OptionInfo(", ".join(ui_reorder_categories), "txt2img/ing2img UI item order"), 'localization': OptionInfo("None", "Localization (requires restart)", gr.Dropdown, lambda: {"choices": ["None"] + list(localization.localizations.keys())}, refresh=lambda: localization.list_localizations(cmd_opts.localizations_dir)), })) @@ -543,6 +561,12 @@ def reorder(self): if os.path.exists(config_filename): opts.load(config_filename) +latent_upscale_default_mode = "Latent" +latent_upscale_modes = { + "Latent": "bilinear", + "Latent (nearest)": "nearest", +} + sd_upscalers = [] sd_model = None diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index f6112578e0a..fd2534776f7 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -23,6 +23,8 @@ def __init__(self, vec, name, step=None): self.vec = vec self.name = name self.step = step + self.shape = None + self.vectors = 0 self.cached_checksum = None self.sd_checkpoint = None self.sd_checkpoint_name = None @@ -57,8 +59,10 @@ class EmbeddingDatabase: def __init__(self, embeddings_dir): self.ids_lookup = {} self.word_embeddings = {} + self.skipped_embeddings = {} self.dir_mtime = None self.embeddings_dir = embeddings_dir + self.expected_shape = -1 def register_embedding(self, embedding, model): @@ -75,20 +79,24 @@ def register_embedding(self, embedding, model): return embedding - def load_textual_inversion_embeddings(self): + def get_expected_shape(self): + vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1) + return vec.shape[1] + + def load_textual_inversion_embeddings(self, force_reload = False): mt = os.path.getmtime(self.embeddings_dir) - if self.dir_mtime is not None and mt <= self.dir_mtime: + if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime: return self.dir_mtime = mt self.ids_lookup.clear() self.word_embeddings.clear() + self.skipped_embeddings.clear() + self.expected_shape = self.get_expected_shape() def process_file(path, filename): name = os.path.splitext(filename)[0] - data = [] - if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']: embed_image = Image.open(path) if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text: @@ -122,7 +130,13 @@ def process_file(path, filename): embedding.step = data.get('step', None) embedding.sd_checkpoint = data.get('sd_checkpoint', None) embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None) - self.register_embedding(embedding, shared.sd_model) + embedding.vectors = vec.shape[0] + embedding.shape = vec.shape[-1] + + if self.expected_shape == -1 or self.expected_shape == embedding.shape: + self.register_embedding(embedding, shared.sd_model) + else: + self.skipped_embeddings[name] = embedding for fn in os.listdir(self.embeddings_dir): try: @@ -137,8 +151,9 @@ def process_file(path, filename): print(traceback.format_exc(), file=sys.stderr) continue - print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.") - print("Embeddings:", ', '.join(self.word_embeddings.keys())) + print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}") + if len(self.skipped_embeddings) > 0: + print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}") def find_embedding_at_position(self, tokens, offset): token = tokens[offset] @@ -267,7 +282,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ return embedding, filename scheduler = LearnRateScheduler(learn_rate, steps, initial_step) - # dataset loading may take a while, so input validations and early returns should be done before this + # dataset loading may take a while, so input validations and early returns should be done before this shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..." old_parallel_processing_allowed = shared.parallel_processing_allowed @@ -295,7 +310,6 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_ loss_step = 0 _loss_step = 0 #internal - last_saved_file = "
Process images in a directory on the same machine where the server is running.
Use an empty output directory to save pictures normally instead of writing to the output directory.{hidden}
See wiki for detailed explanation.
