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Merge branch 'comfyanonymous:master' into master

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104
comfy/k_diffusion/sampling.py
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@ -841,3 +841,107 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
d_prime = w1 * d + w2 * d_2 + w3 * d_3 d_prime = w1 * d + w2 * d_2 + w3 * d_3
x = x + d_prime * dt x = x + d_prime * dt
return x return x
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
#under Apache 2 license
def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
x_next = x
buffer_model = []
for i in trange(len(sigmas) - 1, disable=disable):
t_cur = sigmas[i]
t_next = sigmas[i + 1]
x_cur = x_next
denoised = model(x_cur, t_cur * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d_cur = (x_cur - denoised) / t_cur
order = min(max_order, i+1)
if order == 1: # First Euler step.
x_next = x_cur + (t_next - t_cur) * d_cur
elif order == 2: # Use one history point.
x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
elif order == 3: # Use two history points.
x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
elif order == 4: # Use three history points.
x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
if len(buffer_model) == max_order - 1:
for k in range(max_order - 2):
buffer_model[k] = buffer_model[k+1]
buffer_model[-1] = d_cur
else:
buffer_model.append(d_cur)
return x_next
#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
#under Apache 2 license
def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
extra_args = {} if extra_args is None else extra_args
s_in = x.new_ones([x.shape[0]])
x_next = x
t_steps = sigmas
buffer_model = []
for i in trange(len(sigmas) - 1, disable=disable):
t_cur = sigmas[i]
t_next = sigmas[i + 1]
x_cur = x_next
denoised = model(x_cur, t_cur * s_in, **extra_args)
if callback is not None:
callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
d_cur = (x_cur - denoised) / t_cur
order = min(max_order, i+1)
if order == 1: # First Euler step.
x_next = x_cur + (t_next - t_cur) * d_cur
elif order == 2: # Use one history point.
h_n = (t_next - t_cur)
h_n_1 = (t_cur - t_steps[i-1])
coeff1 = (2 + (h_n / h_n_1)) / 2
coeff2 = -(h_n / h_n_1) / 2
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
elif order == 3: # Use two history points.
h_n = (t_next - t_cur)
h_n_1 = (t_cur - t_steps[i-1])
h_n_2 = (t_steps[i-1] - t_steps[i-2])
temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
coeff3 = temp * h_n_1 / h_n_2
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
elif order == 4: # Use three history points.
h_n = (t_next - t_cur)
h_n_1 = (t_cur - t_steps[i-1])
h_n_2 = (t_steps[i-1] - t_steps[i-2])
h_n_3 = (t_steps[i-2] - t_steps[i-3])
temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
* (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
if len(buffer_model) == max_order - 1:
for k in range(max_order - 2):
buffer_model[k] = buffer_model[k+1]
buffer_model[-1] = d_cur.detach()
else:
buffer_model.append(d_cur.detach())
return x_next

33
comfy/lora.py
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@ -218,12 +218,21 @@ def model_lora_keys_clip(model, key_map={}):
lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
key_map[lora_key] = k key_map[lora_key] = k
for k in sdk: #OneTrainer SD3 lora
if k.startswith("t5xxl.transformer.") and k.endswith(".weight"):
l_key = k[len("t5xxl.transformer."):-len(".weight")]
lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
key_map[lora_key] = k
k = "clip_g.transformer.text_projection.weight" k = "clip_g.transformer.text_projection.weight"
if k in sdk: if k in sdk:
key_map["lora_prior_te_text_projection"] = k #cascade lora? key_map["lora_prior_te_text_projection"] = k #cascade lora?
# key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
# key_map["lora_te_text_projection"] = k key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora
k = "clip_l.transformer.text_projection.weight"
if k in sdk:
key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning
return key_map return key_map
@ -252,15 +261,17 @@ def model_lora_keys_unet(model, key_map={}):
key_map[diffusers_lora_key] = unet_key key_map[diffusers_lora_key] = unet_key
if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3 if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
for i in range(model.model_config.unet_config.get("depth", 0)): diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
k = "transformer.transformer_blocks.{}.attn.".format(i) for k in diffusers_keys:
qkv = "diffusion_model.joint_blocks.{}.x_block.attn.qkv.weight".format(i) if k.endswith(".weight"):
proj = "diffusion_model.joint_blocks.{}.x_block.attn.proj.weight".format(i) to = diffusers_keys[k]
if qkv in sd: key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
offset = sd[qkv].shape[0] // 3 key_map[key_lora] = to
key_map["{}to_q".format(k)] = (qkv, (0, 0, offset))
key_map["{}to_k".format(k)] = (qkv, (0, offset, offset)) key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
key_map["{}to_v".format(k)] = (qkv, (0, offset * 2, offset)) key_map[key_lora] = to
key_map["{}to_out.0".format(k)] = proj
key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
key_map[key_lora] = to
return key_map return key_map

