From b35d8bf3feb1ad756d2c4e39b2a0af66061a05dd Mon Sep 17 00:00:00 2001 From: tazlin Date: Sun, 21 Jul 2024 07:24:59 -0400 Subject: [PATCH] improve hires fix efficiency; fix: better SDXL hires fix default implementation; fix: cascade bug (#287) * fix: don't collect custom node stdout ComfyUI uses logger now and all of the emitted messages are captured/handled elsewhere * fix: use latest compat. horde deps * fix: better SDXL hires + calc steps more effectively * fix: resolve bug with cascade steps not applying correctly * tests: group test runs by model within modules This will help minimize the situation where models were loading and unloading an unnecessary number of times * tests: always run cascade tests last * tests: be less likely to download timeout for problem lora * tests: use more threads during CI for lora downloads * tests: add sdxl character hires fix test case * fix: use latest compat. horde deps * style: fix * style: fix typing --- .pre-commit-config.yaml | 3 +- hordelib/comfy_horde.py | 7 +- hordelib/horde.py | 89 +++++-- hordelib/initialisation.py | 3 +- hordelib/model_manager/base.py | 17 +- hordelib/model_manager/lora.py | 2 +- hordelib/nodes/facerestore_cf/__init__.py | 1 + .../r_chainner/gfpganv1_clean_arch.py | 28 +-- .../r_chainner/model_loading.py | 6 +- .../r_chainner/stylegan2_clean_arch.py | 28 +-- .../nodes/facerestore_cf/r_chainner/types.py | 4 +- hordelib/shared_model_manager.py | 8 +- hordelib/utils/image_utils.py | 111 +++++++++ images_expected/lora_blue_hires_fix.png | Bin 1199964 -> 1197085 bytes images_expected/lora_character_hires_fix.png | Bin 2854892 -> 2908218 bytes .../lora_character_hires_fix_sdxl.png | Bin 0 -> 2454213 bytes .../sdxl_text_to_image_hires_fix.png | Bin 2588076 -> 2630266 bytes pyproject.toml | 6 + requirements.txt | 4 +- tests/conftest.py | 219 +++++++++++++++++- tests/model_managers/test_mm_lora.py | 2 +- tests/test_horde_inference.py | 12 + tests/test_horde_inference_cascade.py | 9 + tests/test_horde_inference_controlnet.py | 7 + tests/test_horde_inference_img2img.py | 16 ++ tests/test_horde_inference_layerdiffusion.py | 5 + tests/test_horde_inference_qr_code.py | 7 + tests/test_horde_lcm.py | 16 +- tests/test_horde_lora.py | 70 ++++++ tests/test_horde_samplers.py | 7 + tests/test_horde_ti.py | 3 + tests/test_image_metadata.py | 3 + tests/test_internal_comfyui_failures.py | 1 + tests/test_utils.py | 59 +++++ tests/testing_shared_classes.py | 20 ++ 35 files changed, 663 insertions(+), 110 deletions(-) create mode 100644 images_expected/lora_character_hires_fix_sdxl.png create mode 100644 tests/testing_shared_classes.py diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index fe2d3b21..1ccb773c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -34,5 +34,6 @@ repos: types-tabulate, types-tqdm, types-urllib3, - horde_sdk==0.12.0, + horde_sdk==0.14.0, + horde_model_reference==0.8.1, ] diff --git a/hordelib/comfy_horde.py b/hordelib/comfy_horde.py index 996132b8..72109f48 100644 --- a/hordelib/comfy_horde.py +++ b/hordelib/comfy_horde.py @@ -366,11 +366,8 @@ def __init__( # Load our pipelines self._load_pipelines() - stdio = OutputCollector() - with contextlib.redirect_stdout(stdio): - # Load our custom nodes - self._load_custom_nodes() - stdio.replay() + # Load our custom nodes + self._load_custom_nodes() self._comfyui_callback = comfyui_callback diff --git a/hordelib/horde.py b/hordelib/horde.py index 404dea7e..fe273add 100644 --- a/hordelib/horde.py +++ b/hordelib/horde.py @@ -13,6 +13,7 @@ from enum import Enum, auto from types import FunctionType +from horde_model_reference.meta_consts import STABLE_DIFFUSION_BASELINE_CATEGORY, get_baseline_native_resolution from horde_sdk.ai_horde_api.apimodels import ImageGenerateJobPopResponse from horde_sdk.ai_horde_api.apimodels.base import ( GenMetadataEntry, @@ -78,15 +79,55 @@ def __init__( self.faults = faults -def _calc_upscale_sampler_steps(payload): - """Calculates the amount of hires_fix upscaler steps based on the denoising used and the steps used for the - primary image""" - upscale_steps = round(payload["ddim_steps"] * (0.9 - payload["hires_fix_denoising_strength"])) - if upscale_steps < 3: - upscale_steps = 3 +def _calc_upscale_sampler_steps( + payload: dict, +) -> int: + """Use `ImageUtils.calc_upscale_sampler_steps(...)