mirror of
https://github.com/lllyasviel/stable-diffusion-webui-forge.git
synced 2026-06-13 21:01:06 +08:00
1637 lines
81 KiB
Python
1637 lines
81 KiB
Python
import datetime
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import mimetypes
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import os
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import sys
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from functools import reduce
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import warnings
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from contextlib import ExitStack
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import gradio as gr
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import gradio.utils
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from gradio.components.image_editor import Brush
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from PIL import Image, PngImagePlugin # noqa: F401
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from modules.call_queue import wrap_gradio_gpu_call, wrap_queued_call, wrap_gradio_call, wrap_gradio_call_no_job # noqa: F401
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from modules import gradio_extensions, sd_schedulers # noqa: F401
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from modules import sd_hijack, sd_models, script_callbacks, ui_extensions, deepbooru, extra_networks, ui_common, ui_postprocessing, progress, ui_loadsave, shared_items, ui_settings, timer, sysinfo, ui_checkpoint_merger, scripts, sd_samplers, processing, ui_extra_networks, ui_toprow, launch_utils
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from modules.ui_components import FormRow, FormGroup, ToolButton, FormHTML, InputAccordion, ResizeHandleRow
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from modules.paths import script_path
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from modules.ui_common import create_refresh_button
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from modules.ui_gradio_extensions import reload_javascript
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from modules.shared import opts, cmd_opts
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import modules.infotext_utils as parameters_copypaste
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import modules.shared as shared
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from modules import prompt_parser
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from modules.infotext_utils import image_from_url_text, PasteField
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from modules_forge.forge_canvas.canvas import ForgeCanvas, canvas_head
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from modules_forge import main_entry, forge_space
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import modules.processing_scripts.comments as comments
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create_setting_component = ui_settings.create_setting_component
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warnings.filterwarnings("default" if opts.show_warnings else "ignore", category=UserWarning)
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warnings.filterwarnings("default" if opts.show_gradio_deprecation_warnings else "ignore", category=gradio_extensions.GradioDeprecationWarning)
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# this is a fix for Windows users. Without it, javascript files will be served with text/html content-type and the browser will not show any UI
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mimetypes.init()
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mimetypes.add_type('application/javascript', '.js')
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mimetypes.add_type('application/javascript', '.mjs')
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# Likewise, add explicit content-type header for certain missing image types
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mimetypes.add_type('image/webp', '.webp')
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mimetypes.add_type('image/avif', '.avif')
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if not cmd_opts.share and not cmd_opts.listen:
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# fix gradio phoning home
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gradio.utils.version_check = lambda: None
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gradio.utils.get_local_ip_address = lambda: '127.0.0.1'
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if cmd_opts.ngrok is not None:
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import modules.ngrok as ngrok
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print('ngrok authtoken detected, trying to connect...')
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ngrok.connect(
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cmd_opts.ngrok,
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cmd_opts.port if cmd_opts.port is not None else 7860,
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cmd_opts.ngrok_options
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)
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def gr_show(visible=True):
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return {"visible": visible, "__type__": "update"}
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sample_img2img = "assets/stable-samples/img2img/sketch-mountains-input.jpg"
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sample_img2img = sample_img2img if os.path.exists(sample_img2img) else None
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# Using constants for these since the variation selector isn't visible.
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# Important that they exactly match script.js for tooltip to work.
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random_symbol = '\U0001f3b2\ufe0f' # 🎲️
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reuse_symbol = '\u267b\ufe0f' # ♻️
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paste_symbol = '\u2199\ufe0f' # ↙
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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apply_style_symbol = '\U0001f4cb' # 📋
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clear_prompt_symbol = '\U0001f5d1\ufe0f' # 🗑️
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extra_networks_symbol = '\U0001F3B4' # 🎴
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switch_values_symbol = '\U000021C5' # ⇅
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restore_progress_symbol = '\U0001F300' # 🌀
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detect_image_size_symbol = '\U0001F4D0' # 📐
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plaintext_to_html = ui_common.plaintext_to_html
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def send_gradio_gallery_to_image(x):
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if len(x) == 0:
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return None
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return image_from_url_text(x[0])
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def calc_resolution_hires(enable, width, height, hr_scale, hr_resize_x, hr_resize_y):
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if not enable:
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return ""
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p = processing.StableDiffusionProcessingTxt2Img(width=width, height=height, enable_hr=True, hr_scale=hr_scale, hr_resize_x=hr_resize_x, hr_resize_y=hr_resize_y)
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p.calculate_target_resolution()
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new_width = p.hr_resize_x or p.hr_upscale_to_x
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new_height = p.hr_resize_y or p.hr_upscale_to_y
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new_width -= new_width % 8 # note: hardcoded latent size 8
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new_height -= new_height % 8
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return f"from <span class='resolution'>{p.width}x{p.height}</span> to <span class='resolution'>{new_width}x{new_height}</span>"
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def resize_from_to_html(width, height, scale_by):
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target_width = int(float(width) * scale_by)
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target_height = int(float(height) * scale_by)
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if not target_width or not target_height:
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return "no image selected"
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target_width -= target_width % 8 # note: hardcoded latent size 8
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target_height -= target_height % 8
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return f"resize: from <span class='resolution'>{width}x{height}</span> to <span class='resolution'>{target_width}x{target_height}</span>"
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def process_interrogate(interrogation_function, mode, ii_input_dir, ii_output_dir, *ii_singles):
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mode = int(mode)
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if mode in (0, 1, 3, 4):
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return [interrogation_function(ii_singles[mode]), None]
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elif mode == 2:
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return [interrogation_function(ii_singles[mode]), None]
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elif mode == 5:
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assert not shared.cmd_opts.hide_ui_dir_config, "Launched with --hide-ui-dir-config, batch img2img disabled"
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images = shared.listfiles(ii_input_dir)
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print(f"Will process {len(images)} images.")
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if ii_output_dir != "":
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os.makedirs(ii_output_dir, exist_ok=True)
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else:
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ii_output_dir = ii_input_dir
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for image in images:
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img = Image.open(image)
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filename = os.path.basename(image)
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left, _ = os.path.splitext(filename)
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print(interrogation_function(img), file=open(os.path.join(ii_output_dir, f"{left}.txt"), 'a', encoding='utf-8'))
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return [gr.update(), None]
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def interrogate(image):
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prompt = shared.interrogator.interrogate(image.convert("RGB"))
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return gr.update() if prompt is None else prompt
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def interrogate_deepbooru(image):
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prompt = deepbooru.model.tag(image)
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return gr.update() if prompt is None else prompt
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def expand_prompt_with_llm(prompt, image=None, llm_model=None, system_prompt=None, is_negative=False, positive_prompt=None):
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"""
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Expand the prompt using LLM capabilities.
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Args:
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prompt: The user's input prompt to expand
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image: Optional PIL Image for context (from img2img)
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llm_model: The LLM model folder name to use (from models/LLM)
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system_prompt: Custom system prompt to use for expansion
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is_negative: Whether this is a negative prompt expansion
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positive_prompt: The positive prompt (used as context for negative expansion)
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"""
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import os
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import time
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prompt_type = "negative" if is_negative else "positive"
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start_time = time.time()
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try:
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print("\n" + "#"*70, flush=True)
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print(f"# {prompt_type.upper()} PROMPT EXPANSION REQUEST", flush=True)
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print("#"*70, flush=True)
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print(f" Timestamp: {time.strftime('%Y-%m-%d %H:%M:%S')}", flush=True)
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print(f" LLM Model: {llm_model}", flush=True)
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print(f" Has image context: {image is not None}", flush=True)
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print(f" Has custom system prompt: {system_prompt is not None and len(system_prompt) > 0}", flush=True)
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if is_negative and positive_prompt:
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print(f" Using positive prompt as context: Yes ({len(positive_prompt)} chars)", flush=True)
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print(f" Original prompt ({len(prompt)} chars):", flush=True)
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print(f" \"{prompt[:200]}{'...' if len(prompt) > 200 else ''}\"", flush=True)
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print("#"*70, flush=True)
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if not prompt or prompt.strip() == "":
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gr.Warning(f"Please enter a {prompt_type} prompt to expand.")
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return prompt
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# Determine model path
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if llm_model and llm_model != "No LLM models found":
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model_path = os.path.join("models", "LLM", llm_model)
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else:
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# Fallback to default model
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model_path = "models/LLM/Qwen3-VL-8B-Caption-V4.5"
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if not os.path.exists(model_path):
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gr.Warning(f"LLM model not found at: {model_path}")
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print(f"ERROR: Model path does not exist: {model_path}", flush=True)
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return prompt
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# Extract PIL image if provided (from ForgeCanvas or gr.Image)
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pil_image = None
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if image is not None:
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if hasattr(image, 'convert'):
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pil_image = image
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elif isinstance(image, dict) and 'image' in image:
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pil_image = image.get('image')
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elif isinstance(image, dict) and 'background' in image:
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pil_image = image.get('background')
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if pil_image is not None:
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gr.Info(f"Expanding {prompt_type} prompt with image context... This may take a moment.")