37
comfy/model_detection.py
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@ -1,7 +1,9 @@
import comfy.supported_models import comfy.supported_models
import comfy.supported_models_base import comfy.supported_models_base
import comfy.utils
import math import math
import logging import logging
import torch
def count_blocks(state_dict_keys, prefix_string): def count_blocks(state_dict_keys, prefix_string):
count = 0 count = 0
@ -431,3 +433,38 @@ def model_config_from_diffusers_unet(state_dict):
if unet_config is not None: if unet_config is not None:
return model_config_from_unet_config(unet_config) return model_config_from_unet_config(unet_config)
return None return None
def convert_diffusers_mmdit(state_dict, output_prefix=""):
depth = count_blocks(state_dict, 'transformer_blocks.{}.')
if depth > 0:
out_sd = {}
sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth}, output_prefix=output_prefix)
for k in sd_map:
weight = state_dict.get(k, None)
if weight is not None:
t = sd_map[k]
if not isinstance(t, str):
if len(t) > 2:
fun = t[2]
else:
fun = lambda a: a
offset = t[1]
if offset is not None:
old_weight = out_sd.get(t[0], None)
if old_weight is None:
old_weight = torch.empty_like(weight)
old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
w = old_weight.narrow(offset[0], offset[1], offset[2])
else:
old_weight = weight
w = weight
w[:] = fun(weight)
t = t[0]
out_sd[t] = old_weight
else:
out_sd[t] = weight
state_dict.pop(k)
return out_sd

26
comfy/model_patcher.py
View File

@ -210,16 +210,19 @@ class ModelPatcher:
model_sd = self.model.state_dict() model_sd = self.model.state_dict()
for k in patches: for k in patches:
offset = None offset = None
function = None
if isinstance(k, str): if isinstance(k, str):
key = k key = k
else: else:
offset = k[1] offset = k[1]
key = k[0] key = k[0]
if len(k) > 2:
function = k[2]
if key in model_sd: if key in model_sd:
p.add(k) p.add(k)
current_patches = self.patches.get(key, []) current_patches = self.patches.get(key, [])
current_patches.append((strength_patch, patches[k], strength_model, offset)) current_patches.append((strength_patch, patches[k], strength_model, offset, function))
self.patches[key] = current_patches self.patches[key] = current_patches
self.patches_uuid = uuid.uuid4() self.patches_uuid = uuid.uuid4()
@ -347,6 +350,9 @@ class ModelPatcher:
v = p[1] v = p[1]
strength_model = p[2] strength_model = p[2]
offset = p[3] offset = p[3]
function = p[4]
if function is None:
function = lambda a: a
old_weight = None old_weight = None
if offset is not None: if offset is not None:
@ -371,7 +377,7 @@ class ModelPatcher:
if w1.shape != weight.shape: if w1.shape != weight.shape:
logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape)) logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
else: else:
weight += strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype) weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
elif patch_type == "lora": #lora/locon elif patch_type == "lora": #lora/locon
mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32) mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32) mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
@ -389,9 +395,9 @@ class ModelPatcher:
try: try:
lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape) lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
if dora_scale is not None: if dora_scale is not None:
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
else: else:
weight += ((strength * alpha) * lora_diff).type(weight.dtype) weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e: except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e)) logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "lokr": elif patch_type == "lokr":
@ -435,9 +441,9 @@ class ModelPatcher:
try: try:
lora_diff = torch.kron(w1, w2).reshape(weight.shape) lora_diff = torch.kron(w1, w2).reshape(weight.shape)
if dora_scale is not None: if dora_scale is not None:
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
else: else:
weight += ((strength * alpha) * lora_diff).type(weight.dtype) weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e: except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e)) logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "loha": elif patch_type == "loha":
@ -472,9 +478,9 @@ class ModelPatcher:
try: try:
lora_diff = (m1 * m2).reshape(weight.shape) lora_diff = (m1 * m2).reshape(weight.shape)
if dora_scale is not None: if dora_scale is not None:
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
else: else:
weight += ((strength * alpha) * lora_diff).type(weight.dtype) weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e: except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e)) logging.error("ERROR {} {} {}".format(patch_type, key, e))
elif patch_type == "glora": elif patch_type == "glora":
@ -493,9 +499,9 @@ class ModelPatcher:
try: try:
lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape) lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
if dora_scale is not None: if dora_scale is not None:
weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength) weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
else: else:
weight += ((strength * alpha) * lora_diff).type(weight.dtype) weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
except Exception as e: except Exception as e:
logging.error("ERROR {} {} {}".format(patch_type, key, e)) logging.error("ERROR {} {} {}".format(patch_type, key, e))
else: else:

3
comfy/samplers.py
View File

@ -539,7 +539,8 @@ class Sampler:
KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral", KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
"lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu", "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
"dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"] "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
"ipndm", "ipndm_v"]
class KSAMPLER(Sampler): class KSAMPLER(Sampler):
def __init__(self, sampler_function, extra_options={}, inpaint_options={}): def __init__(self, sampler_function, extra_options={}, inpaint_options={}):

29
comfy/sd.py
View File

@ -298,16 +298,9 @@ class VAE:
/ 3.0) / 3.0)
return output return output
def decode_tiled_1d(self, samples, tile_x=128, overlap=64): def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
output = torch.empty((samples.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples.shape[2:])), device=self.output_device) decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
for j in range(samples.shape[0]):
for i in range(0, samples.shape[-1], tile_x - overlap):
f = i
t = i + tile_x
output[j:j+1,:,f * self.upscale_ratio:t * self.upscale_ratio] = self.first_stage_model.decode(samples[j:j+1,:,f:t].to(self.vae_dtype).to(self.device)).float()
return output
def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64): def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap) steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
@ -322,6 +315,10 @@ class VAE:
samples /= 3.0 samples /= 3.0
return samples return samples
def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
def decode(self, samples_in): def decode(self, samples_in):
try: try:
memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype) memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
@ -365,6 +362,9 @@ class VAE:
except model_management.OOM_EXCEPTION as e: except model_management.OOM_EXCEPTION as e:
logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.") logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
if len(pixel_samples.shape) == 3:
samples = self.encode_tiled_1d(pixel_samples)
else:
samples = self.encode_tiled_(pixel_samples) samples = self.encode_tiled_(pixel_samples)
return samples return samples
@ -568,7 +568,14 @@ def load_unet_state_dict(sd): #load unet in diffusers format
unet_dtype = model_management.unet_dtype(model_params=parameters) unet_dtype = model_management.unet_dtype(model_params=parameters)
load_device = model_management.get_torch_device() load_device = model_management.get_torch_device()
if "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade if 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
new_sd = model_detection.convert_diffusers_mmdit(sd, "")
if new_sd is None:
return None
model_config = model_detection.model_config_from_unet(new_sd, "")
if model_config is None:
return None
elif "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
model_config = model_detection.model_config_from_unet(sd, "") model_config = model_detection.model_config_from_unet(sd, "")
if model_config is None: if model_config is None:
return None return None

5
comfy/sd1_clip.py
View File

@ -9,6 +9,7 @@ from . import model_management
import comfy.clip_model import comfy.clip_model
import json import json
import logging import logging
import numbers
def gen_empty_tokens(special_tokens, length): def gen_empty_tokens(special_tokens, length):
start_token = special_tokens.get("start", None) start_token = special_tokens.get("start", None)
@ -130,10 +131,10 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
for x in tokens: for x in tokens:
tokens_temp = [] tokens_temp = []
for y in x: for y in x:
if isinstance(y, int): if isinstance(y, numbers.Integral):
if y == token_dict_size: #EOS token if y == token_dict_size: #EOS token
y = -1 y = -1
tokens_temp += [y] tokens_temp += [int(y)]
else: else:
if y.shape[0] == current_embeds.weight.shape[1]: if y.shape[0] == current_embeds.weight.shape[1]:
embedding_weights += [y] embedding_weights += [y]

