nodes/city96-ComfyUI_ExtraModels
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

fix the conversation and run success;

Browse Source
This commit is contained in:
junsong
2024-12-05 00:18:56 -08:00
parent 81f58d87cd
commit dde1273804
7 changed files with 33 additions and 769 deletions
Download Patch File Download Diff File Expand all files Collapse all files
Sana/diffusers_convert.py
+15 -29
View File
@@ -1,20 +1,8 @@
# For using the diffusers format weights
# Based on the original ComfyUI function +
# https://github.com/PixArt-alpha/PixArt-alpha/blob/master/tools/convert_pixart_alpha_to_diffusers.py
# https://github.com/NVlabs/Sana/blob/main/tools/convert_sana_to_diffusers.py
import torch
conversion_map_ms = [ # for multi_scale_train (MS)
# Resolution
("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
# Aspect ratio
("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
]
def get_depth(state_dict):
return sum(key.endswith('.attn1.to_k.bias') for key in state_dict.keys())
@@ -30,7 +18,7 @@ def get_lora_depth(state_dict):
return cnt
def get_conversion_map(state_dict):
conversion_map = [ # main SD conversion map (PixArt reference, HF Diffusers)
conversion_map = [ # main SD conversion map (Sana reference, HF Diffusers)
# Patch embeddings
("x_embedder.proj.weight", "pos_embed.proj.weight"),
("x_embedder.proj.bias", "pos_embed.proj.bias"),
@@ -82,10 +70,7 @@ def find_prefix(state_dict, target_key):
return prefix
def convert_state_dict(state_dict):
if "adaln_single.emb.resolution_embedder.linear_1.weight" in state_dict.keys():
cmap = get_conversion_map(state_dict) + conversion_map_ms
else:
cmap = get_conversion_map(state_dict)
cmap = get_conversion_map(state_dict)
missing = [k for k,v in cmap if v not in state_dict]
new_state_dict = {k: state_dict[v] for k,v in cmap if k not in missing}
@@ -109,16 +94,17 @@ def convert_state_dict(state_dict):
matched += [key('q'), key('k'), key('v')]
if len(matched) < len(state_dict):
print(f"PixArt: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(f"Sana: UNET conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(list( set(state_dict.keys()) - set(matched) ))
if len(missing) > 0:
print(f"PixArt: UNET conversion has missing keys!")
print(f"Sana: UNET conversion has missing keys!")
print(missing)
return new_state_dict
# Same as above but for LoRA weights:
# TODO: Not used yet, need to support LoRA for Sana
def convert_lora_state_dict(state_dict, peft=True):
# koyha
rep_ak = lambda x: x.replace(".weight", ".lora_down.weight")
@@ -137,18 +123,18 @@ def convert_lora_state_dict(state_dict, peft=True):
rep_pp = lambda x: x.replace(".", "_")[:-7] + ".alpha"
prefix = "lora_transformer_"
t5_marker = "lora_te_encoder"
t5_keys = []
gemma_marker = "lora_te_encoder"
gemma_keys = []
for key in list(state_dict.keys()):
if key.startswith(prefix):
state_dict[key[len(prefix):]] = state_dict.pop(key)
elif t5_marker in key:
t5_keys.append(state_dict.pop(key))
if len(t5_keys) > 0:
print(f"Text Encoder not supported for PixArt LoRA, ignoring {len(t5_keys)} keys")
elif gemma_marker in key:
gemma_keys.append(state_dict.pop(key))
if len(gemma_keys) > 0:
print(f"Text Encoder not supported for Sana LoRA, ignoring {len(gemma_keys)} keys")
cmap = []
cmap_unet = get_conversion_map(state_dict) + conversion_map_ms # todo: 512 model
cmap_unet = get_conversion_map(state_dict) # todo: 512 model
for k, v in cmap_unet:
if v.endswith(".weight"):
cmap.append((rep_ak(k), rep_ap(v)))
@@ -213,11 +199,11 @@ def convert_lora_state_dict(state_dict, peft=True):
pass
if len(matched) < len(state_dict):
print(f"PixArt: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(f"Sana: LoRA conversion has leftover keys! ({len(matched)} vs {len(state_dict)})")
print(list( set(state_dict.keys()) - set(matched) ))
if len(missing) > 0:
print(f"PixArt: LoRA conversion has missing keys! (probably)")
print(f"Sana: LoRA conversion has missing keys! (probably)")
print(missing)
return new_state_dict
Sana/lora.py
-146
View File
@@ -1,146 +0,0 @@
import os
import copy
import json
import torch
import comfy.lora
import comfy.model_management
from comfy.model_patcher import ModelPatcher
from .diffusers_convert import convert_lora_state_dict
class EXM_PixArt_ModelPatcher(ModelPatcher):
def calculate_weight(self, patches, weight, key):
"""
This is almost the same as the comfy function, but stripped down to just the LoRA patch code.
