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import spaces |
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import argparse |
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from ast import parse |
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import datetime |
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import json |
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import os |
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import time |
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import hashlib |
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import re |
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import gradio as gr |
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import requests |
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import random |
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from filelock import FileLock |
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from io import BytesIO |
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from PIL import Image, ImageDraw, ImageFont |
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from constants import LOGDIR |
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from utils import ( |
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build_logger, |
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server_error_msg, |
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violates_moderation, |
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moderation_msg, |
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load_image_from_base64, |
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get_log_filename, |
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) |
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from conversation import Conversation |
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logger = build_logger("gradio_web_server", "gradio_web_server.log") |
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headers = {"User-Agent": "InternVL-Chat Client"} |
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no_change_btn = gr.Button() |
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enable_btn = gr.Button(interactive=True) |
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disable_btn = gr.Button(interactive=False) |
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@spaces.GPU(duration=10) |
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def make_zerogpu_happy(): |
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pass |
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def write2file(path, content): |
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lock = FileLock(f"{path}.lock") |
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with lock: |
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with open(path, "a") as fout: |
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fout.write(content) |
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def sort_models(models): |
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def custom_sort_key(model_name): |
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if model_name == "InternVL2-Pro": |
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return (2, model_name) |
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elif model_name.startswith("InternVL2-8B"): |
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return (1, model_name) |
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else: |
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return (0, model_name) |
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models.sort(key=custom_sort_key, reverse=True) |
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return models |
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def get_model_list(): |
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logger.info(f"Call `get_model_list`") |
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ret = requests.post(args.controller_url + "/refresh_all_workers") |
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logger.info(f"status_code from `get_model_list`: {ret.status_code}") |
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assert ret.status_code == 200 |
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ret = requests.post(args.controller_url + "/list_models") |
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logger.info(f"status_code from `list_models`: {ret.status_code}") |
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models = ret.json()["models"] |
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models = sort_models(models) |
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logger.info(f"Models (from {args.controller_url}): {models}") |
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return models |
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get_window_url_params = """ |
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function() { |
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const params = new URLSearchParams(window.location.search); |
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url_params = Object.fromEntries(params); |
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console.log(url_params); |
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return url_params; |
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} |
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""" |
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def init_state(state=None): |
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if state is not None: |
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del state |
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return Conversation() |
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def find_bounding_boxes(state, response): |
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pattern = re.compile(r"<ref>\s*(.*?)\s*</ref>\s*<box>\s*(\[\[.*?\]\])\s*</box>") |
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matches = pattern.findall(response) |
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results = [] |
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for match in matches: |
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results.append((match[0], eval(match[1]))) |
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returned_image = None |
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latest_image = state.get_images(source=state.USER)[-1] |
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returned_image = latest_image.copy() |
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width, height = returned_image.size |
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draw = ImageDraw.Draw(returned_image) |
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for result in results: |
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line_width = max(1, int(min(width, height) / 200)) |
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random_color = ( |
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random.randint(0, 128), |
