本文基于webUI API编写了类似于webUI的Gradio交互式界面，支持文生图/图生图（SD1.x，SD2.x，SDXL），Embedding，Lora，X/Y/Z Plot，ADetailer、ControlNet，超分放大（Extras），图片信息读取（PNG Info）。
1. 在线体验
本文代码已部署到百度飞桨AI Studio平台，以供大家在线体验Stable Diffusion ComfyUI/webUI 原版界面及自制Gradio界面。
项目链接：Stable Diffusion webUI 在线体验
2. 自制Gradio界面展示
文生图界面：
Adetailer 设置界面：
ControlNet 设置界面：
X/Y/Z Plot 设置界面：
图生图界面：
图片放大界面：
图片信息读取界面：
3. Gradio界面设计及webUI API调用
import base64import datetimeimport ioimport osimport reimport subprocessimport gradio as grimport requestsfrom PIL import Image, PngImagePlugindesign_mode = 1save_images = "Yes"url = "http://127.0.0.1:7860"if design_mode == 0: cmd = "netstat -tulnp" netstat_output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout.splitlines() for i in netstat_output: if "stable-diffus" in i: port = int(re.findall(r'\d+', i)[6]) url = f"http://127.0.0.1:{port}"output_dir = os.getcwd() + "/output/" + datetime.date.today().strftime("%Y-%m-%d")os.makedirs(output_dir, exist_ok=True)os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"default = { "prompt": "(best quality:1), (high quality:1), detailed/(extreme, highly, ultra/), realistic, 1girl/(beautiful, delicate, perfect/)", "negative_prompt": "(worst quality:1), (low quality:1), (normal quality:1), lowres, signature, blurry, watermark, duplicate, bad link, plump, bad anatomy, extra arms, extra digits, missing finger, bad hands, bad feet, deformed, error, mutation, text", "clip_skip": 1, "width": 512, "height": 768, "size_step": 64, "steps": 20, "cfg": 7, "ad_nums": 2, "ad_model": ["face_yolov8n.pt", "hand_yolov8n.pt"], "cn_nums": 3, "cn_type": "Canny", "gallery_height": 600, "lora_weight": 0.8, "hidden_models": ["stable_cascade_stage_c", "stable_cascade_stage_b", "svd_xt_1_1", "control_v11p_sd15_canny", "control_v11f1p_sd15_depth", "control_v11p_sd15_openpose"]}samplers = []response = requests.get(url=f"{url}/sdapi/v1/samplers").json()for i in range(len(response)): samplers.append(response[i]["name"])schedulers = []response = requests.get(url=f"{url}/sdapi/v1/schedulers").json()for i in range(len(response)): schedulers.append(response[i]["label"])upscalers = []response = requests.get(url=f"{url}/sdapi/v1/upscalers").json()for i in range(len(response)): upscalers.append(response[i]["name"])sd_models = []sd_models_list = {}response = requests.get(url=f"{url}/sdapi/v1/sd-models").json()for i in range(len(response)): path, sd_model = os.path.split(response[i]["title"]) sd_model_name, sd_model_extension = os.path.splitext(sd_model) if not sd_model_name in default["hidden_models"]: sd_models.append(sd_model) sd_models_list[sd_model] = response[i]["title"]sd_models = sorted(sd_models)sd_vaes = ["Automatic", "None"]response = requests.get(url=f"{url}/sdapi/v1/sd-vae").json()for i in range(len(response)): sd_vaes.append(response[i]["model_name"])embeddings = []response = requests.get(url=f"{url}/sdapi/v1/embeddings").json()for key in response["loaded"]: embeddings.append(key)extensions = []response = requests.get(url=f"{url}/sdapi/v1/extensions").json()for i in range(len(response)): extensions.append(response[i]["name"])loras = []loras_name = {}loras_activation_text = {}response = requests.get(url=f"{url}/sdapi/v1/loras").json()for i in range(len(response)): lora_name = response[i]["name"] lora_info = requests.get(url=f"{url}/tacapi/v1/lora-info/{lora_name}").json() if lora_info and "sd version" in lora_info: lora_type = lora_info["sd version"] lora_name_type = f"{lora_name} ({lora_type})" else: lora_name_type = f"{lora_name}" loras.append(lora_name_type) loras_name[lora_name_type] = lora_name if "activation text" in loras_activation_text: loras_activation_text[lora_name_type] = lora_info["activation text"]xyz_args = {}xyz_plot_types = {}last_choice = "Size"response = requests.get(url=f"{url}/sdapi/v1/script-info").json()for i in range(len(response)): if response[i]["name"] == "x/y/z plot": if response[i]["is_img2img"] == False: xyz_plot_types["txt2img"] = response[i]["args"][0]["choices"] choice_index = xyz_plot_types["txt2img"].index(last_choice) + 1 xyz_plot_types["txt2img"] = xyz_plot_types["txt2img"][:choice_index] else: xyz_plot_types["img2img"] = response[i]["args"][0]["choices"] choice_index = xyz_plot_types["img2img"].index(last_choice) + 1 xyz_plot_types["img2img"] = xyz_plot_types["img2img"][:choice_index]if "adetailer" in extensions: ad_args = {"txt2img": {}, "img2img": {}} ad_skip_img2img = False ad_models = ["None"] response = requests.get(url=f"{url}/adetailer/v1/ad_model").json() for key in response["ad_model"]: ad_models.append(key)if "sd-webui-controlnet" in extensions: cn_args = {"txt2img": {}, "img2img": {}} cn_types = [] cn_types_list = {} response = requests.get(url=f"{url}/controlnet/control_types").json() for key in response["control_types"]: cn_types.append(key) cn_types_list[key] = response["control_types"][key] cn_default_type = default["cn_type"] cn_module_list = cn_types_list[cn_default_type]["module_list"] cn_model_list = cn_types_list[cn_default_type]["model_list"] cn_default_option = cn_types_list[cn_default_type]["default_option"] cn_default_model = cn_types_list[cn_default_type]["default_model"]def save_image(image, part1, part2): counter = 1 image_name = f"{part1}-{part2}-{counter}.png" while os.path.exists(os.path.join(output_dir, image_name)): counter += 1 image_name = f"{part1}-{part2}-{counter}.png" image_path = os.path.join(output_dir, image_name) image_metadata = PngImagePlugin.PngInfo() for key, value in image.info.items(): if isinstance(key, str) and isinstance(value, str): image_metadata.add_text(key, value) image.save(image_path, format="PNG", pnginfo=image_metadata)def pil_to_base64(image_pil): buffer = io.BytesIO() image_pil.save(buffer, format="png") image_buffer = buffer.getbuffer() image_base64 = base64.b64encode(image_buffer).decode("utf-8") return image_base64def base64_to_pil(image_base64): image_binary = base64.b64decode(image_base64) image_pil = Image.open(io.BytesIO(image_binary)) return image_pildef format_prompt(prompt): prompt = re.sub(r"\s+,", ",", prompt) prompt = re.sub(r"\s+", " ", prompt) prompt = re.sub(",,+", ",", prompt) prompt = re.sub(",", ", ", prompt) prompt = re.sub(r"\s+", " ", prompt) prompt = re.sub(r"^,", "", prompt) prompt = re.sub(r"^ ", "", prompt) prompt = re.sub(r" $", "", prompt) prompt = re.sub(r",$", "", prompt) prompt = re.sub(": ", ":", prompt) return promptdef post_interrupt(): global interrupt interrupt = True requests.post(url=f"{url}/sdapi/v1/interrupt").json()def gr_update_visible(visible): return gr.update(visible=visible)def ordinal(n: int) -> str: d = {1: "st", 2: "nd", 3: "rd"} return str(n) + ("th" if 11 <= n % 100 <= 13 else d.get(n % 10, "th"))def add_lora(prompt, lora): lora_weight = default["lora_weight"] prompt = re.sub(r"<[^<>]+>", "", prompt) for elem in loras_activation_text: prompt = re.sub(loras_activation_text[elem], "", prompt) prompt = format_prompt(prompt) for elem in lora: lora_name = loras_name[elem] if elem in loras_activation_text: lora_activation_text = loras_activation_text[elem] else: lora_activation_text = "" if lora_activation_text == "": prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}>" else: prompt = f"{prompt}, <lora:{lora_name}:{lora_weight}> {lora_activation_text}" return promptdef add_embedding(negative_prompt, embedding): for elem in embeddings: negative_prompt = re.sub(f"{elem},", "", negative_prompt) negative_prompt = format_prompt(negative_prompt) for elem in embedding[::-1]: negative_prompt = f"{elem}, {negative_prompt}" return negative_promptdef add_xyz_plot(payload, gen_type): global xyz_args if gen_type in xyz_args: payload["script_name"] = "X/Y/Z plot" payload["script_args"] = xyz_args[gen_type] return payloaddef xyz_update_args(*args): gen_type, enable_xyz_plot, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode = args global xyz_args x_type = xyz_plot_types[gen_type].index(x_type) y_type = xyz_plot_types[gen_type].index(y_type) z_type = xyz_plot_types[gen_type].index(z_type) args = [x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode] if enable_xyz_plot == True: xyz_args[gen_type] = args else: del xyz_args[gen_type]def xyz_update_choices(xyz_type): choices = [] if xyz_type == "Checkpoint name": choices = sd_models if xyz_type == "VAE": choices = sd_vaes if xyz_type == "Sampler": choices = samplers if xyz_type == "Schedule type": choices = schedulers if xyz_type == "Hires sampler": choices = samplers if xyz_type == "Hires upscaler": choices = upscalers if xyz_type == "Always discard next-to-last sigma": choices = ["False", "True"] if xyz_type == "SGM noise multiplier": choices = ["False", "True"] if xyz_type == "Refiner checkpoint": choices = sd_models if xyz_type == "RNG source": choices = ["GPU", "CPU", "NV"] if