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Zero
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import math
import torch
from typing import Any, Dict, Optional, Tuple
from modules.AutoDetailer import AD_util, bbox, tensor_util
from modules.AutoDetailer import SEGS
from modules.Utilities import util
from modules.AutoEncoders import VariationalAE
from modules.Device import Device
from modules.sample import ksampler_util, samplers, sampling, sampling_util
# FIXME: Improve slow inference times
class DifferentialDiffusion:
"""#### Class for applying differential diffusion to a model."""
def apply(self, model: torch.nn.Module) -> Tuple[torch.nn.Module]:
"""#### Apply differential diffusion to a model.
#### Args:
- `model` (torch.nn.Module): The input model.
#### Returns:
- `Tuple[torch.nn.Module]`: The modified model.
"""
model = model.clone()
model.set_model_denoise_mask_function(self.forward)
return (model,)
def forward(
self,
sigma: torch.Tensor,
denoise_mask: torch.Tensor,
extra_options: Dict[str, Any],
) -> torch.Tensor:
"""#### Forward function for differential diffusion.
#### Args:
- `sigma` (torch.Tensor): The sigma tensor.
- `denoise_mask` (torch.Tensor): The denoise mask tensor.
- `extra_options` (Dict[str, Any]): Additional options.
#### Returns:
- `torch.Tensor`: The processed denoise mask tensor.
"""
model = extra_options["model"]
step_sigmas = extra_options["sigmas"]
sigma_to = model.inner_model.model_sampling.sigma_min
sigma_from = step_sigmas[0]
ts_from = model.inner_model.model_sampling.timestep(sigma_from)
ts_to = model.inner_model.model_sampling.timestep(sigma_to)
current_ts = model.inner_model.model_sampling.timestep(sigma[0])
threshold = (current_ts - ts_to) / (ts_from - ts_to)
return (denoise_mask >= threshold).to(denoise_mask.dtype)
def to_latent_image(pixels: torch.Tensor, vae: VariationalAE.VAE) -> torch.Tensor:
"""#### Convert pixels to a latent image using a VAE.
#### Args:
- `pixels` (torch.Tensor): The input pixel tensor.
- `vae` (VariationalAE.VAE): The VAE model.
#### Returns:
- `torch.Tensor`: The latent image tensor.
"""
pixels.shape[1]
pixels.shape[2]
return VariationalAE.VAEEncode().encode(vae, pixels)[0]
def calculate_sigmas2(
model: torch.nn.Module, sampler: str, scheduler: str, steps: int
) -> torch.Tensor:
"""#### Calculate sigmas for a model.
#### Args:
- `model` (torch.nn.Module): The input model.
- `sampler` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `steps` (int): The number of steps.
#### Returns:
- `torch.Tensor`: The calculated sigmas.
"""
return ksampler_util.calculate_sigmas(
model.get_model_object("model_sampling"), scheduler, steps
)
def get_noise_sampler(
x: torch.Tensor, cpu: bool, total_sigmas: torch.Tensor, **kwargs
) -> Optional[sampling_util.BrownianTreeNoiseSampler]:
"""#### Get a noise sampler.
#### Args:
- `x` (torch.Tensor): The input tensor.
- `cpu` (bool): Whether to use CPU.
- `total_sigmas` (torch.Tensor): The total sigmas tensor.
- `kwargs` (dict): Additional arguments.
#### Returns:
- `Optional[sampling_util.BrownianTreeNoiseSampler]`: The noise sampler.
"""
if "extra_args" in kwargs and "seed" in kwargs["extra_args"]:
sigma_min, sigma_max = total_sigmas[total_sigmas > 0].min(), total_sigmas.max()
seed = kwargs["extra_args"].get("seed", None)
return sampling_util.BrownianTreeNoiseSampler(
x, sigma_min, sigma_max, seed=seed, cpu=cpu
)
return None
def ksampler2(
sampler_name: str,
total_sigmas: torch.Tensor,
extra_options: Dict[str, Any] = {},
inpaint_options: Dict[str, Any] = {},
pipeline: bool = False,
) -> sampling.KSAMPLER:
"""#### Get a ksampler.
