411 lines
15 KiB
Python
411 lines
15 KiB
Python
import torch
|
|
import contextlib
|
|
import comfy.model_base
|
|
import comfy.ldm.modules.diffusionmodules.openaimodel
|
|
import comfy.samplers
|
|
import comfy.k_diffusion.external
|
|
import comfy.model_management
|
|
import modules.anisotropic as anisotropic
|
|
import comfy.ldm.modules.attention
|
|
import comfy.k_diffusion.sampling
|
|
import comfy.sd1_clip
|
|
import modules.inpaint_worker as inpaint_worker
|
|
import comfy.ldm.modules.diffusionmodules.openaimodel
|
|
import comfy.sd
|
|
|
|
from comfy.k_diffusion import utils
|
|
from comfy.k_diffusion.sampling import BrownianTreeNoiseSampler, trange
|
|
from comfy.ldm.modules.diffusionmodules.openaimodel import timestep_embedding, forward_timestep_embed
|
|
|
|
|
|
sharpness = 2.0
|
|
negative_adm = True
|
|
|
|
cfg_x0 = 0.0
|
|
cfg_s = 1.0
|
|
cfg_cin = 1.0
|
|
|
|
|
|
def calculate_weight_patched(self, patches, weight, key):
|
|
for p in patches:
|
|
alpha = p[0]
|
|
v = p[1]
|
|
strength_model = p[2]
|
|
|
|
if strength_model != 1.0:
|
|
weight *= strength_model
|
|
|
|
if isinstance(v, list):
|
|
v = (self.calculate_weight(v[1:], v[0].clone(), key),)
|
|
|
|
if len(v) == 1:
|
|
w1 = v[0]
|
|
if alpha != 0.0:
|
|
if w1.shape != weight.shape:
|
|
print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
|
|
else:
|
|
weight += alpha * w1.type(weight.dtype).to(weight.device)
|
|
elif len(v) == 3:
|
|
# fooocus
|
|
w1 = v[0].float()
|
|
w_min = v[1].float()
|
|
w_max = v[2].float()
|
|
w1 = (w1 / 255.0) * (w_max - w_min) + w_min
|
|
if alpha != 0.0:
|
|
if w1.shape != weight.shape:
|
|
print("WARNING SHAPE MISMATCH {} FOOOCUS WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
|
|
else:
|
|
weight += alpha * w1.type(weight.dtype).to(weight.device)
|
|
elif len(v) == 4: # lora/locon
|
|
mat1 = v[0].float().to(weight.device)
|
|
mat2 = v[1].float().to(weight.device)
|
|
if v[2] is not None:
|
|
alpha *= v[2] / mat2.shape[0]
|
|
if v[3] is not None:
|
|
mat3 = v[3].float().to(weight.device)
|
|
final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
|
|
mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1),
|
|
mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
|
|
try:
|
|
weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(
|
|
weight.shape).type(weight.dtype)
|
|
except Exception as e:
|
|
print("ERROR", key, e)
|
|
elif len(v) == 8: # lokr
|
|
w1 = v[0]
|
|
w2 = v[1]
|
|
w1_a = v[3]
|
|
w1_b = v[4]
|
|
w2_a = v[5]
|
|
w2_b = v[6]
|
|
t2 = v[7]
|
|
dim = None
|
|
|
|
if w1 is None:
|
|
dim = w1_b.shape[0]
|
|
w1 = torch.mm(w1_a.float(), w1_b.float())
|
|
else:
|
|
w1 = w1.float().to(weight.device)
|
|
|
|
if w2 is None:
|
|
dim = w2_b.shape[0]
|
|
if t2 is None:
|
|
w2 = torch.mm(w2_a.float().to(weight.device), w2_b.float().to(weight.device))
|
|
else:
|
|
w2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device),
|
|
w2_b.float().to(weight.device), w2_a.float().to(weight.device))
|
|
else:
|
|
w2 = w2.float().to(weight.device)
|
|
|
|
if len(w2.shape) == 4:
|
|
w1 = w1.unsqueeze(2).unsqueeze(2)
|
|
if v[2] is not None and dim is not None:
|
|
alpha *= v[2] / dim
|
|
|
|
try:
|
|
weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
|
|
except Exception as e:
|
|
print("ERROR", key, e)
|
|
else: # loha
|
|
w1a = v[0]
|
|
w1b = v[1]
|
|
if v[2] is not None:
|
|
alpha *= v[2] / w1b.shape[0]
|
|
w2a = v[3]
|
|
w2b = v[4]
|
|
if v[5] is not None: # cp decomposition
|
|
t1 = v[5]
|
|
t2 = v[6]
|
|
m1 = torch.einsum('i j k l, j r, i p -> p r k l', t1.float().to(weight.device),
