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Stable Diffusion | Gradio界面设计及ComfyUI API调用_gradio comfyui

作者：木道寻08 | 2024-07-01 14:47:01

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gradio comfyui

本文基于ComfyUI 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，Stable Cascade），Lora，ControlNet，图生视频（SVD_xt_1_1），图像修复（FaceDetailer），图像放大（Extras），图片/API工作流信息读取（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 ComfyUI 在线体验

2. 自制Gradio界面展示

Stable Diffusion 文生图/图生图界面：

Stable Cascade 文生图/图生图界面：

Stable Video Diffusion 图生视频界面：

图片放大界面：

图片/API工作流信息读取界面：

3. Gradio界面设计及ComfyUI API调用源码

  1. import io
  2. import json
  3. import os
  4. import random
  5. import re
  6. import subprocess
  7. import sys
  8. import urllib.parse
  9. import uuid
  10. sys.path.append("/home/aistudio/ComfyUI/venv/lib/python3.10/site-packages")
  11. import gradio as gr
  12. import requests
  13. import websocket
  14. from collections import OrderedDict, deque
  15. from PIL import Image
  16.  
  17. class Default:
  18.     # 1表示启用，0表示禁用
  19.     design_mode = 0
  20.     lora_weight = 0.8
  21.     controlnet_num = 5
  22.     controlnet_saveimage = 1
  23.     facedetailer_num = 3
  24.     prompt = "(best quality:1), (high quality:1), detailed/(extreme, highly, ultra/), realistic, 1girl/(beautiful, delicate, perfect/), "
  25.     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"
  26.     if design_mode == 1:
  27.         width = 64
  28.         hight = 64
  29.         steps = 2
  30.     else:
  31.         width = 512
  32.         hight = 768
  33.         steps = 20
  34.  
  35. class Initial:
  36.     os.environ["GRADIO_ANALYTICS_ENABLED"] = "False"
  37.     client_id = str(uuid.uuid4())
  38.     server_address = "127.0.0.1:8188"
  39.     if Default.design_mode == 0:
  40.         cmd = "ps -eo pid,args | grep 'export GRADIO_SERVER_PORT=' | awk '{print $8, $14}'"
  41.         ps_output = subprocess.run(cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True).stdout.splitlines()
  42.         for i in ps_output:
  43.             if "/home/aistudio/ComfyUI.gradio.py" in i:
  44.                 port = i.split(" ")[0].split("=")[1]
  45.                 server_address = f"127.0.0.1:{port}"
  46.     output_dir = os.path.join(os.getcwd(), "ComfyUI/output/")
  47.     uploaded_image = {}
  48.  
  49. class Choices:
  50.     ws = websocket.WebSocket()
  51.     ws.connect("ws://{}/ws?clientId={}".format(Initial.server_address, Initial.client_id))
  52.     object_info = requests.get(url="http://{}/object_info".format(Initial.server_address)).json()
  53.     embedding = requests.get(url="http://{}/embeddings".format(Initial.server_address)).json()
  54.     ws.close()
  55.     ckpt = []
  56.     ckpt_list = {}
  57.     ckpt_name = object_info["ImageOnlyCheckpointLoader"]["input"]["required"]["ckpt_name"][0]
  58.     hidden_ckpt = ["stable_cascade_stage_c.safetensors", "stable_cascade_stage_b.safetensors", "svd_xt_1_1.safetensors", "control_v11p_sd15_canny.safetensors", "control_v11f1p_sd15_depth.safetensors", "control_v11p_sd15_openpose.safetensors"]
  59.     for i in ckpt_name:
  60.         path, file = os.path.split(i)
  61.         if file not in hidden_ckpt:
  62.             ckpt.append(file)
  63.         ckpt_list[file] = i
  64.     ckpt = sorted(ckpt)
  65.     controlnet_model = []
  66.     controlnet_model_list = {}
  67.     controlnet_name = object_info["ControlNetLoader"]["input"]["required"]["control_net_name"][0]
  68.     for i in controlnet_name:
  69.         path, file = os.path.split(i)
  70.         controlnet_model.append(file)
  71.         controlnet_model_list[file] = i
  72.     controlnet_model = sorted(controlnet_model)
  73.     preprocessor = ["Canny"]
  74.     if "AIO_Preprocessor" in object_info:
  75.         preprocessor = ["none", "Canny", "CannyEdgePreprocessor", "DepthAnythingPreprocessor", "DWPreprocessor", "OpenposePreprocessor"]
  76.         for i in sorted(object_info["AIO_Preprocessor"]["input"]["optional"]["preprocessor"][0]):
  77.             if i not in preprocessor:
  78.                 preprocessor.append(i)
  79.     if "FaceDetailer" in object_info:
  80.         facedetailer_detector_model = []
  81.         facedetailer_detector_model_list = {}
  82.         facedetailer_detector_model_name = object_info["UltralyticsDetectorProvider"]["input"]["required"]["model_name"][0]
  83.         for i in facedetailer_detector_model_name:
  84.             path, file = os.path.split(i)
  85.             facedetailer_detector_model.append(file)
  86.             facedetailer_detector_model_list[file] = i
  87.         facedetailer_detector_model = sorted(facedetailer_detector_model)
  88.     lora = object_info["LoraLoader"]["input"]["required"]["lora_name"][0]
  89.     sampler = object_info["KSampler"]["input"]["required"]["sampler_name"][0]
  90.     scheduler = object_info["KSampler"]["input"]["required"]["scheduler"][0]
  91.     upscale_method = object_info["ImageScaleBy"]["input"]["required"]["upscale_method"][0]
  92.     upscale_model = object_info["UpscaleModelLoader"]["input"]["required"]["model_name"][0]
  93.     vae = ["Automatic"]
  94.     for i in sorted(object_info["VAELoader"]["input"]["required"]["vae_name"][0]):
  95.         vae.append(i)
  96.  
  97. class Function:
  98.     def format_prompt(prompt):
  99.         prompt = re.sub(r"\s+,", ",", prompt)
  100.         prompt = re.sub(r"\s+", " ", prompt)
  101.         prompt = re.sub(",,+", ",", prompt)
  102.         prompt = re.sub(",", ", ", prompt)
  103.         prompt = re.sub(r"\s+", " ", prompt)
  104.         prompt = re.sub(r"^,", "", prompt)
  105.         prompt = re.sub(r"^ ", "", prompt)
  106.         prompt = re.sub(r" $", "", prompt)
  107.         prompt = re.sub(r",$", "", prompt)
  108.         prompt = re.sub(": ", ":", prompt)
  109.         return prompt
  110.  
  111.     def get_model_path(model_name):
  112.         return Choices.ckpt_list[model_name]
  113.  
  114.     def gen_seed(seed):
  115.         seed = int(seed)
  116.         if seed < 0:
  117.             seed = random.randint(0, 18446744073709551615)
  118.         if seed > 18446744073709551615:
  119.             seed = 18446744073709551615
  120.         return seed
  121.  
  122.     def initialize():
  123.         Lora.cache = {}
  124.         Upscale.cache = {}
  125.         UpscaleWithModel.cache = {}
  126.         ControlNet.cache = {}
  127.         FaceDetailer.cache = {}
  128.  
  129.     def upload_image(image):
  130.         buffer = io.BytesIO()
  131.         image.save(buffer, format="png")
  132.         image = buffer.getbuffer()
  133.         image_hash = hash(image.tobytes())
  134.         if image_hash in Initial.uploaded_image:
  135.             return Initial.uploaded_image[image_hash]
  136.         image_name = str(uuid.uuid4()) + ".png"
  137.         Initial.uploaded_image[image_hash] = image_name
  138.         image_file = {"image": (image_name, image)}
  139.         ws = websocket.WebSocket()
  140.         ws.connect("ws://{}/ws?clientId={}".format(Initial.server_address, Initial.client_id))
  141.         requests.post(url="http://{}/upload/image".format(Initial.server_address), files=image_file)
  142.         ws.close()
  143.         return image_name
  144.  