#### Args:
- `sampler_name` (str): The sampler name.
- `total_sigmas` (torch.Tensor): The total sigmas tensor.
- `extra_options` (Dict[str, Any], optional): Additional options. Defaults to {}.
- `inpaint_options` (Dict[str, Any], optional): Inpaint options. Defaults to {}.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `sampling.KSAMPLER`: The ksampler.
"""
if sampler_name == "dpmpp_2m_sde":
def sample_dpmpp_sde(model, x, sigmas, pipeline, **kwargs):
noise_sampler = get_noise_sampler(x, True, total_sigmas, **kwargs)
if noise_sampler is not None:
kwargs["noise_sampler"] = noise_sampler
return samplers.sample_dpmpp_2m_sde(
model, x, sigmas, pipeline=pipeline, **kwargs
)
sampler_function = sample_dpmpp_sde
else:
return sampling.sampler_object(sampler_name, pipeline=pipeline)
return sampling.KSAMPLER(sampler_function, extra_options, inpaint_options)
class Noise_RandomNoise:
"""#### Class for generating random noise."""
def __init__(self, seed: int):
"""#### Initialize the Noise_RandomNoise class.
#### Args:
- `seed` (int): The seed for random noise.
"""
self.seed = seed
def generate_noise(self, input_latent: Dict[str, torch.Tensor]) -> torch.Tensor:
"""#### Generate random noise.
#### Args:
- `input_latent` (Dict[str, torch.Tensor]): The input latent tensor.
#### Returns:
- `torch.Tensor`: The generated noise tensor.
"""
latent_image = input_latent["samples"]
batch_inds = (
input_latent["batch_index"] if "batch_index" in input_latent else None
)
return ksampler_util.prepare_noise(latent_image, self.seed, batch_inds)
def sample_with_custom_noise(
model: torch.nn.Module,
add_noise: bool,
noise_seed: int,
cfg: int,
positive: Any,
negative: Any,
sampler: Any,
sigmas: torch.Tensor,
latent_image: Dict[str, torch.Tensor],
noise: Optional[torch.Tensor] = None,
callback: Optional[callable] = None,
pipeline: bool = False,
) -> Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]:
"""#### Sample with custom noise.
#### Args:
- `model` (torch.nn.Module): The input model.
- `add_noise` (bool): Whether to add noise.
- `noise_seed` (int): The noise seed.
- `cfg` (int): Classifier-Free Guidance Scale
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `sampler` (Any): The sampler.
- `sigmas` (torch.Tensor): The sigmas tensor.
- `latent_image` (Dict[str, torch.Tensor]): The latent image tensor.
- `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None.
- `callback` (Optional[callable], optional): The callback function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Tuple[Dict[str, torch.Tensor], Dict[str, torch.Tensor]]`: The sampled and denoised tensors.
"""
latent = latent_image
latent_image = latent["samples"]
out = latent.copy()
out["samples"] = latent_image
if noise is None:
noise = Noise_RandomNoise(noise_seed).generate_noise(out)
noise_mask = None
if "noise_mask" in latent:
noise_mask = latent["noise_mask"]
disable_pbar = not util.PROGRESS_BAR_ENABLED
device = Device.get_torch_device()
noise = noise.to(device)
latent_image = latent_image.to(device)
if noise_mask is not None:
noise_mask = noise_mask.to(device)
samples = sampling.sample_custom(
model,
noise,
cfg,
sampler,
sigmas,
positive,
negative,
latent_image,
noise_mask=noise_mask,
disable_pbar=disable_pbar,
seed=noise_seed,
pipeline=pipeline,
)
samples = samples.to(Device.intermediate_device())
out["samples"] = samples
out_denoised = out
return out, out_denoised
def separated_sample(
model: torch.nn.Module,
add_noise: bool,
seed: int,
steps: int,
cfg: int,
sampler_name: str,
scheduler: str,
positive: Any,
negative: Any,
latent_image: Dict[str, torch.Tensor],
start_at_step: Optional[int],
end_at_step: Optional[int],
return_with_leftover_noise: bool,
sigma_ratio: float = 1.0,
sampler_opt: Optional[Dict[str, Any]] = None,
noise: Optional[torch.Tensor] = None,
callback: Optional[callable] = None,
scheduler_func: Optional[callable] = None,
pipeline: bool = False,
) -> Dict[str, torch.Tensor]:
"""#### Perform separated sampling.