The problem with the original code is the q/k/v keys being combined into one for the attention.
In the diffusers code, they're treated as separate keys, but in the reference code they're recombined (q+kv|qkv).
This means, for example, that the [1152,1152] weights become [3456,1152] in the state dict.
The issue with this is that the LoRA weights are [128,1152],[1152,128] and become [384,1162],[3456,128] instead.
This is the best thing I could think of that would fix that, but it's very fragile.
- Check key shape to determine if it needs the fallback logic
- Cut the input into parts based on the shape (undoing the torch.cat)
- Do the matrix multiplication logic
- Recombine them to match the expected shape
"""
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) == 2:
patch_type = v[0]
v = v[1]
if patch_type == "lora":
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)
if v[2] is not None:
alpha *= v[2] / mat2.shape[0]
try:
mat1 = mat1.flatten(start_dim=1)
mat2 = mat2.flatten(start_dim=1)
ch1 = mat1.shape[0] // mat2.shape[1]
ch2 = mat2.shape[0] // mat1.shape[1]
### Fallback logic for shape mismatch ###
if mat1.shape[0] != mat2.shape[1] and ch1 == ch2 and (mat1.shape[0]/mat2.shape[1])%1 == 0:
mat1 = mat1.chunk(ch1, dim=0)
mat2 = mat2.chunk(ch1, dim=0)
weight += torch.cat(
[alpha * torch.mm(mat1[x], mat2[x]) for x in range(ch1)],
dim=0,
).reshape(weight.shape).type(weight.dtype)
else:
weight += (alpha * torch.mm(mat1, mat2)).reshape(weight.shape).type(weight.dtype)
except Exception as e:
print("ERROR", key, e)
return weight
def clone(self):
n = EXM_PixArt_ModelPatcher(self.model, self.load_device, self.offload_device, self.size, self.current_device, weight_inplace_update=self.weight_inplace_update)
n.patches = {}
for k in self.patches:
n.patches[k] = self.patches[k][:]
n.object_patches = self.object_patches.copy()
n.model_options = copy.deepcopy(self.model_options)
n.model_keys = self.model_keys
return n
def replace_model_patcher(model):
n = EXM_PixArt_ModelPatcher(
model = model.model,
size = model.size,
load_device = model.load_device,
offload_device = model.offload_device,
weight_inplace_update = model.weight_inplace_update,
)
n.patches = {}
for k in model.patches:
n.patches[k] = model.patches[k][:]
n.object_patches = model.object_patches.copy()
n.model_options = copy.deepcopy(model.model_options)
return n
def find_peft_alpha(path):
def load_json(json_path):
with open(json_path) as f:
data = json.load(f)
alpha = data.get("lora_alpha")
alpha = alpha or data.get("alpha")
if not alpha:
print(" Found config but `lora_alpha` is missing!")
else:
print(f" Found config at {json_path} [alpha:{alpha}]")
return alpha
# For some weird reason peft doesn't include the alpha in the actual model
print("PixArt: Warning! This is a PEFT LoRA. Trying to find config...")
files = [
f"{os.path.splitext(path)[0]}.json",
f"{os.path.splitext(path)[0]}.config.json",
os.path.join(os.path.dirname(path),"adapter_config.json"),
]
for file in files:
if os.path.isfile(file):
return load_json(file)
print(" Missing config/alpha! assuming alpha of 8. Consider converting it/adding a config json to it.")
