lllyasviel/stable-diffusion-webui-forge
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Text Processing Engine is Finished

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100% reproduce all previous results, including TI embeddings, LoRAs in CLIP, emphasize settings, BREAK, timestep swap scheduling, AB mixture, advanced uncond, etc
Backend is 85% finished
This commit is contained in:
layerdiffusion
2024-08-04 14:13:37 -07:00
parent 2791203d5b
commit a72154405e
8 changed files with 123 additions and 90 deletions
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2
README.md
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@@ -2,7 +2,7 @@
WebUI Forge is under a week of major revision right now between 2024 Aug 1 and Aug 7. To join the test, just update to the latest unstable version.
**Current Progress (2024 Aug 3):** Backend Rewrite is 81% finished - remaining 30 hours to begin making it stable; remaining 48 hours to begin supporting many new things.
**Current Progress (2024 Aug 3):** Backend Rewrite is 85% finished - remaining 30 hours to begin making it stable; remaining 48 hours to begin supporting many new things.
For downloading previous versions, see [Previous Versions](https://github.com/lllyasviel/stable-diffusion-webui-forge/discussions/849).

70
backend/text_processing/classic_engine.py
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@@ -3,10 +3,11 @@ import torch
from collections import namedtuple
from backend.text_processing import parsing, emphasis
from textual_inversion import EmbeddingDatabase
from backend.text_processing.textual_inversion import EmbeddingDatabase
PromptChunkFix = namedtuple('PromptChunkFix', ['offset', 'embedding'])
last_extra_generation_params = {}
class PromptChunk:
@@ -37,6 +38,7 @@ class CLIPEmbeddingForTextualInversion(torch.nn.Module):
for fixes, tensor in zip(batch_fixes, inputs_embeds):
for offset, embedding in fixes:
emb = embedding.vec[self.textual_inversion_key] if isinstance(embedding.vec, dict) else embedding.vec
emb = emb.to(inputs_embeds)
emb_len = min(tensor.shape[0] - offset - 1, emb.shape[0])
tensor = torch.cat([tensor[0:offset + 1], emb[0:emb_len], tensor[offset + 1 + emb_len:]]).to(dtype=inputs_embeds.dtype)
@@ -45,8 +47,11 @@ class CLIPEmbeddingForTextualInversion(torch.nn.Module):
return torch.stack(vecs)
class ClassicTextProcessingEngine:
def __init__(self, text_encoder, tokenizer, chunk_length=75, embedding_dir='./embeddings', embedding_key='clip_l', embedding_expected_shape=768, emphasis_name="original", text_projection=None, minimal_clip_skip=1, clip_skip=1, return_pooled=False, callback_before_encode=None):
class ClassicTextProcessingEngine(torch.nn.Module):
def __init__(self, text_encoder, tokenizer, chunk_length=75,
embedding_dir='./embeddings', embedding_key='clip_l', embedding_expected_shape=768, emphasis_name="original",
text_projection=False, minimal_clip_skip=1, clip_skip=1, return_pooled=False, final_layer_norm=True,
callback_before_encode=None):
super().__init__()
self.embeddings = EmbeddingDatabase(tokenizer, embedding_expected_shape)
@@ -56,20 +61,21 @@ class ClassicTextProcessingEngine:
self.text_encoder = text_encoder
self.tokenizer = tokenizer
self.emphasis = emphasis.get_current_option(emphasis_name)
self.emphasis = emphasis.get_current_option(emphasis_name)()
self.text_projection = text_projection
self.minimal_clip_skip = minimal_clip_skip
self.clip_skip = clip_skip
self.return_pooled = return_pooled
self.final_layer_norm = final_layer_norm
self.callback_before_encode = callback_before_encode
self.chunk_length = chunk_length
self.id_start = self.tokenizer.bos_token_id
self.id_end = self.tokenizer.eos_token_id
self.id_pad = self.id_end
self.id_pad = self.tokenizer.pad_token_id
model_embeddings = text_encoder.text_model.embeddings
model_embeddings = text_encoder.transformer.text_model.embeddings
model_embeddings.token_embedding = CLIPEmbeddingForTextualInversion(model_embeddings.token_embedding, self.embeddings, textual_inversion_key=embedding_key)
vocab = self.tokenizer.get_vocab()
@@ -94,9 +100,6 @@ class ClassicTextProcessingEngine:
if mult != 1.0:
self.token_mults[ident] = mult
# # Todo: remove these
# self.legacy_ucg_val = None # for sgm codebase
def empty_chunk(self):
chunk = PromptChunk()
chunk.tokens = [self.id_start] + [self.id_end] * (self.chunk_length + 1)
@@ -112,27 +115,25 @@ class ClassicTextProcessingEngine:
return tokenized
def encode_with_transformers(self, tokens):
self.text_encoder.transformer.text_model.embeddings.to(tokens.device)
tokens = tokens.to(self.text_encoder.transformer.text_model.embeddings.token_embedding.weight.device)
outputs = self.text_encoder.transformer(tokens, output_hidden_states=True)
layer_id = - max(self.clip_skip, self.minimal_clip_skip)
z = outputs.hidden_states[layer_id]
if self.final_layer_norm:
z = self.text_encoder.transformer.text_model.final_layer_norm(z)
if self.return_pooled:
pooled_output = outputs.pooler_output
if self.text_projection:
pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
pooled_output = pooled_output.float().to(self.text_encoder.text_projection.device) @ self.text_encoder.text_projection.float()
z.pooled = pooled_output
return z
def encode_embedding_init_text(self, init_text, nvpt):
embedding_layer = self.text_encoder.transformer.text_model.embeddings
ids = self.text_encoder.tokenizer(init_text, max_length=nvpt, return_tensors="pt", add_special_tokens=False)["input_ids"]
embedded = embedding_layer.token_embedding.wrapped(ids.to(embedding_layer.token_embedding.wrapped.weight.device)).squeeze(0)
return embedded
def tokenize_line(self, line):
parsed = parsing.parse_prompt_attention(line)
@@ -235,9 +236,9 @@ class ClassicTextProcessingEngine:
return batch_chunks, token_count
def __call__(self, texts):
def forward(self, texts):
if self.callback_before_encode is not None:
self.callback_before_encode()
self.callback_before_encode(self, texts)
batch_chunks, token_count = self.process_texts(texts)
@@ -259,28 +260,21 @@ class ClassicTextProcessingEngine:
z = self.process_tokens(tokens, multipliers)
zs.append(z)
global last_extra_generation_params
last_extra_generation_params = {}
if used_embeddings:
names = []
for name, embedding in used_embeddings.items():
print(f'Used Embedding: {name}')
names.append(name.replace(":", "").replace(",", ""))
# Todo:
# if opts.textual_inversion_add_hashes_to_infotext and used_embeddings:
# hashes = []
# for name, embedding in used_embeddings.items():
# shorthash = embedding.shorthash
# if not shorthash:
# continue
#
# name = name.replace(":", "").replace(",", "")
# hashes.append(f"{name}: {shorthash}")
#
# if hashes:
# if self.hijack.extra_generation_params.get("TI hashes"):
# hashes.append(self.hijack.extra_generation_params.get("TI hashes"))
# self.hijack.extra_generation_params["TI hashes"] = ", ".join(hashes)
#
# 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
last_extra_generation_params["TI"] = ", ".join(names)
if any(x for x in texts if "(" in x or "[" in x) and self.emphasis.name != "Original":
last_extra_generation_params["Emphasis"] = self.emphasis.name
if self.return_pooled:
return torch.hstack(zs), zs[0].pooled
@@ -300,7 +294,7 @@ class ClassicTextProcessingEngine:
pooled = getattr(z, 'pooled', None)
self.emphasis.tokens = remade_batch_tokens
self.emphasis.multipliers = torch.asarray(batch_multipliers)
self.emphasis.multipliers = torch.asarray(batch_multipliers).to(z)
self.emphasis.z = z
self.emphasis.after_transformers()
z = self.emphasis.z

