import torch
from modules import prompt_parser, devices, sd_samplers_common
from modules.shared import opts, state
import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
def catenate_conds(conds):
if not isinstance(conds[0], dict):
return torch.cat(conds)
return {key: torch.cat([x[key] for x in conds]) for key in conds[0].keys()}
def subscript_cond(cond, a, b):
if not isinstance(cond, dict):
return cond[a:b]
return {key: vec[a:b] for key, vec in cond.items()}
def pad_cond(tensor, repeats, empty):
if not isinstance(tensor, dict):
return torch.cat([tensor, empty.repeat((tensor.shape[0], repeats, 1))], axis=1)
tensor['crossattn'] = pad_cond(tensor['crossattn'], repeats, empty)
return tensor
class CFGDenoiser(torch.nn.Module):
"""
Classifier free guidance denoiser. A wrapper for stable diffusion model (specifically for unet)
that can take a noisy picture and produce a noise-free picture using two guidances (prompts)
instead of one. Originally, the second prompt is just an empty string, but we use non-empty
negative prompt.
"""
def __init__(self, sampler):
super().__init__()
self.model_wrap = None
self.mask = None
self.nmask = None
self.mask_blend_power = 1
self.mask_blend_scale = 0.5
self.inpaint_detail_preservation = 4
self.init_latent = None
self.steps = None
"""number of steps as specified by user in UI"""
self.total_steps = None
"""expected number of calls to denoiser calculated from self.steps and specifics of the selected sampler"""
self.step = 0
self.image_cfg_scale = None
self.padded_cond_uncond = False
self.sampler = sampler
self.model_wrap = None
self.p = None
# NOTE: masking before denoising can cause the original latents to be oversmoothed
# as the original latents do not have noise
self.mask_before_denoising = False
@property
def inner_model(self):
raise NotImplementedError()
def combine_denoised(self, x_out, conds_list, uncond, cond_scale):
denoised_uncond = x_out[-uncond.shape[0]:]
denoised = torch.clone(denoised_uncond)
for i, conds in enumerate(conds_list):
for cond_index, weight in conds:
denoised[i] += (x_out[cond_index] - denoised_uncond[i]) * (weight * cond_scale)
return denoised
def combine_denoised_for_edit_model(self, x_out, cond_scale):
out_cond, out_img_cond, out_uncond = x_out.chunk(3)
denoised = out_uncond + cond_scale * (out_cond - out_img_cond) + self.image_cfg_scale * (out_img_cond - out_uncond)
return denoised
def get_pred_x0(self, x_in, x_out, sigma):
return x_out
def update_inner_model(self):
self.model_wrap = None
c, uc = self.p.get_conds()
self.sampler.sampler_extra_args['cond'] = c
self.sampler.sampler_extra_args['uncond'] = uc
def forward(self, x, sigma, uncond, cond, cond_scale, s_min_uncond, image_cond):
def latent_blend(a, b, t):
"""
Interpolates two latent image representations according to the parameter t,
where the interpolated vectors' magnitudes are also interpolated separately.
The "detail_preservation" factor biases the magnitude interpolation towards
the larger of the two magnitudes.
"""
# NOTE: We use inplace operations wherever possible.
one_minus_t = 1 - t
# Linearly interpolate the image vectors.
a_scaled = a * one_minus_t
b_scaled = b * t
image_interp = a_scaled
image_interp.add_(b_scaled)
result_type = image_interp.dtype
del a_scaled, b_scaled
# Calculate the magnitude of the interpolated vectors. (We will remove this magnitude.)
# 64-bit operations are used here to allow large exponents.
current_magnitude = torch.norm(image_interp, p=2, dim=1).to(torch.float64).add_(0.00001)
# Interpolate the powered magnitudes, then un-power them (bring them back to a power of 1).
a_magnitude = torch.norm(a, p=2, dim=1).to(torch.float64).pow_(self.inpaint_detail_preservation) * one_minus_t
b_magnitude = torch.norm(b, p=2, dim=1).to(torch.float64).pow_(self.inpaint_detail_preservation) * t
desired_magnitude = a_magnitude
desired_magnitude.add_(b_magnitude).pow_(1 / self.inpaint_detail_preservation)