  145.     def order_workflow(workflow):
  146.         link_list = {}
  147.         for node in workflow:
  148.             node_link = []
  149.             for input in workflow[node]["inputs"]:
  150.                 if isinstance(workflow[node]["inputs"][input], list):
  151.                     node_link.append(workflow[node]["inputs"][input][0])
  152.             link_list[node] = node_link
  153.         in_degree = {v: 0 for v in link_list}
  154.         for node in link_list:
  155.             for neighbor in link_list[node]:
  156.                 in_degree[neighbor] += 1
  157.         queue = deque([node for node in in_degree if in_degree[node] == 0])
  158.         order_list = []
  159.         while queue:
  160.             node = queue.popleft()
  161.             order_list.append(node)
  162.             for neighbor in link_list[node]:
  163.                 in_degree[neighbor] -= 1
  164.                 if in_degree[neighbor] == 0:
  165.                     queue.append(neighbor)
  166.         order_list = order_list[::-1]
  167.         max_nodes = 1000
  168.         new_node_id = max_nodes * 10 + 1
  169.         workflow_string = json.dumps(workflow)
  170.         for node in order_list:
  171.             workflow_string = workflow_string.replace(f'"{node}"', f'"{new_node_id}"')
  172.             new_node_id += 1
  173.         workflow = json.loads(workflow_string)
  174.         workflow = OrderedDict(sorted(workflow.items()))
  175.         new_node_id = 1
  176.         workflow_string = json.dumps(workflow)
  177.         for node in workflow:
  178.             workflow_string = workflow_string.replace(f'"{node}"', f'"{new_node_id}"')
  179.             new_node_id += 1
  180.         workflow = json.loads(workflow_string)
  181.         for node in workflow:
  182.             if "_meta" in workflow[node]:
  183.                 del workflow[node]["_meta"]
  184.         return workflow
  185.  
  186.     def post_interrupt():
  187.         Initial.interrupt = True
  188.         ws = websocket.WebSocket()
  189.         ws.connect("ws://{}/ws?clientId={}".format(Initial.server_address, Initial.client_id))
  190.         requests.post(url="http://{}/interrupt".format(Initial.server_address))
  191.         ws.close()
  192.  
  193.     def add_embedding(embedding, negative_prompt):
  194.         for i in Choices.embedding:
  195.             negative_prompt = negative_prompt.replace(f"embedding:{i},", "")
  196.         negative_prompt = Function.format_prompt(negative_prompt)
  197.         for i in embedding[::-1]:
  198.             negative_prompt = f"embedding:{i}, {negative_prompt}"
  199.         return negative_prompt
  200.  
  201.     def gen_image(workflow, counter, batch_count, progress):
  202.         if counter == 1:
  203.             progress(0, desc="Processing...")
  204.         if batch_count == 1:
  205.             batch_info = ""
  206.         else:
  207.             batch_info = f"Batch {counter}/{batch_count}: "
  208.         workflow = Function.order_workflow(workflow)
  209.         current_progress = 0
  210.         Initial.interrupt = False
  211.         ws = websocket.WebSocket()
  212.         ws.connect("ws://{}/ws?clientId={}".format(Initial.server_address, Initial.client_id))
  213.         data = {"prompt": workflow, "client_id": Initial.client_id}
  214.         prompt_id = requests.post(url="http://{}/prompt".format(Initial.server_address), json=data).json()["prompt_id"]
  215.         while True:
  216.             try:
  217.                 ws.settimeout(0.1)
  218.                 wsrecv = ws.recv()
  219.                 if isinstance(wsrecv, str):
  220.                     data = json.loads(wsrecv)["data"]
  221.                     if "node" in data:
  222.                         if data["node"] is not None:
  223.                             if "value" in data and "max" in data:
  224.                                 if data["max"] > 1:
  225.                                     current_progress = data["value"]/data["max"]
  226.                                 progress(current_progress, desc=f"{batch_info}" + workflow[data["node"]]["class_type"] + " " + str(data["value"]) + "/" + str(data["max"]))
  227.                             else:
  228.                                 progress(current_progress, desc=f"{batch_info}" + workflow[data["node"]]["class_type"])
  229.                         if data["node"] is None and data["prompt_id"] == prompt_id:
  230.                             break
  231.                 else:
  232.                     continue
  233.             except websocket.WebSocketTimeoutException:
  234.                 if Initial.interrupt is True:
  235.                     ws.close()
  236.                     return None, None
  237.         history = requests.get(url="http://{}/history/{}".format(Initial.server_address, prompt_id)).json()[prompt_id]
  238.         images = []
  239.         file_path = ""
  240.         for node_id in history["outputs"]:
  241.             node_output = history["outputs"][node_id]
  242.             if "images" in node_output:
  243.                 for image in node_output["images"]:
  244.                     file_path = Initial.output_dir + image["filename"]
  245.                     data = {"filename": image["filename"], "subfolder": image["subfolder"], "type": image["type"]}
  246.                     url_values = urllib.parse.urlencode(data)
  247.                     image_data = requests.get("http://{}/view?{}".format(Initial.server_address, url_values)).content
  248.                     image = Image.open(io.BytesIO(image_data))
  249.                     images.append(image)
  250.         ws.close()
  251.         return images, file_path
  252.  
  253.     def get_gallery_index(evt: gr.SelectData):
  254.         return evt.index
  255.  
  256.     def get_image_info(image_pil):
  257.         image_info=[]
  258.         if image_pil is None:
  259.             return
  260.         for key, value in image_pil.info.items():
  261.             image_info.append(value)
  262.         if image_info != []:
  263.             image_info = image_info[0]
  264.             if image_info == 0:
  265.                 image_info = "None"
  266.         else:
  267.             image_info = "None"
  268.         return image_info
  269.  
  270.     def send_to(data, index):
  271.         if data == [] or data is None:
  272.             return None
  273.         return data[index]
  274.  
  275. class Lora:
  276.     cache = {}
  277.  
  278.     def add_node(module, workflow, node_id, model_port, clip_port):
  279.         for lora in Lora.cache[module]:
  280.             strength_model = Lora.cache[module][lora]
  281.             strength_clip = Lora.cache[module][lora]
  282.             node_id += 1
  283.             workflow[str(node_id)] = {"inputs": {"lora_name": lora, "strength_model": strength_model, "strength_clip": strength_clip, "model": model_port, "clip": clip_port}, "class_type": "LoraLoader"}
  284.             model_port = [str(node_id), 0]
  285.             clip_port = [str(node_id), 1]
  286.         return workflow, node_id, model_port, clip_port
  287.  
  288.     def update_cache(module, lora, lora_weight):
  289.         if Initial.initialized is False:
  290.             Function.initialize()
  291.         if lora == []:
  292.             Lora.cache[module] = {}
  293.             return True, [], gr.update(value="", visible=False)
  294.         lora_list = {}
  295.         for i in lora_weight.split("<"):
  296.             for j in i.split(">"):
  297.                 if j != "" and ":" in j:
  298.                     lora_name, weight = j.split(":")
  299.                     lora_list[lora_name] = weight
  300.         lora_weight = ""
  301.         Lora.cache[module] = {}
  302.         for i in lora:
  303.             if i in lora_list:
  304.                 weight = lora_list[i]
  305.             else:
  306.                 weight = Default.lora_weight
  307.             if lora.index(i) == 0:
  308.                 lora_weight = f"<{i}:{weight}>"
  309.             else:
  310.                 lora_weight = f"{lora_weight}\n\n<{i}:{weight}>"
  311.             if weight != "":
  312.                 weight = float(weight)
  313.             Lora.cache[module][i] = weight
  314.         return True, gr.update(), gr.update(value=lora_weight, visible=True)
  315.  
  316.     def blocks(module):
  317.         module = gr.Textbox(value=module, visible=False)
  318.         lora = gr.Dropdown(Choices.lora, label="Lora", multiselect=True, interactive=True)
  319.         lora_weight = gr.Textbox(label="Lora weight | Lora 权重", visible=False)
  320.         for gr_block in [lora, lora_weight]:
  321.             gr_block.change(fn=Lora.update_cache, inputs=[module, lora, lora_weight], outputs=[Initial.initialized, lora, lora_weight])
  322.  
  323. class Upscale:
  324.     cache = {}
  325.  
  326.     def add_node(module, workflow, node_id, image_port):
  327.         upscale_method = Upscale.cache[module]["upscale_method"]
  328.         scale_by = Upscale.cache[module]["scale_by"]
  329.         node_id += 1
  330.         workflow[str(node_id)] = {"inputs": {"upscale_method": upscale_method, "scale_by": scale_by, "image": image_port}, "class_type": "ImageScaleBy"}
  331.         image_port = [str(node_id), 0]
  332.         return workflow, node_id, image_port
  333.  
  334.     def auto_enable(scale_by):
  335.         if scale_by > 1:
  336.             return True
  337.         else:
  338.             return False
  339.  