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random.randint(0, 128), |
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random.randint(0, 128), |
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) |
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category_name, coordinates = result |
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coordinates = [ |
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( |
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float(x[0]) / 1000, |
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float(x[1]) / 1000, |
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float(x[2]) / 1000, |
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float(x[3]) / 1000, |
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) |
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for x in coordinates |
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] |
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coordinates = [ |
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( |
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int(x[0] * width), |
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int(x[1] * height), |
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int(x[2] * width), |
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int(x[3] * height), |
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) |
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for x in coordinates |
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] |
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for box in coordinates: |
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draw.rectangle(box, outline=random_color, width=line_width) |
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font = ImageFont.truetype("assets/SimHei.ttf", int(20 * line_width / 2)) |
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text_size = font.getbbox(category_name) |
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text_width, text_height = ( |
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text_size[2] - text_size[0], |
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text_size[3] - text_size[1], |
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) |
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text_position = (box[0], max(0, box[1] - text_height)) |
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draw.rectangle( |
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[ |
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text_position, |
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(text_position[0] + text_width, text_position[1] + text_height), |
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], |
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fill=random_color, |
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) |
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draw.text(text_position, category_name, fill="white", font=font) |
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return returned_image if len(matches) > 0 else None |
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def query_image_generation(response, sd_worker_url, timeout=15): |
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if not sd_worker_url: |
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return None |
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sd_worker_url = f"{sd_worker_url}/generate_image/" |
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pattern = r"```drawing-instruction\n(.*?)\n```" |
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match = re.search(pattern, response, re.DOTALL) |
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if match: |
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payload = {"caption": match.group(1)} |
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print("drawing-instruction:", payload) |
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response = requests.post(sd_worker_url, json=payload, timeout=timeout) |
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response.raise_for_status() |
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image = Image.open(BytesIO(response.content)) |
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return image |
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else: |
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return None |
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def load_demo(url_params, request: gr.Request = None): |
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if not request: |
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logger.info(f"load_demo. ip: {request.client.host}. params: {url_params}") |
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dropdown_update = gr.Dropdown(visible=True) |
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if "model" in url_params: |
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model = url_params["model"] |
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if model in models: |
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dropdown_update = gr.Dropdown(value=model, visible=True) |
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state = init_state() |
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return state, dropdown_update |
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def load_demo_refresh_model_list(request: gr.Request = None): |
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if not request: |
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logger.info(f"load_demo. ip: {request.client.host}") |
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models = get_model_list() |
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state = init_state() |
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dropdown_update = gr.Dropdown( |
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choices=models, value=models[0] if len(models) > 0 else "" |
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) |
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return state, dropdown_update |
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def vote_last_response(state, liked, model_selector, request: gr.Request): |
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conv_data = { |
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"tstamp": round(time.time(), 4), |
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"like": liked, |
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"model": model_selector, |
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"state": state.dict(), |
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"ip": request.client.host, |
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} |
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write2file(get_log_filename(), json.dumps(conv_data) + "\n") |
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def upvote_last_response(state, model_selector, request: gr.Request): |
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logger.info(f"upvote. ip: {request.client.host}") |
