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Added Vision Languate Adapter usage for pixtral vd adapter

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This commit is contained in:
Jaret Burkett
2024-09-29 19:39:56 -06:00
parent b4f64de4c2
commit f05224970f
2 changed files with 41 additions and 18 deletions
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8
toolkit/custom_adapter.py
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@@ -744,6 +744,7 @@ class CustomAdapter(torch.nn.Module):
batch_size=1, batch_size=1,
) -> PromptEmbeds: ) -> PromptEmbeds:
if self.adapter_type == 'ilora' or self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter': if self.adapter_type == 'ilora' or self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter':
skip_unconditional = self.sd_ref().is_flux
if tensors_0_1 is None: if tensors_0_1 is None:
tensors_0_1 = self.get_empty_clip_image(batch_size) tensors_0_1 = self.get_empty_clip_image(batch_size)
has_been_preprocessed = True has_been_preprocessed = True
@@ -797,7 +798,7 @@ class CustomAdapter(torch.nn.Module):
batch_size = clip_image.shape[0] batch_size = clip_image.shape[0]
if self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter': if (self.adapter_type == 'vision_direct' or self.adapter_type == 'te_augmenter') and not skip_unconditional:
# add an unconditional so we can save it # add an unconditional so we can save it
unconditional = self.get_empty_clip_image(batch_size, shape=clip_image.shape).to( unconditional = self.get_empty_clip_image(batch_size, shape=clip_image.shape).to(
clip_image.device, dtype=clip_image.dtype clip_image.device, dtype=clip_image.dtype
@@ -895,7 +896,10 @@ class CustomAdapter(torch.nn.Module):
# save them to the conditional and unconditional # save them to the conditional and unconditional
try: try:
self.unconditional_embeds, self.conditional_embeds = clip_image_embeds.chunk(2, dim=0) if skip_unconditional:
self.unconditional_embeds, self.conditional_embeds = None, clip_image_embeds
else:
self.unconditional_embeds, self.conditional_embeds = clip_image_embeds.chunk(2, dim=0)
except ValueError: except ValueError:
raise ValueError(f"could not split the clip image embeds into 2. Got shape: {clip_image_embeds.shape}") raise ValueError(f"could not split the clip image embeds into 2. Got shape: {clip_image_embeds.shape}")

