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support for SD3: infinite prompt length, token counting

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AUTOMATIC1111 2024-06-26 23:22:00 +03:00
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commit d686e73daa
6 changed files with 278 additions and 139 deletions
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225
modules/models/sd3/sd3_cond.py Normal file
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@ -0,0 +1,225 @@
import os
import safetensors
import torch
import typing
from transformers import CLIPTokenizer, T5TokenizerFast
from modules import shared, devices, modelloader, sd_hijack_clip, prompt_parser
from modules.models.sd3.other_impls import SDClipModel, SDXLClipG, T5XXLModel, SD3Tokenizer
class SafetensorsMapping(typing.Mapping):
def __init__(self, file):
self.file = file
def __len__(self):
return len(self.file.keys())
def __iter__(self):
for key in self.file.keys():
yield key
def __getitem__(self, key):
return self.file.get_tensor(key)
CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
CLIPL_CONFIG = {
"hidden_act": "quick_gelu",
"hidden_size": 768,
"intermediate_size": 3072,
"num_attention_heads": 12,
"num_hidden_layers": 12,
}
CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
CLIPG_CONFIG = {
"hidden_act": "gelu",
"hidden_size": 1280,
"intermediate_size": 5120,
"num_attention_heads": 20,
"num_hidden_layers": 32,
}
T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
T5_CONFIG = {
"d_ff": 10240,
"d_model": 4096,
"num_heads": 64,
"num_layers": 24,
"vocab_size": 32128,
}
class Sd3ClipLG(sd_hijack_clip.TextConditionalModel):
def __init__(self, clip_l, clip_g):
super().__init__()
self.clip_l = clip_l
self.clip_g = clip_g
self.tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
empty = self.tokenizer('')["input_ids"]
self.id_start = empty[0]
self.id_end = empty[1]
self.id_pad = empty[1]
self.return_pooled = True
def tokenize(self, texts):
return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
def encode_with_transformers(self, tokens):
tokens_g = tokens.clone()
for batch_pos in range(tokens_g.shape[0]):
index = tokens_g[batch_pos].cpu().tolist().index(self.id_end)
tokens_g[batch_pos, index+1:tokens_g.shape[1]] = 0
l_out, l_pooled = self.clip_l(tokens)
g_out, g_pooled = self.clip_g(tokens_g)
lg_out = torch.cat([l_out, g_out], dim=-1)
lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
vector_out = torch.cat((l_pooled, g_pooled), dim=-1)
lg_out.pooled = vector_out
return lg_out
def encode_embedding_init_text(self, init_text, nvpt):
return torch.zeros((nvpt, 768+1280), device=devices.device) # XXX
class Sd3T5(torch.nn.Module):
def __init__(self, t5xxl):
super().__init__()
self.t5xxl = t5xxl
self.tokenizer = T5TokenizerFast.from_pretrained("google/t5-v1_1-xxl")
empty = self.tokenizer('', padding='max_length', max_length=2)["input_ids"]
self.id_end = empty[0]
self.id_pad = empty[1]
def tokenize(self, texts):
return self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"]
def tokenize_line(self, line, *, target_token_count=None):
if shared.opts.emphasis != "None":
parsed = prompt_parser.parse_prompt_attention(line)
else:
parsed = [[line, 1.0]]
tokenized = self.tokenize([text for text, _ in parsed])
tokens = []
multipliers = []
for text_tokens, (text, weight) in zip(tokenized, parsed):
if text == 'BREAK' and weight == -1:
continue
tokens += text_tokens
multipliers += [weight] * len(text_tokens)
tokens += [self.id_end]
multipliers += [1.0]
if target_token_count is not None:
if len(tokens) < target_token_count:
tokens += [self.id_pad] * (target_token_count - len(tokens))
multipliers += [1.0] * (target_token_count - len(tokens))
else:
tokens = tokens[0:target_token_count]
multipliers = multipliers[0:target_token_count]
return tokens, multipliers
def forward(self, texts, *, token_count):
if not self.t5xxl or not shared.opts.sd3_enable_t5:
return torch.zeros((len(texts), token_count, 4096), device=devices.device, dtype=devices.dtype)
tokens_batch = []
for text in texts:
tokens, multipliers = self.tokenize_line(text, target_token_count=token_count)
tokens_batch.append(tokens)
t5_out, t5_pooled = self.t5xxl(tokens_batch)
return t5_out
def encode_embedding_init_text(self, init_text, nvpt):
return torch.zeros((nvpt, 4096), device=devices.device) # XXX
class SD3Cond(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = SD3Tokenizer()
with torch.no_grad():
self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
if shared.opts.sd3_enable_t5:
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
else:
self.t5xxl = None
self.model_lg = Sd3ClipLG(self.clip_l, self.clip_g)
self.model_t5 = Sd3T5(self.t5xxl)
self.weights_loaded = False
def forward(self, prompts: list[str]):
lg_out, vector_out = self.model_lg(prompts)
token_count = lg_out.shape[1]
t5_out = self.model_t5(prompts, token_count=token_count)
lgt_out = torch.cat([lg_out, t5_out], dim=-2)
return {
'crossattn': lgt_out,
'vector': vector_out,
}
def load_weights(self):
if self.weights_loaded:
return
clip_path = os.path.join(shared.models_path, "CLIP")
clip_g_file = modelloader.load_file_from_url(CLIPG_URL, model_dir=clip_path, file_name="clip_g.safetensors")
with safetensors.safe_open(clip_g_file, framework="pt") as file:
self.clip_g.transformer.load_state_dict(SafetensorsMapping(file))
clip_l_file = modelloader.load_file_from_url(CLIPL_URL, model_dir=clip_path, file_name="clip_l.safetensors")
with safetensors.safe_open(clip_l_file, framework="pt") as file:
self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
if self.t5xxl:
t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
with safetensors.safe_open(t5_file, framework="pt") as file:
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
self.weights_loaded = True
def encode_embedding_init_text(self, init_text, nvpt):
return torch.tensor([[0]], device=devices.device) # XXX
def medvram_modules(self):
return [self.clip_g, self.clip_l, self.t5xxl]
def get_token_count(self, text):
_, token_count = self.model_lg.process_texts([text])
return token_count
def get_target_prompt_token_count(self, token_count):
return self.model_lg.get_target_prompt_token_count(token_count)

