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0ef5557d6a
ComfyUI/comfy/sd1_clip.py
Sasbom 0ef5557d6a Add QOL feature for changing the custom nodes folder location through cli args. 
bugfix: fix typo in apply_directory for custom_nodes_directory

allow for PATH style ';' delimited custom_node directories.

change delimiter type for seperate folders per platform.

feat(API-nodes): move Rodin3D nodes to new client; removed old api client.py (#10645)

Fix qwen controlnet regression. (#10657)

Enable pinned memory by default on Nvidia. (#10656)

Removed the --fast pinned_memory flag.

You can use --disable-pinned-memory to disable it. Please report if it
causes any issues.

Pinned mem also seems to work on AMD. (#10658)

Remove environment variable.

Removed environment variable fallback for custom nodes directory.

Update documentation for custom nodes directory

Clarified documentation on custom nodes directory argument, removed documentation on environment variable

Clarify release cycle. (#10667)

Tell users they need to upload their logs in bug reports. (#10671)

mm: guard against double pin and unpin explicitly (#10672)

As commented, if you let cuda be the one to detect double pin/unpinning
it actually creates an asyc GPU error.

Only unpin tensor if it was pinned by ComfyUI (#10677)

Make ScaleROPE node work on Flux. (#10686)

Add logging for model unloading. (#10692)

Unload weights if vram usage goes up between runs. (#10690)

ops: Put weight cast on the offload stream (#10697)

This needs to be on the offload stream. This reproduced a black screen
with low resolution images on a slow bus when using FP8.

Update CI workflow to remove dead macOS runner. (#10704)

* Update CI workflow to remove dead macOS runner.

* revert

* revert

Don't pin tensor if not a torch.nn.parameter.Parameter (#10718)

Update README.md for Intel Arc GPU installation, remove IPEX (#10729)

IPEX is no longer needed for Intel Arc GPUs.  Removing instruction to setup ipex.

mm/mp: always unload re-used but modified models (#10724)

The partial unloader path in model re-use flow skips straight to the
actual unload without any check of the patching UUID. This means that
if you do an upscale flow with a model patch on an existing model, it
will not apply your patchings.

Fix by delaying the partial_unload until after the uuid checks. This
is done by making partial_unload a model of partial_load where extra_mem
is -ve.

qwen: reduce VRAM usage (#10725)

Clean up a bunch of stacked and no-longer-needed tensors on the QWEN
VRAM peak (currently FFN).

With this I go from OOMing at B=37x1328x1328 to being able to
succesfully run B=47 (RTX5090).

 Update Python 3.14 compatibility notes in README  (#10730)

Quantized Ops fixes (#10715)

* offload support, bug fixes, remove mixins

* add readme

add PR template for API-Nodes (#10736)

feat: add create_time dict to prompt field in /history and /queue (#10741)

flux: reduce VRAM usage (#10737)

Cleanup a bunch of stack tensors on Flux. This take me from B=19 to B=22
for 1600x1600 on RTX5090.

Better instructions for the portable. (#10743)

Use same code for chroma and flux blocks so that optimizations are shared. (#10746)

Fix custom nodes import error. (#10747)

This should fix the import errors but will break if the custom nodes actually try to use the class.

revert import reordering

revert imports pt 2

Add left padding support to tokenizers. (#10753)

chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757)

Revert "chore(api-nodes): mark OpenAIDalle2 and OpenAIDalle3 nodes as deprecated (#10757)" (#10759)

This reverts commit 9a02382568.