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else:
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gr.Info(f"Expanding {prompt_type} prompt... This may take a moment.")
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# Use the standalone expansion function
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expanded = expand_prompt_standalone(
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prompt=prompt.strip(),
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model_path=model_path,
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system_prompt=system_prompt,
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image=pil_image,
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is_negative=is_negative,
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positive_prompt=positive_prompt.strip() if positive_prompt else None
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)
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total_time = time.time() - start_time
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if expanded and expanded != prompt:
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gr.Info(f"{prompt_type.capitalize()} prompt expanded successfully! ({total_time:.1f}s)")
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print("\n" + "#"*70, flush=True)
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print(f"# {prompt_type.upper()} PROMPT EXPANSION SUCCESS", flush=True)
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print("#"*70, flush=True)
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print(f" Total request time: {total_time:.2f}s", flush=True)
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print("#"*70 + "\n", flush=True)
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return expanded
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else:
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gr.Warning("Prompt expansion returned empty result, keeping original.")
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return prompt
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except Exception as e:
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import traceback
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total_time = time.time() - start_time
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print("\n" + "!"*70, flush=True)
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print(f"! {prompt_type.upper()} PROMPT EXPANSION FAILED", flush=True)
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print("!"*70, flush=True)
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print(f" Error: {str(e)}", flush=True)
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print(f" Time elapsed: {total_time:.2f}s", flush=True)
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print("!"*70, flush=True)
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traceback.print_exc()
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gr.Warning(f"Error during prompt expansion: {str(e)}")
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return prompt
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# Global cache for LLM model to avoid reloading
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_expansion_model_cache = {
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'model': None,
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'processor': None,
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'model_path': None,
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'model_type': None # 'vl' for standard VL, 'vl_moe' for VL+MoE
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}
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def expand_prompt_standalone(prompt: str, model_path: str, system_prompt: str = None, image=None, is_negative: bool = False, positive_prompt: str = None):
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"""
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Standalone prompt expansion using Qwen3-VL models (standard VL or VL+MoE).
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Args:
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prompt: The user's input prompt to expand
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model_path: Path to the LLM model
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system_prompt: System prompt to use (with {prompt} placeholder)
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image: Optional PIL Image for context
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is_negative: Whether this is a negative prompt expansion
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positive_prompt: The positive prompt (used as context for negative expansion)
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Returns:
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Expanded prompt string
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"""
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import torch
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import gc
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import time
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import json
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from modules.shared import opts
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global _expansion_model_cache
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# Track total time
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total_start_time = time.time()
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def log_step(message, start_time=None):
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"""Helper to log with timestamp and optional elapsed time."""
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timestamp = time.strftime("%H:%M:%S")
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if start_time is not None:
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elapsed = time.time() - start_time
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print(f"[{timestamp}] {message} ({elapsed:.2f}s)", flush=True)
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else:
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print(f"[{timestamp}] {message}", flush=True)
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def get_gpu_memory():
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"""Get GPU memory usage if available."""
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try:
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if torch.cuda.is_available():
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allocated = torch.cuda.memory_allocated() / 1024**3
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reserved = torch.cuda.memory_reserved() / 1024**3
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return f"GPU Memory: {allocated:.2f}GB allocated, {reserved:.2f}GB reserved"
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except:
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pass
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return None
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def detect_model_type(model_path):
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"""Detect whether model is standard VL or VL+MoE from config."""
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config_path = os.path.join(model_path, "config.json")
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if os.path.exists(config_path):
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try:
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with open(config_path, 'r', encoding='utf-8') as f:
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config = json.load(f)
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architectures = config.get("architectures", [])
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model_type = config.get("model_type", "")
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# Check for VL+MoE models (e.g., Qwen3VLMoeForConditionalGeneration)
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if any("Moe" in arch or "MoE" in arch for arch in architectures):
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return "vl_moe"
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if "moe" in model_type.lower():
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return "vl_moe"
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# Default to standard VL
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return "vl"
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except Exception as e:
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log_step(f" Warning: Could not read config.json: {e}")
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return "vl"
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prompt_type = "NEGATIVE" if is_negative else "POSITIVE"
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print("\n" + "="*70, flush=True)
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log_step(f"{prompt_type} PROMPT EXPANSION PIPELINE STARTED")
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print("="*70, flush=True)
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# Get settings
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max_new_tokens = getattr(opts, 'zimage_prompt_expansion_max_tokens', 512)
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temperature = getattr(opts, 'zimage_prompt_expansion_temperature', 0.7)
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log_step(f"Settings: max_tokens={max_new_tokens}, temperature={temperature}")
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# Load model if not cached or different model requested
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if (_expansion_model_cache['model'] is None or
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_expansion_model_cache['model_path'] != model_path):
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# Clear old model if exists
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if _expansion_model_cache['model'] is not None:
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log_step("Unloading previous LLM model...")
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unload_start = time.time()
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del _expansion_model_cache['model']
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del _expansion_model_cache['processor']
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_expansion_model_cache['model'] = None
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_expansion_model_cache['processor'] = None
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_expansion_model_cache['model_type'] = None
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gc.collect()
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torch.cuda.empty_cache()
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log_step("Previous model unloaded", unload_start)
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log_step(f"Loading LLM model: {model_path}")
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load_start = time.time()
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# Detect model type
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detected_type = detect_model_type(model_path)
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log_step(f" Detected model type: {detected_type}")
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try:
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from transformers import AutoProcessor
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log_step(" Loading processor...")
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processor_start = time.time()
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_expansion_model_cache['processor'] = AutoProcessor.from_pretrained(model_path)
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log_step(" Processor loaded", processor_start)
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log_step(" Loading model weights (this may take a moment)...")
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model_start = time.time()
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# Get the device the main program is using to avoid loading on wrong GPU
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from backend import memory_management
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main_device = memory_management.get_torch_device()
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device_index = main_device.index if hasattr(main_device, 'index') and main_device.index is not None else 0
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log_step(f" Target device: cuda:{device_index}")
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# Calculate available VRAM for LLM (leave some headroom for diffusion model)
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total_vram = torch.cuda.get_device_properties(device_index).total_memory / 1024**3
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# Use 90% of total VRAM, let accelerate handle the split with CPU
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max_gpu_memory = f"{int(total_vram * 0.9)}GiB"
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max_memory = {device_index: max_gpu_memory, "cpu": "32GiB"}
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log_step(f" GPU {device_index} has {total_vram:.1f}GB, allowing up to {max_gpu_memory}")
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if detected_type == "vl_moe":
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# Try loading VL+MoE model
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try:
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from transformers import Qwen3VLMoeForConditionalGeneration
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log_step(" Using Qwen3VLMoeForConditionalGeneration")
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_expansion_model_cache['model'] = Qwen3VLMoeForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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max_memory=max_memory,
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)
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except ImportError:
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# Fall back to AutoModelForVision2Seq if specific class not available
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log_step(" Qwen3VLMoeForConditionalGeneration not available, using AutoModelForVision2Seq")
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from transformers import AutoModelForVision2Seq
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_expansion_model_cache['model'] = AutoModelForVision2Seq.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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max_memory=max_memory,
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trust_remote_code=True,
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)
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else:
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# Standard VL model
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from transformers import Qwen3VLForConditionalGeneration
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log_step(" Using Qwen3VLForConditionalGeneration")
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_expansion_model_cache['model'] = Qwen3VLForConditionalGeneration.from_pretrained(
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model_path,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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max_memory=max_memory,
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)
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_expansion_model_cache['model_path'] = model_path
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_expansion_model_cache['model_type'] = detected_type
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log_step(" Model weights loaded", model_start)
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gpu_mem = get_gpu_memory()
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if gpu_mem:
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log_step(f" {gpu_mem}")
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log_step("LLM model ready", load_start)
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except Exception as e:
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raise RuntimeError(f"Failed to load LLM model: {e}")
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else:
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log_step("Using cached LLM model")
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processor = _expansion_model_cache['processor']
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model = _expansion_model_cache['model']
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# Build the full prompt with system instruction
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log_step("Preparing prompt...")