119
modules/models/sd3/sd3_model.py
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@ -1,127 +1,12 @@
import contextlib
import os
from typing import Mapping
import safetensors
import torch
import k_diffusion
from modules.models.sd3.other_impls import SDClipModel, SDXLClipG, T5XXLModel, SD3Tokenizer
from modules.models.sd3.sd3_impls import BaseModel, SDVAE, SD3LatentFormat
from modules.models.sd3.sd3_cond import SD3Cond
from modules import shared, modelloader, devices
CLIPG_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_g.safetensors"
CLIPG_CONFIG = {
"hidden_act": "gelu",
"hidden_size": 1280,
"intermediate_size": 5120,
"num_attention_heads": 20,
"num_hidden_layers": 32,
}
CLIPL_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/clip_l.safetensors"
CLIPL_CONFIG = {
"hidden_act": "quick_gelu",
"hidden_size": 768,
"intermediate_size": 3072,
"num_attention_heads": 12,
"num_hidden_layers": 12,
}
T5_URL = "https://huggingface.co/AUTOMATIC/stable-diffusion-3-medium-text-encoders/resolve/main/t5xxl_fp16.safetensors"
T5_CONFIG = {
"d_ff": 10240,
"d_model": 4096,
"num_heads": 64,
"num_layers": 24,
"vocab_size": 32128,
}
class SafetensorsMapping(Mapping):
def __init__(self, file):
self.file = file
def __len__(self):
return len(self.file.keys())
def __iter__(self):
for key in self.file.keys():
yield key
def __getitem__(self, key):
return self.file.get_tensor(key)
class SD3Cond(torch.nn.Module):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.tokenizer = SD3Tokenizer()
with torch.no_grad():
self.clip_g = SDXLClipG(CLIPG_CONFIG, device="cpu", dtype=devices.dtype)
self.clip_l = SDClipModel(layer="hidden", layer_idx=-2, device="cpu", dtype=devices.dtype, layer_norm_hidden_state=False, return_projected_pooled=False, textmodel_json_config=CLIPL_CONFIG)
if shared.opts.sd3_enable_t5:
self.t5xxl = T5XXLModel(T5_CONFIG, device="cpu", dtype=devices.dtype)
else:
self.t5xxl = None
self.weights_loaded = False
def forward(self, prompts: list[str]):
res = []
for prompt in prompts:
tokens = self.tokenizer.tokenize_with_weights(prompt)
l_out, l_pooled = self.clip_l.encode_token_weights(tokens["l"])
g_out, g_pooled = self.clip_g.encode_token_weights(tokens["g"])
if self.t5xxl and shared.opts.sd3_enable_t5:
t5_out, t5_pooled = self.t5xxl.encode_token_weights(tokens["t5xxl"])
else:
t5_out = torch.zeros(l_out.shape[0:2] + (4096,), dtype=l_out.dtype, device=l_out.device)
lg_out = torch.cat([l_out, g_out], dim=-1)
lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
lgt_out = torch.cat([lg_out, t5_out], dim=-2)
vector_out = torch.cat((l_pooled, g_pooled), dim=-1)
res.append({
'crossattn': lgt_out[0].to(devices.device),
'vector': vector_out[0].to(devices.device),
})
return res
def load_weights(self):
if self.weights_loaded:
return
clip_path = os.path.join(shared.models_path, "CLIP")
clip_g_file = modelloader.load_file_from_url(CLIPG_URL, model_dir=clip_path, file_name="clip_g.safetensors")
with safetensors.safe_open(clip_g_file, framework="pt") as file:
self.clip_g.transformer.load_state_dict(SafetensorsMapping(file))
clip_l_file = modelloader.load_file_from_url(CLIPL_URL, model_dir=clip_path, file_name="clip_l.safetensors")
with safetensors.safe_open(clip_l_file, framework="pt") as file:
self.clip_l.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
if self.t5xxl:
t5_file = modelloader.load_file_from_url(T5_URL, model_dir=clip_path, file_name="t5xxl_fp16.safetensors")
with safetensors.safe_open(t5_file, framework="pt") as file:
self.t5xxl.transformer.load_state_dict(SafetensorsMapping(file), strict=False)
self.weights_loaded = True
def encode_embedding_init_text(self, init_text, nvpt):
return torch.tensor([[0]], device=devices.device) # XXX
def medvram_modules(self):
return [self.clip_g, self.clip_l, self.t5xxl]
from modules import shared, devices
class SD3Denoiser(k_diffusion.external.DiscreteSchedule):

