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-rw-r--r--configs/altdiffusion/ad-inference.yaml72
-rw-r--r--modules/sd_hijack.py23
-rw-r--r--modules/sd_hijack_clip.py10
-rw-r--r--modules/shared.py8
-rw-r--r--modules/xlmr.py137
-rw-r--r--v2-inference-v.yaml68
6 files changed, 310 insertions, 8 deletions
diff --git a/configs/altdiffusion/ad-inference.yaml b/configs/altdiffusion/ad-inference.yaml
new file mode 100644
index 00000000..cfbee72d
--- /dev/null
+++ b/configs/altdiffusion/ad-inference.yaml
@@ -0,0 +1,72 @@
+model:
+ base_learning_rate: 1.0e-04
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false # Note: different from the one we trained before
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+
+ scheduler_config: # 10000 warmup steps
+ target: ldm.lr_scheduler.LambdaLinearScheduler
+ params:
+ warm_up_steps: [ 10000 ]
+ cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases
+ f_start: [ 1.e-6 ]
+ f_max: [ 1. ]
+ f_min: [ 1. ]
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: modules.xlmr.BertSeriesModelWithTransformation
+ params:
+ name: "XLMR-Large" \ No newline at end of file
diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 690a9ec2..bce23b03 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -78,17 +78,24 @@ class StableDiffusionModelHijack:
embedding_db = modules.textual_inversion.textual_inversion.EmbeddingDatabase(cmd_opts.embeddings_dir)
def hijack(self, m):
- if type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
+
+ if shared.text_model_name == "XLMR-Large":
+ model_embeddings = m.cond_stage_model.roberta.embeddings
+ model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
+ m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+
+ elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
m.cond_stage_model = sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
+ apply_optimizations()
elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder:
m.cond_stage_model.model.token_embedding = EmbeddingsWithFixes(m.cond_stage_model.model.token_embedding, self)
m.cond_stage_model = sd_hijack_open_clip.FrozenOpenCLIPEmbedderWithCustomWords(m.cond_stage_model, self)
-
+ apply_optimizations()
+
self.clip = m.cond_stage_model
-
- apply_optimizations()
+
fix_checkpoint()
def flatten(el):
@@ -101,7 +108,11 @@ class StableDiffusionModelHijack:
self.layers = flatten(m)
def undo_hijack(self, m):
- if type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
+
+ if shared.text_model_name == "XLMR-Large":
+ m.cond_stage_model = m.cond_stage_model.wrapped
+
+ elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
m.cond_stage_model = m.cond_stage_model.wrapped
model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
@@ -129,8 +140,8 @@ class StableDiffusionModelHijack:
def tokenize(self, text):
_, remade_batch_tokens, _, _, _, token_count = self.clip.process_text([text])
- return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
+ return remade_batch_tokens[0], token_count, sd_hijack_clip.get_target_prompt_token_count(token_count)
class EmbeddingsWithFixes(torch.nn.Module):
diff --git a/modules/sd_hijack_clip.py b/modules/sd_hijack_clip.py
index b451d1cf..9ea6e1ce 100644
--- a/modules/sd_hijack_clip.py
+++ b/modules/sd_hijack_clip.py
@@ -4,7 +4,7 @@ import torch
from modules import prompt_parser, devices
from modules.shared import opts
-
+import modules.shared as shared
def get_target_prompt_token_count(token_count):
return math.ceil(max(token_count, 1) / 75) * 75
@@ -177,6 +177,9 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
return batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count
def forward(self, text):
+ if shared.text_model_name == "XLMR-Large":
+ return self.wrapped.encode(text)
+
use_old = opts.use_old_emphasis_implementation
if use_old:
batch_multipliers, remade_batch_tokens, used_custom_terms, hijack_comments, hijack_fixes, token_count = self.process_text_old(text)
@@ -254,7 +257,10 @@ class FrozenCLIPEmbedderWithCustomWords(FrozenCLIPEmbedderWithCustomWordsBase):
def __init__(self, wrapped, hijack):
super().__init__(wrapped, hijack)
self.tokenizer = wrapped.tokenizer
- self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
+ if shared.text_model_name == "XLMR-Large":
+ self.comma_token = None
+ else :
+ self.comma_token = [v for k, v in self.tokenizer.get_vocab().items() if k == ',</w>'][0]
self.token_mults = {}
tokens_with_parens = [(k, v) for k, v in self.tokenizer.get_vocab().items() if '(' in k or ')' in k or '[' in k or ']' in k]
diff --git a/modules/shared.py b/modules/shared.py
index c494a3b9..2b31e717 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -108,6 +108,14 @@ restricted_opts = {
"outdir_txt2img_grids",
"outdir_save",
}
+from omegaconf import OmegaConf
+config = OmegaConf.load(f"{cmd_opts.config}")
+# XLMR-Large
+try:
+ text_model_name = config.model.params.cond_stage_config.params.name
+
+except :
+ text_model_name = "stable_diffusion"
cmd_opts.disable_extension_access = (cmd_opts.share or cmd_opts.listen or cmd_opts.server_name) and not cmd_opts.enable_insecure_extension_access
diff --git a/modules/xlmr.py b/modules/xlmr.py
new file mode 100644
index 00000000..beab3fdf
--- /dev/null
+++ b/modules/xlmr.py
@@ -0,0 +1,137 @@
