From caf84e8233236cd36fcd42e39e4199262dc3f4e7 Mon Sep 17 00:00:00 2001 From: catboxanon <122327233+catboxanon@users.noreply.github.com> Date: Wed, 22 Mar 2023 17:51:40 +0000 Subject: Expose inpainting mask and composite For inpainting, this exposes the mask and masked composite and gives the user the ability to display these in the web UI, save to disk, or both. --- modules/processing.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) (limited to 'modules/processing.py') diff --git a/modules/processing.py b/modules/processing.py index 59717b4c..2e5a363f 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -689,6 +689,22 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed: image.info["parameters"] = text output_images.append(image) + if hasattr(p, 'mask_for_overlay') and p.mask_for_overlay: + image_mask = p.mask_for_overlay.convert('RGB') + image_mask_composite = Image.composite(image.convert('RGBA').convert('RGBa'), Image.new('RGBa', image.size), p.mask_for_overlay.convert('L')).convert('RGBA') + + if opts.save_mask: + images.save_image(image_mask, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask") + + if opts.save_mask_composite: + images.save_image(image_mask_composite, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-mask-composite") + + if opts.return_mask: + output_images.append(image_mask) + + if opts.return_mask_composite: + output_images.append(image_mask_composite) + del x_samples_ddim devices.torch_gc() -- cgit v1.2.3 From 68999d0b15d612965e7bc7feb62d6b4d55e112fa Mon Sep 17 00:00:00 2001 From: space-nuko <24979496+space-nuko@users.noreply.github.com> Date: Sat, 25 Mar 2023 12:52:14 -0400 Subject: Add upscale slider to img2img --- javascript/ui.js | 25 +- modules/generation_parameters_copypaste.py | 3 + modules/img2img.py | 3 +- modules/processing.py | 18 +- modules/ui.py | 67 ++- style.css | 772 +++++++++++++++++++---------- 6 files changed, 623 insertions(+), 265 deletions(-) (limited to 'modules/processing.py') diff --git a/javascript/ui.js b/javascript/ui.js index fcaf5608..8aa4a459 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -8,8 +8,8 @@ function set_theme(theme){ } function selected_gallery_index(){ - var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item') - var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .gallery-item.\\!ring-2') + var buttons = gradioApp().querySelectorAll('[style="display: block;"].tabitem div[id$=_gallery] .thumbnails > .thumbnail-item') + var button = gradioApp().querySelector('[style="display: block;"].tabitem div[id$=_gallery] .thumbnails > .thumbnail-item.selected') var result = -1 buttons.forEach(function(v, i){ if(v==button) { result = i } }) @@ -111,6 +111,14 @@ function get_img2img_tab_index() { return res } +function get_img2img_tab_index_for_res_preview() { + let res = args_to_array(arguments) + res.splice(-1) // gradio also sends outputs to the arguments, pop them off + res[0] = get_tab_index('mode_img2img') + debugger; + return res +} + function create_submit_args(args){ res = [] for(var i=0;i 1) + setInactive(i2iHeight, scale > 1) + + return [init_img, width, height, scale, resize_mode] +} diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 6df76858..459de080 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -282,6 +282,9 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model res["Hires resize-1"] = 0 res["Hires resize-2"] = 0 + if "Img2Img Upscale" not in res: + res["Img2Img Upscale"] = 1 + restore_old_hires_fix_params(res) return res diff --git a/modules/img2img.py b/modules/img2img.py index c973b770..d05fa750 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): +def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): override_settings = create_override_settings_dict(override_settings_texts) is_batch = mode == 5 @@ -149,6 +149,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, override_settings=override_settings, + scale=scale, ) p.scripts = modules.scripts.scripts_txt2img diff --git a/modules/processing.py b/modules/processing.py index 2e5a363f..fc4b166c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): + def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -949,11 +949,27 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = None self.nmask = None self.image_conditioning = None + self.scale = scale + + def get_final_size(self): + if self.scale > 1: + img = self.init_images[0] + width = int(img.width * self.scale) + height = int(img.height * self.scale) + return width, height + else: + return self.width, self.height + def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None + if self.scale > 1: + self.extra_generation_params["Img2Img Upscale"] = self.scale + + self.width, self.height = self.get_final_size() + image_mask = self.image_mask if image_mask is not None: diff --git a/modules/ui.py b/modules/ui.py index af8546c2..bb548f92 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -15,6 +15,7 @@ import warnings import gradio as gr import gradio.routes import gradio.utils +from gradio.events import Releaseable import numpy as np from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call @@ -138,6 +139,26 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}" +def calc_resolution_img2img(mode, scale, resize_x, resize_y, resize_mode, *i2i_images): + init_img = None + if mode in {0, 1, 3, 4}: + init_img = i2i_images[mode] + elif mode == 2: + init_img = i2i_images[mode]["image"] + + if not init_img: + return "" + + if scale > 1: + width = int(init_img.width * scale) + height = int(init_img.height * scale) + else: + width = resize_x + height = resize_y + + return f"resize: from {init_img.width}x{init_img.height} to {width}x{height}" + + def apply_styles(prompt, prompt_neg, styles): prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles) prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles) @@ -755,8 +776,13 @@ def create_ui(): elif category == "dimensions": with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): - width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") - height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") + with FormRow(variant="compact"): + final_resolution = FormHTML(value="", elem_id="img2img_finalres", label="Upscaled resolution", interactive=False) + with FormRow(variant="compact"): + scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=1.0, elem_id="img2img_scale") + with FormRow(variant="compact"): + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") @@ -824,6 +850,41 @@ def create_ui(): outputs=[inpaint_controls, mask_alpha], ) + img2img_resolution_preview_inputs = [dummy_component, # filled in by selected img2img tab index in _js + scale, width, height, resize_mode, + init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint] + for input in img2img_resolution_preview_inputs: + if isinstance(input, Releaseable): + input.release( + fn=calc_resolution_img2img, + _js="get_img2img_tab_index_for_res_preview", + inputs=img2img_resolution_preview_inputs, + outputs=[final_resolution], + show_progress=False, + ) + input.release( + None, + _js="onCalcResolutionImg2Img", + inputs=img2img_resolution_preview_inputs, + outputs=[], + show_progress=False, + ) + else: + input.change( + fn=calc_resolution_img2img, + _js="get_img2img_tab_index_for_res_preview", + inputs=img2img_resolution_preview_inputs, + outputs=[final_resolution], + show_progress=False, + ) + input.change( + None, + _js="onCalcResolutionImg2Img", + inputs=img2img_resolution_preview_inputs, + outputs=[], + show_progress=False, + ) + img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) @@ -872,6 +933,7 @@ def create_ui(): subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, + scale, resize_mode, inpaint_full_res, inpaint_full_res_padding, @@ -957,6 +1019,7 @@ def create_ui(): (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), + (scale, "Img2Img Upscale"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), diff --git a/style.css b/style.css index 0dcc3e25..7d58b3b2 100644 --- a/style.css +++ b/style.css @@ -1,316 +1,270 @@ - -/* general gradio fixes */ - -:root, .dark{ - --checkbox-label-gap: 0.25em 0.1em; - --section-header-text-size: 12pt; - --block-background-fill: transparent; +.container { + max-width: 100%; } -.block.padded{ - padding: 0 !important; +.token-counter{ + position: absolute; + display: inline-block; + right: 2em; + min-width: 0 !important; + width: auto; + z-index: 100; } -div.gradio-container{ - max-width: unset !important; +.token-counter.error span{ + box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075); + border: 2px solid rgba(255,0,0,0.4) !important; } -.hidden{ - display: none; +.token-counter div{ + display: inline; } -.compact{ - background: transparent !important; - padding: 0 !important; +.token-counter span{ + padding: 0.1em 0.75em; } -div.form{ - border-width: 0; - box-shadow: none; - background: transparent; - overflow: visible; - gap: 0.5em; +#sh{ + min-width: 2em; + min-height: 2em; + max-width: 2em; + max-height: 2em; + flex-grow: 0; + padding-left: 0.25em; + padding-right: 0.25em; + margin: 0.1em 0; + opacity: 0%; + cursor: default; } -.block.gradio-dropdown, -.block.gradio-slider, -.block.gradio-checkbox, -.block.gradio-textbox, -.block.gradio-radio, -.block.gradio-checkboxgroup, -.block.gradio-number, -.block.gradio-colorpicker -{ - border-width: 0 !important; - box-shadow: none !important; -} +.output-html p {margin: 0 0.5em;} -.gap.compact{ - padding: 0; - gap: 0.2em 0; +.row > *, +.row > .gr-form > * { + min-width: min(120px, 100%); + flex: 1 1 0%; } -div.compact{ - gap: 1em; +.performance { + font-size: 0.85em; + color: #444; } -.gradio-dropdown ul.options{ - z-index: 3000; +.performance p{ + display: inline-block; } -.gradio-dropdown label span:not(.has-info), -.gradio-textbox label span:not(.has-info), -.gradio-number label span:not(.has-info) -{ - margin-bottom: 0; +.performance .time { + margin-right: 0; } -.gradio-dropdown div.wrap.wrap.wrap.wrap{ - box-shadow: 0 1px 2px 0 rgba(0, 0, 0, 0.05); +.performance .vram { } -.gradio-dropdown .wrap-inner.wrap-inner.wrap-inner{ - flex-wrap: unset; +#txt2img_generate, #img2img_generate { + min-height: 4.5em; } -.gradio-dropdown .single-select{ - white-space: nowrap; - overflow: hidden; +@media screen and (min-width: 2500px) { + #txt2img_gallery, #img2img_gallery { + min-height: 768px; + } } -.gradio-dropdown .token-remove.remove-all.remove-all{ - display: none; +#txt2img_gallery img, #img2img_gallery img{ + object-fit: scale-down; } - -.gradio-dropdown.multiselect .token-remove.remove-all.remove-all{ - display: flex; +#txt2img_actions_column, #img2img_actions_column { + margin: 0.35rem 0.75rem 0.35rem 0; } - -.gradio-slider input[type="number"]{ - width: 6em; +#script_list { + padding: .625rem .75rem 0 .625rem; } - -.block.gradio-checkbox { - margin: 0.75em 1.5em 0 0; +.justify-center.overflow-x-scroll { + justify-content: left; } -.gradio-html div.wrap{ - height: 100%; -} -div.gradio-html.min{ - min-height: 0; +.justify-center.overflow-x-scroll button:first-of-type { + margin-left: auto; } -.block.gradio-gallery{ - background: var(--input-background-fill); +.justify-center.overflow-x-scroll button:last-of-type { + margin-right: auto; } -.gradio-container .prose a, .gradio-container .prose a:visited{ - color: unset; - text-decoration: none; +[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; } - - -/* general styled components */ - -.gradio-button.tool{ - max-width: 2.2em; - min-width: 2.2em !important; - height: 2.4em; - align-self: end; - line-height: 1em; - border-radius: 0.5em; +#hidden_element{ + display: none; } -.checkboxes-row{ - margin-bottom: 0.5em; - margin-left: 0em; +[id$=_seed_row], [id$=_subseed_row]{ + gap: 0.5rem; + padding: 0.6em; } -.checkboxes-row > div{ - flex: 0; - white-space: nowrap; + +[id$=_subseed_show_box]{ min-width: auto; + flex-grow: 0; } -button.custom-button{ - border-radius: var(--button-large-radius); - padding: var(--button-large-padding); - font-weight: var(--button-large-text-weight); - border: var(--button-border-width) solid var(--button-secondary-border-color); - background: var(--button-secondary-background-fill); - color: var(--button-secondary-text-color); - font-size: var(--button-large-text-size); - display: inline-flex; - justify-content: center; - align-items: center; - transition: var(--button-transition); - box-shadow: var(--button-shadow); - text-align: center; +[id$=_subseed_show_box] > div{ + border: 0; + height: 100%; } - -/* txt2img/img2img specific */ - -.block.token-counter{ - position: absolute; - display: inline-block; - right: 1em; - min-width: 0 !important; - width: auto; - z-index: 100; - top: -0.75em; +[id$=_subseed_show]{ + min-width: auto; + flex-grow: 0; + padding: 0; } -.block.token-counter span{ - background: var(--input-background-fill) !important; - box-shadow: 0 0 0.0 0.3em rgba(192,192,192,0.15), inset 0 0 0.6em rgba(192,192,192,0.075); - border: 2px solid rgba(192,192,192,0.4) !important; - border-radius: 0.4em; +[id$=_subseed_show] label{ + height: 100%; } -.block.token-counter.error span{ - box-shadow: 0 0 0.0 0.3em rgba(255,0,0,0.15), inset 0 0 0.6em rgba(255,0,0,0.075); - border: 2px solid rgba(255,0,0,0.4) !important; +#txt2img_actions_column, #img2img_actions_column{ + gap: 0; + margin-right: .75rem; } -.block.token-counter div{ - display: inline; +#txt2img_tools, #img2img_tools{ + gap: 0.4em; } -.block.token-counter span{ - padding: 0.1em 0.75em; +#interrogate_col{ + min-width: 0 !important; + max-width: 8em !important; + margin-right: 1em; + gap: 0; } - -[id$=_subseed_show]{ - min-width: auto !important; - flex-grow: 0 !important; - display: flex; +#interrogate, #deepbooru{ + margin: 0em 0.25em 0.5em 0.25em; + min-width: 8em; + max-width: 8em; } -[id$=_subseed_show] label{ - margin-bottom: 0.5em; - align-self: end; +#style_pos_col, #style_neg_col{ + min-width: 8em !important; } -.performance { - font-size: 0.85em; - color: #444; +#txt2img_styles_row, #img2img_styles_row{ + gap: 0.25em; + margin-top: 0.3em; } -.performance p{ - display: inline-block; +#txt2img_styles_row > button, #img2img_styles_row > button{ + margin: 0; } -.performance .time { - margin-right: 0; +#txt2img_styles, #img2img_styles{ + padding: 0; } -.performance .vram { +#txt2img_styles > label > div, #img2img_styles > label > div{ + min-height: 3.2em; } -#txt2img_generate, #img2img_generate { - min-height: 4.5em; +ul.list-none{ + max-height: 35em; + z-index: 2000; } -@media screen and (min-width: 2500px) { - #txt2img_gallery, #img2img_gallery { - min-height: 768px; - } +.gr-form{ + background: transparent; } -#txt2img_gallery img, #img2img_gallery img{ - object-fit: scale-down; -} -#txt2img_actions_column, #img2img_actions_column { - gap: 0.5em; +.my-4{ + margin-top: 0; + margin-bottom: 0; } -#txt2img_tools, #img2img_tools{ - gap: 0.4em; + +#resize_mode{ + flex: 1.5; } -.interrogate-col{ - min-width: 0 !important; - max-width: fit-content; - gap: 0.5em; +button{ + align-self: stretch !important; } -.interrogate-col > button{ - flex: 1; + +.overflow-hidden, .gr-panel{ + overflow: visible !important; } -.generate-box{ - position: relative; +#x_type, #y_type{ + max-width: 10em; } -.gradio-button.generate-box-skip, .gradio-button.generate-box-interrupt{ + +#txt2img_preview, #img2img_preview, #ti_preview{ position: absolute; - width: 50%; - height: 100%; - display: none; - background: #b4c0cc; -} -.gradio-button.generate-box-skip:hover, .gradio-button.generate-box-interrupt:hover{ - background: #c2cfdb; -} -.gradio-button.generate-box-interrupt{ + width: 320px; left: 0; - border-radius: 0.5rem 0 0 0.5rem; -} -.gradio-button.generate-box-skip{ right: 0; - border-radius: 0 0.5rem 0.5rem 0; + margin-left: auto; + margin-right: auto; + margin-top: 34px; + z-index: 100; + border: none; + border-top-left-radius: 0; + border-top-right-radius: 0; } -#txtimg_hr_finalres{ - min-height: 0 !important; - padding: .625rem .75rem; - margin-left: -0.75em +@media screen and (min-width: 768px) { + #txt2img_preview, #img2img_preview, #ti_preview { + position: absolute; + } } -#txtimg_hr_finalres .resolution{ - font-weight: bold; +@media screen and (max-width: 767px) { + #txt2img_preview, #img2img_preview, #ti_preview { + position: relative; + } } -.inactive{ - opacity: 0.5; +#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{ + display: none; } -[id$=_column_batch]{ - min-width: min(13.5em, 100%) !important; -} +fieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{ + position: absolute; + top: -0.7em; + line-height: 1.2em; + padding: 0; + margin: 0 0.5em; -div.dimensions-tools{ - min-width: 0 !important; - max-width: fit-content; - flex-direction: row; - align-content: center; -} + background-color: white; + box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white; -#mode_img2img .gradio-image > div.fixed-height, #mode_img2img .gradio-image > div.fixed-height img{ - height: 480px !important; - max-height: 480px !important; - min-height: 480px !important; + z-index: 300; } -.image-buttons button{ - min-width: auto; +.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); + box-shadow: none; + border: 1px solid rgba(128, 128, 128, 0.1); + border-radius: 6px; + padding: 0.1em 0.5em; } -.infotext { - overflow-wrap: break-word; +#txt2img_column_batch, #img2img_column_batch{ + min-width: min(13.5em, 100%) !important; } -/* settings */ -#quicksettings { - width: fit-content; +#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{ + position: relative; + border: none; + margin-right: 8em; } -#quicksettings > div, #quicksettings > fieldset{ - max-width: 24em; - min-width: 24em; - padding: 0; - border: none; - box-shadow: none; - background: none; +#settings .gr-panel div.flex-col div.justify-between div{ + position: relative; + z-index: 200; } #settings{ @@ -322,18 +276,17 @@ div.dimensions-tools{ margin-left: 10em; } -#settings > div.tab-nav{ +#settings > div.flex-wrap{ float: left; display: block; margin-left: 0; width: 10em; } -#settings > div.tab-nav button{ +#settings > div.flex-wrap button{ display: block; border: none; text-align: left; - white-space: initial; } #settings_result{ @@ -341,8 +294,29 @@ div.dimensions-tools{ margin: 0 1.2em; } +input[type="range"]{ + margin: 0.5em 0 -0.3em 0; +} + +#mask_bug_info { + text-align: center; + display: block; + margin-top: -0.75em; + margin-bottom: -0.75em; +} + +#txt2img_negative_prompt, #img2img_negative_prompt{ +} + +/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */ +.transition.opacity-20 { + opacity: 1 !important; +} + +/* more gradio's garbage cleanup */ +.min-h-\[4rem\] { min-height: unset !important; } +.min-h-\[6rem\] { min-height: unset !important; } -/* live preview */ .progressDiv{ position: relative; height: 20px; @@ -388,8 +362,6 @@ div.dimensions-tools{ height: 100%; } -/* fullscreen popup (ie in Lora's (i) button) */ - .popup-metadata{ color: black; background: white; @@ -430,54 +402,87 @@ div.dimensions-tools{ padding: 2em; } -/* fullpage image viewer */ - #lightboxModal{ - display: none; - position: fixed; - z-index: 1001; - left: 0; - top: 0; - width: 100%; - height: 100%; - overflow: auto; - background-color: rgba(20, 20, 20, 0.95); - user-select: none; - -webkit-user-select: none; - flex-direction: column; + display: none; + position: fixed; + z-index: 1001; + padding-top: 100px; + left: 0; + top: 0; + width: 100%; + height: 100%; + overflow: auto; + background-color: rgba(20, 20, 20, 0.95); + user-select: none; + -webkit-user-select: none; } .modalControls { - display: flex; - gap: 1em; - padding: 1em; + display: grid; + grid-template-columns: 32px 32px 32px 1fr 32px; + grid-template-areas: "zoom tile save space close"; + position: absolute; + top: 0; + left: 0; + right: 0; + padding: 16px; + gap: 16px; background-color: rgba(0,0,0,0.2); } + .modalClose { - margin-left: auto; + grid-area: close; +} + +.modalZoom { + grid-area: zoom; } -.modalControls span{ + +.modalSave { + grid-area: save; +} + +.modalTileImage { + grid-area: tile; +} + +.modalClose, +.modalZoom, +.modalTileImage { + color: white; + font-size: 35px; + font-weight: bold; + cursor: pointer; +} + +.modalSave { color: white; - font-size: 35px; + font-size: 28px; + margin-top: 8px; font-weight: bold; cursor: pointer; - width: 1em; } -.modalControls span:hover, .modalControls span:focus{ - color: #999; - text-decoration: none; +.modalClose:hover, +.modalClose:focus, +.modalSave:hover, +.modalSave:focus, +.modalZoom:hover, +.modalZoom:focus { + color: #999; + text-decoration: none; + cursor: pointer; } -#lightboxModal > img { +#modalImage { display: block; margin: auto; width: auto; } -#lightboxModal > img.modalImageFullscreen{ +.modalImageFullscreen { object-fit: contain; - height: 100%; + height: 90%; } .modalPrev, @@ -507,7 +512,45 @@ div.dimensions-tools{ background-color: rgba(0, 0, 0, 0.8); } -/* context menu (ie for the generate button) */ +#imageARPreview{ + position:absolute; + top:0px; + left:0px; + border:2px solid red; + background:rgba(255, 0, 0, 0.3); + z-index: 900; + pointer-events:none; + display:none +} + +#txt2img_generate_box, #img2img_generate_box{ + position: relative; +} + +#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{ + position: absolute; + width: 50%; + height: 100%; + background: #b4c0cc; + display: none; +} + +#txt2img_interrupt, #img2img_interrupt{ + left: 0; + border-radius: 0.5rem 0 0 0.5rem; +} +#txt2img_skip, #img2img_skip{ + right: 0; + border-radius: 0 0.5rem 0.5rem 0; +} + +.red { + color: red; +} + +.gallery-item { + --tw-bg-opacity: 0 !important; +} #context-menu{ z-index:9999; @@ -536,8 +579,61 @@ div.dimensions-tools{ background: #a55000; } +#quicksettings { + width: fit-content; +} + +#quicksettings > div, #quicksettings > fieldset{ + max-width: 24em; + min-width: 24em; + padding: 0; + border: none; + box-shadow: none; + background: none; + margin-right: 10px; +} + +#quicksettings > div > div > div > label > span { + position: relative; + margin-right: 9em; + margin-bottom: -1em; +} + +canvas[key="mask"] { + z-index: 12 !important; + filter: invert(); + mix-blend-mode: multiply; + pointer-events: none; +} + + +/* gradio 3.4.1 stuff for editable scrollbar values */ +.gr-box > div > div > input.gr-text-input{ + position: absolute; + right: 0.5em; + top: -0.6em; + z-index: 400; + width: 6em; +} +#quicksettings .gr-box > div > div > input.gr-text-input { + top: -1.12em; +} + +.row.gr-compact{ + overflow: visible; +} + +#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img, +#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img, +#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img, +#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img +{ + height: 480px !important; + max-height: 480px !important; + min-height: 480px !important; +} -/* extensions */ +/* Extensions */ #tab_extensions table{ border-collapse: collapse; @@ -550,7 +646,6 @@ div.dimensions-tools{ #tab_extensions table input[type="checkbox"]{ margin-right: 0.5em; - appearance: checkbox; } #tab_extensions button{ @@ -575,7 +670,74 @@ div.dimensions-tools{ font-size: 90%; } -/* replace original footer with ours */ +#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, .gr-button-tool-top{ + max-width: 2.5em; + min-width: 2.5em !important; + height: 2.4em; +} + +.gr-button-tool{ + margin: 0.6em 0em 0.55em 0; +} + +.gr-button-tool-top, #settings .gr-button-tool{ + margin: 1.6em 0.7em 0.55em 0; +} + + +#modelmerger_results_container{ + margin-top: 1em; + overflow: visible; +} + +#modelmerger_models{ + gap: 0; +} + + +#quicksettings .gr-button-tool{ + margin: 0; + border-color: unset; + background-color: unset; +} + +#modelmerger_interp_description>p { + margin: 0!important; + text-align: center; +} +#modelmerger_interp_description { + margin: 0.35rem 0.75rem 1.23rem; +} +#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form { + padding-top: 0.9em; + padding-bottom: 0.9em; +} +#txt2img_settings { + padding-top: 1.16em; + padding-bottom: 0.9em; +} +#img2img_settings { + padding-bottom: 0.9em; +} + +#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{ + border: none; + padding-bottom: 0.5em; +} footer { display: none !important; @@ -594,7 +756,99 @@ footer { opacity: 0.85; } -/* extra networks UI */ +#txtimg_hr_finalres{ + min-height: 0 !important; + padding: .625rem .75rem; + margin-left: -0.75em +} + +#txtimg_hr_finalres .resolution{ + font-weight: bold; +} + +#txt2img_checkboxes, #img2img_checkboxes{ + margin-bottom: 0.5em; + margin-left: 0em; +} +#txt2img_checkboxes > div, #img2img_checkboxes > div{ + flex: 0; + white-space: nowrap; + min-width: auto; +} + +#img2img_finalres{ + min-height: 0 !important; + padding: .625rem .75rem; + margin-left: -0.75em +} + +#img2img_finalres .resolution{ + font-weight: bold; +} + +#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{ + margin-left: 0em; +} + +#axis_options { + margin-left: 0em; +} + +.inactive{ + opacity: 0.5; +} + +[id*='_prompt_container']{ + gap: 0; +} + +[id*='_prompt_container'] > div{ + margin: -0.4em 0 0 0; +} + +.gr-compact { + border: none; +} + +.dark .gr-compact{ + background-color: rgb(31 41 55 / var(--tw-bg-opacity)); + margin-left: 0; +} + +.gr-compact{ + overflow: visible; +} + +.gr-compact > *{ +} + +.gr-compact .gr-block, .gr-compact .gr-form{ + border: none; + box-shadow: none; +} + +.gr-compact .gr-box{ + border-radius: .5rem !important; + border-width: 1px !important; +} + +#mode_img2img > div > div{ + gap: 0 !important; +} + +[id*='img2img_copy_to_'] { + border: none; +} + +[id*='img2img_copy_to_'] > button { +} + +[id*='img2img_label_copy_to_'] { + font-size: 1.0em; + font-weight: bold; + text-align: center; + line-height: 2.4em; +} .extra-networks > div > [id *= '_extra_']{ margin: 0.3em; @@ -607,12 +861,12 @@ footer { .extra-network-subdirs button{ margin: 0 0.15em; } -.extra-networks .tab-nav .search{ + +#txt2img_extra_networks .search, #img2img_extra_networks .search{ display: inline-block; max-width: 16em; margin: 0.3em; align-self: center; - width: 16em; } #txt2img_extra_view, #img2img_extra_view { @@ -644,7 +898,6 @@ footer { text-shadow: 2px 2px 3px black; padding: 0.25em; font-size: 22pt; - width: 1.5em; } .extra-network-cards .card:hover .metadata-button, .extra-network-thumbs .card:hover .metadata-button{ display: inline-block; @@ -738,15 +991,12 @@ footer { left: 0; right: 0; padding: 0.5em; + color: white; background: rgba(0,0,0,0.5); box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5); text-shadow: 0 0 0.2em black; } -.extra-network-cards .card .actions *{ - color: white; -} - .extra-network-cards .card .actions:hover{ box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important; } @@ -784,3 +1034,7 @@ footer { .extra-network-cards .card ul a:hover{ color: red; } + +[id*='_prompt_container'] > div { + margin: 0!important; +} -- cgit v1.2.3 From 7ea5d395c44be208f654b07ec7993aa2952f2510 Mon Sep 17 00:00:00 2001 From: space-nuko <24979496+space-nuko@users.noreply.github.com> Date: Sun, 19 Feb 2023 03:45:43 -0800 Subject: Add upscaler to img2img --- modules/generation_parameters_copypaste.py | 4 ++-- modules/img2img.py | 3 ++- modules/processing.py | 23 +++++++++++++++++------ modules/ui.py | 10 +++++++--- scripts/xyz_grid.py | 1 + style.css | 2 +- 6 files changed, 30 insertions(+), 13 deletions(-) (limited to 'modules/processing.py') diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 459de080..0ad2ad4f 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -282,8 +282,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model res["Hires resize-1"] = 0 res["Hires resize-2"] = 0 - if "Img2Img Upscale" not in res: - res["Img2Img Upscale"] = 1 + if "Img2Img upscale" not in res: + res["Img2Img upscale"] = 1 restore_old_hires_fix_params(res) diff --git a/modules/img2img.py b/modules/img2img.py index d05fa750..959dd96e 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): +def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, upscaler: str, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): override_settings = create_override_settings_dict(override_settings_texts) is_batch = mode == 5 @@ -150,6 +150,7 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s inpainting_mask_invert=inpainting_mask_invert, override_settings=override_settings, scale=scale, + upscaler=upscaler, ) p.scripts = modules.scripts.scripts_txt2img diff --git a/modules/processing.py b/modules/processing.py index fc4b166c..afb8cfd1 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -929,7 +929,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, **kwargs): + def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -950,6 +950,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.nmask = None self.image_conditioning = None self.scale = scale + self.upscaler = upscaler def get_final_size(self): if self.scale > 1: @@ -966,7 +967,16 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): crop_region = None if self.scale > 1: - self.extra_generation_params["Img2Img Upscale"] = self.scale + self.extra_generation_params["Img2Img upscale"] = self.scale + + # Non-latent upscalers are run before sampling + # Latent upscalers are run during sampling + init_upscaler = None + if self.upscaler is not None: + self.extra_generation_params["Img2Img upscaler"] = self.upscaler + if self.upscaler not in shared.latent_upscale_modes: + assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}" + init_upscaler = self.upscaler self.width, self.height = self.get_final_size() @@ -992,7 +1002,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: - image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height) + image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler) np_mask = np.array(image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) @@ -1009,7 +1019,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: - image = images.resize_image(self.resize_mode, image, self.width, self.height) + image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler) if image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) @@ -1054,8 +1064,9 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) - if self.resize_mode == 3: - self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") + latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") + if