xyz_type == "FP8 mode": choices = ["Disable", "Enable for SDXL", "Enable"] if choices == []: return gr.update(visible=True, value=None), gr.update(visible=False) else: return gr.update(visible=False), gr.update(visible=True, choices=choices)def xyz_blocks(gen_type): with gr.Blocks() as demo: with gr.Row(): xyz_gen_type = gr.Textbox(visible=False, value=gen_type) enable_xyz_plot = gr.Checkbox(label="Enable") with gr.Row(): x_type = gr.Dropdown(xyz_plot_types[gen_type], label="X type", value=xyz_plot_types[gen_type][1]) x_values = gr.Textbox(label="X values", lines=1) x_values_dropdown = gr.Dropdown(label="X values", visible=False, multiselect=True, interactive=True) with gr.Row(): y_type = gr.Dropdown(xyz_plot_types[gen_type], label="Y type", value=xyz_plot_types[gen_type][0]) y_values = gr.Textbox(label="Y values", lines=1) y_values_dropdown = gr.Dropdown(label="Y values", visible=False, multiselect=True, interactive=True) with gr.Row(): z_type = gr.Dropdown(xyz_plot_types[gen_type], label="Z type", value=xyz_plot_types[gen_type][0]) z_values = gr.Textbox(label="Z values", lines=1) z_values_dropdown = gr.Dropdown(label="Z values", visible=False, multiselect=True, interactive=True) with gr.Row(): with gr.Column(): draw_legend = gr.Checkbox(label='Draw legend', value=True) no_fixed_seeds = gr.Checkbox(label='Keep -1 for seeds', value=False) vary_seeds_x = gr.Checkbox(label='Vary seeds for X', value=False) vary_seeds_y = gr.Checkbox(label='Vary seeds for Y', value=False) vary_seeds_z = gr.Checkbox(label='Vary seeds for Z', value=False) with gr.Column(): include_lone_images = gr.Checkbox(label='Include Sub Images', value=True) include_sub_grids = gr.Checkbox(label='Include Sub Grids', value=False) csv_mode = gr.Checkbox(label='Use text inputs instead of dropdowns', value=False) margin_size = gr.Slider(label="Grid margins (px)", minimum=0, maximum=500, value=0, step=2) x_type.change(fn=xyz_update_choices, inputs=x_type, outputs=[x_values, x_values_dropdown]) y_type.change(fn=xyz_update_choices, inputs=y_type, outputs=[y_values, y_values_dropdown]) z_type.change(fn=xyz_update_choices, inputs=z_type, outputs=[z_values, z_values_dropdown]) xyz_inputs = [xyz_gen_type, enable_xyz_plot, x_type, x_values, x_values_dropdown, y_type, y_values, y_values_dropdown, z_type, z_values, z_values_dropdown, draw_legend, include_lone_images, include_sub_grids, no_fixed_seeds, vary_seeds_x, vary_seeds_y, vary_seeds_z, margin_size, csv_mode] for gr_block in xyz_inputs: if type(gr_block) is gr.components.slider.Slider: gr_block.release(fn=xyz_update_args, inputs=xyz_inputs, outputs=None) else: gr_block.change(fn=xyz_update_args, inputs=xyz_inputs, outputs=None) return demodef add_adetailer(payload, gen_type): global ad_args, ad_skip_img2img args = ad_args[gen_type] args = dict(sorted(args.items(), key=lambda x: x[0])) payload["alwayson_scripts"]["adetailer"] = {"args": []} if args == {}: return payload if gen_type == "img2img": payload["alwayson_scripts"]["adetailer"]["args"] = [True, ad_skip_img2img] else: payload["alwayson_scripts"]["adetailer"]["args"] = [True, False] for i in args: payload["alwayson_scripts"]["adetailer"]["args"].append(args[i]) return payloaddef ad_update_args(*args): if "sd-webui-controlnet" in extensions: ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face, ad_controlnet_model, ad_controlnet_module, ad_controlnet_weight, ad_controlnet_guidance_start, ad_controlnet_guidance_end = args else: ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face = args global ad_args args = { "ad_model": ad_model, "ad_model_classes": "", "ad_prompt": ad_prompt, "ad_negative_prompt": ad_negative_prompt, "ad_confidence": ad_confidence, "ad_mask_k_largest": ad_mask_k_largest, "ad_mask_min_ratio": ad_mask_min_ratio, "ad_mask_max_ratio": ad_mask_max_ratio, "ad_dilate_erode": ad_dilate_erode, "ad_x_offset": ad_x_offset, "ad_y_offset": ad_y_offset, "ad_mask_merge_invert": ad_mask_merge_invert, "ad_mask_blur": ad_mask_blur, "ad_denoising_strength": ad_denoising_strength, "ad_inpaint_only_masked": ad_inpaint_only_masked, "ad_inpaint_only_masked_padding": ad_inpaint_only_masked_padding, "ad_use_inpaint_width_height": ad_use_inpaint_width_height, "ad_inpaint_width": ad_inpaint_width, "ad_inpaint_height": ad_inpaint_height, "ad_use_steps": ad_use_steps, "ad_steps": ad_steps, "ad_use_cfg_scale": ad_use_cfg_scale, "ad_cfg_scale": ad_cfg_scale, "ad_use_checkpoint": ad_use_checkpoint, "ad_checkpoint": ad_checkpoint, "ad_use_vae": ad_use_vae, "ad_vae": ad_vae, "ad_use_sampler": ad_use_sampler, "ad_sampler": ad_sampler, "ad_scheduler": ad_scheduler, "ad_use_noise_multiplier": ad_use_noise_multiplier, "ad_noise_multiplier": ad_noise_multiplier, "ad_use_clip_skip": ad_use_clip_skip, "ad_clip_skip": ad_clip_skip, "ad_restore_face": ad_restore_face, } if "sd-webui-controlnet" in extensions: args["ad_controlnet_model"] = ad_controlnet_model args["ad_controlnet_module"] = ad_controlnet_module args["ad_controlnet_weight"] = ad_controlnet_weight args["ad_controlnet_guidance_start"] = ad_controlnet_guidance_start args["ad_controlnet_guidance_end"] = ad_controlnet_guidance_end if enable_ad == True: ad_args[ad_gen_type][ad_num] = args else: del ad_args[ad_gen_type][ad_num]def ad_update_cn_module_choices(ad_controlnet_model): if ad_controlnet_model == "control_v11f1p_sd15_depth [1a8eb83c]": return gr.update(choices=["depth_midas", "depth_hand_refiner"], visible=True, value="depth_midas") if ad_controlnet_model == "control_v11p_sd15_inpaint [dfe64acb]": return gr.update(choices=["inpaint_global_harmonious", "inpaint_only", "inpaint_only+lama"], visible=True, value="inpaint_global_harmonious") if ad_controlnet_model == "control_v11p_sd15_lineart [2c3004a6]": return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse") if ad_controlnet_model == "control_v11p_sd15_openpose [52e0ea54]": return gr.update(choices=["openpose_full", "dw_openpose_full"], visible=True, value="openpose_full") if ad_controlnet_model == "control_v11p_sd15_scribble [46a6fcd7]": return gr.update(choices=["t2ia_sketch_pidi"], visible=True, value="t2ia_sketch_pidi") if ad_controlnet_model == "control_v11p_sd15s2_lineart_anime [19a26aa8]": return gr.update(choices=["lineart_coarse", "lineart_realistic", "lineart_anime", "lineart_anime_denoise"], visible=True, value="lineart_coarse") return gr.update(visible=False)def ad_update_skip_img2img(arg): global ad_skip_img2img ad_skip_img2img = argdef ad_blocks(i, gen_type): with gr.Blocks() as demo: ad_gen_type = gr.Textbox(visible=False, value=gen_type) ad_num = gr.Textbox(visible=False, value=i) enable_ad = gr.Checkbox(label="Enable") ad_model = gr.Dropdown(ad_models, label="ADetailer model", value=default["ad_model"][i]) ad_prompt = gr.Textbox(show_label=False, placeholder="ADetailer prompt" + "\nIf blank, the main prompt is used.", lines=3) ad_negative_prompt = gr.Textbox(show_label=False, placeholder="ADetailer negative prompt" + "\nIf blank, the main negative prompt is used.", lines=3) with gr.Tab("Detection"): with gr.Row(): ad_confidence = gr.Slider(label="Detection model confidence threshold", minimum=0, maximum=1, step=0.01, value=0.3) ad_mask_min_ratio = gr.Slider(label="Mask min area ratio", minimum=0, maximum=1, step=0.001, value=0) with gr.Row(): ad_mask_k_largest = gr.Slider(label="Mask only the top k largest (0 to disable)", minimum=0, maximum=10, step=1, value=0) ad_mask_max_ratio = gr.Slider(label="Mask max area ratio", minimum=0, maximum=1, step=0.001, value=1) with gr.Tab("Mask Preprocessing"): with gr.Row(): ad_x_offset = gr.Slider(label="Mask x(→) offset", minimum=-200, maximum=200, step=1, value=0) ad_y_offset = gr.Slider(label="Mask y(↑) offset", minimum=-200, maximum=200, step=1, value=0) ad_dilate_erode = gr.Slider(label="Mask erosion (-) / dilation (+)", minimum=-128, maximum=128, step=4, value=4) ad_mask_merge_invert = gr.Radio(["None", "Merge", "Merge and Invert"], label="Mask merge mode", value="None") with gr.Tab("Inpainting"): with gr.Row(): ad_mask_blur = gr.Slider(label="Inpaint mask blur", minimum=0, maximum=64, step=1, value=4) ad_denoising_strength = gr.Slider(label="Inpaint denoising strength", minimum=0, maximum=1, step=0.01, value=0.4) with gr.Row(): ad_inpaint_only_masked = gr.Checkbox(label="Inpaint only masked", value=True) ad_use_inpaint_width_height = gr.Checkbox(label="Use separate width/height") with gr.Row(): ad_inpaint_only_masked_padding = gr.Slider(label="Inpaint only masked padding, pixels", minimum=0, maximum=256, step=4, value=32) with gr.Column(): ad_inpaint_width = gr.Slider(label="inpaint width", minimum=64, maximum=2048, step=default["size_step"], value=512) ad_inpaint_height = gr.Slider(label="inpaint height", minimum=64, maximum=2048, step=default["size_step"], value=512) with