return 8.0
def load_pixart_lora(model, lora, lora_path, strength):
k_back = lambda x: x.replace(".lora_up.weight", "")
# need to convert the actual weights for this to work.
if any(True for x in lora.keys() if x.endswith("adaln_single.linear.lora_A.weight")):
lora = convert_lora_state_dict(lora, peft=True)
alpha = find_peft_alpha(lora_path)
lora.update({f"{k_back(x)}.alpha":torch.tensor(alpha) for x in lora.keys() if "lora_up" in x})
else: # OneTrainer
lora = convert_lora_state_dict(lora, peft=False)
key_map = {k_back(x):f"diffusion_model.{k_back(x)}.weight" for x in lora.keys() if "lora_up" in x} # fake
loaded = comfy.lora.load_lora(lora, key_map)
if model is not None:
# switch to custom model patcher when using LoRAs
if isinstance(model, EXM_PixArt_ModelPatcher):
new_modelpatcher = model.clone()
else:
new_modelpatcher = replace_model_patcher(model)
k = new_modelpatcher.add_patches(loaded, strength)
else:
k = ()
new_modelpatcher = None
k = set(k)
for x in loaded:
if (x not in k):
print("NOT LOADED", x)
return new_modelpatcher
Sana/models/sana.py
-2
View File
@@ -226,8 +226,6 @@ class Sana(nn.Module):
)
self.final_layer = T2IFinalLayer(hidden_size, patch_size, self.out_channels)
self.initialize_weights()
def forward(self, x, timestep, y, mask=None, data_info=None, **kwargs):
"""
Forward pass of Sana.
Sana/models/sana_blocks.py
-2
View File
@@ -16,10 +16,8 @@
# This file is modified from https://github.com/PixArt-alpha/PixArt-sigma
import math
import os
from typing import Optional
import xformers.ops
import torch
import torch.nn as nn
import torch.nn.functional as F
Sana/models/utils.py
+2 -497
View File
@@ -14,21 +14,12 @@
#
# SPDX-License-Identifier: Apache-2.0
import math
import os
import random
import re
import sys
from collections.abc import Iterable
from itertools import repeat
from typing import Union, Tuple
import torch
import torch.distributed as dist
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
from torch.utils.checkpoint import checkpoint, checkpoint_sequential
from torchvision import transforms as T
def _ntuple(n):
@@ -44,25 +35,6 @@ to_1tuple = _ntuple(1)
to_2tuple = _ntuple(2)
def set_grad_checkpoint(model, gc_step=1):
assert isinstance(model, nn.Module)
def set_attr(module):
module.grad_checkpointing = True
module.grad_checkpointing_step = gc_step
model.apply(set_attr)
def set_fp32_attention(model):
assert isinstance(model, nn.Module)
def set_attr(module):
module.fp32_attention = True
model.apply(set_attr)
def auto_grad_checkpoint(module, *args, **kwargs):
if getattr(module, "grad_checkpointing", False):
if isinstance(module, Iterable):
@@ -99,478 +71,12 @@ def checkpoint_sequential(functions, step, input, *args, **kwargs):
input = checkpoint(run_function(start, end, functions), input, preserve_rng_state=preserve)
return run_function(end + 1, len(functions) - 1, functions)(input)
def window_partition(x, window_size):
"""
Partition into non-overlapping windows with padding if needed.
Args:
x (tensor): input tokens with [B, H, W, C].
window_size (int): window size.
Returns:
windows: windows after partition with [B * num_windows, window_size, window_size, C].
(Hp, Wp): padded height and width before partition
"""
B, H, W, C = x.shape
pad_h = (window_size - H % window_size) % window_size
pad_w = (window_size - W % window_size) % window_size
if pad_h > 0 or pad_w > 0:
x = F.pad(x, (0, 0, 0, pad_w, 0, pad_h))
Hp, Wp = H + pad_h, W + pad_w
x = x.view(B, Hp // window_size, window_size, Wp // window_size, window_size, C)
windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
return windows, (Hp, Wp)
def window_unpartition(windows, window_size, pad_hw, hw):
"""
Window unpartition into original sequences and removing padding.
Args:
x (tensor): input tokens with [B * num_windows, window_size, window_size, C].
window_size (int): window size.
pad_hw (Tuple): padded height and width (Hp, Wp).
hw (Tuple): original height and width (H, W) before padding.
Returns:
x: unpartitioned sequences with [B, H, W, C].
