Recommended settings: Sampling Steps: 80-100, Sampler: Euler a, Denoising strength: 0.8
\")\r\n\r\n pixels = gr.Slider(label=\"Pixels to expand\", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id(\"pixels\"))\r\n mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=8, elem_id=self.elem_id(\"mask_blur\"))\r\n direction = gr.CheckboxGroup(label=\"Outpainting direction\", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id(\"direction\"))\r\n noise_q = gr.Slider(label=\"Fall-off exponent (lower=higher detail)\", minimum=0.0, maximum=4.0, step=0.01, value=1.0, elem_id=self.elem_id(\"noise_q\"))\r\n color_variation = gr.Slider(label=\"Color variation\", minimum=0.0, maximum=1.0, step=0.01, value=0.05, elem_id=self.elem_id(\"color_variation\"))\r\n\r\n return [info, pixels, mask_blur, direction, noise_q, color_variation]\r\n\r\n def run(self, p, _, pixels, mask_blur, direction, noise_q, color_variation):\r\n initial_seed_and_info = [None, None]\r\n\r\n process_width = p.width\r\n process_height = p.height\r\n\r\n p.mask_blur = mask_blur*4\r\n p.inpaint_full_res = False\r\n p.inpainting_fill = 1\r\n p.do_not_save_samples = True\r\n p.do_not_save_grid = True\r\n\r\n left = pixels if \"left\" in direction else 0\r\n right = pixels if \"right\" in direction else 0\r\n up = pixels if \"up\" in direction else 0\r\n down = pixels if \"down\" in direction else 0\r\n\r\n init_img = p.init_images[0]\r\n target_w = math.ceil((init_img.width + left + right) / 64) * 64\r\n target_h = math.ceil((init_img.height + up + down) / 64) * 64\r\n\r\n if left > 0:\r\n left = left * (target_w - init_img.width) // (left + right)\r\n\r\n if right > 0:\r\n right = target_w - init_img.width - left\r\n\r\n if up > 0:\r\n up = up * (target_h - init_img.height) // (up + down)\r\n\r\n if down > 0:\r\n down = target_h - init_img.height - up\r\n\r\n def expand(init, count, expand_pixels, is_left=False, is_right=False, is_top=False, is_bottom=False):\r\n is_horiz = is_left or is_right\r\n is_vert = is_top or is_bottom\r\n pixels_horiz = expand_pixels if is_horiz else 0\r\n pixels_vert = expand_pixels if is_vert else 0\r\n\r\n images_to_process = []\r\n output_images = []\r\n for n in range(count):\r\n res_w = init[n].width + pixels_horiz\r\n res_h = init[n].height + pixels_vert\r\n process_res_w = math.ceil(res_w / 64) * 64\r\n process_res_h = math.ceil(res_h / 64) * 64\r\n\r\n img = Image.new(\"RGB\", (process_res_w, process_res_h))\r\n img.paste(init[n], (pixels_horiz if is_left else 0, pixels_vert if is_top else 0))\r\n mask = Image.new(\"RGB\", (process_res_w, process_res_h), \"white\")\r\n draw = ImageDraw.Draw(mask)\r\n draw.rectangle((\r\n expand_pixels + mask_blur if is_left else 0,\r\n expand_pixels + mask_blur if is_top else 0,\r\n mask.width - expand_pixels - mask_blur if is_right else res_w,\r\n mask.height - expand_pixels - mask_blur if is_bottom else res_h,\r\n ), fill=\"black\")\r\n\r\n np_image = (np.asarray(img) / 255.0).astype(np.float64)\r\n np_mask = (np.asarray(mask) / 255.0).astype(np.float64)\r\n noised = get_matched_noise(np_image, np_mask, noise_q, color_variation)\r\n output_images.append(Image.fromarray(np.clip(noised * 255., 0., 255.).astype(np.uint8), mode=\"RGB\"))\r\n\r\n target_width = min(process_width, init[n].width + pixels_horiz) if is_horiz else img.width\r\n target_height = min(process_height, init[n].height + pixels_vert) if is_vert else img.height\r\n p.width = target_width if is_horiz else img.width\r\n p.height = target_height if is_vert else img.height\r\n\r\n crop_region = (\r\n 0 if is_left else output_images[n].width - target_width,\r\n 0 if is_top else output_images[n].height - target_height,\r\n target_width if is_left else output_images[n].width,\r\n target_height if is_top else output_images[n].height,\r\n )\r\n mask = mask.crop(crop_region)\r\n p.image_mask = mask\r\n\r\n image_to_process = output_images[n].crop(crop_region)\r\n images_to_process.append(image_to_process)\r\n\r\n p.init_images = images_to_process\r\n\r\n latent_mask = Image.new(\"RGB\", (p.width, p.height), \"white\")\r\n draw = ImageDraw.Draw(latent_mask)\r\n draw.rectangle((\r\n expand_pixels + mask_blur * 2 if is_left else 0,\r\n expand_pixels + mask_blur * 2 if is_top else 0,\r\n mask.width - expand_pixels - mask_blur * 2 if is_right else res_w,\r\n mask.height - expand_pixels - mask_blur * 2 if is_bottom else res_h,\r\n ), fill=\"black\")\r\n p.latent_mask = latent_mask\r\n\r\n proc = process_images(p)\r\n\r\n if initial_seed_and_info[0] is None:\r\n initial_seed_and_info[0] = proc.seed\r\n initial_seed_and_info[1] = proc.info\r\n\r\n for n in range(count):\r\n output_images[n].paste(proc.images[n], (0 if is_left else output_images[n].width - proc.images[n].width, 0 if is_top else output_images[n].height - proc.images[n].height))\r\n output_images[n] = output_images[n].crop((0, 0, res_w, res_h))\r\n\r\n return output_images\r\n\r\n batch_count = p.n_iter\r\n batch_size = p.batch_size\r\n p.n_iter = 1\r\n state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))\r\n all_processed_images = []\r\n\r\n for i in range(batch_count):\r\n imgs = [init_img] * batch_size\r\n state.job = f\"Batch {i + 1} out of {batch_count}\"\r\n\r\n if left > 0:\r\n imgs = expand(imgs, batch_size, left, is_left=True)\r\n if right > 0:\r\n imgs = expand(imgs, batch_size, right, is_right=True)\r\n if up > 0:\r\n imgs = expand(imgs, batch_size, up, is_top=True)\r\n if down > 0:\r\n imgs = expand(imgs, batch_size, down, is_bottom=True)\r\n\r\n all_processed_images += imgs\r\n\r\n all_images = all_processed_images\r\n\r\n combined_grid_image = images.image_grid(all_processed_images)\r\n unwanted_grid_because_of_img_count = len(all_processed_images) < 2 and opts.grid_only_if_multiple\r\n if opts.return_grid and not unwanted_grid_because_of_img_count:\r\n all_images = [combined_grid_image] + all_processed_images\r\n\r\n res = Processed(p, all_images, initial_seed_and_info[0], initial_seed_and_info[1])\r\n\r\n if opts.samples_save:\r\n for img in all_processed_images:\r\n images.save_image(img, p.outpath_samples, \"\", res.seed, p.prompt, opts.grid_format, info=res.info, p=p)\r\n\r\n if opts.grid_save and not unwanted_grid_because_of_img_count:\r\n images.save_image(combined_grid_image, p.outpath_grids, \"grid\", res.seed, p.prompt, opts.grid_format, info=res.info, short_filename=not opts.grid_extended_filename, grid=True, p=p)\r\n\r\n return res\r\n" }, { "path": "scripts/poor_mans_outpainting.py", "content": "import math\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\nfrom PIL import Image, ImageDraw\r\n\r\nfrom modules import images, processing, devices\r\nfrom modules.processing import Processed, process_images\r\nfrom modules.shared import opts, cmd_opts, state\r\n\r\n\r\nclass Script(scripts.Script):\r\n def title(self):\r\n return \"Poor man's outpainting\"\r\n\r\n def show(self, is_img2img):\r\n return is_img2img\r\n\r\n def ui(self, is_img2img):\r\n if not is_img2img:\r\n return None\r\n \r\n pixels = gr.Slider(label=\"Pixels to expand\", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id(\"pixels\"))\r\n mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id(\"mask_blur\"))\r\n inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type=\"index\", elem_id=self.elem_id(\"inpainting_fill\"))\r\n direction = gr.CheckboxGroup(label=\"Outpainting direction\", choices=['left', 'right', 'up', 'down'], value=['left', 'right', 'up', 'down'], elem_id=self.elem_id(\"direction\"))\r\n\r\n return [pixels, mask_blur, inpainting_fill, direction]\r\n\r\n def run(self, p, pixels, mask_blur, inpainting_fill, direction):\r\n initial_seed = None\r\n initial_info = None\r\n\r\n p.mask_blur = mask_blur * 2\r\n p.inpainting_fill = inpainting_fill\r\n p.inpaint_full_res = False\r\n\r\n left = pixels if \"left\" in direction else 0\r\n right = pixels if \"right\" in direction else 0\r\n up = pixels if \"up\" in direction else 0\r\n down = pixels if \"down\" in direction else 0\r\n\r\n init_img = p.init_images[0]\r\n target_w = math.ceil((init_img.width + left + right) / 64) * 64\r\n target_h = math.ceil((init_img.height + up + down) / 64) * 64\r\n\r\n if left > 0:\r\n left = left * (target_w - init_img.width) // (left + right)\r\n if right > 0:\r\n right = target_w - init_img.width - left\r\n\r\n if up > 0:\r\n up = up * (target_h - init_img.height) // (up + down)\r\n\r\n if down > 0:\r\n down = target_h - init_img.height - up\r\n\r\n img = Image.new(\"RGB\", (target_w, target_h))\r\n img.paste(init_img, (left, up))\r\n\r\n mask = Image.new(\"L\", (img.width, img.height), \"white\")\r\n draw = ImageDraw.Draw(mask)\r\n draw.rectangle((\r\n left + (mask_blur * 2 if left > 0 else 0),\r\n up + (mask_blur * 2 if up > 0 else 0),\r\n mask.width - right - (mask_blur * 2 if right > 0 else 0),\r\n mask.height - down - (mask_blur * 2 if down > 0 else 0)\r\n ), fill=\"black\")\r\n\r\n latent_mask = Image.new(\"L\", (img.width, img.height), \"white\")\r\n latent_draw = ImageDraw.Draw(latent_mask)\r\n latent_draw.rectangle((\r\n left + (mask_blur//2 if left > 0 else 0),\r\n up + (mask_blur//2 if up > 0 else 0),\r\n mask.width - right - (mask_blur//2 if right > 0 else 0),\r\n mask.height - down - (mask_blur//2 if down > 0 else 0)\r\n ), fill=\"black\")\r\n\r\n devices.torch_gc()\r\n\r\n grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=pixels)\r\n grid_mask = images.split_grid(mask, tile_w=p.width, tile_h=p.height, overlap=pixels)\r\n grid_latent_mask = images.split_grid(latent_mask, tile_w=p.width, tile_h=p.height, overlap=pixels)\r\n\r\n p.n_iter = 1\r\n p.batch_size = 1\r\n p.do_not_save_grid = True\r\n p.do_not_save_samples = True\r\n\r\n work = []\r\n work_mask = []\r\n work_latent_mask = []\r\n work_results = []\r\n\r\n for (y, h, row), (_, _, row_mask), (_, _, row_latent_mask) in zip(grid.tiles, grid_mask.tiles, grid_latent_mask.tiles):\r\n for tiledata, tiledata_mask, tiledata_latent_mask in zip(row, row_mask, row_latent_mask):\r\n x, w = tiledata[0:2]\r\n\r\n if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:\r\n continue\r\n\r\n work.append(tiledata[2])\r\n work_mask.append(tiledata_mask[2])\r\n work_latent_mask.append(tiledata_latent_mask[2])\r\n\r\n batch_count = len(work)\r\n print(f\"Poor man's outpainting will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)}.\")\r\n\r\n state.job_count = batch_count\r\n\r\n for i in range(batch_count):\r\n p.init_images = [work[i]]\r\n p.image_mask = work_mask[i]\r\n p.latent_mask = work_latent_mask[i]\r\n\r\n state.job = f\"Batch {i + 1} out of {batch_count}\"\r\n processed = process_images(p)\r\n\r\n if initial_seed is None:\r\n initial_seed = processed.seed\r\n initial_info = processed.info\r\n\r\n p.seed = processed.seed + 1\r\n work_results += processed.images\r\n\r\n\r\n image_index = 0\r\n for y, h, row in grid.tiles:\r\n for tiledata in row:\r\n x, w = tiledata[0:2]\r\n\r\n if x >= left and x+w <= img.width - right and y >= up and y+h <= img.height - down:\r\n continue\r\n\r\n tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new(\"RGB\", (p.width, p.height))\r\n image_index += 1\r\n\r\n combined_image = images.combine_grid(grid)\r\n\r\n if opts.samples_save:\r\n images.save_image(combined_image, p.outpath_samples, \"\", initial_seed, p.prompt, opts.grid_format, info=initial_info, p=p)\r\n\r\n processed = Processed(p, [combined_image], initial_seed, initial_info)\r\n\r\n return processed\r\n\r\n" }, { "path": "scripts/postprocessing_codeformer.py", "content": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, codeformer_model\r\nimport gradio as gr\r\n\r\nfrom modules.ui_components import FormRow\r\n\r\n\r\nclass ScriptPostprocessingCodeFormer(scripts_postprocessing.ScriptPostprocessing):\r\n name = \"CodeFormer\"\r\n order = 3000\r\n\r\n def ui(self):\r\n with FormRow():\r\n codeformer_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label=\"CodeFormer visibility\", value=0, elem_id=\"extras_codeformer_visibility\")\r\n codeformer_weight = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label=\"CodeFormer weight (0 = maximum effect, 1 = minimum effect)\", value=0, elem_id=\"extras_codeformer_weight\")\r\n\r\n return {\r\n \"codeformer_visibility\": codeformer_visibility,\r\n \"codeformer_weight\": codeformer_weight,\r\n }\r\n\r\n def process(self, pp: scripts_postprocessing.PostprocessedImage, codeformer_visibility, codeformer_weight):\r\n if codeformer_visibility == 0:\r\n return\r\n\r\n restored_img = codeformer_model.codeformer.restore(np.array(pp.image, dtype=np.uint8), w=codeformer_weight)\r\n res = Image.fromarray(restored_img)\r\n\r\n if codeformer_visibility < 1.0:\r\n res = Image.blend(pp.image, res, codeformer_visibility)\r\n\r\n pp.image = res\r\n pp.info[\"CodeFormer visibility\"] = round(codeformer_visibility, 3)\r\n pp.info[\"CodeFormer weight\"] = round(codeformer_weight, 3)\r\n" }, { "path": "scripts/postprocessing_gfpgan.py", "content": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, gfpgan_model\r\nimport gradio as gr\r\n\r\nfrom modules.ui_components import FormRow\r\n\r\n\r\nclass ScriptPostprocessingGfpGan(scripts_postprocessing.ScriptPostprocessing):\r\n name = \"GFPGAN\"\r\n order = 2000\r\n\r\n def ui(self):\r\n with FormRow():\r\n gfpgan_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label=\"GFPGAN visibility\", value=0, elem_id=\"extras_gfpgan_visibility\")\r\n\r\n return {\r\n \"gfpgan_visibility\": gfpgan_visibility,\r\n }\r\n\r\n def process(self, pp: scripts_postprocessing.PostprocessedImage, gfpgan_visibility):\r\n if gfpgan_visibility == 0:\r\n return\r\n\r\n restored_img = gfpgan_model.gfpgan_fix_faces(np.array(pp.image, dtype=np.uint8))\r\n res = Image.fromarray(restored_img)\r\n\r\n if gfpgan_visibility < 1.0:\r\n res = Image.blend(pp.image, res, gfpgan_visibility)\r\n\r\n pp.image = res\r\n pp.info[\"GFPGAN visibility\"] = round(gfpgan_visibility, 3)\r\n" }, { "path": "scripts/postprocessing_upscale.py", "content": "from PIL import Image\r\nimport numpy as