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vote_last_response(state, True, model_selector, request) |
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textbox = gr.MultimodalTextbox(value=None, interactive=True) |
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return (textbox,) + (disable_btn,) * 3 |
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def downvote_last_response(state, model_selector, request: gr.Request): |
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logger.info(f"downvote. ip: {request.client.host}") |
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vote_last_response(state, False, model_selector, request) |
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textbox = gr.MultimodalTextbox(value=None, interactive=True) |
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return (textbox,) + (disable_btn,) * 3 |
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def vote_selected_response( |
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state, model_selector, request: gr.Request, data: gr.LikeData |
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): |
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logger.info( |
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f"Vote: {data.liked}, index: {data.index}, value: {data.value} , ip: {request.client.host}" |
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) |
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conv_data = { |
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"tstamp": round(time.time(), 4), |
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"like": data.liked, |
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"index": data.index, |
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"model": model_selector, |
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"state": state.dict(), |
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"ip": request.client.host, |
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} |
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write2file(get_log_filename(), json.dumps(conv_data) + "\n") |
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return |
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def flag_last_response(state, model_selector, request: gr.Request): |
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logger.info(f"flag. ip: {request.client.host}") |
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vote_last_response(state, "flag", model_selector, request) |
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textbox = gr.MultimodalTextbox(value=None, interactive=True) |
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return (textbox,) + (disable_btn,) * 3 |
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def regenerate(state, image_process_mode, request: gr.Request): |
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logger.info(f"regenerate. ip: {request.client.host}") |
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state.update_message(Conversation.ASSISTANT, None, -1) |
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prev_human_msg = state.messages[-2] |
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if type(prev_human_msg[1]) in (tuple, list): |
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prev_human_msg[1] = (*prev_human_msg[1][:2], image_process_mode) |
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state.skip_next = False |
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textbox = gr.MultimodalTextbox(value=None, interactive=True) |
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return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 |
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def clear_history(request: gr.Request): |
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logger.info(f"clear_history. ip: {request.client.host}") |
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state = init_state() |
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textbox = gr.MultimodalTextbox(value=None, interactive=True) |
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return (state, state.to_gradio_chatbot(), textbox) + (disable_btn,) * 5 |
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def change_system_prompt(state, system_prompt, request: gr.Request): |
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logger.info(f"Change system prompt. ip: {request.client.host}") |
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state.set_system_message(system_prompt) |
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return state |
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def add_text(state, message, system_prompt, model_selector, request: gr.Request): |
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print(f"state: {state}") |
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if not state: |
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state = init_state() |
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images = message.get("files", []) |
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text = message.get("text", "").strip() |
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logger.info(f"add_text. ip: {request.client.host}. len: {len(text)}") |
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textbox = gr.MultimodalTextbox(value=None, interactive=False) |
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if len(text) <= 0 and len(images) == 0: |
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state.skip_next = True |
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return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 |
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if args.moderate: |
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flagged = violates_moderation(text) |
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if flagged: |
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state.skip_next = True |
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textbox = gr.MultimodalTextbox( |
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value={"text": moderation_msg}, interactive=True |
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) |
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return (state, state.to_gradio_chatbot(), textbox) + (no_change_btn,) * 5 |
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images = [Image.open(path).convert("RGB") for path in images] |
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if len(images) > 0 and len(state.get_images(source=state.USER)) > 0: |
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state = init_state(state) |
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state.set_system_message(system_prompt) |
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state.append_message(Conversation.USER, text, images) |
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state.skip_next = False |
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return (state, state.to_gradio_chatbot(), textbox, model_selector) + ( |
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disable_btn, |
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) * 5 |