  340.     def update_cache(module, enable, upscale_method, scale_by):
  341.         if Initial.initialized is False:
  342.             Function.initialize()
  343.         if module not in Upscale.cache:
  344.             Upscale.cache[module] = {}
  345.         if enable is True:
  346.             Upscale.cache[module]["upscale_method"] = upscale_method
  347.             Upscale.cache[module]["scale_by"] = scale_by
  348.         else:
  349.             del Upscale.cache[module]
  350.         return True
  351.  
  352.     def blocks(module):
  353.         module = gr.Textbox(value=module, visible=False)
  354.         enable = gr.Checkbox(label="Enable（放大系数大于1后自动启用）")
  355.         with gr.Row():
  356.             upscale_method = gr.Dropdown(Choices.upscale_method, label="Upscale method | 放大方法", value=Choices.upscale_method[-1])
  357.             scale_by = gr.Slider(minimum=1, maximum=8, step=1, label="Scale by | 放大系数", value=1)
  358.         scale_by.release(fn=Upscale.auto_enable, inputs=[scale_by], outputs=[enable])
  359.         inputs = [module, enable, upscale_method, scale_by]
  360.         for gr_block in inputs:
  361.             if type(gr_block) is gr.components.slider.Slider:
  362.                 gr_block.release(fn=Upscale.update_cache, inputs=inputs, outputs=[Initial.initialized])
  363.             else:
  364.                 gr_block.change(fn=Upscale.update_cache, inputs=inputs, outputs=[Initial.initialized])
  365.  
  366. class UpscaleWithModel:
  367.     cache = {}
  368.  
  369.     def add_node(module, workflow, node_id, image_port):
  370.         upscale_model = UpscaleWithModel.cache[module]["upscale_model"]
  371.         node_id += 1
  372.         workflow[str(node_id)] = {"inputs": {"model_name": upscale_model}, "class_type": "UpscaleModelLoader"}
  373.         upscale_model_port = [str(node_id), 0]
  374.         node_id += 1
  375.         workflow[str(node_id)] = {"inputs": {"upscale_model": upscale_model_port, "image": image_port}, "class_type": "ImageUpscaleWithModel"}
  376.         image_port = [str(node_id), 0]
  377.         return workflow, node_id, image_port
  378.  
  379.     def update_cache(module, enable, upscale_model):
  380.         if Initial.initialized is False:
  381.             Function.initialize()
  382.         if module not in UpscaleWithModel.cache:
  383.             UpscaleWithModel.cache[module] = {}
  384.         if enable is True:
  385.             UpscaleWithModel.cache[module]["upscale_model"] = upscale_model
  386.         else:
  387.             del UpscaleWithModel.cache[module]
  388.         return True
  389.  
  390.     def blocks(module):
  391.         module = gr.Textbox(value=module, visible=False)
  392.         enable = gr.Checkbox(label="Enable")
  393.         upscale_model = gr.Dropdown(Choices.upscale_model, label="Upscale model | 超分模型", value=Choices.upscale_model[0])
  394.         inputs = [module, enable, upscale_model]
  395.         for gr_block in inputs:
  396.             gr_block.change(fn=UpscaleWithModel.update_cache, inputs=inputs, outputs=[Initial.initialized])
  397.  
  398. class ControlNet:
  399.     cache = {}
  400.     model_preprocessor_list = {
  401.         "control_v11e_sd15_ip2p.safetensors": [],
  402.         "control_v11e_sd15_shuffle.safetensors": ["ShufflePreprocessor"],
  403.         "control_v11f1e_sd15_tile.bin": ["TilePreprocessor", "TTPlanet_TileGF_Preprocessor", "TTPlanet_TileSimple_Preprocessor"],
  404.         "control_v11f1p_sd15_depth.safetensors": ["DepthAnythingPreprocessor", "LeReS-DepthMapPreprocessor", "MiDaS-NormalMapPreprocessor", "MeshGraphormer-DepthMapPreprocessor", "MeshGraphormer+ImpactDetector-DepthMapPreprocessor", "MiDaS-DepthMapPreprocessor", "Zoe_DepthAnythingPreprocessor", "Zoe-DepthMapPreprocessor"],
  405.         "control_v11p_sd15_canny.safetensors": ["Canny", "CannyEdgePreprocessor"],
  406.         "control_v11p_sd15_inpaint.safetensors": [],
  407.         "control_v11p_sd15_lineart.safetensors": ["LineArtPreprocessor", "LineartStandardPreprocessor"],
  408.         "control_v11p_sd15_mlsd.safetensors": ["M-LSDPreprocessor"],
  409.         "control_v11p_sd15_normalbae.safetensors": ["BAE-NormalMapPreprocessor", "DSINE-NormalMapPreprocessor"],
  410.         "control_v11p_sd15_openpose.safetensors": ["DWPreprocessor", "OpenposePreprocessor", "DensePosePreprocessor"],
  411.         "control_v11p_sd15_scribble.safetensors": ["ScribblePreprocessor", "Scribble_XDoG_Preprocessor", "Scribble_PiDiNet_Preprocessor", "FakeScribblePreprocessor"],
  412.         "control_v11p_sd15_seg.safetensors": ["AnimeFace_SemSegPreprocessor", "OneFormer-COCO-SemSegPreprocessor", "OneFormer-ADE20K-SemSegPreprocessor", "SemSegPreprocessor", "UniFormer-SemSegPreprocessor"],
  413.         "control_v11p_sd15_softedge.safetensors": ["HEDPreprocessor", "PiDiNetPreprocessor", "TEEDPreprocessor", "DiffusionEdge_Preprocessor"],
  414.         "control_v11p_sd15s2_lineart_anime.safetensors": ["AnimeLineArtPreprocessor", "Manga2Anime_LineArt_Preprocessor"],
  415.         "control_scribble.safetensors": ["BinaryPreprocessor"],
  416.         "ioclab_sd15_recolor.safetensors": ["ImageLuminanceDetector", "ImageIntensityDetector"],
  417.         "control_sd15_animal_openpose_fp16.pth": ["AnimalPosePreprocessor"],
  418.         "controlnet_sd21_laion_face_v2.safetensors": ["MediaPipe-FaceMeshPreprocessor"]
  419.     }
  420.  
  421.     def add_node(module, counter, workflow, node_id, positive_port, negative_port):
  422.         for unit_id in ControlNet.cache[module]:
  423.             preprocessor = ControlNet.cache[module][unit_id]["preprocessor"]
  424.             model = ControlNet.cache[module][unit_id]["model"]
  425.             input_image = ControlNet.cache[module][unit_id]["input_image"]
  426.             resolution = ControlNet.cache[module][unit_id]["resolution"]
  427.             strength = ControlNet.cache[module][unit_id]["strength"]
  428.             start_percent = ControlNet.cache[module][unit_id]["start_percent"]
  429.             end_percent = ControlNet.cache[module][unit_id]["end_percent"]
  430.             node_id += 1
  431.             workflow[str(node_id)] = {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"}
  432.             image_port = [str(node_id), 0]
  433.             if preprocessor == "Canny":
  434.                 node_id += 1
  435.                 workflow[str(node_id)] = {"inputs": {"low_threshold": 0.3, "high_threshold": 0.7, "image": image_port}, "class_type": "Canny"}
  436.                 image_port = [str(node_id), 0]
  437.             else:
  438.                 node_id += 1
  439.                 workflow[str(node_id)] = {"inputs": {"preprocessor": preprocessor, "resolution": resolution, "image": image_port}, "class_type": "AIO_Preprocessor"}
  440.                 image_port = [str(node_id), 0]
  441.             if counter == 1 and Default.controlnet_saveimage == 1:
  442.                 node_id += 1
  443.                 workflow[str(node_id)] = {"inputs": {"filename_prefix": "ControlNet", "images": image_port}, "class_type": "SaveImage"}
  444.             node_id += 1
  445.             workflow[str(node_id)] = {"inputs": {"control_net_name": model}, "class_type": "ControlNetLoader"}
  446.             control_net_port = [str(node_id), 0]
  447.             node_id += 1
  448.             workflow[str(node_id)] = {"inputs": {"strength": strength, "start_percent": start_percent, "end_percent": end_percent, "positive": positive_port, "negative": negative_port, "control_net": control_net_port, "image": image_port}, "class_type": "ControlNetApplyAdvanced"}
  449.             positive_port = [str(node_id), 0]
  450.             negative_port = [str(node_id), 1]
  451.         return workflow, node_id, positive_port, negative_port
  452.  
  453.     def auto_enable():
  454.         return True
  455.  