+from transformers import BertPreTrainedModel,BertModel,BertConfig
+import torch.nn as nn
+import torch
+from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
+from transformers import XLMRobertaModel,XLMRobertaTokenizer
+from typing import Optional
+
+class BertSeriesConfig(BertConfig):
+ def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs):
+
+ super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs)
+ self.project_dim = project_dim
+ self.pooler_fn = pooler_fn
+ self.learn_encoder = learn_encoder
+
+class RobertaSeriesConfig(XLMRobertaConfig):
+ def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs):
+ super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
+ self.project_dim = project_dim
+ self.pooler_fn = pooler_fn
+ self.learn_encoder = learn_encoder
+
+
+class BertSeriesModelWithTransformation(BertPreTrainedModel):
+
+ _keys_to_ignore_on_load_unexpected = [r"pooler"]
+ _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
+ config_class = BertSeriesConfig
+
+ def __init__(self, config=None, **kargs):
+ # modify initialization for autoloading
+ if config is None:
+ config = XLMRobertaConfig()
+ config.attention_probs_dropout_prob= 0.1
+ config.bos_token_id=0
+ config.eos_token_id=2
+ config.hidden_act='gelu'
+ config.hidden_dropout_prob=0.1
+ config.hidden_size=1024
+ config.initializer_range=0.02
+ config.intermediate_size=4096
+ config.layer_norm_eps=1e-05
+ config.max_position_embeddings=514
+
+ config.num_attention_heads=16
+ config.num_hidden_layers=24
+ config.output_past=True
+ config.pad_token_id=1
+ config.position_embedding_type= "absolute"
+
+ config.type_vocab_size= 1
+ config.use_cache=True
+ config.vocab_size= 250002
+ config.project_dim = 768
+ config.learn_encoder = False
+ super().__init__(config)
+ self.roberta = XLMRobertaModel(config)
+ self.transformation = nn.Linear(config.hidden_size,config.project_dim)
+ self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+ self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large')
+ self.pooler = lambda x: x[:,0]
+ self.post_init()
+
+ def encode(self,c):
+ device = next(self.parameters()).device
+ text = self.tokenizer(c,
+ truncation=True,
+ max_length=77,
+ return_length=False,
+ return_overflowing_tokens=False,
+ padding="max_length",
+ return_tensors="pt")
+ text["input_ids"] = torch.tensor(text["input_ids"]).to(device)
+ text["attention_mask"] = torch.tensor(
+ text['attention_mask']).to(device)
+ features = self(**text)
+ return features['projection_state']
+
+ def forward(
+ self,
+ input_ids: Optional[torch.Tensor] = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ token_type_ids: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.Tensor] = None,
+ head_mask: Optional[torch.Tensor] = None,
+ inputs_embeds: Optional[torch.Tensor] = None,
+ encoder_hidden_states: Optional[torch.Tensor] = None,
+ encoder_attention_mask: Optional[torch.Tensor] = None,
+ output_attentions: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ ) :
+ r"""
+ """
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+
+ outputs = self.roberta(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ token_type_ids=token_type_ids,
+ position_ids=position_ids,
+ head_mask=head_mask,
+ inputs_embeds=inputs_embeds,
+ encoder_hidden_states=encoder_hidden_states,
+ encoder_attention_mask=encoder_attention_mask,
+ output_attentions=output_attentions,
+ output_hidden_states=True,
+ return_dict=return_dict,
+ )
+
+ # last module outputs
+ sequence_output = outputs[0]
+
+
+ # project every module
+ sequence_output_ln = self.pre_LN(sequence_output)
+
+ # pooler
+ pooler_output = self.pooler(sequence_output_ln)
+ pooler_output = self.transformation(pooler_output)
+ projection_state = self.transformation(outputs.last_hidden_state)
+
+ return {
+ 'pooler_output':pooler_output,
+ 'last_hidden_state':outputs.last_hidden_state,
+ 'hidden_states':outputs.hidden_states,
+ 'attentions':outputs.attentions,
+ 'projection_state':projection_state,
+ 'sequence_out': sequence_output
+ }
+
+
+class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
+ base_model_prefix = 'roberta'
+ config_class= RobertaSeriesConfig \ No newline at end of file
diff --git a/v2-inference-v.yaml b/v2-inference-v.yaml
new file mode 100644
index 00000000..513cd635
--- /dev/null
+++ b/v2-inference-v.yaml
@@ -0,0 +1,68 @@
+model:
+ base_learning_rate: 1.0e-4
+ target: ldm.models.diffusion.ddpm.LatentDiffusion
+ params:
+ parameterization: "v"
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "jpg"
+ cond_stage_key: "txt"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False # we set this to false because this is an inference only config
+
+ unet_config:
+ target: ldm.modules.diffusionmodules.openaimodel.UNetModel
+ params:
+ use_checkpoint: True
+ use_fp16: True
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_head_channels: 64 # need to fix for flash-attn
+ use_spatial_transformer: True
+ use_linear_in_transformer: True
+ transformer_depth: 1
+ context_dim: 1024
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ #attn_type: "vanilla-xformers"
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder
+ params:
+ freeze: True
+ layer: "penultimate" \ No newline at end of file
generated by cgit v1.2.3 (git 2.25.1) at 2025-04-28 16:42:02 +0000