#### Args:
- `model` (torch.nn.Module): The input model.
- `add_noise` (bool): Whether to add noise.
- `seed` (int): The seed for random noise.
- `steps` (int): The number of steps.
- `cfg` (int): Classifier-Free Guidance Scale
- `sampler_name` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `latent_image` (Dict[str, torch.Tensor]): The latent image tensor.
- `start_at_step` (Optional[int]): The step to start at.
- `end_at_step` (Optional[int]): The step to end at.
- `return_with_leftover_noise` (bool): Whether to return with leftover noise.
- `sigma_ratio` (float, optional): The sigma ratio. Defaults to 1.0.
- `sampler_opt` (Optional[Dict[str, Any]], optional): The sampler options. Defaults to None.
- `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None.
- `callback` (Optional[callable], optional): The callback function. Defaults to None.
- `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Dict[str, torch.Tensor]`: The sampled tensor.
"""
total_sigmas = calculate_sigmas2(model, sampler_name, scheduler, steps)
sigmas = total_sigmas
if start_at_step is not None:
sigmas = sigmas[start_at_step:] * sigma_ratio
impact_sampler = ksampler2(sampler_name, total_sigmas, pipeline=pipeline)
res = sample_with_custom_noise(
model,
add_noise,
seed,
cfg,
positive,
negative,
impact_sampler,
sigmas,
latent_image,
noise=noise,
callback=callback,
pipeline=pipeline,
)
return res[1]
def ksampler_wrapper(
model: torch.nn.Module,
seed: int,
steps: int,
cfg: int,
sampler_name: str,
scheduler: str,
positive: Any,
negative: Any,
latent_image: Dict[str, torch.Tensor],
denoise: float,
refiner_ratio: Optional[float] = None,
refiner_model: Optional[torch.nn.Module] = None,
refiner_clip: Optional[Any] = None,
refiner_positive: Optional[Any] = None,
refiner_negative: Optional[Any] = None,
sigma_factor: float = 1.0,
noise: Optional[torch.Tensor] = None,
scheduler_func: Optional[callable] = None,
pipeline: bool = False,
) -> Dict[str, torch.Tensor]:
"""#### Wrapper for ksampler.
#### Args:
- `model` (torch.nn.Module): The input model.
- `seed` (int): The seed for random noise.
- `steps` (int): The number of steps.
- `cfg` (int): Classifier-Free Guidance Scale
- `sampler_name` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `latent_image` (Dict[str, torch.Tensor]): The latent image tensor.
- `denoise` (float): The denoise factor.
- `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None.
- `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None.
- `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None.
- `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None.
- `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None.
- `sigma_factor` (float, optional): The sigma factor. Defaults to 1.0.
- `noise` (Optional[torch.Tensor], optional): The noise tensor. Defaults to None.
- `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Dict[str, torch.Tensor]`: The refined latent tensor.