2
modules/prompt_parser.py
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@ -268,7 +268,7 @@ def get_multicond_learned_conditioning(model, prompts, steps, hires_steps=None,
class DictWithShape(dict):
def __init__(self, x, shape):
def __init__(self, x, shape=None):
super().__init__()
self.update(x)

3
modules/sd_hijack.py
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@ -325,6 +325,9 @@ class StableDiffusionModelHijack:
if self.clip is None:
return "-", "-"
if hasattr(self.clip, 'get_token_count'):
token_count = self.clip.get_token_count(text)
else:
_, token_count = self.clip.process_texts([text])
return token_count, self.clip.get_target_prompt_token_count(token_count)

59
modules/sd_hijack_clip.py
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@ -27,24 +27,21 @@ chunk. Those objects are found in PromptChunk.fixes and, are placed into FrozenC
are applied by sd_hijack.EmbeddingsWithFixes's forward function."""
class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
class TextConditionalModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.hijack: sd_hijack.StableDiffusionModelHijack = hijack
self.hijack = sd_hijack.model_hijack
self.chunk_length = 75
self.is_trainable = getattr(wrapped, 'is_trainable', False)
self.input_key = getattr(wrapped, 'input_key', 'txt')
self.legacy_ucg_val = None
self.is_trainable = False
self.input_key = 'txt'
self.return_pooled = False
self.comma_token = None
self.id_start = None
self.id_end = None
self.id_pad = None
def empty_chunk(self):
"""creates an empty PromptChunk and returns it"""
@ -210,10 +207,6 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
is when you do prompt editing: "a picture of a [cat:dog:0.4] eating ice cream"
"""
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
batch_chunks, token_count = self.process_texts(texts)
used_embeddings = {}
@ -252,7 +245,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
if any(x for x in texts if "(" in x or "[" in x) and opts.emphasis != "Original":
self.hijack.extra_generation_params["Emphasis"] = opts.emphasis
if getattr(self.wrapped, 'return_pooled', False):
if self.return_pooled:
return torch.hstack(zs), zs[0].pooled
else:
return torch.hstack(zs)
@ -292,6 +285,34 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
return z
class FrozenCLIPEmbedderWithCustomWordsBase(TextConditionalModel):
"""A pytorch module that is a wrapper for FrozenCLIPEmbedder module. it enhances FrozenCLIPEmbedder, making it possible to
have unlimited prompt length and assign weights to tokens in prompt.
"""
def __init__(self, wrapped, hijack):
super().__init__()
self.hijack = hijack
self.wrapped = wrapped
"""Original FrozenCLIPEmbedder module; can also be FrozenOpenCLIPEmbedder or xlmr.BertSeriesModelWithTransformation,
depending on model."""
self.is_trainable = getattr(wrapped, 'is_trainable', False)
self.input_key = getattr(wrapped, 'input_key', 'txt')
self.return_pooled = getattr(self.wrapped, 'return_pooled', False)
self.legacy_ucg_val = None # for sgm codebase
def forward(self, texts):
if opts.use_old_emphasis_implementation:
import modules.sd_hijack_clip_old
return modules.sd_hijack_clip_old.forward_old(self, texts)
return super().forward(texts)
class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
def __init__(self, wrapped, hijack):
super().__init__(wrapped, hijack)

7
modules/sd_models.py
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@ -722,7 +722,12 @@ def get_empty_cond(sd_model):
d = sd_model.get_learned_conditioning([""])
return d['crossattn']
else:
return sd_model.cond_stage_model([""])
d = sd_model.cond_stage_model([""])
if isinstance(d, dict):
d = d['crossattn']
return d
def send_model_to_cpu(m):