` to calculate the number of steps for the upscale sampler. - logger.debug(f"Upscale steps calculated as {upscale_steps}") - return upscale_steps + Args: + payload (dict): The payload to use for the calculation. + + Returns: + int: The number of steps to use. + """ + model_name = payload.get("model_name") + baseline = None + native_resolution = None + if model_name is not None: + baseline = SharedModelManager.model_reference_manager.stable_diffusion.get_model_baseline(model_name) + if baseline is not None: + try: + baseline = STABLE_DIFFUSION_BASELINE_CATEGORY(baseline) + except ValueError: + baseline = None + logger.warning( + f"Model {model_name} has an invalid baseline {baseline} so we cannot calculate " + "hires fix upscale steps.", + ) + if baseline is not None: + native_resolution = get_baseline_native_resolution(baseline) + + width: int | None = payload.get("width") + height: int | None = payload.get("height") + hires_fix_denoising_strength: float | None = payload.get("hires_fix_denoising_strength") + ddim_steps: int | None = payload.get("ddim_steps") + + if width is None or height is None: + raise ValueError("Width and height must be set to calculate upscale sampler steps") + + if hires_fix_denoising_strength is None: + raise ValueError("Hires fix denoising strength must be set to calculate upscale sampler steps") + + if ddim_steps is None: + raise ValueError("DDIM steps must be set to calculate upscale sampler steps") + + return ImageUtils.calc_upscale_sampler_steps( + model_native_resolution=native_resolution, + width=width, + height=height, + hires_fix_denoising_strength=hires_fix_denoising_strength, + ddim_steps=ddim_steps, + ) class HordeLib: @@ -825,13 +866,15 @@ def _final_pipeline_adjustments(self, payload, pipeline_data) -> tuple[dict, lis raise RuntimeError(f"Invalid key {key}") elif "*" in key: key, multiplier = key.split("*", 1) - elif key in payload: + + if key in payload: if multiplier: pipeline_params[newkey] = round(payload.get(key) * float(multiplier)) else: pipeline_params[newkey] = payload.get(key) - else: + elif not isinstance(key, FunctionType): logger.error(f"Parameter {key} not found") + # We inject these parameters to ensure the HordeCheckpointLoader knows what file to load, if necessary # We don't want to hardcode this into the pipeline.json as we export this directly from ComfyUI # and don't want to have to rememebr to re-add those keys @@ -874,16 +917,22 @@ def _final_pipeline_adjustments(self, payload, pipeline_data) -> tuple[dict, lis baseline = None if model_details: baseline = model_details.get("baseline") - if baseline and (baseline == "stable_cascade" or baseline == "stable_diffusion_xl"): - new_width, new_height = ImageUtils.get_first_pass_image_resolution_max( - original_width, - original_height, - ) - else: - new_width, new_height = ImageUtils.get_first_pass_image_resolution_min( - original_width, - original_height, - ) + if baseline: + if baseline == "stable_cascade": + new_width, new_height = ImageUtils.get_first_pass_image_resolution_max( + original_width, + original_height, + ) + elif baseline == "stable_diffusion_xl": + new_width, new_height = ImageUtils.get_first_pass_image_resolution_sdxl( + original_width, + original_height, + ) + else: # fall through case; only `stable diffusion 1`` at time of writing + new_width, new_height = ImageUtils.get_first_pass_image_resolution_min( + original_width, + original_height, + ) # This is the *target* resolution