"""
advanced_steps = math.floor(steps / denoise)
start_at_step = advanced_steps - steps
end_at_step = start_at_step + steps
refined_latent = separated_sample(
model,
True,
seed,
advanced_steps,
cfg,
sampler_name,
scheduler,
positive,
negative,
latent_image,
start_at_step,
end_at_step,
False,
sigma_ratio=sigma_factor,
noise=noise,
scheduler_func=scheduler_func,
pipeline=pipeline,
)
return refined_latent
def enhance_detail(
image: torch.Tensor,
model: torch.nn.Module,
clip: Any,
vae: VariationalAE.VAE,
guide_size: int,
guide_size_for_bbox: bool,
max_size: int,
bbox: Tuple[int, int, int, int],
seed: int,
steps: int,
cfg: int,
sampler_name: str,
scheduler: str,
positive: Any,
negative: Any,
denoise: float,
noise_mask: Optional[torch.Tensor],
force_inpaint: bool,
wildcard_opt: Optional[Any] = None,
wildcard_opt_concat_mode: Optional[Any] = None,
detailer_hook: Optional[callable] = None,
refiner_ratio: Optional[float] = None,
refiner_model: Optional[torch.nn.Module] = None,
refiner_clip: Optional[Any] = None,
refiner_positive: Optional[Any] = None,
refiner_negative: Optional[Any] = None,
control_net_wrapper: Optional[Any] = None,
cycle: int = 1,
inpaint_model: bool = False,
noise_mask_feather: int = 0,
scheduler_func: Optional[callable] = None,
pipeline: bool = False,
) -> Tuple[torch.Tensor, Optional[Any]]:
"""#### Enhance detail of an image.
#### Args:
- `image` (torch.Tensor): The input image tensor.
- `model` (torch.nn.Module): The model.
- `clip` (Any): The clip model.
- `vae` (VariationalAE.VAE): The VAE model.
- `guide_size` (int): The guide size.
- `guide_size_for_bbox` (bool): Whether to use guide size for bbox.
- `max_size` (int): The maximum size.
- `bbox` (Tuple[int, int, int, int]): The bounding box.
- `seed` (int): The seed for random noise.
- `steps` (int): The number of steps.
- `cfg` (int): Classifier-Free Guidance Scale
- `sampler_name` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `denoise` (float): The denoise factor.
- `noise_mask` (Optional[torch.Tensor]): The noise mask tensor.
- `force_inpaint` (bool): Whether to force inpaint.
- `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None.
- `wildcard_opt_concat_mode` (Optional[Any], optional): The wildcard concat mode. Defaults to None.
- `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None.
- `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None.
- `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None.
- `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None.
- `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None.
- `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None.
- `control_net_wrapper` (Optional[Any], optional): The control net wrapper. Defaults to None.
- `cycle` (int, optional): The number of cycles. Defaults to 1.
- `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False.
- `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0.
- `scheduler_func` (Optional[callable], optional): The scheduler function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Tuple[torch.Tensor, Optional[Any]]`: The refined image tensor and optional cnet_pils.
"""
if noise_mask is not None:
noise_mask = tensor_util.tensor_gaussian_blur_mask(
noise_mask, noise_mask_feather
)
noise_mask = noise_mask.squeeze(3)
h = image.shape[1]
w = image.shape[2]
bbox_h = bbox[3] - bbox[1]
bbox_w = bbox[2] - bbox[0]
# for cropped_size
upscale = guide_size / min(w, h)
new_w = int(w * upscale)
new_h = int(h * upscale)
if new_w > max_size or new_h > max_size:
upscale *= max_size / max(new_w, new_h)
new_w = int(w * upscale)
new_h = int(h * upscale)
if upscale <= 1.0 or new_w == 0 or new_h == 0:
print("Detailer: force inpaint")
upscale = 1.0
new_w = w
new_h = h
print(
f"Detailer: segment upscale for ({bbox_w, bbox_h}) | crop region {w, h} x {upscale} -> {new_w, new_h}"
)
# upscale
upscaled_image = tensor_util.tensor_resize(image, new_w, new_h)
cnet_pils = None
# prepare mask
latent_image = to_latent_image(upscaled_image, vae)
if noise_mask is not None:
latent_image["noise_mask"] = noise_mask
refined_latent = latent_image
# ksampler
for i in range(0, cycle):
(
model2,
seed2,
steps2,
cfg2,
sampler_name2,
scheduler2,
positive2,
negative2,
_upscaled_latent2,
denoise2,
) = (
model,
seed + i,
steps,
cfg,
sampler_name,
scheduler,
positive,
negative,
latent_image,
denoise,
)
noise = None
refined_latent = ksampler_wrapper(
model2,
seed2,
steps2,
cfg2,
sampler_name2,
scheduler2,
positive2,
negative2,
refined_latent,
denoise2,
refiner_ratio,
refiner_model,
refiner_clip,
refiner_positive,
refiner_negative,
noise=noise,
scheduler_func=scheduler_func,
pipeline=pipeline,
)
# non-latent downscale - latent downscale cause bad quality
try:
# try to decode image normally
refined_image = vae.decode(refined_latent["samples"])
except Exception:
# usually an out-of-memory exception from the decode, so try a tiled approach
refined_image = vae.decode_tiled(
refined_latent["samples"],
tile_x=64,
tile_y=64,
)
# downscale
refined_image = tensor_util.tensor_resize(refined_image, w, h)
# prevent mixing of device
refined_image = refined_image.cpu()
# don't convert to latent - latent break image
# preserving pil is much better
return refined_image, cnet_pils
class DetailerForEach:
"""#### Class for detailing each segment of an image."""
