| |
| |
| import argparse |
| import glob |
| import io |
| import os |
|
|
| from PIL import Image |
| from scepter.modules.transform.io import pillow_convert |
| from scepter.modules.utils.config import Config |
| from scepter.modules.utils.file_system import FS |
|
|
| from examples.examples import all_examples |
| from inference.ace_plus_diffusers import ACEPlusDiffuserInference |
| inference_dict = { |
| "ACE_DIFFUSER_PLUS": ACEPlusDiffuserInference |
| } |
|
|
| fs_list = [ |
| Config(cfg_dict={"NAME": "HuggingfaceFs", "TEMP_DIR": "./cache"}, load=False), |
| Config(cfg_dict={"NAME": "ModelscopeFs", "TEMP_DIR": "./cache"}, load=False), |
| Config(cfg_dict={"NAME": "HttpFs", "TEMP_DIR": "./cache"}, load=False), |
| Config(cfg_dict={"NAME": "LocalFs", "TEMP_DIR": "./cache"}, load=False), |
| ] |
|
|
| for one_fs in fs_list: |
| FS.init_fs_client(one_fs) |
|
|
|
|
| def run_one_case(pipe, |
| input_image = None, |
| input_mask = None, |
| input_reference_image = None, |
| save_path = "examples/output/example.png", |
| instruction = "", |
| output_h = 1024, |
| output_w = 1024, |
| seed = -1, |
| sample_steps = None, |
| guide_scale = None, |
| repainting_scale = None, |
| model_path = None, |
| **kwargs): |
| if input_image is not None: |
| input_image = Image.open(io.BytesIO(FS.get_object(input_image))) |
| input_image = pillow_convert(input_image, "RGB") |
| if input_mask is not None: |
| input_mask = Image.open(io.BytesIO(FS.get_object(input_mask))) |
| input_mask = pillow_convert(input_mask, "L") |
| if input_reference_image is not None: |
| input_reference_image = Image.open(io.BytesIO(FS.get_object(input_reference_image))) |
| input_reference_image = pillow_convert(input_reference_image, "RGB") |
|
|
| image, seed = pipe( |
| reference_image=input_reference_image, |
| edit_image=input_image, |
| edit_mask=input_mask, |
| prompt=instruction, |
| output_height=output_h, |
| output_width=output_w, |
| sampler='flow_euler', |
| sample_steps=sample_steps or pipe.input.get("sample_steps", 28), |
| guide_scale=guide_scale or pipe.input.get("guide_scale", 50), |
| seed=seed, |
| repainting_scale=repainting_scale or pipe.input.get("repainting_scale", 1.0), |
| lora_path = model_path |
| ) |
| with FS.put_to(save_path) as local_path: |
| image.save(local_path) |
| return local_path, seed |
|
|
|
|
| def run(): |
| parser = argparse.ArgumentParser(description='Argparser for Scepter:\n') |
| parser.add_argument('--instruction', |
| dest='instruction', |
| help='The instruction for editing or generating!', |
| default="") |
| parser.add_argument('--output_h', |
| dest='output_h', |
| help='The height of output image for generation tasks!', |
| type=int, |
| default=1024) |
| parser.add_argument('--output_w', |
| dest='output_w', |
| help='The width of output image for generation tasks!', |
| type=int, |
| default=1024) |
| parser.add_argument('--input_reference_image', |
| dest='input_reference_image', |
| help='The input reference image!', |
| default=None |
| ) |
| parser.add_argument('--input_image', |
| dest='input_image', |
| help='The input image!', |
| default=None |
| ) |
| parser.add_argument('--input_mask', |
| dest='input_mask', |
| help='The input mask!', |
| default=None |
| ) |
| parser.add_argument('--save_path', |
| dest='save_path', |
| help='The save path for output image!', |
| default='examples/output_images/output.png' |
| ) |
| parser.add_argument('--seed', |
| dest='seed', |
| help='The seed for generation!', |
| type=int, |
| default=-1) |
|
|
| parser.add_argument('--step', |
| dest='step', |