Change ROCm nightly install command to 7.1 (#10764)
2025-11-17 06:16:21 +01:00

701 lines
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import os
from transformers import CLIPTokenizer
import comfy.ops
import torch
import traceback
import zipfile
from . import model_management
import comfy.clip_model
import json
import logging
import numbers
import re
def gen_empty_tokens(special_tokens, length):
start_token = special_tokens.get("start", None)
end_token = special_tokens.get("end", None)
pad_token = special_tokens.get("pad")
output = []
if start_token is not None:
output.append(start_token)
if end_token is not None:
output.append(end_token)
output += [pad_token] * (length - len(output))
return output
class ClipTokenWeightEncoder:
def encode_token_weights(self, token_weight_pairs):
to_encode = list()
max_token_len = 0
has_weights = False
for x in token_weight_pairs:
tokens = list(map(lambda a: a[0], x))
max_token_len = max(len(tokens), max_token_len)
has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
to_encode.append(tokens)
sections = len(to_encode)
if has_weights or sections == 0:
if hasattr(self, "gen_empty_tokens"):
to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))
else:
to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
o = self.encode(to_encode)
out, pooled = o[:2]
if pooled is not None:
first_pooled = pooled[0:1].to(model_management.intermediate_device())
else:
first_pooled = pooled
output = []
for k in range(0, sections):
z = out[k:k+1]
if has_weights:
z_empty = out[-1]
for i in range(len(z)):
for j in range(len(z[i])):
weight = token_weight_pairs[k][j][1]
if weight != 1.0:
z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
output.append(z)
if (len(output) == 0):
r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
else:
r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
if len(o) > 2:
extra = {}
for k in o[2]:
v = o[2][k]
if k == "attention_mask":
v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
extra[k] = v
r = r + (extra,)
return r
class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
LAYERS = [
"last",
"pooled",
"hidden",
"all"
]
def __init__(self, device="cpu", max_length=77,
freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
if textmodel_json_config is None:
textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
if "model_name" not in model_options:
model_options = {**model_options, "model_name": "clip_l"}
if isinstance(textmodel_json_config, dict):
config = textmodel_json_config
else:
with open(textmodel_json_config) as f:
config = json.load(f)
te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {})
for k, v in te_model_options.items():
config[k] = v
operations = model_options.get("custom_operations", None)
scaled_fp8 = None
if operations is None:
scaled_fp8 = model_options.get("scaled_fp8", None)
if scaled_fp8 is not None:
operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
else:
operations = comfy.ops.manual_cast
self.operations = operations
self.transformer = model_class(config, dtype, device, self.operations)
if scaled_fp8 is not None:
self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
self.num_layers = self.transformer.num_layers
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = None
self.special_tokens = special_tokens
self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
self.enable_attention_masks = enable_attention_masks
self.zero_out_masked = zero_out_masked
self.layer_norm_hidden_state = layer_norm_hidden_state
self.return_projected_pooled = return_projected_pooled
self.return_attention_masks = return_attention_masks
if layer == "hidden":
assert layer_idx is not None
assert abs(layer_idx) < self.num_layers
self.set_clip_options({"layer": layer_idx})
self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
def freeze(self):
self.transformer = self.transformer.eval()
#self.train = disabled_train
for param in self.parameters():
param.requires_grad = False
def set_clip_options(self, options):
layer_idx = options.get("layer", self.layer_idx)
self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
if self.layer == "all":
pass
elif layer_idx is None or abs(layer_idx) > self.num_layers:
self.layer = "last"
else:
self.layer = "hidden"
self.layer_idx = layer_idx
def reset_clip_options(self):
self.layer = self.options_default[0]
self.layer_idx = self.options_default[1]
self.return_projected_pooled = self.options_default[2]
def process_tokens(self, tokens, device):
end_token = self.special_tokens.get("end", None)
if end_token is None:
cmp_token = self.special_tokens.get("pad", -1)
else:
cmp_token = end_token
embeds_out = []
attention_masks = []
num_tokens = []
for x in tokens:
attention_mask = []
tokens_temp = []
other_embeds = []
eos = False
index = 0
for y in x:
if isinstance(y, numbers.Integral):
if eos:
attention_mask.append(0)
else:
attention_mask.append(1)
token = int(y)
tokens_temp += [token]
if not eos and token == cmp_token:
if end_token is None:
attention_mask[-1] = 0
eos = True
else:
other_embeds.append((index, y))
index += 1
tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long)
tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32)
index = 0
pad_extra = 0
embeds_info = []
for o in other_embeds:
emb = o[1]
if torch.is_tensor(emb):
emb = {"type": "embedding", "data": emb}
extra = None
emb_type = emb.get("type", None)
if emb_type == "embedding":
emb = emb.get("data", None)
else:
if hasattr(self.transformer, "preprocess_embed"):
emb, extra = self.transformer.preprocess_embed(emb, device=device)
else:
emb = None
if emb is None:
index += -1
continue
ind = index + o[0]
emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32)
emb_shape = emb.shape[1]
if emb.shape[-1] == tokens_embed.shape[-1]:
tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1)
attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:]
index += emb_shape - 1
embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra})
else:
index += -1
pad_extra += emb_shape
logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1]))
if pad_extra > 0:
padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32)
tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1)
attention_mask = attention_mask + [0] * pad_extra
embeds_out.append(tokens_embed)
attention_masks.append(attention_mask)
num_tokens.append(sum(attention_mask))
return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens, embeds_info
def forward(self, tokens):
device = self.transformer.get_input_embeddings().weight.device
embeds, attention_mask, num_tokens, embeds_info = self.process_tokens(tokens, device)
attention_mask_model = None
if self.enable_attention_masks:
attention_mask_model = attention_mask
if self.layer == "all":
intermediate_output = "all"
else:
intermediate_output = self.layer_idx
outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32, embeds_info=embeds_info)
if self.layer == "last":
z = outputs[0].float()
else:
z = outputs[1].float()
if self.zero_out_masked:
z *= attention_mask.unsqueeze(-1).float()
pooled_output = None
if len(outputs) >= 3:
if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
pooled_output = outputs[3].float()
elif outputs[2] is not None:
pooled_output = outputs[2].float()
extra = {}
if self.return_attention_masks:
extra["attention_mask"] = attention_mask
if len(extra) > 0:
return z, pooled_output, extra
return z, pooled_output
def encode(self, tokens):
return self(tokens)
def load_sd(self, sd):
return self.transformer.load_state_dict(sd, strict=False)
def parse_parentheses(string):
result = []
current_item = ""
nesting_level = 0
for char in string:
if char == "(":
if nesting_level == 0:
if current_item:
result.append(current_item)
current_item = "("
else:
current_item = "("
else:
current_item += char
nesting_level += 1
elif char == ")":
nesting_level -= 1
if nesting_level == 0:
result.append(current_item + ")")
current_item = ""
else:
current_item += char
else:
current_item += char
if current_item:
result.append(current_item)
return result
def token_weights(string, current_weight):
a = parse_parentheses(string)
out = []
for x in a:
weight = current_weight
if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
x = x[1:-1]
xx = x.rfind(":")
weight *= 1.1
if xx > 0:
try:
weight = float(x[xx+1:])
x = x[:xx]
except:
pass
out += token_weights(x, weight)
else:
out += [(x, current_weight)]
return out
def escape_important(text):
text = text.replace("\\)", "\0\1")
text = text.replace("\\(", "\0\2")
return text
def unescape_important(text):
text = text.replace("\0\1", ")")
text = text.replace("\0\2", "(")
return text
def safe_load_embed_zip(embed_path):
with zipfile.ZipFile(embed_path) as myzip:
names = list(filter(lambda a: "data/" in a, myzip.namelist()))
names.reverse()
for n in names:
with myzip.open(n) as myfile:
data = myfile.read()
number = len(data) // 4
length_embed = 1024 #sd2.x
if number < 768:
continue
if number % 768 == 0:
length_embed = 768 #sd1.x
num_embeds = number // length_embed
embed = torch.frombuffer(data, dtype=torch.float)
out = embed.reshape((num_embeds, length_embed)).clone()