del a_magnitude, b_magnitude, one_minus_t
# Change the linearly interpolated image vectors' magnitudes to the value we want.
# This is the last 64-bit operation.
image_interp_scaling_factor = desired_magnitude
image_interp_scaling_factor.div_(current_magnitude)
image_interp_scaled = image_interp
image_interp_scaled.mul_(image_interp_scaling_factor)
del current_magnitude
del desired_magnitude
del image_interp
del image_interp_scaling_factor
image_interp_scaled = image_interp_scaled.to(result_type)
del result_type
return image_interp_scaled
def get_modified_nmask(nmask, _sigma):
"""
Converts a negative mask representing the transparency of the original latent vectors being overlayed
to a mask that is scaled according to the denoising strength for this step.
Where:
0 = fully opaque, infinite density, fully masked
1 = fully transparent, zero density, fully unmasked
We bring this transparency to a power, as this allows one to simulate N number of blending operations
where N can be any positive real value. Using this one can control the balance of influence between
the denoiser and the original latents according to the sigma value.
NOTE: "mask" is not used
"""
return torch.pow(nmask, (_sigma ** self.mask_blend_power) * self.mask_blend_scale)
if state.interrupted or state.skipped:
raise sd_samplers_common.InterruptedException
if sd_samplers_common.apply_refiner(self):
cond = self.sampler.sampler_extra_args['cond']
uncond = self.sampler.sampler_extra_args['uncond']
# at self.image_cfg_scale == 1.0 produced results for edit model are the same as with normal sampling,
# so is_edit_model is set to False to support AND composition.
is_edit_model = shared.sd_model.cond_stage_key == "edit" and self.image_cfg_scale is not None and self.image_cfg_scale != 1.0
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
uncond = prompt_parser.reconstruct_cond_batch(uncond, self.step)
assert not is_edit_model or all(len(conds) == 1 for conds in conds_list), "AND is not supported for InstructPix2Pix checkpoint (unless using Image CFG scale = 1.0)"
# Blend in the original latents (before)
if self.mask_before_denoising and self.mask is not None:
x = latent_blend(self.init_latent, x, get_modified_nmask(self.nmask, sigma))
batch_size = len(conds_list)
repeats = [len(conds_list[i]) for i in range(batch_size)]
if shared.sd_model.model.conditioning_key == "crossattn-adm":
image_uncond = torch.zeros_like(image_cond)
make_condition_dict = lambda c_crossattn, c_adm: {"c_crossattn": [c_crossattn], "c_adm": c_adm}
else:
image_uncond = image_cond
if isinstance(uncond, dict):
make_condition_dict = lambda c_crossattn, c_concat: {**c_crossattn, "c_concat": [c_concat]}
else:
make_condition_dict = lambda c_crossattn, c_concat: {"c_crossattn": [c_crossattn], "c_concat": [c_concat]}
if not is_edit_model:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond])
else:
x_in = torch.cat([torch.stack([x[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [x] + [x])
sigma_in = torch.cat([torch.stack([sigma[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [sigma] + [sigma])
image_cond_in = torch.cat([torch.stack([image_cond[i] for _ in range(n)]) for i, n in enumerate(repeats)] + [image_uncond] + [torch.zeros_like(self.init_latent)])
denoiser_params = CFGDenoiserParams(x_in, image_cond_in, sigma_in, state.sampling_step, state.sampling_steps, tensor, uncond)
cfg_denoiser_callback(denoiser_params)
x_in = denoiser_params.x
image_cond_in = denoiser_params.image_cond
sigma_in = denoiser_params.sigma
tensor = denoiser_params.text_cond