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prep_start = time.time()
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# For negative prompt expansion, inject positive prompt context if available
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effective_prompt = prompt
|
|
if is_negative and positive_prompt:
|
|
log_step(f" Injecting positive prompt context for negative expansion")
|
|
# Prepend context about what the image will contain
|
|
effective_prompt = f"The image being generated is: \"{positive_prompt}\"\n\nNow expand this negative prompt to exclude unwanted elements: {prompt}"
|
|
|
|
if system_prompt:
|
|
if '{prompt}' in system_prompt:
|
|
full_prompt = system_prompt.replace('{prompt}', effective_prompt)
|
|
else:
|
|
full_prompt = system_prompt + effective_prompt
|
|
else:
|
|
full_prompt = effective_prompt
|
|
|
|
log_step(f" System prompt: {len(system_prompt) if system_prompt else 0} chars")
|
|
log_step(f" User prompt: {len(prompt)} chars")
|
|
if is_negative and positive_prompt:
|
|
log_step(f" Positive prompt context: {len(positive_prompt)} chars")
|
|
log_step(f" Combined prompt: {len(full_prompt)} chars")
|
|
|
|
# Build message content based on whether image is provided
|
|
if image is not None:
|
|
log_step(" Including image context")
|
|
content = [
|
|
{"type": "image", "image": image},
|
|
{"type": "text", "text": full_prompt}
|
|
]
|
|
else:
|
|
content = [{"type": "text", "text": full_prompt}]
|
|
|
|
messages = [{"role": "user", "content": content}]
|
|
|
|
# Apply chat template
|
|
log_step("Applying chat template...")
|
|
template_start = time.time()
|
|
text_input = processor.apply_chat_template(
|
|
messages,
|
|
tokenize=False,
|
|
add_generation_prompt=True,
|
|
)
|
|
log_step("Chat template applied", template_start)
|
|
|
|
# Process/tokenize inputs
|
|
log_step("Tokenizing inputs...")
|
|
tokenize_start = time.time()
|
|
if image is not None:
|
|
inputs = processor(
|
|
text=[text_input],
|
|
images=[image],
|
|
padding=True,
|
|
return_tensors="pt",
|
|
)
|
|
else:
|
|
inputs = processor(
|
|
text=[text_input],
|
|
padding=True,
|
|
return_tensors="pt",
|
|
)
|
|
|
|
# Move to device
|
|
device = next(model.parameters()).device
|
|
inputs = {k: v.to(device) for k, v in inputs.items()}
|
|
|
|
input_token_count = inputs['input_ids'].shape[1]
|
|
log_step(f"Tokenization complete: {input_token_count} input tokens", tokenize_start)
|
|
|
|
# Generate expanded prompt
|
|
print("-"*70, flush=True)
|
|
log_step(f"GENERATING (max {max_new_tokens} tokens)...")
|
|
print("-"*70, flush=True)
|
|
generate_start = time.time()
|
|
|
|
with torch.no_grad():
|
|
outputs = model.generate(
|
|
**inputs,
|
|
max_new_tokens=max_new_tokens,
|
|
do_sample=True,
|
|
temperature=temperature,
|
|
top_p=0.9,
|
|
top_k=50,
|
|
repetition_penalty=1.1,
|
|
)
|
|
|
|
generate_time = time.time() - generate_start
|
|
|
|
# Decode the generated text
|
|
log_step("Decoding output...")
|
|
decode_start = time.time()
|
|
input_len = inputs['input_ids'].shape[1]
|
|
generated_ids = outputs[0][input_len:]
|
|
output_token_count = len(generated_ids)
|
|
raw_output = processor.decode(generated_ids, skip_special_tokens=True)
|
|
log_step(f"Decoding complete", decode_start)
|
|
|
|
# Calculate generation stats
|
|
tokens_per_second = output_token_count / generate_time if generate_time > 0 else 0
|
|
|
|
print("-"*70, flush=True)
|
|
log_step("GENERATION COMPLETE")
|
|
print("-"*70, flush=True)
|
|
log_step(f" Output tokens: {output_token_count}")
|
|
log_step(f" Generation time: {generate_time:.2f}s")
|
|
log_step(f" Speed: {tokens_per_second:.2f} tokens/sec")
|
|
|
|
# Print raw output to console
|
|
print("\n" + "="*70, flush=True)
|
|
print("RAW LLM OUTPUT:", flush=True)
|
|
print("="*70, flush=True)
|
|
print(raw_output, flush=True)
|
|
print("="*70, flush=True)
|
|
|
|
# Clean up output - remove thinking tags if present
|
|
expanded_prompt = raw_output
|
|
if "</think>" in expanded_prompt:
|
|
log_step("Removing <think> tags from output...")
|
|
expanded_prompt = expanded_prompt.split("</think>")[-1].strip()
|
|
|
|
print("\n" + "="*70, flush=True)
|
|
print("FINAL EXPANDED PROMPT:", flush=True)
|
|
print("="*70, flush=True)
|
|
print(expanded_prompt, flush=True)
|
|
print("="*70 + "\n", flush=True)
|
|
|
|
# Unload model to free VRAM
|
|
log_step("Unloading LLM model to free VRAM...")
|
|
unload_start = time.time()
|
|
|
|
gpu_before = get_gpu_memory()
|
|
if gpu_before:
|
|
log_step(f" Before unload: {gpu_before}")
|
|
|
|
# Properly unload Hugging Face model with device_map
|
|
if _expansion_model_cache['model'] is not None:
|
|
try:
|
|
# Move model to CPU first to release GPU memory
|
|
_expansion_model_cache['model'].to('cpu')
|
|
except:
|
|
pass
|
|
# Clear any internal hooks from accelerate
|
|
try:
|
|
from accelerate.hooks import remove_hook_from_submodules
|
|
remove_hook_from_submodules(_expansion_model_cache['model'])
|
|
except:
|
|
pass
|
|
del _expansion_model_cache['model']
|
|
_expansion_model_cache['model'] = None
|
|
|
|
if _expansion_model_cache['processor'] is not None:
|
|
del _expansion_model_cache['processor']
|
|
_expansion_model_cache['processor'] = None
|
|
_expansion_model_cache['model_path'] = None
|
|
|
|
# Force garbage collection multiple times for thorough cleanup
|
|
gc.collect()
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
torch.cuda.synchronize()
|
|
|
|
gpu_after = get_gpu_memory()
|
|
if gpu_after:
|
|
log_step(f" After unload: {gpu_after}")
|
|
|
|
log_step("LLM model unloaded", unload_start)
|
|
|
|
# Final summary
|
|
total_time = time.time() - total_start_time
|
|
print("\n" + "="*70, flush=True)
|
|
log_step("PROMPT EXPANSION PIPELINE COMPLETE")
|
|
print("="*70, flush=True)
|
|
log_step(f" Total time: {total_time:.2f}s")
|
|
log_step(f" Input: {len(prompt)} chars -> Output: {len(expanded_prompt)} chars")
|
|
log_step(f" Expansion ratio: {len(expanded_prompt)/len(prompt):.1f}x")
|
|
print("="*70 + "\n", flush=True)
|
|
|
|
return expanded_prompt.strip()
|
|
|
|
|
|
def connect_clear_prompt(button):
|
|
"""Given clear button, prompt, and token_counter objects, setup clear prompt button click event"""
|
|
button.click(
|
|
_js="clear_prompt",
|
|
fn=None,
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
|
|
def update_token_counter(text, steps, styles, *, is_positive=True):
|
|
params = script_callbacks.BeforeTokenCounterParams(text, steps, styles, is_positive=is_positive)
|
|
script_callbacks.before_token_counter_callback(params)
|
|
text = params.prompt
|
|
steps = params.steps
|
|
styles = params.styles
|
|
is_positive = params.is_positive
|
|
|
|
if shared.opts.include_styles_into_token_counters:
|
|
apply_styles = shared.prompt_styles.apply_styles_to_prompt if is_positive else shared.prompt_styles.apply_negative_styles_to_prompt
|
|
text = apply_styles(text, styles)
|
|
else:
|
|
text = comments.strip_comments(text).strip()
|
|
|
|
try:
|
|
text, _ = extra_networks.parse_prompt(text)
|
|
|
|
if is_positive:
|
|
_, prompt_flat_list, _ = prompt_parser.get_multicond_prompt_list([text])
|
|
else:
|
|
prompt_flat_list = [text]
|
|
|
|
prompt_schedules = prompt_parser.get_learned_conditioning_prompt_schedules(prompt_flat_list, steps)
|
|
|
|
except Exception:
|
|
# a parsing error can happen here during typing, and we don't want to bother the user with
|
|
# messages related to it in console
|
|
prompt_schedules = [[[steps, text]]]
|
|
|
|
try:
|
|
get_prompt_lengths_on_ui = sd_models.model_data.sd_model.get_prompt_lengths_on_ui
|
|
assert get_prompt_lengths_on_ui is not None
|
|
except Exception:
|
|
return f"<span class='gr-box gr-text-input'>?/?</span>"
|
|
|
|
flat_prompts = reduce(lambda list1, list2: list1+list2, prompt_schedules)
|
|
prompts = [prompt_text for step, prompt_text in flat_prompts]
|
|
token_count, max_length = max([get_prompt_lengths_on_ui(prompt) for prompt in prompts], key=lambda args: args[0])
|
|
return f"<span class='gr-box gr-text-input'>{token_count}/{max_length}</span>"
|
|
|
|
|
|
def update_negative_prompt_token_counter(*args):