np\r\n\r\nfrom modules import scripts_postprocessing, shared\r\nimport gradio as gr\r\n\r\nfrom modules.ui_components import FormRow\r\n\r\n\r\nupscale_cache = {}\r\n\r\n\r\nclass ScriptPostprocessingUpscale(scripts_postprocessing.ScriptPostprocessing):\r\n name = \"Upscale\"\r\n order = 1000\r\n\r\n def ui(self):\r\n selected_tab = gr.State(value=0)\r\n\r\n with gr.Tabs(elem_id=\"extras_resize_mode\"):\r\n with gr.TabItem('Scale by', elem_id=\"extras_scale_by_tab\") as tab_scale_by:\r\n upscaling_resize = gr.Slider(minimum=1.0, maximum=8.0, step=0.05, label=\"Resize\", value=4, elem_id=\"extras_upscaling_resize\")\r\n\r\n with gr.TabItem('Scale to', elem_id=\"extras_scale_to_tab\") as tab_scale_to:\r\n with FormRow():\r\n upscaling_resize_w = gr.Number(label=\"Width\", value=512, precision=0, elem_id=\"extras_upscaling_resize_w\")\r\n upscaling_resize_h = gr.Number(label=\"Height\", value=512, precision=0, elem_id=\"extras_upscaling_resize_h\")\r\n upscaling_crop = gr.Checkbox(label='Crop to fit', value=True, elem_id=\"extras_upscaling_crop\")\r\n\r\n with FormRow():\r\n extras_upscaler_1 = gr.Dropdown(label='Upscaler 1', elem_id=\"extras_upscaler_1\", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)\r\n\r\n with FormRow():\r\n extras_upscaler_2 = gr.Dropdown(label='Upscaler 2', elem_id=\"extras_upscaler_2\", choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)\r\n extras_upscaler_2_visibility = gr.Slider(minimum=0.0, maximum=1.0, step=0.001, label=\"Upscaler 2 visibility\", value=0.0, elem_id=\"extras_upscaler_2_visibility\")\r\n\r\n tab_scale_by.select(fn=lambda: 0, inputs=[], outputs=[selected_tab])\r\n tab_scale_to.select(fn=lambda: 1, inputs=[], outputs=[selected_tab])\r\n\r\n return {\r\n \"upscale_mode\": selected_tab,\r\n \"upscale_by\": upscaling_resize,\r\n \"upscale_to_width\": upscaling_resize_w,\r\n \"upscale_to_height\": upscaling_resize_h,\r\n \"upscale_crop\": upscaling_crop,\r\n \"upscaler_1_name\": extras_upscaler_1,\r\n \"upscaler_2_name\": extras_upscaler_2,\r\n \"upscaler_2_visibility\": extras_upscaler_2_visibility,\r\n }\r\n\r\n def upscale(self, image, info, upscaler, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop):\r\n if upscale_mode == 1:\r\n upscale_by = max(upscale_to_width/image.width, upscale_to_height/image.height)\r\n info[\"Postprocess upscale to\"] = f\"{upscale_to_width}x{upscale_to_height}\"\r\n else:\r\n info[\"Postprocess upscale by\"] = upscale_by\r\n\r\n cache_key = (hash(np.array(image.getdata()).tobytes()), upscaler.name, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)\r\n cached_image = upscale_cache.pop(cache_key, None)\r\n\r\n if cached_image is not None:\r\n image = cached_image\r\n else:\r\n image = upscaler.scaler.upscale(image, upscale_by, upscaler.data_path)\r\n\r\n upscale_cache[cache_key] = image\r\n if len(upscale_cache) > shared.opts.upscaling_max_images_in_cache:\r\n upscale_cache.pop(next(iter(upscale_cache), None), None)\r\n\r\n if upscale_mode == 1 and upscale_crop:\r\n cropped = Image.new(\"RGB\", (upscale_to_width, upscale_to_height))\r\n cropped.paste(image, box=(upscale_to_width // 2 - image.width // 2, upscale_to_height // 2 - image.height // 2))\r\n image = cropped\r\n info[\"Postprocess crop to\"] = f\"{image.width}x{image.height}\"\r\n\r\n return image\r\n\r\n def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1, upscale_by=2.0, upscale_to_width=None, upscale_to_height=None, upscale_crop=False, upscaler_1_name=None, upscaler_2_name=None, upscaler_2_visibility=0.0):\r\n if upscaler_1_name == \"None\":\r\n upscaler_1_name = None\r\n\r\n upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_1_name]), None)\r\n assert upscaler1 or (upscaler_1_name is None), f'could not find upscaler named {upscaler_1_name}'\r\n\r\n if not upscaler1:\r\n return\r\n\r\n if upscaler_2_name == \"None\":\r\n upscaler_2_name = None\r\n\r\n upscaler2 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_2_name and x.name != \"None\"]), None)\r\n assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'\r\n\r\n upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)\r\n pp.info[f\"Postprocess upscaler\"] = upscaler1.name\r\n\r\n if upscaler2 and upscaler_2_visibility > 0:\r\n second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)\r\n upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)\r\n\r\n pp.info[f\"Postprocess upscaler 2\"] = upscaler2.name\r\n\r\n pp.image = upscaled_image\r\n\r\n def image_changed(self):\r\n upscale_cache.clear()\r\n\r\n\r\nclass ScriptPostprocessingUpscaleSimple(ScriptPostprocessingUpscale):\r\n name = \"Simple Upscale\"\r\n order = 900\r\n\r\n def ui(self):\r\n with FormRow():\r\n upscaler_name = gr.Dropdown(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name)\r\n upscale_by = gr.Slider(minimum=0.05, maximum=8.0, step=0.05, label=\"Upscale by\", value=2)\r\n\r\n return {\r\n \"upscale_by\": upscale_by,\r\n \"upscaler_name\": upscaler_name,\r\n }\r\n\r\n def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0, upscaler_name=None):\r\n if upscaler_name is None or upscaler_name == \"None\":\r\n return\r\n\r\n upscaler1 = next(iter([x for x in shared.sd_upscalers if x.name == upscaler_name]), None)\r\n assert upscaler1, f'could not find upscaler named {upscaler_name}'\r\n\r\n pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)\r\n pp.info[f\"Postprocess upscaler\"] = upscaler1.name\r\n" }, { "path": "scripts/prompt_matrix.py", "content": "import math\r\nfrom collections import namedtuple\r\nfrom copy import copy\r\nimport random\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\n\r\nfrom modules import images\r\nfrom modules.processing import process_images, Processed\r\nfrom modules.shared import opts, cmd_opts, state\r\nimport modules.sd_samplers\r\n\r\n\r\ndef draw_xy_grid(xs, ys, x_label, y_label, cell):\r\n res = []\r\n\r\n ver_texts = [[images.GridAnnotation(y_label(y))] for y in ys]\r\n hor_texts = [[images.GridAnnotation(x_label(x))] for x in xs]\r\n\r\n first_processed = None\r\n\r\n state.job_count = len(xs) * len(ys)\r\n\r\n for iy, y in enumerate(ys):\r\n for ix, x in enumerate(xs):\r\n state.job = f\"{ix + iy * len(xs) + 1} out of {len(xs) * len(ys)}\"\r\n\r\n processed = cell(x, y)\r\n if first_processed is None:\r\n first_processed = processed\r\n\r\n res.append(processed.images[0])\r\n\r\n grid = images.image_grid(res, rows=len(ys))\r\n grid = images.draw_grid_annotations(grid, res[0].width, res[0].height, hor_texts, ver_texts)\r\n\r\n first_processed.images = [grid]\r\n\r\n return first_processed\r\n\r\n\r\nclass Script(scripts.Script):\r\n def title(self):\r\n return \"Prompt matrix\"\r\n\r\n def ui(self, is_img2img):\r\n gr.HTML('Will upscale the image by the selected scale factor; use width and height sliders to set tile size
\")\r\n overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id(\"overlap\"))\r\n scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id(\"scale_factor\"))\r\n upscaler_index = gr.Radio(label='Upscaler', choices=[x.name for x in shared.sd_upscalers], value=shared.sd_upscalers[0].name, type=\"index\", elem_id=self.elem_id(\"upscaler_index\"))\r\n\r\n return [info, overlap, upscaler_index, scale_factor]\r\n\r\n def run(self, p, _, overlap, upscaler_index, scale_factor):\r\n if isinstance(upscaler_index, str):\r\n upscaler_index = [x.name.lower() for x in shared.sd_upscalers].index(upscaler_index.lower())\r\n processing.fix_seed(p)\r\n upscaler = shared.sd_upscalers[upscaler_index]\r\n\r\n p.extra_generation_params[\"SD upscale overlap\"] = overlap\r\n p.extra_generation_params[\"SD upscale upscaler\"] = upscaler.name\r\n\r\n initial_info = None\r\n seed = p.seed\r\n\r\n init_img = p.init_images[0]\r\n init_img = images.flatten(init_img, opts.img2img_background_color)\r\n\r\n if upscaler.name != \"None\":\r\n img = upscaler.scaler.upscale(init_img, scale_factor, upscaler.data_path)\r\n else:\r\n img = init_img\r\n\r\n devices.torch_gc()\r\n\r\n grid = images.split_grid(img, tile_w=p.width, tile_h=p.height, overlap=overlap)\r\n\r\n batch_size = p.batch_size\r\n upscale_count = p.n_iter\r\n p.n_iter = 1\r\n p.do_not_save_grid = True\r\n p.do_not_save_samples = True\r\n\r\n work = []\r\n\r\n for y, h, row in grid.tiles:\r\n for tiledata in row:\r\n work.append(tiledata[2])\r\n\r\n batch_count = math.ceil(len(work) / batch_size)\r\n state.job_count = batch_count * upscale_count\r\n\r\n print(f\"SD upscaling will process a total of {len(work)} images tiled as {len(grid.tiles[0][2])}x{len(grid.tiles)} per upscale in a total of {state.job_count} batches.\")\r\n\r\n result_images = []\r\n for n in range(upscale_count):\r\n start_seed = seed + n\r\n p.seed = start_seed\r\n\r\n work_results = []\r\n for i in range(batch_count):\r\n p.batch_size = batch_size\r\n p.init_images = work[i * batch_size:(i + 1) * batch_size]\r\n\r\n state.job = f\"Batch {i + 1 + n * batch_count} out of {state.job_count}\"\r\n processed = processing.process_images(p)\r\n\r\n if initial_info is None:\r\n initial_info = processed.info\r\n\r\n p.seed = processed.seed + 1\r\n work_results += processed.images\r\n\r\n image_index = 0\r\n for y, h, row in grid.tiles:\r\n for tiledata in row:\r\n tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new(\"RGB\", (p.width, p.height))\r\n image_index += 1\r\n\r\n combined_image = images.combine_grid(grid)\r\n result_images.append(combined_image)\r\n\r\n if opts.samples_save:\r\n images.save_image(combined_image, p.outpath_samples, \"\", start_seed, p.prompt, opts.samples_format, info=initial_info, p=p)\r\n\r\n processed = Processed(p, result_images, seed, initial_info)\r\n\r\n return processed\r\n" }, { "path": "scripts/xyz_grid.py", "content": "from collections import namedtuple\r\nfrom copy import copy\r\nfrom itertools import permutations, chain\r\nimport random\r\nimport csv\r\nfrom io import StringIO\r\nfrom PIL import Image\r\nimport numpy as np\r\n\r\nimport modules.scripts as scripts\r\nimport gradio as gr\r\n\r\nfrom modules import images, paths, sd_samplers, processing, sd_models, sd_vae\r\nfrom modules.processing import process_images, Processed, StableDiffusionProcessingTxt2Img\r\nfrom modules.shared import opts, cmd_opts, state\r\nimport modules.shared as shared\r\nimport modules.sd_samplers\r\nimport modules.sd_models\r\nimport modules.sd_vae\r\nimport glob\r\nimport os\r\nimport re\r\n\r\nfrom modules.ui_components import ToolButton\r\n\r\nfill_values_symbol = \"\\U0001f4d2\" # 📒\r\n\r\nAxisInfo = namedtuple('AxisInfo', ['axis', 'values'])\r\n\r\n\r\ndef apply_field(field):\r\n def fun(p, x, xs):\r\n setattr(p, field, x)\r\n\r\n return fun\r\n\r\n\r\ndef apply_prompt(p, x, xs):\r\n if xs[0] not in p.prompt and xs[0] not in p.negative_prompt:\r\n raise RuntimeError(f\"Prompt S/R did not find {xs[0]} in prompt or negative prompt.\")\r\n\r\n p.prompt = p.prompt.replace(xs[0], x)\r\n p.negative_prompt = p.negative_prompt.replace(xs[0], x)\r\n\r\n\r\ndef apply_order(p, x, xs):\r\n token_order = []\r\n\r\n # Initally grab the tokens from the prompt, so they can be replaced in order of earliest seen\r\n for token in x:\r\n token_order.append((p.prompt.find(token), token))\r\n\r\n token_order.sort(key=lambda t: t[0])\r\n\r\n prompt_parts = []\r\n\r\n # Split the prompt up, taking out the tokens\r\n for _, token in token_order:\r\n n = p.prompt.find(token)\r\n prompt_parts.append(p.prompt[0:n])\r\n p.prompt = p.prompt[n + len(token):]\r\n\r\n # Rebuild the prompt with the tokens in the order we want\r\n prompt_tmp = \"\"\r\n for idx, part in enumerate(prompt_parts):\r\n prompt_tmp += part\r\n prompt_tmp += x[idx]\r\n p.prompt = prompt_tmp + p.prompt\r\n\r\n\r\ndef apply_sampler(p, x, xs):\r\n sampler_name = sd_samplers.samplers_map.get(x.lower(), None)\r\n if sampler_name is None:\r\n raise RuntimeError(f\"Unknown sampler: {x}\")\r\n\r\n p.sampler_name = sampler_name\r\n\r\n\r\ndef confirm_samplers(p, xs):\r\n for x in xs:\r\n if x.lower() not in sd_samplers.samplers_map:\r\n raise RuntimeError(f\"Unknown sampler: {x}\")\r\n\r\n\r\ndef apply_checkpoint(p, x, xs):\r\n info = modules.sd_models.get_closet_checkpoint_match(x)\r\n if info is None:\r\n raise RuntimeError(f\"Unknown checkpoint: {x}\")\r\n modules.sd_models.reload_model_weights(shared.sd_model, info)\r\n\r\n\r\ndef confirm_checkpoints(p, xs):\r\n for x in xs:\r\n if modules.sd_models.get_closet_checkpoint_match(x) is None:\r\n raise RuntimeError(f\"Unknown checkpoint: {x}\")\r\n\r\n\r\ndef apply_clip_skip(p, x, xs):\r\n opts.data[\"CLIP_stop_at_last_layers\"] = x\r\n\r\n\r\ndef apply_upscale_latent_space(p, x, xs):\r\n if x.lower().strip() != '0':\r\n opts.data[\"use_scale_latent_for_hires_fix\"] = True\r\n else:\r\n opts.data[\"use_scale_latent_for_hires_fix\"] = False\r\n\r\n\r\ndef find_vae(name: str):\r\n if name.lower() in ['auto', 'automatic']:\r\n return modules.sd_vae.unspecified\r\n if name.lower() == 'none':\r\n return None\r\n else:\r\n choices = [x for x in sorted(modules.sd_vae.vae_dict, key=lambda x: len(x)) if name.lower().strip() in x.lower()]\r\n if len(choices) == 0:\r\n print(f\"No VAE found for {name}; using automatic\")\r\n return modules.sd_vae.unspecified\r\n else:\r\n return modules.sd_vae.vae_dict[choices[0]]\r\n\r\n\r\ndef apply_vae(p, x, xs):\r\n modules.sd_vae.reload_vae_weights(shared.sd_model, vae_file=find_vae(x))\r\n\r\n\r\ndef apply_styles(p: StableDiffusionProcessingTxt2Img, x: str, _):\r\n p.styles.extend(x.split(','))\r\n\r\n\r\ndef format_value_add_label(p, opt, x):\r\n if type(x) == float:\r\n x = round(x, 8)\r\n\r\n return f\"{opt.label}: {x}\"\r\n\r\n\r\ndef format_value(p, opt, x):\r\n if type(x) == float:\r\n x = round(x, 8)\r\n return x\r\n\r\n\r\ndef format_value_join_list(p, opt, x):\r\n return \", \".join(x)\r\n\r\n\r\ndef do_nothing(p, x, xs):\r\n pass\r\n\r\n\r\ndef format_nothing(p, opt, x):\r\n return \"\"\r\n\r\n\r\ndef str_permutations(x):\r\n \"\"\"dummy function for specifying it in AxisOption's type when you want to get a list of permutations\"\"\"\r\n return x\r\n\r\n\r\nclass AxisOption:\r\n def __init__(self, label, type, apply, format_value=format_value_add_label, confirm=None, cost=0.0, choices=None):\r\n self.label = label\r\n self.type = type\r\n self.apply = apply\r\n self.format_value = format_value\r\n self.confirm = confirm\r\n self.cost = cost\r\n self.choices = choices\r\n\r\n\r\nclass AxisOptionImg2Img(AxisOption):\r\n def __init__(self, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n self.is_img2img = True\r\n\r\nclass AxisOptionTxt2Img(AxisOption):\r\n def __init__(self, *args, **kwargs):\r\n super().