Hires fix rework
This commit is contained in:
AUTOMATIC
parent
fd4461d44c
commit
ef27a18b6b
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@ -1,5 +1,6 @@
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import base64
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import io
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import math
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import os
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import re
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from pathlib import Path
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@ -164,6 +165,35 @@ def find_hypernetwork_key(hypernet_name, hypernet_hash=None):
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return None
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def restore_old_hires_fix_params(res):
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"""for infotexts that specify old First pass size parameter, convert it into
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width, height, and hr scale"""
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firstpass_width = res.get('First pass size-1', None)
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firstpass_height = res.get('First pass size-2', None)
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if firstpass_width is None or firstpass_height is None:
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return
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firstpass_width, firstpass_height = int(firstpass_width), int(firstpass_height)
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width = int(res.get("Size-1", 512))
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height = int(res.get("Size-2", 512))
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if firstpass_width == 0 or firstpass_height == 0:
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# old algorithm for auto-calculating first pass size
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desired_pixel_count = 512 * 512
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actual_pixel_count = width * height
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scale = math.sqrt(desired_pixel_count / actual_pixel_count)
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firstpass_width = math.ceil(scale * width / 64) * 64
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firstpass_height = math.ceil(scale * height / 64) * 64
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hr_scale = width / firstpass_width if firstpass_width > 0 else height / firstpass_height
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res['Size-1'] = firstpass_width
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res['Size-2'] = firstpass_height
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res['Hires upscale'] = hr_scale
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def parse_generation_parameters(x: str):
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"""parses generation parameters string, the one you see in text field under the picture in UI:
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```
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@ -221,6 +251,8 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
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hypernet_hash = res.get("Hypernet hash", None)
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res["Hypernet"] = find_hypernetwork_key(hypernet_name, hypernet_hash)
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restore_old_hires_fix_params(res)
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return res
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@ -230,16 +230,32 @@ def draw_prompt_matrix(im, width, height, all_prompts):
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return draw_grid_annotations(im, width, height, hor_texts, ver_texts)
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def resize_image(resize_mode, im, width, height):
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def resize_image(resize_mode, im, width, height, upscaler_name=None):
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"""
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Resizes an image with the specified resize_mode, width, and height.
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Args:
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resize_mode: The mode to use when resizing the image.
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0: Resize the image to the specified width and height.
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1: Resize the image to fill the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, cropping the excess.
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2: Resize the image to fit within the specified width and height, maintaining the aspect ratio, and then center the image within the dimensions, filling empty with data from image.
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im: The image to resize.
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width: The width to resize the image to.
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height: The height to resize the image to.
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upscaler_name: The name of the upscaler to use. If not provided, defaults to opts.upscaler_for_img2img.
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"""
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upscaler_name = upscaler_name or opts.upscaler_for_img2img
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def resize(im, w, h):
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if opts.upscaler_for_img2img is None or opts.upscaler_for_img2img == "None" or im.mode == 'L':
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if upscaler_name is None or upscaler_name == "None" or im.mode == 'L':
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return im.resize((w, h), resample=LANCZOS)
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scale = max(w / im.width, h / im.height)
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if scale > 1.0:
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upscalers = [x for x in shared.sd_upscalers if x.name == opts.upscaler_for_img2img]
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assert len(upscalers) > 0, f"could not find upscaler named {opts.upscaler_for_img2img}"
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upscalers = [x for x in shared.sd_upscalers if x.name == upscaler_name]
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assert len(upscalers) > 0, f"could not find upscaler named {upscaler_name}"
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upscaler = upscalers[0]
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im = upscaler.scaler.upscale(im, scale, upscaler.data_path)
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@ -658,14 +658,18 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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sampler = None
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def __init__(self, enable_hr: bool=False, denoising_strength: float=0.75, firstphase_width: int=0, firstphase_height: int=0, **kwargs):
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def __init__(self, enable_hr: bool = False, denoising_strength: float = 0.75, firstphase_width: int = 0, firstphase_height: int = 0, hr_scale: float = 2.0, hr_upscaler: str = None, **kwargs):
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super().__init__(**kwargs)
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self.enable_hr = enable_hr
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self.denoising_strength = denoising_strength
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self.firstphase_width = firstphase_width
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self.firstphase_height = firstphase_height
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self.truncate_x = 0
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self.truncate_y = 0
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self.hr_scale = hr_scale
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self.hr_upscaler = hr_upscaler
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if firstphase_width != 0 or firstphase_height != 0:
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print("firstphase_width/firstphase_height no longer supported; use hr_scale", file=sys.stderr)
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self.hr_scale = self.width / firstphase_width
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self.width = firstphase_width
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self.height = firstphase_height
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def init(self, all_prompts, all_seeds, all_subseeds):
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if self.enable_hr:
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@ -674,47 +678,29 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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else:
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state.job_count = state.job_count * 2
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self.extra_generation_params["First pass size"] = f"{self.firstphase_width}x{self.firstphase_height}"
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if self.firstphase_width == 0 or self.firstphase_height == 0:
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desired_pixel_count = 512 * 512
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actual_pixel_count = self.width * self.height
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scale = math.sqrt(desired_pixel_count / actual_pixel_count)
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self.firstphase_width = math.ceil(scale * self.width / 64) * 64
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self.firstphase_height = math.ceil(scale * self.height / 64) * 64
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firstphase_width_truncated = int(scale * self.width)
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firstphase_height_truncated = int(scale * self.height)
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else:
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width_ratio = self.width / self.firstphase_width
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height_ratio = self.height / self.firstphase_height
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if width_ratio > height_ratio:
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firstphase_width_truncated = self.firstphase_width
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firstphase_height_truncated = self.firstphase_width * self.height / self.width
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else:
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firstphase_width_truncated = self.firstphase_height * self.width / self.height
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firstphase_height_truncated = self.firstphase_height
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self.truncate_x = int(self.firstphase_width - firstphase_width_truncated) // opt_f
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self.truncate_y = int(self.firstphase_height - firstphase_height_truncated) // opt_f
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self.extra_generation_params["Hires upscale"] = self.hr_scale
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if self.hr_upscaler is not None:
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self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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latent_scale_mode = shared.latent_upscale_modes.get(self.hr_upscaler, None) if self.hr_upscaler is not None else shared.latent_upscale_default_mode
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if self.enable_hr and latent_scale_mode is None:
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assert len([x for x in shared.sd_upscalers if x.name == self.hr_upscaler]) > 0, f"could not find upscaler named {self.hr_upscaler}"
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x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
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if not self.enable_hr:
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x = create_random_tensors([opt_C, self.height // opt_f, self.width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
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return samples
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x = create_random_tensors([opt_C, self.firstphase_height // opt_f, self.firstphase_width // opt_f], seeds=seeds, subseeds=subseeds, subseed_strength=self.subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x, self.firstphase_width, self.firstphase_height))
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target_width = int(self.width * self.hr_scale)
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target_height = int(self.height * self.hr_scale)
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samples = samples[:, :, self.truncate_y//2:samples.shape[2]-self.truncate_y//2, self.truncate_x//2:samples.shape[3]-self.truncate_x//2]
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"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
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def save_intermediate(image, index):
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"""saves image before applying hires fix, if enabled in options; takes as an argument either an image or batch with latent space images"""
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if not opts.save or self.do_not_save_samples or not opts.save_images_before_highres_fix:
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return
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@ -723,11 +709,11 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, suffix="-before-highres-fix")
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if opts.use_scale_latent_for_hires_fix:
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if latent_scale_mode is not None:
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for i in range(samples.shape[0]):
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save_intermediate(samples, i)
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samples = torch.nn.functional.interpolate(samples, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
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samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode)
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# Avoid making the inpainting conditioning unless necessary as
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# this does need some extra compute to decode / encode the image again.
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@ -747,7 +733,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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save_intermediate(image, i)
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image = images.resize_image(0, image, self.width, self.height)
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image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
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image = np.array(image).astype(np.float32) / 255.0
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image = np.moveaxis(image, 2, 0)
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batch_images.append(image)
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@ -764,7 +750,7 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
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self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, seed_resize_from_h=self.seed_resize_from_h, seed_resize_from_w=self.seed_resize_from_w, p=self)
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noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
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# GC now before running the next img2img to prevent running out of memory
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x = None
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@ -327,7 +327,6 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
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"ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for ESRGAN upscalers. Low values = visible seam.", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
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"realesrgan_enabled_models": OptionInfo(["R-ESRGAN 4x+", "R-ESRGAN 4x+ Anime6B"], "Select which Real-ESRGAN models to show in the web UI. (Requires restart)", gr.CheckboxGroup, lambda: {"choices": realesrgan_models_names()}),
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"upscaler_for_img2img": OptionInfo(None, "Upscaler for img2img", gr.Dropdown, lambda: {"choices": [x.name for x in sd_upscalers]}),
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"use_scale_latent_for_hires_fix": OptionInfo(False, "Upscale latent space image when doing hires. fix"),
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}))
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options_templates.update(options_section(('face-restoration', "Face restoration"), {
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@ -545,6 +544,12 @@ opts = Options()
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if os.path.exists(config_filename):
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opts.load(config_filename)
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latent_upscale_default_mode = "Latent"
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latent_upscale_modes = {
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"Latent": "bilinear",
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"Latent (nearest)": "nearest",