|
|
return update_token_counter(*args, is_positive=False)
|
|
|
|
|
|
def setup_progressbar(*args, **kwargs):
|
|
pass
|
|
|
|
|
|
def apply_setting(key, value):
|
|
if value is None:
|
|
return gr.update()
|
|
|
|
if shared.cmd_opts.freeze_settings:
|
|
return gr.update()
|
|
|
|
# dont allow model to be swapped when model hash exists in prompt
|
|
if key == "sd_model_checkpoint" and opts.disable_weights_auto_swap:
|
|
return gr.update()
|
|
|
|
if key == "sd_model_checkpoint":
|
|
ckpt_info = sd_models.get_closet_checkpoint_match(value)
|
|
|
|
if ckpt_info is not None:
|
|
value = ckpt_info.title
|
|
else:
|
|
return gr.update()
|
|
|
|
comp_args = opts.data_labels[key].component_args
|
|
if comp_args and isinstance(comp_args, dict) and comp_args.get('visible') is False:
|
|
return
|
|
|
|
valtype = type(opts.data_labels[key].default)
|
|
oldval = opts.data.get(key, None)
|
|
opts.data[key] = valtype(value) if valtype != type(None) else value
|
|
if oldval != value and opts.data_labels[key].onchange is not None:
|
|
opts.data_labels[key].onchange()
|
|
|
|
opts.save(shared.config_filename)
|
|
return getattr(opts, key)
|
|
|
|
|
|
def create_output_panel(tabname, outdir, toprow=None):
|
|
return ui_common.create_output_panel(tabname, outdir, toprow)
|
|
|
|
|
|
def ordered_ui_categories():
|
|
user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder_list)}
|
|
|
|
for _, category in sorted(enumerate(shared_items.ui_reorder_categories()), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
|
|
yield category
|
|
|
|
|
|
def create_override_settings_dropdown(tabname, row):
|
|
dropdown = gr.Dropdown([], label="Override settings", visible=False, elem_id=f"{tabname}_override_settings", multiselect=True)
|
|
|
|
dropdown.change(
|
|
fn=lambda x: gr.Dropdown.update(visible=bool(x)),
|
|
inputs=[dropdown],
|
|
outputs=[dropdown],
|
|
)
|
|
|
|
return dropdown
|
|
|
|
|
|
def create_ui():
|
|
import modules.img2img
|
|
import modules.txt2img
|
|
|
|
reload_javascript()
|
|
|
|
parameters_copypaste.reset()
|
|
|
|
settings = ui_settings.UiSettings()
|
|
settings.register_settings()
|
|
|
|
scripts.scripts_current = scripts.scripts_txt2img
|
|
scripts.scripts_txt2img.initialize_scripts(is_img2img=False)
|
|
|
|
with gr.Blocks(analytics_enabled=False, head=canvas_head) as txt2img_interface:
|
|
toprow = ui_toprow.Toprow(is_img2img=False, is_compact=shared.opts.compact_prompt_box)
|
|
|
|
dummy_component = gr.Textbox(visible=False)
|
|
dummy_component_number = gr.Number(visible=False)
|
|
|
|
extra_tabs = gr.Tabs(elem_id="txt2img_extra_tabs", elem_classes=["extra-networks"])
|
|
extra_tabs.__enter__()
|
|
|
|
with gr.Tab("Generation", id="txt2img_generation") as txt2img_generation_tab, ResizeHandleRow(equal_height=False):
|
|
with ExitStack() as stack:
|
|
if shared.opts.txt2img_settings_accordion:
|
|
stack.enter_context(gr.Accordion("Open for Settings", open=False))
|
|
stack.enter_context(gr.Column(variant='compact', elem_id="txt2img_settings"))
|
|
|
|
scripts.scripts_txt2img.prepare_ui()
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "prompt":
|
|
toprow.create_inline_toprow_prompts()
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="txt2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="txt2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="txt2img_height")
|
|
|
|
with gr.Column(elem_id="txt2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
|
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="txt2img_res_switch_btn", tooltip="Switch width/height")
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "cfg":
|
|
with gr.Row():
|
|
distilled_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Distilled CFG Scale', value=3.5, elem_id="txt2img_distilled_cfg_scale")
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='CFG Scale', value=7.0, elem_id="txt2img_cfg_scale")
|
|
zimage_shift = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Z-Image Shift', value=0.0, elem_id="txt2img_zimage_shift")
|
|
cfg_scale.change(lambda x: gr.update(interactive=(x != 1)), inputs=[cfg_scale], outputs=[toprow.negative_prompt], queue=False, show_progress=False)
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_classes="checkboxes-row", variant="compact"):
|
|
pass
|
|
|
|
elif category == "accordions":
|
|
with gr.Row(elem_id="txt2img_accordions", elem_classes="accordions"):
|
|
with InputAccordion(False, label="Hires. fix", elem_id="txt2img_hr") as enable_hr:
|
|
with enable_hr.extra():
|
|
hr_final_resolution = FormHTML(value="", elem_id="txtimg_hr_finalres", label="Upscaled resolution")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row1", variant="compact"):
|
|
hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
|
|
hr_second_pass_steps = gr.Slider(minimum=0, maximum=150, step=1, label='Hires steps', value=0, elem_id="txt2img_hires_steps")
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row2", variant="compact"):
|
|
hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
|
|
hr_resize_x = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize width to", value=0, elem_id="txt2img_hr_resize_x")
|
|
hr_resize_y = gr.Slider(minimum=0, maximum=2048, step=8, label="Resize height to", value=0, elem_id="txt2img_hr_resize_y")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row_cfg", variant="compact"):
|
|
hr_distilled_cfg = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label="Hires Distilled CFG Scale", value=3.5, elem_id="txt2img_hr_distilled_cfg")
|
|
hr_cfg = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label="Hires CFG Scale", value=7.0, elem_id="txt2img_hr_cfg")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row3", variant="compact", visible=shared.opts.hires_fix_show_sampler) as hr_checkpoint_container:
|
|
hr_checkpoint_name = gr.Dropdown(label='Hires Checkpoint', elem_id="hr_checkpoint", choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True), value="Use same checkpoint", scale=2)
|
|
|
|
hr_checkpoint_refresh = ToolButton(value=refresh_symbol)
|
|
|
|
def get_additional_modules():
|
|
modules_list = ['Use same choices']
|
|
if main_entry.module_list == {}:
|
|
_, modules = main_entry.refresh_models()
|
|
modules_list += list(modules)
|
|
else:
|
|
modules_list += list(main_entry.module_list.keys())
|
|
return modules_list
|
|
|
|
modules_list = get_additional_modules()
|
|
|
|
def refresh_model_and_modules():
|
|
modules_list = get_additional_modules()
|
|
return gr.update(choices=["Use same checkpoint"] + modules.sd_models.checkpoint_tiles(use_short=True)), gr.update(choices=modules_list)
|
|
|
|
hr_additional_modules = gr.Dropdown(label='Hires VAE / Text Encoder', elem_id="hr_vae_te", choices=modules_list, value=["Use same choices"], multiselect=True, scale=3)
|
|
|
|
hr_checkpoint_refresh.click(fn=refresh_model_and_modules, outputs=[hr_checkpoint_name, hr_additional_modules], show_progress=False)
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row3b", variant="compact", visible=shared.opts.hires_fix_show_sampler) as hr_sampler_container:
|
|
hr_sampler_name = gr.Dropdown(label='Hires sampling method', elem_id="hr_sampler", choices=["Use same sampler"] + sd_samplers.visible_sampler_names(), value="Use same sampler")
|
|
hr_scheduler = gr.Dropdown(label='Hires schedule type', elem_id="hr_scheduler", choices=["Use same scheduler"] + [x.label for x in sd_schedulers.schedulers], value="Use same scheduler")
|
|
|
|
with FormRow(elem_id="txt2img_hires_fix_row4", variant="compact", visible=shared.opts.hires_fix_show_prompts) as hr_prompts_container:
|
|
with gr.Column():
|
|
hr_prompt = gr.Textbox(label="Hires prompt", elem_id="hires_prompt", show_label=False, lines=3, placeholder="Prompt for hires fix pass.\nLeave empty to use the same prompt as in first pass.", elem_classes=["prompt"])
|
|
with gr.Column():
|
|
hr_negative_prompt = gr.Textbox(label="Hires negative prompt", elem_id="hires_neg_prompt", show_label=False, lines=3, placeholder="Negative prompt for hires fix pass.\nLeave empty to use the same negative prompt as in first pass.", elem_classes=["prompt"])
