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