1049 lines
48 KiB
Python
1049 lines
48 KiB
Python
import json
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import math
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import os
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import sys
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import warnings
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import torch
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import numpy as np
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from PIL import Image, ImageFilter, ImageOps
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import random
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import cv2
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from skimage import exposure
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from typing import Any, Dict, List, Optional
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import modules.sd_hijack
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from modules import devices, prompt_parser, masking, sd_samplers, lowvram, generation_parameters_copypaste, script_callbacks, extra_networks, sd_vae_approx, scripts
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from modules.sd_hijack import model_hijack
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from modules.shared import opts, cmd_opts, state
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import modules.shared as shared
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import modules.paths as paths
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import modules.face_restoration
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import modules.images as images
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import modules.styles
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import modules.sd_models as sd_models
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import modules.sd_vae as sd_vae
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import logging
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from ldm.data.util import AddMiDaS
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from ldm.models.diffusion.ddpm import LatentDepth2ImageDiffusion
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from einops import repeat, rearrange
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from blendmodes.blend import blendLayers, BlendType
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# some of those options should not be changed at all because they would break the model, so I removed them from options.
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opt_C = 4
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opt_f = 8
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def setup_color_correction(image):
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logging.info("Calibrating color correction.")
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correction_target = cv2.cvtColor(np.asarray(image.copy()), cv2.COLOR_RGB2LAB)
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return correction_target
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def apply_color_correction(correction, original_image):
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logging.info("Applying color correction.")
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image = Image.fromarray(cv2.cvtColor(exposure.match_histograms(
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cv2.cvtColor(
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np.asarray(original_image),
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cv2.COLOR_RGB2LAB
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),
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correction,
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channel_axis=2
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), cv2.COLOR_LAB2RGB).astype("uint8"))
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image = blendLayers(image, original_image, BlendType.LUMINOSITY)
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return image
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def apply_overlay(image, paste_loc, index, overlays):
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if overlays is None or index >= len(overlays):
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return image
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overlay = overlays[index]
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if paste_loc is not None:
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x, y, w, h = paste_loc
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base_image = Image.new('RGBA', (overlay.width, overlay.height))
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image = images.resize_image(1, image, w, h)
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base_image.paste(image, (x, y))
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image = base_image
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image = image.convert('RGBA')
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image.alpha_composite(overlay)
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image = image.convert('RGB')
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return image
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def txt2img_image_conditioning(sd_model, x, width, height):
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if sd_model.model.conditioning_key not in {'hybrid', 'concat'}:
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# Dummy zero conditioning if we're not using inpainting model.
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# Still takes up a bit of memory, but no encoder call.
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# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
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return x.new_zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
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# The "masked-image" in this case will just be all zeros since the entire image is masked.
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image_conditioning = torch.zeros(x.shape[0], 3, height, width, device=x.device)
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image_conditioning = sd_model.get_first_stage_encoding(sd_model.encode_first_stage(image_conditioning))
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# Add the fake full 1s mask to the first dimension.
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image_conditioning = torch.nn.functional.pad(image_conditioning, (0, 0, 0, 0, 1, 0), value=1.0)
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image_conditioning = image_conditioning.to(x.dtype)
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return image_conditioning
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class StableDiffusionProcessing:
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"""
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The first set of paramaters: sd_models -> do_not_reload_embeddings represent the minimum required to create a StableDiffusionProcessing
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"""
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def __init__(self, sd_model=None, outpath_samples=None, outpath_grids=None, prompt: str = "", styles: List[str] = None, seed: int = -1, subseed: int = -1, subseed_strength: float = 0, seed_resize_from_h: int = -1, seed_resize_from_w: int = -1, seed_enable_extras: bool = True, sampler_name: str = None, batch_size: int = 1, n_iter: int = 1, steps: int = 50, cfg_scale: float = 7.0, width: int = 512, height: int = 512, restore_faces: bool = False, tiling: bool = False, do_not_save_samples: bool = False, do_not_save_grid: bool = False, extra_generation_params: Dict[Any, Any] = None, overlay_images: Any = None, negative_prompt: str = None, eta: float = None, do_not_reload_embeddings: bool = False, denoising_strength: float = 0, ddim_discretize: str = None, s_churn: float = 0.0, s_tmax: float = None, s_tmin: float = 0.0, s_noise: float = 1.0, override_settings: Dict[str, Any] = None, override_settings_restore_afterwards: bool = True, sampler_index: int = None, script_args: list = None):
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if sampler_index is not None:
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print("sampler_index argument for StableDiffusionProcessing does not do anything; use sampler_name", file=sys.stderr)
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self.outpath_samples: str = outpath_samples
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self.outpath_grids: str = outpath_grids
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self.prompt: str = prompt
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self.prompt_for_display: str = None
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self.negative_prompt: str = (negative_prompt or "")
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self.styles: list = styles or []
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self.seed: int = seed
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self.subseed: int = subseed
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self.subseed_strength: float = subseed_strength
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self.seed_resize_from_h: int = seed_resize_from_h
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self.seed_resize_from_w: int = seed_resize_from_w
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self.sampler_name: str = sampler_name
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self.batch_size: int = batch_size
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self.n_iter: int = n_iter
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self.steps: int = steps
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self.cfg_scale: float = cfg_scale
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self.width: int = width
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self.height: int = height