130
comfy/utils.py
View File

@ -6,6 +6,7 @@ import safetensors.torch
import numpy as np import numpy as np
from PIL import Image from PIL import Image
import logging import logging
import itertools
def load_torch_file(ckpt, safe_load=False, device=None): def load_torch_file(ckpt, safe_load=False, device=None):
if device is None: if device is None:
@ -249,6 +250,87 @@ def unet_to_diffusers(unet_config):
return diffusers_unet_map return diffusers_unet_map
def swap_scale_shift(weight):
shift, scale = weight.chunk(2, dim=0)
new_weight = torch.cat([scale, shift], dim=0)
return new_weight
MMDIT_MAP_BASIC = {
("context_embedder.bias", "context_embedder.bias"),
("context_embedder.weight", "context_embedder.weight"),
("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
("x_embedder.proj.bias", "pos_embed.proj.bias"),
("x_embedder.proj.weight", "pos_embed.proj.weight"),
("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
("pos_embed", "pos_embed.pos_embed"),
("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
("final_layer.linear.bias", "proj_out.bias"),
("final_layer.linear.weight", "proj_out.weight"),
}
MMDIT_MAP_BLOCK = {
("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
("context_block.attn.proj.bias", "attn.to_add_out.bias"),
("context_block.attn.proj.weight", "attn.to_add_out.weight"),
("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
("x_block.attn.proj.bias", "attn.to_out.0.bias"),
("x_block.attn.proj.weight", "attn.to_out.0.weight"),
("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
("x_block.mlp.fc2.bias", "ff.net.2.bias"),
("x_block.mlp.fc2.weight", "ff.net.2.weight"),
}
def mmdit_to_diffusers(mmdit_config, output_prefix=""):
key_map = {}
depth = mmdit_config.get("depth", 0)
for i in range(depth):
block_from = "transformer_blocks.{}".format(i)
block_to = "{}joint_blocks.{}".format(output_prefix, i)
offset = depth * 64
for end in ("weight", "bias"):
k = "{}.attn.".format(block_from)
qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
for k in MMDIT_MAP_BLOCK:
key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
map_basic = MMDIT_MAP_BASIC.copy()
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
for k in map_basic:
if len(k) > 2:
key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
else:
key_map[k[1]] = "{}{}".format(output_prefix, k[0])
return key_map
def repeat_to_batch_size(tensor, batch_size, dim=0): def repeat_to_batch_size(tensor, batch_size, dim=0):
if tensor.shape[dim] > batch_size: if tensor.shape[dim] > batch_size:
return tensor.narrow(dim, 0, batch_size) return tensor.narrow(dim, 0, batch_size)
@ -425,34 +507,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap))) return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
@torch.inference_mode() @torch.inference_mode()
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None): def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device) dims = len(tile)
output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
for b in range(samples.shape[0]): for b in range(samples.shape[0]):
s = samples[b:b+1] s = samples[b:b+1]
out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device) out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
for y in range(0, s.shape[2], tile_y - overlap):
for x in range(0, s.shape[3], tile_x - overlap):
x = max(0, min(s.shape[-1] - overlap, x))
y = max(0, min(s.shape[-2] - overlap, y))
s_in = s[:,:,y:y+tile_y,x:x+tile_x]
for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
s_in = s
upscaled = []
for d in range(dims):
pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
l = min(tile[d], s.shape[d + 2] - pos)
s_in = s_in.narrow(d + 2, pos, l)
upscaled.append(round(pos * upscale_amount))
ps = function(s_in).to(output_device) ps = function(s_in).to(output_device)
mask = torch.ones_like(ps) mask = torch.ones_like(ps)
feather = round(overlap * upscale_amount) feather = round(overlap * upscale_amount)
for t in range(feather): for t in range(feather):
mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1)) for d in range(2, dims + 2):
mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1)) m = mask.narrow(d, t, 1)
mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1)) m *= ((1.0/feather) * (t + 1))
mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1)) m = mask.narrow(d, mask.shape[d] -1 -t, 1)
out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask m *= ((1.0/feather) * (t + 1))
out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
o = out
o_d = out_div
for d in range(dims):
o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
o += ps * mask
o_d += mask
if pbar is not None: if pbar is not None:
pbar.update(1) pbar.update(1)
output[b:b+1] = out/out_div output[b:b+1] = out/out_div
return output return output
def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
PROGRESS_BAR_ENABLED = True PROGRESS_BAR_ENABLED = True
def set_progress_bar_enabled(enabled): def set_progress_bar_enabled(enabled):
global PROGRESS_BAR_ENABLED global PROGRESS_BAR_ENABLED