uncond = denoiser_params.text_uncond
skip_uncond = False
# alternating uncond allows for higher thresholds without the quality loss normally expected from raising it
if self.step % 2 and s_min_uncond > 0 and sigma[0] < s_min_uncond and not is_edit_model:
skip_uncond = True
x_in = x_in[:-batch_size]
sigma_in = sigma_in[:-batch_size]
self.padded_cond_uncond = False
if shared.opts.pad_cond_uncond and tensor.shape[1] != uncond.shape[1]:
empty = shared.sd_model.cond_stage_model_empty_prompt
num_repeats = (tensor.shape[1] - uncond.shape[1]) // empty.shape[1]
if num_repeats < 0:
tensor = pad_cond(tensor, -num_repeats, empty)
self.padded_cond_uncond = True
elif num_repeats > 0:
uncond = pad_cond(uncond, num_repeats, empty)
self.padded_cond_uncond = True
if tensor.shape[1] == uncond.shape[1] or skip_uncond:
if is_edit_model:
cond_in = catenate_conds([tensor, uncond, uncond])
elif skip_uncond:
cond_in = tensor
else:
cond_in = catenate_conds([tensor, uncond])
if shared.opts.batch_cond_uncond:
x_out = self.inner_model(x_in, sigma_in, cond=make_condition_dict(cond_in, image_cond_in))
else:
x_out = torch.zeros_like(x_in)
for batch_offset in range(0, x_out.shape[0], batch_size):
a = batch_offset
b = a + batch_size
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(subscript_cond(cond_in, a, b), image_cond_in[a:b]))
else:
x_out = torch.zeros_like(x_in)
batch_size = batch_size*2 if shared.opts.batch_cond_uncond else batch_size
for batch_offset in range(0, tensor.shape[0], batch_size):
a = batch_offset
b = min(a + batch_size, tensor.shape[0])
if not is_edit_model:
c_crossattn = subscript_cond(tensor, a, b)
else:
c_crossattn = torch.cat([tensor[a:b]], uncond)
x_out[a:b] = self.inner_model(x_in[a:b], sigma_in[a:b], cond=make_condition_dict(c_crossattn, image_cond_in[a:b]))
if not skip_uncond:
x_out[-uncond.shape[0]:] = self.inner_model(x_in[-uncond.shape[0]:], sigma_in[-uncond.shape[0]:], cond=make_condition_dict(uncond, image_cond_in[-uncond.shape[0]:]))
denoised_image_indexes = [x[0][0] for x in conds_list]
if skip_uncond:
fake_uncond = torch.cat([x_out[i:i+1] for i in denoised_image_indexes])
x_out = torch.cat([x_out, fake_uncond]) # we skipped uncond denoising, so we put cond-denoised image to where the uncond-denoised image should be
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params)
devices.test_for_nans(x_out, "unet")
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
elif skip_uncond:
denoised = self.combine_denoised(x_out, conds_list, uncond, 1.0)
else:
denoised = self.combine_denoised(x_out, conds_list, uncond, cond_scale)
# Blend in the original latents (after)
if not self.mask_before_denoising and self.mask is not None:
denoised = latent_blend(self.init_latent, denoised, get_modified_nmask(self.nmask, sigma))
self.sampler.last_latent = self.get_pred_x0(torch.cat([x_in[i:i + 1] for i in denoised_image_indexes]), torch.cat([x_out[i:i + 1] for i in denoised_image_indexes]), sigma)
if opts.live_preview_content == "Prompt":
preview = self.sampler.last_latent
elif opts.live_preview_content == "Negative prompt":
preview = self.get_pred_x0(x_in[-uncond.shape[0]:], x_out[-uncond.shape[0]:], sigma)
else:
preview = self.get_pred_x0(torch.cat([x_in[i:i+1] for i in denoised_image_indexes]), torch.cat([denoised[i:i+1] for i in denoised_image_indexes]), sigma)
sd_samplers_common.store_latent(preview)
after_cfg_callback_params = AfterCFGCallbackParams(denoised, state.sampling_step, state.sampling_steps)
cfg_after_cfg_callback(after_cfg_callback_params)
denoised = after_cfg_callback_params.x
self.step += 1
return denoised