"""
Hp, Wp = pad_hw
H, W = hw
B = windows.shape[0] // (Hp * Wp // window_size // window_size)
x = windows.view(B, Hp // window_size, Wp // window_size, window_size, window_size, -1)
x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, Hp, Wp, -1)
if Hp > H or Wp > W:
x = x[:, :H, :W, :].contiguous()
return x
def get_rel_pos(q_size, k_size, rel_pos):
"""
Get relative positional embeddings according to the relative positions of
query and key sizes.
Args:
q_size (int): size of query q.
k_size (int): size of key k.
rel_pos (Tensor): relative position embeddings (L, C).
Returns:
Extracted positional embeddings according to relative positions.
"""
max_rel_dist = int(2 * max(q_size, k_size) - 1)
# Interpolate rel pos if needed.
if rel_pos.shape[0] != max_rel_dist:
# Interpolate rel pos.
rel_pos_resized = F.interpolate(
rel_pos.reshape(1, rel_pos.shape[0], -1).permute(0, 2, 1),
size=max_rel_dist,
mode="linear",
)
rel_pos_resized = rel_pos_resized.reshape(-1, max_rel_dist).permute(1, 0)
else:
rel_pos_resized = rel_pos
# Scale the coords with short length if shapes for q and k are different.
q_coords = torch.arange(q_size)[:, None] * max(k_size / q_size, 1.0)
k_coords = torch.arange(k_size)[None, :] * max(q_size / k_size, 1.0)
relative_coords = (q_coords - k_coords) + (k_size - 1) * max(q_size / k_size, 1.0)
return rel_pos_resized[relative_coords.long()]
def add_decomposed_rel_pos(attn, q, rel_pos_h, rel_pos_w, q_size, k_size):
"""
Calculate decomposed Relative Positional Embeddings from :paper:`mvitv2`.
https://github.com/facebookresearch/mvit/blob/19786631e330df9f3622e5402b4a419a263a2c80/mvit/models/attention.py # noqa B950
Args:
attn (Tensor): attention map.
q (Tensor): query q in the attention layer with shape (B, q_h * q_w, C).
rel_pos_h (Tensor): relative position embeddings (Lh, C) for height axis.
rel_pos_w (Tensor): relative position embeddings (Lw, C) for width axis.
q_size (Tuple): spatial sequence size of query q with (q_h, q_w).
k_size (Tuple): spatial sequence size of key k with (k_h, k_w).
Returns:
attn (Tensor): attention map with added relative positional embeddings.
"""
q_h, q_w = q_size
k_h, k_w = k_size
Rh = get_rel_pos(q_h, k_h, rel_pos_h)
Rw = get_rel_pos(q_w, k_w, rel_pos_w)
B, _, dim = q.shape
r_q = q.reshape(B, q_h, q_w, dim)
rel_h = torch.einsum("bhwc,hkc->bhwk", r_q, Rh)
rel_w = torch.einsum("bhwc,wkc->bhwk", r_q, Rw)
attn = (attn.view(B, q_h, q_w, k_h, k_w) + rel_h[:, :, :, :, None] + rel_w[:, :, :, None, :]).view(
B, q_h * q_w, k_h * k_w
)
return attn
def mean_flat(tensor):
return tensor.mean(dim=list(range(1, tensor.ndim)))
#################################################################################
# Token Masking and Unmasking #
#################################################################################
def get_mask(batch, length, mask_ratio, device, mask_type=None, data_info=None, extra_len=0):
"""
Get the binary mask for the input sequence.