|
|
|
|
hr_cfg.change(lambda x: gr.update(interactive=(x != 1)), inputs=[hr_cfg], outputs=[hr_negative_prompt], queue=False, show_progress=False)
|
|
|
|
scripts.scripts_txt2img.setup_ui_for_section(category)
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="txt2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="txt2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="txt2img_batch_size")
|
|
|
|
elif category == "override_settings":
|
|
with FormRow(elem_id="txt2img_override_settings_row") as row:
|
|
override_settings = create_override_settings_dropdown('txt2img', row)
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="txt2img_script_container"):
|
|
custom_inputs = scripts.scripts_txt2img.setup_ui()
|
|
|
|
if category not in {"accordions"}:
|
|
scripts.scripts_txt2img.setup_ui_for_section(category)
|
|
|
|
hr_resolution_preview_inputs = [enable_hr, width, height, hr_scale, hr_resize_x, hr_resize_y]
|
|
|
|
for component in hr_resolution_preview_inputs:
|
|
event = component.release if isinstance(component, gr.Slider) else component.change
|
|
|
|
event(
|
|
fn=calc_resolution_hires,
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[hr_final_resolution],
|
|
show_progress=False,
|
|
)
|
|
event(
|
|
None,
|
|
_js="onCalcResolutionHires",
|
|
inputs=hr_resolution_preview_inputs,
|
|
outputs=[],
|
|
show_progress=False,
|
|
)
|
|
|
|
output_panel = create_output_panel("txt2img", opts.outdir_txt2img_samples, toprow)
|
|
|
|
txt2img_inputs = [
|
|
dummy_component,
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
toprow.ui_styles.dropdown,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
distilled_cfg_scale,
|
|
zimage_shift,
|
|
height,
|
|
width,
|
|
enable_hr,
|
|
denoising_strength,
|
|
hr_scale,
|
|
hr_upscaler,
|
|
hr_second_pass_steps,
|
|
hr_resize_x,
|
|
hr_resize_y,
|
|
hr_checkpoint_name,
|
|
hr_additional_modules,
|
|
hr_sampler_name,
|
|
hr_scheduler,
|
|
hr_prompt,
|
|
hr_negative_prompt,
|
|
hr_cfg,
|
|
hr_distilled_cfg,
|
|
override_settings,
|
|
] + custom_inputs
|
|
|
|
txt2img_outputs = [
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
]
|
|
|
|
txt2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img, extra_outputs=[None, '', '']),
|
|
_js="submit",
|
|
inputs=txt2img_inputs,
|
|
outputs=txt2img_outputs,
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.prompt.submit(**txt2img_args)
|
|
toprow.submit.click(**txt2img_args)
|
|
|
|
def select_gallery_image(index):
|
|
index = int(index)
|
|
if getattr(shared.opts, 'hires_button_gallery_insert', False):
|
|
index += 1
|
|
return gr.update(selected_index=index)
|
|
|
|
txt2img_upscale_inputs = txt2img_inputs[0:1] + [output_panel.gallery, dummy_component_number, output_panel.generation_info] + txt2img_inputs[1:]
|
|
output_panel.button_upscale.click(
|
|
fn=wrap_gradio_gpu_call(modules.txt2img.txt2img_upscale, extra_outputs=[None, '', '']),
|
|
_js="submit_txt2img_upscale",
|
|
inputs=txt2img_upscale_inputs,
|
|
outputs=txt2img_outputs,
|
|
show_progress=False,
|
|
).then(fn=select_gallery_image, js="selected_gallery_index", inputs=[dummy_component], outputs=[output_panel.gallery])
|
|
|
|
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
|
|
|
|
toprow.restore_progress_button.click(
|
|
fn=progress.restore_progress,
|
|
_js="restoreProgressTxt2img",
|
|
inputs=[dummy_component],
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
txt2img_paste_fields = [
|
|
PasteField(toprow.prompt, "Prompt", api="prompt"),
|
|
PasteField(toprow.negative_prompt, "Negative prompt", api="negative_prompt"),
|
|
PasteField(cfg_scale, "CFG scale", api="cfg_scale"),
|
|
PasteField(distilled_cfg_scale, "Distilled CFG Scale", api="distilled_cfg_scale"),
|
|
PasteField(width, "Size-1", api="width"),
|
|
PasteField(height, "Size-2", api="height"),
|
|
PasteField(batch_size, "Batch size", api="batch_size"),
|
|
PasteField(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update(), api="styles"),
|
|
PasteField(denoising_strength, "Denoising strength", api="denoising_strength"),
|
|
PasteField(enable_hr, lambda d: "Denoising strength" in d and ("Hires upscale" in d or "Hires upscaler" in d or "Hires resize-1" in d), api="enable_hr"),
|
|
PasteField(hr_scale, "Hires upscale", api="hr_scale"),
|
|
PasteField(hr_upscaler, "Hires upscaler", api="hr_upscaler"),
|
|
PasteField(hr_second_pass_steps, "Hires steps", api="hr_second_pass_steps"),
|
|
PasteField(hr_resize_x, "Hires resize-1", api="hr_resize_x"),
|
|
PasteField(hr_resize_y, "Hires resize-2", api="hr_resize_y"),
|
|
PasteField(hr_checkpoint_name, "Hires checkpoint", api="hr_checkpoint_name"),
|
|
PasteField(hr_additional_modules, "Hires VAE/TE", api="hr_additional_modules"),
|
|
PasteField(hr_sampler_name, sd_samplers.get_hr_sampler_from_infotext, api="hr_sampler_name"),
|
|
PasteField(hr_scheduler, sd_samplers.get_hr_scheduler_from_infotext, api="hr_scheduler"),
|
|
PasteField(hr_sampler_container, lambda d: gr.update(visible=True) if d.get("Hires sampler", "Use same sampler") != "Use same sampler" or d.get("Hires checkpoint", "Use same checkpoint") != "Use same checkpoint" or d.get("Hires schedule type", "Use same scheduler") != "Use same scheduler" else gr.update()),
|
|
PasteField(hr_prompt, "Hires prompt", api="hr_prompt"),
|
|
PasteField(hr_negative_prompt, "Hires negative prompt", api="hr_negative_prompt"),
|
|
PasteField(hr_cfg, "Hires CFG Scale", api="hr_cfg"),
|
|
PasteField(hr_distilled_cfg, "Hires Distilled CFG Scale", api="hr_distilled_cfg"),
|
|
PasteField(hr_prompts_container, lambda d: gr.update(visible=True) if d.get("Hires prompt", "") != "" or d.get("Hires negative prompt", "") != "" else gr.update()),
|
|
*scripts.scripts_txt2img.infotext_fields
|
|
]
|
|
parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields, override_settings)
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=toprow.paste, tabname="txt2img", source_text_component=toprow.prompt, source_image_component=None,
|
|
))
|
|
|
|
steps = scripts.scripts_txt2img.script('Sampler').steps
|
|
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
toprow.token_button.click(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
|
|
# Connect expand prompt buttons for Prompt Expansion accordion
|
|
# Expand Positive Prompt button
|
|
toprow.expand_positive_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[
|
|
toprow.prompt,
|
|
gr.State(None), # No image for txt2img positive
|
|
toprow.llm_model_dropdown,
|
|
toprow.positive_system_prompt,
|
|
gr.State(False), # is_negative=False
|
|
gr.State(None) # positive_prompt (not needed for positive expansion)
|
|
],
|
|
outputs=[toprow.prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
# Expand Negative Prompt button - uses positive prompt as context
|
|
toprow.expand_negative_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[
|
|
toprow.negative_prompt,
|
|
gr.State(None), # No image for txt2img negative
|
|
toprow.llm_model_dropdown,
|
|
toprow.negative_system_prompt,
|
|
gr.State(True), # is_negative=True
|
|
toprow.prompt # Pass positive prompt as context
|
|
],
|
|
outputs=[toprow.negative_prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
# Legacy expand prompt button (hidden, but kept for backward compatibility)
|
|
toprow.expand_prompt_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[toprow.prompt],
|
|
outputs=[toprow.prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
extra_networks_ui = ui_extra_networks.create_ui(txt2img_interface, [txt2img_generation_tab], 'txt2img')
|
|
ui_extra_networks.setup_ui(extra_networks_ui, output_panel.gallery)
|
|
|
|
extra_tabs.__exit__()
|
|
|
|
scripts.scripts_current = scripts.scripts_img2img
|
|
scripts.scripts_img2img.initialize_scripts(is_img2img=True)
|
|
|
|
with gr.Blocks(analytics_enabled=False, head=canvas_head) as img2img_interface:
|
|
toprow = ui_toprow.Toprow(is_img2img=True, is_compact=shared.opts.compact_prompt_box)
|
|
|
|
extra_tabs = gr.Tabs(elem_id="img2img_extra_tabs", elem_classes=["extra-networks"])
|
|