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def http_bot( |
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state, |
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model_selector, |
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temperature, |
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top_p, |
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repetition_penalty, |
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max_new_tokens, |
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max_input_tiles, |
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request: gr.Request, |
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): |
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logger.info(f"http_bot. ip: {request.client.host}") |
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start_tstamp = time.time() |
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model_name = model_selector |
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if hasattr(state, "skip_next") and state.skip_next: |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=False), |
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) + (no_change_btn,) * 5 |
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return |
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controller_url = args.controller_url |
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ret = requests.post( |
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controller_url + "/get_worker_address", json={"model": model_name} |
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) |
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worker_addr = ret.json()["address"] |
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if worker_addr.startswith("http://0.0.0.0") and args.worker_ip: |
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worker_addr = worker_addr.replace("0.0.0.0", args.worker_ip) |
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logger.info(f"model_name: {model_name}, worker_addr: {worker_addr}") |
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|
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if worker_addr == "": |
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state.update_message(Conversation.ASSISTANT, server_error_msg) |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=False), |
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disable_btn, |
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disable_btn, |
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disable_btn, |
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enable_btn, |
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enable_btn, |
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) |
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return |
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|
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all_images = state.get_images(source=state.USER) |
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all_image_paths = [state.save_image(image) for image in all_images] |
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pload = { |
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"model": model_name, |
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"prompt": state.get_prompt(), |
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"temperature": float(temperature), |
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"top_p": float(top_p), |
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"max_new_tokens": max_new_tokens, |
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"max_input_tiles": max_input_tiles, |
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|
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"repetition_penalty": repetition_penalty, |
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"images": f"List of {len(all_images)} images: {all_image_paths}", |
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} |
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logger.info(f"==== request ====\n{pload}") |
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pload.pop("images") |
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pload["prompt"] = state.get_prompt(inlude_image=True) |
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state.append_message(Conversation.ASSISTANT, state.streaming_placeholder) |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=False), |
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) + (disable_btn,) * 5 |
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try: |
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|
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response = requests.post( |
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worker_addr + "/worker_generate_stream", |
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headers=headers, |
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json=pload, |
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stream=True, |
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timeout=20, |
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) |
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for chunk in response.iter_lines(decode_unicode=False, delimiter=b"\0"): |
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if chunk: |
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data = json.loads(chunk.decode()) |
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if data["error_code"] == 0: |
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if "text" in data: |
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output = data["text"].strip() |
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output += state.streaming_placeholder |
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|
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image = None |
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if "image" in data: |
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image = load_image_from_base64(data["image"]) |
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_ = state.save_image(image) |
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|
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state.update_message(Conversation.ASSISTANT, output, image) |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=False), |
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) + (disable_btn,) * 5 |
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else: |
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output = ( |
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f"**{data['text']}**" + f" (error_code: {data['error_code']})" |
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) |
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|
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state.update_message(Conversation.ASSISTANT, output, None) |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=True), |
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) + ( |
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disable_btn, |
