204 lines
9.6 KiB
Python
204 lines
9.6 KiB
Python
import torch
|
|
import argparse
|
|
import io
|
|
|
|
import requests
|
|
import torch
|
|
from omegaconf import OmegaConf
|
|
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
|
|
assign_to_checkpoint,
|
|
conv_attn_to_linear,
|
|
create_vae_diffusers_config,
|
|
renew_vae_attention_paths,
|
|
renew_vae_resnet_paths,
|
|
)
|
|
|
|
from diffusers import (
|
|
AutoencoderKL,
|
|
DDIMScheduler,
|
|
DDPMScheduler,
|
|
DEISMultistepScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
DPMSolverSinglestepScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
EulerDiscreteScheduler,
|
|
HeunDiscreteScheduler,
|
|
KDPM2AncestralDiscreteScheduler,
|
|
KDPM2DiscreteScheduler,
|
|
UniPCMultistepScheduler,
|
|
)
|
|
|
|
SCHEDULERS = {
|
|
'DDIM' : DDIMScheduler,
|
|
'DDPM' : DDPMScheduler,
|
|
'DEISMultistep' : DEISMultistepScheduler,
|
|
'DPMSolverMultistep' : DPMSolverMultistepScheduler,
|
|
'DPMSolverSinglestep' : DPMSolverSinglestepScheduler,
|
|
'EulerAncestralDiscrete' : EulerAncestralDiscreteScheduler,
|
|
'EulerDiscrete' : EulerDiscreteScheduler,
|
|
'HeunDiscrete' : HeunDiscreteScheduler,
|
|
'KDPM2AncestralDiscrete' : KDPM2AncestralDiscreteScheduler,
|
|
'KDPM2Discrete' : KDPM2DiscreteScheduler,
|
|
'UniPCMultistep' : UniPCMultistepScheduler
|
|
}
|
|
|
|
def token_auto_concat_embeds(pipe, positive, negative):
|
|
max_length = pipe.tokenizer.model_max_length
|
|
positive_length = pipe.tokenizer(positive, return_tensors="pt").input_ids.shape[-1]
|
|
negative_length = pipe.tokenizer(negative, return_tensors="pt").input_ids.shape[-1]
|
|
|
|
print(f'Token length is model maximum: {max_length}, positive length: {positive_length}, negative length: {negative_length}.')
|
|
if max_length < positive_length or max_length < negative_length:
|
|
print('Concatenated embedding.')
|
|
if positive_length > negative_length:
|
|
positive_ids = pipe.tokenizer(positive, return_tensors="pt").input_ids.to("cuda")
|
|
negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=positive_ids.shape[-1], return_tensors="pt").input_ids.to("cuda")
|
|
else:
|
|
negative_ids = pipe.tokenizer(negative, return_tensors="pt").input_ids.to("cuda")
|
|
positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=negative_ids.shape[-1], return_tensors="pt").input_ids.to("cuda")
|
|
else:
|
|
positive_ids = pipe.tokenizer(positive, truncation=False, padding="max_length", max_length=max_length, return_tensors="pt").input_ids.to("cuda")
|
|
negative_ids = pipe.tokenizer(negative, truncation=False, padding="max_length", max_length=max_length, return_tensors="pt").input_ids.to("cuda")
|
|
|
|
positive_concat_embeds = []
|
|
negative_concat_embeds = []
|
|
for i in range(0, positive_ids.shape[-1], max_length):
|
|
positive_concat_embeds.append(pipe.text_encoder(positive_ids[:, i: i + max_length])[0])
|
|
negative_concat_embeds.append(pipe.text_encoder(negative_ids[:, i: i + max_length])[0])
|
|
|
|
positive_prompt_embeds = torch.cat(positive_concat_embeds, dim=1)
|
|
negative_prompt_embeds = torch.cat(negative_concat_embeds, dim=1)
|
|
return positive_prompt_embeds, negative_prompt_embeds
|
|
|
|
|
|
def custom_convert_ldm_vae_checkpoint(checkpoint, config):
|
|
vae_state_dict = checkpoint
|
|
|
|
new_checkpoint = {}
|
|
|
|
new_checkpoint["encoder.conv_in.weight"] = vae_state_dict["encoder.conv_in.weight"]
|
|
new_checkpoint["encoder.conv_in.bias"] = vae_state_dict["encoder.conv_in.bias"]
|
|
new_checkpoint["encoder.conv_out.weight"] = vae_state_dict["encoder.conv_out.weight"]
|
|
new_checkpoint["encoder.conv_out.bias"] = vae_state_dict["encoder.conv_out.bias"]
|
|
new_checkpoint["encoder.conv_norm_out.weight"] = vae_state_dict["encoder.norm_out.weight"]
|
|
new_checkpoint["encoder.conv_norm_out.bias"] = vae_state_dict["encoder.norm_out.bias"]
|
|
|
|
new_checkpoint["decoder.conv_in.weight"] = vae_state_dict["decoder.conv_in.weight"]
|
|
new_checkpoint["decoder.conv_in.bias"] = vae_state_dict["decoder.conv_in.bias"]
|
|
new_checkpoint["decoder.conv_out.weight"] = vae_state_dict["decoder.conv_out.weight"]
|
|
new_checkpoint["decoder.conv_out.bias"] = vae_state_dict["decoder.conv_out.bias"]
|
|
new_checkpoint["decoder.conv_norm_out.weight"] = vae_state_dict["decoder.norm_out.weight"]
|
|