gr.Row(): ad_use_steps = gr.Checkbox(label="Use separate steps") ad_use_cfg_scale = gr.Checkbox(label="Use separate CFG scale") with gr.Row(): ad_steps = gr.Slider(label="ADetailer steps", minimum=1, maximum=150, step=1, value=28) ad_cfg_scale = gr.Slider(label="ADetailer CFG scale", minimum=0, maximum=30, step=0.5, value=7) with gr.Row(): ad_use_checkpoint = gr.Checkbox(label="Use separate checkpoint") ad_use_vae = gr.Checkbox(label="Use separate VAE") with gr.Row(): ckpts = ["Use same checkpoint"] for model in sd_models: ckpts.append(model) ad_checkpoint = gr.Dropdown(ckpts, label="ADetailer checkpoint", value=ckpts[0]) vaes = ["Use same VAE"] for vae in sd_vaes: vaes.append(vae) ad_vae = gr.Dropdown(vaes, label="ADetailer VAE", value=vaes[0]) ad_use_sampler = gr.Checkbox(label="Use separate sampler") with gr.Row(): ad_sampler = gr.Dropdown(samplers, label="ADetailer sampler", value=samplers[0]) scheduler_names = ["Use same scheduler"] for scheduler in schedulers: scheduler_names.append(scheduler) ad_scheduler = gr.Dropdown(scheduler_names, label="ADetailer scheduler", value=scheduler_names[0]) with gr.Row(): ad_use_noise_multiplier = gr.Checkbox(label="Use separate noise multiplier") ad_use_clip_skip = gr.Checkbox(label="Use separate CLIP skip") with gr.Row(): ad_noise_multiplier = gr.Slider(label="Noise multiplier for img2img", minimum=0.5, maximum=1.5, step=0.01, value=1) ad_clip_skip = gr.Slider(label="ADetailer CLIP skip", minimum=1, maximum=12, step=1, value=1) ad_restore_face = gr.Checkbox(label="Restore faces after ADetailer") if "sd-webui-controlnet" in extensions: with gr.Tab("ControlNet"): with gr.Row(): ad_cn_models = ["None", "Passthrough", "control_v11f1p_sd15_depth [1a8eb83c]", "control_v11p_sd15_inpaint [dfe64acb]", "control_v11p_sd15_lineart [2c3004a6]", "control_v11p_sd15_openpose [52e0ea54]", "control_v11p_sd15_scribble [46a6fcd7]", "control_v11p_sd15s2_lineart_anime [19a26aa8]"] ad_controlnet_model = gr.Dropdown(ad_cn_models, label="ControlNet model", value="None") ad_controlnet_module = gr.Dropdown(["None"], label="ControlNet module", value="None", visible=False) ad_controlnet_model.change(fn= ad_update_cn_module_choices, inputs=ad_controlnet_model, outputs=ad_controlnet_module) with gr.Row(): ad_controlnet_weight = gr.Slider(label="Control Weight", minimum=0, maximum=1, step=0.01, value=1) ad_controlnet_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0) ad_controlnet_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1) if "sd-webui-controlnet" in extensions: ad_inputs = [ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face, ad_controlnet_model, ad_controlnet_module, ad_controlnet_weight, ad_controlnet_guidance_start, ad_controlnet_guidance_end] else: ad_inputs = [ad_gen_type, ad_num, enable_ad, ad_model, ad_prompt, ad_negative_prompt, ad_confidence, ad_mask_min_ratio, ad_mask_k_largest, ad_mask_max_ratio, ad_x_offset, ad_y_offset, ad_dilate_erode, ad_mask_merge_invert, ad_mask_blur, ad_denoising_strength, ad_inpaint_only_masked, ad_use_inpaint_width_height, ad_inpaint_only_masked_padding, ad_inpaint_width, ad_inpaint_height, ad_use_steps, ad_use_cfg_scale, ad_steps, ad_cfg_scale, ad_use_checkpoint, ad_use_vae, ad_checkpoint, ad_vae, ad_use_sampler, ad_sampler, ad_scheduler, ad_use_noise_multiplier, ad_use_clip_skip, ad_noise_multiplier, ad_clip_skip, ad_restore_face] for gr_block in ad_inputs: if type(gr_block) is gr.components.slider.Slider: gr_block.release(fn=ad_update_args, inputs=ad_inputs, outputs=None) else: gr_block.change(fn=ad_update_args, inputs=ad_inputs, outputs=None) return demodef add_controlnet(payload, gen_type): global cn_args args = cn_args[gen_type] args = dict(sorted(args.items(), key=lambda x: x[0])) payload["alwayson_scripts"]["controlnet"] = {"args": []} if args == {}: return payload for i in args: payload["alwayson_scripts"]["controlnet"]["args"].append(args[i]) return payloaddef cn_preprocess(cn_module, cn_input_image): if cn_input_image is None: return None cn_input_image = pil_to_base64(cn_input_image) payload = { "controlnet_module": cn_module, "controlnet_input_images": [cn_input_image] } response = requests.post(url=f"{url}/controlnet/detect", json=payload) images_base64 = response.json()["images"][0] image_pil = base64_to_pil(images_base64) if save_images == "Yes": save_image(image_pil, "ControlNet", "detect") return