extra_tabs.__enter__()
|
|
|
|
with gr.Tab("Generation", id="img2img_generation") as img2img_generation_tab, ResizeHandleRow(equal_height=False):
|
|
with ExitStack() as stack:
|
|
if shared.opts.img2img_settings_accordion:
|
|
stack.enter_context(gr.Accordion("Open for Settings", open=False))
|
|
stack.enter_context(gr.Column(variant='compact', elem_id="img2img_settings"))
|
|
|
|
copy_image_buttons = []
|
|
copy_image_destinations = {}
|
|
|
|
def add_copy_image_controls(tab_name, elem):
|
|
with gr.Row(variant="compact", elem_id=f"img2img_copy_to_{tab_name}"):
|
|
for title, name in zip(['to img2img', 'to sketch', 'to inpaint', 'to inpaint sketch'], ['img2img', 'sketch', 'inpaint', 'inpaint_sketch']):
|
|
if name == tab_name:
|
|
gr.Button(title, interactive=False)
|
|
copy_image_destinations[name] = elem
|
|
continue
|
|
|
|
button = gr.Button(title)
|
|
copy_image_buttons.append((button, name, elem))
|
|
|
|
scripts.scripts_img2img.prepare_ui()
|
|
|
|
for category in ordered_ui_categories():
|
|
if category == "prompt":
|
|
toprow.create_inline_toprow_prompts()
|
|
|
|
if category == "image":
|
|
with gr.Tabs(elem_id="mode_img2img"):
|
|
img2img_selected_tab = gr.Number(value=0, visible=False)
|
|
|
|
with gr.TabItem('img2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
|
|
init_img = ForgeCanvas(elem_id="img2img_image", height=512, no_scribbles=True)
|
|
add_copy_image_controls('img2img', init_img)
|
|
|
|
with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
|
|
sketch = ForgeCanvas(elem_id="img2img_sketch", height=512, scribble_color=opts.img2img_sketch_default_brush_color)
|
|
add_copy_image_controls('sketch', sketch)
|
|
|
|
with gr.TabItem('Inpaint', id='inpaint', elem_id="img2img_inpaint_tab") as tab_inpaint:
|
|
init_img_with_mask = ForgeCanvas(elem_id="img2maskimg", height=512, contrast_scribbles=opts.img2img_inpaint_mask_high_contrast, scribble_color=opts.img2img_inpaint_mask_brush_color, scribble_color_fixed=True, scribble_alpha=opts.img2img_inpaint_mask_scribble_alpha, scribble_alpha_fixed=True, scribble_softness_fixed=True)
|
|
add_copy_image_controls('inpaint', init_img_with_mask)
|
|
|
|
with gr.TabItem('Inpaint sketch', id='inpaint_sketch', elem_id="img2img_inpaint_sketch_tab") as tab_inpaint_color:
|
|
inpaint_color_sketch = ForgeCanvas(elem_id="inpaint_sketch", height=512, scribble_color=opts.img2img_inpaint_sketch_default_brush_color)
|
|
add_copy_image_controls('inpaint_sketch', inpaint_color_sketch)
|
|
|
|
with gr.TabItem('Inpaint upload', id='inpaint_upload', elem_id="img2img_inpaint_upload_tab") as tab_inpaint_upload:
|
|
init_img_inpaint = gr.Image(label="Image for img2img", show_label=False, source="upload", interactive=True, type="pil", elem_id="img_inpaint_base")
|
|
init_mask_inpaint = gr.Image(label="Mask", source="upload", interactive=True, type="pil", image_mode="RGBA", elem_id="img_inpaint_mask")
|
|
|
|
with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
|
|
with gr.Tabs(elem_id="img2img_batch_source"):
|
|
img2img_batch_source_type = gr.Textbox(visible=False, value="upload")
|
|
with gr.TabItem('Upload', id='batch_upload', elem_id="img2img_batch_upload_tab") as tab_batch_upload:
|
|
img2img_batch_upload = gr.Files(label="Files", interactive=True, elem_id="img2img_batch_upload")
|
|
with gr.TabItem('From directory', id='batch_from_dir', elem_id="img2img_batch_from_dir_tab") as tab_batch_from_dir:
|
|
hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
|
|
gr.HTML(
|
|
"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
|
|
"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
|
|
f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
|
|
f"{hidden}</p>"
|
|
)
|
|
img2img_batch_input_dir = gr.Textbox(label="Input directory", **shared.hide_dirs, elem_id="img2img_batch_input_dir")
|
|
img2img_batch_output_dir = gr.Textbox(label="Output directory", **shared.hide_dirs, elem_id="img2img_batch_output_dir")
|
|
img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
|
|
tab_batch_upload.select(fn=lambda: "upload", inputs=[], outputs=[img2img_batch_source_type])
|
|
tab_batch_from_dir.select(fn=lambda: "from dir", inputs=[], outputs=[img2img_batch_source_type])
|
|
with gr.Accordion("PNG info", open=False):
|
|
img2img_batch_use_png_info = gr.Checkbox(label="Append png info to prompts", elem_id="img2img_batch_use_png_info")
|
|
img2img_batch_png_info_dir = gr.Textbox(label="PNG info directory", **shared.hide_dirs, placeholder="Leave empty to use input directory", elem_id="img2img_batch_png_info_dir")
|
|
img2img_batch_png_info_props = gr.CheckboxGroup(["Prompt", "Negative prompt", "Seed", "CFG scale", "Sampler", "Steps", "Model hash"], label="Parameters to take from png info", info="Prompts from png info will be appended to prompts set in ui.")
|
|
|
|
img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
|
|
|
|
for i, tab in enumerate(img2img_tabs):
|
|
tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
|
|
|
|
def copyCanvas_img2img (background, foreground, source):
|
|
if source == 1 or source == 3: # 1 is sketch, 3 is Inpaint sketch
|
|
bg = Image.alpha_composite(background, foreground)
|
|
return bg, None
|
|
return background, None
|
|
|
|
for button, name, elem in copy_image_buttons:
|
|
button.click(
|
|
fn=copyCanvas_img2img,
|
|
inputs=[elem.background, elem.foreground, img2img_selected_tab],
|
|
outputs=[copy_image_destinations[name].background, copy_image_destinations[name].foreground],
|
|
)
|
|
button.click(
|
|
fn=None,
|
|
_js=f"switch_to_{name.replace(' ', '_')}",
|
|
inputs=[],
|
|
outputs=[],
|
|
)
|
|
|
|
with FormRow():
|
|
resize_mode = gr.Radio(label="Resize mode", elem_id="resize_mode", choices=["Just resize", "Crop and resize", "Resize and fill", "Just resize (latent upscale)"], type="index", value="Just resize")
|
|
|
|
elif category == "dimensions":
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
selected_scale_tab = gr.Number(value=0, visible=False)
|
|
|
|
with gr.Tabs(elem_id="img2img_tabs_resize"):
|
|
with gr.Tab(label="Resize to", id="to", elem_id="img2img_tab_resize_to") as tab_scale_to:
|
|
with FormRow():
|
|
with gr.Column(elem_id="img2img_column_size", scale=4):
|
|
width = gr.Slider(minimum=64, maximum=2048, step=8, label="Width", value=512, elem_id="img2img_width")
|
|
height = gr.Slider(minimum=64, maximum=2048, step=8, label="Height", value=512, elem_id="img2img_height")
|
|
with gr.Column(elem_id="img2img_dimensions_row", scale=1, elem_classes="dimensions-tools"):
|
|
res_switch_btn = ToolButton(value=switch_values_symbol, elem_id="img2img_res_switch_btn", tooltip="Switch width/height")
|
|
detect_image_size_btn = ToolButton(value=detect_image_size_symbol, elem_id="img2img_detect_image_size_btn", tooltip="Auto detect size from img2img")
|
|
|
|
with gr.Tab(label="Resize by", id="by", elem_id="img2img_tab_resize_by") as tab_scale_by:
|
|
scale_by = gr.Slider(minimum=0.05, maximum=4.0, step=0.01, label="Scale", value=1.0, elem_id="img2img_scale")
|
|
|
|
with FormRow():
|
|
scale_by_html = FormHTML(resize_from_to_html(0, 0, 0.0), elem_id="img2img_scale_resolution_preview")
|
|
gr.Slider(label="Unused", elem_id="img2img_unused_scale_by_slider")
|
|
button_update_resize_to = gr.Button(visible=False, elem_id="img2img_update_resize_to")
|
|
|
|
on_change_args = dict(
|
|
fn=resize_from_to_html,
|
|
_js="currentImg2imgSourceResolution",
|
|
inputs=[dummy_component, dummy_component, scale_by],
|
|
outputs=scale_by_html,
|
|
show_progress=False,
|
|
)
|
|
|
|
scale_by.change(**on_change_args)
|
|
button_update_resize_to.click(**on_change_args)
|
|
|
|
def updateWH (img, w, h):
|
|
if img and shared.opts.img2img_autosize == True:
|
|
return img.size[0], img.size[1]
|
|
else:
|
|
return w, h
|
|
|
|
img_sources = [init_img.background, sketch.background, init_img_with_mask.background, inpaint_color_sketch.background, init_img_inpaint]
|
|
for i in img_sources:
|
|
i.change(fn=updateWH, inputs=[i, width, height], outputs=[width, height], show_progress='hidden')
|
|
i.change(**on_change_args)
|
|
|
|
