import torch

import comfy.model_base

import comfy.ldm.modules.diffusionmodules.openaimodel

import comfy.samplers

from comfy.samplers import model_management, lcm, math

from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed

from modules.filters import gaussian_filter_2d

sharpness = 2.0

def sampling_function_patched(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={},

seed=None):

def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):

area = (x_in.shape[2], x_in.shape[3], 0, 0)

strength = 1.0

if 'timestep_start' in cond[1]:

timestep_start = cond[1]['timestep_start']

if timestep_in[0] > timestep_start:

return None

if 'timestep_end' in cond[1]:

timestep_end = cond[1]['timestep_end']

if timestep_in[0] < timestep_end:

return None

if 'area' in cond[1]:

area = cond[1]['area']

if 'strength' in cond[1]:

strength = cond[1]['strength']

adm_cond = None

if 'adm_encoded' in cond[1]:

adm_cond = cond[1]['adm_encoded']

input_x = x_in[:, :, area[2]:area[0] + area[2], area[3]:area[1] + area[3]]

if 'mask' in cond[1]:

# Scale the mask to the size of the input

# The mask should have been resized as we began the sampling process

mask_strength = 1.0

if "mask_strength" in cond[1]:

mask_strength = cond[1]["mask_strength"]

mask = cond[1]['mask']

assert (mask.shape[1] == x_in.shape[2])

assert (mask.shape[2] == x_in.shape[3])

mask = mask[:, area[2]:area[0] + area[2], area[3]:area[1] + area[3]] * mask_strength

mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)

else:

mask = torch.ones_like(input_x)

mult = mask * strength

if 'mask' not in cond[1]:

rr = 8

if area[2] != 0:

for t in range(rr):

mult[:, :, t:1 + t, :] *= ((1.0 / rr) * (t + 1))

if (area[0] + area[2]) < x_in.shape[2]:

for t in range(rr):

mult[:, :, area[0] - 1 - t:area[0] - t, :] *= ((1.0 / rr) * (t + 1))

if area[3] != 0:

for t in range(rr):

mult[:, :, :, t:1 + t] *= ((1.0 / rr) * (t + 1))

if (area[1] + area[3]) < x_in.shape[3]:

for t in range(rr):

mult[:, :, :, area[1] - 1 - t:area[1] - t] *= ((1.0 / rr) * (t + 1))

conditionning = {}

conditionning['c_crossattn'] = cond[0]

if cond_concat_in is not None and len(cond_concat_in) > 0:

cropped = []

for x in cond_concat_in:

cr = x[:, :, area[2]:area[0] + area[2], area[3]:area[1] + area[3]]

cropped.append(cr)

conditionning['c_concat'] = torch.cat(cropped, dim=1)

if adm_cond is not None:

conditionning['c_adm'] = adm_cond

control = None

if 'control' in cond[1]:

control = cond[1]['control']

patches = None

if 'gligen' in cond[1]:

gligen = cond[1]['gligen']

patches = {}

gligen_type = gligen[0]

gligen_model = gligen[1]

if gligen_type == "position":

gligen_patch = gligen_model.set_position(input_x.shape, gligen[2], input_x.device)

else:

gligen_patch = gligen_model.set_empty(input_x.shape, input_x.device)

patches['middle_patch'] = [gligen_patch]

return (input_x, mult, conditionning, area, control, patches)

def cond_equal_size(c1, c2):

if c1 is c2:

return True

if c1.keys() != c2.keys():

return False

if 'c_crossattn' in c1:

s1 = c1['c_crossattn'].shape

s2 = c2['c_crossattn'].shape

if s1 != s2:

if s1[0] != s2[0] or s1[2] != s2[2]: # these 2 cases should not happen

return False

mult_min = lcm(s1[1], s2[1])

diff = mult_min // min(s1[1], s2[1])

if diff > 4: # arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much

return False

if 'c_concat' in c1:

if c1['c_concat'].shape != c2['c_concat'].shape:

return False

if 'c_adm' in c1:

if c1['c_adm'].shape != c2['c_adm'].shape:

return False

return True

def can_concat_cond(c1, c2):

if c1[0].shape != c2[0].shape:

return False

# control

if (c1[4] is None) != (c2[4] is None):

return False

if c1[4] is not None:

if c1[4] is not c2[4]:

return False

# patches

if (c1[5] is None) != (c2[5] is None):

return False

if (c1[5] is not None):

if c1[5] is not c2[5]:

return False

return cond_equal_size(c1[2], c2[2])

def cond_cat(c_list):

c_crossattn = []

c_concat = []

c_adm = []

crossattn_max_len = 0

for x in c_list:

if 'c_crossattn' in x:

c = x['c_crossattn']

if crossattn_max_len == 0:

crossattn_max_len = c.shape[1]

else:

crossattn_max_len = lcm(crossattn_max_len, c.shape[1])

c_crossattn.append(c)

if 'c_concat' in x:

c_concat.append(x['c_concat'])

if 'c_adm' in x:

c_adm.append(x['c_adm'])

out = {}

c_crossattn_out = []

for c in c_crossattn:

if c.shape[1] < crossattn_max_len:

c = c.repeat(1, crossattn_max_len // c.shape[1], 1) # padding with repeat doesn't change result

c_crossattn_out.append(c)

if len(c_crossattn_out) > 0:

out['c_crossattn'] = [torch.cat(c_crossattn_out)]

if len(c_concat) > 0:

out['c_concat'] = [torch.cat(c_concat)]

if len(c_adm) > 0:

out['c_adm'] = torch.cat(c_adm)

return out

def calc_cond_uncond_batch(model_function, cond, uncond, x_in, timestep, max_total_area, cond_concat_in,

model_options):

out_cond = torch.zeros_like(x_in)

out_count = torch.ones_like(x_in) / 100000.0

out_uncond = torch.zeros_like(x_in)

out_uncond_count = torch.ones_like(x_in) / 100000.0

COND = 0

UNCOND = 1

to_run = []

for x in cond:

p = get_area_and_mult(x, x_in, cond_concat_in, timestep)

if p is None:

continue

to_run += [(p, COND)]

if uncond is not None:

for x in uncond:

p = get_area_and_mult(x, x_in, cond_concat_in, timestep)

if p is None:

continue

to_run += [(p, UNCOND)]

while len(to_run) > 0:

first = to_run[0]

first_shape = first[0][0].shape

to_batch_temp = []

for x in range(len(to_run)):

if can_concat_cond(to_run[x][0], first[0]):

to_batch_temp += [x]

to_batch_temp.reverse()

to_batch = to_batch_temp[:1]

for i in range(1, len(to_batch_temp) + 1):

batch_amount = to_batch_temp[:len(to_batch_temp) // i]

if (len(batch_amount) * first_shape[0] * first_shape[2] * first_shape[3] < max_total_area):

to_batch = batch_amount

break

input_x = []

mult = []

c = []

cond_or_uncond = []

area = []

control = None

patches = None

for x in to_batch:

o = to_run.pop(x)

p = o[0]

input_x += [p[0]]

mult += [p[1]]

c += [p[2]]

area += [p[3]]

cond_or_uncond += [o[1]]

control = p[4]

patches = p[5]

batch_chunks = len(cond_or_uncond)

input_x = torch.cat(input_x)

c = cond_cat(c)

timestep_ = torch.cat([timestep] * batch_chunks)

if control is not None:

c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond))

transformer_options = {}

if 'transformer_options' in model_options:

transformer_options = model_options['transformer_options'].copy()

if patches is not None:

if "patches" in transformer_options:

cur_patches = transformer_options["patches"].copy()

for p in patches:

if p in cur_patches:

cur_patches[p] = cur_patches[p] + patches[p]

else:

cur_patches[p] = patches[p]

else:

transformer_options["patches"] = patches

c['transformer_options'] = transformer_options

transformer_options['uc_mask'] = torch.Tensor(cond_or_uncond).to(input_x).float()[:, None, None, None]

if 'model_function_wrapper' in model_options:

output = model_options['model_function_wrapper'](model_function,

{"input": input_x, "timestep": timestep_, "c": c,

"cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)