  456.     def auto_select_model(preprocessor):
  457.         for model in Choices.controlnet_model:
  458.             if model in ControlNet.model_preprocessor_list:
  459.                 if preprocessor in ControlNet.model_preprocessor_list[model]:
  460.                     return gr.update(value=model)
  461.         return gr.update(value="未定义/检测到对应的模型，请自行选择！")
  462.  
  463.     def preprocess(unit_id, preview, preprocessor, input_image, resolution, progress=gr.Progress()):
  464.         if preview is False or input_image is None:
  465.             return
  466.         input_image = Function.upload_image(input_image)
  467.         workflow = {}
  468.         node_id = 1
  469.         workflow[str(node_id)] = {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"}
  470.         image_port = [str(node_id), 0]
  471.         if preprocessor == "Canny":
  472.             node_id += 1
  473.             workflow[str(node_id)] = {"inputs": {"low_threshold": 0.3, "high_threshold": 0.7, "image": image_port}, "class_type": "Canny"}
  474.             image_port = [str(node_id), 0]
  475.         else:
  476.             node_id += 1
  477.             workflow[str(node_id)] = {"inputs": {"preprocessor": preprocessor, "resolution": resolution, "image": image_port}, "class_type": "AIO_Preprocessor"}
  478.             image_port = [str(node_id), 0]
  479.         node_id += 1
  480.         workflow[str(node_id)] = {"inputs": {"images": image_port}, "class_type": "PreviewImage"}
  481.         output = Function.gen_image(workflow, 1, 1, progress)[0]
  482.         if output is not None:
  483.             output = output[0]
  484.         return output
  485.  
  486.     def update_cache(module, unit_id, enable, preprocessor, model, input_image, resolution, strength, start_percent, end_percent):
  487.         if Initial.initialized is False:
  488.             Function.initialize()
  489.         if module not in ControlNet.cache:
  490.             ControlNet.cache[module] = {}
  491.         ControlNet.cache[module][unit_id] = {}
  492.         if input_image is None:
  493.             del ControlNet.cache[module][unit_id]
  494.             return True, False
  495.         if model not in Choices.controlnet_model:
  496.             del ControlNet.cache[module][unit_id]
  497.             return True, False
  498.         if enable is True:
  499.             ControlNet.cache[module][unit_id]["preprocessor"] = preprocessor
  500.             ControlNet.cache[module][unit_id]["model"] = Choices.controlnet_model_list[model]
  501.             ControlNet.cache[module][unit_id]["input_image"] = Function.upload_image(input_image)
  502.             ControlNet.cache[module][unit_id]["resolution"] = resolution
  503.             ControlNet.cache[module][unit_id]["strength"] = strength
  504.             ControlNet.cache[module][unit_id]["start_percent"] = start_percent
  505.             ControlNet.cache[module][unit_id]["end_percent"] = end_percent
  506.         else:
  507.             del ControlNet.cache[module][unit_id]
  508.         return True, gr.update()
  509.  
  510.     def unit(module, i):
  511.         module = gr.Textbox(value=module, visible=False)
  512.         unit_id = gr.Textbox(value=i, visible=False)
  513.         with gr.Row():
  514.             enable = gr.Checkbox(label="Enable（上传图片后自动启用）")
  515.             preview = gr.Checkbox(label="Preview")
  516.         with gr.Row():
  517.             preprocessor = gr.Dropdown(Choices.preprocessor, label="Preprocessor", value="Canny")
  518.             model = gr.Dropdown(Choices.controlnet_model, label="ControlNet model", value="control_v11p_sd15_canny.safetensors")
  519.         with gr.Row():
  520.             input_image = gr.Image(type="pil")
  521.             preprocess_preview = gr.Image(label="Preprocessor preview")
  522.         with gr.Row():
  523.             resolution = gr.Slider(label="Resolution", minimum=64, maximum=2048, step=64, value=512)
  524.             strength = gr.Slider(label="Strength", minimum=0, maximum=2, step=0.01, value=1)
  525.         with gr.Row():
  526.             start_percent = gr.Slider(label="Start percent", minimum=0, maximum=1, step=0.01, value=0)
  527.             end_percent = gr.Slider(label="End percent", minimum=0, maximum=1, step=0.01, value=1)
  528.         input_image.upload(fn=ControlNet.auto_enable, inputs=None, outputs=[enable])
  529.         preprocessor.change(fn=ControlNet.auto_select_model, inputs=[preprocessor], outputs=[model])
  530.         for gr_block in [preview, preprocessor, input_image]:
  531.             gr_block.change(fn=ControlNet.preprocess, inputs=[unit_id, preview, preprocessor, input_image, resolution], outputs=[preprocess_preview])
  532.         inputs = [module, unit_id, enable, preprocessor, model, input_image, resolution, strength, start_percent, end_percent]
  533.         for gr_block in inputs:
  534.             if type(gr_block) is gr.components.slider.Slider:
  535.                 gr_block.release(fn=ControlNet.update_cache, inputs=inputs, outputs=[Initial.initialized, enable])
  536.             else:
  537.                 gr_block.change(fn=ControlNet.update_cache, inputs=inputs, outputs=[Initial.initialized, enable])
  538.  
  539.     def blocks(module):
  540.         with gr.Tab(label="控制网络"):
  541.             if Default.controlnet_num == 1:
  542.                 ControlNet.unit(module, 1)
  543.             else:
  544.                 for i in range(Default.controlnet_num):
  545.                     with gr.Tab(label=f"ControlNet Unit {i + 1}"):
  546.                         ControlNet.unit(module, i + 1)
  547.  
  548. class FaceDetailer:
  549.     cache = {}
  550.  
  551.     def add_node(module, workflow, node_id, image_port, model_port, clip_port, vae_port, positive_port, negative_port, seed, steps, cfg, sampler_name, scheduler):
  552.         for unit_id in FaceDetailer.cache[module]:
  553.             model = Choices.facedetailer_detector_model_list[FaceDetailer.cache[module][unit_id]["model"]]
  554.             node_id += 1
  555.             workflow[str(node_id)] = {"inputs": {"model_name": model}, "class_type": "UltralyticsDetectorProvider"}
  556.             bbox_detector_port = [str(node_id), 0]
  557.             segm_detector_opt_port = [str(node_id), 1]
  558.             node_id += 1
  559.             workflow[str(node_id)] = {"inputs": {"model_name": "sam_vit_b_01ec64.pth", "device_mode": "AUTO"}, "class_type": "SAMLoader"}
  560.             sam_model_opt_port = [str(node_id), 0]
  561.             node_id += 1
  562.             workflow[str(node_id)] = {"inputs": {"guide_size": 384, "guide_size_for": "True", "max_size": 1024, "seed": seed, "steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": 0.5, "feather": 5, "noise_mask": "True", "force_inpaint": "True", "bbox_threshold": 0.5, "bbox_dilation": 10, "bbox_crop_factor": 3, "sam_detection_hint": "center-1", "sam_dilation": 0, "sam_threshold": 0.93, "sam_bbox_expansion": 0, "sam_mask_hint_threshold": 0.7, "sam_mask_hint_use_negative": "False", "drop_size": 10, "wildcard": "", "cycle": 1, "inpaint_model": "False", "noise_mask_feather": 20, "image": image_port, "model": model_port, "clip": clip_port, "vae": vae_port, "positive": positive_port, "negative": negative_port, "bbox_detector": bbox_detector_port, "sam_model_opt": sam_model_opt_port, "segm_detector_opt": segm_detector_opt_port}, "class_type": "FaceDetailer"}
  563.             image_port = [str(node_id), 0]
  564.         return workflow, node_id, image_port
  565.  
  566.     def update_cache(module, unit_id, enable, model):
  567.         if Initial.initialized is False:
  568.             Function.initialize()
  569.         if module not in FaceDetailer.cache:
  570.             FaceDetailer.cache[module] = {}
  571.         FaceDetailer.cache[module][unit_id] = {}
  572.         if enable is True:
  573.             FaceDetailer.cache[module][unit_id]["model"] = model
  574.         else:
  575.             del FaceDetailer.cache[module][unit_id]
  576.         return True
  577.  
  578.     def unit(module, i):
  579.         module = gr.Textbox(value=module, visible=False)
  580.         unit_id = gr.Textbox(value=i, visible=False)
  581.         enable = gr.Checkbox(label="Enable")
  582.         if i == 1:
  583.             model = gr.Dropdown(Choices.facedetailer_detector_model, label="Detector model", value="face_yolov8m.pt")
  584.         if i == 2:
  585.             model = gr.Dropdown(Choices.facedetailer_detector_model, label="Detector model", value="hand_yolov8s.pt")
  586.         if i == 3:
  587.             model = gr.Dropdown(Choices.facedetailer_detector_model, label="Detector model", value="person_yolov8m-seg.pt")
  588.         inputs = [module, unit_id, enable, model]
  589.         for gr_block in inputs:
  590.             gr_block.change(fn=FaceDetailer.update_cache, inputs=inputs, outputs=[Initial.initialized])
  591.  