del embed
return out
def expand_directory_list(directories):
dirs = set()
for x in directories:
dirs.add(x)
for root, subdir, file in os.walk(x, followlinks=True):
dirs.add(root)
return list(dirs)
def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
out_list = []
for k in embed:
if k.startswith(prefix) and k.endswith(suffix):
out_list.append(embed[k])
if len(out_list) == 0:
return None
return torch.cat(out_list, dim=0)
def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
if isinstance(embedding_directory, str):
embedding_directory = [embedding_directory]
embedding_directory = expand_directory_list(embedding_directory)
valid_file = None
for embed_dir in embedding_directory:
embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
embed_dir = os.path.abspath(embed_dir)
try:
if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
continue
except:
continue
if not os.path.isfile(embed_path):
extensions = ['.safetensors', '.pt', '.bin']
for x in extensions:
t = embed_path + x
if os.path.isfile(t):
valid_file = t
break
else:
valid_file = embed_path
if valid_file is not None:
break
if valid_file is None:
return None
embed_path = valid_file
embed_out = None
try:
if embed_path.lower().endswith(".safetensors"):
import safetensors.torch
embed = safetensors.torch.load_file(embed_path, device="cpu")
else:
try:
embed = torch.load(embed_path, weights_only=True, map_location="cpu")
except:
embed_out = safe_load_embed_zip(embed_path)
except Exception:
logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
return None
if embed_out is None:
if 'string_to_param' in embed:
values = embed['string_to_param'].values()
embed_out = next(iter(values))
elif isinstance(embed, list):
out_list = []
for x in range(len(embed)):
for k in embed[x]:
t = embed[x][k]
if t.shape[-1] != embedding_size:
continue
out_list.append(t.reshape(-1, t.shape[-1]))
embed_out = torch.cat(out_list, dim=0)
elif embed_key is not None and embed_key in embed:
embed_out = embed[embed_key]
else:
embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')
if embed_out is None:
embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))
if embed_out is None:
values = embed.values()
embed_out = next(iter(values))
return embed_out
class SDTokenizer:
def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, pad_left=False, tokenizer_data={}, tokenizer_args={}):
if tokenizer_path is None:
tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
self.end_token = None
self.min_padding = min_padding
self.pad_left = pad_left
empty = self.tokenizer('')["input_ids"]
self.tokenizer_adds_end_token = has_end_token
if has_start_token:
self.tokens_start = 1
self.start_token = empty[0]
if end_token is not None:
self.end_token = end_token
else:
if has_end_token:
self.end_token = empty[1]
else:
self.tokens_start = 0
self.start_token = None
if end_token is not None:
self.end_token = end_token
else:
if has_end_token:
self.end_token = empty[0]
if pad_token is not None:
self.pad_token = pad_token
elif pad_with_end:
self.pad_token = self.end_token
else:
self.pad_token = 0
self.pad_with_end = pad_with_end
self.pad_to_max_length = pad_to_max_length
vocab = self.tokenizer.get_vocab()
self.inv_vocab = {v: k for k, v in vocab.items()}
self.embedding_directory = embedding_directory
self.max_word_length = 8
self.embedding_identifier = "embedding:"
self.embedding_size = embedding_size
self.embedding_key = embedding_key
def _try_get_embedding(self, embedding_name:str):
'''
Takes a potential embedding name and tries to retrieve it.
Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
'''
split_embed = embedding_name.split()
embedding_name = split_embed[0]
leftover = ' '.join(split_embed[1:])
embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
if embed is None:
stripped = embedding_name.strip(',')
if len(stripped) < len(embedding_name):
embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
return (embed, leftover)
def pad_tokens(self, tokens, amount):
if self.pad_left:
for i in range(amount):
tokens.insert(0, (self.pad_token, 1.0, 0))
else:
tokens.extend([(self.pad_token, 1.0, 0)] * amount)
def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):
'''
Takes a prompt and converts it to a list of (token, weight, word id) elements.
Tokens can both be integer tokens and pre computed CLIP tensors.
Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
Returned list has the dimensions NxM where M is the input size of CLIP
'''
min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
text = escape_important(text)