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self.restore_faces: bool = restore_faces
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self.tiling: bool = tiling
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self.do_not_save_samples: bool = do_not_save_samples
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self.do_not_save_grid: bool = do_not_save_grid
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self.extra_generation_params: dict = extra_generation_params or {}
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self.overlay_images = overlay_images
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self.eta = eta
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self.do_not_reload_embeddings = do_not_reload_embeddings
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self.paste_to = None
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self.color_corrections = None
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self.denoising_strength: float = denoising_strength
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self.sampler_noise_scheduler_override = None
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self.ddim_discretize = ddim_discretize or opts.ddim_discretize
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self.s_churn = s_churn or opts.s_churn
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self.s_tmin = s_tmin or opts.s_tmin
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self.s_tmax = s_tmax or float('inf') # not representable as a standard ui option
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self.s_noise = s_noise or opts.s_noise
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self.override_settings = {k: v for k, v in (override_settings or {}).items() if k not in shared.restricted_opts}
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self.override_settings_restore_afterwards = override_settings_restore_afterwards
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self.is_using_inpainting_conditioning = False
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self.disable_extra_networks = False
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if not seed_enable_extras:
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self.subseed = -1
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self.subseed_strength = 0
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self.seed_resize_from_h = 0
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self.seed_resize_from_w = 0
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self.scripts = None
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self.script_args = script_args
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self.all_prompts = None
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self.all_negative_prompts = None
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self.all_seeds = None
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self.all_subseeds = None
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self.iteration = 0
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@property
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def sd_model(self):
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return shared.sd_model
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def txt2img_image_conditioning(self, x, width=None, height=None):
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self.is_using_inpainting_conditioning = self.sd_model.model.conditioning_key in {'hybrid', 'concat'}
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return txt2img_image_conditioning(self.sd_model, x, width or self.width, height or self.height)
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def depth2img_image_conditioning(self, source_image):
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# Use the AddMiDaS helper to Format our source image to suit the MiDaS model
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transformer = AddMiDaS(model_type="dpt_hybrid")
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transformed = transformer({"jpg": rearrange(source_image[0], "c h w -> h w c")})
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midas_in = torch.from_numpy(transformed["midas_in"][None, ...]).to(device=shared.device)
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midas_in = repeat(midas_in, "1 ... -> n ...", n=self.batch_size)
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
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conditioning = torch.nn.functional.interpolate(
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self.sd_model.depth_model(midas_in),
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size=conditioning_image.shape[2:],
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mode="bicubic",
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align_corners=False,
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)
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(depth_min, depth_max) = torch.aminmax(conditioning)
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conditioning = 2. * (conditioning - depth_min) / (depth_max - depth_min) - 1.
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return conditioning
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def edit_image_conditioning(self, source_image):
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(source_image))
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return conditioning_image
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def inpainting_image_conditioning(self, source_image, latent_image, image_mask=None):
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self.is_using_inpainting_conditioning = True
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# Handle the different mask inputs
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if image_mask is not None:
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if torch.is_tensor(image_mask):
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conditioning_mask = image_mask
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else:
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conditioning_mask = np.array(image_mask.convert("L"))
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conditioning_mask = conditioning_mask.astype(np.float32) / 255.0
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conditioning_mask = torch.from_numpy(conditioning_mask[None, None])
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# Inpainting model uses a discretized mask as input, so we round to either 1.0 or 0.0
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conditioning_mask = torch.round(conditioning_mask)
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else:
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conditioning_mask = source_image.new_ones(1, 1, *source_image.shape[-2:])
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# Create another latent image, this time with a masked version of the original input.
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# Smoothly interpolate between the masked and unmasked latent conditioning image using a parameter.
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conditioning_mask = conditioning_mask.to(device=source_image.device, dtype=source_image.dtype)
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conditioning_image = torch.lerp(
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source_image,
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source_image * (1.0 - conditioning_mask),
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getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight)
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)
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# Encode the new masked image using first stage of network.
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conditioning_image = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(conditioning_image))
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# Create the concatenated conditioning tensor to be fed to `c_concat`
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conditioning_mask = torch.nn.functional.interpolate(conditioning_mask, size=latent_image.shape[-2:])
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conditioning_mask = conditioning_mask.expand(conditioning_image.shape[0], -1, -1, -1)
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image_conditioning = torch.cat([conditioning_mask, conditioning_image], dim=1)
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image_conditioning = image_conditioning.to(shared.device).type(self.sd_model.dtype)
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return image_conditioning
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def img2img_image_conditioning(self, source_image, latent_image, image_mask=None):
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source_image = devices.cond_cast_float(source_image)
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# HACK: Using introspection as the Depth2Image model doesn't appear to uniquely
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# identify itself with a field common to all models. The conditioning_key is also hybrid.
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if isinstance(self.sd_model, LatentDepth2ImageDiffusion):
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return self.depth2img_image_conditioning(source_image)
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if self.sd_model.cond_stage_key == "edit":
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return self.edit_image_conditioning(source_image)
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if self.sampler.conditioning_key in {'hybrid', 'concat'}:
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return self.inpainting_image_conditioning(source_image, latent_image, image_mask=image_mask)
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# Dummy zero conditioning if we're not using inpainting or depth model.