  592.     def blocks(module):
  593.         with gr.Tab(label="图像修复"):
  594.             if Default.facedetailer_num == 1:
  595.                 FaceDetailer.unit(module, 1)
  596.             else:
  597.                 with gr.Row():
  598.                     for i in range(Default.facedetailer_num):
  599.                         with gr.Column():
  600.                             with gr.Tab(label=f"FaceDetailer Unit {i + 1}"):
  601.                                 FaceDetailer.unit(module, i + 1)
  602.                     if Default.facedetailer_num % 2 != 0:
  603.                         with gr.Column():
  604.                             gr.HTML("")
  605.  
  606. class Postprocess:
  607.     def add_node(module, *args):
  608.         if module == "SD":
  609.             workflow, node_id, image_port, model_port, clip_port, vae_port, positive_port, negative_port, seed, steps, cfg, sampler_name, scheduler = args
  610.         else:
  611.             workflow, node_id, image_port = args
  612.         if module in FaceDetailer.cache:
  613.             workflow, node_id, image_port = FaceDetailer.add_node(module, workflow, node_id, image_port, model_port, clip_port, vae_port, positive_port, negative_port, seed, steps, cfg, sampler_name, scheduler)
  614.         if module in Upscale.cache:
  615.             workflow, node_id, image_port = Upscale.add_node(module, workflow, node_id, image_port)
  616.         if module in UpscaleWithModel.cache:
  617.             workflow, node_id, image_port = UpscaleWithModel.add_node(module, workflow, node_id, image_port)
  618.         return workflow, node_id, image_port
  619.  
  620.     def blocks(module):
  621.         if module == "SD":
  622.             if "FaceDetailer" in Choices.object_info:
  623.                 FaceDetailer.blocks(module)
  624.         with gr.Tab(label="图像放大"):
  625.             with gr.Row():
  626.                 with gr.Tab(label="算术放大"):
  627.                     Upscale.blocks(module)
  628.             with gr.Row():
  629.                 with gr.Tab(label="超分放大"):
  630.                     UpscaleWithModel.blocks(module)
  631.             gr.HTML("注意：同时启用两种放大模式将先执行算术放大，再执行超分放大，最终放大倍数为二者放大倍数的乘积！")
  632.  
  633. class SD:
  634.     def generate(initialized, batch_count, ckpt_name, vae_name, clip_mode, clip_skip, width, height, batch_size, negative_prompt, positive_prompt, seed, steps, cfg, sampler_name, scheduler, denoise, input_image, progress=gr.Progress()):
  635.         module = "SD"
  636.         ckpt_name = Function.get_model_path(ckpt_name)
  637.         seed = Function.gen_seed(seed)
  638.         if input_image is not None:
  639.             input_image = Function.upload_image(input_image)
  640.         counter = 1
  641.         output_images = []
  642.         node_id = 0
  643.         while counter <= batch_count:
  644.             workflow = {}
  645.             node_id += 1
  646.             workflow[str(node_id)] = {"inputs": {"ckpt_name": ckpt_name}, "class_type": "CheckpointLoaderSimple"}
  647.             model_port = [str(node_id), 0]
  648.             clip_port = [str(node_id), 1]
  649.             vae_port = [str(node_id), 2]
  650.             if vae_name != "Automatic":
  651.                 node_id += 1
  652.                 workflow[str(node_id)] = {"inputs": {"vae_name": vae_name}, "class_type": "VAELoader"}
  653.                 vae_port = [str(node_id), 0]
  654.             if input_image is None:
  655.                 node_id += 1
  656.                 workflow[str(node_id)] = {"inputs": {"width": width, "height": height, "batch_size": batch_size}, "class_type": "EmptyLatentImage"}
  657.                 latent_image_port = [str(node_id), 0]
  658.             else:
  659.                 node_id += 1
  660.                 workflow[str(node_id)] = {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"}
  661.                 pixels_port = [str(node_id), 0]
  662.                 node_id += 1
  663.                 workflow[str(node_id)] = {"inputs": {"pixels": pixels_port, "vae": vae_port}, "class_type": "VAEEncode"}
  664.                 latent_image_port = [str(node_id), 0]
  665.             node_id += 1
  666.             workflow[str(node_id)] = {"inputs": {"stop_at_clip_layer": -clip_skip, "clip": clip_port}, "class_type": "CLIPSetLastLayer"}
  667.             clip_port = [str(node_id), 0]
  668.             if initialized is True and module in Lora.cache:
  669.                 workflow, node_id, model_port, clip_port = Lora.add_node(module, workflow, node_id, model_port, clip_port)
  670.             node_id += 1
  671.             if clip_mode == "ComfyUI":
  672.                 workflow[str(node_id)] = {"inputs": {"text": positive_prompt, "clip": clip_port}, "class_type": "CLIPTextEncode"}
  673.             else:
  674.                 workflow[str(node_id)] = {"inputs": {"text": positive_prompt, "token_normalization": "none", "weight_interpretation": "A1111", "clip": clip_port}, "class_type": "BNK_CLIPTextEncodeAdvanced"}
  675.             positive_port = [str(node_id), 0]
  676.             node_id += 1
  677.             if clip_mode == "ComfyUI":
  678.                 workflow[str(node_id)] = {"inputs": {"text": negative_prompt, "clip": clip_port}, "class_type": "CLIPTextEncode"}
  679.             else:
  680.                 workflow[str(node_id)] = {"inputs": {"text": negative_prompt, "token_normalization": "none", "weight_interpretation": "A1111", "clip": clip_port}, "class_type": "BNK_CLIPTextEncodeAdvanced"}
  681.             negative_port = [str(node_id), 0]
  682.             if initialized is True and module in ControlNet.cache:
  683.                 workflow, node_id, positive_port, negative_port = ControlNet.add_node(module, counter, workflow, node_id, positive_port, negative_port)
  684.             node_id += 1
  685.             workflow[str(node_id)] = {"inputs": {"seed": seed, "steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": denoise, "model": model_port, "positive": positive_port, "negative": negative_port, "latent_image": latent_image_port}, "class_type": "KSampler"}
  686.             samples_port = [str(node_id), 0]
  687.             node_id += 1
  688.             workflow[str(node_id)] = {"inputs": {"samples": samples_port, "vae": vae_port}, "class_type": "VAEDecode"}
  689.             image_port = [str(node_id), 0]
  690.             if initialized is True:
  691.                 workflow, node_id, image_port = Postprocess.add_node(module, workflow, node_id, image_port, model_port, clip_port, vae_port, positive_port, negative_port, seed, steps, cfg, sampler_name, scheduler)
  692.             node_id += 1
  693.             workflow[str(node_id)] = {"inputs": {"filename_prefix": "ComfyUI", "images": image_port}, "class_type": "SaveImage"}
  694.             images = Function.gen_image(workflow, counter, batch_count, progress)[0]
  695.             if images is None:
  696.                 break
  697.             for image in images:
  698.                 output_images.append(image)
  699.             seed += 1
  700.             counter += 1
  701.         return output_images, output_images
  702.  