") @@ -1167,58 +1203,58 @@ def update_orig(image, state): with gr.Tabs(elem_id="train_tabs"): with gr.Tab(label="Create embedding"): - new_embedding_name = gr.Textbox(label="Name") - initialization_text = gr.Textbox(label="Initialization text", value="*") - nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1) - overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding") + new_embedding_name = gr.Textbox(label="Name", elem_id="train_new_embedding_name") + initialization_text = gr.Textbox(label="Initialization text", value="*", elem_id="train_initialization_text") + nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1, elem_id="train_nvpt") + overwrite_old_embedding = gr.Checkbox(value=False, label="Overwrite Old Embedding", elem_id="train_overwrite_old_embedding") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): - create_embedding = gr.Button(value="Create embedding", variant='primary') + create_embedding = gr.Button(value="Create embedding", variant='primary', elem_id="train_create_embedding") with gr.Tab(label="Create hypernetwork"): - new_hypernetwork_name = gr.Textbox(label="Name") - new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"]) - new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'") - new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys) - new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"]) - new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization") - new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout") - overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork") + new_hypernetwork_name = gr.Textbox(label="Name", elem_id="train_new_hypernetwork_name") + new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "1024", "320", "640", "1280"], elem_id="train_new_hypernetwork_sizes") + new_hypernetwork_layer_structure = gr.Textbox("1, 2, 1", label="Enter hypernetwork layer structure", placeholder="1st and last digit must be 1. ex:'1, 2, 1'", elem_id="train_new_hypernetwork_layer_structure") + new_hypernetwork_activation_func = gr.Dropdown(value="linear", label="Select activation function of hypernetwork. Recommended : Swish / Linear(none)", choices=modules.hypernetworks.ui.keys, elem_id="train_new_hypernetwork_activation_func") + new_hypernetwork_initialization_option = gr.Dropdown(value = "Normal", label="Select Layer weights initialization. Recommended: Kaiming for relu-like, Xavier for sigmoid-like, Normal otherwise", choices=["Normal", "KaimingUniform", "KaimingNormal", "XavierUniform", "XavierNormal"], elem_id="train_new_hypernetwork_initialization_option") + new_hypernetwork_add_layer_norm = gr.Checkbox(label="Add layer normalization", elem_id="train_new_hypernetwork_add_layer_norm") + new_hypernetwork_use_dropout = gr.Checkbox(label="Use dropout", elem_id="train_new_hypernetwork_use_dropout") + overwrite_old_hypernetwork = gr.Checkbox(value=False, label="Overwrite Old Hypernetwork", elem_id="train_overwrite_old_hypernetwork") with gr.Row(): with gr.Column(scale=3): gr.HTML(value="") with gr.Column(): - create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary') + create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary', elem_id="train_create_hypernetwork") with gr.Tab(label="Preprocess images"): - process_src = gr.Textbox(label='Source directory') - process_dst = gr.Textbox(label='Destination directory') - process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512) - process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512) - preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"]) + process_src = gr.Textbox(label='Source directory', elem_id="train_process_src") + process_dst = gr.Textbox(label='Destination directory', elem_id="train_process_dst") + process_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_process_width") + process_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_process_height") + preprocess_txt_action = gr.Dropdown(label='Existing Caption txt Action', value="ignore", choices=["ignore", "copy", "prepend", "append"], elem_id="train_preprocess_txt_action") with gr.Row(): - process_flip = gr.Checkbox(label='Create flipped copies') - process_split = gr.Checkbox(label='Split oversized images') - process_focal_crop = gr.Checkbox(label='Auto focal point crop') - process_caption = gr.Checkbox(label='Use BLIP for caption') - process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True) + process_flip = gr.Checkbox(label='Create flipped copies', elem_id="train_process_flip") + process_split = gr.Checkbox(label='Split oversized images', elem_id="train_process_split") + process_focal_crop = gr.Checkbox(label='Auto focal point crop', elem_id="train_process_focal_crop") + process_caption = gr.Checkbox(label='Use BLIP for caption', elem_id="train_process_caption") + process_caption_deepbooru = gr.Checkbox(label='Use deepbooru for caption', visible=True, elem_id="train_process_caption_deepbooru") with gr.Row(visible=False) as process_split_extra_row: - process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05) + process_split_threshold = gr.Slider(label='Split image threshold', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_split_threshold") + process_overlap_ratio = gr.Slider(label='Split image overlap ratio', value=0.2, minimum=0.0, maximum=0.9, step=0.05, elem_id="train_process_overlap_ratio") with gr.Row(visible=False) as process_focal_crop_row: - process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05) - process_focal_crop_debug = gr.Checkbox(label='Create debug image') + process_focal_crop_face_weight = gr.Slider(label='Focal point face weight', value=0.9, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_face_weight") + process_focal_crop_entropy_weight = gr.Slider(label='Focal point entropy weight', value=0.15, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_entropy_weight") + process_focal_crop_edges_weight = gr.Slider(label='Focal point edges weight', value=0.5, minimum=0.0, maximum=1.0, step=0.05, elem_id="train_process_focal_crop_edges_weight") + process_focal_crop_debug = gr.Checkbox(label='Create debug image', elem_id="train_process_focal_crop_debug") with gr.Row(): with gr.Column(scale=3): @@ -1226,8 +1262,8 @@ def update_orig(image, state): with gr.Column(): with gr.Row(): - interrupt_preprocessing = gr.Button("Interrupt") - run_preprocess = gr.Button(value="Preprocess", variant='primary') + interrupt_preprocessing = gr.Button("Interrupt", elem_id="train_interrupt_preprocessing") + run_preprocess = gr.Button(value="Preprocess", variant='primary', elem_id="train_run_preprocess") process_split.change( fn=lambda show: gr_show(show), @@ -1250,31 +1286,31 @@ def update_orig(image, state): train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', elem_id="train_hypernetwork", choices=[x for x in shared.hypernetworks.keys()]) create_refresh_button(train_hypernetwork_name, shared.reload_hypernetworks, lambda: {"choices": sorted([x for x in shared.hypernetworks.keys()])}, "refresh_train_hypernetwork_name") with gr.Row(): - embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005") - hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001") - - batch_size = gr.Number(label='Batch size', value=1, precision=0) - gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0) - dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images") - log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion") - template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt")) - training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512) - training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512) - steps = gr.Number(label='Max steps', value=100000, precision=0) - create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0) - save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0) - save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True) - preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False) + embedding_learn_rate = gr.Textbox(label='Embedding Learning rate', placeholder="Embedding Learning rate", value="0.005", elem_id="train_embedding_learn_rate") + hypernetwork_learn_rate = gr.Textbox(label='Hypernetwork Learning rate', placeholder="Hypernetwork Learning rate", value="0.00001", elem_id="train_hypernetwork_learn_rate") + + batch_size = gr.Number(label='Batch size', value=1, precision=0, elem_id="train_batch_size") + gradient_step = gr.Number(label='Gradient accumulation steps', value=1, precision=0, elem_id="train_gradient_step") + dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images", elem_id="train_dataset_directory") + log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion", elem_id="train_log_directory") + template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"), elem_id="train_template_file") + training_width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="train_training_width") + training_height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="train_training_height") + steps = gr.Number(label='Max steps', value=100000, precision=0, elem_id="train_steps") + create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_create_image_every") + save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0, elem_id="train_save_embedding_every") + save_image_with_stored_embedding = gr.Checkbox(label='Save images with embedding in PNG chunks', value=True, elem_id="train_save_image_with_stored_embedding") + preview_from_txt2img = gr.Checkbox(label='Read parameters (prompt, etc...) from txt2img tab when making previews', value=False, elem_id="train_preview_from_txt2img") with gr.Row(): - shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False) - tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0) + shuffle_tags = gr.Checkbox(label="Shuffle tags by ',' when creating prompts.", value=False, elem_id="train_shuffle_tags") + tag_drop_out = gr.Slider(minimum=0, maximum=1, step=0.1, label="Drop out tags when creating prompts.", value=0, elem_id="train_tag_drop_out") with gr.Row(): - latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random']) + latent_sampling_method = gr.Radio(label='Choose latent sampling method', value="once", choices=['once', 'deterministic', 'random'], elem_id="train_latent_sampling_method") with gr.Row(): - interrupt_training = gr.Button(value="Interrupt") - train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary') - train_embedding = gr.Button(value="Train Embedding", variant='primary') + interrupt_training = gr.Button(value="Interrupt", elem_id="train_interrupt_training") + train_hypernetwork = gr.Button(value="Train Hypernetwork", variant='primary', elem_id="train_train_hypernetwork") + train_embedding = gr.Button(value="Train Embedding", variant='primary', elem_id="train_train_embedding") params = script_callbacks.UiTrainTabParams(txt2img_preview_params) @@ -1447,7 +1483,7 @@ def fun(): res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) else: - with gr.Row(variant="compact"): + with FormRow(): res = comp(label=info.label, value=fun(), elem_id=elem_id, **(args or {})) create_refresh_button(res, info.refresh, info.component_args, "refresh_" + key) else: @@ -1492,41 +1528,36 @@ def run_settings_single(value, key): return gr.update(value=value), opts.dumpjson() with gr.Blocks(analytics_enabled=False) as settings_interface: - settings_submit = gr.Button(value="Apply settings", variant='primary') - result = gr.HTML() + with gr.Row(): + with gr.Column(scale=6): + settings_submit = gr.Button(value="Apply settings", variant='primary', elem_id="settings_submit") + with gr.Column(): + restart_gradio = gr.Button(value='Reload UI', variant='primary', elem_id="settings_restart_gradio") - settings_cols = 3 - items_per_col = int(len(opts.data_labels) * 0.9 / settings_cols) + result = gr.HTML(elem_id="settings_result") quicksettings_names = [x.strip() for x in opts.quicksettings.split(",")] - quicksettings_names = set(x for x in quicksettings_names if x != 'quicksettings') + quicksettings_names = {x: i for i, x in enumerate(quicksettings_names) if x != 'quicksettings'} quicksettings_list = [] - cols_displayed = 0 - items_displayed = 0 previous_section = None - column = None - with gr.Row(elem_id="settings").style(equal_height=False): + current_tab = None + with gr.Tabs(elem_id="settings"): for i, (k, item) in enumerate(opts.data_labels.items()): section_must_be_skipped = item.section[0] is None if previous_section != item.section and not section_must_be_skipped: - if cols_displayed < settings_cols and (items_displayed >= items_per_col or previous_section is None): - if column is not None: - column.__exit__() + elem_id, text = item.section - column = gr.Column(variant='panel') - column.__enter__() + if current_tab is not None: + current_tab.__exit__() - items_displayed = 0 - cols_displayed += 1 + current_tab = gr.TabItem(elem_id="settings_{}".format(elem_id), label=text) + current_tab.__enter__() previous_section = item.section - elem_id, text = item.section - gr.HTML(elem_id="settings_header_text_{}".format(elem_id), value='Will upscale the image by the selected scale factor; use width and height sliders to set tile size
") overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64) - scale_factor = gr.Slider(minimum=1, maximum=4, step=1, label='Scale Factor', value=2) + scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0) upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type="index") return [info, overlap, upscaler_index, scale_factor] diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py index 3e0b28055ae..f92f9776fcf 100644 --- a/scripts/xy_grid.py +++ b/scripts/xy_grid.py @@ -202,7 +202,7 @@ def str_permutations(x): AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None), AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None), AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None), - AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None), + AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), format_value_add_label, None), AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None), AxisOption("VAE", str, apply_vae, format_value_add_label, None), AxisOption("Styles", str, apply_styles, format_value_add_label, None), @@ -267,7 +267,6 @@ def __enter__(self): self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers self.hypernetwork = opts.sd_hypernetwork self.model = shared.sd_model - self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix self.vae = opts.sd_vae def __exit__(self, exc_type, exc_value, tb): @@ -278,7 +277,6 @@ def __exit__(self, exc_type, exc_value, tb): hypernetwork.apply_strength() opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers - opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*") diff --git a/style.css b/style.css index 3ad78006240..2116ec3c815 100644 --- a/style.css +++ b/style.css @@ -73,8 +73,9 @@ margin-right: auto; } -#random_seed, #random_subseed, #reuse_seed, #reuse_subseed, #open_folder{ - min-width: auto; +[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{ + min-width: 2.3em; + height: 2.5em; flex-grow: 0; padding-left: 0.25em; padding-right: 0.25em; @@ -84,27 +85,28 @@ display: none; } -#seed_row, #subseed_row{ +[id$=_seed_row], [id$=_subseed_row]{ gap: 0.5rem; + padding: 0.6em; } -#subseed_show_box{ +[id$=_subseed_show_box]{ min-width: auto; flex-grow: 0; } -#subseed_show_box > div{ +[id$=_subseed_show_box] > div{ border: 0; height: 100%; } -#subseed_show{ +[id$=_subseed_show]{ min-width: auto; flex-grow: 0; padding: 0; } -#subseed_show label{ +[id$=_subseed_show] label{ height: 100%; } @@ -206,24 +208,24 @@ button{ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{ position: absolute; - top: -0.6em; + top: -0.7em; line-height: 1.2em; - padding: 0 0.5em; - margin: 0; + padding: 0; + margin: 0 0.5em; background-color: white; - border-top: 1px solid #eee; - border-left: 1px solid #eee; - border-right: 1px solid #eee; + box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white; z-index: 300; } .dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{ background-color: rgb(31, 41, 55); - border-top: 1px solid rgb(55 65 81); - border-left: 1px solid rgb(55 65 81); - border-right: 1px solid rgb(55 65 81); + box-shadow: 6px 0 6px 0px rgb(31, 41, 55), -6px 0 6px 0px rgb(31, 41, 55); +} + +#txt2img_column_batch, #img2img_column_batch{ + min-width: min(13.5em, 100%) !important; } #settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{ @@ -232,22 +234,40 @@ fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block s margin-right: 8em; } -.gr-panel div.flex-col div.justify-between label span{ - margin: 0; -} - #settings .gr-panel div.flex-col div.justify-between div{ position: relative; z-index: 200; } -input[type="range"]{ - margin: 0.5em 0 -0.3em 0; +#settings{ + display: block; } -#txt2img_sampling label{ - padding-left: 0.6em; - padding-right: 0.6em; +#settings > div{ + border: none; + margin-left: 10em; +} + +#settings > div.flex-wrap{ + float: left; + display: block; + margin-left: 0; + width: 10em; +} + +#settings > div.flex-wrap button{ + display: block; + border: none; + text-align: left; +} + +#settings_result{ + height: 1.4em; + margin: 0 1.2em; +} + +input[type="range"]{ + margin: 0.5em 0 -0.3em 0; } #mask_bug_info { @@ -501,13 +521,6 @@ input[type="range"]{ padding: 0; } -#refresh_sd_model_checkpoint, #refresh_sd_vae, #refresh_sd_hypernetwork, #refresh_train_hypernetwork_name, #refresh_train_embedding_name, #refresh_localization{ - max-width: 2.5em; - min-width: 2.5em; - height: 2.4em; -} - - canvas[key="mask"] { z-index: 12 !important; filter: invert(); @@ -521,7 +534,7 @@ canvas[key="mask"] { position: absolute; right: 0.5em; top: -0.6em; - z-index: 200; + z-index: 400; width: 8em; } #quicksettings .gr-box > div > div > input.gr-text-input { @@ -568,6 +581,53 @@ img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h font-size: 95%; } +#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{ + min-width: auto; + padding-left: 0.5em; + padding-right: 0.5em; +} + +.gr-form{ + background-color: white; +} + +.dark .gr-form{ + background-color: rgb(31 41 55 / var(--tw-bg-opacity)); +} + +.gr-button-tool{ + max-width: 2.5em; + min-width: 2.5em !important; + height: 2.4em; + margin: 0.55em 0; +} + +#quicksettings .gr-button-tool{ + margin: 0; +} + + +#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form { + padding-top: 0.9em; +} + +#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form{ + border: none; + padding-bottom: 0.5em; +} + +footer { + display: none !important; +} + +#footer{ + text-align: center; +} + +#footer div{ + display: inline-block; +} + /* The following handles localization for right-to-left (RTL) languages like Arabic. The rtl media type will only be activated by the logic in javascript/localization.js. If you change anything above, you need to make sure it is RTL compliant by just running diff --git a/v2-inference-v.yaml b/v2-inference-v.yaml new file mode 100644 index 00000000000..513cd635c0d --- /dev/null +++ b/v2-inference-v.yaml @@ -0,0 +1,68 @@ +model: + base_learning_rate: 1.0e-4 + target: ldm.models.diffusion.ddpm.LatentDiffusion + params: + parameterization: "v" + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: crossattn + monitor: val/loss_simple_ema + scale_factor: 0.18215 + use_ema: False # we set this to false because this is an inference only config + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + use_fp16: True + image_size: 32 # unused + in_channels: 4 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" \ No newline at end of file diff --git a/webui.py b/webui.py index 3aee8792477..c7d55a97889 100644 --- a/webui.py +++ b/webui.py @@ -1,4 +1,5 @@ import os +import sys import threading import time import importlib @@ -55,8 +56,8 @@ def initialize(): gfpgan.setup_model(cmd_opts.gfpgan_models_path) shared.face_restorers.append(modules.face_restoration.FaceRestoration()) + modelloader.list_builtin_upscalers() modules.scripts.load_scripts() - modelloader.load_upscalers() modules.sd_vae.refresh_vae_list() @@ -169,23 +170,22 @@ def webui(): modules.script_callbacks.app_started_callback(shared.demo, app) wait_on_server(shared.demo) + print('Restarting UI...') sd_samplers.set_samplers() - print('Reloading extensions') extensions.list_extensions() localization.list_localizations(cmd_opts.localizations_dir) - print('Reloading custom scripts') + modelloader.forbid_loaded_nonbuiltin_upscalers() modules.scripts.reload_scripts() modelloader.load_upscalers() - print('Reloading modules: modules.ui') - importlib.reload(modules.ui) - print('Refreshing Model List') + for module in [module for name, module in sys.modules.items() if name.startswith("modules.ui")]: + importlib.reload(module) + modules.sd_models.list_models() - print('Restarting Gradio') if __name__ == "__main__":