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return latent_image.new_zeros(latent_image.shape[0], 5, 1, 1)
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def init(self, all_prompts, all_seeds, all_subseeds):
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pass
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def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
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raise NotImplementedError()
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def close(self):
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self.sampler = None
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class Processed:
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def __init__(self, p: StableDiffusionProcessing, images_list, seed=-1, info="", subseed=None, all_prompts=None, all_negative_prompts=None, all_seeds=None, all_subseeds=None, index_of_first_image=0, infotexts=None, comments=""):
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self.images = images_list
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self.prompt = p.prompt
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self.negative_prompt = p.negative_prompt
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self.seed = seed
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self.subseed = subseed
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self.subseed_strength = p.subseed_strength
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self.info = info
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self.comments = comments
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self.width = p.width
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self.height = p.height
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self.sampler_name = p.sampler_name
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self.cfg_scale = p.cfg_scale
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self.steps = p.steps
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self.batch_size = p.batch_size
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self.restore_faces = p.restore_faces
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self.face_restoration_model = opts.face_restoration_model if p.restore_faces else None
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self.sd_model_hash = shared.sd_model.sd_model_hash
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self.seed_resize_from_w = p.seed_resize_from_w
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self.seed_resize_from_h = p.seed_resize_from_h
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self.denoising_strength = getattr(p, 'denoising_strength', None)
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self.extra_generation_params = p.extra_generation_params
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self.index_of_first_image = index_of_first_image
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self.styles = p.styles
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self.job_timestamp = state.job_timestamp
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self.clip_skip = opts.CLIP_stop_at_last_layers
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self.eta = p.eta
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self.ddim_discretize = p.ddim_discretize
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self.s_churn = p.s_churn
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self.s_tmin = p.s_tmin
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self.s_tmax = p.s_tmax
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self.s_noise = p.s_noise
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self.sampler_noise_scheduler_override = p.sampler_noise_scheduler_override
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self.prompt = self.prompt if type(self.prompt) != list else self.prompt[0]
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self.negative_prompt = self.negative_prompt if type(self.negative_prompt) != list else self.negative_prompt[0]
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self.seed = int(self.seed if type(self.seed) != list else self.seed[0]) if self.seed is not None else -1
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self.subseed = int(self.subseed if type(self.subseed) != list else self.subseed[0]) if self.subseed is not None else -1
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self.is_using_inpainting_conditioning = p.is_using_inpainting_conditioning
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self.all_prompts = all_prompts or p.all_prompts or [self.prompt]
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self.all_negative_prompts = all_negative_prompts or p.all_negative_prompts or [self.negative_prompt]
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self.all_seeds = all_seeds or p.all_seeds or [self.seed]
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self.all_subseeds = all_subseeds or p.all_subseeds or [self.subseed]
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self.infotexts = infotexts or [info]
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def js(self):
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obj = {
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"prompt": self.all_prompts[0],
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"all_prompts": self.all_prompts,
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"negative_prompt": self.all_negative_prompts[0],
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"all_negative_prompts": self.all_negative_prompts,
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"seed": self.seed,
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"all_seeds": self.all_seeds,
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"subseed": self.subseed,
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"all_subseeds": self.all_subseeds,
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"subseed_strength": self.subseed_strength,
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"width": self.width,
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"height": self.height,
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"sampler_name": self.sampler_name,
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"cfg_scale": self.cfg_scale,
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"steps": self.steps,
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"batch_size": self.batch_size,
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"restore_faces": self.restore_faces,
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"face_restoration_model": self.face_restoration_model,
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"sd_model_hash": self.sd_model_hash,
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"seed_resize_from_w": self.seed_resize_from_w,
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"seed_resize_from_h": self.seed_resize_from_h,
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"denoising_strength": self.denoising_strength,
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"extra_generation_params": self.extra_generation_params,
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"index_of_first_image": self.index_of_first_image,
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"infotexts": self.infotexts,
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"styles": self.styles,
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"job_timestamp": self.job_timestamp,
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"clip_skip": self.clip_skip,
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"is_using_inpainting_conditioning": self.is_using_inpainting_conditioning,
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}
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return json.dumps(obj)
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def infotext(self, p: StableDiffusionProcessing, index):
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return create_infotext(p, self.all_prompts, self.all_seeds, self.all_subseeds, comments=[], position_in_batch=index % self.batch_size, iteration=index // self.batch_size)
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# from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475/3
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def slerp(val, low, high):
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low_norm = low/torch.norm(low, dim=1, keepdim=True)
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high_norm = high/torch.norm(high, dim=1, keepdim=True)
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dot = (low_norm*high_norm).sum(1)
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if dot.mean() > 0.9995:
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return low * val + high * (1 - val)
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omega = torch.acos(dot)
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so = torch.sin(omega)
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res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high
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return res
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def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, seed_resize_from_h=0, seed_resize_from_w=0, p=None):
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eta_noise_seed_delta = opts.eta_noise_seed_delta or 0
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xs = []
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# if we have multiple seeds, this means we are working with batch size>1; this then
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# enables the generation of additional tensors with noise that the sampler will use during its processing.
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# Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
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# produce the same images as with two batches [100], [101].
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if p is not None and p.sampler is not None and (len(seeds) > 1 and opts.enable_batch_seeds or eta_noise_seed_delta > 0):
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sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
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else:
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sampler_noises = None
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for i, seed in enumerate(seeds):
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noise_shape = shape if seed_resize_from_h <= 0 or seed_resize_from_w <= 0 else (shape[0], seed_resize_from_h//8, seed_resize_from_w//8)
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subnoise = None
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if subseeds is not None:
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subseed = 0 if i >= len(subseeds) else subseeds[i]
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subnoise = devices.randn(subseed, noise_shape)
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# randn results depend on device; gpu and cpu get different results for same seed;
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# the way I see it, it's better to do this on CPU, so that everyone gets same result;
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# but the original script had it like this, so I do not dare change it for now because
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# it will break everyone's seeds.