  703.     def blocks():
  704.         with gr.Row():
  705.             with gr.Column():
  706.                 positive_prompt = gr.Textbox(placeholder="Positive prompt | 正向提示词", show_label=False, value=Default.prompt, lines=3)
  707.                 negative_prompt = gr.Textbox(placeholder="Negative prompt | 负向提示词", show_label=False, value=Default.negative_prompt, lines=3)
  708.                 with gr.Tab(label="基础设置"):
  709.                     with gr.Row():
  710.                         ckpt_name = gr.Dropdown(Choices.ckpt, label="Ckpt name | Ckpt 模型名称", value=Choices.ckpt[0])
  711.                         vae_name = gr.Dropdown(Choices.vae, label="VAE name | VAE 模型名称", value=Choices.vae[0])
  712.                         if "BNK_CLIPTextEncodeAdvanced" in Choices.object_info:
  713.                             clip_mode = gr.Dropdown(["ComfyUI", "WebUI"], label="Clip 编码类型", value="ComfyUI")
  714.                         else:
  715.                             clip_mode = gr.Dropdown(["ComfyUI", "WebUI"], label="Clip 编码类型", value="ComfyUI", visible=False)
  716.                         clip_skip = gr.Slider(minimum=1, maximum=12, step=1, label="Clip 跳过", value=1)
  717.                     with gr.Row():
  718.                         width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width | 图像宽度", value=Default.width)
  719.                         batch_size = gr.Slider(minimum=1, maximum=8, step=1, label="Batch size | 批次大小", value=1)
  720.                     with gr.Row():
  721.                         height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height | 图像高度", value=Default.hight)
  722.                         batch_count = gr.Slider(minimum=1, maximum=100, step=1, label="Batch count | 生成批次", value=1)
  723.                     with gr.Row():
  724.                         if Choices.lora != []:
  725.                             Lora.blocks("SD")
  726.                         if Choices.embedding != []:
  727.                             embedding = gr.Dropdown(Choices.embedding, label="Embedding", multiselect=True, interactive=True)
  728.                             embedding.change(fn=Function.add_embedding, inputs=[embedding, negative_prompt], outputs=[negative_prompt])
  729.                     with gr.Row():
  730.                         SD.input_image = gr.Image(value=None, type="pil")
  731.                         gr.HTML("<br>上传图片即自动转为图生图模式。<br><br>文生图、图生图模式共享设置参数。<br><br>图像宽度、图像高度、批次大小对图生图无效。")
  732.                 with gr.Tab(label="采样设置"):
  733.                     with gr.Row():
  734.                         sampler_name = gr.Dropdown(Choices.sampler, label="Sampling method | 采样方法", value=Choices.sampler[12])
  735.                         scheduler = gr.Dropdown(Choices.scheduler, label="Schedule type | 采样计划表类型", value=Choices.scheduler[1])
  736.                     with gr.Row():
  737.                         denoise = gr.Slider(minimum=0, maximum=1, step=0.05, label="Denoise | 去噪强度", value=1)
  738.                         steps = gr.Slider(minimum=1, maximum=100, step=1, label="Sampling steps | 采样次数", value=Default.steps)
  739.                     with gr.Row():
  740.                         cfg = gr.Slider(minimum=0, maximum=20, step=0.1, label="CFG Scale | CFG权重", value=7)
  741.                         seed = gr.Slider(minimum=-1, maximum=18446744073709550000, step=1, label="Seed | 种子数", value=-1)
  742.                 if Choices.controlnet_model != []:
  743.                     ControlNet.blocks("SD")
  744.                 Postprocess.blocks("SD")
  745.             with gr.Column():
  746.                 with gr.Row():
  747.                     btn = gr.Button("Generate | 生成", elem_id="button")
  748.                     btn2 = gr.Button("Interrupt | 终止")
  749.                 output = gr.Gallery(preview=True, height=600)
  750.                 with gr.Row():
  751.                     SD.send_to_sd = gr.Button("发送图片至 SD")
  752.                     if SC.enable is True:
  753.                         SD.send_to_sc = gr.Button("发送图片至 SC")
  754.                     if SVD.enable is True:
  755.                         SD.send_to_svd = gr.Button("发送图片至 SVD")
  756.                     SD.send_to_extras = gr.Button("发送图片至 Extras")
  757.                     SD.send_to_info = gr.Button("发送图片至 Info")
  758.         SD.data = gr.State()
  759.         SD.index = gr.State()
  760.         btn.click(fn=SD.generate, inputs=[Initial.initialized, batch_count, ckpt_name, vae_name, clip_mode, clip_skip, width, height, batch_size, negative_prompt, positive_prompt, seed, steps, cfg, sampler_name, scheduler, denoise, SD.input_image], outputs=[output, SD.data])
  761.         btn2.click(fn=Function.post_interrupt, inputs=None, outputs=None)
  762.         output.select(fn=Function.get_gallery_index, inputs=None, outputs=[SD.index])
  763.  
  764. class SC:
  765.     if Default.design_mode == 1:
  766.         enable = True
  767.     elif "stable_cascade_stage_c.safetensors" in Choices.ckpt_list and "stable_cascade_stage_b.safetensors" in Choices.ckpt_list:
  768.         enable = True
  769.     else:
  770.         enable = False
  771.  
  772.     def generate(initialized, batch_count, positive_prompt, negative_prompt, width, height, batch_size, seed_c, steps_c, cfg_c, sampler_name_c, scheduler_c, denoise_c, seed_b, steps_b, cfg_b, sampler_name_b, scheduler_b, denoise_b, input_image, progress=gr.Progress()):
  773.         module = "SC"
  774.         ckpt_name_c = Function.get_model_path("stable_cascade_stage_c.safetensors")
  775.         ckpt_name_b = Function.get_model_path("stable_cascade_stage_b.safetensors")
  776.         seed_c = Function.gen_seed(seed_c)
  777.         seed_b = Function.gen_seed(seed_b)
  778.         if input_image is not None:
  779.             input_image = Function.upload_image(input_image)
  780.         counter = 1
  781.         output_images = []
  782.         while counter <= batch_count:
  783.             workflow = {
  784. "1": {"inputs": {"ckpt_name": ckpt_name_c}, "class_type": "CheckpointLoaderSimple"},
  785. "2": {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"},
  786. "3": {"inputs": {"compression": 42, "image": ["2", 0], "vae": ["1", 2]}, "class_type": "StableCascade_StageC_VAEEncode"},
  787. "4": {"inputs": {"text": negative_prompt, "clip": ["1", 1]}, "class_type": "CLIPTextEncode"},
  788. "5": {"inputs": {"text": positive_prompt, "clip": ["1", 1]}, "class_type": "CLIPTextEncode"},
  789. "6": {"inputs": {"seed": seed_c, "steps": steps_c, "cfg": cfg_c, "sampler_name": sampler_name_c, "scheduler": scheduler_c, "denoise": denoise_c, "model": ["1", 0], "positive": ["5", 0], "negative": ["4", 0], "latent_image": ["3", 0]}, "class_type": "KSampler"},
  790. "7": {"inputs": {"conditioning": ["5", 0], "stage_c": ["6", 0]}, "class_type": "StableCascade_StageB_Conditioning"},
  791. "8": {"inputs": {"ckpt_name": ckpt_name_b}, "class_type": "CheckpointLoaderSimple"},
  792. "9": {"inputs": {"seed": seed_b, "steps": steps_b, "cfg": cfg_b, "sampler_name": sampler_name_b, "scheduler": scheduler_b, "denoise": denoise_b, "model": ["8", 0], "positive": ["7", 0], "negative": ["4", 0], "latent_image": ["3", 1]}, "class_type": "KSampler"},
  793. "10": {"inputs": {"samples": ["9", 0], "vae": ["8", 2]}, "class_type": "VAEDecode"}
  794. }
  795.             if input_image is None:
  796.                 del workflow["2"]
  797.                 workflow["3"] = {"inputs": {"width": width, "height": height, "compression": 42, "batch_size": batch_size}, "class_type": "StableCascade_EmptyLatentImage"}
  798.             node_id = 10
  799.             image_port = [str(node_id), 0]
  800.             if initialized is True:
  801.                 workflow, node_id, image_port = Postprocess.add_node(module, workflow, node_id, image_port)
  802.             node_id += 1
  803.             workflow[str(node_id)] = {"inputs": {"filename_prefix": "ComfyUI", "images": image_port}, "class_type": "SaveImage"}
  804.             images = Function.gen_image(workflow, counter, batch_count, progress)[0]
  805.             if images is None:
  806.                 break
  807.             for image in images:
  808.                 output_images.append(image)
  809.             seed_c += 1
  810.             counter += 1
  811.         return output_images, output_images
  812.  