@staticmethod
def do_detail(
image: torch.Tensor,
segs: Tuple[torch.Tensor, Any],
model: torch.nn.Module,
clip: Any,
vae: VariationalAE.VAE,
guide_size: int,
guide_size_for_bbox: bool,
max_size: int,
seed: int,
steps: int,
cfg: int,
sampler_name: str,
scheduler: str,
positive: Any,
negative: Any,
denoise: float,
feather: int,
noise_mask: Optional[torch.Tensor],
force_inpaint: bool,
wildcard_opt: Optional[Any] = None,
detailer_hook: Optional[callable] = None,
refiner_ratio: Optional[float] = None,
refiner_model: Optional[torch.nn.Module] = None,
refiner_clip: Optional[Any] = None,
refiner_positive: Optional[Any] = None,
refiner_negative: Optional[Any] = None,
cycle: int = 1,
inpaint_model: bool = False,
noise_mask_feather: int = 0,
scheduler_func_opt: Optional[callable] = None,
pipeline: bool = False,
) -> Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]:
"""#### Perform detailing on each segment of an image.
#### Args:
- `image` (torch.Tensor): The input image tensor.
- `segs` (Tuple[torch.Tensor, Any]): The segments.
- `model` (torch.nn.Module): The model.
- `clip` (Any): The clip model.
- `vae` (VariationalAE.VAE): The VAE model.
- `guide_size` (int): The guide size.
- `guide_size_for_bbox` (bool): Whether to use guide size for bbox.
- `max_size` (int): The maximum size.
- `seed` (int): The seed for random noise.
- `steps` (int): The number of steps.
- `cfg` (int): Classifier-Free Guidance Scale.
- `sampler_name` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `denoise` (float): The denoise factor.
- `feather` (int): The feather value.
- `noise_mask` (Optional[torch.Tensor]): The noise mask tensor.
- `force_inpaint` (bool): Whether to force inpaint.
- `wildcard_opt` (Optional[Any], optional): The wildcard options. Defaults to None.
- `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None.
- `refiner_ratio` (Optional[float], optional): The refiner ratio. Defaults to None.
- `refiner_model` (Optional[torch.nn.Module], optional): The refiner model. Defaults to None.
- `refiner_clip` (Optional[Any], optional): The refiner clip. Defaults to None.
- `refiner_positive` (Optional[Any], optional): The refiner positive prompt. Defaults to None.
- `refiner_negative` (Optional[Any], optional): The refiner negative prompt. Defaults to None.
- `cycle` (int, optional): The number of cycles. Defaults to 1.
- `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False.
- `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0.
- `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Tuple[torch.Tensor, list, list, list, list, Tuple[torch.Tensor, list]]`: The detailed image tensor, cropped list, enhanced list, enhanced alpha list, cnet PIL list, and new segments.