__init__(*args, **kwargs)\r\n self.is_img2img = False\r\n\r\n\r\naxis_options = [\r\n AxisOption(\"Nothing\", str, do_nothing, format_value=format_nothing),\r\n AxisOption(\"Seed\", int, apply_field(\"seed\")),\r\n AxisOption(\"Var. seed\", int, apply_field(\"subseed\")),\r\n AxisOption(\"Var. strength\", float, apply_field(\"subseed_strength\")),\r\n AxisOption(\"Steps\", int, apply_field(\"steps\")),\r\n AxisOptionTxt2Img(\"Hires steps\", int, apply_field(\"hr_second_pass_steps\")),\r\n AxisOption(\"CFG Scale\", float, apply_field(\"cfg_scale\")),\r\n AxisOptionImg2Img(\"Image CFG Scale\", float, apply_field(\"image_cfg_scale\")),\r\n AxisOption(\"Prompt S/R\", str, apply_prompt, format_value=format_value),\r\n AxisOption(\"Prompt order\", str_permutations, apply_order, format_value=format_value_join_list),\r\n AxisOptionTxt2Img(\"Sampler\", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers]),\r\n AxisOptionImg2Img(\"Sampler\", str, apply_sampler, format_value=format_value, confirm=confirm_samplers, choices=lambda: [x.name for x in sd_samplers.samplers_for_img2img]),\r\n AxisOption(\"Checkpoint name\", str, apply_checkpoint, format_value=format_value, confirm=confirm_checkpoints, cost=1.0, choices=lambda: list(sd_models.checkpoints_list)),\r\n AxisOption(\"Sigma Churn\", float, apply_field(\"s_churn\")),\r\n AxisOption(\"Sigma min\", float, apply_field(\"s_tmin\")),\r\n AxisOption(\"Sigma max\", float, apply_field(\"s_tmax\")),\r\n AxisOption(\"Sigma noise\", float, apply_field(\"s_noise\")),\r\n AxisOption(\"Eta\", float, apply_field(\"eta\")),\r\n AxisOption(\"Clip skip\", int, apply_clip_skip),\r\n AxisOption(\"Denoising\", float, apply_field(\"denoising_strength\")),\r\n AxisOptionTxt2Img(\"Hires upscaler\", str, apply_field(\"hr_upscaler\"), choices=lambda: [*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]]),\r\n AxisOptionImg2Img(\"Cond. Image Mask Weight\", float, apply_field(\"inpainting_mask_weight\")),\r\n AxisOption(\"VAE\", str, apply_vae, cost=0.7, choices=lambda: list(sd_vae.vae_dict)),\r\n AxisOption(\"Styles\", str, apply_styles, choices=lambda: list(shared.prompt_styles.styles)),\r\n]\r\n\r\n\r\ndef draw_xyz_grid(p, xs, ys, zs, x_labels, y_labels, z_labels, cell, draw_legend, include_lone_images, include_sub_grids, first_axes_processed, second_axes_processed, margin_size):\r\n hor_texts = [[images.GridAnnotation(x)] for x in x_labels]\r\n ver_texts = [[images.GridAnnotation(y)] for y in y_labels]\r\n title_texts = [[images.GridAnnotation(z)] for z in z_labels]\r\n\r\n # Temporary list of all the images that are generated to be populated into the grid.\r\n # Will be filled with empty images for any individual step that fails to process properly\r\n image_cache = [None] * (len(xs) * len(ys) * len(zs))\r\n\r\n processed_result = None\r\n cell_mode = \"P\"\r\n cell_size = (1, 1)\r\n\r\n state.job_count = len(xs) * len(ys) * len(zs) * p.n_iter\r\n\r\n def process_cell(x, y, z, ix, iy, iz):\r\n nonlocal image_cache, processed_result, cell_mode, cell_size\r\n\r\n def index(ix, iy, iz):\r\n return ix + iy * len(xs) + iz * len(xs) * len(ys)\r\n\r\n state.job = f\"{index(ix, iy, iz) + 1} out of {len(xs) * len(ys) * len(zs)}\"\r\n\r\n processed: Processed = cell(x, y, z)\r\n\r\n try:\r\n # this dereference will throw an exception if the image was not processed\r\n # (this happens in cases such as if the user stops the process from the UI)\r\n processed_image = processed.images[0]\r\n\r\n if processed_result is None:\r\n # Use our first valid processed result as a template container to hold our full results\r\n processed_result = copy(processed)\r\n cell_mode = processed_image.mode\r\n cell_size = processed_image.size\r\n processed_result.images = [Image.new(cell_mode, cell_size)]\r\n processed_result.all_prompts = [processed.prompt]\r\n processed_result.all_seeds = [processed.seed]\r\n processed_result.infotexts = [processed.infotexts[0]]\r\n\r\n image_cache[index(ix, iy, iz)] = processed_image\r\n if include_lone_images:\r\n processed_result.images.append(processed_image)\r\n processed_result.all_prompts.append(processed.prompt)\r\n processed_result.all_seeds.append(processed.seed)\r\n processed_result.infotexts.append(processed.infotexts[0])\r\n except:\r\n image_cache[index(ix, iy, iz)] = Image.new(cell_mode, cell_size)\r\n\r\n if first_axes_processed == 'x':\r\n for ix, x in enumerate(xs):\r\n if second_axes_processed == 'y':\r\n for iy, y in enumerate(ys):\r\n for iz, z in enumerate(zs):\r\n process_cell(x, y, z, ix, iy, iz)\r\n else:\r\n for iz, z in enumerate(zs):\r\n for iy, y in enumerate(ys):\r\n process_cell(x, y, z, ix, iy, iz)\r\n elif first_axes_processed == 'y':\r\n for iy, y in enumerate(ys):\r\n if second_axes_processed == 'x':\r\n for ix, x in enumerate(xs):\r\n for iz, z in enumerate(zs):\r\n process_cell(x, y, z, ix, iy, iz)\r\n else:\r\n for iz, z in enumerate(zs):\r\n for ix, x in enumerate(xs):\r\n process_cell(x, y, z, ix, iy, iz)\r\n elif first_axes_processed == 'z':\r\n for iz, z in enumerate(zs):\r\n if second_axes_processed == 'x':\r\n for ix, x in enumerate(xs):\r\n for iy, y in enumerate(ys):\r\n process_cell(x, y, z, ix, iy, iz)\r\n else:\r\n for iy, y in enumerate(ys):\r\n for ix, x in enumerate(xs):\r\n process_cell(x, y, z, ix, iy, iz)\r\n\r\n if not processed_result:\r\n print(\"Unexpected error: draw_xyz_grid failed to return even a single processed image\")\r\n return Processed(p, [])\r\n\r\n sub_grids = [None] * len(zs)\r\n for i in range(len(zs)):\r\n start_index = i * len(xs) * len(ys)\r\n end_index = start_index + len(xs) * len(ys)\r\n grid = images.image_grid(image_cache[start_index:end_index], rows=len(ys))\r\n if draw_legend:\r\n grid = images.draw_grid_annotations(grid, cell_size[0], cell_size[1], hor_texts, ver_texts, margin_size)\r\n sub_grids[i] = grid\r\n if include_sub_grids and len(zs) > 1:\r\n processed_result.images.insert(i+1, grid)\r\n\r\n sub_grid_size = sub_grids[0].size\r\n z_grid = images.image_grid(sub_grids, rows=1)\r\n if draw_legend:\r\n z_grid = images.draw_grid_annotations(z_grid, sub_grid_size[0], sub_grid_size[1], title_texts, [[images.GridAnnotation()]])\r\n processed_result.images[0] = z_grid\r\n\r\n return processed_result, sub_grids\r\n\r\n\r\nclass SharedSettingsStackHelper(object):\r\n def __enter__(self):\r\n self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers\r\n self.vae = opts.sd_vae\r\n \r\n def __exit__(self, exc_type, exc_value, tb):\r\n opts.data[\"sd_vae\"] = self.vae\r\n modules.sd_models.reload_model_weights()\r\n modules.sd_vae.reload_vae_weights()\r\n\r\n opts.data[\"CLIP_stop_at_last_layers\"] = self.CLIP_stop_at_last_layers\r\n\r\n\r\nre_range = re.compile(r\"\\s*([+-]?\\s*\\d+)\\s*-\\s*([+-]?\\s*\\d+)(?:\\s*\\(([+-]\\d+)\\s*\\))?\\s*\")\r\nre_range_float = re.compile(r\"\\s*([+-]?\\s*\\d+(?:.\\d*)?)\\s*-\\s*([+-]?\\s*\\d+(?:.\\d*)?)(?:\\s*\\(([+-]\\d+(?:.\\d*)?)\\s*\\))?\\s*\")\r\n\r\nre_range_count = re.compile(r\"\\s*([+-]?\\s*\\d+)\\s*-\\s*([+-]?\\s*\\d+)(?:\\s*\\[(\\d+)\\s*\\])?\\s*\")\r\nre_range_count_float = re.compile(r\"\\s*([+-]?\\s*\\d+(?:.\\d*)?)\\s*-\\s*([+-]?\\s*\\d+(?:.\\d*)?)(?:\\s*\\[(\\d+(?:.\\d*)?)\\s*\\])?\\s*\")\r\n\r\n\r\nclass Script(scripts.Script):\r\n def title(self):\r\n return \"X/Y/Z plot\"\r\n\r\n def ui(self, is_img2img):\r\n self.current_axis_options = [x for x in axis_options if type(x) == AxisOption or x.is_img2img == is_img2img]\r\n\r\n with gr.Row():\r\n with gr.Column(scale=19):\r\n with gr.Row():\r\n x_type = gr.Dropdown(label=\"X type\", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[1].label, type=\"index\", elem_id=self.elem_id(\"x_type\"))\r\n x_values = gr.Textbox(label=\"X values\", lines=1, elem_id=self.elem_id(\"x_values\"))\r\n fill_x_button = ToolButton(value=fill_values_symbol, elem_id=\"xyz_grid_fill_x_tool_button\", visible=False)\r\n\r\n with gr.Row():\r\n y_type = gr.Dropdown(label=\"Y type\", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type=\"index\", elem_id=self.elem_id(\"y_type\"))\r\n y_values = gr.Textbox(label=\"Y values\", lines=1, elem_id=self.elem_id(\"y_values\"))\r\n fill_y_button = ToolButton(value=fill_values_symbol, elem_id=\"xyz_grid_fill_y_tool_button\", visible=False)\r\n\r\n with gr.Row():\r\n z_type = gr.Dropdown(label=\"Z type\", choices=[x.label for x in self.current_axis_options], value=self.current_axis_options[0].label, type=\"index\", elem_id=self.elem_id(\"z_type\"))\r\n z_values = gr.Textbox(label=\"Z values\", lines=1, elem_id=self.elem_id(\"z_values\"))\r\n fill_z_button = ToolButton(value=fill_values_symbol, elem_id=\"xyz_grid_fill_z_tool_button\", visible=False)\r\n\r\n with gr.Row(variant=\"compact\", elem_id=\"axis_options\"):\r\n with gr.Column():\r\n draw_legend = gr.Checkbox(label='Draw legend', value=True, elem_id=self.elem_id(\"draw_legend\"))\r\n no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False, elem_id=self.elem_id(\"no_fixed_seeds\"))\r\n with gr.Column():\r\n include_lone_images = gr.Checkbox(label='Include Sub Images', value=False, elem_id=self.elem_id(\"include_lone_images\"))\r\n include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False, elem_id=self.elem_id(\"include_sub_grids\"))\r\n with gr.Column():\r\n margin_size = gr.Slider(label=\"Grid margins (px)\", minimum=0, maximum=500, value=0, step=2, elem_id=self.elem_id(\"margin_size\"))\r\n \r\n with gr.Row(variant=\"compact\", elem_id=\"swap_axes\"):\r\n swap_xy_axes_button = gr.Button(value=\"Swap X/Y axes\", elem_id=\"xy_grid_swap_axes_button\")\r\n swap_yz_axes_button = gr.Button(value=\"Swap Y/Z axes\", elem_id=\"yz_grid_swap_axes_button\")\r\n swap_xz_axes_button = gr.Button(value=\"Swap X/Z axes\", elem_id=\"xz_grid_swap_axes_button\")\r\n\r\n def swap_axes(axis1_type, axis1_values, axis2_type, axis2_values):\r\n return self.current_axis_options[axis2_type].label, axis2_values, self.current_axis_options[axis1_type].label, axis1_values\r\n\r\n xy_swap_args = [x_type, x_values, y_type, y_values]\r\n swap_xy_axes_button.click(swap_axes, inputs=xy_swap_args, outputs=xy_swap_args)\r\n yz_swap_args = [y_type, y_values, z_type, z_values]\r\n swap_yz_axes_button.click(swap_axes, inputs=yz_swap_args, outputs=yz_swap_args)\r\n xz_swap_args = [x_type, x_values, z_type, z_values]\r\n swap_xz_axes_button.click(swap_axes, inputs=xz_swap_args, outputs=xz_swap_args)\r\n\r\n def fill(x_type):\r\n axis = self.current_axis_options[x_type]\r\n return \", \".join(axis.choices()) if axis.choices else gr.update()\r\n\r\n fill_x_button.click(fn=fill, inputs=[x_type], outputs=[x_values])\r\n fill_y_button.click(fn=fill, inputs=[y_type], outputs=[y_values])\r\n fill_z_button.click(fn=fill, inputs=[z_type], outputs=[z_values])\r\n\r\n def select_axis(x_type):\r\n return gr.Button.update(visible=self.current_axis_options[x_type].choices is not None)\r\n\r\n x_type.change(fn=select_axis, inputs=[x_type], outputs=[fill_x_button])\r\n y_type.change(fn=select_axis, inputs=[y_type], outputs=[fill_y_button])\r\n z_type.change(fn=select_axis, inputs=[z_type], outputs=[fill_z_button])\r\n\r\n self.infotext_fields = (\r\n (x_type, \"X Type\"),\r\n (x_values, \"X Values\"),\r\n (y_type, \"Y Type\"),\r\n (y_values, \"Y Values\"),\r\n (z_type, \"Z Type\"),\r\n (z_values, \"Z Values\"),\r\n )\r\n\r\n return [x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size]\r\n\r\n def run(self, p, x_type, x_values, y_type, y_values, z_type, z_values, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, margin_size):\r\n if not no_fixed_seeds:\r\n modules.processing.fix_seed(p)\r\n\r\n if not opts.return_grid:\r\n p.batch_size = 1\r\n\r\n def process_axis(opt, vals):\r\n if opt.label == 'Nothing':\r\n return [0]\r\n\r\n valslist = [x.strip() for x in chain.from_iterable(csv.reader(StringIO(vals)))]\r\n\r\n if opt.type == int:\r\n valslist_ext = []\r\n\r\n for val in valslist:\r\n m = re_range.fullmatch(val)\r\n mc = re_range_count.fullmatch(val)\r\n if m is not None:\r\n start = int(m.group(1))\r\n end = int(m.group(2))+1\r\n step = int(m.group(3)) if m.group(3) is not None else 1\r\n\r\n valslist_ext += list(range(start, end, step))\r\n elif mc is not None:\r\n start = int(mc.group(1))\r\n end = int(mc.group(2))\r\n num = int(mc.group(3)) if mc.group(3) is not None else 1\r\n \r\n valslist_ext += [int(x) for x in np.linspace(start=start, stop=end, num=num).tolist()]\r\n else:\r\n valslist_ext.append(val)\r\n\r\n valslist = valslist_ext\r\n elif opt.type == float:\r\n valslist_ext = []\r\n\r\n for val in valslist:\r\n m = re_range_float.fullmatch(val)\r\n mc = re_range_count_float.fullmatch(val)\r\n if m is not None:\r\n start = float(m.group(1))\r\n end = float(m.group(2))\r\n step = float(m.group(3)) if m.group(3) is not None else 1\r\n\r\n valslist_ext += np.arange(start, end + step, step).tolist()\r\n elif mc is not None:\r\n start = float(mc.group(1))\r\n end = float(mc.group(2))\r\n num = int(mc.group(3)) if mc.group(3) is not None else 1\r\n \r\n valslist_ext += np.linspace(start=start, stop=end, num=num).tolist()\r\n else:\r\n valslist_ext.append(val)\r\n\r\n valslist = valslist_ext\r\n elif opt.type == str_permutations:\r\n valslist = list(permutations(valslist))\r\n\r\n valslist = [opt.type(x) for x in valslist]\r\n\r\n # Confirm options are valid before starting\r\n if opt.confirm:\r\n opt.confirm(p, valslist)\r\n\r\n return valslist\r\n\r\n x_opt = self.current_axis_options[x_type]\r\n xs = process_axis(x_opt, x_values)\r\n\r\n y_opt = self.current_axis_options[y_type]\r\n ys = process_axis(y_opt, y_values)\r\n\r\n z_opt = self.current_axis_options[z_type]\r\n zs = process_axis(z_opt, z_values)\r\n\r\n def fix_axis_seeds(axis_opt, axis_list):\r\n if axis_opt.label in ['Seed', 'Var. seed']:\r\n return [int(random.randrange(4294967294)) if val is None or val == '' or val == -1 else val for val in axis_list]\r\n else:\r\n return axis_list\r\n\r\n if not no_fixed_seeds:\r\n xs = fix_axis_seeds(x_opt, xs)\r\n ys = fix_axis_seeds(y_opt, ys)\r\n zs = fix_axis_seeds(z_opt, zs)\r\n\r\n if x_opt.label == 'Steps':\r\n total_steps = sum(xs) * len(ys) * len(zs)\r\n elif y_opt.label == 'Steps':\r\n total_steps = sum(ys) * len(xs) * len(zs)\r\n elif z_opt.label == 'Steps':\r\n total_steps = sum(zs) * len(xs) * len(ys)\r\n else:\r\n total_steps = p.steps * len(xs) * len(ys) * len(zs)\r\n\r\n if isinstance(p, StableDiffusionProcessingTxt2Img) and p.enable_hr:\r\n if x_opt.label == \"Hires steps\":\r\n total_steps += sum(xs) * len(ys) * len(zs)\r\n elif y_opt.label == \"Hires steps\":\r\n total_steps += sum(ys) * len(xs) * len(zs)\r\n elif z_opt.label == \"Hires steps\":\r\n total_steps += sum(zs) * len(xs) * len(ys)\r\n elif p.hr_second_pass_steps:\r\n total_steps += p.hr_second_pass_steps * len(xs) * len(ys) * len(zs)\r\n else:\r\n total_steps *= 2\r\n\r\n total_steps *= p.n_iter\r\n\r\n image_cell_count = p.n_iter * p.batch_size\r\n cell_console_text = f\"; {image_cell_count} images per cell\" if image_cell_count > 1 else \"\"\r\n plural_s = 's' if len(zs) > 1 else ''\r\n print(f\"X/Y/Z plot will create {len(xs) * len(ys) * len(zs) * image_cell_count} images on {len(zs)} {len(xs)}x{len(ys)} grid{plural_s}{cell_console_text}. (Total steps to process: {total_steps})\")\r\n shared.total_tqdm.updateTotal(total_steps)\r\n\r\n grid_infotext = [None]\r\n\r\n state.xyz_plot_x = AxisInfo(x_opt, xs)\r\n state.xyz_plot_y = AxisInfo(y_opt, ys)\r\n state.xyz_plot_z = AxisInfo(z_opt, zs)\r\n\r\n # If one of the axes is very slow to change between (like SD model\r\n # checkpoint), then make sure it is in the outer iteration of the nested\r\n # `for` loop.\r\n first_axes_processed = 'x'\r\n second_axes_processed = 'y'\r\n if x_opt.cost > y_opt.cost and x_opt.cost > z_opt.cost:\r\n first_axes_processed = 'x'\r\n if y_opt.cost > z_opt.cost:\r\n second_axes_processed = 'y'\r\n else:\r\n second_axes_processed = 'z'\r\n elif y_opt.cost > x_opt.cost and y_opt.cost > z_opt.cost:\r\n first_axes_processed = 'y'\r\n if x_opt.cost > z_opt.cost:\r\n second_axes_processed = 'x'\r\n else:\r\n second_axes_processed = 'z'\r\n elif z_opt.cost > x_opt.cost and z_opt.cost > y_opt.cost:\r\n first_axes_processed = 'z'\r\n if x_opt.cost > y_opt.cost:\r\n second_axes_processed = 'x'\r\n else:\r\n second_axes_processed = 'y'\r\n\r\n def cell(x, y, z):\r\n if shared.state.interrupted:\r\n return Processed(p, [], p.seed, \"\")\r\n\r\n pc = copy(p)\r\n pc.styles = pc.styles[:]\r\n x_opt.apply(pc, x, xs)\r\n y_opt.apply(pc, y, ys)\r\n z_opt.apply(pc, z, zs)\r\n\r\n res = process_images(pc)\r\n\r\n if grid_infotext[0] is None:\r\n pc.extra_generation_params = copy(pc.extra_generation_params)\r\n pc.extra_generation_params['Script'] = self.title()\r\n\r\n if x_opt.label != 'Nothing':\r\n pc.extra_generation_params[\"X Type\"] = x_opt.label\r\n pc.extra_generation_params[\"X Values\"] = x_values\r\n if x_opt.label in [\"Seed\", \"Var. seed\"] and not no_fixed_seeds:\r\n pc.extra_generation_params[\"Fixed X Values\"] = \", \".join([str(x) for x in xs])\r\n\r\n if y_opt.label != 'Nothing':\r\n pc.extra_generation_params[\"Y Type\"] = y_opt.label\r\n pc.extra_generation_params[\"Y Values\"] = y_values\r\n if y_opt.label in [\"Seed\", \"Var. seed\"] and not no_fixed_seeds:\r\n pc.extra_generation_params[\"Fixed Y Values\"] = \", \".join([str(y) for y in ys])\r\n\r\n if z_opt.label != 'Nothing':\r\n pc.extra_generation_params[\"Z Type\"] = z_opt.label\r\n pc.extra_generation_params[\"Z Values\"] = z_values\r\n if z_opt.label in [\"Seed\", \"Var. seed\"] and not no_fixed_seeds:\r\n pc.extra_generation_params[\"Fixed Z Values\"] = \", \".join([str(z) for z in zs])\r\n\r\n grid_infotext[0] = processing.create_infotext(pc, pc.all_prompts, pc.all_seeds, pc.all_subseeds)\r\n\r\n return res\r\n\r\n with SharedSettingsStackHelper():\r\n processed, sub_grids = draw_xyz_grid(\r\n p,\r\n xs=xs,\r\n ys=ys,\r\n zs=zs,\r\n x_labels=[x_opt.format_value(p, x_opt, x) for x in xs],\r\n y_labels=[y_opt.format_value(p, y_opt, y) for y in ys],\r\n z_labels=[z_opt.format_value(p, z_opt, z) for z in zs],\r\n cell=cell,\r\n draw_legend=draw_legend,\r\n include_lone_images=include_lone_images,\r\n include_sub_grids=include_sub_grids,\r\n first_axes_processed=first_axes_processed,\r\n second_axes_processed=second_axes_processed,\r\n margin_size=margin_size\r\n )\r\n\r\n if opts.grid_save and len(sub_grids) > 1:\r\n for sub_grid in sub_grids:\r\n images.save_image(sub_grid, p.outpath_grids, \"xyz_grid\", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)\r\n\r\n if opts.grid_save:\r\n images.save_image(processed.images[0], p.outpath_grids, \"xyz_grid\", info=grid_infotext[0], extension=opts.grid_format, prompt=p.prompt, seed=processed.seed, grid=True, p=p)\r\n\r\n return processed\r\n" }, { "path": "style.css", "content": ".container {\r\n max-width: 100%;\r\n}\r\n\r\n.token-counter{\r\n position: absolute;\r\n display: inline-block;\r\n right: 2em;\r\n min-width: 0 !important;\r\n width: auto;\r\n z-index: 100;\r\n}\r\n\r\n.token-counter.error span{\r\n 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);\r\n border: 2px solid rgba(255,0,0,0.4) !important;\r\n}\r\n\r\n.token-counter div{\r\n display: inline;\r\n}\r\n\r\n.token-counter span{\r\n padding: 0.1em 0.75em;\r\n}\r\n\r\n#sh{\r\n min-width: 2em;\r\n min-height: 2em;\r\n max-width: 2em;\r\n max-height: 2em;\r\n flex-grow: 0;\r\n padding-left: 0.25em;\r\n padding-right: 0.25em;\r\n margin: 0.1em 0;\r\n opacity: 0%;\r\n cursor: default;\r\n}\r\n\r\n.output-html p {margin: 0 0.5em;}\r\n\r\n.row > *,\r\n.row > .gr-form > * {\r\n min-width: min(120px, 100%);\r\n flex: 1 1 0%;\r\n}\r\n\r\n.performance {\r\n font-size: 0.85em;\r\n color: #444;\r\n}\r\n\r\n.performance p{\r\n display: inline-block;\r\n}\r\n\r\n.performance .time {\r\n margin-right: 0;\r\n}\r\n\r\n.performance .vram {\r\n}\r\n\r\n#txt2img_generate, #img2img_generate {\r\n min-height: 4.5em;\r\n}\r\n\r\n@media screen and (min-width: 2500px) {\r\n #txt2img_gallery, #img2img_gallery {\r\n min-height: 768px;\r\n }\r\n}\r\n\r\n#txt2img_gallery img, #img2img_gallery img{\r\n object-fit: scale-down;\r\n}\r\n#txt2img_actions_column, #img2img_actions_column {\r\n\tmargin: 0.35rem 0.75rem 0.35rem 0;\t\r\n}\r\n#script_list {\r\n padding: .625rem .75rem 0 .625rem;\r\n}\r\n.justify-center.overflow-x-scroll {\r\n justify-content: left;\r\n}\r\n\r\n.justify-center.overflow-x-scroll button:first-of-type {\r\n margin-left: auto;\r\n}\r\n\r\n.justify-center.overflow-x-scroll button:last-of-type {\r\n margin-right: auto;\r\n}\r\n\r\n[id$=_random_seed], [id$=_random_subseed], [id$=_reuse_seed], [id$=_reuse_subseed], #open_folder{\r\n min-width: 2.3em;\r\n height: 2.5em;\r\n flex-grow: 0;\r\n padding-left: 0.25em;\r\n padding-right: 0.25em;\r\n}\r\n\r\n#hidden_element{\r\n display: none;\r\n}\r\n\r\n[id$=_seed_row], [id$=_subseed_row]{\r\n gap: 0.5rem;\r\n padding: 0.6em;\r\n}\r\n\r\n[id$=_subseed_show_box]{\r\n min-width: auto;\r\n flex-grow: 0;\r\n}\r\n\r\n[id$=_subseed_show_box] > div{\r\n border: 0;\r\n height: 100%;\r\n}\r\n\r\n[id$=_subseed_show]{\r\n min-width: auto;\r\n flex-grow: 0;\r\n padding: 0;\r\n}\r\n\r\n[id$=_subseed_show] label{\r\n height: 100%;\r\n}\r\n\r\n#txt2img_actions_column, #img2img_actions_column{\r\n gap: 0;\r\n margin-right: .75rem;\r\n}\r\n\r\n#txt2img_tools, #img2img_tools{\r\n gap: 0.4em;\r\n}\r\n\r\n#interrogate_col{\r\n min-width: 0 !important;\r\n max-width: 8em !important;\r\n margin-right: 1em;\r\n gap: 0;\r\n}\r\n#interrogate, #deepbooru{\r\n margin: 0em 0.25em 0.5em 0.25em;\r\n min-width: 8em;\r\n max-width: 8em;\r\n}\r\n\r\n#style_pos_col, #style_neg_col{\r\n min-width: 8em !important;\r\n}\r\n\r\n#txt2img_styles_row, #img2img_styles_row{\r\n gap: 0.25em;\r\n margin-top: 0.3em;\r\n}\r\n\r\n#txt2img_styles_row > button, #img2img_styles_row > button{\r\n margin: 0;\r\n}\r\n\r\n#txt2img_styles, #img2img_styles{\r\n padding: 0;\r\n}\r\n\r\n#txt2img_styles > label > div, #img2img_styles > label > div{\r\n min-height: 3.2em;\r\n}\r\n\r\nul.list-none{\r\n max-height: 35em;\r\n z-index: 2000;\r\n}\r\n\r\n.gr-form{\r\n background: transparent;\r\n}\r\n\r\n.my-4{\r\n margin-top: 0;\r\n margin-bottom: 0;\r\n}\r\n\r\n#resize_mode{\r\n flex: 1.5;\r\n}\r\n\r\nbutton{\r\n align-self: stretch !important;\r\n}\r\n\r\n.overflow-hidden, .gr-panel{\r\n overflow: visible !important;\r\n}\r\n\r\n#x_type, #y_type{\r\n max-width: 10em;\r\n}\r\n\r\n#txt2img_preview, #img2img_preview, #ti_preview{\r\n position: absolute;\r\n width: 320px;\r\n left: 0;\r\n right: 0;\r\n margin-left: auto;\r\n margin-right: auto;\r\n margin-top: 34px;\r\n z-index: 100;\r\n border: none;\r\n border-top-left-radius: 0;\r\n border-top-right-radius: 0;\r\n}\r\n\r\n@media screen and (min-width: 768px) {\r\n #txt2img_preview, #img2img_preview, #ti_preview {\r\n position: absolute;\r\n }\r\n}\r\n\r\n@media screen and (max-width: 767px) {\r\n #txt2img_preview, #img2img_preview, #ti_preview {\r\n position: relative;\r\n }\r\n}\r\n\r\n#txt2img_preview div.left-0.top-0, #img2img_preview div.left-0.top-0, #ti_preview div.left-0.top-0{\r\n display: none;\r\n}\r\n\r\nfieldset span.text-gray-500, .gr-block.gr-box span.text-gray-500, label.block span{\r\n position: absolute;\r\n top: -0.7em;\r\n line-height: 1.2em;\r\n padding: 0;\r\n margin: 0 0.5em;\r\n\r\n background-color: white;\r\n box-shadow: 6px 0 6px 0px white, -6px 0 6px 0px white;\r\n\r\n z-index: 300;\r\n}\r\n\r\n.dark fieldset span.text-gray-500, .dark .gr-block.gr-box span.text-gray-500, .dark label.block span{\r\n background-color: rgb(31, 41, 55);\r\n box-shadow: none;\r\n border: 1px solid rgba(128, 128, 128, 0.1);\r\n border-radius: 6px;\r\n padding: 0.1em 0.5em;\r\n}\r\n\r\n#txt2img_column_batch, #img2img_column_batch{\r\n min-width: min(13.5em, 100%) !important;\r\n}\r\n\r\n#settings fieldset span.text-gray-500, #settings .gr-block.gr-box span.text-gray-500, #settings label.block span{\r\n position: relative;\r\n border: none;\r\n margin-right: 8em;\r\n}\r\n\r\n#settings .gr-panel div.flex-col div.justify-between div{\r\n position: relative;\r\n z-index: 200;\r\n}\r\n\r\n#settings{\r\n display: block;\r\n}\r\n\r\n#settings > div{\r\n border: none;\r\n margin-left: 10em;\r\n}\r\n\r\n#settings > div.flex-wrap{\r\n float: left;\r\n display: block;\r\n margin-left: 0;\r\n width: 10em;\r\n}\r\n\r\n#settings > div.flex-wrap button{\r\n display: block;\r\n border: none;\r\n text-align: left;\r\n}\r\n\r\n#settings_result{\r\n height: 1.4em;\r\n margin: 0 1.2em;\r\n}\r\n\r\ninput[type=\"range\"]{\r\n margin: 0.5em 0 -0.3em 0;\r\n}\r\n\r\n#mask_bug_info {\r\n text-align: center;\r\n display: block;\r\n margin-top: -0.75em;\r\n margin-bottom: -0.75em;\r\n}\r\n\r\n#txt2img_negative_prompt, #img2img_negative_prompt{\r\n}\r\n\r\n/* gradio 3.8 adds opacity to progressbar which makes it blink; disable it here */\r\n.transition.opacity-20 {\r\n opacity: 1 !important;\r\n}\r\n\r\n/* more gradio's garbage cleanup */\r\n.min-h-\\[4rem\\] { min-height: unset !important; }\r\n.min-h-\\[6rem\\] { min-height: unset !important; }\r\n\r\n.progressDiv{\r\n position: relative;\r\n height: 20px;\r\n background: #b4c0cc;\r\n border-radius: 3px !important;\r\n margin-bottom: -3px;\r\n}\r\n\r\n.dark .progressDiv{\r\n background: #424c5b;\r\n}\r\n\r\n.progressDiv .progress{\r\n width: 0%;\r\n height: 20px;\r\n background: #0060df;\r\n color: white;\r\n font-weight: bold;\r\n line-height: 20px;\r\n padding: 0 8px 0 0;\r\n text-align: right;\r\n border-radius: 3px;\r\n overflow: visible;\r\n white-space: nowrap;\r\n padding: 0 0.5em;\r\n}\r\n\r\n.livePreview{\r\n position: absolute;\r\n z-index: 300;\r\n background-color: white;\r\n margin: -4px;\r\n}\r\n\r\n.dark .livePreview{\r\n background-color: rgb(17 24 39 / var(--tw-bg-opacity));\r\n}\r\n\r\n.livePreview img{\r\n position: absolute;\r\n object-fit: contain;\r\n width: 100%;\r\n height: 100%;\r\n}\r\n\r\n#lightboxModal{\r\n display: none;\r\n position: fixed;\r\n z-index: 1001;\r\n padding-top: 100px;\r\n left: 0;\r\n top: 0;\r\n width: 100%;\r\n height: 100%;\r\n overflow: auto;\r\n background-color: rgba(20, 20, 20, 0.95);\r\n user-select: none;\r\n -webkit-user-select: none;\r\n}\r\n\r\n.modalControls {\r\n display: grid;\r\n grid-template-columns: 32px 32px 32px 1fr 32px;\r\n grid-template-areas: \"zoom tile save space close\";\r\n position: absolute;\r\n top: 0;\r\n left: 0;\r\n right: 0;\r\n padding: 16px;\r\n gap: 16px;\r\n background-color: rgba(0,0,0,0.2);\r\n}\r\n\r\n.modalClose {\r\n grid-area: close;\r\n}\r\n\r\n.modalZoom {\r\n grid-area: zoom;\r\n}\r\n\r\n.modalSave {\r\n grid-area: save;\r\n}\r\n\r\n.modalTileImage {\r\n grid-area: tile;\r\n}\r\n\r\n.modalClose,\r\n.modalZoom,\r\n.modalTileImage {\r\n color: white;\r\n font-size: 35px;\r\n font-weight: bold;\r\n cursor: pointer;\r\n}\r\n\r\n.modalSave {\r\n color: white;\r\n font-size: 28px;\r\n margin-top: 8px;\r\n font-weight: bold;\r\n cursor: pointer;\r\n}\r\n\r\n.modalClose:hover,\r\n.modalClose:focus,\r\n.modalSave:hover,\r\n.modalSave:focus,\r\n.modalZoom:hover,\r\n.modalZoom:focus {\r\n color: #999;\r\n text-decoration: none;\r\n cursor: pointer;\r\n}\r\n\r\n#modalImage {\r\n display: block;\r\n margin-left: auto;\r\n margin-right: auto;\r\n margin-top: auto;\r\n width: auto;\r\n}\r\n\r\n.modalImageFullscreen {\r\n object-fit: contain;\r\n height: 90%;\r\n}\r\n\r\n.modalPrev,\r\n.modalNext {\r\n cursor: pointer;\r\n position: absolute;\r\n top: 50%;\r\n width: auto;\r\n