2
backend/text_processing/textual_inversion.py
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@@ -128,7 +128,7 @@ class EmbeddingDatabase:
return self.register_embedding_by_name(embedding, embedding.name)
def register_embedding_by_name(self, embedding, name):
ids = self.tokenizer.tokenize([name])[0]
ids = self.tokenizer([name], truncation=False, add_special_tokens=False)["input_ids"][0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []

12
modules/processing.py
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@@ -498,8 +498,14 @@ class StableDiffusionProcessing:
with devices.autocast():
cache[1] = function(shared.sd_model, required_prompts, steps, hires_steps, shared.opts.use_old_scheduling)
import backend.text_processing.classic_engine
last_extra_generation_params = backend.text_processing.classic_engine.last_extra_generation_params.copy()
modules.sd_hijack.model_hijack.extra_generation_params.update(last_extra_generation_params)
if len(cache) > 2:
cache[2] = modules.sd_hijack.model_hijack.extra_generation_params
cache[2] = last_extra_generation_params
cache[0] = cached_params
return cache[1]
@@ -880,7 +886,9 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
p.all_subseeds = [int(subseed) + x for x in range(len(p.all_prompts))]
if os.path.exists(cmd_opts.embeddings_dir) and not p.do_not_reload_embeddings:
model_hijack.embedding_db.load_textual_inversion_embeddings()
# todo: reload ti
# model_hijack.embedding_db.load_textual_inversion_embeddings()
pass
if p.scripts is not None:
p.scripts.process(p)

