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import numpy as np |
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from fastapi import FastAPI, Body |
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from fastapi.exceptions import HTTPException |
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from PIL import Image |
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import gradio as gr |
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from modules.api.models import * |
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from modules.api import api |
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from scripts import external_code, global_state |
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from scripts.processor import preprocessor_sliders_config |
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def encode_to_base64(image): |
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if type(image) is str: |
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return image |
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elif type(image) is Image.Image: |
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return api.encode_pil_to_base64(image) |
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elif type(image) is np.ndarray: |
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return encode_np_to_base64(image) |
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else: |
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return "" |
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def encode_np_to_base64(image): |
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pil = Image.fromarray(image) |
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return api.encode_pil_to_base64(pil) |
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def controlnet_api(_: gr.Blocks, app: FastAPI): |
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@app.get("/controlnet/version") |
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async def version(): |
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return {"version": external_code.get_api_version()} |
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@app.get("/controlnet/model_list") |
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async def model_list(): |
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up_to_date_model_list = external_code.get_models(update=True) |
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print(up_to_date_model_list) |
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return {"model_list": up_to_date_model_list} |
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@app.get("/controlnet/module_list") |
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async def module_list(alias_names: bool = False): |
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_module_list = external_code.get_modules(alias_names) |
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print(_module_list) |
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return { |
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"module_list": _module_list, |
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"module_detail": external_code.get_modules_detail(alias_names) |
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} |
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@app.get("/controlnet/settings") |
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async def settings(): |
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max_models_num = external_code.get_max_models_num() |
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return {"control_net_max_models_num":max_models_num} |
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cached_cn_preprocessors = global_state.cache_preprocessors(global_state.cn_preprocessor_modules) |
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@app.post("/controlnet/detect") |
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async def detect( |
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controlnet_module: str = Body("none", title='Controlnet Module'), |
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controlnet_input_images: List[str] = Body([], title='Controlnet Input Images'), |
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controlnet_processor_res: int = Body(512, title='Controlnet Processor Resolution'), |
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controlnet_threshold_a: float = Body(64, title='Controlnet Threshold a'), |
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controlnet_threshold_b: float = Body(64, title='Controlnet Threshold b') |
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): |
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controlnet_module = global_state.reverse_preprocessor_aliases.get(controlnet_module, controlnet_module) |
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if controlnet_module not in cached_cn_preprocessors: |
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raise HTTPException( |
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status_code=422, detail="Module not available") |
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if len(controlnet_input_images) == 0: |
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raise HTTPException( |
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status_code=422, detail="No image selected") |
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print(f"Detecting {str(len(controlnet_input_images))} images with the {controlnet_module} module.") |
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results = [] |
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processor_module = cached_cn_preprocessors[controlnet_module] |
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for input_image in controlnet_input_images: |
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img = external_code.to_base64_nparray(input_image) |
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results.append(processor_module(img, res=controlnet_processor_res, thr_a=controlnet_threshold_a, thr_b=controlnet_threshold_b)[0]) |
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global_state.cn_preprocessor_unloadable.get(controlnet_module, lambda: None)() |
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results64 = list(map(encode_to_base64, results)) |
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return {"images": results64, "info": "Success"} |
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try: |
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import modules.script_callbacks as script_callbacks |
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script_callbacks.on_app_started(controlnet_api) |
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except: |
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pass |
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