"""
image = image.clone()
enhanced_alpha_list = []
enhanced_list = []
cropped_list = []
cnet_pil_list = []
segs = AD_util.segs_scale_match(segs, image.shape)
new_segs = []
wildcard_concat_mode = None
wmode, wildcard_chooser = bbox.process_wildcard_for_segs(wildcard_opt)
ordered_segs = segs[1]
if (
noise_mask_feather > 0
and "denoise_mask_function" not in model.model_options
):
model = DifferentialDiffusion().apply(model)[0]
for i, seg in enumerate(ordered_segs):
cropped_image = AD_util.crop_ndarray4(
image.cpu().numpy(), seg.crop_region
) # Never use seg.cropped_image to handle overlapping area
cropped_image = tensor_util.to_tensor(cropped_image)
mask = tensor_util.to_tensor(seg.cropped_mask)
mask = tensor_util.tensor_gaussian_blur_mask(mask, feather)
is_mask_all_zeros = (seg.cropped_mask == 0).all().item()
if is_mask_all_zeros:
print("Detailer: segment skip [empty mask]")
continue
cropped_mask = seg.cropped_mask
seg_seed, wildcard_item = wildcard_chooser.get(seg)
seg_seed = seed + i if seg_seed is None else seg_seed
cropped_positive = [
[
condition,
{
k: (
crop_condition_mask(v, image, seg.crop_region)
if k == "mask"
else v
)
for k, v in details.items()
},
]
for condition, details in positive
]
cropped_negative = [
[
condition,
{
k: (
crop_condition_mask(v, image, seg.crop_region)
if k == "mask"
else v
)
for k, v in details.items()
},
]
for condition, details in negative
]
orig_cropped_image = cropped_image.clone()
enhanced_image, cnet_pils = enhance_detail(
cropped_image,
model,
clip,
vae,
guide_size,
guide_size_for_bbox,
max_size,
seg.bbox,
seg_seed,
steps,
cfg,
sampler_name,
scheduler,
cropped_positive,
cropped_negative,
denoise,
cropped_mask,
force_inpaint,
wildcard_opt=wildcard_item,
wildcard_opt_concat_mode=wildcard_concat_mode,
detailer_hook=detailer_hook,
refiner_ratio=refiner_ratio,
refiner_model=refiner_model,
refiner_clip=refiner_clip,
refiner_positive=refiner_positive,
refiner_negative=refiner_negative,
control_net_wrapper=seg.control_net_wrapper,
cycle=cycle,
inpaint_model=inpaint_model,
noise_mask_feather=noise_mask_feather,
scheduler_func=scheduler_func_opt,
pipeline=pipeline,
)
if enhanced_image is not None:
# don't latent composite-> converting to latent caused poor quality
# use image paste
image = image.cpu()
enhanced_image = enhanced_image.cpu()
tensor_util.tensor_paste(
image,
enhanced_image,
(seg.crop_region[0], seg.crop_region[1]),
mask,
) # this code affecting to `cropped_image`.
enhanced_list.append(enhanced_image)
# Convert enhanced_pil_alpha to RGBA mode
enhanced_image_alpha = tensor_util.tensor_convert_rgba(enhanced_image)
new_seg_image = (
enhanced_image.numpy()
) # alpha should not be applied to seg_image
# Apply the mask
mask = tensor_util.tensor_resize(
mask, *tensor_util.tensor_get_size(enhanced_image)
)
tensor_util.tensor_putalpha(enhanced_image_alpha, mask)
enhanced_alpha_list.append(enhanced_image_alpha)
cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image`
new_seg = SEGS.SEG(
new_seg_image,
seg.cropped_mask,
seg.confidence,
seg.crop_region,
seg.bbox,
seg.label,
seg.control_net_wrapper,
)
new_segs.append(new_seg)
image_tensor = tensor_util.tensor_convert_rgb(image)
cropped_list.sort(key=lambda x: x.shape, reverse=True)
enhanced_list.sort(key=lambda x: x.shape, reverse=True)
enhanced_alpha_list.sort(key=lambda x: x.shape, reverse=True)
return (
image_tensor,
cropped_list,
enhanced_list,
enhanced_alpha_list,
cnet_pil_list,
(segs[0], new_segs),
)
def empty_pil_tensor(w: int = 64, h: int = 64) -> torch.Tensor:
"""#### Create an empty PIL tensor.
