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Copy pathconvert_blipdiffusion_to_diffusers.py
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344 lines (297 loc) · 13.1 KB
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"""
This script requires you to build `LAVIS` from source, since the pip version doesn't have BLIP Diffusion. Follow instructions here: https://github.com/salesforce/LAVIS/tree/main.
"""
import argparse
import os
import tempfile
import torch
from lavis.models import load_model_and_preprocess
from transformers import CLIPTokenizer
from transformers.models.blip_2.configuration_blip_2 import Blip2Config
from diffusers import (
AutoencoderKL,
PNDMScheduler,
UNet2DConditionModel,
)
from diffusers.pipelines import BlipDiffusionPipeline
from diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
from diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
from diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
BLIP2_CONFIG = {
"vision_config": {
"hidden_size": 1024,
"num_hidden_layers": 23,
"num_attention_heads": 16,
"image_size": 224,
"patch_size": 14,
"intermediate_size": 4096,
"hidden_act": "quick_gelu",
},
"qformer_config": {
"cross_attention_frequency": 1,
"encoder_hidden_size": 1024,
"vocab_size": 30523,
},
"num_query_tokens": 16,
}
blip2config = Blip2Config(**BLIP2_CONFIG)
def qformer_model_from_original_config():
qformer = Blip2QFormerModel(blip2config)
return qformer
def embeddings_from_original_checkpoint(model, diffuser_embeddings_prefix, original_embeddings_prefix):
embeddings = {}
embeddings.update(
{
f"{diffuser_embeddings_prefix}.word_embeddings.weight": model[
f"{original_embeddings_prefix}.word_embeddings.weight"
]
}
)
embeddings.update(
{
f"{diffuser_embeddings_prefix}.position_embeddings.weight": model[
f"{original_embeddings_prefix}.position_embeddings.weight"
]
}
)
embeddings.update(
{f"{diffuser_embeddings_prefix}.LayerNorm.weight": model[f"{original_embeddings_prefix}.LayerNorm.weight"]}
)
embeddings.update(
{f"{diffuser_embeddings_prefix}.LayerNorm.bias": model[f"{original_embeddings_prefix}.LayerNorm.bias"]}
)
return embeddings
def proj_layer_from_original_checkpoint(model, diffuser_proj_prefix, original_proj_prefix):
proj_layer = {}
proj_layer.update({f"{diffuser_proj_prefix}.dense1.weight": model[f"{original_proj_prefix}.dense1.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense1.bias": model[f"{original_proj_prefix}.dense1.bias"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense2.weight": model[f"{original_proj_prefix}.dense2.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.dense2.bias": model[f"{original_proj_prefix}.dense2.bias"]})
proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.weight": model[f"{original_proj_prefix}.LayerNorm.weight"]})
proj_layer.update({f"{diffuser_proj_prefix}.LayerNorm.bias": model[f"{original_proj_prefix}.LayerNorm.bias"]})
return proj_layer
def attention_from_original_checkpoint(model, diffuser_attention_prefix, original_attention_prefix):
attention = {}
attention.update(
{
f"{diffuser_attention_prefix}.attention.query.weight": model[
f"{original_attention_prefix}.self.query.weight"
]
}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.query.bias": model[f"{original_attention_prefix}.self.query.bias"]}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.key.weight": model[f"{original_attention_prefix}.self.key.weight"]}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.key.bias": model[f"{original_attention_prefix}.self.key.bias"]}
)
attention.update(
{
f"{diffuser_attention_prefix}.attention.value.weight": model[
f"{original_attention_prefix}.self.value.weight"
]
}
)
attention.update(
{f"{diffuser_attention_prefix}.attention.value.bias": model[f"{original_attention_prefix}.self.value.bias"]}
)
attention.update(
{f"{diffuser_attention_prefix}.output.dense.weight": model[f"{original_attention_prefix}.output.dense.weight"]}
)
attention.update(
{f"{diffuser_attention_prefix}.output.dense.bias": model[f"{original_attention_prefix}.output.dense.bias"]}
)
attention.update(
{
f"{diffuser_attention_prefix}.output.LayerNorm.weight": model[
f"{original_attention_prefix}.output.LayerNorm.weight"
]
}
)
attention.update(
{
f"{diffuser_attention_prefix}.output.LayerNorm.bias": model[
f"{original_attention_prefix}.output.LayerNorm.bias"
]
}
)
return attention
def output_layers_from_original_checkpoint(model, diffuser_output_prefix, original_output_prefix):
output_layers = {}
output_layers.update({f"{diffuser_output_prefix}.dense.weight": model[f"{original_output_prefix}.dense.weight"]})
output_layers.update({f"{diffuser_output_prefix}.dense.bias": model[f"{original_output_prefix}.dense.bias"]})
output_layers.update(
{f"{diffuser_output_prefix}.LayerNorm.weight": model[f"{original_output_prefix}.LayerNorm.weight"]}
)
output_layers.update(
{f"{diffuser_output_prefix}.LayerNorm.bias": model[f"{original_output_prefix}.LayerNorm.bias"]}
)
return output_layers
def encoder_from_original_checkpoint(model, diffuser_encoder_prefix, original_encoder_prefix):
encoder = {}
for i in range(blip2config.qformer_config.num_hidden_layers):
encoder.update(
attention_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.attention", f"{original_encoder_prefix}.{i}.attention"
)
)
encoder.update(
attention_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.crossattention", f"{original_encoder_prefix}.{i}.crossattention"
)
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate.dense.weight": model[
f"{original_encoder_prefix}.{i}.intermediate.dense.weight"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate.dense.bias": model[