|
|
w1b.float().to(weight.device), w1a.float().to(weight.device))
|
|
m2 = torch.einsum('i j k l, j r, i p -> p r k l', t2.float().to(weight.device),
|
|
w2b.float().to(weight.device), w2a.float().to(weight.device))
|
|
else:
|
|
m1 = torch.mm(w1a.float().to(weight.device), w1b.float().to(weight.device))
|
|
m2 = torch.mm(w2a.float().to(weight.device), w2b.float().to(weight.device))
|
|
|
|
try:
|
|
weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
|
|
except Exception as e:
|
|
print("ERROR", key, e)
|
|
|
|
return weight
|
|
|
|
|
|
def cfg_patched(args):
|
|
global cfg_x0, cfg_s
|
|
positive_eps = args['cond'].clone()
|
|
positive_x0 = args['cond'] * cfg_s + cfg_x0
|
|
uncond = args['uncond'] * cfg_s + cfg_x0
|
|
cond_scale = args['cond_scale']
|
|
t = args['timestep']
|
|
|
|
alpha = 1.0 - (t / 999.0)[:, None, None, None].clone()
|
|
alpha *= 0.001 * sharpness
|
|
|
|
eps_degraded = anisotropic.adaptive_anisotropic_filter(x=positive_eps, g=positive_x0)
|
|
eps_degraded_weighted = eps_degraded * alpha + positive_eps * (1.0 - alpha)
|
|
|
|
cond = eps_degraded_weighted * cfg_s + cfg_x0
|
|
|
|
return uncond + (cond - uncond) * cond_scale
|
|
|
|
|
|
def patched_discrete_eps_ddpm_denoiser_forward(self, input, sigma, **kwargs):
|
|
global cfg_x0, cfg_s, cfg_cin
|
|
c_out, c_in = [utils.append_dims(x, input.ndim) for x in self.get_scalings(sigma)]
|
|
cfg_x0 = input
|
|
cfg_s = c_out
|
|
cfg_cin = c_in
|
|
return self.get_eps(input * c_in, self.sigma_to_t(sigma), **kwargs)
|
|
|
|
|
|
def patched_model_function(func, args):
|
|
global cfg_cin
|
|
x = args['input']
|
|
t = args['timestep']
|
|
c = args['c']
|
|
# is_uncond = torch.tensor(args['cond_or_uncond'])[:, None, None, None].to(x) * 5e-3
|
|
return func(x, t, **c)
|
|
|
|
|
|
def sdxl_encode_adm_patched(self, **kwargs):
|
|
global negative_adm
|
|
|
|
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 negative_adm:
|
|
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 text_encoder_device_patched():
|
|
# Fooocus's style system uses text encoder much more times than comfy so this makes things much faster.
|
|
return comfy.model_management.get_torch_device()
|
|
|
|
|
|
def encode_token_weights_patched_with_a1111_method(self, token_weight_pairs):
|
|
to_encode = list(self.empty_tokens)
|
|
for x in token_weight_pairs:
|
|
tokens = list(map(lambda a: a[0], x))
|
|
to_encode.append(tokens)
|
|
|
|
out, pooled = self.encode(to_encode)
|
|
|
|
z_empty = out[0:1]
|
|
if pooled.shape[0] > 1:
|
|
first_pooled = pooled[1:2]
|
|
else:
|
|
first_pooled = pooled[0:1]
|
|
|
|
output = []
|
|
for k in range(1, out.shape[0]):
|
|
z = out[k:k + 1]
|
|
original_mean = z.mean()
|
|
|
|
for i in range(len(z)):
|
|
for j in range(len(z[i])):
|
|
weight = token_weight_pairs[k - 1][j][1]
|
|
z[i][j] = (z[i][j] - z_empty[0][j]) * weight + z_empty[0][j]
|
|
|
|
new_mean = z.mean()
|
|
z = z * (original_mean / new_mean)
|
|
output.append(z)
|
|
|
|
if len(output) == 0:
|
|
return z_empty.cpu(), first_pooled.cpu()
|
|
return torch.cat(output, dim=-2).cpu(), first_pooled.cpu()
|
|
|
|
|
|
@torch.no_grad()
|
|
def sample_dpmpp_fooocus_2m_sde_inpaint_seamless(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, **kwargs):
|
|
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
|
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
|
|
|
|
seed = extra_args.get("seed", None)
|
|
assert isinstance(seed, int)
|
|
|
|
energy_generator = torch.Generator(device='cpu')
|
|
energy_generator.manual_seed(seed + 1) # avoid bad results by using different seeds.