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noise = devices.randn(seed, noise_shape)
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if subnoise is not None:
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noise = slerp(subseed_strength, noise, subnoise)
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if noise_shape != shape:
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x = devices.randn(seed, shape)
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dx = (shape[2] - noise_shape[2]) // 2
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dy = (shape[1] - noise_shape[1]) // 2
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w = noise_shape[2] if dx >= 0 else noise_shape[2] + 2 * dx
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h = noise_shape[1] if dy >= 0 else noise_shape[1] + 2 * dy
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tx = 0 if dx < 0 else dx
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ty = 0 if dy < 0 else dy
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dx = max(-dx, 0)
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dy = max(-dy, 0)
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x[:, ty:ty+h, tx:tx+w] = noise[:, dy:dy+h, dx:dx+w]
|
|
noise = x
|
|
|
|
if sampler_noises is not None:
|
|
cnt = p.sampler.number_of_needed_noises(p)
|
|
|
|
if eta_noise_seed_delta > 0:
|
|
torch.manual_seed(seed + eta_noise_seed_delta)
|
|
|
|
for j in range(cnt):
|
|
sampler_noises[j].append(devices.randn_without_seed(tuple(noise_shape)))
|
|
|
|
xs.append(noise)
|
|
|
|
if sampler_noises is not None:
|
|
p.sampler.sampler_noises = [torch.stack(n).to(shared.device) for n in sampler_noises]
|
|
|
|
x = torch.stack(xs).to(shared.device)
|
|
return x
|
|
|
|
|
|
def decode_first_stage(model, x):
|
|
with devices.autocast(disable=x.dtype == devices.dtype_vae):
|
|
x = model.decode_first_stage(x)
|
|
|
|
return x
|
|
|
|
|
|
def get_fixed_seed(seed):
|
|
if seed is None or seed == '' or seed == -1:
|
|
return int(random.randrange(4294967294))
|
|
|
|
return seed
|
|
|
|
|
|
def fix_seed(p):
|
|
p.seed = get_fixed_seed(p.seed)
|
|
p.subseed = get_fixed_seed(p.subseed)
|
|
|
|
|
|
def create_infotext(p, all_prompts, all_seeds, all_subseeds, comments=None, iteration=0, position_in_batch=0):
|
|
index = position_in_batch + iteration * p.batch_size
|
|
|
|
clip_skip = getattr(p, 'clip_skip', opts.CLIP_stop_at_last_layers)
|
|
|
|
generation_params = {
|
|
"Steps": p.steps,
|
|
"Sampler": p.sampler_name,
|
|
"CFG scale": p.cfg_scale,
|
|
"Seed": all_seeds[index],
|
|
"Face restoration": (opts.face_restoration_model if p.restore_faces else None),
|
|
"Size": f"{p.width}x{p.height}",
|
|
"Model hash": getattr(p, 'sd_model_hash', None if not opts.add_model_hash_to_info or not shared.sd_model.sd_model_hash else shared.sd_model.sd_model_hash),
|
|
"Model": (None if not opts.add_model_name_to_info or not shared.sd_model.sd_checkpoint_info.model_name else shared.sd_model.sd_checkpoint_info.model_name.replace(',', '').replace(':', '')),
|
|
"Variation seed": (None if p.subseed_strength == 0 else all_subseeds[index]),
|
|
"Variation seed strength": (None if p.subseed_strength == 0 else p.subseed_strength),
|
|
"Seed resize from": (None if p.seed_resize_from_w == 0 or p.seed_resize_from_h == 0 else f"{p.seed_resize_from_w}x{p.seed_resize_from_h}"),
|
|
"Denoising strength": getattr(p, 'denoising_strength', None),
|
|
"Conditional mask weight": getattr(p, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) if p.is_using_inpainting_conditioning else None,
|
|
"Clip skip": None if clip_skip <= 1 else clip_skip,
|
|
"ENSD": None if opts.eta_noise_seed_delta == 0 else opts.eta_noise_seed_delta,
|
|
}
|
|
|
|
generation_params.update(p.extra_generation_params)
|
|
|
|
generation_params_text = ", ".join([k if k == v else f'{k}: {generation_parameters_copypaste.quote(v)}' for k, v in generation_params.items() if v is not None])
|
|
|
|
negative_prompt_text = "\nNegative prompt: " + p.all_negative_prompts[index] if p.all_negative_prompts[index] else ""
|
|
|
|
return f"{all_prompts[index]}{negative_prompt_text}\n{generation_params_text}".strip()
|
|
|
|
|
|
def process_images(p: StableDiffusionProcessing) -> Processed:
|
|
stored_opts = {k: opts.data[k] for k in p.override_settings.keys()}
|
|
|
|
try:
|
|
for k, v in p.override_settings.items():
|
|
setattr(opts, k, v)
|
|
|
|
if k == 'sd_model_checkpoint':
|
|
sd_models.reload_model_weights()
|
|
|
|
if k == 'sd_vae':
|
|
sd_vae.reload_vae_weights()
|
|
|
|
res = process_images_inner(p)
|
|
|
|
finally:
|
|
# restore opts to original state
|
|
if p.override_settings_restore_afterwards:
|
|
for k, v in stored_opts.items():
|
|
setattr(opts, k, v)
|
|
if k == 'sd_model_checkpoint':
|
|
sd_models.reload_model_weights()
|
|
|
|
if k == 'sd_vae':
|
|
sd_vae.reload_vae_weights()
|
|
|
|
return res
|
|
|
|
|
|
def process_images_inner(p: StableDiffusionProcessing) -> Processed:
|
|
"""this is the main loop that both txt2img and img2img use; it calls func_init once inside all the scopes and func_sample once per batch"""
|
|
|
|
if type(p.prompt) == list:
|
|
assert(len(p.prompt) > 0)
|
|
else:
|
|
assert p.prompt is not None
|
|
|
|
devices.torch_gc()
|
|
|
|
seed = get_fixed_seed(p.seed)
|
|
subseed = get_fixed_seed(p.subseed)
|
|
|
|
modules.sd_hijack.model_hijack.apply_circular(p.tiling)
|
|
modules.sd_hijack.model_hijack.clear_comments()
|
|
|
|
comments = {}
|
|
|
|
if type(p.prompt) == list:
|
|
p.all_prompts = [shared.prompt_styles.apply_styles_to_prompt(x, p.styles) for x in p.prompt]
|
|
else:
|
|
p.all_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_styles_to_prompt(p.prompt, p.styles)]
|
|
|
|
if type(p.negative_prompt) == list:
|
|
p.all_negative_prompts = [shared.prompt_styles.apply_negative_styles_to_prompt(x, p.styles) for x in p.negative_prompt]
|
|
else:
|
|
p.all_negative_prompts = p.batch_size * p.n_iter * [shared.prompt_styles.apply_negative_styles_to_prompt(p.negative_prompt, p.styles)]
|
|
|
|
if type(seed) == list:
|
|
p.all_seeds = seed
|
|
else:
|
|
p.all_seeds = [int(seed) + (x if p.subseed_strength == 0 else 0) for x in range(len(p.all_prompts))]
|
|
|
|
if type(subseed) == list:
|
|
p.all_subseeds = subseed
|
|
else:
|
|
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
|
|
|
|
def infotext(iteration=0, position_in_batch=0):
|
|
return create_infotext(p, p.all_prompts, p.all_seeds, p.all_subseeds, comments, iteration, position_in_batch)
|
|
|
|
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
|
|
model_hijack.embedding_db.load_textual_inversion_embeddings()
|
|
|
|
_, extra_network_data = extra_networks.parse_prompts(p.all_prompts[0:1])
|
|
|
|
if p.scripts is not None:
|
|
p.scripts.process(p)
|
|
|
|
infotexts = []
|
|
output_images = []
|
|
|
|
cached_uc = [None, None]
|
|
cached_c = [None, None]
|
|
|
|
def get_conds_with_caching(function, required_prompts, steps, cache):
|
|
"""
|
|
Returns the result of calling function(shared.sd_model, required_prompts, steps)
|
|
using a cache to store the result if the same arguments have been used before.
