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import gc
import os
from typing import Dict, List, Tuple, Union
from PIL import Image, ImageFilter
from controlnet_aux import LineartDetector
from diffusers import (
ControlNetModel,
StableDiffusionXLControlNetPipeline,
UNet2DConditionModel,
)
from huggingface_hub import hf_hub_download
import safetensors
import torch
from tqdm import tqdm
from transformers import AutoTokenizer, CLIPTextModelWithProjection
# Base models
SDXL_REPO = "stabilityai/stable-diffusion-xl-base-1.0"
DPO_REPO = "mhdang/dpo-sdxl-text2image-v1"
JN_REPO = "RunDiffusion/Juggernaut-XL-v9"
JSDXL_REPO = "stabilityai/japanese-stable-diffusion-xl"
# Evo-Ukiyoe
UKIYOE_REPO = "SakanaAI/Evo-Ukiyoe-v1"
# Evo-Nishikie
NISHIKIE_REPO = "SakanaAI/Evo-Nishikie-v1"
class EvoNishikieConditioningImageProcessor:
def __init__(self, device="cpu"):
self.lineart_detector = LineartDetector.from_pretrained("lllyasviel/Annotators").to(device)
self.image_filter = ImageFilter.MedianFilter(size=3)
def __call__(self, original_image: Image.Image) -> Image.Image:
lineart_image = self.lineart_detector(original_image, coarse=False, image_resolution=1024)
lineart_image_filtered = lineart_image.filter(self.image_filter)
conditioning_image = lineart_image_filtered.point(lambda p: 255 if p > 40 else 0).convert("L")
return conditioning_image
def load_state_dict(checkpoint_file: Union[str, os.PathLike], device: str = "cpu"):
file_extension = os.path.basename(checkpoint_file).split(".")[-1]
if file_extension == "safetensors":
return safetensors.torch.load_file(checkpoint_file, device=device)
else:
return torch.load(checkpoint_file, map_location=device)
def load_from_pretrained(
repo_id,
filename="diffusion_pytorch_model.fp16.safetensors",
subfolder="unet",
device="cuda",
) -> Dict[str, torch.Tensor]:
return load_state_dict(
hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder,
),
device=device,
)
def reshape_weight_task_tensors(task_tensors, weights):
"""
Reshapes `weights` to match the shape of `task_tensors` by unsqueezing in the remaining dimensions.
Args:
task_tensors (`torch.Tensor`): The tensors that will be used to reshape `weights`.
weights (`torch.Tensor`): The tensor to be reshaped.
Returns:
`torch.Tensor`: The reshaped tensor.
"""
new_shape = weights.shape + (1,) * (task_tensors.dim() - weights.dim())
weights = weights.view(new_shape)
return weights
def linear(task_tensors: List[torch.Tensor], weights: torch.Tensor) -> torch.Tensor:
"""
Merge the task tensors using `linear`.
Args:
task_tensors(`List[torch.Tensor]`):The task tensors to merge.
weights (`torch.Tensor`):The weights of the task tensors.
Returns:
`torch.Tensor`: The merged tensor.
"""
task_tensors = torch.stack(task_tensors, dim=0)
# weighted task tensors
weights = reshape_weight_task_tensors(task_tensors, weights)
weighted_task_tensors = task_tensors * weights
mixed_task_tensors = weighted_task_tensors.sum(dim=0)
return mixed_task_tensors
def merge_models(task_tensors, weights):
keys = list(task_tensors[0].keys())
weights = torch.tensor(weights, device=task_tensors[0][keys[0]].device)
state_dict = {}
for key in tqdm(keys, desc="Merging"):
w_list = []
for i, sd in enumerate(task_tensors):
w = sd.pop(key)
w_list.append(w)
new_w = linear(task_tensors=w_list, weights=weights)
state_dict[key] = new_w
return state_dict
def split_conv_attn(weights):
attn_tensors = {}
conv_tensors = {}
for key in list(weights.keys()):
if any(k in key for k in ["to_k", "to_q", "to_v", "to_out.0"]):
attn_tensors[key] = weights.pop(key)
else:
conv_tensors[key] = weights.pop(key)
return {"conv": conv_tensors, "attn": attn_tensors}
def load_evo_nishikie(device="cuda", processor_device="cpu") -> Tuple[
StableDiffusionXLControlNetPipeline, EvoNishikieConditioningImageProcessor
]:
# Load base models
sdxl_weights = split_conv_attn(load_from_pretrained(SDXL_REPO, device=device))
dpo_weights = split_conv_attn(
load_from_pretrained(
DPO_REPO, "diffusion_pytorch_model.safetensors", device=device
)
)
jn_weights = split_conv_attn(load_from_pretrained(JN_REPO, device=device))
jsdxl_weights = split_conv_attn(load_from_pretrained(JSDXL_REPO, device=device))
# Merge base models
tensors = [sdxl_weights, dpo_weights, jn_weights, jsdxl_weights]
new_conv = merge_models(
[sd["conv"] for sd in tensors],
[
0.15928833971605916,
0.1032449268871776,
0.6503217149752791,
0.08714501842148402,
],
)
new_attn = merge_models(
[sd["attn"] for sd in tensors],
[
0.1877279276437178,
0.20014114603909822,
0.3922685507065275,
0.2198623756106564,
],
)
# Delete no longer needed variables to free
del sdxl_weights, dpo_weights, jn_weights, jsdxl_weights
gc.collect()
if "cuda" in device:
torch.cuda.empty_cache()
# Instantiate UNet
unet_config = UNet2DConditionModel.load_config(SDXL_REPO, subfolder="unet")
unet = UNet2DConditionModel.from_config(unet_config).to(device=device)
unet.load_state_dict({**new_conv, **new_attn})
# Load other modules
text_encoder = CLIPTextModelWithProjection.from_pretrained(
JSDXL_REPO, subfolder="text_encoder", torch_dtype=torch.float16, variant="fp16",
)
tokenizer = AutoTokenizer.from_pretrained(
JSDXL_REPO, subfolder="tokenizer", use_fast=False,
)
# Load Evo-Nishikie weights
controlnet = ControlNetModel.from_pretrained(
NISHIKIE_REPO, torch_dtype=torch.float16, device=device,
)
# Load pipeline
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
SDXL_REPO,
unet=unet,
text_encoder=text_encoder,
tokenizer=tokenizer,
controlnet=controlnet,
torch_dtype=torch.float16,
variant="fp16",
)
# Load Evo-Ukiyoe weights
pipe.load_lora_weights(UKIYOE_REPO)
pipe.fuse_lora(lora_scale=1.0)
pipe = pipe.to(device, dtype=torch.float16)
# Load conditioning image processor
processor = EvoNishikieConditioningImageProcessor(device=processor_device)
return pipe, processor
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