padding: 16px;\r\n margin-top: -50px;\r\n color: white;\r\n font-weight: bold;\r\n font-size: 20px;\r\n transition: 0.6s ease;\r\n border-radius: 0 3px 3px 0;\r\n user-select: none;\r\n -webkit-user-select: none;\r\n}\r\n\r\n.modalNext {\r\n right: 0;\r\n border-radius: 3px 0 0 3px;\r\n}\r\n\r\n.modalPrev:hover,\r\n.modalNext:hover {\r\n background-color: rgba(0, 0, 0, 0.8);\r\n}\r\n\r\n#imageARPreview{\r\n position:absolute;\r\n top:0px;\r\n left:0px;\r\n border:2px solid red;\r\n background:rgba(255, 0, 0, 0.3);\r\n z-index: 900;\r\n pointer-events:none;\r\n display:none\r\n}\r\n\r\n#txt2img_generate_box, #img2img_generate_box{\r\n position: relative;\r\n}\r\n\r\n#txt2img_interrupt, #img2img_interrupt, #txt2img_skip, #img2img_skip{\r\n position: absolute;\r\n width: 50%;\r\n height: 100%;\r\n background: #b4c0cc;\r\n display: none;\r\n}\r\n\r\n#txt2img_interrupt, #img2img_interrupt{\r\n left: 0;\r\n border-radius: 0.5rem 0 0 0.5rem;\r\n}\r\n#txt2img_skip, #img2img_skip{\r\n right: 0;\r\n border-radius: 0 0.5rem 0.5rem 0;\r\n}\r\n\r\n.red {\r\n\tcolor: red;\r\n}\r\n\r\n.gallery-item {\r\n --tw-bg-opacity: 0 !important;\r\n}\r\n\r\n#context-menu{\r\n z-index:9999;\r\n position:absolute;\r\n display:block;\r\n padding:0px 0;\r\n border:2px solid #a55000;\r\n border-radius:8px;\r\n box-shadow:1px 1px 2px #CE6400;\r\n width: 200px;\r\n}\r\n\r\n.context-menu-items{\r\n list-style: none;\r\n margin: 0;\r\n padding: 0;\r\n}\r\n\r\n.context-menu-items a{\r\n display:block;\r\n padding:5px;\r\n cursor:pointer;\r\n}\r\n\r\n.context-menu-items a:hover{\r\n background: #a55000;\r\n}\r\n\r\n#quicksettings {\r\n width: fit-content;\r\n}\r\n\r\n#quicksettings > div, #quicksettings > fieldset{\r\n max-width: 24em;\r\n min-width: 24em;\r\n padding: 0;\r\n border: none;\r\n box-shadow: none;\r\n background: none;\r\n margin-right: 10px;\r\n}\r\n\r\n#quicksettings > div > div > div > label > span {\r\n position: relative;\r\n margin-right: 9em;\r\n margin-bottom: -1em;\r\n}\r\n\r\ncanvas[key=\"mask\"] {\r\n z-index: 12 !important;\r\n filter: invert();\r\n mix-blend-mode: multiply;\r\n pointer-events: none;\r\n}\r\n\r\n\r\n/* gradio 3.4.1 stuff for editable scrollbar values */\r\n.gr-box > div > div > input.gr-text-input{\r\n position: absolute;\r\n right: 0.5em;\r\n top: -0.6em;\r\n z-index: 400;\r\n width: 6em;\r\n}\r\n#quicksettings .gr-box > div > div > input.gr-text-input {\r\n top: -1.12em;\r\n}\r\n\r\n.row.gr-compact{\r\n overflow: visible;\r\n}\r\n\r\n#img2img_image, #img2img_image > .h-60, #img2img_image > .h-60 > div, #img2img_image > .h-60 > div > img,\r\n#img2img_sketch, #img2img_sketch > .h-60, #img2img_sketch > .h-60 > div, #img2img_sketch > .h-60 > div > img,\r\n#img2maskimg, #img2maskimg > .h-60, #img2maskimg > .h-60 > div, #img2maskimg > .h-60 > div > img,\r\n#inpaint_sketch, #inpaint_sketch > .h-60, #inpaint_sketch > .h-60 > div, #inpaint_sketch > .h-60 > div > img\r\n{\r\n height: 480px !important;\r\n max-height: 480px !important;\r\n min-height: 480px !important;\r\n}\r\n\r\n/* Extensions */\r\n\r\n#tab_extensions table{\r\n border-collapse: collapse;\r\n}\r\n\r\n#tab_extensions table td, #tab_extensions table th{\r\n border: 1px solid #ccc;\r\n padding: 0.25em 0.5em;\r\n}\r\n\r\n#tab_extensions table input[type=\"checkbox\"]{\r\n margin-right: 0.5em;\r\n}\r\n\r\n#tab_extensions button{\r\n max-width: 16em;\r\n}\r\n\r\n#tab_extensions input[disabled=\"disabled\"]{\r\n opacity: 0.5;\r\n}\r\n\r\n.extension-tag{\r\n font-weight: bold;\r\n font-size: 95%;\r\n}\r\n\r\n#available_extensions .info{\r\n margin: 0;\r\n}\r\n\r\n#available_extensions .date_added{\r\n opacity: 0.85;\r\n font-size: 90%;\r\n}\r\n\r\n#image_buttons_txt2img button, #image_buttons_img2img button, #image_buttons_extras button{\r\n min-width: auto;\r\n padding-left: 0.5em;\r\n padding-right: 0.5em;\r\n}\r\n\r\n.gr-form{\r\n background-color: white;\r\n}\r\n\r\n.dark .gr-form{\r\n background-color: rgb(31 41 55 / var(--tw-bg-opacity));\r\n}\r\n\r\n.gr-button-tool, .gr-button-tool-top{\r\n max-width: 2.5em;\r\n min-width: 2.5em !important;\r\n height: 2.4em;\r\n}\r\n\r\n.gr-button-tool{\r\n margin: 0.6em 0em 0.55em 0;\r\n}\r\n\r\n.gr-button-tool-top, #settings .gr-button-tool{\r\n margin: 1.6em 0.7em 0.55em 0;\r\n}\r\n\r\n\r\n#modelmerger_results_container{\r\n margin-top: 1em;\r\n overflow: visible;\r\n}\r\n\r\n#modelmerger_models{\r\n gap: 0;\r\n}\r\n\r\n\r\n#quicksettings .gr-button-tool{\r\n margin: 0;\r\n border-color: unset;\r\n\tbackground-color: unset;\r\n}\r\n\r\n#modelmerger_interp_description>p {\r\n margin: 0!important;\r\n text-align: center;\r\n}\r\n#modelmerger_interp_description {\r\n margin: 0.35rem 0.75rem 1.23rem;\r\n}\r\n#img2img_settings > div.gr-form, #txt2img_settings > div.gr-form {\r\n padding-top: 0.9em;\r\n padding-bottom: 0.9em;\r\n}\r\n#txt2img_settings {\r\n padding-top: 1.16em;\r\n padding-bottom: 0.9em;\r\n}\r\n#img2img_settings {\r\n padding-bottom: 0.9em;\r\n}\r\n\r\n#img2img_settings div.gr-form .gr-form, #txt2img_settings div.gr-form .gr-form, #train_tabs div.gr-form .gr-form{\r\n border: none;\r\n padding-bottom: 0.5em;\r\n}\r\n\r\nfooter {\r\n display: none !important;\r\n}\r\n\r\n#footer{\r\n text-align: center;\r\n}\r\n\r\n#footer div{\r\n display: inline-block;\r\n}\r\n\r\n#footer .versions{\r\n font-size: 85%;\r\n opacity: 0.85;\r\n}\r\n\r\n#txtimg_hr_finalres{\r\n min-height: 0 !important;\r\n padding: .625rem .75rem;\r\n margin-left: -0.75em\r\n\r\n}\r\n\r\n#txtimg_hr_finalres .resolution{\r\n font-weight: bold;\r\n}\r\n\r\n#txt2img_checkboxes, #img2img_checkboxes{\r\n margin-bottom: 0.5em;\r\n margin-left: 0em;\r\n}\r\n#txt2img_checkboxes > div, #img2img_checkboxes > div{\r\n flex: 0;\r\n white-space: nowrap;\r\n min-width: auto;\r\n}\r\n\r\n#img2img_copy_to_img2img, #img2img_copy_to_sketch, #img2img_copy_to_inpaint, #img2img_copy_to_inpaint_sketch{\r\n margin-left: 0em;\r\n}\r\n\r\n#axis_options {\r\n margin-left: 0em;\r\n}\r\n\r\n.inactive{\r\n opacity: 0.5;\r\n}\r\n\r\n[id*='_prompt_container']{\r\n gap: 0;\r\n}\r\n\r\n[id*='_prompt_container'] > div{\r\n margin: -0.4em 0 0 0;\r\n}\r\n\r\n.gr-compact {\r\n border: none;\r\n}\r\n\r\n.dark .gr-compact{\r\n background-color: rgb(31 41 55 / var(--tw-bg-opacity));\r\n\tmargin-left: 0;\r\n}\r\n\r\n.gr-compact{\r\n overflow: visible;\r\n}\r\n\r\n.gr-compact > *{\r\n}\r\n\r\n.gr-compact .gr-block, .gr-compact .gr-form{\r\n border: none;\r\n box-shadow: none;\r\n}\r\n\r\n.gr-compact .gr-box{\r\n border-radius: .5rem !important;\r\n border-width: 1px !important;\r\n}\r\n\r\n#mode_img2img > div > div{\r\n gap: 0 !important;\r\n}\r\n\r\n[id*='img2img_copy_to_'] {\r\n border: none;\r\n}\r\n\r\n[id*='img2img_copy_to_'] > button {\r\n}\r\n\r\n[id*='img2img_label_copy_to_'] {\r\n font-size: 1.0em;\r\n font-weight: bold;\r\n text-align: center;\r\n line-height: 2.4em;\r\n}\r\n\r\n.extra-networks > div > [id *= '_extra_']{\r\n margin: 0.3em;\r\n}\r\n\r\n.extra-network-subdirs{\r\n padding: 0.2em 0.35em;\r\n}\r\n\r\n.extra-network-subdirs button{\r\n margin: 0 0.15em;\r\n}\r\n\r\n#txt2img_extra_networks .search, #img2img_extra_networks .search{\r\n display: inline-block;\r\n max-width: 16em;\r\n margin: 0.3em;\r\n align-self: center;\r\n}\r\n\r\n#txt2img_extra_view, #img2img_extra_view {\r\n width: auto;\r\n}\r\n\r\n.extra-network-cards .nocards, .extra-network-thumbs .nocards{\r\n margin: 1.25em 0.5em 0.5em 0.5em;\r\n}\r\n\r\n.extra-network-cards .nocards h1, .extra-network-thumbs .nocards h1{\r\n font-size: 1.5em;\r\n margin-bottom: 1em;\r\n}\r\n\r\n.extra-network-cards .nocards li, .extra-network-thumbs .nocards li{\r\n margin-left: 0.5em;\r\n}\r\n\r\n.extra-network-thumbs {\r\n display: flex;\r\n flex-flow: row wrap;\r\n gap: 10px;\r\n}\r\n\r\n.extra-network-thumbs .card {\r\n height: 6em;\r\n width: 6em;\r\n cursor: pointer;\r\n background-image: url('./file=html/card-no-preview.png');\r\n background-size: cover;\r\n background-position: center center;\r\n position: relative;\r\n}\r\n\r\n.extra-network-thumbs .card:hover .additional a {\r\n display: block;\r\n}\r\n\r\n.extra-network-thumbs .actions .additional a {\r\n background-image: url('./file=html/image-update.svg');\r\n background-repeat: no-repeat;\r\n background-size: cover;\r\n background-position: center center;\r\n position: absolute;\r\n top: 0;\r\n left: 0;\r\n width: 24px;\r\n height: 24px;\r\n display: none;\r\n font-size: 0;\r\n text-align: -9999;\r\n}\r\n\r\n.extra-network-thumbs .actions .name {\r\n position: absolute;\r\n bottom: 0;\r\n font-size: 10px;\r\n padding: 3px;\r\n width: 100%;\r\n overflow: hidden;\r\n white-space: nowrap;\r\n text-overflow: ellipsis;\r\n background: rgba(0,0,0,.5);\r\n color: white;\r\n}\r\n\r\n.extra-network-thumbs .card:hover .actions .name {\r\n white-space: normal;\r\n word-break: break-all;\r\n}\r\n\r\n.extra-network-cards .card{\r\n display: inline-block;\r\n margin: 0.5em;\r\n width: 16em;\r\n height: 24em;\r\n box-shadow: 0 0 5px rgba(128, 128, 128, 0.5);\r\n border-radius: 0.2em;\r\n position: relative;\r\n\r\n background-size: auto 100%;\r\n background-position: center;\r\n overflow: hidden;\r\n cursor: pointer;\r\n\r\n background-image: url('./file=html/card-no-preview.png')\r\n}\r\n\r\n.extra-network-cards .card:hover{\r\n box-shadow: 0 0 2px 0.3em rgba(0, 128, 255, 0.35);\r\n}\r\n\r\n.extra-network-cards .card .actions .additional{\r\n display: none;\r\n}\r\n\r\n.extra-network-cards .card .actions{\r\n position: absolute;\r\n bottom: 0;\r\n left: 0;\r\n right: 0;\r\n padding: 0.5em;\r\n color: white;\r\n background: rgba(0,0,0,0.5);\r\n box-shadow: 0 0 0.25em 0.25em rgba(0,0,0,0.5);\r\n text-shadow: 0 0 0.2em black;\r\n}\r\n\r\n.extra-network-cards .card .actions:hover{\r\n box-shadow: 0 0 0.75em 0.75em rgba(0,0,0,0.5) !important;\r\n}\r\n\r\n.extra-network-cards .card .actions .name{\r\n font-size: 1.7em;\r\n font-weight: bold;\r\n line-break: anywhere;\r\n}\r\n\r\n.extra-network-cards .card .actions:hover .additional{\r\n display: block;\r\n}\r\n\r\n.extra-network-cards .card ul{\r\n margin: 0.25em 0 0.75em 0.25em;\r\n cursor: unset;\r\n}\r\n\r\n.extra-network-cards .card ul a{\r\n cursor: pointer;\r\n}\r\n\r\n.extra-network-cards .card ul a:hover{\r\n color: red;\r\n}\r\n\r\n[id*='_prompt_container'] > div {\r\n\tmargin: 0!important;\r\n}\r\n" }, { "path": "styles.csv", "content": "name,prompt,negative_prompt\r\nNone,,\r\nnaifu基础起手式,\"masterpiece, best quality, \",\"lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry\"\r\n" }, { "path": "tags/temp/emb.txt", "content": "" }, { "path": "tags/temp/wc.txt", "content": "" }, { "path": "test/__init__.py", "content": "" }, { "path": "test/basic_features/__init__.py", "content": "" }, { "path": "test/basic_features/extras_test.py", "content": "import unittest\r\nimport requests\r\nfrom gradio.processing_utils import encode_pil_to_base64\r\nfrom PIL import Image\r\n\r\nclass TestExtrasWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.url_extras_single = \"http://localhost:7860/sdapi/v1/extra-single-image\"\r\n self.extras_single = {\r\n \"resize_mode\": 0,\r\n \"show_extras_results\": True,\r\n \"gfpgan_visibility\": 0,\r\n \"codeformer_visibility\": 0,\r\n \"codeformer_weight\": 0,\r\n \"upscaling_resize\": 2,\r\n \"upscaling_resize_w\": 128,\r\n \"upscaling_resize_h\": 128,\r\n \"upscaling_crop\": True,\r\n \"upscaler_1\": \"None\",\r\n \"upscaler_2\": \"None\",\r\n \"extras_upscaler_2_visibility\": 0,\r\n \"image\": encode_pil_to_base64(Image.open(r\"test/test_files/img2img_basic.png\"))\r\n }\r\n\r\n def test_simple_upscaling_performed(self):\r\n self.extras_single[\"upscaler_1\"] = \"Lanczos\"\r\n self.assertEqual(requests.post(self.url_extras_single, json=self.extras_single).status_code, 200)\r\n\r\n\r\nclass TestPngInfoWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.url_png_info = \"http://localhost:7860/sdapi/v1/extra-single-image\"\r\n self.png_info = {\r\n \"image\": encode_pil_to_base64(Image.open(r\"test/test_files/img2img_basic.png\"))\r\n }\r\n\r\n def test_png_info_performed(self):\r\n self.assertEqual(requests.post(self.url_png_info, json=self.png_info).status_code, 200)\r\n\r\n\r\nclass TestInterrogateWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.url_interrogate = \"http://localhost:7860/sdapi/v1/extra-single-image\"\r\n self.interrogate = {\r\n \"image\": encode_pil_to_base64(Image.open(r\"test/test_files/img2img_basic.png\")),\r\n \"model\": \"clip\"\r\n }\r\n\r\n def test_interrogate_performed(self):\r\n self.assertEqual(requests.post(self.url_interrogate, json=self.interrogate).status_code, 200)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n" }, { "path": "test/basic_features/img2img_test.py", "content": "import unittest\r\nimport requests\r\nfrom gradio.processing_utils import encode_pil_to_base64\r\nfrom PIL import Image\r\n\r\n\r\nclass TestImg2ImgWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.url_img2img = \"http://localhost:7860/sdapi/v1/img2img\"\r\n self.simple_img2img = {\r\n \"init_images\": [encode_pil_to_base64(Image.open(r\"test/test_files/img2img_basic.png\"))],\r\n \"resize_mode\": 0,\r\n \"denoising_strength\": 0.75,\r\n \"mask\": None,\r\n \"mask_blur\": 4,\r\n \"inpainting_fill\": 0,\r\n \"inpaint_full_res\": False,\r\n \"inpaint_full_res_padding\": 0,\r\n \"inpainting_mask_invert\": False,\r\n \"prompt\": \"example prompt\",\r\n \"styles\": [],\r\n \"seed\": -1,\r\n \"subseed\": -1,\r\n \"subseed_strength\": 0,\r\n \"seed_resize_from_h\": -1,\r\n \"seed_resize_from_w\": -1,\r\n \"batch_size\": 1,\r\n \"n_iter\": 1,\r\n \"steps\": 3,\r\n \"cfg_scale\": 7,\r\n \"width\": 64,\r\n \"height\": 64,\r\n \"restore_faces\": False,\r\n \"tiling\": False,\r\n \"negative_prompt\": \"\",\r\n \"eta\": 0,\r\n \"s_churn\": 0,\r\n \"s_tmax\": 0,\r\n \"s_tmin\": 0,\r\n \"s_noise\": 1,\r\n \"override_settings\": {},\r\n \"sampler_index\": \"Euler a\",\r\n \"include_init_images\": False\r\n }\r\n\r\n def test_img2img_simple_performed(self):\r\n