tab_scale_to.select(fn=lambda: 0, inputs=[], outputs=[selected_scale_tab])
|
|
tab_scale_by.select(fn=lambda: 1, inputs=[], outputs=[selected_scale_tab])
|
|
|
|
if opts.dimensions_and_batch_together:
|
|
with gr.Column(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "denoising":
|
|
denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.75, elem_id="img2img_denoising_strength")
|
|
|
|
elif category == "cfg":
|
|
with gr.Row():
|
|
distilled_cfg_scale = gr.Slider(minimum=0.0, maximum=30.0, step=0.1, label='Distilled CFG Scale', value=3.5, elem_id="img2img_distilled_cfg_scale")
|
|
cfg_scale = gr.Slider(minimum=1.0, maximum=30.0, step=0.1, label='CFG Scale', value=7.0, elem_id="img2img_cfg_scale")
|
|
zimage_shift = gr.Slider(minimum=0.0, maximum=30.0, step=0.05, label='Z-Image Shift', value=0.0, elem_id="img2img_zimage_shift")
|
|
image_cfg_scale = gr.Slider(minimum=0, maximum=3.0, step=0.05, label='Image CFG Scale', value=1.5, elem_id="img2img_image_cfg_scale", visible=False)
|
|
cfg_scale.change(lambda x: gr.update(interactive=(x != 1)), inputs=[cfg_scale], outputs=[toprow.negative_prompt], queue=False, show_progress=False)
|
|
|
|
elif category == "checkboxes":
|
|
with FormRow(elem_classes="checkboxes-row", variant="compact"):
|
|
pass
|
|
|
|
elif category == "accordions":
|
|
with gr.Row(elem_id="img2img_accordions", elem_classes="accordions"):
|
|
scripts.scripts_img2img.setup_ui_for_section(category)
|
|
|
|
elif category == "batch":
|
|
if not opts.dimensions_and_batch_together:
|
|
with FormRow(elem_id="img2img_column_batch"):
|
|
batch_count = gr.Slider(minimum=1, step=1, label='Batch count', value=1, elem_id="img2img_batch_count")
|
|
batch_size = gr.Slider(minimum=1, maximum=8, step=1, label='Batch size', value=1, elem_id="img2img_batch_size")
|
|
|
|
elif category == "override_settings":
|
|
with FormRow(elem_id="img2img_override_settings_row") as row:
|
|
override_settings = create_override_settings_dropdown('img2img', row)
|
|
|
|
elif category == "scripts":
|
|
with FormGroup(elem_id="img2img_script_container"):
|
|
custom_inputs = scripts.scripts_img2img.setup_ui()
|
|
|
|
elif category == "inpaint":
|
|
with FormGroup(elem_id="inpaint_controls", visible=False) as inpaint_controls:
|
|
with FormRow():
|
|
mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id="img2img_mask_blur")
|
|
mask_alpha = gr.Slider(label="Mask transparency", visible=False, elem_id="img2img_mask_alpha")
|
|
|
|
with FormRow():
|
|
inpainting_mask_invert = gr.Radio(label='Mask mode', choices=['Inpaint masked', 'Inpaint not masked'], value='Inpaint masked', type="index", elem_id="img2img_mask_mode")
|
|
|
|
with FormRow():
|
|
inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='original', type="index", elem_id="img2img_inpainting_fill")
|
|
|
|
with FormRow():
|
|
with gr.Column():
|
|
inpaint_full_res = gr.Radio(label="Inpaint area", choices=["Whole picture", "Only masked"], type="index", value="Whole picture", elem_id="img2img_inpaint_full_res")
|
|
|
|
with gr.Column(scale=4):
|
|
inpaint_full_res_padding = gr.Slider(label='Only masked padding, pixels', minimum=0, maximum=256, step=4, value=32, elem_id="img2img_inpaint_full_res_padding")
|
|
|
|
if category not in {"accordions"}:
|
|
scripts.scripts_img2img.setup_ui_for_section(category)
|
|
|
|
def select_img2img_tab(tab):
|
|
return gr.update(visible=tab in [2, 3, 4]), gr.update(visible=tab == 3),
|
|
|
|
for i, elem in enumerate(img2img_tabs):
|
|
elem.select(
|
|
fn=lambda tab=i: select_img2img_tab(tab),
|
|
inputs=[],
|
|
outputs=[inpaint_controls, mask_alpha],
|
|
)
|
|
|
|
output_panel = create_output_panel("img2img", opts.outdir_img2img_samples, toprow)
|
|
|
|
submit_img2img_inputs = [
|
|
dummy_component,
|
|
img2img_selected_tab,
|
|
toprow.prompt,
|
|
toprow.negative_prompt,
|
|
toprow.ui_styles.dropdown,
|
|
init_img.background,
|
|
sketch.background,
|
|
sketch.foreground,
|
|
init_img_with_mask.background,
|
|
init_img_with_mask.foreground,
|
|
inpaint_color_sketch.background,
|
|
inpaint_color_sketch.foreground,
|
|
init_img_inpaint,
|
|
init_mask_inpaint,
|
|
mask_blur,
|
|
mask_alpha,
|
|
inpainting_fill,
|
|
batch_count,
|
|
batch_size,
|
|
cfg_scale,
|
|
distilled_cfg_scale,
|
|
zimage_shift,
|
|
image_cfg_scale,
|
|
denoising_strength,
|
|
selected_scale_tab,
|
|
height,
|
|
width,
|
|
scale_by,
|
|
resize_mode,
|
|
inpaint_full_res,
|
|
inpaint_full_res_padding,
|
|
inpainting_mask_invert,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
img2img_batch_inpaint_mask_dir,
|
|
override_settings,
|
|
img2img_batch_use_png_info,
|
|
img2img_batch_png_info_props,
|
|
img2img_batch_png_info_dir,
|
|
img2img_batch_source_type,
|
|
img2img_batch_upload,
|
|
] + custom_inputs
|
|
|
|
img2img_args = dict(
|
|
fn=wrap_gradio_gpu_call(modules.img2img.img2img, extra_outputs=[None, '', '']),
|
|
_js="submit_img2img",
|
|
inputs=submit_img2img_inputs,
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
interrogate_args = dict(
|
|
_js="get_img2img_tab_index",
|
|
inputs=[
|
|
dummy_component,
|
|
img2img_batch_input_dir,
|
|
img2img_batch_output_dir,
|
|
init_img.background,
|
|
sketch.background,
|
|
init_img_with_mask.background,
|
|
inpaint_color_sketch.background,
|
|
init_img_inpaint,
|
|
],
|
|
outputs=[toprow.prompt, dummy_component],
|
|
)
|
|
|
|
toprow.prompt.submit(**img2img_args)
|
|
toprow.submit.click(**img2img_args)
|
|
|
|
res_switch_btn.click(lambda w, h: (h, w), inputs=[width, height], outputs=[width, height], show_progress=False)
|
|
|
|
detect_image_size_btn.click(
|
|
fn=lambda w, h: (w or gr.update(), h or gr.update()),
|
|
_js="currentImg2imgSourceResolution",
|
|
inputs=[dummy_component, dummy_component],
|
|
outputs=[width, height],
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.restore_progress_button.click(
|
|
fn=progress.restore_progress,
|
|
_js="restoreProgressImg2img",
|
|
inputs=[dummy_component],
|
|
outputs=[
|
|
output_panel.gallery,
|
|
output_panel.generation_info,
|
|
output_panel.infotext,
|
|
output_panel.html_log,
|
|
],
|
|
show_progress=False,
|
|
)
|
|
|
|
toprow.button_interrogate.click(
|
|
fn=lambda *args: process_interrogate(interrogate, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
toprow.button_deepbooru.click(
|
|
fn=lambda *args: process_interrogate(interrogate_deepbooru, *args),
|
|
**interrogate_args,
|
|
)
|
|
|
|
steps = scripts.scripts_img2img.script('Sampler').steps
|
|
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_token_counter), inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.ui_styles.dropdown.change(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
toprow.token_button.click(fn=update_token_counter, inputs=[toprow.prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.token_counter])
|
|
toprow.negative_token_button.click(fn=wrap_queued_call(update_negative_prompt_token_counter), inputs=[toprow.negative_prompt, steps, toprow.ui_styles.dropdown], outputs=[toprow.negative_token_counter])
|
|
|
|
# Connect expand prompt buttons for Prompt Expansion accordion (img2img with image context)
|
|
# Expand Positive Prompt button - uses image from img2img
|
|
toprow.expand_positive_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[
|
|
toprow.prompt,
|
|
init_img.background, # Image context from img2img
|
|
toprow.llm_model_dropdown,
|
|
toprow.positive_system_prompt,
|
|
gr.State(False), # is_negative=False
|
|
gr.State(None) # positive_prompt (not needed for positive expansion)
|
|
],
|
|
outputs=[toprow.prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
# Expand Negative Prompt button - uses image from img2img and positive prompt as context
|
|
toprow.expand_negative_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[
|
|
toprow.negative_prompt,
|
|
init_img.background, # Image context from img2img
|
|
toprow.llm_model_dropdown,
|
|
toprow.negative_system_prompt,
|
|
gr.State(True), # is_negative=True
|
|
toprow.prompt # Pass positive prompt as context
|
|
],
|
|
outputs=[toprow.negative_prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
