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| """ | |
| Prediction interface for Cog ⚙️ | |
| https://github.com/replicate/cog/blob/main/docs/python.md | |
| """ | |
| import copy | |
| import os | |
| from typing import List | |
| import numpy as np | |
| from PIL import Image | |
| from cog import BasePredictor, BaseModel, Input, Path | |
| from fooocusapi.utils.lora_manager import LoraManager | |
| from fooocusapi.utils.file_utils import output_dir | |
| from fooocusapi.models.common.task import GenerationFinishReason | |
| from fooocusapi.configs.default import ( | |
| available_aspect_ratios, | |
| uov_methods, | |
| outpaint_expansions, | |
| default_styles, | |
| default_base_model_name, | |
| default_refiner_model_name, | |
| default_loras, | |
| default_refiner_switch, | |
| default_cfg_scale, | |
| default_prompt_negative | |
| ) | |
| from fooocusapi.parameters import ImageGenerationParams | |
| from fooocusapi.task_queue import TaskType | |
| class Output(BaseModel): | |
| """ | |
| Output model | |
| """ | |
| seeds: List[str] | |
| paths: List[Path] | |
| class Predictor(BasePredictor): | |
| """Predictor""" | |
| def setup(self) -> None: | |
| """ | |
| Load the model into memory to make running multiple predictions efficient | |
| """ | |
| from main import pre_setup | |
| pre_setup() | |
| def predict( | |
| self, | |
| prompt: str = Input( | |
| default='', | |
| description="Prompt for image generation"), | |
| negative_prompt: str = Input( | |
| default=default_prompt_negative, | |
| description="Negative prompt for image generation"), | |
| style_selections: str = Input( | |
| default=','.join(default_styles), | |
| description="Fooocus styles applied for image generation, separated by comma"), | |
| performance_selection: str = Input( | |
| default='Speed', | |
| choices=['Speed', 'Quality', 'Extreme Speed', 'Lightning'], | |
| description="Performance selection"), | |
| aspect_ratios_selection: str = Input( | |
| default='1152*896', | |
| choices=available_aspect_ratios, | |
| description="The generated image's size"), | |
| image_number: int = Input( | |
| default=1, | |
| ge=1, le=8, | |
| description="How many image to generate"), | |
| image_seed: int = Input( | |
| default=-1, | |
| description="Seed to generate image, -1 for random"), | |
| use_default_loras: bool = Input( | |
| default=True, | |
| description="Use default LoRAs"), | |
| loras_custom_urls: str = Input( | |
| default="", | |
| description="Custom LoRAs URLs in the format 'url,weight' provide multiple separated by ; (example 'url1,0.3;url2,0.1')"), | |
| sharpness: float = Input( | |
| default=2.0, | |
| ge=0.0, le=30.0), | |
| guidance_scale: float = Input( | |
| default=default_cfg_scale, | |
| ge=1.0, le=30.0), | |
| refiner_switch: float = Input( | |
| default=default_refiner_switch, | |
| ge=0.1, le=1.0), | |
| uov_input_image: Path = Input( | |
| default=None, | |
| description="Input image for upscale or variation, keep None for not upscale or variation"), | |
| uov_method: str = Input( | |
| default='Disabled', | |
| choices=uov_methods), | |
| uov_upscale_value: float = Input( | |
| default=0, | |
| description="Only when Upscale (Custom)"), | |
| inpaint_additional_prompt: str = Input( | |
| default='', | |
| description="Prompt for image generation"), | |
| inpaint_input_image: Path = Input( | |
| default=None, | |
| description="Input image for inpaint or outpaint, keep None for not inpaint or outpaint. Please noticed, `uov_input_image` has bigger priority is not None."), | |
| inpaint_input_mask: Path = Input( | |
| default=None, | |
| description="Input mask for inpaint"), | |
| outpaint_selections: str = Input( | |
| default='', | |