self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)\r\n\r\n def test_inpainting_masked_performed(self):\r\n self.simple_img2img[\"mask\"] = encode_pil_to_base64(Image.open(r\"test/test_files/mask_basic.png\"))\r\n self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)\r\n\r\n def test_inpainting_with_inverted_masked_performed(self):\r\n self.simple_img2img[\"mask\"] = encode_pil_to_base64(Image.open(r\"test/test_files/mask_basic.png\"))\r\n self.simple_img2img[\"inpainting_mask_invert\"] = True\r\n self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)\r\n\r\n def test_img2img_sd_upscale_performed(self):\r\n self.simple_img2img[\"script_name\"] = \"sd upscale\"\r\n self.simple_img2img[\"script_args\"] = [\"\", 8, \"Lanczos\", 2.0]\r\n\r\n self.assertEqual(requests.post(self.url_img2img, json=self.simple_img2img).status_code, 200)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n" }, { "path": "test/basic_features/txt2img_test.py", "content": "import unittest\r\nimport requests\r\n\r\n\r\nclass TestTxt2ImgWorking(unittest.TestCase):\r\n def setUp(self):\r\n self.url_txt2img = \"http://localhost:7860/sdapi/v1/txt2img\"\r\n self.simple_txt2img = {\r\n \"enable_hr\": False,\r\n \"denoising_strength\": 0,\r\n \"firstphase_width\": 0,\r\n \"firstphase_height\": 0,\r\n \"prompt\": \"example prompt\",\r\n \"styles\": [],\r\n \"seed\": -1,\r\n \"subseed\": -1,\r\n \"subseed_strength\": 0,\r\n \"seed_resize_from_h\": -1,\r\n \"seed_resize_from_w\": -1,\r\n \"batch_size\": 1,\r\n \"n_iter\": 1,\r\n \"steps\": 3,\r\n \"cfg_scale\": 7,\r\n \"width\": 64,\r\n \"height\": 64,\r\n \"restore_faces\": False,\r\n \"tiling\": False,\r\n \"negative_prompt\": \"\",\r\n \"eta\": 0,\r\n \"s_churn\": 0,\r\n \"s_tmax\": 0,\r\n \"s_tmin\": 0,\r\n \"s_noise\": 1,\r\n \"sampler_index\": \"Euler a\"\r\n }\r\n\r\n def test_txt2img_simple_performed(self):\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_negative_prompt_performed(self):\r\n self.simple_txt2img[\"negative_prompt\"] = \"example negative prompt\"\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_complex_prompt_performed(self):\r\n self.simple_txt2img[\"prompt\"] = \"((emphasis)), (emphasis1:1.1), [to:1], [from::2], [from:to:0.3], [alt|alt1]\"\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_not_square_image_performed(self):\r\n self.simple_txt2img[\"height\"] = 128\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_hrfix_performed(self):\r\n self.simple_txt2img[\"enable_hr\"] = True\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_tiling_performed(self):\r\n self.simple_txt2img[\"tiling\"] = True\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_restore_faces_performed(self):\r\n self.simple_txt2img[\"restore_faces\"] = True\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_with_vanilla_sampler_performed(self):\r\n self.simple_txt2img[\"sampler_index\"] = \"PLMS\"\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n self.simple_txt2img[\"sampler_index\"] = \"DDIM\"\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_multiple_batches_performed(self):\r\n self.simple_txt2img[\"n_iter\"] = 2\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n def test_txt2img_batch_performed(self):\r\n self.simple_txt2img[\"batch_size\"] = 2\r\n self.assertEqual(requests.post(self.url_txt2img, json=self.simple_txt2img).status_code, 200)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n" }, { "path": "test/basic_features/utils_test.py", "content": "import unittest\r\nimport requests\r\n\r\nclass UtilsTests(unittest.TestCase):\r\n def setUp(self):\r\n self.url_options = \"http://localhost:7860/sdapi/v1/options\"\r\n self.url_cmd_flags = \"http://localhost:7860/sdapi/v1/cmd-flags\"\r\n self.url_samplers = \"http://localhost:7860/sdapi/v1/samplers\"\r\n self.url_upscalers = \"http://localhost:7860/sdapi/v1/upscalers\"\r\n self.url_sd_models = \"http://localhost:7860/sdapi/v1/sd-models\"\r\n self.url_hypernetworks = \"http://localhost:7860/sdapi/v1/hypernetworks\"\r\n self.url_face_restorers = \"http://localhost:7860/sdapi/v1/face-restorers\"\r\n self.url_realesrgan_models = \"http://localhost:7860/sdapi/v1/realesrgan-models\"\r\n self.url_prompt_styles = \"http://localhost:7860/sdapi/v1/prompt-styles\"\r\n self.url_embeddings = \"http://localhost:7860/sdapi/v1/embeddings\"\r\n\r\n def test_options_get(self):\r\n self.assertEqual(requests.get(self.url_options).status_code, 200)\r\n\r\n def test_options_write(self):\r\n response = requests.get(self.url_options)\r\n self.assertEqual(response.status_code, 200)\r\n\r\n pre_value = response.json()[\"send_seed\"]\r\n\r\n self.assertEqual(requests.post(self.url_options, json={\"send_seed\":not pre_value}).status_code, 200)\r\n\r\n response = requests.get(self.url_options)\r\n self.assertEqual(response.status_code, 200)\r\n self.assertEqual(response.json()[\"send_seed\"], not pre_value)\r\n\r\n requests.post(self.url_options, json={\"send_seed\": pre_value})\r\n\r\n def test_cmd_flags(self):\r\n self.assertEqual(requests.get(self.url_cmd_flags).status_code, 200)\r\n\r\n def test_samplers(self):\r\n self.assertEqual(requests.get(self.url_samplers).status_code, 200)\r\n\r\n def test_upscalers(self):\r\n self.assertEqual(requests.get(self.url_upscalers).status_code, 200)\r\n\r\n def test_sd_models(self):\r\n self.assertEqual(requests.get(self.url_sd_models).status_code, 200)\r\n\r\n def test_hypernetworks(self):\r\n self.assertEqual(requests.get(self.url_hypernetworks).status_code, 200)\r\n\r\n def test_face_restorers(self):\r\n self.assertEqual(requests.get(self.url_face_restorers).status_code, 200)\r\n \r\n def test_realesrgan_models(self):\r\n self.assertEqual(requests.get(self.url_realesrgan_models).status_code, 200)\r\n \r\n def test_prompt_styles(self):\r\n self.assertEqual(requests.get(self.url_prompt_styles).status_code, 200)\r\n\r\n def test_embeddings(self):\r\n self.assertEqual(requests.get(self.url_embeddings).status_code, 200)\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n" }, { "path": "test/server_poll.py", "content": "import unittest\r\nimport requests\r\nimport time\r\n\r\n\r\ndef run_tests(proc, test_dir):\r\n timeout_threshold = 240\r\n start_time = time.time()\r\n while time.time()-start_time < timeout_threshold:\r\n try:\r\n requests.head(\"http://localhost:7860/\")\r\n break\r\n except requests.exceptions.ConnectionError:\r\n if proc.poll() is not None:\r\n break\r\n if proc.poll() is None:\r\n if test_dir is None:\r\n test_dir = \"test\"\r\n suite = unittest.TestLoader().discover(test_dir, pattern=\"*_test.py\", top_level_dir=\"test\")\r\n result = unittest.TextTestRunner(verbosity=2).run(suite)\r\n return len(result.failures) + len(result.errors)\r\n else:\r\n print(\"Launch unsuccessful\")\r\n return 1\r\n" }, { "path": "textual_inversion_templates/hypernetwork.txt", "content": "a photo of a [filewords]\r\na rendering of a [filewords]\r\na cropped photo of the [filewords]\r\nthe photo of a [filewords]\r\na photo of a clean [filewords]\r\na photo of a dirty [filewords]\r\na dark photo of the [filewords]\r\na photo of my [filewords]\r\na photo of the cool [filewords]\r\na close-up photo of a [filewords]\r\na bright photo of the [filewords]\r\na cropped photo of a [filewords]\r\na photo of the [filewords]\r\na good photo of the [filewords]\r\na photo of one [filewords]\r\na close-up photo of the [filewords]\r\na rendition of the [filewords]\r\na photo of the clean [filewords]\r\na rendition of a [filewords]\r\na photo of a nice [filewords]\r\na good photo of a [filewords]\r\na photo of the nice [filewords]\r\na photo of the small [filewords]\r\na photo of the weird [filewords]\r\na photo of the large [filewords]\r\na photo of a cool [filewords]\r\na photo of a small [filewords]\r\n" }, { "path": "textual_inversion_templates/none.txt", "content": "picture\r\n" }, { "path": "textual_inversion_templates/style.txt", "content": "a painting, art by [name]\r\na rendering, art by [name]\r\na cropped painting, art by [name]\r\nthe painting, art by [name]\r\na clean painting, art by [name]\r\na dirty painting, art by [name]\r\na dark painting, art by [name]\r\na picture, art by [name]\r\na cool painting, art by [name]\r\na close-up painting, art by [name]\r\na bright painting, art by [name]\r\na cropped painting, art by [name]\r\na good painting, art by [name]\r\na close-up painting, art by [name]\r\na rendition, art by [name]\r\na nice painting, art by [name]\r\na small painting, art by [name]\r\na weird painting, art by [name]\r\na large painting, art by [name]\r\n" }, { "path": "textual_inversion_templates/style_filewords.txt", "content": "a painting of [filewords], art by [name]\r\na rendering of [filewords], art by [name]\r\na cropped painting of [filewords], art by [name]\r\nthe painting of [filewords], art by [name]\r\na clean painting of [filewords], art by [name]\r\na dirty painting of [filewords], art by [name]\r\na dark painting of [filewords], art by [name]\r\na picture of [filewords], art by [name]\r\na cool painting of [filewords], art by [name]\r\na close-up painting of [filewords], art by [name]\r\na bright painting of [filewords], art by [name]\r\na cropped painting of [filewords], art by [name]\r\na good painting of [filewords], art by [name]\r\na close-up painting of [filewords], art by [name]\r\na rendition of [filewords], art by [name]\r\na nice painting of [filewords], art by [name]\r\na small painting of [filewords], art by [name]\r\na weird painting of [filewords], art by [name]\r\na large painting of [filewords], art by [name]\r\n" }, { "path": "textual_inversion_templates/subject.txt", "content": "a photo of a [name]\r\na rendering of a [name]\r\na cropped photo of the [name]\r\nthe photo of a [name]\r\na photo of a clean [name]\r\na photo of a dirty [name]\r\na dark photo of the [name]\r\na photo of my [name]\r\na photo of the cool [name]\r\na close-up photo of a [name]\r\na bright photo of the [name]\r\na cropped photo of a [name]\r\na photo of the [name]\r\na good photo of the [name]\r\na photo of one [name]\r\na close-up photo of the [name]\r\na rendition of the [name]\r\na photo of the clean [name]\r\na rendition of a [name]\r\na photo of a nice [name]\r\na good photo of a [name]\r\na photo of the nice [name]\r\na photo of the small [name]\r\na photo of the weird [name]\r\na photo of the large [name]\r\na photo of a cool [name]\r\na photo of a small [name]\r\n" }, { "path": "textual_inversion_templates/subject_filewords.txt", "content": "a photo of a [name], [filewords]\r\na rendering of a [name], [filewords]\r\na cropped photo of the [name], [filewords]\r\nthe photo of a [name], [filewords]\r\na photo of a clean [name], [filewords]\r\na photo of a dirty [name], [filewords]\r\na dark photo of the [name], [filewords]\r\na photo of my [name], [filewords]\r\na photo of the cool [name], [filewords]\r\na close-up photo of a [name], [filewords]\r\na bright photo of the [name], [filewords]\r\na cropped photo of a [name], [filewords]\r\na photo of the [name], [filewords]\r\na good photo of the [name], [filewords]\r\na photo of one [name], [filewords]\r\na close-up photo of the [name], [filewords]\r\na rendition of the [name], [filewords]\r\na photo of the clean [name], [filewords]\r\na rendition of a [name], [filewords]\r\na photo of a nice [name], [filewords]\r\na good photo of a [name], [filewords]\r\na photo of the nice [name], [filewords]\r\na photo of the small [name], [filewords]\r\na photo of the weird [name], [filewords]\r\na photo of the large [name], [filewords]\r\na photo of a cool [name], [filewords]\r\na photo of a small [name], [filewords]\r\n" }, { "path": "tmp/stderr.txt", "content": "^C" }, { "path": "tmp/stdout.txt", "content": "" }, { "path": "tmp/tagAutocompletePath.txt", "content": "extensions/a1111-sd-webui-tagcomplete/tags" }, { "path": "ui-config.json", "content": "{\r\n \"txt2img/Prompt/visible\": true,\r\n \"txt2img/Prompt/value\": \"\",\r\n \"txt2img/Negative prompt/visible\": true,\r\n \"txt2img/Negative prompt/value\": \"\",\r\n \"txt2img/Styles/visible\": true,\r\n \"txt2img/Styles/value\": [],\r\n \"txt2img/Sampling method/visible\": true,\r\n \"txt2img/Sampling method/value\": \"Euler a\",\r\n \"txt2img/Sampling steps/visible\": true,\r\n \"txt2img/Sampling steps/value\": 20,\r\n \"txt2img/Sampling steps/minimum\": 1,\r\n \"txt2img/Sampling steps/maximum\": 150,\r\n \"txt2img/Sampling steps/step\": 1,\r\n \"txt2img/Width/visible\": true,\r\n \"txt2img/Width/value\": 512,\r\n \"txt2img/Width/minimum\": 64,\r\n \"txt2img/Width/maximum\": 2048,\r\n \"txt2img/Width/step\": 8,\r\n \"txt2img/Height/visible\": true,\r\n \"txt2img/Height/value\": 512,\r\n \"txt2img/Height/minimum\": 64,\r\n \"txt2img/Height/maximum\": 2048,\r\n \"txt2img/Height/step\": 8,\r\n \"txt2img/Batch count/visible\": true,\r\n \"txt2img/Batch count/value\": 1,\r\n \"txt2img/Batch count/minimum\": 1,\r\n \"txt2img/Batch count/maximum\": 100,\r\n \"txt2img/Batch count/step\": 1,\r\n \"txt2img/Batch size/visible\": true,\r\n \"txt2img/Batch size/value\": 1,\r\n \"txt2img/Batch size/minimum\": 1,\r\n \"txt2img/Batch size/maximum\": 8,\r\n \"txt2img/Batch size/step\": 1,\r\n \"txt2img/CFG Scale/visible\": true,\r\n \"txt2img/CFG Scale/value\": 7.0,\r\n \"txt2img/CFG Scale/minimum\": 1.0,\r\n \"txt2img/CFG Scale/maximum\": 30.0,\r\n \"txt2img/CFG Scale/step\": 0.5,\r\n \"txt2img/Seed/visible\": true,\r\n \"txt2img/Seed/value\": -1.0,\r\n \"txt2img/Extra/visible\": true,\r\n \"txt2img/Extra/value\": false,\r\n \"txt2img/Variation seed/visible\": true,\r\n \"txt2img/Variation seed/value\": -1.0,\r\n \"txt2img/Variation strength/visible\": true,\r\n \"txt2img/Variation strength/value\": 0.0,\r\n \"txt2img/Variation strength/minimum\": 0,\r\n \"txt2img/Variation strength/maximum\": 1,\r\n \"txt2img/Variation strength/step\": 0.01,\r\n \"txt2img/Resize seed from width/visible\": true,\r\n \"txt2img/Resize seed from width/value\": 0,\r\n \"txt2img/Resize seed from width/minimum\": 0,\r\n \"txt2img/Resize seed from width/maximum\": 