Args:
- batch: batch size
- length: sequence length
- mask_ratio: ratio of tokens to mask
- data_info: dictionary with info for reconstruction
return:
mask_dict with following keys:
- mask: binary mask, 0 is keep, 1 is remove
- ids_keep: indices of tokens to keep
- ids_restore: indices to restore the original order
"""
assert mask_type in ["random", "fft", "laplacian", "group"]
mask = torch.ones([batch, length], device=device)
len_keep = int(length * (1 - mask_ratio)) - extra_len
if mask_type == "random" or mask_type == "group":
noise = torch.rand(batch, length, device=device) # noise in [0, 1]
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
ids_removed = ids_shuffle[:, len_keep:]
elif mask_type in ["fft", "laplacian"]:
if "strength" in data_info:
strength = data_info["strength"]
else:
N = data_info["N"][0]
img = data_info["ori_img"]
# 获取原图的尺寸信息
_, C, H, W = img.shape
if mask_type == "fft":
# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
reshaped_image = img.reshape((batch, -1, H // N, N, W // N, N))
fft_image = torch.fft.fftn(reshaped_image, dim=(3, 5))
# 取绝对值并求和获取频率强度
strength = torch.sum(torch.abs(fft_image), dim=(1, 3, 5)).reshape(
(
batch,
-1,
)
)
elif type == "laplacian":
laplacian_kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32).reshape(
1, 1, 3, 3
)
laplacian_kernel = laplacian_kernel.repeat(C, 1, 1, 1)
# 对图片进行reshape，将其变为patch (3, H/N, N, W/N, N)
reshaped_image = img.reshape(-1, C, H // N, N, W // N, N).permute(0, 2, 4, 1, 3, 5).reshape(-1, C, N, N)
laplacian_response = F.conv2d(reshaped_image, laplacian_kernel, padding=1, groups=C)
strength = laplacian_response.sum(dim=[1, 2, 3]).reshape(
(
batch,
-1,
)
)
# 对频率强度进行归一化，然后使用torch.multinomial进行采样
probabilities = strength / (strength.max(dim=1)[0][:, None] + 1e-5)
ids_shuffle = torch.multinomial(probabilities.clip(1e-5, 1), length, replacement=False)
ids_keep = ids_shuffle[:, :len_keep]
ids_restore = torch.argsort(ids_shuffle, dim=1)
ids_removed = ids_shuffle[:, len_keep:]
mask[:, :len_keep] = 0
mask = torch.gather(mask, dim=1, index=ids_restore)
return {"mask": mask, "ids_keep": ids_keep, "ids_restore": ids_restore, "ids_removed": ids_removed}
def mask_out_token(x, ids_keep, ids_removed=None):
"""
Mask out the tokens specified by ids_keep.
Args:
- x: input sequence, [N, L, D]
- ids_keep: indices of tokens to keep
return:
- x_masked: masked sequence
"""
N, L, D = x.shape # batch, length, dim
x_remain = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
if ids_removed is not None:
x_masked = torch.gather(x, dim=1, index=ids_removed.unsqueeze(-1).repeat(1, 1, D))
return x_remain, x_masked
else:
return x_remain
def mask_tokens(x, mask_ratio):
"""
Perform per-sample random masking by per-sample shuffling.
Per-sample shuffling is done by argsort random noise.
x: [N, L, D], sequence
"""
N, L, D = x.shape # batch, length, dim
len_keep = int(L * (1 - mask_ratio))
noise = torch.rand(N, L, device=x.device) # noise in [0, 1]
# sort noise for each sample
ids_shuffle = torch.argsort(noise, dim=1) # ascend: small is keep, large is remove
ids_restore = torch.argsort(ids_shuffle, dim=1)
# keep the first subset
ids_keep = ids_shuffle[:, :len_keep]
x_masked = torch.gather(x, dim=1, index=ids_keep.unsqueeze(-1).repeat(1, 1, D))
# generate the binary mask: 0 is keep, 1 is remove
mask = torch.ones([N, L], device=x.device)
mask[:, :len_keep] = 0
mask = torch.gather(mask, dim=1, index=ids_restore)
return x_masked, mask, ids_restore
def unmask_tokens(x, ids_restore, mask_token):
# x: [N, T, D] if extras == 0 (i.e., no cls token) else x: [N, T+1, D]
mask_tokens = mask_token.repeat(x.shape[0], ids_restore.shape[1] - x.shape[1], 1)
x = torch.cat([x, mask_tokens], dim=1)
x = torch.gather(x, dim=1, index=ids_restore.unsqueeze(-1).repeat(1, 1, x.shape[2])) # unshuffle
return x
# Parse 'None' to None and others to float value
def parse_float_none(s):
assert isinstance(s, str)
return None if s == "None" else float(s)
# ----------------------------------------------------------------------------
# Parse a comma separated list of numbers or ranges and return a list of ints.
# Example: '1,2,5-10' returns [1, 2, 5, 6, 7, 8, 9, 10]
def parse_int_list(s):
if isinstance(s, list):
return s
ranges = []
range_re = re.compile(r"^(\d+)-(\d+)$")
for p in s.split(","):
m = range_re.match(p)
if m:
ranges.extend(range(int(m.group(1)), int(m.group(2)) + 1))
else:
ranges.append(int(p))
return ranges
def init_processes(fn, args):
"""Initialize the distributed environment."""