|
|
|
|
cache is an array containing two elements. The first element is a tuple
|
|
representing the previously used arguments, or None if no arguments
|
|
have been used before. The second element is where the previously
|
|
computed result is stored.
|
|
"""
|
|
|
|
if cache[0] is not None and (required_prompts, steps) == cache[0]:
|
|
return cache[1]
|
|
|
|
with devices.autocast():
|
|
cache[1] = function(shared.sd_model, required_prompts, steps)
|
|
|
|
cache[0] = (required_prompts, steps)
|
|
return cache[1]
|
|
|
|
with torch.no_grad(), p.sd_model.ema_scope():
|
|
with devices.autocast():
|
|
p.init(p.all_prompts, p.all_seeds, p.all_subseeds)
|
|
|
|
# for OSX, loading the model during sampling changes the generated picture, so it is loaded here
|
|
if shared.opts.live_previews_enable and opts.show_progress_type == "Approx NN":
|
|
sd_vae_approx.model()
|
|
|
|
if not p.disable_extra_networks:
|
|
extra_networks.activate(p, extra_network_data)
|
|
|
|
with open(os.path.join(paths.data_path, "params.txt"), "w", encoding="utf8") as file:
|
|
processed = Processed(p, [], p.seed, "")
|
|
file.write(processed.infotext(p, 0))
|
|
|
|
if state.job_count == -1:
|
|
state.job_count = p.n_iter
|
|
|
|
for n in range(p.n_iter):
|
|
p.iteration = n
|
|
|
|
if state.skipped:
|
|
state.skipped = False
|
|
|
|
if state.interrupted:
|
|
break
|
|
|
|
prompts = p.all_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
|
negative_prompts = p.all_negative_prompts[n * p.batch_size:(n + 1) * p.batch_size]
|
|
seeds = p.all_seeds[n * p.batch_size:(n + 1) * p.batch_size]
|
|
subseeds = p.all_subseeds[n * p.batch_size:(n + 1) * p.batch_size]
|
|
|
|
if len(prompts) == 0:
|
|
break
|
|
|
|
prompts, _ = extra_networks.parse_prompts(prompts)
|
|
|
|
if p.scripts is not None:
|
|
p.scripts.process_batch(p, batch_number=n, prompts=prompts, seeds=seeds, subseeds=subseeds)
|
|
|
|
uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps, cached_uc)
|
|
c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps, cached_c)
|
|
|
|
if len(model_hijack.comments) > 0:
|
|
for comment in model_hijack.comments:
|
|
comments[comment] = 1
|
|
|
|
if p.n_iter > 1:
|
|
shared.state.job = f"Batch {n+1} out of {p.n_iter}"
|
|
|
|
with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
|
|
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
|
|
|
|
x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
|
|
for x in x_samples_ddim:
|
|
devices.test_for_nans(x, "vae")
|
|
|
|
x_samples_ddim = torch.stack(x_samples_ddim).float()
|
|
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
|
|
|
|
del samples_ddim
|
|
|
|
if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
|
|
lowvram.send_everything_to_cpu()
|
|
|
|
devices.torch_gc()
|
|
|
|
if p.scripts is not None:
|
|
p.scripts.postprocess_batch(p, x_samples_ddim, batch_number=n)
|
|
|
|
for i, x_sample in enumerate(x_samples_ddim):
|
|
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
|
x_sample = x_sample.astype(np.uint8)
|
|
|
|
if p.restore_faces:
|
|
if opts.save and not p.do_not_save_samples and opts.save_images_before_face_restoration:
|
|
images.save_image(Image.fromarray(x_sample), p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-face-restoration")
|
|
|
|
devices.torch_gc()
|
|
|
|
x_sample = modules.face_restoration.restore_faces(x_sample)
|
|
devices.torch_gc()
|
|
|
|
image = Image.fromarray(x_sample)
|
|
|
|
if p.scripts is not None:
|
|
pp = scripts.PostprocessImageArgs(image)
|
|
p.scripts.postprocess_image(p, pp)
|
|
image = pp.image
|
|
|
|
if p.color_corrections is not None and i < len(p.color_corrections):
|
|
if opts.save and not p.do_not_save_samples and opts.save_images_before_color_correction:
|
|
image_without_cc = apply_overlay(image, p.paste_to, i, p.overlay_images)
|
|
images.save_image(image_without_cc, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p, suffix="-before-color-correction")
|
|
image = apply_color_correction(p.color_corrections[i], image)
|
|
|
|
image = apply_overlay(image, p.paste_to, i, p.overlay_images)
|
|
|
|
if opts.samples_save and not p.do_not_save_samples:
|
|
images.save_image(image, p.outpath_samples, "", seeds[i], prompts[i], opts.samples_format, info=infotext(n, i), p=p)
|
|
|
|
text = infotext(n, i)
|
|
infotexts.append(text)
|
|
if opts.enable_pnginfo:
|
|
image.info["parameters"] = text
|
|
output_images.append(image)
|
|
|
|
del x_samples_ddim
|
|
|
|
devices.torch_gc()
|
|
|
|
state.nextjob()
|
|
|
|
p.color_corrections = None
|
|
|
|
index_of_first_image = 0
|
|
unwanted_grid_because_of_img_count = len(output_images) < 2 and opts.grid_only_if_multiple
|
|
if (opts.return_grid or opts.grid_save) and not p.do_not_save_grid and not unwanted_grid_because_of_img_count:
|
|
grid = images.image_grid(output_images, p.batch_size)
|
|
|
|
if opts.return_grid:
|
|
text = infotext()
|
|
infotexts.insert(0, text)
|
|
if opts.enable_pnginfo:
|
|