|
|
|
|
def get_energy():
|
|
return torch.randn(x.size(), dtype=x.dtype, generator=energy_generator, device="cpu").to(x)
|
|
|
|
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
|
|
noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
|
|
extra_args = {} if extra_args is None else extra_args
|
|
s_in = x.new_ones([x.shape[0]])
|
|
|
|
old_denoised, h_last, h = None, None, None
|
|
|
|
latent_processor = model.inner_model.inner_model.inner_model.process_latent_in
|
|
inpaint_latent = None
|
|
inpaint_mask = None
|
|
|
|
if inpaint_worker.current_task is not None:
|
|
inpaint_latent = latent_processor(inpaint_worker.current_task.latent).to(x)
|
|
inpaint_mask = inpaint_worker.current_task.latent_mask.to(x)
|
|
|
|
def blend_latent(a, b, w):
|
|
return a * w + b * (1 - w)
|
|
|
|
for i in trange(len(sigmas) - 1, disable=disable):
|
|
if inpaint_latent is None:
|
|
denoised = model(x, sigmas[i] * s_in, **extra_args)
|
|
else:
|
|
energy = get_energy() * sigmas[i] + inpaint_latent
|
|
x_prime = blend_latent(x, energy, inpaint_mask)
|
|
denoised = model(x_prime, sigmas[i] * s_in, **extra_args)
|
|
denoised = blend_latent(denoised, inpaint_latent, inpaint_mask)
|
|
if callback is not None:
|
|
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
|
|
if sigmas[i + 1] == 0:
|
|
x = denoised
|
|
else:
|
|
t, s = -sigmas[i].log(), -sigmas[i + 1].log()
|
|
h = s - t
|
|
eta_h = eta * h
|
|
|
|
x = sigmas[i + 1] / sigmas[i] * (-eta_h).exp() * x + (-h - eta_h).expm1().neg() * denoised
|
|
if old_denoised is not None:
|
|
r = h_last / h
|
|
x = x + 0.5 * (-h - eta_h).expm1().neg() * (1 / r) * (denoised - old_denoised)
|
|
|
|
x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (
|
|
-2 * eta_h).expm1().neg().sqrt() * s_noise
|
|
|
|
old_denoised = denoised
|
|
h_last = h
|
|
|
|
return x
|
|
|
|
|
|
def patched_unet_forward(self, x, timesteps=None, context=None, y=None, control=None, transformer_options={}, **kwargs):
|
|
inpaint_fix = None
|
|
if inpaint_worker.current_task is not None:
|
|
inpaint_fix = inpaint_worker.current_task.inpaint_head_feature
|
|
|
|
transformer_options["original_shape"] = list(x.shape)
|
|
transformer_options["current_index"] = 0
|
|
|
|
assert (y is not None) == (
|
|
self.num_classes is not None
|
|
), "must specify y if and only if the model is class-conditional"
|
|
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 inpaint_fix is not None:
|
|
if int(h.shape[1]) == int(inpaint_fix.shape[1]):
|
|
h = h + inpaint_fix.to(h)
|
|
inpaint_fix = None
|
|
|
|
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)
|
|
if self.predict_codebook_ids:
|
|
return self.id_predictor(h)
|
|
else:
|
|
return self.out(h)
|
|
|
|
|
|
def patched_SD1ClipModel_forward(self, tokens):
|
|
backup_embeds = self.transformer.get_input_embeddings()
|
|
device = backup_embeds.weight.device
|
|
tokens = self.set_up_textual_embeddings(tokens, backup_embeds)
|
|
tokens = torch.LongTensor(tokens).to(device)
|
|
|
|
if backup_embeds.weight.dtype != torch.float32:
|
|
precision_scope = torch.autocast
|
|
else:
|
|
precision_scope = contextlib.nullcontext
|
|
|
|
with precision_scope(comfy.model_management.get_autocast_device(device)):
|
|
outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
|
|
self.transformer.set_input_embeddings(backup_embeds)
|
|
|
|
if self.layer == "last":
|
|
z = outputs.last_hidden_state
|
|
elif self.layer == "pooled":
|
|
z = outputs.pooler_output[:, None, :]
|
|
else:
|
|
z = outputs.hidden_states[self.layer_idx]
|
|
if self.layer_norm_hidden_state:
|
|
z = self.transformer.text_model.final_layer_norm(z)
|
|
|
|
pooled_output = outputs.pooler_output
|
|
if self.text_projection is not None:
|
|
pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
|
|
return z.float(), pooled_output.float()
|
|
|
|
|
|
def patch_all():
|
|
comfy.sd1_clip.SD1ClipModel.forward = patched_SD1ClipModel_forward
|
|
|
|
comfy.sd.ModelPatcher.calculate_weight = calculate_weight_patched
|
|
comfy.ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = patched_unet_forward
|
|
|
|
comfy.ldm.modules.attention.print = lambda x: None
|
|
comfy.k_diffusion.sampling.sample_dpmpp_fooocus_2m_sde_inpaint_seamless = sample_dpmpp_fooocus_2m_sde_inpaint_seamless
|
|
|
|
comfy.model_management.text_encoder_device = text_encoder_device_patched
|
|
print(f'Fooocus Text Processing Pipelines are retargeted to {str(comfy.model_management.text_encoder_device())}')
|
|
|
|
comfy.k_diffusion.external.DiscreteEpsDDPMDenoiser.forward = patched_discrete_eps_ddpm_denoiser_forward
|
|
comfy.model_base.SDXL.encode_adm = sdxl_encode_adm_patched
|
|
|
|
comfy.sd1_clip.ClipTokenWeightEncoder.encode_token_weights = encode_token_weights_patched_with_a1111_method
|
|
return
|