pipeline_params["latent_upscale.width"] = original_width diff --git a/hordelib/initialisation.py b/hordelib/initialisation.py index 0577637c..014d8e4c 100644 --- a/hordelib/initialisation.py +++ b/hordelib/initialisation.py @@ -27,6 +27,7 @@ def initialise( force_normal_vram_mode: bool = True, extra_comfyui_args: list[str] | None = None, disable_smart_memory: bool = False, + do_not_load_model_mangers: bool = False, ): """Initialise hordelib. This is required before using any other hordelib functions. @@ -96,7 +97,7 @@ def initialise( # Initialise model manager from hordelib.shared_model_manager import SharedModelManager - SharedModelManager() + SharedModelManager(do_not_load_model_mangers=do_not_load_model_mangers) sys.argv = sys_arg_bkp diff --git a/hordelib/model_manager/base.py b/hordelib/model_manager/base.py index eca5042c..56abfe71 100644 --- a/hordelib/model_manager/base.py +++ b/hordelib/model_manager/base.py @@ -138,22 +138,7 @@ def load_model_database(self) -> None: ) def download_model_reference(self) -> dict: - try: - logger.debug(f"Downloading Model Reference for {self.models_db_name}") - response = requests.get(self.remote_db) - logger.debug("Downloaded Model Reference successfully") - models = response.json() - logger.info("Updated Model Reference from remote.") - return models - except Exception as e: # XXX Double check and/or rework this - logger.error( - f"Download failed: {e}", - ) - logger.warning("Model Reference not downloaded, using local copy") - if self.models_db_path.exists(): - return json.loads(self.models_db_path.read_text()) - logger.error("No local copy of Model Reference found!") - return {} + raise NotImplementedError("Downloading model databases is no longer supported within hordelib.") def get_free_ram_mb(self) -> int: """Returns the amount of free RAM in MB rounded down to the nearest integer. diff --git a/hordelib/model_manager/lora.py b/hordelib/model_manager/lora.py index 104095b7..04b3b9b1 100644 --- a/hordelib/model_manager/lora.py +++ b/hordelib/model_manager/lora.py @@ -44,7 +44,7 @@ class LoraModelManager(BaseModelManager): ) LORA_API = "https://civitai.com/api/v1/models?types=LORA&sort=Highest%20Rated&primaryFileOnly=true" MAX_RETRIES = 10 if not TESTS_ONGOING else 3 - MAX_DOWNLOAD_THREADS = 5 if not TESTS_ONGOING else 15 + MAX_DOWNLOAD_THREADS = 5 if not TESTS_ONGOING else 75 RETRY_DELAY = 3 if not TESTS_ONGOING else 0.2 """The time to wait between retries in seconds""" REQUEST_METADATA_TIMEOUT = 20 # Longer because civitai performs poorly on metadata requests for more than 5 models diff --git a/hordelib/nodes/facerestore_cf/__init__.py b/hordelib/nodes/facerestore_cf/__init__.py index 81c6c1c6..bcad12b1 100644 --- a/hordelib/nodes/facerestore_cf/__init__.py +++ b/hordelib/nodes/facerestore_cf/__init__.py @@ -8,6 +8,7 @@ import model_management import numpy as np import torch + # from comfy_extras.chainner_models import model_loading from hordelib.nodes.facerestore_cf.r_chainner import model_loading from torchvision.transforms.functional import normalize diff --git a/hordelib/nodes/facerestore_cf/r_chainner/gfpganv1_clean_arch.py b/hordelib/nodes/facerestore_cf/r_chainner/gfpganv1_clean_arch.py index 7f2f0e75..455bdbb3 100644 --- a/hordelib/nodes/facerestore_cf/r_chainner/gfpganv1_clean_arch.py +++ b/hordelib/nodes/facerestore_cf/r_chainner/gfpganv1_clean_arch.py @@ -72,16 +72,12 @@ def forward( if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] + noise = [getattr(self.noises, f"noise{i}") for i in range(self.num_layers)] # style truncation if truncation < 1: style_truncation = [] for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) styles = style_truncation # get style latents with injection if len(styles) == 1: @@ -96,9 +92,7 @@ def forward( if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) latent = torch.cat([latent1, latent2], 1) # main