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}
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sd_upscalers = []
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sd_model = None
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@ -8,7 +8,7 @@ import modules.processing as processing
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from modules.ui import plaintext_to_html
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def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, firstphase_width: int, firstphase_height: int, *args):
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def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2: str, steps: int, sampler_index: int, restore_faces: bool, tiling: bool, n_iter: int, batch_size: int, cfg_scale: float, seed: int, subseed: int, subseed_strength: float, seed_resize_from_h: int, seed_resize_from_w: int, seed_enable_extras: bool, height: int, width: int, enable_hr: bool, denoising_strength: float, hr_scale: float, hr_upscaler: str, *args):
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p = StableDiffusionProcessingTxt2Img(
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sd_model=shared.sd_model,
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outpath_samples=opts.outdir_samples or opts.outdir_txt2img_samples,
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@ -33,8 +33,8 @@ def txt2img(prompt: str, negative_prompt: str, prompt_style: str, prompt_style2:
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tiling=tiling,
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enable_hr=enable_hr,
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denoising_strength=denoising_strength if enable_hr else None,
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firstphase_width=firstphase_width if enable_hr else None,
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firstphase_height=firstphase_height if enable_hr else None,
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hr_scale=hr_scale,
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hr_upscaler=hr_upscaler,
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)
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p.scripts = modules.scripts.scripts_txt2img
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@ -684,11 +684,11 @@ def create_ui():
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with gr.Row():
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restore_faces = gr.Checkbox(label='Restore faces', value=False, visible=len(shared.face_restorers) > 1, elem_id="txt2img_restore_faces")
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tiling = gr.Checkbox(label='Tiling', value=False, elem_id="txt2img_tiling")
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enable_hr = gr.Checkbox(label='Highres. fix', value=False, elem_id="txt2img_enable_hr")
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enable_hr = gr.Checkbox(label='Hires. fix', value=False, elem_id="txt2img_enable_hr")
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with gr.Row(visible=False) as hr_options:
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firstphase_width = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass width", value=0, elem_id="txt2img_firstphase_width")
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firstphase_height = gr.Slider(minimum=0, maximum=1024, step=8, label="Firstpass height", value=0, elem_id="txt2img_firstphase_height")
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hr_upscaler = gr.Dropdown(label="Upscaler", elem_id="txt2img_hr_upscaler", choices=[*shared.latent_upscale_modes, *[x.name for x in shared.sd_upscalers]], value=shared.latent_upscale_default_mode)
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hr_scale = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label="Upscale by", value=2.0, elem_id="txt2img_hr_scale")
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denoising_strength = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Denoising strength', value=0.7, elem_id="txt2img_denoising_strength")
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with gr.Row(equal_height=True):
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@ -729,8 +729,8 @@ def create_ui():
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width,
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enable_hr,
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denoising_strength,
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firstphase_width,
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firstphase_height,
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hr_scale,
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hr_upscaler,
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] + custom_inputs,
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outputs=[
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@ -762,7 +762,6 @@ def create_ui():
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outputs=[hr_options],
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)
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txt2img_paste_fields = [
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(txt2img_prompt, "Prompt"),
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(txt2img_negative_prompt, "Negative prompt"),
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@ -781,8 +780,8 @@ def create_ui():
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(denoising_strength, "Denoising strength"),
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(enable_hr, lambda d: "Denoising strength" in d),
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(hr_options, lambda d: gr.Row.update(visible="Denoising strength" in d)),
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(firstphase_width, "First pass size-1"),
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(firstphase_height, "First pass size-2"),
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(hr_scale, "Hires upscale"),
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(hr_upscaler, "Hires upscaler"),
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*modules.scripts.scripts_txt2img.infotext_fields
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]
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parameters_copypaste.add_paste_fields("txt2img", None, txt2img_paste_fields)
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@ -202,7 +202,7 @@ axis_options = [
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AxisOption("Eta", float, apply_field("eta"), format_value_add_label, None),
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AxisOption("Clip skip", int, apply_clip_skip, format_value_add_label, None),
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AxisOption("Denoising", float, apply_field("denoising_strength"), format_value_add_label, None),
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AxisOption("Upscale latent space for hires.", str, apply_upscale_latent_space, format_value_add_label, None),
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AxisOption("Hires upscaler", str, apply_field("hr_upscaler"), format_value_add_label, None),
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AxisOption("Cond. Image Mask Weight", float, apply_field("inpainting_mask_weight"), format_value_add_label, None),
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AxisOption("VAE", str, apply_vae, format_value_add_label, None),
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AxisOption("Styles", str, apply_styles, format_value_add_label, None),
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@ -267,7 +267,6 @@ class SharedSettingsStackHelper(object):
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self.CLIP_stop_at_last_layers = opts.CLIP_stop_at_last_layers
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self.hypernetwork = opts.sd_hypernetwork
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self.model = shared.sd_model
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self.use_scale_latent_for_hires_fix = opts.use_scale_latent_for_hires_fix
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self.vae = opts.sd_vae
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def __exit__(self, exc_type, exc_value, tb):
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@ -278,7 +277,6 @@ class SharedSettingsStackHelper(object):
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hypernetwork.apply_strength()
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opts.data["CLIP_stop_at_last_layers"] = self.CLIP_stop_at_last_layers
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opts.data["use_scale_latent_for_hires_fix"] = self.use_scale_latent_for_hires_fix
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re_range = re.compile(r"\s*([+-]?\s*\d+)\s*-\s*([+-]?\s*\d+)(?:\s*\(([+-]\d+)\s*\))?\s*")
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user