grid.info["parameters"] = text
|
|
output_images.insert(0, grid)
|
|
index_of_first_image = 1
|
|
|
|
if opts.grid_save:
|
|
images.save_image(grid, p.outpath_grids, "grid", p.all_seeds[0], p.all_prompts[0], opts.grid_format, info=infotext(), short_filename=not opts.grid_extended_filename, p=p, grid=True)
|
|
|
|
if not p.disable_extra_networks:
|
|
extra_networks.deactivate(p, extra_network_data)
|
|
|
|
devices.torch_gc()
|
|
|
|
res = Processed(p, output_images, p.all_seeds[0], infotext(), comments="".join(["\n\n" + x for x in comments]), subseed=p.all_subseeds[0], index_of_first_image=index_of_first_image, infotexts=infotexts)
|
|
|
|
if p.scripts is not None:
|
|
p.scripts.postprocess(p, res)
|
|
|
|
return res
|
|
|
|
|
|
def old_hires_fix_first_pass_dimensions(width, height):
|
|
"""old algorithm for auto-calculating first pass size"""
|
|
|
|
desired_pixel_count = 512 * 512
|
|
actual_pixel_count = width * height
|
|
scale = math.sqrt(desired_pixel_count / actual_pixel_count)
|
|
width = math.ceil(scale * width / 64) * 64
|
|
height = math.ceil(scale * height / 64) * 64
|
|
|
|
return width, height
|
|
|
|
|
|
class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
|
|
sampler = None
|
|
|
|
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, hr_second_pass_steps: int = 0, hr_resize_x: int = 0, hr_resize_y: int = 0, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.enable_hr = enable_hr
|
|
self.denoising_strength = denoising_strength
|
|
self.hr_scale = hr_scale
|
|
self.hr_upscaler = hr_upscaler
|
|
self.hr_second_pass_steps = hr_second_pass_steps
|
|
self.hr_resize_x = hr_resize_x
|
|
self.hr_resize_y = hr_resize_y
|
|
self.hr_upscale_to_x = hr_resize_x
|
|
self.hr_upscale_to_y = hr_resize_y
|
|
|
|
if firstphase_width != 0 or firstphase_height != 0:
|
|
self.hr_upscale_to_x = self.width
|
|
self.hr_upscale_to_y = self.height
|
|
self.width = firstphase_width
|
|
self.height = firstphase_height
|
|
|
|
self.truncate_x = 0
|
|
self.truncate_y = 0
|
|
self.applied_old_hires_behavior_to = None
|
|
|
|
def init(self, all_prompts, all_seeds, all_subseeds):
|
|
if self.enable_hr:
|
|
if opts.use_old_hires_fix_width_height and self.applied_old_hires_behavior_to != (self.width, self.height):
|
|
self.hr_resize_x = self.width
|
|
self.hr_resize_y = self.height
|
|
self.hr_upscale_to_x = self.width
|
|
self.hr_upscale_to_y = self.height
|
|
|
|
self.width, self.height = old_hires_fix_first_pass_dimensions(self.width, self.height)
|
|
self.applied_old_hires_behavior_to = (self.width, self.height)
|
|
|
|
if self.hr_resize_x == 0 and self.hr_resize_y == 0:
|
|
self.extra_generation_params["Hires upscale"] = self.hr_scale
|
|
self.hr_upscale_to_x = int(self.width * self.hr_scale)
|
|
self.hr_upscale_to_y = int(self.height * self.hr_scale)
|
|
else:
|
|
self.extra_generation_params["Hires resize"] = f"{self.hr_resize_x}x{self.hr_resize_y}"
|
|
|
|
if self.hr_resize_y == 0:
|
|
self.hr_upscale_to_x = self.hr_resize_x
|
|
self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
|
|
elif self.hr_resize_x == 0:
|
|
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
|
|
self.hr_upscale_to_y = self.hr_resize_y
|
|
else:
|
|
target_w = self.hr_resize_x
|
|
target_h = self.hr_resize_y
|
|
src_ratio = self.width / self.height
|
|
dst_ratio = self.hr_resize_x / self.hr_resize_y
|
|
|
|
if src_ratio < dst_ratio:
|
|
self.hr_upscale_to_x = self.hr_resize_x
|
|
self.hr_upscale_to_y = self.hr_resize_x * self.height // self.width
|
|
else:
|
|
self.hr_upscale_to_x = self.hr_resize_y * self.width // self.height
|
|
self.hr_upscale_to_y = self.hr_resize_y
|
|
|
|
self.truncate_x = (self.hr_upscale_to_x - target_w) // opt_f
|
|
self.truncate_y = (self.hr_upscale_to_y - target_h) // opt_f
|
|
|
|
# special case: the user has chosen to do nothing
|
|
if self.hr_upscale_to_x == self.width and self.hr_upscale_to_y == self.height:
|
|
self.enable_hr = False
|
|
self.denoising_strength = None
|
|
self.extra_generation_params.pop("Hires upscale", None)
|
|
self.extra_generation_params.pop("Hires resize", None)
|
|
return
|
|
|
|
if not state.processing_has_refined_job_count:
|
|
if state.job_count == -1:
|
|
state.job_count = self.n_iter
|
|
|
|
shared.total_tqdm.updateTotal((self.steps + (self.hr_second_pass_steps or self.steps)) * state.job_count)
|
|
state.job_count = state.job_count * 2
|
|
state.processing_has_refined_job_count = True
|
|
|
|
if self.hr_second_pass_steps:
|
|
self.extra_generation_params["Hires steps"] = self.hr_second_pass_steps
|
|
|
|
if self.hr_upscaler is not None:
|
|
self.extra_generation_params["Hires upscaler"] = self.hr_upscaler
|
|
|
|
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
|
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_modes.get(shared.latent_upscale_default_mode, "nearest")
|
|
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}"
|
|
|
|
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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|