generation @@ -160,14 +154,10 @@ def __init__(self, in_channels, out_channels, mode="down"): def forward(self, x): out = F.leaky_relu_(self.conv1(x), negative_slope=0.2) # upsample/downsample - out = F.interpolate( - out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False - ) + out = F.interpolate(out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False) out = F.leaky_relu_(self.conv2(out), negative_slope=0.2) # skip - x = F.interpolate( - x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False - ) + x = F.interpolate(x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False) skip = self.skip(x) out = out + skip return out @@ -283,9 +273,7 @@ def __init__( # load pre-trained stylegan2 model if necessary if decoder_load_path: self.stylegan_decoder.load_state_dict( - torch.load( - decoder_load_path, map_location=lambda storage, loc: storage - )["params_ema"] + torch.load(decoder_load_path, map_location=lambda storage, loc: storage)["params_ema"] ) # fix decoder without updating params if fix_decoder: @@ -317,9 +305,7 @@ def __init__( ) self.load_state_dict(state_dict) - def forward( - self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs - ): + def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs): """Forward function for GFPGANv1Clean. Args: x (Tensor): Input images. diff --git a/hordelib/nodes/facerestore_cf/r_chainner/model_loading.py b/hordelib/nodes/facerestore_cf/r_chainner/model_loading.py index 598e605c..7d10bb1c 100644 --- a/hordelib/nodes/facerestore_cf/r_chainner/model_loading.py +++ b/hordelib/nodes/facerestore_cf/r_chainner/model_loading.py @@ -1,4 +1,3 @@ - from hordelib.nodes.facerestore_cf.r_chainner.gfpganv1_clean_arch import GFPGANv1Clean from hordelib.nodes.facerestore_cf.r_chainner.types import PyTorchModel @@ -21,9 +20,6 @@ def load_state_dict(state_dict) -> PyTorchModel: state_dict_keys = list(state_dict.keys()) # GFPGAN - if ( - "toRGB.0.weight" in state_dict_keys - and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys - ): + if "toRGB.0.weight" in state_dict_keys and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys: model = GFPGANv1Clean(state_dict) return model diff --git a/hordelib/nodes/facerestore_cf/r_chainner/stylegan2_clean_arch.py b/hordelib/nodes/facerestore_cf/r_chainner/stylegan2_clean_arch.py index c48de9af..ddd013ba 100644 --- a/hordelib/nodes/facerestore_cf/r_chainner/stylegan2_clean_arch.py +++ b/hordelib/nodes/facerestore_cf/r_chainner/stylegan2_clean_arch.py @@ -117,9 +117,7 @@ def forward(self, x, style): demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps) weight = weight * demod.view(b, self.out_channels, 1, 1, 1) - weight = weight.view( - b * self.out_channels, c, self.kernel_size, self.kernel_size - ) + weight = weight.view(b * self.out_channels, c, self.kernel_size, self.kernel_size) # upsample or downsample if necessary if self.sample_mode == "upsample": @@ -224,9 +222,7 @@ def forward(self, x, style, skip=None): out = out + self.bias if skip is not None: if self.upsample: - skip = F.interpolate( - skip, scale_factor=2, mode="bilinear", align_corners=False - ) + skip = F.interpolate(skip, scale_factor=2, mode="bilinear", align_corners=False) out = out + skip return out @@ -257,9 +253,7 @@ class StyleGAN2GeneratorClean(nn.Module): narrow (float): Narrow ratio for channels. Default: 1.0. """ - def __init__( - self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1 - ): + def __init__(self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1): super(StyleGAN2GeneratorClean, self).