  813.     def blocks():
  814.         with gr.Row():
  815.             with gr.Column():
  816.                 positive_prompt = gr.Textbox(placeholder="Positive prompt | 正向提示词", show_label=False, value=Default.prompt, lines=3)
  817.                 negative_prompt = gr.Textbox(placeholder="Negative prompt | 负向提示词", show_label=False, value=Default.negative_prompt, lines=3)
  818.                 with gr.Tab(label="基础设置"):
  819.                     with gr.Row():
  820.                         width = gr.Slider(minimum=128, maximum=2048, step=128, label="Width | 图像宽度", value=1024)
  821.                         batch_size = gr.Slider(minimum=1, maximum=8, step=1, label="Batch size | 批次大小", value=1)
  822.                     with gr.Row():
  823.                         height = gr.Slider(minimum=128, maximum=2048, step=128, label="Height | 图像高度", value=1024)
  824.                         batch_count = gr.Slider(minimum=1, maximum=100, step=1, label="Batch count | 生成批次", value=1)
  825.                     with gr.Row():
  826.                         SC.input_image = gr.Image(value=None, type="pil")
  827.                         gr.HTML("<br>上传图片即自动转为图生图模式。<br><br>文生图、图生图模式共享设置参数。<br><br>图像宽度、图像高度、批次大小对图生图无效。")
  828.                 with gr.Tab(label="Stage C 采样设置"):
  829.                     with gr.Row():
  830.                         sampler_name_c = gr.Dropdown(Choices.sampler, label="Sampling method | 采样方法", value=Choices.sampler[12])
  831.                         scheduler_c = gr.Dropdown(Choices.scheduler, label="Schedule type | 采样计划表类型", value=Choices.scheduler[1])
  832.                     with gr.Row():
  833.                         denoise_c = gr.Slider(minimum=0, maximum=1, step=0.05, label="Denoise | 去噪强度", value=1)
  834.                         steps_c = gr.Slider(minimum=10, maximum=30, step=1, label="Sampling steps | 采样次数", value=20)
  835.                     with gr.Row():
  836.                         cfg_c = gr.Slider(minimum=0, maximum=20, step=0.1, label="CFG Scale | CFG权重", value=4)
  837.                         seed_c = gr.Slider(minimum=-1, maximum=18446744073709550000, step=1, label="Seed | 种子数", value=-1)
  838.                 with gr.Tab(label="Stage B 采样设置"):
  839.                     with gr.Row():
  840.                         sampler_name_b = gr.Dropdown(Choices.sampler, label="Sampling method | 采样方法", value=Choices.sampler[12])
  841.                         scheduler_b = gr.Dropdown(Choices.scheduler, label="Schedule type | 采样计划表类型", value=Choices.scheduler[1])
  842.                     with gr.Row():
  843.                         denoise_b = gr.Slider(minimum=0, maximum=1, step=0.05, label="Denoise | 去噪强度", value=1)
  844.                         steps_b = gr.Slider(minimum=4, maximum=12, step=1, label="Sampling steps | 采样次数", value=10)
  845.                     with gr.Row():
  846.                         cfg_b = gr.Slider(minimum=0, maximum=20, step=0.1, label="CFG Scale | CFG权重", value=1.1)
  847.                         seed_b = gr.Slider(minimum=-1, maximum=18446744073709550000, step=1, label="Seed | 种子数", value=-1)
  848.                 Postprocess.blocks("SC")
  849.             with gr.Column():
  850.                 with gr.Row():
  851.                     btn = gr.Button("Generate | 生成", elem_id="button")
  852.                     btn2 = gr.Button("Interrupt | 终止")
  853.                 output = gr.Gallery(preview=True, height=600)
  854.                 with gr.Row():
  855.                     SC.send_to_sd = gr.Button("发送图片至 SD")
  856.                     SC.send_to_sc = gr.Button("发送图片至 SC")
  857.                     if SVD.enable is True:
  858.                         SC.send_to_svd = gr.Button("发送图片至 SVD")
  859.                     SC.send_to_extras = gr.Button("发送图片至 Extras")
  860.                     SC.send_to_info = gr.Button("发送图片至 Info")
  861.         SC.data = gr.State()
  862.         SC.index = gr.State()
  863.         btn.click(fn=SC.generate, inputs=[Initial.initialized, batch_count, positive_prompt, negative_prompt, width, height, batch_size, seed_c, steps_c, cfg_c, sampler_name_c, scheduler_c, denoise_c, seed_b, steps_b, cfg_b, sampler_name_b, scheduler_b, denoise_b, SC.input_image], outputs=[output, SC.data])
  864.         btn2.click(fn=Function.post_interrupt, inputs=None, outputs=None)
  865.         output.select(fn=Function.get_gallery_index, inputs=None, outputs=[SC.index])
  866.  
  867. class SVD:
  868.     if Default.design_mode == 1:
  869.         enable = True
  870.     elif "svd_xt_1_1.safetensors" in Choices.ckpt_list:
  871.         enable = True
  872.     else:
  873.         enable = False
  874.  
  875.     def generate(input_image, width, height, video_frames, motion_bucket_id, fps, augmentation_level, min_cfg, seed, steps, cfg, sampler_name, scheduler, denoise, fps2, lossless, quality, method, progress=gr.Progress()):
  876.         ckpt_name = Function.get_model_path("svd_xt_1_1.safetensors")
  877.         seed = Function.gen_seed(seed)
  878.         if input_image is None:
  879.             return
  880.         else:
  881.             input_image = Function.upload_image(input_image)
  882.         workflow = {
  883. "1": {"inputs": {"ckpt_name": ckpt_name}, "class_type": "ImageOnlyCheckpointLoader"},
  884. "2": {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"},
  885. "3": {"inputs": {"width": width, "height": height, "video_frames": video_frames, "motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "clip_vision": ["1", 1], "init_image": ["2", 0], "vae": ["1", 2]}, "class_type": "SVD_img2vid_Conditioning"},
  886. "4": {"inputs": {"min_cfg": min_cfg, "model": ["1", 0]}, "class_type": "VideoLinearCFGGuidance"},
  887. "5": {"inputs": {"seed": seed, "steps": steps, "cfg": cfg, "sampler_name": sampler_name, "scheduler": scheduler, "denoise": denoise, "model": ["4", 0], "positive": ["3", 0], "negative": ["3", 1], "latent_image": ["3", 2]}, "class_type": "KSampler"},
  888. "6": {"inputs": {"samples": ["5", 0], "vae": ["1", 2]}, "class_type": "VAEDecode"},
  889. "7": {"inputs": {"filename_prefix": "ComfyUI", "fps": fps2, "lossless": False, "quality": quality, "method": method, "images": ["6", 0]}, "class_type": "SaveAnimatedWEBP"}
  890. }
  891.         return Function.gen_image(workflow, 1, 1, progress)[1]
  892.  
  893.     def blocks():
  894.         with gr.Row():
  895.             with gr.Column():
  896.                 SVD.input_image = gr.Image(value=None, type="pil")
  897.                 with gr.Tab(label="基础设置"):
  898.                     with gr.Row():
  899.                         width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width | 图像宽度", value=512)
  900.                         video_frames = gr.Slider(minimum=1, maximum=25, step=1, label="Video frames | 视频帧", value=25)
  901.                     with gr.Row():
  902.                         height = gr.Slider(minimum=64, maximum=2048, step=64, label="Height | 图像高度", value=512)
  903.                         fps = gr.Slider(minimum=1, maximum=30, step=1, label="FPS | 帧率", value=6)
  904.                     with gr.Row():
  905.                         with gr.Column():
  906.                             augmentation_level = gr.Slider(minimum=0, maximum=1, step=0.01, label="Augmentation level | 增强级别", value=0)
  907.                             motion_bucket_id = gr.Slider(minimum=1, maximum=256, step=1, label="Motion bucket id | 运动参数", value=127)
  908.                         with gr.Column():
  909.                             min_cfg = gr.Slider(minimum=0, maximum=20, step=0.5, label="Min CFG | 最小CFG权重", value=1)
  910.                 with gr.Tab(label="采样设置"):
  911.                     with gr.Row():
  912.                         sampler_name = gr.Dropdown(Choices.sampler, label="Sampling method | 采样方法", value=Choices.sampler[12])
  913.                         scheduler = gr.Dropdown(Choices.scheduler, label="Schedule type | 采样计划表类型", value=Choices.scheduler[1])
  914.                     with gr.Row():
  915.                         denoise = gr.Slider(minimum=0, maximum=1, step=0.05, label="Denoise | 去噪强度", value=1)
  916.                         steps = gr.Slider(minimum=10, maximum=30, step=1, label="Sampling steps | 采样次数", value=20)
  917.                     with gr.Row():
  918.                         cfg = gr.Slider(minimum=0, maximum=20, step=0.1, label="CFG Scale | CFG权重", value=2.5)
  919.                         seed = gr.Slider(minimum=-1, maximum=18446744073709550000, step=1, label="Seed | 种子数", value=-1)
  920.                 with gr.Tab(label="输出设置"):
  921.                     with gr.Row():
  922.                         method = gr.Dropdown(["default", "fastest", "slowest"], label="Method | 输出方法", value="default")
  923.                         lossless = gr.Dropdown(["true", "false"], label="Lossless | 无损压缩", value="false")
  924.                     with gr.Row():
  925.                         quality = gr.Slider(minimum=70, maximum=100, step=1, label="Quality | 输出质量", value=85)
  926.                         fps2 = gr.Slider(minimum=1, maximum=30, step=1, label="FPS | 帧率", value=10)
  927.             with gr.Column():
  928.                 with gr.Row():
  929.                     btn = gr.Button("Generate | 生成", elem_id="button")
  930.                     btn2 = gr.Button("Interrupt | 终止")
  931.                 output = gr.Image(height=600)
  932.         btn.click(fn=SVD.generate, inputs=[SVD.input_image, width, height, video_frames, motion_bucket_id, fps, augmentation_level, min_cfg, seed, steps, cfg, sampler_name, scheduler, denoise, fps2, lossless, quality, method], outputs=[output])
  933.         btn2.click(fn=Function.post_interrupt, inputs=None, outputs=None)
  934.  