| help='The sample step for generation!', |
| type=int, |
| default=None) |
|
|
| parser.add_argument('--guide_scale', |
| dest='guide_scale', |
| help='The guide scale for generation!', |
| type=int, |
| default=None) |
|
|
| parser.add_argument('--repainting_scale', |
| dest='repainting_scale', |
| help='The repainting scale for content filling generation!', |
| type=int, |
| default=None) |
|
|
| parser.add_argument('--task_type', |
| dest='task_type', |
| choices=['portrait', 'subject', 'local_editing'], |
| help="Choose the task type.", |
| default='') |
|
|
| parser.add_argument('--task_model', |
| dest='task_model', |
| help='The models list for different tasks!', |
| default="./models/model_zoo.yaml") |
|
|
|
|
| parser.add_argument('--infer_type', |
| dest='infer_type', |
| choices=['diffusers'], |
| default='diffusers', |
| help="Choose the inference scripts. 'native' refers to using the official implementation of ace++, " |
| "while 'diffusers' refers to using the adaptation for diffusers") |
|
|
| parser.add_argument('--cfg_folder', |
| dest='cfg_folder', |
| help='The inference config!', |
| default="./config") |
|
|
| cfg = Config(load=True, parser_ins=parser) |
|
|
| model_yamls = glob.glob(os.path.join(cfg.args.cfg_folder, '*.yaml')) |
| model_choices = dict() |
| for i in model_yamls: |
| model_cfg = Config(load=True, cfg_file=i) |
| model_name = model_cfg.NAME |
| model_choices[model_name] = model_cfg |
|
|
| if cfg.args.infer_type == "native": |
| infer_name = "ace_plus_native_infer" |
| elif cfg.args.infer_type == "diffusers": |
| infer_name = "ace_plus_diffuser_infer" |
| else: |
| raise ValueError("infer_type should be native or diffusers") |
|
|
| assert infer_name in model_choices |
|
|
| |
| task_model_cfg = Config(load=True, cfg_file=cfg.args.task_model) |
|
|
| task_model_dict = {} |
| for task_name, task_model in task_model_cfg.MODEL.items(): |
| task_model_dict[task_name] = task_model |
|
|
|
|
| |
| pipe_cfg = model_choices[infer_name] |
| infer_name = pipe_cfg.get("INFERENCE_TYPE", "ACE_PLUS") |
| pipe = inference_dict[infer_name]() |
| pipe.init_from_cfg(pipe_cfg) |
|
|
| if cfg.args.instruction == "" and cfg.args.input_image is None and cfg.args.input_reference_image is None: |
| params = { |
| "output_h": cfg.args.output_h, |
| "output_w": cfg.args.output_w, |
| "sample_steps": cfg.args.step, |
| "guide_scale": cfg.args.guide_scale |
| } |
| |
|
|
| for example in all_examples: |
| example["model_path"] = FS.get_from(task_model_dict[example["task_type"].upper()]["MODEL_PATH"]) |
| example.update(params) |
| if example["edit_type"] == "repainting": |
| example["repainting_scale"] = 1.0 |
| else: |
| example["repainting_scale"] = task_model_dict[example["task_type"].upper()].get("REPAINTING_SCALE", 1.0) |
| print(example) |
| local_path, seed = run_one_case(pipe, **example) |
|
|
| else: |
| assert cfg.args.task_type.upper() in task_model_cfg |
| params = { |
| "input_image": cfg.args.input_image, |
| "input_mask": cfg.args.input_mask, |
| "input_reference_image": cfg.args.input_reference_image, |
| "save_path": cfg.args.save_path, |
| "instruction": cfg.args.instruction, |
| "output_h": cfg.args.output_h, |
| "output_w": cfg.args.output_w, |
| "sample_steps": cfg.args.step, |
| "guide_scale": cfg.args.guide_scale, |
| "repainting_scale": cfg.args.repainting_scale, |
| "model_path": FS.get_from(task_model_dict[cfg.args.task_type.upper()]["MODEL_PATH"]) |
| } |
| local_path, seed = run_one_case(pipe, **params) |
| print(local_path, seed) |
|
|
| if __name__ == '__main__': |
| run() |
|
|
|
|