2048,\r\n \"txt2img/Resize seed from width/step\": 8,\r\n \"txt2img/Resize seed from height/visible\": true,\r\n \"txt2img/Resize seed 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1/value\": 1.0,\r\n \"txt2img/Weight 1/minimum\": -1.0,\r\n \"txt2img/Weight 1/maximum\": 2.0,\r\n \"txt2img/Weight 1/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 1/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 1/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 1/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 1/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 1/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 1/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 1/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 1/step\": 0.05,\r\n \"txt2img/Weight 2/visible\": true,\r\n \"txt2img/Weight 2/value\": 1.0,\r\n \"txt2img/Weight 2/minimum\": -1.0,\r\n \"txt2img/Weight 2/maximum\": 2.0,\r\n \"txt2img/Weight 2/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 2/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 2/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 2/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 2/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 2/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 2/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 2/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 2/step\": 0.05,\r\n \"txt2img/Weight 3/visible\": true,\r\n \"txt2img/Weight 3/value\": 1.0,\r\n \"txt2img/Weight 3/minimum\": -1.0,\r\n \"txt2img/Weight 3/maximum\": 2.0,\r\n \"txt2img/Weight 3/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 3/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 3/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 3/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 3/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 3/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 3/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 3/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 3/step\": 0.05,\r\n \"txt2img/Weight 4/visible\": true,\r\n \"txt2img/Weight 4/value\": 1.0,\r\n \"txt2img/Weight 4/minimum\": -1.0,\r\n \"txt2img/Weight 4/maximum\": 2.0,\r\n \"txt2img/Weight 4/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 4/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 4/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 4/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 4/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 4/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 4/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 4/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 4/step\": 0.05,\r\n \"txt2img/Weight 5/visible\": true,\r\n \"txt2img/Weight 5/value\": 1.0,\r\n \"txt2img/Weight 5/minimum\": -1.0,\r\n \"txt2img/Weight 5/maximum\": 2.0,\r\n \"txt2img/Weight 5/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 5/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 5/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 5/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/UNet Weight 5/step\": 0.05,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 5/value\": 1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 5/minimum\": -1.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 5/maximum\": 2.0,\r\n \"customscript/additional_networks.py/txt2img/TEnc Weight 5/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/Separate UNet/Text Encoder weights/visible\": true,\r\n \"customscript/additional_networks.py/img2img/Separate UNet/Text Encoder weights/value\": false,\r\n \"img2img/Weight 1/visible\": true,\r\n \"img2img/Weight 1/value\": 1.0,\r\n \"img2img/Weight 1/minimum\": -1.0,\r\n \"img2img/Weight 1/maximum\": 2.0,\r\n \"img2img/Weight 1/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 1/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 1/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 1/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 1/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 1/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 1/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 1/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 1/step\": 0.05,\r\n \"img2img/Weight 2/visible\": true,\r\n \"img2img/Weight 2/value\": 1.0,\r\n \"img2img/Weight 2/minimum\": -1.0,\r\n \"img2img/Weight 2/maximum\": 2.0,\r\n \"img2img/Weight 2/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 2/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 2/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 2/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 2/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 2/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 2/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 2/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 2/step\": 0.05,\r\n \"img2img/Weight 3/visible\": true,\r\n \"img2img/Weight 3/value\": 1.0,\r\n \"img2img/Weight 3/minimum\": -1.0,\r\n \"img2img/Weight 3/maximum\": 2.0,\r\n \"img2img/Weight 3/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 3/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 3/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 3/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 3/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 3/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 3/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 3/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 3/step\": 0.05,\r\n \"img2img/Weight 4/visible\": true,\r\n \"img2img/Weight 4/value\": 1.0,\r\n \"img2img/Weight 4/minimum\": -1.0,\r\n \"img2img/Weight 4/maximum\": 2.0,\r\n \"img2img/Weight 4/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 4/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 4/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 4/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 4/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 4/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 4/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 4/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 4/step\": 0.05,\r\n \"img2img/Weight 5/visible\": true,\r\n \"img2img/Weight 5/value\": 1.0,\r\n \"img2img/Weight 5/minimum\": -1.0,\r\n \"img2img/Weight 5/maximum\": 2.0,\r\n \"img2img/Weight 5/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 5/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 5/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 5/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/UNet Weight 5/step\": 0.05,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 5/value\": 1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 5/minimum\": -1.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 5/maximum\": 2.0,\r\n \"customscript/additional_networks.py/img2img/TEnc Weight 5/step\": 0.05\r\n}" }, { "path": "webui-macos-env.sh", "content": "#!/bin/bash\r\n####################################################################\r\n# macOS defaults #\r\n# Please modify webui-user.sh to change these instead of this file #\r\n####################################################################\r\n\r\nif [[ -x \"$(command -v python3.10)\" ]]\r\nthen\r\n python_cmd=\"python3.10\"\r\nfi\r\n\r\nexport install_dir=\"$HOME\"\r\nexport COMMANDLINE_ARGS=\"--skip-torch-cuda-test --upcast-sampling --no-half-vae --use-cpu interrogate\"\r\nexport TORCH_COMMAND=\"pip install torch==1.12.1 torchvision==0.13.1\"\r\nexport K_DIFFUSION_REPO=\"https://github.com/brkirch/k-diffusion.git\"\r\nexport K_DIFFUSION_COMMIT_HASH=\"51c9778f269cedb55a4d88c79c0246d35bdadb71\"\r\nexport PYTORCH_ENABLE_MPS_FALLBACK=1\r\n\r\n####################################################################\r\n" }, { "path": "webui-user.bat", "content": "@echo off\r\n\r\nset PYTHON=\r\nset GIT=\r\nset VENV_DIR=\r\nset COMMANDLINE_ARGS=\r\n\r\ncall webui.bat\r\n" }, { "path": "webui-user.sh", "content": "#!/bin/bash\r\n#########################################################\r\n# Uncomment and change the variables below to your need:#\r\n#########################################################\r\n\r\n# Install directory without trailing slash\r\n#install_dir=\"/home/$(whoami)\"\r\n\r\n# Name of the subdirectory\r\n#clone_dir=\"stable-diffusion-webui\"\r\n\r\n# Commandline arguments for webui.py, for example: export COMMANDLINE_ARGS=\"--medvram --opt-split-attention\"\r\n#export COMMANDLINE_ARGS=\"\"\r\n\r\n# python3 executable\r\n#python_cmd=\"python3\"\r\n\r\n# git executable\r\n#export GIT=\"git\"\r\n\r\n# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)\r\n#venv_dir=\"venv\"\r\n\r\n# script to launch to start the app\r\n#export LAUNCH_SCRIPT=\"launch.py\"\r\n\r\n# install command for torch\r\n#export TORCH_COMMAND=\"pip install torch==1.12.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113\"\r\n\r\n# Requirements file to use for stable-diffusion-webui\r\n#export REQS_FILE=\"requirements_versions.txt\"\r\n\r\n# Fixed git repos\r\n#export K_DIFFUSION_PACKAGE=\"\"\r\n#export GFPGAN_PACKAGE=\"\"\r\n\r\n# Fixed git commits\r\n#export STABLE_DIFFUSION_COMMIT_HASH=\"\"\r\n#export TAMING_TRANSFORMERS_COMMIT_HASH=\"\"\r\n#export CODEFORMER_COMMIT_HASH=\"\"\r\n#export BLIP_COMMIT_HASH=\"\"\r\n\r\n# Uncomment to enable accelerated launch\r\n#export ACCELERATE=\"True\"\r\n\r\n###########################################\r\n" }, { "path": "webui.bat", "content": "@echo off\r\n\r\nif not defined PYTHON (set PYTHON=python)\r\nif not defined VENV_DIR (set \"VENV_DIR=%~dp0%venv\")\r\n\r\n\r\nset ERROR_REPORTING=FALSE\r\n\r\nmkdir tmp 2>NUL\r\n\r\n%PYTHON% -c \"\" >tmp/stdout.txt 2>tmp/stderr.txt\r\nif %ERRORLEVEL% == 0 goto :check_pip\r\necho Couldn't launch python\r\ngoto :show_stdout_stderr\r\n\r\n:check_pip\r\n%PYTHON% -mpip --help >tmp/stdout.txt 2>tmp/stderr.txt\r\nif %ERRORLEVEL% == 0 goto :start_venv\r\nif \"%PIP_INSTALLER_LOCATION%\" == \"\" goto :show_stdout_stderr\r\n%PYTHON% \"%PIP_INSTALLER_LOCATION%\" >tmp/stdout.txt 2>tmp/stderr.txt\r\nif %ERRORLEVEL% == 0 goto :start_venv\r\necho Couldn't install pip\r\ngoto :show_stdout_stderr\r\n\r\n:start_venv\r\nif [\"%VENV_DIR%\"] == [\"-\"] goto :skip_venv\r\nif [\"%SKIP_VENV%\"] == [\"1\"] goto :skip_venv\r\n\r\ndir \"%VENV_DIR%\\Scripts\\Python.exe\" >tmp/stdout.txt 2>tmp/stderr.txt\r\nif %ERRORLEVEL% == 0 goto :activate_venv\r\n\r\nfor /f \"delims=\" %%i in ('CALL %PYTHON% -c \"import sys; print(sys.executable)\"') do set PYTHON_FULLNAME=\"%%i\"\r\necho Creating venv in directory %VENV_DIR% using python %PYTHON_FULLNAME%\r\n%PYTHON_FULLNAME% -m venv \"%VENV_DIR%\" >tmp/stdout.txt 2>tmp/stderr.txt\r\nif %ERRORLEVEL% == 0 goto :activate_venv\r\necho Unable to create venv in directory \"%VENV_DIR%\"\r\ngoto :show_stdout_stderr\r\n\r\n:activate_venv\r\nset PYTHON=\"%VENV_DIR%\\Scripts\\Python.exe\"\r\necho venv %PYTHON%\r\n\r\n:skip_venv\r\nif [%ACCELERATE%] == [\"True\"] goto :accelerate\r\ngoto :launch\r\n\r\n:accelerate\r\necho Checking for accelerate\r\nset ACCELERATE=\"%VENV_DIR%\\Scripts\\accelerate.exe\"\r\nif EXIST %ACCELERATE% goto :accelerate_launch\r\n\r\n:launch\r\n%PYTHON% launch.py %*\r\npause\r\nexit /b\r\n\r\n:accelerate_launch\r\necho Accelerating\r\n%ACCELERATE% launch --num_cpu_threads_per_process=6 launch.py\r\npause\r\nexit /b\r\n\r\n:show_stdout_stderr\r\n\r\necho.\r\necho exit code: %errorlevel%\r\n\r\nfor /f %%i in (\"tmp\\stdout.txt\") do set size=%%~zi\r\nif %size% equ 0 goto :show_stderr\r\necho.\r\necho stdout:\r\ntype tmp\\stdout.txt\r\n\r\n:show_stderr\r\nfor /f %%i in (\"tmp\\stderr.txt\") do set size=%%~zi\r\nif %size% equ 0 goto :show_stderr\r\necho.\r\necho stderr:\r\ntype tmp\\stderr.txt\r\n\r\n:endofscript\r\n\r\necho.\r\necho Launch unsuccessful. Exiting.\r\npause\r\n" }, { "path": "webui.py", "content": "import os\r\nimport sys\r\nimport time\r\nimport importlib\r\nimport signal\r\nimport re\r\nfrom fastapi import FastAPI\r\nfrom fastapi.middleware.cors import CORSMiddleware\r\nfrom fastapi.middleware.gzip import GZipMiddleware\r\nfrom packaging import version\r\n\r\nimport logging\r\nlogging.getLogger(\"xformers\").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())\r\n\r\nfrom modules import import_hook, errors, extra_networks, ui_extra_networks_checkpoints\r\nfrom modules import extra_networks_hypernet, ui_extra_networks_hypernets, ui_extra_networks_textual_inversion\r\nfrom modules.call_queue import wrap_queued_call, queue_lock, wrap_gradio_gpu_call\r\n\r\nimport torch\r\n\r\n# Truncate version number of nightly/local build of PyTorch to not cause exceptions with CodeFormer or Safetensors\r\nif \".dev\" in torch.__version__ or \"+git\" in torch.__version__:\r\n torch.__long_version__ = torch.__version__\r\n torch.__version__ = re.search(r'[\\d.]+[\\d]', torch.__version__).group(0)\r\n\r\nfrom modules import shared, devices, sd_samplers, upscaler, extensions, localization, ui_tempdir, ui_extra_networks\r\nimport modules.codeformer_model as codeformer\r\nimport modules.face_restoration\r\nimport modules.gfpgan_model as gfpgan\r\nimport modules.img2img\r\n\r\nimport modules.lowvram\r\nimport modules.paths\r\nimport modules.scripts\r\nimport modules.sd_hijack\r\nimport modules.sd_models\r\nimport modules.sd_vae\r\nimport modules.txt2img\r\nimport modules.script_callbacks\r\nimport modules.textual_inversion.textual_inversion\r\nimport modules.progress\r\n\r\nimport modules.ui\r\nfrom modules import modelloader\r\nfrom modules.shared import cmd_opts\r\nimport modules.hypernetworks.hypernetwork\r\n\r\n\r\nif cmd_opts.server_name:\r\n server_name = cmd_opts.server_name\r\nelse:\r\n server_name = \"0.0.0.0\" if cmd_opts.listen else None\r\n\r\n\r\ndef check_versions():\r\n if shared.cmd_opts.skip_version_check:\r\n return\r\n\r\n expected_torch_version = \"1.13.1\"\r\n\r\n if version.parse(torch.__version__) < version.parse(expected_torch_version):\r\n errors.print_error_explanation(f\"\"\"\r\nYou are running torch {torch.__version__}.