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disable_btn, |
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disable_btn, |
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enable_btn, |
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enable_btn, |
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) |
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return |
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except requests.exceptions.RequestException as e: |
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state.update_message(Conversation.ASSISTANT, server_error_msg, None) |
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=True), |
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) + ( |
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disable_btn, |
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disable_btn, |
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disable_btn, |
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enable_btn, |
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enable_btn, |
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) |
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return |
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|
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ai_response = state.return_last_message() |
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if "<ref>" in ai_response: |
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returned_image = find_bounding_boxes(state, ai_response) |
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returned_image = [returned_image] if returned_image else [] |
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state.update_message(Conversation.ASSISTANT, ai_response, returned_image) |
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if "```drawing-instruction" in ai_response: |
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returned_image = query_image_generation( |
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ai_response, sd_worker_url=sd_worker_url |
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) |
|
returned_image = [returned_image] if returned_image else [] |
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state.update_message(Conversation.ASSISTANT, ai_response, returned_image) |
|
|
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state.end_of_current_turn() |
|
|
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yield ( |
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state, |
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state.to_gradio_chatbot(), |
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gr.MultimodalTextbox(interactive=True), |
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) + (enable_btn,) * 5 |
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|
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finish_tstamp = time.time() |
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logger.info(f"{output}") |
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data = { |
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"tstamp": round(finish_tstamp, 4), |
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"like": None, |
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"model": model_name, |
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"start": round(start_tstamp, 4), |
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"finish": round(start_tstamp, 4), |
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"state": state.dict(), |
|
"images": all_image_paths, |
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"ip": request.client.host, |
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} |
|
write2file(get_log_filename(), json.dumps(data) + "\n") |
|
|
|
|
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title_html = """ |
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<h2> <span class="gradient-text" id="text">InternVL2</span><span class="plain-text">: Better than the Best—Expanding Performance Boundaries of Open-Source Multimodal Models with the Progressive Scaling Strategy</span></h2> |
|
<a href="https://internvl.github.io/blog/2024-07-02-InternVL-2.0/">[📜 InternVL2 Blog]</a> |
|
<a href="https://huggingface.co/spaces/OpenGVLab/InternVL">[🤗 HF Demo]</a> |
|
<a href="https://github.com/OpenGVLab/InternVL?tab=readme-ov-file#quick-start-with-huggingface">[🚀 Quick Start]</a> |
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<a href="https://github.com/OpenGVLab/InternVL/blob/main/document/How_to_use_InternVL_API.md">[🌐 API]</a> |
|
""" |
|
|
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tos_markdown = """ |
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### Terms of use |
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By using this service, users are required to agree to the following terms: |
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The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research. |
|
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator. |
|
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality. |
|
""" |
|
|
|
|
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learn_more_markdown = """ |
|
### License |
|
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation. |
|
|
|
### Acknowledgement |
|
This demo is modified from LLaVA's demo. Thanks for their awesome work! |
|
""" |
|
|
|
block_css = """ |
|
.gradio-container {margin: 0.1% 1% 0 1% !important; max-width: 98% !important;}; |
|
#buttons button { |
|
min-width: min(120px,100%); |
|
} |
|
|
|
.gradient-text { |
|
font-size: 28px; |
|
width: auto; |
|
font-weight: bold; |
|
background: linear-gradient(45deg, red, orange, yellow, green, blue, indigo, violet); |
|
background-clip: text; |
|
-webkit-background-clip: text; |
|
color: transparent; |
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} |
|
|
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.plain-text { |
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font-size: 22px; |
|
width: auto; |
|
font-weight: bold; |
|
} |
|
""" |
|
|
|
js = """ |
|
function createWaveAnimation() { |
|
const text = document.getElementById('text'); |
|
var i = 0; |
|
setInterval(function() { |
|
const colors = [ |
|
'red, orange, yellow, green, blue, indigo, violet, purple', |
|
'orange, yellow, green, blue, indigo, violet, purple, red', |
|
'yellow, green, blue, indigo, violet, purple, red, orange', |
|
'green, blue, indigo, violet, purple, red, orange, yellow', |
|
'blue, indigo, violet, purple, red, orange, yellow, green', |
|
'indigo, violet, purple, red, orange, yellow, green, blue', |
|
'violet, purple, red, orange, yellow, green, blue, indigo', |
|
'purple, red, orange, yellow, green, blue, indigo, violet', |
|
]; |
|
const angle = 45; |
|
const colorIndex = i % colors.length; |
|
text.style.background = `linear-gradient(${angle}deg, ${colors[colorIndex]})`; |
|
text.style.webkitBackgroundClip = 'text'; |
|
text.style.backgroundClip = 'text'; |
|
text.style.color = 'transparent'; |
|
text.style.fontSize = '28px'; |
|
text.style.width = 'auto'; |
|
text.textContent = 'InternVL2'; |
|
text.style.fontWeight = 'bold'; |
|