__init__() # Style MLP layers self.num_style_feat = num_style_feat @@ -362,9 +356,7 @@ def get_latent(self, x): return self.style_mlp(x) def mean_latent(self, num_latent): - latent_in = torch.randn( - num_latent, self.num_style_feat, device=self.constant_input.weight.device - ) + latent_in = torch.randn(num_latent, self.num_style_feat, device=self.constant_input.weight.device) latent = self.style_mlp(latent_in).mean(0, keepdim=True) return latent @@ -398,16 +390,12 @@ def forward( if randomize_noise: noise = [None] * self.num_layers # for each style conv layer else: # use the stored noise - noise = [ - getattr(self.noises, f"noise{i}") for i in range(self.num_layers) - ] + noise = [getattr(self.noises, f"noise{i}") for i in range(self.num_layers)] # style truncation if truncation < 1: style_truncation = [] for style in styles: - style_truncation.append( - truncation_latent + truncation * (style - truncation_latent) - ) + style_truncation.append(truncation_latent + truncation * (style - truncation_latent)) styles = style_truncation # get style latents with injection if len(styles) == 1: @@ -422,9 +410,7 @@ def forward( if inject_index is None: inject_index = random.randint(1, self.num_latent - 1) latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1) - latent2 = ( - styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) - ) + latent2 = styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1) latent = torch.cat([latent1, latent2], 1) # main generation diff --git a/hordelib/nodes/facerestore_cf/r_chainner/types.py b/hordelib/nodes/facerestore_cf/r_chainner/types.py index 20e39f68..db215364 100644 --- a/hordelib/nodes/facerestore_cf/r_chainner/types.py +++ b/hordelib/nodes/facerestore_cf/r_chainner/types.py @@ -1,4 +1,3 @@ - from typing import Union from hordelib.nodes.facerestore_cf.r_chainner.gfpganv1_clean_arch import GFPGANv1Clean @@ -11,7 +10,8 @@ def is_pytorch_face_model(model: object): return isinstance(model, PyTorchFaceModels) -PyTorchModels = (*PyTorchFaceModels, ) + +PyTorchModels = (*PyTorchFaceModels,) PyTorchModel = Union[PyTorchFaceModel] diff --git a/hordelib/shared_model_manager.py b/hordelib/shared_model_manager.py index e5464237..6ac25d3e 100644 --- a/hordelib/shared_model_manager.py +++ b/hordelib/shared_model_manager.py @@ -6,7 +6,7 @@ import torch from horde_model_reference import get_model_reference_file_path -from horde_model_reference.legacy import LegacyReferenceDownloadManager +from horde_model_reference.model_reference_manager import ModelReferenceManager from loguru import logger from typing_extensions import Self @@ -59,6 +59,7 @@ def do_migrations(): class SharedModelManager: _instance: Self = None # type: ignore manager: ModelManager + model_reference_manager: ModelReferenceManager cuda_available: bool def __new__(cls, do_not_load_model_mangers: bool = False): @@ -101,16 +102,17 @@ def load_model_managers( args_passed = locals().copy() # XXX This is temporary args_passed.pop("cls") # XXX This is temporary - lrdm = LegacyReferenceDownloadManager() if download_legacy_references: try: - lrdm.download_all_legacy_model_references(overwrite_existing=overwrite_existing_references) + cls.model_reference_manager = ModelReferenceManager(download_and_convert_legacy_dbs=True) except Exception as e: logger.error(f"Failed to download legacy model references: {e}") logger.error( "If this continues to happen, " "github may be down or your internet connection may be having issues.", ) + else: + cls.model_reference_manager = ModelReferenceManager() references = {} for reference in managers_to_load: diff --git a/hordelib/utils/image_utils.py b/hordelib/utils/image_utils.py index 19c0dd23..6d75937c 100644 --- a/hordelib/utils/image_utils.py +++ b/hordelib/utils/image_utils.py @@ -10,6 +10,20 @@ DEFAULT_IMAGE_MIN_RESOLUTION = 512 DEFAULT_HIGHER_RES_MAX_RESOLUTION = 1024 +IDEAL_SDXL_RESOLUTIONS = [ + (1024, 1024), + (1152, 896), + (896, 1152), + (1216, 832), + (832, 1216), + (1344, 768), + (768, 1344), + (1536, 640), + (640, 1536), +] + +IDEAL_SDXL_RESOLUTIONS_ASPECT_RATIOS = [width / height for width, height in IDEAL_SDXL_RESOLUTIONS] + class ImageUtils: @classmethod @@ -110,6 +124,103 @@ def get_first_pass_image_resolution_max( ) return