5
modules/sd_hijack.py
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@@ -127,14 +127,9 @@ class StableDiffusionModelHijack:
optimization_method = None
def __init__(self):
import modules.textual_inversion.textual_inversion
self.extra_generation_params = {}
self.comments = []
self.embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase()
self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
def apply_optimizations(self, option=None):
pass

9
modules/sd_models.py
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@@ -686,19 +686,10 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
model_data.set_sd_model(sd_model)
model_data.was_loaded_at_least_once = True
sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True) # Reload embeddings after model load as they may or may not fit the model
timer.record("load textual inversion embeddings")
script_callbacks.model_loaded_callback(sd_model)
timer.record("scripts callbacks")
with torch.no_grad():
sd_model.cond_stage_model_empty_prompt = get_empty_cond(sd_model)
timer.record("calculate empty prompt")
print(f"Model loaded in {timer.summary()}.")
return sd_model

8
modules/textual_inversion/textual_inversion.py
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@@ -127,7 +127,7 @@ class EmbeddingDatabase:
return self.register_embedding_by_name(embedding, model, embedding.name)
def register_embedding_by_name(self, embedding, model, name):
ids = model.cond_stage_model.tokenize([name])[0]
ids = [0, 0, 0] # model.cond_stage_model.tokenize([name])[0]
first_id = ids[0]
if first_id not in self.ids_lookup:
self.ids_lookup[first_id] = []
@@ -183,11 +183,7 @@ class EmbeddingDatabase:
if data is not None:
embedding = create_embedding_from_data(data, name, filename=filename, filepath=path)
if self.expected_shape == -1 or self.expected_shape == embedding.shape:
self.register_embedding(embedding, shared.sd_model)
else:
self.skipped_embeddings[name] = embedding
self.register_embedding(embedding, None)
else:
print(f"Unable to load Textual inversion embedding due to data issue: '{name}'.")