image_pildef cn_update_args(*args): cn_gen_type, cn_num, enable_cn, enable_low_vram, enable_pixel_perfect, cn_module, cn_model, cn_input_image, cn_mask, cn_weight, cn_guidance_start, cn_guidance_end, cn_resolution, cn_control_mode, cn_resize_mode = args global cn_args if not cn_input_image is None: cn_input_image = pil_to_base64(cn_input_image) if not cn_mask is None: cn_mask = pil_to_base64(cn_mask) args = { "input_image": cn_input_image, "module": cn_module, "model": cn_model, "low_vram": enable_low_vram, "pixel_perfect": enable_pixel_perfect, "mask": cn_mask, "weight": cn_weight, "guidance_start": cn_guidance_start, "guidance_end": cn_guidance_end, "processor_res": cn_resolution, "control_mode": cn_control_mode, "resize_mode": cn_resize_mode } if enable_cn == True: cn_args[cn_gen_type][cn_num] = args else: del cn_args[cn_gen_type][cn_num]def cn_update_choices(cn_type): module_list = cn_types_list[cn_type]["module_list"] model_list = cn_types_list[cn_type]["model_list"] default_option = cn_types_list[cn_type]["default_option"] default_model = cn_types_list[cn_type]["default_model"] return gr.update(choices=module_list, value=default_option), gr.update(choices=model_list, value=default_model)def cn_blocks(i, gen_type): with gr.Blocks() as demo: with gr.Row(): cn_gen_type = gr.Textbox(visible=False, value=gen_type) cn_num = gr.Textbox(visible=False, value=i) enable_cn = gr.Checkbox(label="Enable") enable_low_vram = gr.Checkbox(label="Low VRAM") enable_pixel_perfect = gr.Checkbox(label="Pixel Perfect") enable_mask_upload = gr.Checkbox(label="Effective Region Mask") with gr.Row(): cn_type = gr.Dropdown(cn_types, label="ControlNet type", value=cn_default_type) cn_btn = gr.Button("Preprocess | 预处理", elem_id="button") with gr.Row(): cn_module = gr.Dropdown(cn_module_list, label="ControlNet module", value=cn_default_option) cn_model = gr.Dropdown(cn_model_list, label="ControlNet model", value=cn_default_model) with gr.Row(): cn_input_image = gr.Image(type="pil") cn_detect_image = gr.Image(label="Preprocessor Preview") cn_mask = gr.Image(label="Effective Region Mask", interactive=True, visible=False) with gr.Row(): cn_weight = gr.Slider(label="Control Weight", minimum=0, maximum=2, step=0.05, value=1) cn_guidance_start = gr.Slider(label="Starting Control Step", minimum=0, maximum=1, step=0.01, value=0) cn_guidance_end = gr.Slider(label="Ending Control Step", minimum=0, maximum=1, step=0.01, value=1) cn_resolution = gr.Slider(label="Resolution", minimum=64, maximum=2048, step=default["size_step"], value=512) cn_control_mode = gr.Radio(["Balanced", "My prompt is more important", "ControlNet is more important"], label="Control Mode", value="Balanced") cn_resize_mode = gr.Radio(["Just Resize", "Crop and Resize", "Resize and Fill"], label="Resize Mode", value="Crop and Resize") enable_mask_upload.change(fn=gr_update_visible, inputs=enable_mask_upload, outputs=cn_mask) cn_type.change(fn=cn_update_choices, inputs=cn_type, outputs=[cn_module, cn_model]) cn_btn.click(fn=cn_preprocess, inputs=[cn_module, cn_input_image], outputs=cn_detect_image) cn_inputs = [cn_gen_type, cn_num, enable_cn, enable_low_vram, enable_pixel_perfect, cn_module, cn_model, cn_input_image, cn_mask, cn_weight, cn_guidance_start, cn_guidance_end, cn_resolution, cn_control_mode, cn_resize_mode] for gr_block in cn_inputs: if type(gr_block) is gr.components.slider.Slider: gr_block.release(fn=cn_update_args, inputs=cn_inputs, outputs=None) else: gr_block.change(fn=cn_update_args, inputs=cn_inputs, outputs=None) return demodef generate(input_image, sd_model, sd_vae, sampler_name, scheduler, clip_skip, steps, width, batch_size, height, batch_count, cfg_scale, randn_source, seed, denoising_strength, prompt, negative_prompt, progress=gr.Progress()): global interrupt, xyz_args interrupt = False if denoising_strength >= 0: gen_type = "img2img" if input_image is None: return None, None, None else: gen_type = "txt2img" progress(0, desc=f"Loading {sd_model}") payload = { "sd_model_checkpoint": sd_models_list[sd_model], "sd_vae": sd_vae, "CLIP_stop_at_last_layers": clip_skip, "randn_source": randn_source } requests.post(url=f"{url}/sdapi/v1/options", json=payload) if interrupt == True: return None, None, None progress(0, desc="Processing...") images = [] images_info = [] if not input_image is None: input_image = pil_to_base64(input_image) for