width, height + @classmethod + def get_first_pass_image_resolution_sdxl( + cls, + width: int, + height: int, + ): + """Resize the image dimensions fit in one of the pre-defined SDXL resolution buckets which most closely + matches the aspect ratio of the image. + """ + + aspect_ratio = width / height + closest_aspect_ratio = min( + IDEAL_SDXL_RESOLUTIONS_ASPECT_RATIOS, + key=lambda x: abs(aspect_ratio - x), + ) + + index = IDEAL_SDXL_RESOLUTIONS_ASPECT_RATIOS.index(closest_aspect_ratio) + return IDEAL_SDXL_RESOLUTIONS[index] + + @staticmethod + def calc_upscale_sampler_steps( + model_native_resolution: int | None, + width: int, + height: int, + hires_fix_denoising_strength: float, + ddim_steps: int, + ) -> int: + """Calculate the number of upscale steps to use for the upscale sampler based on the input parameters. + + Note: The resulting values are non-linear to the input values. The heuristic is based on the native resolution + of the model, the requested resolution, the denoising strength and the number of steps used for the ddim + sampler. + + Args: + model_name (str | None): The model name to use for the calculation. + width (int): The width of the image to generate. + height (int): The height of the image to generate. + hires_fix_denoising_strength (float): The denoising strength to use for the upscale sampler. + ddim_steps (int): The number of steps used for the sampler. + + Returns: + int: The number of upscale steps to use for the upscale sampler. + """ + MIN_DENOISING_STRENGTH = 0.01 + MAX_DENOISING_STRENGTH = 1.0 + DECAY_RATE = 2 + """The rate at which the upscale steps decay based on the denoising strength""" + MIN_STEPS = 3 + """The minimum number of steps to use for the upscaling sampler""" + UPSCALE_ADJUSTMENT_FACTOR = 0.5 + """The factor by which the upscale steps are adjusted based on the native resolution distance factor""" + UPSCALE_DIVISOR = 2.25 + """The divisor used to adjust the upscale steps based on the native resolution distance factor""" + + STANDARD_RESOLUTION = 512 + + native_resolution_distance_factor: float = 0 + + if model_native_resolution is not None: + native_resolution_pixels = model_native_resolution * model_native_resolution + + requested_pixels = width * height + native_resolution_distance_factor = requested_pixels / native_resolution_pixels + + resolution_penalty = 3 * (STANDARD_RESOLUTION / model_native_resolution) + native_resolution_distance_factor /= resolution_penalty + + hires_fix_denoising_strength = max( + MIN_DENOISING_STRENGTH, + min(MAX_DENOISING_STRENGTH, hires_fix_denoising_strength), + ) + + scale = ddim_steps - MIN_STEPS + upscale_steps = round(MIN_STEPS + scale * (hires_fix_denoising_strength**DECAY_RATE)) + + # if native_resolution_distance_factor > NATIVE_RESOLUTION_THRESHOLD: + upscale_steps = round( + upscale_steps * ((1 / (UPSCALE_ADJUSTMENT_FACTOR**native_resolution_distance_factor)) / UPSCALE_DIVISOR), + ) + + logger.debug(f"Upscale steps calculated as {upscale_steps}") + + if ddim_steps <= 18: + logger.debug(f"Upscale steps increased by {MIN_STEPS} due to low requested ddim steps") + upscale_steps += MIN_STEPS + + if upscale_steps > ddim_steps: + logger.debug(f"Upscale steps adjusted to {ddim_steps} from {upscale_steps}") + upscale_steps = ddim_steps + + step_floor = min(6, ddim_steps) + if step_floor > upscale_steps: + logger.debug(f"Upscale steps adjusted to {step_floor} from {upscale_steps}") + upscale_steps = step_floor + + return upscale_steps + @classmethod def add_image_alpha_channel(cls, source_image, alpha_image): # Convert images to RGBA mode diff --git a/images_expected/lora_blue_hires_fix.png b/images_expected/lora_blue_hires_fix.png index 23550bce7e72c169a7691c01149a31516165943a..8c15b701dcd901cc3734156b7c19bcc05a05e76b 100644 GIT binary patch literal 1197085 zcmV(tKijYzc)=YYcnSZ1|1bZ0 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