if kwargs.get("disable_weights", False):
parsed_weights = [(text, 1.0)]
else:
parsed_weights = token_weights(text, 1.0)
# tokenize words
tokens = []
for weighted_segment, weight in parsed_weights:
to_tokenize = unescape_important(weighted_segment)
split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
to_tokenize = [split[0]]
for i in range(1, len(split)):
to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
to_tokenize = [x for x in to_tokenize if x != ""]
for word in to_tokenize:
# if we find an embedding, deal with the embedding
if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
embedding_name = word[len(self.embedding_identifier):].strip('\n')
embed, leftover = self._try_get_embedding(embedding_name)
if embed is None:
logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
else:
if len(embed.shape) == 1:
tokens.append([(embed, weight)])
else:
tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
#if we accidentally have leftover text, continue parsing using leftover, else move on to next word
if leftover != "":
word = leftover
else:
continue
end = 999999999999
if self.tokenizer_adds_end_token:
end = -1
#parse word
tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]])
#reshape token array to CLIP input size
batched_tokens = []
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0, 0))
batched_tokens.append(batch)
for i, t_group in enumerate(tokens):
#determine if we're going to try and keep the tokens in a single batch
is_large = len(t_group) >= self.max_word_length
if self.end_token is not None:
has_end_token = 1
else:
has_end_token = 0
while len(t_group) > 0:
if len(t_group) + len(batch) > self.max_length - has_end_token:
remaining_length = self.max_length - len(batch) - has_end_token
#break word in two and add end token
if is_large:
batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
t_group = t_group[remaining_length:]
#add end token and pad
else:
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
if self.pad_to_max_length:
self.pad_tokens(batch, remaining_length)
#start new batch
batch = []
if self.start_token is not None:
batch.append((self.start_token, 1.0, 0))
batched_tokens.append(batch)
else:
batch.extend([(t,w,i+1) for t,w in t_group])
t_group = []
#fill last batch
if self.end_token is not None:
batch.append((self.end_token, 1.0, 0))
if min_padding is not None:
self.pad_tokens(batch, min_padding)
if self.pad_to_max_length and len(batch) < self.max_length:
self.pad_tokens(batch, self.max_length - len(batch))
if min_length is not None and len(batch) < min_length:
self.pad_tokens(batch, min_length - len(batch))
if not return_word_ids:
batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
return batched_tokens
def untokenize(self, token_weight_pair):
return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
def state_dict(self):
return {}
class SD1Tokenizer:
def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
if name is not None:
self.clip_name = name
self.clip = "{}".format(self.clip_name)
else:
self.clip_name = clip_name
self.clip = "clip_{}".format(self.clip_name)
tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
out = {}
out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs)
return out
def untokenize(self, token_weight_pair):
return getattr(self, self.clip).untokenize(token_weight_pair)
def state_dict(self):
return getattr(self, self.clip).state_dict()
class SD1CheckpointClipModel(SDClipModel):
def __init__(self, device="cpu", dtype=None, model_options={}):
super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
class SD1ClipModel(torch.nn.Module):
def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs):
super().__init__()
if name is not None:
self.clip_name = name
self.clip = "{}".format(self.clip_name)
else:
self.clip_name = clip_name
self.clip = "clip_{}".format(self.clip_name)
clip_model = model_options.get("{}_class".format(self.clip), clip_model)
model_options = {**model_options, "model_name": self.clip}
setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
self.dtypes = set()
if dtype is not None:
self.dtypes.add(dtype)
def set_clip_options(self, options):
getattr(self, self.clip).set_clip_options(options)
def reset_clip_options(self):
getattr(self, self.clip).reset_clip_options()
def encode_token_weights(self, token_weight_pairs):
token_weight_pairs = token_weight_pairs[self.clip_name]
out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
return out
def load_sd(self, sd):
return getattr(self, self.clip).load_sd(sd)
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