os.environ["MASTER_ADDR"] = args.master_address
os.environ["MASTER_PORT"] = str(random.randint(2000, 6000))
print(f'MASTER_ADDR = {os.environ["MASTER_ADDR"]}')
print(f'MASTER_PORT = {os.environ["MASTER_PORT"]}')
torch.cuda.set_device(args.local_rank)
dist.init_process_group(backend="nccl", init_method="env://", rank=args.global_rank, world_size=args.global_size)
fn(args)
if args.global_size > 1:
cleanup()
def mprint(*args, **kwargs):
"""
Print only from rank 0.
"""
if dist.get_rank() == 0:
print(*args, **kwargs)
def cleanup():
"""
End DDP training.
"""
dist.barrier()
mprint("Done!")
dist.barrier()
dist.destroy_process_group()
# ----------------------------------------------------------------------------
# logging info.
class Logger:
"""
Redirect stderr to stdout, optionally print stdout to a file,
and optionally force flushing on both stdout and the file.
"""
def __init__(self, file_name=None, file_mode="w", should_flush=True):
self.file = None
if file_name is not None:
self.file = open(file_name, file_mode)
self.should_flush = should_flush
self.stdout = sys.stdout
self.stderr = sys.stderr
sys.stdout = self
sys.stderr = self
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
def write(self, text):
"""Write text to stdout (and a file) and optionally flush."""
if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
return
if self.file is not None:
self.file.write(text)
self.stdout.write(text)
if self.should_flush:
self.flush()
def flush(self):
"""Flush written text to both stdout and a file, if open."""
if self.file is not None:
self.file.flush()
self.stdout.flush()
def close(self):
"""Flush, close possible files, and remove stdout/stderr mirroring."""
self.flush()
# if using multiple loggers, prevent closing in wrong order
if sys.stdout is self:
sys.stdout = self.stdout
if sys.stderr is self:
sys.stderr = self.stderr
if self.file is not None:
self.file.close()
class StackedRandomGenerator:
def __init__(self, device, seeds):
super().__init__()
self.generators = [torch.Generator(device).manual_seed(int(seed) % (1 << 32)) for seed in seeds]
def randn(self, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randn(size[1:], generator=gen, **kwargs) for gen in self.generators])
def randn_like(self, input):
return self.randn(input.shape, dtype=input.dtype, layout=input.layout, device=input.device)
def randint(self, *args, size, **kwargs):
assert size[0] == len(self.generators)
return torch.stack([torch.randint(*args, size=size[1:], generator=gen, **kwargs) for gen in self.generators])
def prepare_prompt_ar(prompt, ratios, device="cpu", show=True):
# get aspect_ratio or ar
aspect_ratios = re.findall(r"--aspect_ratio\s+(\d+:\d+)", prompt)
ars = re.findall(r"--ar\s+(\d+:\d+)", prompt)
custom_hw = re.findall(r"--hw\s+(\d+:\d+)", prompt)
if show:
print("aspect_ratios:", aspect_ratios, "ars:", ars, "hws:", custom_hw)
prompt_clean = prompt.split("--aspect_ratio")[0].split("--ar")[0].split("--hw")[0]
if len(aspect_ratios) + len(ars) + len(custom_hw) == 0 and show:
print(
"Wrong prompt format. Set to default ar: 1. change your prompt into format '--ar h:w or --hw h:w' for correct generating"
)
if len(aspect_ratios) != 0:
ar = float(aspect_ratios[0].split(":")[0]) / float(aspect_ratios[0].split(":")[1])
elif len(ars) != 0:
ar = float(ars[0].split(":")[0]) / float(ars[0].split(":")[1])
else:
ar = 1.0
closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - ar))
if len(custom_hw) != 0:
custom_hw = [float(custom_hw[0].split(":")[0]), float(custom_hw[0].split(":")[1])]
else:
custom_hw = ratios[closest_ratio]
default_hw = ratios[closest_ratio]
prompt_show = f"prompt: {prompt_clean.strip()}\nSize: --ar {closest_ratio}, --bin hw {ratios[closest_ratio]}, --custom hw {custom_hw}"
return (
prompt_clean,
prompt_show,
torch.tensor(default_hw, device=device)[None],
torch.tensor([float(closest_ratio)], device=device)[None],
torch.tensor(custom_hw, device=device)[None],
)