\r\nThe program is tested to work with torch {expected_torch_version}.\r\nTo reinstall the desired version, run with commandline flag --reinstall-torch.\r\nBeware that this will cause a lot of large files to be downloaded, as well as\r\nthere are reports of issues with training tab on the latest version.\r\n\r\nUse --skip-version-check commandline argument to disable this check.\r\n \"\"\".strip())\r\n\r\n expected_xformers_version = \"0.0.16rc425\"\r\n if shared.xformers_available:\r\n import xformers\r\n\r\n if version.parse(xformers.__version__) < version.parse(expected_xformers_version):\r\n errors.print_error_explanation(f\"\"\"\r\nYou are running xformers {xformers.__version__}.\r\nThe program is tested to work with xformers {expected_xformers_version}.\r\nTo reinstall the desired version, run with commandline flag --reinstall-xformers.\r\n\r\nUse --skip-version-check commandline argument to disable this check.\r\n \"\"\".strip())\r\n\r\n\r\ndef initialize():\r\n check_versions()\r\n\r\n extensions.list_extensions()\r\n localization.list_localizations(cmd_opts.localizations_dir)\r\n\r\n if cmd_opts.ui_debug_mode:\r\n shared.sd_upscalers = upscaler.UpscalerLanczos().scalers\r\n modules.scripts.load_scripts()\r\n return\r\n\r\n modelloader.cleanup_models()\r\n modules.sd_models.setup_model()\r\n codeformer.setup_model(cmd_opts.codeformer_models_path)\r\n gfpgan.setup_model(cmd_opts.gfpgan_models_path)\r\n\r\n modelloader.list_builtin_upscalers()\r\n modules.scripts.load_scripts()\r\n modelloader.load_upscalers()\r\n\r\n modules.sd_vae.refresh_vae_list()\r\n\r\n modules.textual_inversion.textual_inversion.list_textual_inversion_templates()\r\n\r\n try:\r\n modules.sd_models.load_model()\r\n except Exception as e:\r\n errors.display(e, \"loading stable diffusion model\")\r\n print(\"\", file=sys.stderr)\r\n print(\"Stable diffusion model failed to load, exiting\", file=sys.stderr)\r\n exit(1)\r\n\r\n shared.opts.data[\"sd_model_checkpoint\"] = shared.sd_model.sd_checkpoint_info.title\r\n\r\n shared.opts.onchange(\"sd_model_checkpoint\", wrap_queued_call(lambda: modules.sd_models.reload_model_weights()))\r\n shared.opts.onchange(\"sd_vae\", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)\r\n shared.opts.onchange(\"sd_vae_as_default\", wrap_queued_call(lambda: modules.sd_vae.reload_vae_weights()), call=False)\r\n shared.opts.onchange(\"temp_dir\", ui_tempdir.on_tmpdir_changed)\r\n\r\n shared.reload_hypernetworks()\r\n\r\n ui_extra_networks.intialize()\r\n ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())\r\n ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())\r\n ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())\r\n\r\n extra_networks.initialize()\r\n extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())\r\n\r\n if cmd_opts.tls_keyfile is not None and cmd_opts.tls_keyfile is not None:\r\n\r\n try:\r\n if not os.path.exists(cmd_opts.tls_keyfile):\r\n print(\"Invalid path to TLS keyfile given\")\r\n if not os.path.exists(cmd_opts.tls_certfile):\r\n print(f\"Invalid path to TLS certfile: '{cmd_opts.tls_certfile}'\")\r\n except TypeError:\r\n cmd_opts.tls_keyfile = cmd_opts.tls_certfile = None\r\n print(\"TLS setup invalid, running webui without TLS\")\r\n else:\r\n print(\"Running with TLS\")\r\n\r\n # make the program just exit at ctrl+c without waiting for anything\r\n def sigint_handler(sig, frame):\r\n print(f'Interrupted with signal {sig} in {frame}')\r\n os._exit(0)\r\n\r\n signal.signal(signal.SIGINT, sigint_handler)\r\n\r\n\r\ndef setup_cors(app):\r\n if cmd_opts.cors_allow_origins and cmd_opts.cors_allow_origins_regex:\r\n app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])\r\n elif cmd_opts.cors_allow_origins:\r\n app.add_middleware(CORSMiddleware, allow_origins=cmd_opts.cors_allow_origins.split(','), allow_methods=['*'], allow_credentials=True, allow_headers=['*'])\r\n elif cmd_opts.cors_allow_origins_regex:\r\n app.add_middleware(CORSMiddleware, allow_origin_regex=cmd_opts.cors_allow_origins_regex, allow_methods=['*'], allow_credentials=True, allow_headers=['*'])\r\n\r\n\r\ndef create_api(app):\r\n from modules.api.api import Api\r\n api = Api(app, queue_lock)\r\n return api\r\n\r\n\r\ndef wait_on_server(demo=None):\r\n while 1:\r\n time.sleep(0.5)\r\n if shared.state.need_restart:\r\n shared.state.need_restart = False\r\n time.sleep(0.5)\r\n demo.close()\r\n time.sleep(0.5)\r\n break\r\n\r\n\r\ndef api_only():\r\n initialize()\r\n\r\n app = FastAPI()\r\n setup_cors(app)\r\n app.add_middleware(GZipMiddleware, minimum_size=1000)\r\n api = create_api(app)\r\n\r\n modules.script_callbacks.app_started_callback(None, app)\r\n\r\n api.launch(server_name=\"0.0.0.0\" if cmd_opts.listen else \"127.0.0.1\", port=cmd_opts.port if cmd_opts.port else 7861)\r\n\r\n\r\ndef webui():\r\n launch_api = cmd_opts.api\r\n initialize()\r\n\r\n while 1:\r\n if shared.opts.clean_temp_dir_at_start:\r\n ui_tempdir.cleanup_tmpdr()\r\n\r\n modules.script_callbacks.before_ui_callback()\r\n\r\n shared.demo = modules.ui.create_ui()\r\n\r\n if cmd_opts.gradio_queue:\r\n shared.demo.queue(64)\r\n\r\n gradio_auth_creds = []\r\n if cmd_opts.gradio_auth:\r\n gradio_auth_creds += cmd_opts.gradio_auth.strip('\"').replace('\\n', '').split(',')\r\n if cmd_opts.gradio_auth_path:\r\n with open(cmd_opts.gradio_auth_path, 'r', encoding=\"utf8\") as file:\r\n for line in file.readlines():\r\n gradio_auth_creds += [x.strip() for x in line.split(',')]\r\n\r\n app, local_url, share_url = shared.demo.launch(\r\n share=cmd_opts.share,\r\n server_name=server_name,\r\n server_port=cmd_opts.port,\r\n ssl_keyfile=cmd_opts.tls_keyfile,\r\n ssl_certfile=cmd_opts.tls_certfile,\r\n debug=cmd_opts.gradio_debug,\r\n auth=[tuple(cred.split(':')) for cred in gradio_auth_creds] if gradio_auth_creds else None,\r\n inbrowser=cmd_opts.autolaunch,\r\n prevent_thread_lock=True\r\n )\r\n # after initial launch, disable --autolaunch for subsequent restarts\r\n cmd_opts.autolaunch = False\r\n\r\n # gradio uses a very open CORS policy via app.user_middleware, which makes it possible for\r\n # an attacker to trick the user into opening a malicious HTML page, which makes a request to the\r\n # running web ui and do whatever the attacker wants, including installing an extension and\r\n # running its code. We disable this here. Suggested by RyotaK.\r\n app.user_middleware = [x for x in app.user_middleware if x.cls.__name__ != 'CORSMiddleware']\r\n\r\n setup_cors(app)\r\n\r\n app.add_middleware(GZipMiddleware, minimum_size=1000)\r\n\r\n modules.progress.setup_progress_api(app)\r\n\r\n if launch_api:\r\n create_api(app)\r\n\r\n ui_extra_networks.add_pages_to_demo(app)\r\n\r\n modules.script_callbacks.app_started_callback(shared.demo, app)\r\n\r\n wait_on_server(shared.demo)\r\n print('Restarting UI...')\r\n\r\n sd_samplers.set_samplers()\r\n\r\n modules.script_callbacks.script_unloaded_callback()\r\n extensions.list_extensions()\r\n\r\n localization.list_localizations(cmd_opts.localizations_dir)\r\n\r\n modelloader.forbid_loaded_nonbuiltin_upscalers()\r\n modules.scripts.reload_scripts()\r\n modules.script_callbacks.model_loaded_callback(shared.sd_model)\r\n modelloader.load_upscalers()\r\n\r\n for module in [module for name, module in sys.modules.items() if name.startswith(\"modules.ui\")]:\r\n importlib.reload(module)\r\n\r\n modules.sd_models.list_models()\r\n\r\n shared.reload_hypernetworks()\r\n\r\n ui_extra_networks.intialize()\r\n ui_extra_networks.register_page(ui_extra_networks_textual_inversion.ExtraNetworksPageTextualInversion())\r\n ui_extra_networks.register_page(ui_extra_networks_hypernets.ExtraNetworksPageHypernetworks())\r\n ui_extra_networks.register_page(ui_extra_networks_checkpoints.ExtraNetworksPageCheckpoints())\r\n\r\n extra_networks.initialize()\r\n extra_networks.register_extra_network(extra_networks_hypernet.ExtraNetworkHypernet())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n if cmd_opts.nowebui:\r\n api_only()\r\n else:\r\n webui()\r\n" }, { "path": "webui.sh", "content": "#!/usr/bin/env bash\r\n#################################################\r\n# Please do not make any changes to this file, #\r\n# change the variables in webui-user.sh instead #\r\n#################################################\r\n\r\n# If run from macOS, load defaults from webui-macos-env.sh\r\nif [[ \"$OSTYPE\" == \"darwin\"* ]]; then\r\n if [[ -f webui-macos-env.sh ]]\r\n then\r\n source ./webui-macos-env.sh\r\n fi\r\nfi\r\n\r\n# Read variables from webui-user.sh\r\n# shellcheck source=/dev/null\r\nif [[ -f webui-user.sh ]]\r\nthen\r\n source ./webui-user.sh\r\nfi\r\n\r\n# Set defaults\r\n# Install directory without trailing slash\r\nif [[ -z \"${install_dir}\" ]]\r\nthen\r\n install_dir=\"/home/$(whoami)\"\r\nfi\r\n\r\n# Name of the subdirectory (defaults to stable-diffusion-webui)\r\nif [[ -z \"${clone_dir}\" ]]\r\nthen\r\n clone_dir=\"stable-diffusion-webui\"\r\nfi\r\n\r\n# python3 executable\r\nif [[ -z \"${python_cmd}\" ]]\r\nthen\r\n python_cmd=\"python3\"\r\nfi\r\n\r\n# git executable\r\nif [[ -z \"${GIT}\" ]]\r\nthen\r\n export GIT=\"git\"\r\nfi\r\n\r\n# python3 venv without trailing slash (defaults to ${install_dir}/${clone_dir}/venv)\r\nif [[ -z \"${venv_dir}\" ]]\r\nthen\r\n venv_dir=\"venv\"\r\nfi\r\n\r\nif [[ -z \"${LAUNCH_SCRIPT}\" ]]\r\nthen\r\n LAUNCH_SCRIPT=\"launch.py\"\r\nfi\r\n\r\n# this script cannot be run as root by default\r\ncan_run_as_root=0\r\n\r\n# read any command line flags to the webui.sh script\r\nwhile getopts \"f\" flag > /dev/null 2>&1\r\ndo\r\n case ${flag} in\r\n f) can_run_as_root=1;;\r\n *) break;;\r\n esac\r\ndone\r\n\r\n# Disable sentry logging\r\nexport ERROR_REPORTING=FALSE\r\n\r\n# Do not reinstall existing pip packages on Debian/Ubuntu\r\nexport PIP_IGNORE_INSTALLED=0\r\n\r\n# Pretty print\r\ndelimiter=\"################################################################\"\r\n\r\nprintf \"\\n%s\\n\" \"${delimiter}\"\r\nprintf \"\\e[1m\\e[32mInstall script for stable-diffusion + Web UI\\n\"\r\nprintf \"\\e[1m\\e[34mTested on Debian 11 (Bullseye)\\e[0m\"\r\nprintf \"\\n%s\\n\" \"${delimiter}\"\r\n\r\n# Do not run as root\r\nif [[ $(id -u) -eq 0 && can_run_as_root -eq 0 ]]\r\nthen\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"\\e[1m\\e[31mERROR: This script must not be launched as root, aborting...\\e[0m\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n exit 1\r\nelse\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Running on \\e[1m\\e[32m%s\\e[0m user\" \"$(whoami)\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\nfi\r\n\r\nif [[ -d .git ]]\r\nthen\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Repo already cloned, using it as install directory\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n install_dir=\"${PWD}/../\"\r\n clone_dir=\"${PWD##*/}\"\r\nfi\r\n\r\n# Check prerequisites\r\ngpu_info=$(lspci 2>/dev/null | grep VGA)\r\ncase \"$gpu_info\" in\r\n *\"Navi 1\"*|*\"Navi 2\"*) export HSA_OVERRIDE_GFX_VERSION=10.3.0\r\n ;;\r\n *\"Renoir\"*) export HSA_OVERRIDE_GFX_VERSION=9.0.0\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Experimental support for Renoir: make sure to have at least 4GB of VRAM and 10GB of RAM or enable cpu mode: --use-cpu all --no-half\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n ;;\r\n *) \r\n ;;\r\nesac\r\nif echo \"$gpu_info\" | grep -q \"AMD\" && [[ -z \"${TORCH_COMMAND}\" ]]\r\nthen\r\n export TORCH_COMMAND=\"pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.2\"\r\nfi \r\n\r\nfor preq in \"${GIT}\" \"${python_cmd}\"\r\ndo\r\n if ! hash \"${preq}\" &>/dev/null\r\n then\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"\\e[1m\\e[31mERROR: %s is not installed, aborting...\\e[0m\" \"${preq}\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n exit 1\r\n fi\r\ndone\r\n\r\nif ! \"${python_cmd}\" -c \"import venv\" &>/dev/null\r\nthen\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"\\e[1m\\e[31mERROR: python3-venv is not installed, aborting...\\e[0m\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n exit 1\r\nfi\r\n\r\ncd \"${install_dir}\"/ || { printf \"\\e[1m\\e[31mERROR: Can't cd to %s/, aborting...\\e[0m\" \"${install_dir}\"; exit 1; }\r\nif [[ -d \"${clone_dir}\" ]]\r\nthen\r\n cd \"${clone_dir}\"/ || { printf \"\\e[1m\\e[31mERROR: Can't cd to %s/%s/, aborting...\\e[0m\" \"${install_dir}\" \"${clone_dir}\"; exit 1; }\r\nelse\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Clone stable-diffusion-webui\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n \"${GIT}\" clone https://github.com/AUTOMATIC1111/stable-diffusion-webui.git \"${clone_dir}\"\r\n cd \"${clone_dir}\"/ || { printf \"\\e[1m\\e[31mERROR: Can't cd to %s/%s/, aborting...\\e[0m\" \"${install_dir}\" \"${clone_dir}\"; exit 1; }\r\nfi\r\n\r\nprintf \"\\n%s\\n\" \"${delimiter}\"\r\nprintf \"Create and activate python venv\"\r\nprintf \"\\n%s\\n\" \"${delimiter}\"\r\ncd \"${install_dir}\"/\"${clone_dir}\"/ || { printf \"\\e[1m\\e[31mERROR: Can't cd to %s/%s/, aborting...\\e[0m\" \"${install_dir}\" \"${clone_dir}\"; exit 1; }\r\nif [[ ! -d \"${venv_dir}\" ]]\r\nthen\r\n \"${python_cmd}\" -m venv \"${venv_dir}\"\r\n first_launch=1\r\nfi\r\n# shellcheck source=/dev/null\r\nif [[ -f \"${venv_dir}\"/bin/activate ]]\r\nthen\r\n source \"${venv_dir}\"/bin/activate\r\nelse\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"\\e[1m\\e[31mERROR: Cannot activate python venv, aborting...\\e[0m\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n exit 1\r\nfi\r\n\r\nif [[ ! -z \"${ACCELERATE}\" ]] && [ ${ACCELERATE}=\"True\" ] && [ -x \"$(command -v accelerate)\" ]\r\nthen\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Accelerating launch.py...\"\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n exec accelerate launch --num_cpu_threads_per_process=6 \"${LAUNCH_SCRIPT}\" \"$@\"\r\nelse\r\n printf \"\\n%s\\n\" \"${delimiter}\"\r\n printf \"Launching launch.py...\"\r\n printf \"\\n%s\\n\" \"${delimiter}\" \r\n exec \"${python_cmd}\" \"${LAUNCH_SCRIPT}\" \"$@\"\r\nfi\r\n" } ]