import torch
import comfy.model_base
import comfy.ldm.modules.diffusionmodules.openaimodel
import comfy.samplers
import modules.anisotropic as anisotropic
from comfy.samplers import model_management, lcm, math
from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
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)
alpha = 1.0 - (timesteps / 999.0)[:, None, None, None].clone()
alpha *= 0.001 * sharpness
degraded_x0 = anisotropic.bilateral_blur(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