i += 1; |
|
}, 200); |
|
const params = new URLSearchParams(window.location.search); |
|
url_params = Object.fromEntries(params); |
|
// console.log(url_params); |
|
// console.log('hello world...'); |
|
// console.log(window.location.search); |
|
// console.log('hello world...'); |
|
// alert(window.location.search) |
|
// alert(url_params); |
|
return url_params; |
|
} |
|
|
|
""" |
|
|
|
|
|
def build_demo(embed_mode): |
|
textbox = gr.MultimodalTextbox( |
|
interactive=True, |
|
file_types=["image", "video"], |
|
placeholder="Enter message or upload file...", |
|
show_label=False, |
|
) |
|
|
|
with gr.Blocks( |
|
title="InternVL-Chat", |
|
theme=gr.themes.Default(), |
|
css=block_css, |
|
) as demo: |
|
models = get_model_list() |
|
state = gr.State() |
|
|
|
if not embed_mode: |
|
|
|
gr.HTML(title_html) |
|
|
|
with gr.Row(): |
|
with gr.Column(scale=2): |
|
|
|
with gr.Row(elem_id="model_selector_row"): |
|
model_selector = gr.Dropdown( |
|
choices=models, |
|
value=models[0] if len(models) > 0 else "", |
|
|
|
interactive=True, |
|
show_label=False, |
|
container=False, |
|
) |
|
|
|
with gr.Accordion("System Prompt", open=False) as system_prompt_row: |
|
system_prompt = gr.Textbox( |
|
value="请尽可能详细地回答用户的问题。", |
|
label="System Prompt", |
|
interactive=True, |
|
) |
|
with gr.Accordion("Parameters", open=False) as parameter_row: |
|
temperature = gr.Slider( |
|
minimum=0.0, |
|
maximum=1.0, |
|
value=0.2, |
|
step=0.1, |
|
interactive=True, |
|
label="Temperature", |
|
) |
|
top_p = gr.Slider( |
|
minimum=0.0, |
|
maximum=1.0, |
|
value=0.7, |
|
step=0.1, |
|
interactive=True, |
|
label="Top P", |
|
) |
|
repetition_penalty = gr.Slider( |
|
minimum=1.0, |
|
maximum=1.5, |
|
value=1.1, |
|
step=0.02, |
|
interactive=True, |
|
label="Repetition penalty", |
|
) |
|
max_output_tokens = gr.Slider( |
|
minimum=0, |
|
maximum=4096, |
|
value=1024, |
|
step=64, |
|
interactive=True, |
|
label="Max output tokens", |
|
) |
|
max_input_tiles = gr.Slider( |
|
minimum=1, |
|
maximum=32, |
|
value=12, |
|
step=1, |
|
interactive=True, |
|
label="Max input tiles (control the image size)", |
|
) |
|
examples = gr.Examples( |
|
examples=[ |
|
[ |
|
{ |
|
"files": [ |
|
"gallery/prod_9.jpg", |
|
], |
|
"text": "What's at the far end of the image?", |
|
} |
|
], |
|
[ |
|
{ |
|
"files": [ |
|
"gallery/astro_on_unicorn.png", |
|
], |
|
"text": "What does this image mean?", |
|
} |
|
], |
|
[ |
|
{ |
|
"files": [ |
|
"gallery/prod_12.png", |
|
], |
|
"text": "What are the consequences of the easy decisions shown in this image?", |
|
} |
|
], |
|
[ |
|
{ |
|
"files": [ |
|
"gallery/water.jpg", |
|
], |
|
"text": "Please describe this image.", |
|
} |
|
], |
|
], |
|
inputs=[textbox], |
|
) |
|
|
|
with gr.Column(scale=8): |
|
chatbot = gr.Chatbot( |
|
elem_id="chatbot", |
|
label="InternVL2", |
|
height=580, |
|
show_copy_button=True, |
|
show_share_button=True, |
|
avatar_images=[ |
|
"assets/human.png", |
|
"assets/assistant.png", |
|
], |
|
bubble_full_width=False, |
|
) |
|
with gr.Row(): |
|
with gr.Column(scale=8): |
|
textbox.render() |
|
with gr.Column(scale=1, min_width=50): |
|
submit_btn = gr.Button(value="Send", variant="primary") |
|
with gr.Row(elem_id="buttons") as button_row: |
|
upvote_btn = gr.Button(value="👍 Upvote", interactive=False) |
|
downvote_btn = gr.Button(value="👎 Downvote", interactive=False) |
|
flag_btn = gr.Button(value="⚠️ Flag", interactive=False) |
|
|
|
regenerate_btn = gr.Button( |
|
value="🔄 Regenerate", interactive=False |
|
) |
|
clear_btn = gr.Button(value="🗑️ Clear", interactive=False) |
|
|
|
if not embed_mode: |
|
gr.Markdown(tos_markdown) |
|
gr.Markdown(learn_more_markdown) |
|
url_params = gr.JSON(visible=False) |
|
|
|
|
|
btn_list = [upvote_btn, downvote_btn, flag_btn, regenerate_btn, clear_btn] |
|
upvote_btn.click( |
|
upvote_last_response, |
|
[state, model_selector], |
|
[textbox, upvote_btn, downvote_btn, flag_btn], |
|
) |
|
downvote_btn.click( |
|
downvote_last_response, |
|
[state, model_selector], |
|
[textbox, upvote_btn, downvote_btn, flag_btn], |
|
) |
|
chatbot.like( |
|
vote_selected_response, |
|
[state, model_selector], |
|
[], |
|
) |
|
flag_btn.click( |
|
flag_last_response, |
|
[state, model_selector], |
|
[textbox, upvote_btn, downvote_btn, flag_btn], |
|
) |
|
regenerate_btn.click( |
|
regenerate, |
|
[state, system_prompt], |
|
[state, chatbot, textbox] + btn_list, |
|
).then( |
|
http_bot, |
|
[ |
|
state, |
|
model_selector, |
|
temperature, |
|
top_p, |
|
repetition_penalty, |
|
max_output_tokens, |
|
max_input_tiles, |
|
|
|
|
|
], |
|
[state, chatbot, textbox] + btn_list, |
|
) |
|
clear_btn.click(clear_history, None, [state, chatbot, textbox] + btn_list) |
|
|
|
textbox.submit( |
|
add_text, |
|
[state, textbox, system_prompt, model_selector], |
|
[state, chatbot, textbox, model_selector] + btn_list, |
|
).then( |
|
http_bot, |
|
[ |
|
state, |
|
model_selector, |
|
temperature, |
|
top_p, |
|
repetition_penalty, |
|
max_output_tokens, |
|
max_input_tiles, |
|
|
|
|
|
], |
|
[state, chatbot, textbox] + btn_list, |
|
) |
|
submit_btn.click( |
|
add_text, |
|
[state, textbox, system_prompt, model_selector], |
|
[state, chatbot, textbox, model_selector] + btn_list, |
|
).then( |
|
http_bot, |
|
[ |
|
state, |
|
model_selector, |
|
temperature, |
|
top_p, |
|
repetition_penalty, |
|
max_output_tokens, |
|
max_input_tiles, |
|
|
|
|
|
], |
|
[state, chatbot, textbox] + btn_list, |
|
) |
|
|
|
|
|
|
|
""" |
|
if args.model_list_mode == "once": |
|
demo.load( |
|
load_demo, |
|
[url_params], |
|
[state, model_selector], |
|
js=js, |
|
) |
|
elif args.model_list_mode == "reload": |
|
demo.load( |
|
load_demo_refresh_model_list, |
|
None, |
|
[state, model_selector], |
|
js=js, |
|
) |
|
else: |
|
raise ValueError(f"Unknown model list mode: {args.model_list_mode}") |
|
""" |
|
|
|
return demo |
|
|
|
|
|
if __name__ == "__main__": |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument("--host", type=str, default="0.0.0.0") |
|
parser.add_argument("--port", type=int, default=7860) |
|
parser.add_argument("--controller-url", type=str, default=None) |
|
parser.add_argument("--worker-ip", type=str, default=None) |
|
parser.add_argument("--concurrency-count", type=int, default=10) |
|
parser.add_argument( |
|
"--model-list-mode", type=str, default="reload", choices=["once", "reload"] |
|
) |
|
parser.add_argument("--sd-worker-url", type=str, default=None) |
|
parser.add_argument("--share", action="store_true") |
|
parser.add_argument("--moderate", action="store_true") |
|
parser.add_argument("--embed", action="store_true") |
|
args = parser.parse_args() |
|
logger.info(f"args: {args}") |
|
if not args.controller_url: |
|
args.controller_url = os.environ.get("CONTROLLER_URL", None) |
|
|
|
if not args.controller_url: |
|
raise ValueError("controller-url is required.") |
|
|
|
if not args.worker_ip: |
|
args.worker_ip = os.environ.get("WORKER_IP", None) |
|
|
|
sd_worker_url = args.sd_worker_url |
|
logger.info(args) |
|
demo = build_demo(args.embed) |
|
demo.queue(api_open=False).launch( |
|
server_name=args.host, |
|
server_port=args.port, |
|
share=args.share, |
|
max_threads=args.concurrency_count, |
|
) |
|
|