369
comfy_extras/nodes_gits.py Normal file
View File

@ -0,0 +1,369 @@
# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
import numpy as np
import torch
def loglinear_interp(t_steps, num_steps):
"""
Performs log-linear interpolation of a given array of decreasing numbers.
"""
xs = np.linspace(0, 1, len(t_steps))
ys = np.log(t_steps[::-1])
new_xs = np.linspace(0, 1, num_steps)
new_ys = np.interp(new_xs, xs, ys)
interped_ys = np.exp(new_ys)[::-1].copy()
return interped_ys
NOISE_LEVELS = {
0.80: [
[14.61464119, 7.49001646, 0.02916753],
[14.61464119, 11.54541874, 6.77309084, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
],
0.85: [
[14.61464119, 7.49001646, 0.02916753],
[14.61464119, 7.49001646, 1.84880662, 0.02916753],
[14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
[14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, 0.02916753],
],
0.90: [
[14.61464119, 6.77309084, 0.02916753],
[14.61464119, 7.49001646, 1.56271636, 0.02916753],
[14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753],
[14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753],
[14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.11996698, 4.86714602, 3.07277966, 1.61558151, 0.69515091, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 2.95596409, 1.61558151, 0.69515091, 0.02916753],
[14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
[14.61464119, 12.96784878, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 4.45427561, 3.1956799, 2.19988537, 1.24153244, 0.57119018, 0.02916753],
[14.61464119, 12.96784878, 11.54541874, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.75677586, 2.84484982, 1.84880662, 1.08895338, 0.52423614, 0.02916753],
[14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.44769001, 5.58536053, 4.45427561, 3.32507086, 2.45070267, 1.61558151, 0.95350921, 0.45573691, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
[14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.91689563, 3.07277966, 2.27973175, 1.56271636, 0.95350921, 0.45573691, 0.02916753],
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[14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
1.50: [
[14.61464119, 0.54755926, 0.02916753],
[14.61464119, 0.803307, 0.25053367, 0.02916753],
[14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
[14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
[14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
[14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
],
}
class GITSScheduler:
@classmethod
def INPUT_TYPES(s):
return {"required":
{"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
"steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
}
}
RETURN_TYPES = ("SIGMAS",)
CATEGORY = "sampling/custom_sampling/schedulers"
FUNCTION = "get_sigmas"
def get_sigmas(self, coeff, steps, denoise):
total_steps = steps
if denoise < 1.0:
if denoise <= 0.0:
return (torch.FloatTensor([]),)
total_steps = round(steps * denoise)
if steps <= 20:
sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
else:
sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
sigmas = loglinear_interp(sigmas, steps + 1)
sigmas = sigmas[-(total_steps + 1):]
sigmas[-1] = 0
return (torch.FloatTensor(sigmas), )
NODE_CLASS_MAPPINGS = {
"GITSScheduler": GITSScheduler,
}

23
comfy_extras/nodes_model_merging_model_specific.py
View File

@ -52,9 +52,32 @@ class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
return {"required": arg_dict} return {"required": arg_dict}
class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
CATEGORY = "advanced/model_merging/model_specific"
@classmethod
def INPUT_TYPES(s):
arg_dict = { "model1": ("MODEL",),
"model2": ("MODEL",)}
argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
arg_dict["pos_embed."] = argument
arg_dict["x_embedder."] = argument
arg_dict["context_embedder."] = argument
arg_dict["y_embedder."] = argument
arg_dict["t_embedder."] = argument
for i in range(24):
arg_dict["joint_blocks.{}.".format(i)] = argument
arg_dict["final_layer."] = argument
return {"required": arg_dict}
NODE_CLASS_MAPPINGS = { NODE_CLASS_MAPPINGS = {
"ModelMergeSD1": ModelMergeSD1, "ModelMergeSD1": ModelMergeSD1,
"ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
"ModelMergeSDXL": ModelMergeSDXL, "ModelMergeSDXL": ModelMergeSDXL,
"ModelMergeSD3_2B": ModelMergeSD3_2B,
} }

1
nodes.py
View File

@ -1991,6 +1991,7 @@ def init_custom_nodes():
"nodes_webcam.py", "nodes_webcam.py",
"nodes_audio.py", "nodes_audio.py",
"nodes_sd3.py", "nodes_sd3.py",
"nodes_gits.py",
] ]
import_failed = [] import_failed = []

1
requirements.txt
View File

@ -15,3 +15,4 @@ psutil
#non essential dependencies: #non essential dependencies:
kornia>=0.7.1 kornia>=0.7.1
spandrel spandrel
soundfile

13
server.py
View File

@ -530,6 +530,19 @@ class PromptServer():
def add_routes(self): def add_routes(self):
self.user_manager.add_routes(self.routes) self.user_manager.add_routes(self.routes)
# Prefix every route with /api for easier matching for delegation.
# This is very useful for frontend dev server, which need to forward
# everything except serving of static files.
# Currently both the old endpoints without prefix and new endpoints with
# prefix are supported.
api_routes = web.RouteTableDef()
for route in self.routes:
# Custom nodes might add extra static routes. Only process non-static
# routes to add /api prefix.
if isinstance(route, web.RouteDef):
api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
self.app.add_routes(api_routes)
self.app.add_routes(self.routes) self.app.add_routes(self.routes)
for name, dir in nodes.EXTENSION_WEB_DIRS.items(): for name, dir in nodes.EXTENSION_WEB_DIRS.items():