#### Args:
- `w` (int, optional): The width of the tensor. Defaults to 64.
- `h` (int, optional): The height of the tensor. Defaults to 64.
#### Returns:
- `torch.Tensor`: The empty tensor.
"""
return torch.zeros((1, h, w, 3), dtype=torch.float32)
class DetailerForEachTest(DetailerForEach):
"""#### Test class for DetailerForEach."""
def doit(
self,
image: torch.Tensor,
segs: Any,
model: torch.nn.Module,
clip: Any,
vae: VariationalAE.VAE,
guide_size: int,
guide_size_for: bool,
max_size: int,
seed: int,
steps: int,
cfg: Any,
sampler_name: str,
scheduler: str,
positive: Any,
negative: Any,
denoise: float,
feather: int,
noise_mask: Optional[torch.Tensor],
force_inpaint: bool,
wildcard: Optional[Any],
detailer_hook: Optional[callable] = None,
cycle: int = 1,
inpaint_model: bool = False,
noise_mask_feather: int = 0,
scheduler_func_opt: Optional[callable] = None,
pipeline: bool = False,
) -> Tuple[torch.Tensor, list, list, list, list]:
"""#### Perform detail enhancement for testing.
#### Args:
- `image` (torch.Tensor): The input image tensor.
- `segs` (Any): The segments.
- `model` (torch.nn.Module): The model.
- `clip` (Any): The clip model.
- `vae` (VariationalAE.VAE): The VAE model.
- `guide_size` (int): The guide size.
- `guide_size_for` (bool): Whether to use guide size for.
- `max_size` (int): The maximum size.
- `seed` (int): The seed for random noise.
- `steps` (int): The number of steps.
- `cfg` (Any): The configuration.
- `sampler_name` (str): The sampler name.
- `scheduler` (str): The scheduler name.
- `positive` (Any): The positive prompt.
- `negative` (Any): The negative prompt.
- `denoise` (float): The denoise factor.
- `feather` (int): The feather value.
- `noise_mask` (Optional[torch.Tensor]): The noise mask tensor.
- `force_inpaint` (bool): Whether to force inpaint.
- `wildcard` (Optional[Any]): The wildcard options.
- `detailer_hook` (Optional[callable], optional): The detailer hook. Defaults to None.
- `cycle` (int, optional): The number of cycles. Defaults to 1.
- `inpaint_model` (bool, optional): Whether to use inpaint model. Defaults to False.
- `noise_mask_feather` (int, optional): The noise mask feather. Defaults to 0.
- `scheduler_func_opt` (Optional[callable], optional): The scheduler function. Defaults to None.
- `pipeline` (bool, optional): Whether to use pipeline. Defaults to False.
#### Returns:
- `Tuple[torch.Tensor, list, list, list, list]`: The enhanced image tensor, cropped list, cropped enhanced list, cropped enhanced alpha list, and cnet PIL list.
"""
(
enhanced_img,
cropped,
cropped_enhanced,
cropped_enhanced_alpha,
cnet_pil_list,
new_segs,
) = DetailerForEach.do_detail(
image,
segs,
model,
clip,
vae,
guide_size,
guide_size_for,
max_size,
seed,
steps,
cfg,
sampler_name,
scheduler,
positive,
negative,
denoise,
feather,
noise_mask,
force_inpaint,
wildcard,
detailer_hook,
cycle=cycle,
inpaint_model=inpaint_model,
noise_mask_feather=noise_mask_feather,
scheduler_func_opt=scheduler_func_opt,
pipeline=pipeline,
)
cnet_pil_list = [empty_pil_tensor()]
return (
enhanced_img,
cropped,
cropped_enhanced,
cropped_enhanced_alpha,
cnet_pil_list,
)
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