| description="Outpaint expansion selections, literal 'Left', 'Right', 'Top', 'Bottom' separated by comma"), | |
| outpaint_distance_left: int = Input( | |
| default=0, | |
| description="Outpaint expansion distance from Left of the image"), | |
| outpaint_distance_top: int = Input( | |
| default=0, | |
| description="Outpaint expansion distance from Top of the image"), | |
| outpaint_distance_right: int = Input( | |
| default=0, | |
| description="Outpaint expansion distance from Right of the image"), | |
| outpaint_distance_bottom: int = Input( | |
| default=0, | |
| description="Outpaint expansion distance from Bottom of the image"), | |
| cn_img1: Path = Input( | |
| default=None, | |
| description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."), | |
| cn_stop1: float = Input( | |
| default=None, | |
| ge=0, le=1, | |
| description="Stop at for image prompt, None for default value"), | |
| cn_weight1: float = Input( | |
| default=None, | |
| ge=0, le=2, | |
| description="Weight for image prompt, None for default value"), | |
| cn_type1: str = Input( | |
| default='ImagePrompt', | |
| choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS'], | |
| description="ControlNet type for image prompt"), | |
| cn_img2: Path = Input( | |
| default=None, | |
| description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."), | |
| cn_stop2: float = Input( | |
| default=None, | |
| ge=0, le=1, | |
| description="Stop at for image prompt, None for default value"), | |
| cn_weight2: float = Input( | |
| default=None, | |
| ge=0, le=2, | |
| description="Weight for image prompt, None for default value"), | |
| cn_type2: str = Input( | |
| default='ImagePrompt', | |
| choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS'], | |
| description="ControlNet type for image prompt"), | |
| cn_img3: Path = Input( | |
| default=None, | |
| description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."), | |
| cn_stop3: float = Input( | |
| default=None, | |
| ge=0, le=1, | |
| description="Stop at for image prompt, None for default value"), | |
| cn_weight3: float = Input( | |
| default=None, | |
| ge=0, le=2, | |
| description="Weight for image prompt, None for default value"), | |
| cn_type3: str = Input( | |
| default='ImagePrompt', | |
| choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS'], | |
| description="ControlNet type for image prompt"), | |
| cn_img4: Path = Input( | |
| default=None, | |
| description="Input image for image prompt. If all cn_img[n] are None, image prompt will not applied."), | |
| cn_stop4: float = Input( | |
| default=None, | |
| ge=0, le=1, | |
| description="Stop at for image prompt, None for default value"), | |
| cn_weight4: float = Input( | |
| default=None, | |
| ge=0, le=2, | |
| description="Weight for image prompt, None for default value"), | |
| cn_type4: str = Input( | |
| default='ImagePrompt', | |
| choices=['ImagePrompt', 'FaceSwap', 'PyraCanny', 'CPDS'], | |
| description="ControlNet type for image prompt") | |
| ) -> Output: | |
| """Run a single prediction on the model""" | |
| from modules import flags | |
| from modules.sdxl_styles import legal_style_names | |
| from fooocusapi.worker import blocking_get_task_result, worker_queue | |
| base_model_name = default_base_model_name | |
| refiner_model_name = default_refiner_model_name | |
| lora_manager = LoraManager() | |
| # Use default loras if selected | |
| loras = copy.copy(default_loras) if use_default_loras else [] | |
| # add custom user loras if provided | |
| if loras_custom_urls: | |
| urls = [url.strip() for url in loras_custom_urls.split(';')] | |
| loras_with_weights = [url.split(',') for url in urls] | |
| custom_lora_paths = lora_manager.check([lw[0] for lw in loras_with_weights]) | |
| custom_loras = [[path, float(lw[1]) if len(lw) > 1 else 1.0] for path, lw in | |