f"{original_encoder_prefix}.{i}.intermediate.dense.bias"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.weight": model[
f"{original_encoder_prefix}.{i}.intermediate_query.dense.weight"
]
}
)
encoder.update(
{
f"{diffuser_encoder_prefix}.{i}.intermediate_query.dense.bias": model[
f"{original_encoder_prefix}.{i}.intermediate_query.dense.bias"
]
}
)
encoder.update(
output_layers_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.output", f"{original_encoder_prefix}.{i}.output"
)
)
encoder.update(
output_layers_from_original_checkpoint(
model, f"{diffuser_encoder_prefix}.{i}.output_query", f"{original_encoder_prefix}.{i}.output_query"
)
)
return encoder
def visual_encoder_layer_from_original_checkpoint(model, diffuser_prefix, original_prefix):
visual_encoder_layer = {}
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.weight": model[f"{original_prefix}.ln_1.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm1.bias": model[f"{original_prefix}.ln_1.bias"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.weight": model[f"{original_prefix}.ln_2.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.layer_norm2.bias": model[f"{original_prefix}.ln_2.bias"]})
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.qkv.weight": model[f"{original_prefix}.attn.in_proj_weight"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.qkv.bias": model[f"{original_prefix}.attn.in_proj_bias"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.projection.weight": model[f"{original_prefix}.attn.out_proj.weight"]}
)
visual_encoder_layer.update(
{f"{diffuser_prefix}.self_attn.projection.bias": model[f"{original_prefix}.attn.out_proj.bias"]}
)
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.weight": model[f"{original_prefix}.mlp.c_fc.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc1.bias": model[f"{original_prefix}.mlp.c_fc.bias"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.weight": model[f"{original_prefix}.mlp.c_proj.weight"]})
visual_encoder_layer.update({f"{diffuser_prefix}.mlp.fc2.bias": model[f"{original_prefix}.mlp.c_proj.bias"]})
return visual_encoder_layer
def visual_encoder_from_original_checkpoint(model, diffuser_prefix, original_prefix):
visual_encoder = {}
visual_encoder.update(
{
f"{diffuser_prefix}.embeddings.class_embedding": model[f"{original_prefix}.class_embedding"]
.unsqueeze(0)
.unsqueeze(0)
}
)
visual_encoder.update(
{
f"{diffuser_prefix}.embeddings.position_embedding": model[
f"{original_prefix}.positional_embedding"
].unsqueeze(0)
}
)
visual_encoder.update(
{f"{diffuser_prefix}.embeddings.patch_embedding.weight": model[f"{original_prefix}.conv1.weight"]}
)
visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.weight": model[f"{original_prefix}.ln_pre.weight"]})
visual_encoder.update({f"{diffuser_prefix}.pre_layernorm.bias": model[f"{original_prefix}.ln_pre.bias"]})
for i in range(blip2config.vision_config.num_hidden_layers):
visual_encoder.update(
visual_encoder_layer_from_original_checkpoint(
model, f"{diffuser_prefix}.encoder.layers.{i}", f"{original_prefix}.transformer.resblocks.{i}"
)
)
visual_encoder.update({f"{diffuser_prefix}.post_layernorm.weight": model["blip.ln_vision.weight"]})
visual_encoder.update({f"{diffuser_prefix}.post_layernorm.bias": model["blip.ln_vision.bias"]})
return visual_encoder
def qformer_original_checkpoint_to_diffusers_checkpoint(model):
qformer_checkpoint = {}
qformer_checkpoint.update(embeddings_from_original_checkpoint(model, "embeddings", "blip.Qformer.bert.embeddings"))
qformer_checkpoint.update({"query_tokens": model["blip.query_tokens"]})
qformer_checkpoint.update(proj_layer_from_original_checkpoint(model, "proj_layer", "proj_layer"))
qformer_checkpoint.update(
encoder_from_original_checkpoint(model, "encoder.layer", "blip.Qformer.bert.encoder.layer")
)
qformer_checkpoint.update(visual_encoder_from_original_checkpoint(model, "visual_encoder", "blip.visual_encoder"))
return qformer_checkpoint
def get_qformer(model):
print("loading qformer")
qformer = qformer_model_from_original_config()
qformer_diffusers_checkpoint = qformer_original_checkpoint_to_diffusers_checkpoint(model)
load_checkpoint_to_model(qformer_diffusers_checkpoint, qformer)
print("done loading qformer")
return qformer
def load_checkpoint_to_model(checkpoint, model):
with tempfile.NamedTemporaryFile(delete=False) as file:
torch.save(checkpoint, file.name)
del checkpoint
model.load_state_dict(torch.load(file.name), strict=False)
os.remove(file.name)
def save_blip_diffusion_model(model, args):
qformer = get_qformer(model)
qformer.eval()
text_encoder = ContextCLIPTextModel.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="text_encoder"
)
vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae")
unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
vae.eval()
text_encoder.eval()
scheduler = PNDMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
set_alpha_to_one=False,
skip_prk_steps=True,
)
tokenizer = CLIPTokenizer.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="tokenizer")
image_processor = BlipImageProcessor()
blip_diffusion = BlipDiffusionPipeline(
tokenizer=tokenizer,
text_encoder=text_encoder,
vae=vae,
unet=unet,
scheduler=scheduler,
qformer=qformer,
image_processor=image_processor,
)
blip_diffusion.save_pretrained(args.checkpoint_path)
def main(args):
model, _, _ = load_model_and_preprocess("blip_diffusion", "base", device="cpu", is_eval=True)
save_blip_diffusion_model(model.state_dict(), args)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--checkpoint_path", default=None, type=str, required=True, help="Path to the output model.")
args = parser.parse_args()
main(args)
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