latent_scale_mode is not None: + self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) if image_mask is not None: init_mask = latent_mask diff --git a/modules/ui.py b/modules/ui.py index bb548f92..24ab0af7 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -767,7 +767,7 @@ def create_ui(): ) with FormRow(): - resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize") + resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") for category in ordered_ui_categories(): if category == "sampler": @@ -797,7 +797,9 @@ def create_ui(): with FormRow(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit") - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") + with FormRow(): + upscaler = gr.Dropdown(label="Upscaler", elem_id="img2img_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") elif category == "seed": seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img') @@ -934,6 +936,7 @@ def create_ui(): height, width, scale, + upscaler, resize_mode, inpaint_full_res, inpaint_full_res_padding, @@ -1019,7 +1022,8 @@ def create_ui(): (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), - (scale, "Img2Img Upscale"), + (scale, "Img2Img upscale"), + (upscaler, "Img2Img upscaler"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 3895a795..3f6c1997 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -220,6 +220,7 @@ axis_options = [ AxisOption("Clip skip", int, apply_clip_skip), AxisOption("Denoising", float, apply_field("denoising_strength")), AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), + AxisOptionImg2Img("Upscaler", str, apply_field("upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")), AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)), AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)), diff --git a/style.css b/style.css index 7d58b3b2..e824256f 100644 --- a/style.css +++ b/style.css @@ -779,7 +779,7 @@ footer { #img2img_finalres{ min-height: 0 !important; padding: .625rem .75rem; - margin-left: -0.75em + margin-left: 0.25em } #img2img_finalres .resolution{ -- cgit v1.2.3 From 8a34671fe91e142bce9e5556cca2258b3be9dd6e Mon Sep 17 00:00:00 2001 From: MrCheeze Date: Fri, 24 Mar 2023 22:48:16 -0400 Subject: Add support for the Variations models (unclip-h and unclip-l) --- launch.py | 2 +- models/karlo/ViT-L-14_stats.th | Bin 0 -> 7079 bytes modules/lowvram.py | 10 ++++++---- modules/processing.py | 41 +++++++++++++++++++++++++++----------- modules/sd_models.py | 5 +++++ modules/sd_models_config.py | 7 +++++++ modules/sd_samplers_compvis.py | 31 +++++++++++++++++++++------- modules/sd_samplers_kdiffusion.py | 19 ++++++++++++------ 8 files changed, 85 insertions(+), 30 deletions(-) create mode 100644 models/karlo/ViT-L-14_stats.th (limited to 'modules/processing.py') diff --git a/launch.py b/launch.py index b943fed2..e70df7ba 100644 --- a/launch.py +++ b/launch.py @@ -252,7 +252,7 @@ def prepare_environment(): 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', "47b6b607fdd31875c9279cd2f4f16b92e4ea958e") + 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', "5b3af030dd83e0297272d861c19477735d0317ec") codeformer_commit_hash = os.environ.get('CODEFORMER_COMMIT_HASH', "c5b4593074ba6214284d6acd5f1719b6c5d739af") diff --git a/models/karlo/ViT-L-14_stats.th b/models/karlo/ViT-L-14_stats.th new file mode 100644 index 00000000..a6a06e94 Binary files /dev/null and b/models/karlo/ViT-L-14_stats.th differ diff --git a/modules/lowvram.py b/modules/lowvram.py index 042a0254..e254cc13 100644 --- a/modules/lowvram.py +++ b/modules/lowvram.py @@ -55,12 +55,12 @@ def setup_for_low_vram(sd_model, use_medvram): if hasattr(sd_model.cond_stage_model, 'model'): sd_model.cond_stage_model.transformer = sd_model.cond_stage_model.model - # remove four big modules, cond, first_stage, depth (if applicable), and unet from the model and then + # remove several big modules: cond, first_stage, depth/embedder (if applicable), and unet from the model and then # send the model to GPU. Then put modules back. the modules will be in CPU. - stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), sd_model.model - sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = None, None, None, None + stored = sd_model.cond_stage_model.transformer, sd_model.first_stage_model, getattr(sd_model, 'depth_model', None), getattr(sd_model, 'embedder', None), sd_model.model + sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = None, None, None, None, None sd_model.to(devices.device) - sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.model = stored + sd_model.cond_stage_model.transformer, sd_model.first_stage_model, sd_model.depth_model, sd_model.embedder, sd_model.model = stored # register hooks for those the first three models sd_model.cond_stage_model.transformer.register_forward_pre_hook(send_me_to_gpu) @@ -69,6 +69,8 @@ def setup_for_low_vram(sd_model, use_medvram): sd_model.first_stage_model.decode = first_stage_model_decode_wrap if sd_model.depth_model: sd_model.depth_model.register_forward_pre_hook(send_me_to_gpu) + if sd_model.embedder: + sd_model.embedder.register_forward_pre_hook(send_me_to_gpu) parents[sd_model.cond_stage_model.transformer] = sd_model.cond_stage_model if hasattr(sd_model.cond_stage_model, 'model'): diff --git a/modules/processing.py b/modules/processing.py index 59717b4c..1451811c 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -78,21 +78,27 @@ def apply_overlay(image, paste_loc, index, overlays): def txt2img_image_conditioning(sd_model, x, width, height): - if sd_model.model.conditioning_key not in {'hybrid', 'concat'}: - # Dummy zero conditioning if we're not using inpainting model. - # Still takes up a bit of memory, but no encoder call. - # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. - return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) + if sd_model.model.conditioning_key in {'hybrid', 'concat'}: # Inpainting models - # The "masked-image" in this case will just be all zeros since the entire image is masked. - image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) - image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning)) + # The "masked-image" in this case will just be all zeros since the entire image is masked. + image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device) + image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning)) - # Add the fake full 1s mask to the first dimension. - image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) - image_conditioning = image_conditioning.to(x.dtype) + # Add the fake full 1s mask to the first dimension. + image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0) + image_conditioning = image_conditioning.to(x.dtype) - return image_conditioning + return image_conditioning + + elif sd_model.model.conditioning_key == "crossattn-adm": # UnCLIP models + + return x.new_zeros(x.shape[0], 2*sd_model.noise_augmentor.time_embed.dim, dtype=x.dtype, device=x.device) + + else: + # Dummy zero conditioning if we're not using inpainting or unclip models. + # Still takes up a bit of memory, but no encoder call. + # Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size. + return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device) class StableDiffusionProcessing: @@ -190,6 +196,14 @@ class StableDiffusionProcessing: return conditioning_image + def unclip_image_conditioning(self, source_image): + c_adm = self.sd_model.embedder(source_image) + if self.sd_model.noise_augmentor is not None: + noise_level = 0 # TODO: Allow other noise levels? + c_adm, noise_level_emb = self.sd_model.noise_augmentor(c_adm, noise_level=repeat(torch.tensor([noise_level]).to(c_adm.device), '1 -> b', b=c_adm.shape[0])) + c_adm = torch.cat((c_adm, noise_level_emb), 1) + return c_adm + def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None): self.is_using_inpainting_conditioning = True @@ -241,6 +255,9 @@ class StableDiffusionProcessing: if self.sampler.conditioning_key in {'hybrid', 'concat'}: return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask) + if self.sampler.conditioning_key == "crossattn-adm": + return self.unclip_image_conditioning(source_image) + # Dummy zero conditioning if we're not using inpainting or depth model. return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1) diff --git a/modules/sd_models.py b/modules/sd_models.py index f0cb1240..c1a80d82 100644 --- a/modules/sd_models.py +++ b/modules/sd_models.py @@ -383,6 +383,11 @@ def repair_config(sd_config): elif shared.cmd_opts.upcast_sampling: sd_config.model.params.unet_config.params.use_fp16 = True + # For UnCLIP-L, override the hardcoded karlo directory + if hasattr(sd_config.model.params, "noise_aug_config") and hasattr(sd_config.model.params.noise_aug_config.params, "clip_stats_path"): + karlo_path = os.path.join(paths.models_path, 'karlo') + sd_config.model.params.noise_aug_config.params.clip_stats_path = sd_config.model.params.noise_aug_config.params.clip_stats_path.replace("checkpoints/karlo_models", karlo_path) + sd1_clip_weight = 'cond_stage_model.transformer.text_model.embeddings.token_embedding.weight' sd2_clip_weight = 'cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight' diff --git a/modules/sd_models_config.py b/modules/sd_models_config.py index 91c21700..9398f528 100644 --- a/modules/sd_models_config.py +++ b/modules/sd_models_config.py @@ -14,6 +14,8 @@ config_sd2 = os.path.join(sd_repo_configs_path, "v2-inference.yaml") config_sd2v = os.path.join(sd_repo_configs_path, "v2-inference-v.yaml") config_sd2_inpainting = os.path.join(sd_repo_configs_path, "v2-inpainting-inference.yaml") config_depth_model = os.path.join(sd_repo_configs_path, "v2-midas-inference.yaml") +config_unclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-l-inference.yaml") +config_unopenclip = os.path.join(sd_repo_configs_path, "v2-1-stable-unclip-h-inference.yaml") config_inpainting = os.path.join(sd_configs_path, "v1-inpainting-inference.yaml") config_instruct_pix2pix = os.path.join(sd_configs_path, "instruct-pix2pix.yaml") config_alt_diffusion = os.path.join(sd_configs_path, "alt-diffusion-inference.yaml") @@ -65,9 +67,14 @@ def is_using_v_parameterization_for_sd2(state_dict): def guess_model_config_from_state_dict(sd, filename): sd2_cond_proj_weight = sd.get('cond_stage_model.model.transformer.resblocks.0.attn.in_proj_weight', None) diffusion_model_input = sd.get('model.diffusion_model.input_blocks.0.0.weight', None) + sd2_variations_weight = sd.get('embedder.model.ln_final.weight', None) if sd.get('depth_model.model.pretrained.act_postprocess3.0.project.0.bias', None) is not None: return config_depth_model + elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 768: + return config_unclip + elif sd2_variations_weight is not None and sd2_variations_weight.shape[0] == 1024: + return config_unopenclip if sd2_cond_proj_weight is not None and sd2_cond_proj_weight.shape[1] == 1024: if diffusion_model_input.shape[1] == 9: diff --git a/modules/sd_samplers_compvis.py b/modules/sd_samplers_compvis.py index 083da18c..bfcc5574 100644 --- a/modules/sd_samplers_compvis.py +++ b/modules/sd_samplers_compvis.py @@ -70,8 +70,13 @@ class VanillaStableDiffusionSampler: # Have to unwrap the inpainting conditioning here to perform pre-processing image_conditioning = None + uc_image_conditioning = None if isinstance(cond, dict): - image_conditioning = cond["c_concat"][0] + if self.conditioning_key == "crossattn-adm": + image_conditioning = cond["c_adm"] + uc_image_conditioning = unconditional_conditioning["c_adm"] + else: + image_conditioning = cond["c_concat"][0] cond = cond["c_crossattn"][0] unconditional_conditioning = unconditional_conditioning["c_crossattn"][0] @@ -98,8 +103,12 @@ class VanillaStableDiffusionSampler: # Wrap the image conditioning back up since the DDIM code can accept the dict directly. # Note that they need to be lists because it just concatenates them later. if image_conditioning is not None: - cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} - unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + if self.conditioning_key == "crossattn-adm": + cond = {"c_adm": image_conditioning, "c_crossattn": [cond]} + unconditional_conditioning = {"c_adm": uc_image_conditioning, "c_crossattn": [unconditional_conditioning]} + else: + cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} return x, ts, cond, unconditional_conditioning @@ -176,8 +185,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model if image_conditioning is not None: - conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} - unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} + if self.conditioning_key == "crossattn-adm": + conditioning = {"c_adm": image_conditioning, "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_adm": torch.zeros_like(image_conditioning), "c_crossattn": [unconditional_conditioning]} + else: + conditioning = {"c_concat": [image_conditioning], "c_crossattn": [conditioning]} + unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]} samples = self.launch_sampling(t_enc + 1, lambda: self.sampler.decode(x1, conditioning, t_enc, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning)) @@ -195,8 +208,12 @@ class VanillaStableDiffusionSampler: # Wrap the conditioning models with additional image conditioning for inpainting model # dummy_for_plms is needed because PLMS code checks the first item in the dict to have the right shape if image_conditioning is not None: - conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]} - unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]} + if self.conditioning_key == "crossattn-adm": + conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_adm": image_conditioning} + unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_adm": torch.zeros_like(image_conditioning)} + else: + conditioning = {"dummy_for_plms": np.zeros((conditioning.shape[0],)), "c_crossattn": [conditioning], "c_concat": [image_conditioning]} + unconditional_conditioning = {"c_crossattn": [unconditional_conditioning], "c_concat": [image_conditioning]} samples_ddim = self.launch_sampling(steps, lambda: self.sampler.sample(S=steps, conditioning=conditioning, batch_size=int(x.shape[0]), shape=x[0].shape, verbose=False, unconditional_guidance_scale=p.cfg_scale, unconditional_conditioning=unconditional_conditioning, x_T=x, eta=self.eta)[0]) diff --git a/modules/sd_samplers_kdiffusion.py b/modules/sd_samplers_kdiffusion.py index 93f0e55a..e9f08518 100644 --- a/modules/sd_samplers_kdiffusion.py +++ b/modules/sd_samplers_kdiffusion.py @@ -92,14 +92,21 @@ class CFGDenoiser(torch.nn.Module): batch_size = len(conds_list) repeats = [len(conds_list[i]) for i in range(batch_size)] + if shared.sd_model.model.conditioning_key == "crossattn-adm": + image_uncond = torch.zeros_like(image_cond) + make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": c_crossattn, "c_adm": c_adm} + else: + image_uncond = image_cond + make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": c_crossattn, "c_concat": [c_concat]} + if not is_edit_model: x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma]) - image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond]) else: x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x]) sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma]) - image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_cond] + [torch.zeros_like(self.init_latent)]) + image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)]) denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond) cfg_denoiser_callback(denoiser_params) @@ -116,13 +123,13 @@ class CFGDenoiser(torch.nn.Module): cond_in = torch.cat([tensor, uncond, uncond]) if shared.batch_cond_uncond: - x_out = self.inner_model(x_in, sigma_in, cond={"c_crossattn": [cond_in], "c_concat": [image_cond_in]}) + x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict([cond_in], image_cond_in)) else: x_out = torch.zeros_like(x_in) for batch_offset in range(0, x_out.shape[0], batch_size): a = batch_offset b = a + batch_size - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": [cond_in[a:b]], "c_concat": [image_cond_in[a:b]]}) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict([cond_in[a:b]], image_cond_in[a:b])) else: x_out = torch.zeros_like(x_in) batch_size = batch_size*2 if shared.batch_cond_uncond else batch_size @@ -135,9 +142,9 @@ class CFGDenoiser(torch.nn.Module): else: c_crossattn = torch.cat([tensor[a:b]], uncond) - x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond={"c_crossattn": c_crossattn, "c_concat": [image_cond_in[a:b]]}) + x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b])) - x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond={"c_crossattn": [uncond], "c_concat": [image_cond_in[-uncond.shape[0]:]]}) + x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict([uncond], image_cond_in[-uncond.shape[0]:])) denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps) cfg_denoised_callback(denoised_params) -- cgit v1.2.3 From 433b3ab7017556a19173a86d1215ed0a0b5b1396 Mon Sep 17 00:00:00 2001 From: AUTOMATIC <16777216c@gmail.com> Date: Tue, 28 Mar 2023 20:36:57 +0300 Subject: Revert "Merge pull request #7931 from space-nuko/img2img-enhance" This reverts commit 426875937048e21305ac24bea53df06523bdaa81, reversing changes made to 1b63afbedc7789c0eb9a4742b780ab304d7a9caf. --- javascript/ui.js | 22 +-------- modules/generation_parameters_copypaste.py | 3 -- modules/img2img.py | 4 +- modules/processing.py | 37 ++------------- modules/ui.py | 73 ++---------------------------- scripts/xyz_grid.py | 1 - style.css | 4 +- 7 files changed, 13 insertions(+), 131 deletions(-) (limited to 'modules/processing.py') diff --git a/javascript/ui.js b/javascript/ui.js index a73eeaa2..4a440193 100644 --- a/javascript/ui.js +++ b/javascript/ui.js @@ -132,14 +132,7 @@ function create_tab_index_args(tabId, args){ function get_img2img_tab_index() { let res = args_to_array(arguments) - res.splice(-2) // gradio also sends outputs to the arguments, pop them off - res[0] = get_tab_index('mode_img2img') - return res -} - -function get_img2img_tab_index_for_res_preview() { - let res = args_to_array(arguments) - res.splice(-1) // gradio also sends outputs to the arguments, pop them off + res.splice(-2) res[0] = get_tab_index('mode_img2img') return res } @@ -368,16 +361,3 @@ function selectCheckpoint(name){ desiredCheckpointName = name; gradioApp().getElementById('change_checkpoint').click() } - - -function onCalcResolutionImg2Img(mode, scale, width, height, resize_mode, init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint){ - i2iScale = gradioApp().getElementById('img2img_scale') - i2iWidth = gradioApp().getElementById('img2img_width') - i2iHeight = gradioApp().getElementById('img2img_height') - - setInactive(i2iScale, scale == 1) - setInactive(i2iWidth, scale > 1) - setInactive(i2iHeight, scale > 1) - - return []; -} diff --git a/modules/generation_parameters_copypaste.py b/modules/generation_parameters_copypaste.py index 0ad2ad4f..6df76858 100644 --- a/modules/generation_parameters_copypaste.py +++ b/modules/generation_parameters_copypaste.py @@ -282,9 +282,6 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model res["Hires resize-1"] = 0 res["Hires resize-2"] = 0 - if "Img2Img upscale" not in res: - res["Img2Img upscale"] = 1 - restore_old_hires_fix_params(res) return res diff --git a/modules/img2img.py b/modules/img2img.py index 959dd96e..c973b770 100644 --- a/modules/img2img.py +++ b/modules/img2img.py @@ -78,7 +78,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args): processed_image.save(os.path.join(output_dir, filename)) -def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, scale: float, upscaler: str, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): +def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_styles, init_img, sketch, init_img_with_mask, inpaint_color_sketch, inpaint_color_sketch_orig, init_img_inpaint, init_mask_inpaint, steps: int, sampler_index: int, mask_blur: int, mask_alpha: float, inpainting_fill: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, image_cfg_scale: float, denoising_strength: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, resize_mode: int, inpaint_full_res: bool, inpaint_full_res_padding: int, inpainting_mask_invert: int, img2img_batch_input_dir: str, img2img_batch_output_dir: str, img2img_batch_inpaint_mask_dir: str, override_settings_texts, *args): override_settings = create_override_settings_dict(override_settings_texts) is_batch = mode == 5 @@ -149,8 +149,6 @@ def img2img(id_task: str, mode: int, prompt: str, negative_prompt: str, prompt_s inpaint_full_res_padding=inpaint_full_res_padding, inpainting_mask_invert=inpainting_mask_invert, override_settings=override_settings, - scale=scale, - upscaler=upscaler, ) p.scripts = modules.scripts.scripts_txt2img diff --git a/modules/processing.py b/modules/processing.py index 509b80b9..6d9c6a8d 100644 --- a/modules/processing.py +++ b/modules/processing.py @@ -946,7 +946,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing): class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): sampler = None - def __init__(self, init_images: Optional[list] = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: Optional[float] = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: Optional[float] = None, scale: float = 0, upscaler: Optional[str] = None, **kwargs): + def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, image_cfg_scale: float = None, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs): super().__init__(**kwargs) self.init_images = init_images @@ -966,37 +966,11 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.mask = None self.nmask = None self.image_conditioning = None - self.scale = scale - self.upscaler = upscaler - - def get_final_size(self): - if self.scale > 1: - img = self.init_images[0] - width = int(img.width * self.scale) - height = int(img.height * self.scale) - return width, height - else: - return self.width, self.height - def init(self, all_prompts, all_seeds, all_subseeds): self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model) crop_region = None - if self.scale > 1: - self.extra_generation_params["Img2Img upscale"] = self.scale - - # Non-latent upscalers are run before sampling - # Latent upscalers are run during sampling - init_upscaler = None - if self.upscaler is not None: - self.extra_generation_params["Img2Img upscaler"] = self.upscaler - if self.upscaler not in shared.latent_upscale_modes: - assert len([x for x in shared.sd_upscalers if x.name == self.upscaler]) > 0, f"could not find upscaler named {self.upscaler}" - init_upscaler = self.upscaler - - self.width, self.height = self.get_final_size() - image_mask = self.image_mask if image_mask is not None: @@ -1019,7 +993,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image_mask = images.resize_image(2, mask, self.width, self.height) self.paste_to = (x1, y1, x2-x1, y2-y1) else: - image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height, init_upscaler) + image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height) np_mask = np.array(image_mask) np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8) self.mask_for_overlay = Image.fromarray(np_mask) @@ -1036,7 +1010,7 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): image = images.flatten(img, opts.img2img_background_color) if crop_region is None and self.resize_mode != 3: - image = images.resize_image(self.resize_mode, image, self.width, self.height, init_upscaler) + image = images.resize_image(self.resize_mode, image, self.width, self.height) if image_mask is not None: image_masked = Image.new('RGBa', (image.width, image.height)) @@ -1081,9 +1055,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing): self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image)) - latent_scale_mode = shared.latent_upscale_modes.get(self.upscaler, None) if self.upscaler is not None else shared.latent_upscale_modes.get(shared.latent_upscale_default_mode, "nearest") - if latent_scale_mode is not None: - self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"]) + if self.resize_mode == 3: + self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear") if image_mask is not None: init_mask = latent_mask diff --git a/modules/ui.py b/modules/ui.py index f22da16a..eb5fcd3f 100644 --- a/modules/ui.py +++ b/modules/ui.py @@ -15,7 +15,6 @@ import warnings import gradio as gr import gradio.routes import gradio.utils -from gradio.events import Releaseable import numpy as np from PIL import Image, PngImagePlugin from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call @@ -128,26 +127,6 @@ def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resiz return f"resize: from {p.width}x{p.height} to {p.hr_resize_x or p.hr_upscale_to_x}x{p.hr_resize_y or p.hr_upscale_to_y}" -def calc_resolution_img2img(mode, scale, resize_x, resize_y, resize_mode, *i2i_images): - init_img = None - if mode in {0, 1, 3, 4}: - init_img = i2i_images[mode] - elif mode == 2: - init_img = i2i_images[mode]["image"] - - if not init_img: - return "" - - if scale > 1: - width = int(init_img.width * scale) - height = int(init_img.height * scale) - else: - width = resize_x - height = resize_y - - return f"resize: from {init_img.width}x{init_img.height} to {width}x{height}" - - def apply_styles(prompt, prompt_neg, styles): prompt = shared.prompt_styles.apply_styles_to_prompt(prompt, styles) prompt_neg = shared.prompt_styles.apply_negative_styles_to_prompt(prompt_neg, styles) @@ -756,7 +735,7 @@ def create_ui(): ) with FormRow(): - resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill"], type="index", value="Just resize") + resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize") for category in ordered_ui_categories(): if category == "sampler": @@ -765,13 +744,8 @@ def create_ui(): elif category == "dimensions": with FormRow(): with gr.Column(elem_id="img2img_column_size", scale=4): - with FormRow(variant="compact"): - final_resolution = FormHTML(value="", elem_id="img2img_finalres", label="Upscaled resolution", interactive=False) - with FormRow(variant="compact"): - scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=1.0, elem_id="img2img_scale") - with FormRow(variant="compact"): - width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") - height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") + width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width") + height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height") with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"): res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn") @@ -786,9 +760,7 @@ def create_ui(): with FormRow(): cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.5, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale") image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=shared.sd_model and shared.sd_model.cond_stage_key == "edit") - with FormRow(): - upscaler = gr.Dropdown(label="Upscaler", elem_id="img2img_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode) - denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") + denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength") elif category == "seed": seed, reuse_seed, subseed, reuse_subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox = create_seed_inputs('img2img') @@ -841,39 +813,6 @@ def create_ui(): outputs=[inpaint_controls, mask_alpha], ) - img2img_resolution_preview_inputs = [dummy_component, # filled in by selected img2img tab index in _js - scale, width, height, resize_mode, - init_img, sketch, init_img_with_mask, inpaint_color_sketch, init_img_inpaint] - for input in img2img_resolution_preview_inputs[1:]: - if isinstance(input, Releaseable): - input.release( - fn=calc_resolution_img2img, - _js="get_img2img_tab_index_for_res_preview", - inputs=img2img_resolution_preview_inputs, - outputs=[final_resolution], - show_progress=False, - ).success( - None, - _js="onCalcResolutionImg2Img", - inputs=img2img_resolution_preview_inputs, - outputs=[], - show_progress=False, - ) - else: - input.change( - fn=calc_resolution_img2img, - _js="get_img2img_tab_index_for_res_preview", - inputs=img2img_resolution_preview_inputs, - outputs=[final_resolution], - show_progress=False, - ).success( - None, - _js="onCalcResolutionImg2Img", - inputs=img2img_resolution_preview_inputs, - outputs=[], - show_progress=False, - ) - img2img_gallery, generation_info, html_info, html_log = create_output_panel("img2img", opts.outdir_img2img_samples) connect_reuse_seed(seed, reuse_seed, generation_info, dummy_component, is_subseed=False) @@ -922,8 +861,6 @@ def create_ui(): subseed, subseed_strength, seed_resize_from_h, seed_resize_from_w, seed_checkbox, height, width, - scale, - upscaler, resize_mode, inpaint_full_res, inpaint_full_res_padding, @@ -1009,8 +946,6 @@ def create_ui(): (seed, "Seed"), (width, "Size-1"), (height, "Size-2"), - (scale, "Img2Img upscale"), - (upscaler, "Img2Img upscaler"), (batch_size, "Batch size"), (subseed, "Variation seed"), (subseed_strength, "Variation seed strength"), diff --git a/scripts/xyz_grid.py b/scripts/xyz_grid.py index 3f6c1997..3895a795 100644 --- a/scripts/xyz_grid.py +++ b/scripts/xyz_grid.py @@ -220,7 +220,6 @@ axis_options = [ AxisOption("Clip skip", int, apply_clip_skip), AxisOption("Denoising", float, apply_field("denoising_strength")), AxisOptionTxt2Img("Hires upscaler", str, apply_field("hr_upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), - AxisOptionImg2Img("Upscaler", str, apply_field("upscaler"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]), AxisOptionImg2Img("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight")), AxisOption("VAE", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)), AxisOption("Styles", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)), diff --git a/style.css b/style.css index 379a89dc..de16a7f2 100644 --- a/style.css +++ b/style.css @@ -287,13 +287,13 @@ button.custom-button{ border-radius: 0 0.5rem 0.5rem 0; } -#txtimg_hr_finalres, #img2img_finalres { +#txtimg_hr_finalres{ min-height: 0 !important; padding: .625rem .75rem; margin-left: -0.75em } -#txtimg_hr_finalres .resolution, #img2img_finalres .resolution{ +#txtimg_hr_finalres .resolution{ font-weight: bold; } -- cgit v1.2.3