i in range(batch_count): payload = { "prompt": prompt, "negative_prompt": negative_prompt, "batch_size": batch_size, "seed": seed, "sampler_name": sampler_name, "scheduler": scheduler, "steps": steps, "cfg_scale": cfg_scale, "width": width, "height": height, "init_images": [input_image], "denoising_strength": denoising_strength, "alwayson_scripts": {} } if "adetailer" in extensions: payload = add_adetailer(payload, gen_type) if "sd-webui-controlnet" in extensions: payload = add_controlnet(payload, gen_type) payload = add_xyz_plot(payload, gen_type) response = requests.post(url=f"{url}/sdapi/v1/{gen_type}", json=payload) images_base64 = response.json()["images"] for j in range(len(images_base64)): image_pil = base64_to_pil(images_base64[j]) images.append(image_pil) image_info = get_png_info(image_pil) images_info.append(image_info) if image_info == "None": if save_images == "Yes": if gen_type in xyz_args: save_image(image_pil, "XYZ_Plot", "grid") else: save_image(image_pil, "ControlNet", "detect") else: seed = re.findall("Seed: [0-9]+", image_info)[0].split(": ")[-1] if save_images == "Yes": save_image(image_pil, sd_model, seed) seed = int(seed) + 1 progress((i+1)/batch_count, desc=f"Batch count: {(i+1)}/{batch_count}") if interrupt == True: return images, images_info, datetime.datetime.now() return images, images_info, datetime.datetime.now()def gen_clear_geninfo(): return Nonedef gen_update_geninfo(images_info): if images_info == [] or images_info is None: return None return images_info[0]def gen_update_selected_geninfo(images_info, evt: gr.SelectData): return images_info[evt.index]def gen_blocks(gen_type): with gr.Blocks() as demo: with gr.Row(): with gr.Column(): prompt = gr.Textbox(placeholder="Prompt", show_label=False, value=default["prompt"], lines=3) negative_prompt = gr.Textbox(placeholder="Negative prompt", show_label=False, value=default["negative_prompt"], lines=3) if gen_type == "txt2img": input_image = gr.Image(visible=False) else: input_image = gr.Image(type="pil") with gr.Tab("Generation"): with gr.Row(): sd_model = gr.Dropdown(sd_models, label="SD Model", value=sd_models[0]) sd_vae = gr.Dropdown(sd_vaes, label="SD VAE", value=sd_vaes[0]) clip_skip = gr.Slider(minimum=1, maximum=12, step=1, label="Clip skip", value=default["clip_skip"]) with gr.Row(): sampler_name = gr.Dropdown(samplers, label="Sampling method", value=samplers[0]) scheduler = gr.Dropdown(schedulers, label="Schedule type", value=schedulers[0]) steps = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps", value=default["steps"]) with gr.Row(): width = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Width", value=default["width"]) batch_size = gr.Slider(minimum=1, maximum=8, step=1, label="Batch size", value=1) with gr.Row(): height = gr.Slider(minimum=64, maximum=2048, step=default["size_step"], label="Height", value=default["height"]) batch_count = gr.Slider(minimum=1, maximum=100, step=1, label="Batch count", value=1) with gr.Row(): cfg_scale = gr.Slider(minimum=1, maximum=30, step=0.5, label="CFG Scale", value=default["cfg"]) if gen_type == "txt2img": denoising_strength = gr.Slider(minimum=-1, maximum=1, step=1, value=-1, visible=False) else: denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label="Denoising strength", value=0.7) with gr.Row(): randn_source = gr.Dropdown(["CPU", "GPU"], label="RNG", value="CPU") seed = gr.Textbox(label="Seed", value=-1) if "adetailer" in extensions: with gr.Tab("ADetailer"): if gen_type == "img2img": with gr.Row(): ad_skip_img2img = gr.Checkbox(label="Skip img2img", visible=True) ad_skip_img2img.change(fn=ad_update_skip_img2img, inputs=ad_skip_img2img, outputs=None) for i in range(default["ad_nums"]): with gr.Tab(f"ADetailer {ordinal(i + 1)}"): ad_blocks(i, gen_type) if "sd-webui-controlnet" in extensions: with gr.Tab("ControlNet"): for i in range(default["cn_nums"]): with gr.Tab(f"ControlNet Unit {i}"): cn_blocks(i, gen_type) if not loras == [] or not embeddings == []: with gr.Tab("Extra Networks"): if not loras == []: lora = gr.Dropdown(loras, label="Lora", multiselect=True, interactive=True) lora.change(fn=add_lora, inputs=[prompt, lora], outputs=prompt) if not embeddings == []: embedding = gr.Dropdown(embeddings, label="Embedding", multiselect=True, interactive=True) embedding.change(fn=add_embedding, inputs=[negative_prompt, embedding], outputs=negative_prompt) with