105
modules_forge/loader.py
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@@ -9,7 +9,7 @@ import backend.nn.unet
from omegaconf import OmegaConf
from modules.sd_models_config import find_checkpoint_config
from modules.shared import cmd_opts
from modules.shared import cmd_opts, opts
from modules import sd_hijack
from modules.sd_models_xl import extend_sdxl
from ldm.util import instantiate_from_config
@@ -17,6 +17,7 @@ from modules_forge import clip
from modules_forge.unet_patcher import UnetPatcher
from backend.loader import load_huggingface_components
from backend.modules.k_model import KModel
from backend.text_processing.classic_engine import ClassicTextProcessingEngine
import open_clip
from transformers import CLIPTextModel, CLIPTokenizer
@@ -148,6 +149,15 @@ def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
sd_model.first_stage_model = forge_objects.vae.first_stage_model
sd_model.model.diffusion_model = forge_objects.unet.model
def set_clip_skip_callback(m, ts):
m.clip_skip = opts.CLIP_stop_at_last_layers
return
def set_clip_skip_callback_and_move_model(m, ts):
memory_management.load_model_gpu(sd_model.forge_objects.clip.patcher)
m.clip_skip = opts.CLIP_stop_at_last_layers
return
conditioner = getattr(sd_model, 'conditioner', None)
if conditioner:
text_cond_models = []
@@ -156,23 +166,44 @@ def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
embedder = conditioner.embedders[i]
typename = type(embedder).__name__
if typename == 'FrozenCLIPEmbedder': # SDXL Clip L
embedder.tokenizer = forge_objects.clip.tokenizer.clip_l
embedder.transformer = forge_objects.clip.cond_stage_model.clip_l.transformer
model_embeddings = embedder.transformer.text_model.embeddings
model_embeddings.token_embedding = sd_hijack.EmbeddingsWithFixes(
model_embeddings.token_embedding, sd_hijack.model_hijack)
embedder = clip.CLIP_SD_XL_L(embedder, sd_hijack.model_hijack)
conditioner.embedders[i] = embedder
engine = ClassicTextProcessingEngine(
text_encoder=forge_objects.clip.cond_stage_model.clip_l,
tokenizer=forge_objects.clip.tokenizer.clip_l,
embedding_dir=cmd_opts.embeddings_dir,
embedding_key='clip_l',
embedding_expected_shape=2048,
emphasis_name=opts.emphasis,
text_projection=False,
minimal_clip_skip=2,
clip_skip=2,
return_pooled=False,
final_layer_norm=False,
callback_before_encode=set_clip_skip_callback
)
engine.is_trainable = False # for sgm codebase
engine.legacy_ucg_val = None # for sgm codebase
engine.input_key = 'txt' # for sgm codebase
conditioner.embedders[i] = engine
text_cond_models.append(embedder)
elif typename == 'FrozenOpenCLIPEmbedder2': # SDXL Clip G
embedder.tokenizer = forge_objects.clip.tokenizer.clip_g
embedder.transformer = forge_objects.clip.cond_stage_model.clip_g.transformer
embedder.text_projection = forge_objects.clip.cond_stage_model.clip_g.text_projection
model_embeddings = embedder.transformer.text_model.embeddings
model_embeddings.token_embedding = sd_hijack.EmbeddingsWithFixes(
model_embeddings.token_embedding, sd_hijack.model_hijack, textual_inversion_key='clip_g')
embedder = clip.CLIP_SD_XL_G(embedder, sd_hijack.model_hijack)
conditioner.embedders[i] = embedder
engine = ClassicTextProcessingEngine(
text_encoder=forge_objects.clip.cond_stage_model.clip_g,
tokenizer=forge_objects.clip.tokenizer.clip_g,
embedding_dir=cmd_opts.embeddings_dir,
embedding_key='clip_g',
embedding_expected_shape=2048,
emphasis_name=opts.emphasis,
text_projection=True,
minimal_clip_skip=2,
clip_skip=2,
return_pooled=True,
final_layer_norm=False,
callback_before_encode=set_clip_skip_callback
)
engine.is_trainable = False # for sgm codebase
engine.legacy_ucg_val = None # for sgm codebase
engine.input_key = 'txt' # for sgm codebase
conditioner.embedders[i] = engine
text_cond_models.append(embedder)
if len(text_cond_models) == 1:
@@ -180,19 +211,37 @@ def load_model_for_a1111(timer, checkpoint_info=None, state_dict=None):
else:
sd_model.cond_stage_model = conditioner
elif type(sd_model.cond_stage_model).__name__ == 'FrozenCLIPEmbedder': # SD15 Clip
sd_model.cond_stage_model.tokenizer = forge_objects.clip.tokenizer.clip_l
sd_model.cond_stage_model.transformer = forge_objects.clip.cond_stage_model.clip_l.transformer
model_embeddings = sd_model.cond_stage_model.transformer.text_model.embeddings
model_embeddings.token_embedding = sd_hijack.EmbeddingsWithFixes(
model_embeddings.token_embedding, sd_hijack.model_hijack)
sd_model.cond_stage_model = clip.CLIP_SD_15_L(sd_model.cond_stage_model, sd_hijack.model_hijack)
engine = ClassicTextProcessingEngine(
text_encoder=forge_objects.clip.cond_stage_model.clip_l,
tokenizer=forge_objects.clip.tokenizer.clip_l,
embedding_dir=cmd_opts.embeddings_dir,
embedding_key='clip_l',
embedding_expected_shape=768,
emphasis_name=opts.emphasis,
text_projection=False,
minimal_clip_skip=1,
clip_skip=1,
return_pooled=False,
final_layer_norm=True,
callback_before_encode=set_clip_skip_callback_and_move_model
)
sd_model.cond_stage_model = engine
elif type(sd_model.cond_stage_model).__name__ == 'FrozenOpenCLIPEmbedder': # SD21 Clip
sd_model.cond_stage_model.tokenizer = forge_objects.clip.tokenizer.clip_l
sd_model.cond_stage_model.transformer = forge_objects.clip.cond_stage_model.clip_l.transformer
model_embeddings = sd_model.cond_stage_model.transformer.text_model.embeddings
model_embeddings.token_embedding = sd_hijack.EmbeddingsWithFixes(
model_embeddings.token_embedding, sd_hijack.model_hijack)
sd_model.cond_stage_model = clip.CLIP_SD_21_H(sd_model.cond_stage_model, sd_hijack.model_hijack)
engine = ClassicTextProcessingEngine(
text_encoder=forge_objects.clip.cond_stage_model.clip_l,
tokenizer=forge_objects.clip.tokenizer.clip_l,
embedding_dir=cmd_opts.embeddings_dir,
embedding_key='clip_l',
embedding_expected_shape=1024,
emphasis_name=opts.emphasis,
text_projection=False,
minimal_clip_skip=1,
clip_skip=1,
return_pooled=False,
final_layer_norm=True,
callback_before_encode=set_clip_skip_callback_and_move_model
)
sd_model.cond_stage_model = engine
else:
raise NotImplementedError('Bad Clip Class Name:' + type(sd_model.cond_stage_model).__name__)