return samples
|
|
|
|
target_width = self.hr_upscale_to_x
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|
target_height = self.hr_upscale_to_y
|
|
|
|
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
|
|
|
|
if not isinstance(image, Image.Image):
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|
image = sd_samplers.sample_to_image(image, index, approximation=0)
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|
|
|
info = create_infotext(self, self.all_prompts, self.all_seeds, self.all_subseeds, [], iteration=self.iteration, position_in_batch=index)
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|
images.save_image(image, self.outpath_samples, "", seeds[index], prompts[index], opts.samples_format, info=info, suffix="-before-highres-fix")
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|
|
|
if latent_scale_mode is not None:
|
|
for i in range(samples.shape[0]):
|
|
save_intermediate(samples, i)
|
|
|
|
samples = torch.nn.functional.interpolate(samples, size=(target_height // opt_f, target_width // opt_f), mode=latent_scale_mode["mode"], antialias=latent_scale_mode["antialias"])
|
|
|
|
# Avoid making the inpainting conditioning unless necessary as
|
|
# this does need some extra compute to decode / encode the image again.
|
|
if getattr(self, "inpainting_mask_weight", shared.opts.inpainting_mask_weight) < 1.0:
|
|
image_conditioning = self.img2img_image_conditioning(decode_first_stage(self.sd_model, samples), samples)
|
|
else:
|
|
image_conditioning = self.txt2img_image_conditioning(samples)
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|
else:
|
|
decoded_samples = decode_first_stage(self.sd_model, samples)
|
|
lowres_samples = torch.clamp((decoded_samples + 1.0) / 2.0, min=0.0, max=1.0)
|
|
|
|
batch_images = []
|
|
for i, x_sample in enumerate(lowres_samples):
|
|
x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
|
|
x_sample = x_sample.astype(np.uint8)
|
|
image = Image.fromarray(x_sample)
|
|
|
|
save_intermediate(image, i)
|
|
|
|
image = images.resize_image(0, image, target_width, target_height, upscaler_name=self.hr_upscaler)
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = np.moveaxis(image, 2, 0)
|
|
batch_images.append(image)
|
|
|
|
decoded_samples = torch.from_numpy(np.array(batch_images))
|
|
decoded_samples = decoded_samples.to(shared.device)
|
|
decoded_samples = 2. * decoded_samples - 1.
|
|
|
|
samples = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(decoded_samples))
|
|
|
|
image_conditioning = self.img2img_image_conditioning(decoded_samples, samples)
|
|
|
|
shared.state.nextjob()
|
|
|
|
img2img_sampler_name = self.sampler_name if self.sampler_name != 'PLMS' else 'DDIM' # PLMS does not support img2img so we just silently switch ot DDIM
|
|
self.sampler = sd_samplers.create_sampler(img2img_sampler_name, self.sd_model)
|
|
|
|
samples = samples[:, :, self.truncate_y//2:samples.shape[2]-(self.truncate_y+1)//2, self.truncate_x//2:samples.shape[3]-(self.truncate_x+1)//2]
|
|
|
|
noise = create_random_tensors(samples.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=subseed_strength, p=self)
|
|
|
|
# GC now before running the next img2img to prevent running out of memory
|
|
x = None
|
|
devices.torch_gc()
|
|
|
|
samples = self.sampler.sample_img2img(self, samples, noise, conditioning, unconditional_conditioning, steps=self.hr_second_pass_steps or self.steps, image_conditioning=image_conditioning)
|
|
|
|
return samples
|
|
|
|
|
|
class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
|
|
sampler = None
|
|
|
|
def __init__(self, init_images: list = None, resize_mode: int = 0, denoising_strength: float = 0.75, mask: Any = None, mask_blur: int = 4, inpainting_fill: int = 0, inpaint_full_res: bool = True, inpaint_full_res_padding: int = 0, inpainting_mask_invert: int = 0, initial_noise_multiplier: float = None, **kwargs):
|
|
super().__init__(**kwargs)
|
|
|
|
self.init_images = init_images
|
|
self.resize_mode: int = resize_mode
|
|
self.denoising_strength: float = denoising_strength
|
|
self.init_latent = None
|
|
self.image_mask = mask
|
|
self.latent_mask = None
|
|
self.mask_for_overlay = None
|
|
self.mask_blur = mask_blur
|
|
self.inpainting_fill = inpainting_fill
|
|
self.inpaint_full_res = inpaint_full_res
|
|
self.inpaint_full_res_padding = inpaint_full_res_padding
|
|
self.inpainting_mask_invert = inpainting_mask_invert
|
|
self.initial_noise_multiplier = opts.initial_noise_multiplier if initial_noise_multiplier is None else initial_noise_multiplier
|
|
self.mask = None
|
|
self.nmask = None
|
|
self.image_conditioning = None
|
|
|
|
def init(self, all_prompts, all_seeds, all_subseeds):
|
|
self.sampler = sd_samplers.create_sampler(self.sampler_name, self.sd_model)
|
|
crop_region = None
|
|
|
|
image_mask = self.image_mask
|
|
|
|
if image_mask is not None:
|
|
image_mask = image_mask.convert('L')
|
|
|
|