| zip(custom_lora_paths, loras_with_weights)] | |
| loras.extend(custom_loras) | |
| style_selections_arr = [] | |
| for s in style_selections.strip().split(','): | |
| style = s.strip() | |
| if style in legal_style_names: | |
| style_selections_arr.append(style) | |
| if uov_input_image is not None: | |
| im = Image.open(str(uov_input_image)) | |
| uov_input_image = np.array(im) | |
| inpaint_input_image_dict = None | |
| if inpaint_input_image is not None: | |
| im = Image.open(str(inpaint_input_image)) | |
| inpaint_input_image = np.array(im) | |
| if inpaint_input_mask is not None: | |
| im = Image.open(str(inpaint_input_mask)) | |
| inpaint_input_mask = np.array(im) | |
| inpaint_input_image_dict = { | |
| 'image': inpaint_input_image, | |
| 'mask': inpaint_input_mask | |
| } | |
| outpaint_selections_arr = [] | |
| for e in outpaint_selections.strip().split(','): | |
| expansion = e.strip() | |
| if expansion in outpaint_expansions: | |
| outpaint_selections_arr.append(expansion) | |
| image_prompts = [] | |
| image_prompt_config = [ | |
| (cn_img1, cn_stop1, cn_weight1, cn_type1), | |
| (cn_img2, cn_stop2, cn_weight2, cn_type2), | |
| (cn_img3, cn_stop3, cn_weight3, cn_type3), | |
| (cn_img4, cn_stop4, cn_weight4, cn_type4)] | |
| for config in image_prompt_config: | |
| cn_img, cn_stop, cn_weight, cn_type = config | |
| if cn_img is not None: | |
| im = Image.open(str(cn_img)) | |
| cn_img = np.array(im) | |
| if cn_stop is None: | |
| cn_stop = flags.default_parameters[cn_type][0] | |
| if cn_weight is None: | |
| cn_weight = flags.default_parameters[cn_type][1] | |
| image_prompts.append((cn_img, cn_stop, cn_weight, cn_type)) | |
| advanced_params = None | |
| params = ImageGenerationParams( | |
| prompt=prompt, | |
| negative_prompt=negative_prompt, | |
| style_selections=style_selections_arr, | |
| performance_selection=performance_selection, | |
| aspect_ratios_selection=aspect_ratios_selection, | |
| image_number=image_number, | |
| image_seed=image_seed, | |
| sharpness=sharpness, | |
| guidance_scale=guidance_scale, | |
| base_model_name=base_model_name, | |
| refiner_model_name=refiner_model_name, | |
| refiner_switch=refiner_switch, | |
| loras=loras, | |
| uov_input_image=uov_input_image, | |
| uov_method=uov_method, | |
| upscale_value=uov_upscale_value, | |
| outpaint_selections=outpaint_selections_arr, | |
| inpaint_input_image=inpaint_input_image_dict, | |
| image_prompts=image_prompts, | |
| advanced_params=advanced_params, | |
| inpaint_additional_prompt=inpaint_additional_prompt, | |
| outpaint_distance_left=outpaint_distance_left, | |
| outpaint_distance_top=outpaint_distance_top, | |
| outpaint_distance_right=outpaint_distance_right, | |
| outpaint_distance_bottom=outpaint_distance_bottom, | |
| save_meta=True, | |
| meta_scheme='fooocus', | |
| save_extension='png', | |
| save_name='', | |
| require_base64=False, | |
| ) | |
| print(f"[Predictor Predict] Params: {params.__dict__}") | |
| async_task = worker_queue.add_task( | |
| TaskType.text_2_img, | |
| params) | |
| if async_task is None: | |
| print("[Task Queue] The task queue has reached limit") | |
| raise Exception("The task queue has reached limit.") | |
| results = blocking_get_task_result(async_task.job_id) | |
| output_paths: List[Path] = [] | |
| output_seeds: List[str] = [] | |
| for r in results: | |
| if r.finish_reason == GenerationFinishReason.success and r.im is not None: | |
| output_seeds.append(r.seed) | |
| output_paths.append(Path(os.path.join(output_dir, r.im))) | |
| print(f"[Predictor Predict] Finished with {len(output_paths)} images") | |
| if len(output_paths) == 0: | |
| raise Exception("Process failed.") | |
| return Output(seeds=output_seeds, paths=output_paths) | |