  935. class Extras:
  936.     def generate(initialized, input_image, progress=gr.Progress()):
  937.         module = "Extras"
  938.         if input_image is None:
  939.             return
  940.         else:
  941.             input_image = Function.upload_image(input_image)
  942.         workflow = {}
  943.         node_id = 1
  944.         workflow[str(node_id)] = {"inputs": {"image": input_image, "upload": "image"}, "class_type": "LoadImage"}
  945.         image_port = [str(node_id), 0]
  946.         if initialized is True:
  947.             if module not in Upscale.cache and module not in UpscaleWithModel.cache:
  948.                 return
  949.             if module in Upscale.cache:
  950.                 workflow, node_id, image_port = Upscale.add_node(module, workflow, node_id, image_port)
  951.             if module in UpscaleWithModel.cache:
  952.                 workflow, node_id, image_port = UpscaleWithModel.add_node(module, workflow, node_id, image_port)
  953.         else:
  954.             return
  955.         node_id += 1
  956.         workflow[str(node_id)] = {"inputs": {"filename_prefix": "ComfyUI", "images": image_port}, "class_type": "SaveImage"}
  957.         output = Function.gen_image(workflow, 1, 1, progress)[0]
  958.         if output is not None:
  959.             output = output[0]
  960.         return output
  961.  
  962.     def blocks():
  963.         with gr.Row():
  964.             with gr.Column():
  965.                 Extras.input_image = gr.Image(value=None, type="pil")
  966.                 with gr.Row():
  967.                     with gr.Tab(label="算术放大"):
  968.                         Upscale.blocks("Extras")
  969.                 with gr.Row():
  970.                     with gr.Tab(label="超分放大"):
  971.                         UpscaleWithModel.blocks("Extras")
  972.                 gr.HTML("注意：同时启用两种放大模式将先执行算术放大，再执行超分放大，最终放大倍数为二者放大倍数的乘积！")
  973.             with gr.Column():
  974.                 with gr.Row():
  975.                     btn = gr.Button("Generate | 生成", elem_id="button")
  976.                     btn2 = gr.Button("Interrupt | 终止")
  977.                 output = gr.Image(height=600)
  978.         btn.click(fn=Extras.generate, inputs=[Initial.initialized, Extras.input_image], outputs=[output])
  979.         btn2.click(fn=Function.post_interrupt, inputs=None, outputs=None)
  980.  
  981. class Info:
  982.     def generate(image_info, progress=gr.Progress()):
  983.         if not image_info or image_info is None or image_info == "仅支持API工作流！！！" or "Version:" in image_info or image_info == "None":
  984.             return
  985.         workflow = json.loads(image_info)
  986.         return Function.gen_image(workflow, 1, 1, progress)[0]
  987.  
  988.     def order_workflow(workflow):
  989.         if workflow is None:
  990.             return gr.update(visible=False, value=None)
  991.         workflow = json.loads(workflow)
  992.         if "last_node_id" in workflow:
  993.             return gr.update(show_label=False, visible=True, value="仅支持API工作流！！！", lines=1)
  994.         workflow = Function.order_workflow(workflow)
  995.         lines = len(workflow) + 5
  996.         workflow_string = "{"
  997.         for node in workflow:
  998.             workflow_string = workflow_string + "\n" + f'"{node}": {workflow[node]},'
  999.         workflow_string = workflow_string + "\n}"
  1000.         workflow_string = workflow_string.replace(",\n}", "\n}")
  1001.         workflow_string = workflow_string.replace("'", '"')
  1002.         return gr.update(label="Ordered workflow_api", show_label=True, visible=True, value=workflow_string, lines=lines)
  1003.  
  1004.     def get_image_info(image_pil):
  1005.         if image_pil is None:
  1006.             return gr.update(visible=False, value=None)
  1007.         else:
  1008.             image_info = Function.get_image_info(image_pil)
  1009.             if image_info == "None":
  1010.                 return gr.update(visible=False, value=None)
  1011.             if "Version:" in image_info:
  1012.                 return gr.update(label="Image info", show_label=True, visible=True, value=image_info, lines=3)
  1013.             return Info.order_workflow(image_info)
  1014.  
  1015.     def hide_another_input(this_input):
  1016.         if this_input is None:
  1017.             return gr.update(visible=True)
  1018.         return gr.update(visible=False)
  1019.  
  1020.     def blocks():
  1021.         with gr.Row():
  1022.             with gr.Column():
  1023.                 Info.input_image = gr.Image(value=None, type="pil")
  1024.                 workflow = gr.File(label="workflow_api.json", file_types=[".json"], type="binary")
  1025.                 image_info = gr.Textbox(visible=False)
  1026.             with gr.Column():
  1027.                 with gr.Row():
  1028.                     btn = gr.Button("Generate | 生成", elem_id="button")
  1029.                     btn2 = gr.Button("Interrupt | 终止")
  1030.                 output = gr.Gallery(preview=True, height=600)
  1031.         btn.click(fn=Info.generate, inputs=[image_info], outputs=[output])
  1032.         btn2.click(fn=Function.post_interrupt, inputs=None, outputs=None)
  1033.         Info.input_image.change(fn=Info.hide_another_input, inputs=[Info.input_image], outputs=[workflow])
  1034.         Info.input_image.change(fn=Info.get_image_info, inputs=[Info.input_image], outputs=[image_info])
  1035.         workflow.change(fn=Info.hide_another_input, inputs=[workflow], outputs=[Info.input_image])
  1036.         workflow.change(fn=Info.order_workflow, inputs=[workflow], outputs=[image_info])
  1037.  
  1038. with 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: 1.15em;}") as demo:
  1039.     Initial.initialized = gr.Checkbox(value=False, visible=False)
  1040.     with gr.Tab(label="Stable Diffusion"): SD.blocks()
  1041.     if SC.enable is True:
  1042.         with gr.Tab(label="Stable Cascade"): SC.blocks()
  1043.     if SVD.enable is True:
  1044.         with gr.Tab(label="Stable Video Diffusion"): SVD.blocks()
  1045.     with gr.Tab(label="Extras"): Extras.blocks()
  1046.     with gr.Tab(label="Info"): Info.blocks()
  1047.     SD.send_to_sd.click(fn=Function.send_to, inputs=[SD.data, SD.index], outputs=[SD.input_image])
  1048.     if SC.enable is True:
  1049.         SD.send_to_sc.click(fn=Function.send_to, inputs=[SD.data, SD.index], outputs=[SC.input_image])
  1050.     if SVD.enable is True:
  1051.         SD.send_to_svd.click(fn=Function.send_to, inputs=[SD.data, SD.index], outputs=[SVD.input_image])
  1052.     SD.send_to_extras.click(fn=Function.send_to, inputs=[SD.data, SD.index], outputs=[Extras.input_image])
  1053.     SD.send_to_info.click(fn=Function.send_to, inputs=[SD.data, SD.index], outputs=[Info.input_image])
  1054.     if SC.enable is True:
  1055.         SC.send_to_sd.click(fn=Function.send_to, inputs=[SC.data, SC.index], outputs=[SD.input_image])
  1056.         SC.send_to_sc.click(fn=Function.send_to, inputs=[SC.data, SC.index], outputs=[SC.input_image])
  1057.         if SVD.enable is True:
  1058.             SC.send_to_svd.click(fn=Function.send_to, inputs=[SC.data, SC.index], outputs=[SVD.input_image])
  1059.         SC.send_to_extras.click(fn=Function.send_to, inputs=[SC.data, SC.index], outputs=[Extras.input_image])
  1060.         SC.send_to_info.click(fn=Function.send_to, inputs=[SC.data, SC.index], outputs=[Info.input_image])
  1061.  
  1062. demo.queue(concurrency_count=100).launch(inbrowser=True, inline=False)

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