gr.Tab("X/Y/Z plot"): xyz_blocks(gen_type) with gr.Column(): with gr.Row(): btn = gr.Button("Generate | 生成", elem_id="button") btn2 = gr.Button("Interrupt | 终止") gallery = gr.Gallery(preview=True, height=default["gallery_height"]) image_geninfo = gr.Markdown() images_geninfo = gr.State() update_geninfo = gr.Textbox(visible=False) gen_inputs = [input_image, sd_model, sd_vae, sampler_name, scheduler, clip_skip, steps, width, batch_size, height, batch_count, cfg_scale, randn_source, seed, denoising_strength, prompt, negative_prompt] btn.click(fn=gen_clear_geninfo, inputs=None, outputs=image_geninfo) btn.click(fn=generate, inputs=gen_inputs, outputs=[gallery, images_geninfo, update_geninfo]) btn2.click(fn=post_interrupt, inputs=None, outputs=None) gallery.select(fn=gen_update_selected_geninfo, inputs=images_geninfo, outputs=image_geninfo) update_geninfo.change(fn=gen_update_geninfo, inputs=images_geninfo, outputs=image_geninfo) return demodef extras(input_image, upscaler_1, upscaler_2, upscaling_resize, extras_upscaler_2_visibility, enable_gfpgan, gfpgan_visibility, enable_codeformer, codeformer_visibility, codeformer_weight): if input_image is None: return None input_image = pil_to_base64(input_image) if enable_gfpgan == False: gfpgan_visibility = 0 if enable_codeformer == False: codeformer_visibility = 0 payload = { "gfpgan_visibility": gfpgan_visibility, "codeformer_visibility": codeformer_visibility, "codeformer_weight": codeformer_weight, "upscaling_resize": upscaling_resize, "upscaler_1": upscaler_1, "upscaler_2": upscaler_2, "extras_upscaler_2_visibility": extras_upscaler_2_visibility, "image": input_image } response = requests.post(url=f"{url}/sdapi/v1/extra-single-image", json=payload) images_base64 = response.json()["image"] image_pil = base64_to_pil(images_base64) if save_images == "Yes": save_image(image_pil, "Extras", "image") return image_pildef extras_blocks(): with gr.Blocks() as demo: with gr.Row(): with gr.Column(): input_image = gr.Image(type="pil") with gr.Row(): upscaler_1 = gr.Dropdown(upscalers, label="Upscaler 1", value="R-ESRGAN 4x+") upscaler_2 = gr.Dropdown(upscalers, label="Upscaler 2", value="None") with gr.Row(): upscaling_resize = gr.Slider(minimum=1, maximum=8, step=0.05, label="Scale by", value=4) extras_upscaler_2_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="Upscaler 2 visibility", value=0) enable_gfpgan = gr.Checkbox(label="Enable GFPGAN") gfpgan_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="GFPGAN Visibility", value=1) enable_codeformer = gr.Checkbox(label="Enable CodeFormer") codeformer_visibility = gr.Slider(minimum=0, maximum=1, step=0.001, label="CodeFormer Visibility", value=1) codeformer_weight = gr.Slider(minimum=0, maximum=1, step=0.001, label="Weight (0 = maximum effect, 1 = minimum effect)", value=0) with gr.Column(): with gr.Row(): btn = gr.Button("Generate | 生成", elem_id="button") btn2 = gr.Button("Interrupt | 终止") extra_image = gr.Image(label="Extras image") btn.click(fn=extras, inputs=[input_image, upscaler_1, upscaler_2, upscaling_resize, extras_upscaler_2_visibility, enable_gfpgan, gfpgan_visibility, enable_codeformer, codeformer_visibility, codeformer_weight], outputs=extra_image) btn2.click(fn=post_interrupt, inputs=None, outputs=None) return demodef get_png_info(image_pil): image_info=[] if image_pil is None: return None for key, value in image_pil.info.items(): image_info.append(value) if not image_info == []: image_info = image_info[0] image_info = re.sub(r"<", "\<", image_info) image_info = re.sub(r">", "\>", image_info) image_info = re.sub(r"\n", "<br>", image_info) else: image_info = "None" return image_infodef png_info_blocks(): with gr.Blocks() as demo: with gr.Row(): with gr.Column(): input_image = gr.Image(value=None, type="pil") with gr.Column(): png_info = gr.Markdown() input_image.change(fn=get_png_info, inputs=input_image, outputs=png_info) return demowith gr.Blocks(css="#button {background: #FFE1C0; color: #FF453A} .block.padded:not(.gradio-accordion) {padding: 0 !important;} div.form {border-width: 0; box-shadow: none; background: white; gap: 0.5em;}") as demo: with gr.Tab("txt2img"): gen_blocks("txt2img") with gr.Tab("img2img"): gen_blocks("img2img") with gr.Tab("Extras"): extras_blocks() with gr.Tab("PNG Info"): png_info_blocks()demo.queue(concurrency_count=100).launch(inbrowser=True)