def resize_and_crop_tensor(samples: torch.Tensor, new_width: int, new_height: int) -> torch.Tensor:
orig_height, orig_width = samples.shape[2], samples.shape[3]
# Check if resizing is needed
if orig_height != new_height or orig_width != new_width:
ratio = max(new_height / orig_height, new_width / orig_width)
resized_width = int(orig_width * ratio)
resized_height = int(orig_height * ratio)
# Resize
samples = F.interpolate(samples, size=(resized_height, resized_width), mode="bilinear", align_corners=False)
# Center Crop
start_x = (resized_width - new_width) // 2
end_x = start_x + new_width
start_y = (resized_height - new_height) // 2
end_y = start_y + new_height
samples = samples[:, :, start_y:end_y, start_x:end_x]
return samples
def resize_and_crop_img(img: Image, new_width, new_height):
orig_width, orig_height = img.size
ratio = max(new_width / orig_width, new_height / orig_height)
resized_width = int(orig_width * ratio)
resized_height = int(orig_height * ratio)
img = img.resize((resized_width, resized_height), Image.LANCZOS)
left = (resized_width - new_width) / 2
top = (resized_height - new_height) / 2
right = (resized_width + new_width) / 2
bottom = (resized_height + new_height) / 2
img = img.crop((left, top, right, bottom))
return img
def mask_feature(emb, mask):
if emb.shape[0] == 1:
keep_index = mask.sum().item()
return emb[:, :, :keep_index, :], keep_index
else:
masked_feature = emb * mask[:, None, :, None]
return masked_feature, emb.shape[2]
def val2list(x: list or tuple or any, repeat_time=1) -> list: # type: ignore
"""Repeat `val` for `repeat_time` times and return the list or val if list/tuple."""
if isinstance(x, (list, tuple)):
return list(x)
return [x for _ in range(repeat_time)]
def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -> tuple: # type: ignore
"""Return tuple with min_len by repeating element at idx_repeat."""
# convert to list first
@@ -582,8 +88,7 @@ def val2tuple(x: list or tuple or any, min_len: int = 1, idx_repeat: int = -1) -
return tuple(x)
def get_same_padding(kernel_size: int or tuple[int, ...]) -> int or tuple[int, ...]:
def get_same_padding(kernel_size: Union[int, Tuple[int, ...]]) -> Union[int, Tuple[int, ...]]:
if isinstance(kernel_size, tuple):
return tuple([get_same_padding(ks) for ks in kernel_size])
else:
Sana/nodes.py
+11 -82
View File
@@ -1,13 +1,8 @@
import os
import json
import torch
import folder_paths
from comfy.model_management import get_torch_device, soft_empty_cache, text_encoder_offload_device
from comfy import utils
from .conf import sana_conf, sana_res
from .loader import load_sana
from ..utils.dtype import string_to_dtype
dtypes = [
"auto",
@@ -93,7 +88,6 @@ class SanaTextEncode:
return {
"required": {
"text": ("STRING", {"multiline": True}),
"preset_styles": (STYLE_NAMES,),
"GEMMA": ("GEMMA",),
}
}
@@ -103,19 +97,15 @@ class SanaTextEncode:
CATEGORY = "ExtraModels/Sana"
TITLE = "Sana Text Encode"
def encode(self, text, preset_styles, GEMMA=None):
def encode(self, text, GEMMA=None):
tokenizer = GEMMA["tokenizer"]
text_encoder = GEMMA["text_encoder"]
# 应用预设样式 - 只使用正面提示词部分
text, _ = apply_style(preset_styles, text)
with torch.no_grad():
# 处理正面提示词
chi_prompt = "\n".join(preset_te_prompt)
full_prompt = chi_prompt + text
num_chi_tokens = len(tokenizer.encode(chi_prompt))
max_length = num_chi_tokens + 300 - 2 # 减去[bos]和[_]标记
max_length = num_chi_tokens + 300 - 2
tokens = tokenizer(
[full_prompt],
@@ -128,82 +118,21 @@ class SanaTextEncode:
select_idx = [0] + list(range(-300 + 1, 0))
embs = text_encoder(tokens.input_ids, tokens.attention_mask)[0][:, None][:, :, select_idx]
emb_masks = tokens.attention_mask[:, select_idx]
# 利用emb_masks将有效的embs选出来，其他置零
embs = embs * emb_masks.unsqueeze(-1)
return ([[embs, {}]], )
# 需要添加style相关的辅助函数
style_list = [
{
"name": "(No style)",
"prompt": "{prompt}",
"negative_prompt": "",
},
{
"name": "Cinematic",
"prompt": "cinematic still {prompt} . emotional, harmonious, vignette, highly detailed, high budget, bokeh, "