# Legacy expand prompt button (hidden, but kept for backward compatibility)
|
|
toprow.expand_prompt_button.click(
|
|
fn=expand_prompt_with_llm,
|
|
inputs=[toprow.prompt, init_img.background],
|
|
outputs=[toprow.prompt],
|
|
show_progress=True,
|
|
)
|
|
|
|
img2img_paste_fields = [
|
|
(toprow.prompt, "Prompt"),
|
|
(toprow.negative_prompt, "Negative prompt"),
|
|
(cfg_scale, "CFG scale"),
|
|
(distilled_cfg_scale, "Distilled CFG Scale"),
|
|
(image_cfg_scale, "Image CFG scale"),
|
|
(width, "Size-1"),
|
|
(height, "Size-2"),
|
|
(batch_size, "Batch size"),
|
|
(toprow.ui_styles.dropdown, lambda d: d["Styles array"] if isinstance(d.get("Styles array"), list) else gr.update()),
|
|
(denoising_strength, "Denoising strength"),
|
|
(mask_blur, "Mask blur"),
|
|
(inpainting_mask_invert, 'Mask mode'),
|
|
(inpainting_fill, 'Masked content'),
|
|
(inpaint_full_res, 'Inpaint area'),
|
|
(inpaint_full_res_padding, 'Masked area padding'),
|
|
*scripts.scripts_img2img.infotext_fields
|
|
]
|
|
parameters_copypaste.add_paste_fields("img2img", init_img.background, img2img_paste_fields, override_settings)
|
|
parameters_copypaste.add_paste_fields("inpaint", init_img_with_mask.background, img2img_paste_fields, override_settings)
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=toprow.paste, tabname="img2img", source_text_component=toprow.prompt, source_image_component=None,
|
|
))
|
|
|
|
extra_networks_ui_img2img = ui_extra_networks.create_ui(img2img_interface, [img2img_generation_tab], 'img2img')
|
|
ui_extra_networks.setup_ui(extra_networks_ui_img2img, output_panel.gallery)
|
|
|
|
extra_tabs.__exit__()
|
|
|
|
with gr.Blocks(analytics_enabled=False, head=canvas_head) as space_interface:
|
|
forge_space.main_entry()
|
|
|
|
scripts.scripts_current = None
|
|
|
|
with gr.Blocks(analytics_enabled=False) as extras_interface:
|
|
ui_postprocessing.create_ui()
|
|
|
|
with gr.Blocks(analytics_enabled=False) as pnginfo_interface:
|
|
with ResizeHandleRow(equal_height=False):
|
|
with gr.Column(variant='panel'):
|
|
image = gr.Image(elem_id="pnginfo_image", label="Source", source="upload", interactive=True, type="pil", height="50vh", image_mode="RGBA")
|
|
|
|
with gr.Column(variant='panel'):
|
|
html = gr.HTML()
|
|
generation_info = gr.Textbox(visible=False, elem_id="pnginfo_generation_info")
|
|
html2 = gr.HTML()
|
|
with gr.Row():
|
|
buttons = parameters_copypaste.create_buttons(["txt2img", "img2img", "inpaint", "extras"])
|
|
|
|
for tabname, button in buttons.items():
|
|
parameters_copypaste.register_paste_params_button(parameters_copypaste.ParamBinding(
|
|
paste_button=button, tabname=tabname, source_text_component=generation_info, source_image_component=image,
|
|
))
|
|
|
|
image.change(
|
|
fn=wrap_gradio_call_no_job(modules.extras.run_pnginfo),
|
|
inputs=[image],
|
|
outputs=[html, generation_info, html2],
|
|
)
|
|
|
|
modelmerger_ui = ui_checkpoint_merger.UiCheckpointMerger()
|
|
|
|
loadsave = ui_loadsave.UiLoadsave(cmd_opts.ui_config_file)
|
|
ui_settings_from_file = loadsave.ui_settings.copy()
|
|
|
|
settings.create_ui(loadsave, dummy_component)
|
|
|
|
interfaces = [
|
|
(txt2img_interface, "Txt2img", "txt2img"),
|
|
(img2img_interface, "Img2img", "img2img"),
|
|
(space_interface, "Spaces", "space"),
|
|
(extras_interface, "Extras", "extras"),
|
|
(pnginfo_interface, "PNG Info", "pnginfo"),
|
|
(modelmerger_ui.blocks, "Checkpoint Merger", "modelmerger"),
|
|
]
|
|
|
|
interfaces += script_callbacks.ui_tabs_callback()
|
|
interfaces += [(settings.interface, "Settings", "settings")]
|
|
|
|
extensions_interface = ui_extensions.create_ui()
|
|
interfaces += [(extensions_interface, "Extensions", "extensions")]
|
|
|
|
shared.tab_names = []
|
|
for _interface, label, _ifid in interfaces:
|
|
shared.tab_names.append(label)
|
|
|
|
with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion", head=canvas_head) as demo:
|
|
quicksettings_row = settings.add_quicksettings()
|
|
|
|
parameters_copypaste.connect_paste_params_buttons()
|
|
|
|
with gr.Tabs(elem_id="tabs") as tabs:
|
|
tab_order = {k: i for i, k in enumerate(opts.ui_tab_order)}
|
|
sorted_interfaces = sorted(interfaces, key=lambda x: tab_order.get(x[1], 9999))
|
|
|
|
for interface, label, ifid in sorted_interfaces:
|
|
if label in shared.opts.hidden_tabs:
|
|
continue
|
|
with gr.TabItem(label, id=ifid, elem_id=f"tab_{ifid}"):
|
|
interface.render()
|
|
|
|
if ifid not in ["extensions", "settings"]:
|
|
loadsave.add_block(interface, ifid)
|
|
|
|
loadsave.add_component(f"webui/Tabs@{tabs.elem_id}", tabs)
|
|
|
|
loadsave.setup_ui()
|
|
|
|
def tab_changed(evt: gr.SelectData):
|
|
no_quick_setting = getattr(shared.opts, "tabs_without_quick_settings_bar", [])
|
|
return gr.update(visible=evt.value not in no_quick_setting)
|
|
|
|
tabs.select(tab_changed, outputs=[quicksettings_row], show_progress=False, queue=False)
|
|
|
|
if os.path.exists(os.path.join(script_path, "notification.mp3")) and shared.opts.notification_audio:
|
|
gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
|
|
|
|
footer = shared.html("footer.html")
|
|
footer = footer.format(versions=versions_html(), api_docs="/docs" if shared.cmd_opts.api else "https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/API")
|
|
gr.HTML(footer, elem_id="footer")
|
|
|
|
settings.add_functionality(demo)
|
|
|
|
update_image_cfg_scale_visibility = lambda: gr.update(visible=False)
|
|
settings.text_settings.change(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
|
demo.load(fn=update_image_cfg_scale_visibility, inputs=[], outputs=[image_cfg_scale])
|
|
|
|
modelmerger_ui.setup_ui(dummy_component=dummy_component, sd_model_checkpoint_component=main_entry.ui_checkpoint)
|
|
|
|
main_entry.forge_main_entry()
|
|
|
|
if ui_settings_from_file != loadsave.ui_settings:
|
|
loadsave.dump_defaults()
|
|
demo.ui_loadsave = loadsave
|
|
|
|
return demo
|
|
|
|
|
|
def versions_html():
|
|
import torch
|
|
import launch
|
|
|
|
python_version = ".".join([str(x) for x in sys.version_info[0:3]])
|
|
commit = launch.commit_hash()
|
|
tag = launch.git_tag()
|
|
|
|
if shared.xformers_available:
|
|
import xformers
|
|
xformers_version = xformers.__version__
|
|
else:
|
|
xformers_version = "N/A"
|
|
|
|
return f"""
|
|
version: <a href="https://github.com/lllyasviel/stable-diffusion-webui-forge/commit/{commit}">{tag}</a>
|
|
 • 
|
|
python: <span title="{sys.version}">{python_version}</span>
|
|
 • 
|
|
torch: {getattr(torch, '__long_version__',torch.__version__)}
|
|
 • 
|
|
xformers: {xformers_version}
|
|
 • 
|
|
gradio: {gr.__version__}
|
|
 • 
|
|
checkpoint: <a id="sd_checkpoint_hash">N/A</a>
|
|
"""
|
|
|
|
|
|
def setup_ui_api(app):
|
|
from pydantic import BaseModel, Field
|
|
|
|
class QuicksettingsHint(BaseModel):
|
|
name: str = Field(title="Name of the quicksettings field")
|
|
label: str = Field(title="Label of the quicksettings field")
|
|
|
|
def quicksettings_hint():
|
|
return [QuicksettingsHint(name=k, label=v.label) for k, v in opts.data_labels.items()]
|
|
|
|
app.add_api_route("/internal/quicksettings-hint", quicksettings_hint, methods=["GET"], response_model=list[QuicksettingsHint])
|
|
|
|
app.add_api_route("/internal/ping", lambda: {}, methods=["GET"])
|
|
|
|
app.add_api_route("/internal/profile-startup", lambda: timer.startup_record, methods=["GET"])
|
|
|
|
def download_sysinfo(attachment=False):
|
|
from fastapi.responses import PlainTextResponse
|
|
|
|
text = sysinfo.get()
|
|
filename = f"sysinfo-{datetime.datetime.now(datetime.timezone.utc).strftime('%Y-%m-%d-%H-%M')}.json"
|
|
|
|
return PlainTextResponse(text, headers={'Content-Disposition': f'{"attachment" if attachment else "inline"}; filename="{filename}"'})
|
|
|
|
app.add_api_route("/internal/sysinfo", download_sysinfo, methods=["GET"])
|
|
app.add_api_route("/internal/sysinfo-download", lambda: download_sysinfo(attachment=True), methods=["GET"])
|
|
|
|
import fastapi.staticfiles
|
|
app.mount("/webui-assets", fastapi.staticfiles.StaticFiles(directory=launch_utils.repo_dir('stable-diffusion-webui-assets')), name="webui-assets")
|