else:

output = model_function(input_x, timestep_, **c).chunk(batch_chunks)

del input_x

model_management.throw_exception_if_processing_interrupted()

for o in range(batch_chunks):

if cond_or_uncond[o] == COND:

out_cond[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += output[

o] * \

mult[o]

out_count[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += mult[o]

else:

out_uncond[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += output[

o] * \

mult[o]

out_uncond_count[:, :, area[o][2]:area[o][0] + area[o][2], area[o][3]:area[o][1] + area[o][3]] += \

mult[o]

del mult

out_cond /= out_count

del out_count

out_uncond /= out_uncond_count

del out_uncond_count

return out_cond, out_uncond

max_total_area = model_management.maximum_batch_area()

if math.isclose(cond_scale, 1.0):

uncond = None

cond, uncond = calc_cond_uncond_batch(model_function, cond, uncond, x, timestep, max_total_area, cond_concat,

model_options)

if "sampler_cfg_function" in model_options:

args = {"cond": cond, "uncond": uncond, "cond_scale": cond_scale, "timestep": timestep}

return model_options["sampler_cfg_function"](args)

else:

return uncond + (cond - uncond) * cond_scale

def unet_forward_patched(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):

uc_mask = transformer_options['uc_mask']

transformer_options["original_shape"] = list(x.shape)

transformer_options["current_index"] = 0

hs = []

t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(self.dtype)

emb = self.time_embed(t_emb)

if self.num_classes is not None:

assert y.shape[0] == x.shape[0]

emb = emb + self.label_emb(y)

h = x.type(self.dtype)

for id, module in enumerate(self.input_blocks):

transformer_options["block"] = ("input", id)

h = forward_timestep_embed(module, h, emb, context, transformer_options)

if control is not None and 'input' in control and len(control['input']) > 0:

ctrl = control['input'].pop()

if ctrl is not None:

h += ctrl

hs.append(h)

transformer_options["block"] = ("middle", 0)

h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options)

if control is not None and 'middle' in control and len(control['middle']) > 0:

h += control['middle'].pop()

for id, module in enumerate(self.output_blocks):

transformer_options["block"] = ("output", id)

hsp = hs.pop()

if control is not None and 'output' in control and len(control['output']) > 0:

ctrl = control['output'].pop()

if ctrl is not None:

hsp += ctrl

h = torch.cat([h, hsp], dim=1)

del hsp

if len(hs) > 0:

output_shape = hs[-1].shape

else:

output_shape = None

h = forward_timestep_embed(module, h, emb, context, transformer_options, output_shape)

h = h.type(x.dtype)

x0 = self.out(h)

degraded_x0 = gaussian_filter_2d(x0) * alpha + x0 * (1.0 - alpha)

x0 = x0 * uc_mask + degraded_x0 * (1.0 - uc_mask)

return x0

def sdxl_encode_adm_patched(self, **kwargs):

clip_pooled = kwargs["pooled_output"]

width = kwargs.get("width", 768)

height = kwargs.get("height", 768)

crop_w = kwargs.get("crop_w", 0)

crop_h = kwargs.get("crop_h", 0)

target_width = kwargs.get("target_width", width)

target_height = kwargs.get("target_height", height)

if kwargs.get("prompt_type", "") == "negative":

width *= 0.8

height *= 0.8

elif kwargs.get("prompt_type", "") == "positive":

width *= 1.5

height *= 1.5

out = []

out.append(self.embedder(torch.Tensor([height])))

out.append(self.embedder(torch.Tensor([width])))

out.append(self.embedder(torch.Tensor([crop_h])))

out.append(self.embedder(torch.Tensor([crop_w])))

out.append(self.embedder(torch.Tensor([target_height])))

out.append(self.embedder(torch.Tensor([target_width])))

flat = torch.flatten(torch.cat(out))[None, ]

return torch.cat((clip_pooled.to(flat.device), flat), dim=1)

def patch_all():

comfy.samplers.sampling_function = sampling_function_patched

comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched

comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = unet_forward_patched