"cinemascope, moody, epic, gorgeous, film grain, grainy",
"negative_prompt": "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
},
{
"name": "Photographic",
"prompt": "cinematic photo {prompt} . 35mm photograph, film, bokeh, professional, 4k, highly detailed",
"negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
},
{
"name": "Anime",
"prompt": "anime artwork {prompt} . anime style, key visual, vibrant, studio anime, highly detailed",
"negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
},
{
"name": "Manga",
"prompt": "manga style {prompt} . vibrant, high-energy, detailed, iconic, Japanese comic style",
"negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
},
{
"name": "Digital Art",
"prompt": "concept art {prompt} . digital artwork, illustrative, painterly, matte painting, highly detailed",
"negative_prompt": "photo, photorealistic, realism, ugly",
},
{
"name": "Pixel art",
"prompt": "pixel-art {prompt} . low-res, blocky, pixel art style, 8-bit graphics",
"negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
},
{
"name": "Fantasy art",
"prompt": "ethereal fantasy concept art of {prompt} . magnificent, celestial, ethereal, painterly, epic, "
"majestic, magical, fantasy art, cover art, dreamy",
"negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, "
"glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, "
"disfigured, sloppy, duplicate, mutated, black and white",
},
{
"name": "Neonpunk",
"prompt": "neonpunk style {prompt} . cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, "
"detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, "
"ultra detailed, intricate, professional",
"negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
},
{
"name": "3D Model",
"prompt": "professional 3d model {prompt} . octane render, highly detailed, volumetric, dramatic lighting",
"negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
},
preset_te_prompt = [
'Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:',
'- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.',
'- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.',
'Here are examples of how to transform or refine prompts:',
'- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.',
'- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.',
'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:',
'User Prompt: '
]
styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
STYLE_NAMES = list(styles.keys())
def apply_style(style_name: str, positive: str, negative: str = "") -> tuple[str, str]:
p, n = styles.get(style_name, styles[style_name])
if not negative:
negative = ""
return p.replace("{prompt}", positive), n + negative
preset_te_prompt = ['Given a user prompt, generate an "Enhanced prompt" that provides detailed visual descriptions suitable for image generation. Evaluate the level of detail in the user prompt:', '- If the prompt is simple, focus on adding specifics about colors, shapes, sizes, textures, and spatial relationships to create vivid and concrete scenes.', '- If the prompt is already detailed, refine and enhance the existing details slightly without overcomplicating.', 'Here are examples of how to transform or refine prompts:', '- User Prompt: A cat sleeping -> Enhanced: A small, fluffy white cat curled up in a round shape, sleeping peacefully on a warm sunny windowsill, surrounded by pots of blooming red flowers.', '- User Prompt: A busy city street -> Enhanced: A bustling city street scene at dusk, featuring glowing street lamps, a diverse crowd of people in colorful clothing, and a double-decker bus passing by towering glass skyscrapers.', 'Please generate only the enhanced description for the prompt below and avoid including any additional commentary or evaluations:', 'User Prompt: ']
NODE_CLASS_MAPPINGS = {
"SanaCheckpointLoader" : SanaCheckpointLoader,
"SanaResolutionSelect" : SanaResolutionSelect,