51
toolkit/models/vd_adapter.py
View File

@@ -9,7 +9,7 @@ from collections import OrderedDict
from diffusers import Transformer2DModel, FluxTransformer2DModel from diffusers import Transformer2DModel, FluxTransformer2DModel
from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection from transformers import T5EncoderModel, CLIPTextModel, CLIPTokenizer, T5Tokenizer, CLIPVisionModelWithProjection
from toolkit.models.pixtral_vision import PixtralVisionEncoder, PixtralVisionImagePreprocessor from toolkit.models.pixtral_vision import PixtralVisionEncoder, PixtralVisionImagePreprocessor, VisionLanguageAdapter
from toolkit.config_modules import AdapterConfig from toolkit.config_modules import AdapterConfig
from toolkit.paths import REPOS_ROOT from toolkit.paths import REPOS_ROOT
@@ -462,11 +462,23 @@ class VisionDirectAdapter(torch.nn.Module):
self.sd_ref: weakref.ref = weakref.ref(sd) self.sd_ref: weakref.ref = weakref.ref(sd)
self.config: AdapterConfig = adapter.config self.config: AdapterConfig = adapter.config
self.vision_model_ref: weakref.ref = weakref.ref(vision_model) self.vision_model_ref: weakref.ref = weakref.ref(vision_model)
self.resampler = None
is_pixtral = self.config.image_encoder_arch == "pixtral"
if adapter.config.clip_layer == "image_embeds": if adapter.config.clip_layer == "image_embeds":
self.token_size = vision_model.config.projection_dim self.token_size = vision_model.config.projection_dim
else: else:
self.token_size = vision_model.config.hidden_size self.token_size = vision_model.config.hidden_size
self.mid_size = self.token_size
# if pixtral, use cross attn dim for more sparse representation
if is_pixtral:
if is_flux:
hidden_size = 3072
else:
hidden_size = sd.unet.config['cross_attention_dim']
self.mid_size = hidden_size
# init adapter modules # init adapter modules
attn_procs = {} attn_procs = {}
@@ -487,9 +499,10 @@ class VisionDirectAdapter(torch.nn.Module):
for i, module in transformer.transformer_blocks.named_children(): for i, module in transformer.transformer_blocks.named_children():
attn_processor_keys.append(f"transformer_blocks.{i}.attn") attn_processor_keys.append(f"transformer_blocks.{i}.attn")
# single transformer blocks do not have cross attn, but we will do them anyway if not self.config.flux_only_double:
for i, module in transformer.single_transformer_blocks.named_children(): # single transformer blocks do not have cross attn, but we will do them anyway
attn_processor_keys.append(f"single_transformer_blocks.{i}.attn") for i, module in transformer.single_transformer_blocks.named_children():
attn_processor_keys.append(f"single_transformer_blocks.{i}.attn")
else: else:
attn_processor_keys = list(sd.unet.attn_processors.keys()) attn_processor_keys = list(sd.unet.attn_processors.keys())
@@ -532,27 +545,27 @@ class VisionDirectAdapter(torch.nn.Module):
to_v_adapter = unet_sd[layer_name + ".to_v.weight"] to_v_adapter = unet_sd[layer_name + ".to_v.weight"]
# add zero padding to the adapter # add zero padding to the adapter
if to_k_adapter.shape[1] < self.token_size: if to_k_adapter.shape[1] < self.mid_size:
to_k_adapter = torch.cat([ to_k_adapter = torch.cat([
to_k_adapter, to_k_adapter,
torch.randn(to_k_adapter.shape[0], self.token_size - to_k_adapter.shape[1]).to( torch.randn(to_k_adapter.shape[0], self.mid_size - to_k_adapter.shape[1]).to(
to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01 to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
], ],
dim=1 dim=1
) )
to_v_adapter = torch.cat([ to_v_adapter = torch.cat([
to_v_adapter, to_v_adapter,
torch.randn(to_v_adapter.shape[0], self.token_size - to_v_adapter.shape[1]).to( torch.randn(to_v_adapter.shape[0], self.mid_size - to_v_adapter.shape[1]).to(
to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01 to_k_adapter.device, dtype=to_k_adapter.dtype) * 0.01
], ],
dim=1 dim=1
) )
elif to_k_adapter.shape[1] > self.token_size: elif to_k_adapter.shape[1] > self.mid_size:
to_k_adapter = to_k_adapter[:, :self.token_size] to_k_adapter = to_k_adapter[:, :self.mid_size]
to_v_adapter = to_v_adapter[:, :self.token_size] to_v_adapter = to_v_adapter[:, :self.mid_size]
# if is_pixart: # if is_pixart:
# to_k_bias = to_k_bias[:self.token_size] # to_k_bias = to_k_bias[:self.mid_size]
# to_v_bias = to_v_bias[:self.token_size] # to_v_bias = to_v_bias[:self.mid_size]
else: else:
to_k_adapter = to_k_adapter to_k_adapter = to_k_adapter
to_v_adapter = to_v_adapter to_v_adapter = to_v_adapter
@@ -574,7 +587,7 @@ class VisionDirectAdapter(torch.nn.Module):
cross_attention_dim=cross_attention_dim, cross_attention_dim=cross_attention_dim,
scale=1.0, scale=1.0,
adapter=self, adapter=self,
adapter_hidden_size=self.token_size, adapter_hidden_size=self.mid_size,
has_bias=False, has_bias=False,
block_idx=current_idx block_idx=current_idx
) )
@@ -584,7 +597,7 @@ class VisionDirectAdapter(torch.nn.Module):
cross_attention_dim=cross_attention_dim, cross_attention_dim=cross_attention_dim,
scale=1.0, scale=1.0,
adapter=self, adapter=self,
adapter_hidden_size=self.token_size, adapter_hidden_size=self.mid_size,
has_bias=False, has_bias=False,
) )
current_idx += 1 current_idx += 1
@@ -655,9 +668,15 @@ class VisionDirectAdapter(torch.nn.Module):
self.resampler = MLPR( self.resampler = MLPR(
in_dim=self.token_size, in_dim=self.token_size,
in_channels=max_seq_len, in_channels=max_seq_len,
out_dim=self.token_size, out_dim=self.mid_size,
out_channels=self.config.num_tokens, out_channels=self.config.num_tokens,
) )
elif self.config.image_encoder_arch == "pixtral":
self.resampler = VisionLanguageAdapter(
in_dim=self.token_size,
out_dim=self.mid_size,
)
def state_dict(self, destination=None, prefix='', keep_vars=False): def state_dict(self, destination=None, prefix='', keep_vars=False):
if self.config.train_scaler: if self.config.train_scaler:
@@ -678,7 +697,7 @@ class VisionDirectAdapter(torch.nn.Module):
# todo remove this when we have a real solution # todo remove this when we have a real solution
if self.block_scaler is not None and self.block_scaler.dtype != torch.float32: if self.block_scaler is not None and self.block_scaler.dtype != torch.float32:
self.block_scaler.data = self.block_scaler.data.to(torch.float32) self.block_scaler.data = self.block_scaler.data.to(torch.float32)
if self.config.num_tokens is not None: if self.resampler is not None:
input = self.resampler(input) input = self.resampler(input)
return input return input