if self.inpainting_mask_invert:
|
|
image_mask = ImageOps.invert(image_mask)
|
|
|
|
if self.mask_blur > 0:
|
|
image_mask = image_mask.filter(ImageFilter.GaussianBlur(self.mask_blur))
|
|
|
|
if self.inpaint_full_res:
|
|
self.mask_for_overlay = image_mask
|
|
mask = image_mask.convert('L')
|
|
crop_region = masking.get_crop_region(np.array(mask), self.inpaint_full_res_padding)
|
|
crop_region = masking.expand_crop_region(crop_region, self.width, self.height, mask.width, mask.height)
|
|
x1, y1, x2, y2 = crop_region
|
|
|
|
mask = mask.crop(crop_region)
|
|
image_mask = images.resize_image(2, mask, self.width, self.height)
|
|
self.paste_to = (x1, y1, x2-x1, y2-y1)
|
|
else:
|
|
image_mask = images.resize_image(self.resize_mode, image_mask, self.width, self.height)
|
|
np_mask = np.array(image_mask)
|
|
np_mask = np.clip((np_mask.astype(np.float32)) * 2, 0, 255).astype(np.uint8)
|
|
self.mask_for_overlay = Image.fromarray(np_mask)
|
|
|
|
self.overlay_images = []
|
|
|
|
latent_mask = self.latent_mask if self.latent_mask is not None else image_mask
|
|
|
|
add_color_corrections = opts.img2img_color_correction and self.color_corrections is None
|
|
if add_color_corrections:
|
|
self.color_corrections = []
|
|
imgs = []
|
|
for img in self.init_images:
|
|
image = images.flatten(img, opts.img2img_background_color)
|
|
|
|
if crop_region is None and self.resize_mode != 3:
|
|
image = images.resize_image(self.resize_mode, image, self.width, self.height)
|
|
|
|
if image_mask is not None:
|
|
image_masked = Image.new('RGBa', (image.width, image.height))
|
|
image_masked.paste(image.convert("RGBA").convert("RGBa"), mask=ImageOps.invert(self.mask_for_overlay.convert('L')))
|
|
|
|
self.overlay_images.append(image_masked.convert('RGBA'))
|
|
|
|
# crop_region is not None if we are doing inpaint full res
|
|
if crop_region is not None:
|
|
image = image.crop(crop_region)
|
|
image = images.resize_image(2, image, self.width, self.height)
|
|
|
|
if image_mask is not None:
|
|
if self.inpainting_fill != 1:
|
|
image = masking.fill(image, latent_mask)
|
|
|
|
if add_color_corrections:
|
|
self.color_corrections.append(setup_color_correction(image))
|
|
|
|
image = np.array(image).astype(np.float32) / 255.0
|
|
image = np.moveaxis(image, 2, 0)
|
|
|
|
imgs.append(image)
|
|
|
|
if len(imgs) == 1:
|
|
batch_images = np.expand_dims(imgs[0], axis=0).repeat(self.batch_size, axis=0)
|
|
if self.overlay_images is not None:
|
|
self.overlay_images = self.overlay_images * self.batch_size
|
|
|
|
if self.color_corrections is not None and len(self.color_corrections) == 1:
|
|
self.color_corrections = self.color_corrections * self.batch_size
|
|
|
|
elif len(imgs) <= self.batch_size:
|
|
self.batch_size = len(imgs)
|
|
batch_images = np.array(imgs)
|
|
else:
|
|
raise RuntimeError(f"bad number of images passed: {len(imgs)}; expecting {self.batch_size} or less")
|
|
|
|
image = torch.from_numpy(batch_images)
|
|
image = 2. * image - 1.
|
|
image = image.to(shared.device)
|
|
|
|
self.init_latent = self.sd_model.get_first_stage_encoding(self.sd_model.encode_first_stage(image))
|
|
|
|
if self.resize_mode == 3:
|
|
self.init_latent = torch.nn.functional.interpolate(self.init_latent, size=(self.height // opt_f, self.width // opt_f), mode="bilinear")
|
|
|
|
if image_mask is not None:
|
|
init_mask = latent_mask
|
|
latmask = init_mask.convert('RGB').resize((self.init_latent.shape[3], self.init_latent.shape[2]))
|
|
latmask = np.moveaxis(np.array(latmask, dtype=np.float32), 2, 0) / 255
|
|
latmask = latmask[0]
|
|
latmask = np.around(latmask)
|
|
latmask = np.tile(latmask[None], (4, 1, 1))
|
|
|
|
self.mask = torch.asarray(1.0 - latmask).to(shared.device).type(self.sd_model.dtype)
|
|
self.nmask = torch.asarray(latmask).to(shared.device).type(self.sd_model.dtype)
|
|
|
|
# this needs to be fixed to be done in sample() using actual seeds for batches
|
|
if self.inpainting_fill == 2:
|
|
self.init_latent = self.init_latent * self.mask + create_random_tensors(self.init_latent.shape[1:], all_seeds[0:self.init_latent.shape[0]]) * self.nmask
|
|
elif self.inpainting_fill == 3:
|
|
self.init_latent = self.init_latent * self.mask
|
|
|
|
self.image_conditioning = self.img2img_image_conditioning(image, self.init_latent, image_mask)
|
|
|
|
def sample(self, conditioning, unconditional_conditioning, seeds, subseeds, subseed_strength, prompts):
|
|
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)
|
|
|
|
if self.initial_noise_multiplier != 1.0:
|
|
self.extra_generation_params["Noise multiplier"] = self.initial_noise_multiplier
|
|
x *= self.initial_noise_multiplier
|
|
|
|
samples = self.sampler.sample_img2img(self, self.init_latent, x, conditioning, unconditional_conditioning, image_conditioning=self.image_conditioning)
|
|
|
|
if self.mask is not None:
|
|
samples = samples * self.nmask + self.init_latent * self.mask
|
|
|
|
del x
|
|
devices.torch_gc()
|
|
|
|
return samples
|