new_checkpoint["decoder.conv_norm_out.bias"] = vae_state_dict["decoder.norm_out.bias"]
|
|
|
|
new_checkpoint["quant_conv.weight"] = vae_state_dict["quant_conv.weight"]
|
|
new_checkpoint["quant_conv.bias"] = vae_state_dict["quant_conv.bias"]
|
|
new_checkpoint["post_quant_conv.weight"] = vae_state_dict["post_quant_conv.weight"]
|
|
new_checkpoint["post_quant_conv.bias"] = vae_state_dict["post_quant_conv.bias"]
|
|
|
|
# Retrieves the keys for the encoder down blocks only
|
|
num_down_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "encoder.down" in layer})
|
|
down_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"down.{layer_id}" in key] for layer_id in range(num_down_blocks)
|
|
}
|
|
|
|
# Retrieves the keys for the decoder up blocks only
|
|
num_up_blocks = len({".".join(layer.split(".")[:3]) for layer in vae_state_dict if "decoder.up" in layer})
|
|
up_blocks = {
|
|
layer_id: [key for key in vae_state_dict if f"up.{layer_id}" in key] for layer_id in range(num_up_blocks)
|
|
}
|
|
|
|
for i in range(num_down_blocks):
|
|
resnets = [key for key in down_blocks[i] if f"down.{i}" in key and f"down.{i}.downsample" not in key]
|
|
|
|
if f"encoder.down.{i}.downsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.weight"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.weight"
|
|
)
|
|
new_checkpoint[f"encoder.down_blocks.{i}.downsamplers.0.conv.bias"] = vae_state_dict.pop(
|
|
f"encoder.down.{i}.downsample.conv.bias"
|
|
)
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"down.{i}.block", "new": f"down_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "encoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"encoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "encoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
|
|
for i in range(num_up_blocks):
|
|
block_id = num_up_blocks - 1 - i
|
|
resnets = [
|
|
key for key in up_blocks[block_id] if f"up.{block_id}" in key and f"up.{block_id}.upsample" not in key
|
|
]
|
|
|
|
if f"decoder.up.{block_id}.upsample.conv.weight" in vae_state_dict:
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.weight"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.weight"
|
|
]
|
|
new_checkpoint[f"decoder.up_blocks.{i}.upsamplers.0.conv.bias"] = vae_state_dict[
|
|
f"decoder.up.{block_id}.upsample.conv.bias"
|
|
]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"up.{block_id}.block", "new": f"up_blocks.{i}.resnets"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_resnets = [key for key in vae_state_dict if "decoder.mid.block" in key]
|
|
num_mid_res_blocks = 2
|
|
for i in range(1, num_mid_res_blocks + 1):
|
|
resnets = [key for key in mid_resnets if f"decoder.mid.block_{i}" in key]
|
|
|
|
paths = renew_vae_resnet_paths(resnets)
|
|
meta_path = {"old": f"mid.block_{i}", "new": f"mid_block.resnets.{i - 1}"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
|
|
mid_attentions = [key for key in vae_state_dict if "decoder.mid.attn" in key]
|
|
paths = renew_vae_attention_paths(mid_attentions)
|
|
meta_path = {"old": "mid.attn_1", "new": "mid_block.attentions.0"}
|
|
assign_to_checkpoint(paths, new_checkpoint, vae_state_dict, additional_replacements=[meta_path], config=config)
|
|
conv_attn_to_linear(new_checkpoint)
|
|
return new_checkpoint
|
|
|
|
|
|
def vae_pt_to_vae_diffuser(
|
|
checkpoint_path: str,
|
|
output_path: str,
|
|
):
|
|
# Only support V1
|
|
r = requests.get(
|
|
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
|
|
)
|
|
io_obj = io.BytesIO(r.content)
|
|
|
|
original_config = OmegaConf.load(io_obj)
|
|
image_size = 512
|
|
device = "cuda" if torch.cuda.is_available() else "cpu"
|
|
if checkpoint_path.endswith("safetensors"):
|
|
from safetensors import safe_open
|
|
|
|
checkpoint = {}
|
|
with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
|
|
for key in f.keys():
|
|
checkpoint[key] = f.get_tensor(key)
|
|
else:
|
|
checkpoint = torch.load(checkpoint_path, map_location=device)["state_dict"]
|
|
|
|
# Convert the VAE model.
|
|
vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
|
|
converted_vae_checkpoint = custom_convert_ldm_vae_checkpoint(checkpoint, vae_config)
|
|
|
|
vae = AutoencoderKL(**vae_config)
|
|
vae.load_state_dict(converted_vae_checkpoint)
|
|
vae.save_pretrained(output_path) |