| import math |
| from typing import Dict, List, Optional |
|
|
| from PIL import Image |
|
|
| from internals.data.result import Result |
| from internals.pipelines.commons import AbstractPipeline, Img2Img |
| from internals.util import get_generators |
| from internals.util.cache import clear_cuda_and_gc |
| from internals.util.config import ( |
| get_base_dimension, |
| get_is_sdxl, |
| get_model_dir, |
| get_num_return_sequences, |
| ) |
| from internals.util.sdxl_lightning import LightningMixin |
|
|
|
|
| class HighRes(AbstractPipeline, LightningMixin): |
| def load(self, img2img: Optional[Img2Img] = None): |
| if hasattr(self, "pipe"): |
| return |
|
|
| if not img2img: |
| img2img = Img2Img() |
| img2img.load(get_model_dir()) |
|
|
| self.pipe = img2img.pipe |
| self.img2img = img2img |
|
|
| if get_is_sdxl(): |
| self.configure_sdxl_lightning(img2img.pipe) |
|
|
| def apply( |
| self, |
| prompt: List[str], |
| negative_prompt: List[str], |
| images, |
| width: int, |
| height: int, |
| seed: int, |
| num_inference_steps: int, |
| strength: float = 0.5, |
| guidance_scale: int = 9, |
| **kwargs, |
| ): |
| clear_cuda_and_gc() |
|
|
| generator = get_generators(seed, get_num_return_sequences()) |
|
|
| images = [image.resize((width, height)) for image in images] |
|
|
| |
| |
| |
|
|
| if get_is_sdxl(): |
| extra_args = self.enable_sdxl_lightning() |
| kwargs.update(extra_args) |
|
|
| kwargs = { |
| "prompt": prompt, |
| "image": images, |
| "strength": strength, |
| "negative_prompt": negative_prompt, |
| "guidance_scale": guidance_scale, |
| "num_inference_steps": num_inference_steps, |
| "generator": generator, |
| **kwargs, |
| } |
|
|
| print(kwargs) |
| result = self.pipe.__call__(**kwargs) |
|
|
| if get_is_sdxl(): |
| self.disable_sdxl_lightning() |
|
|
| return Result.from_result(result) |
|
|
| @staticmethod |
| def get_intermediate_dimension(target_width: int, target_height: int): |
| def_size = get_base_dimension() |
|
|
| desired_pixel_count = def_size * def_size |
| actual_pixel_count = target_width * target_height |
|
|
| scale = math.sqrt(desired_pixel_count / actual_pixel_count) |
|
|
| firstpass_width = math.ceil(scale * target_width / 64) * 64 |
| firstpass_height = math.ceil(scale * target_height / 64) * 64 |
|
|
| print("Pass1", firstpass_width, firstpass_height) |
|
|
| if get_is_sdxl(): |
| firstpass_width, firstpass_height = HighRes.find_closest_sdxl_aspect_ratio( |
| firstpass_width, firstpass_height |
| ) |
|
|
| print("Pass2", firstpass_width, firstpass_height) |
| return firstpass_width, firstpass_height |
|
|
| @staticmethod |
| def find_closest_sdxl_aspect_ratio(target_width: int, target_height: int): |
| target_ratio = target_width / target_height |
| closest_ratio = "" |
| min_difference = float("inf") |
|
|
| for ratio_str, (width, height) in SD_XL_BASE_RATIOS.items(): |
| ratio = width / height |
| difference = abs(target_ratio - ratio) |
|
|
| if difference < min_difference: |
| min_difference = difference |
| closest_ratio = ratio_str |
|
|
| new_width, new_height = SD_XL_BASE_RATIOS[closest_ratio] |
| return new_width, new_height |
|
|
|
|
| SD_XL_BASE_RATIOS = { |
| "0.5": (704, 1408), |
| "0.52": (704, 1344), |
| "0.57": (768, 1344), |
| "0.6": (768, 1280), |
| "0.68": (832, 1216), |
| "0.72": (832, 1152), |
| "0.78": (896, 1152), |
| "0.82": (896, 1088), |
| "0.88": (960, 1088), |
| "0.94": (960, 1024), |
| "1.0": (1024, 1024), |
| "1.07": (1024, 960), |
| "1.13": (1088, 960), |
| "1.21": (1088, 896), |
| "1.29": (1152, 896), |
| "1.38": (1152, 832), |
| "1.46": (1216, 832), |
| "1.67": (1280, 768), |
| "1.75": (1344, 768), |
| "1.91": (1344, 704), |
| "2.0": (1408, 704), |
| "2.09": (1472, 704), |
| "2.4": (1536, 640), |
| "2.5": (1600, 640), |
| "2.89": (1664, 576), |
| "3.0": (1728, 576), |
| } |
|
|
