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https://github.com/finegrain-ai/refiners.git
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add IP-Adapter support for SD 1.5
Official repo: https://github.com/tencent-ailab/IP-Adapter
This commit is contained in:
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111
scripts/conversion/convert_diffusers_ip_adapter.py
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111
scripts/conversion/convert_diffusers_ip_adapter.py
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@ -0,0 +1,111 @@
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from pathlib import Path
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from typing import Any
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import argparse
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import torch
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from refiners.foundationals.latent_diffusion import SD1UNet
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from refiners.foundationals.latent_diffusion.stable_diffusion_1 import SD1IPAdapter
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from refiners.fluxion.utils import save_to_safetensors
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def main() -> None:
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parser = argparse.ArgumentParser(description="Converts a IP-Adapter diffusers model to refiners.")
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parser.add_argument(
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"--from",
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type=str,
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dest="source_path",
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default="ip-adapter_sd15.bin",
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help="Path to the source model. (default: 'ip-adapter_sd15.bin').",
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)
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parser.add_argument(
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"--to",
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type=str,
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dest="output_path",
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default="ip-adapter_sd15.safetensors",
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help="Path to save the converted model. (default: 'ip-adapter_sd15.safetensors').",
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)
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parser.add_argument("--verbose", action="store_true", dest="verbose")
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parser.add_argument("--half", action="store_true", dest="half")
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args = parser.parse_args()
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if args.output_path is None:
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args.output_path = f"{Path(args.source_path).stem}.safetensors"
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weights: dict[str, Any] = torch.load(f=args.source_path, map_location="cpu") # type: ignore
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assert isinstance(weights, dict)
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assert sorted(weights.keys()) == ["image_proj", "ip_adapter"]
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unet = SD1UNet(in_channels=4)
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ip_adapter = SD1IPAdapter(target=unet)
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# Manual conversion to avoid any runtime dependency on IP-Adapter[1] custom classes
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# [1]: https://github.com/tencent-ailab/IP-Adapter
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state_dict: dict[str, torch.Tensor] = {}
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image_proj_weights = weights["image_proj"]
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image_proj_state_dict: dict[str, torch.Tensor] = {
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"Linear.weight": image_proj_weights["proj.weight"],
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"Linear.bias": image_proj_weights["proj.bias"],
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"LayerNorm.weight": image_proj_weights["norm.weight"],
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"LayerNorm.bias": image_proj_weights["norm.bias"],
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}
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ip_adapter.image_proj.load_state_dict(state_dict=image_proj_state_dict)
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for k, v in image_proj_state_dict.items():
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state_dict[f"image_proj.{k}"] = v
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ip_adapter_weights: dict[str, torch.Tensor] = weights["ip_adapter"]
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assert len(ip_adapter.sub_adapters) == len(ip_adapter_weights.keys()) // 2
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# Running:
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#
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# from diffusers import UNet2DConditionModel
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# unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
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# for k in unet.attn_processors.keys():
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# print(k)
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#
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# Gives:
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#
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# down_blocks.0.attentions.0.transformer_blocks.0.attn1.processor
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# down_blocks.0.attentions.0.transformer_blocks.0.attn2.processor
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# ...
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# down_blocks.2.attentions.1.transformer_blocks.0.attn2.processor
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# up_blocks.1.attentions.0.transformer_blocks.0.attn1.processor
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# up_blocks.1.attentions.0.transformer_blocks.0.attn2.processor
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# ...
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# up_blocks.3.attentions.2.transformer_blocks.0.attn2.processor
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# mid_block.attentions.0.transformer_blocks.0.attn1.processor
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# mid_block.attentions.0.transformer_blocks.0.attn2.processor
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#
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# With attn1=self-attention and attn2=cross-attention, and middle block in last position. So in terms of increasing
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# indices:
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#
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# DownBlocks -> [1, 3, 5, 7, 9, 11]
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# MiddleBlock -> [31]
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# UpBlocks -> [13, 15, 17, 19, 21, 23, 25, 27, 29]
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cross_attn_mapping: list[int] = [1, 3, 5, 7, 9, 11, 31, 13, 15, 17, 19, 21, 23, 25, 27, 29]
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for i, cross_attn in enumerate(ip_adapter.sub_adapters):
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cross_attn_index = cross_attn_mapping[i]
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k_ip = f"{cross_attn_index}.to_k_ip.weight"
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v_ip = f"{cross_attn_index}.to_v_ip.weight"
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# Ignore Wq, Wk, Wv and Proj (hence strict=False): at runtime, they will be part of the UNet original weights
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cross_attn_state_dict: dict[str, Any] = {
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cross_attn.get_parameter_name("wk_prime"): ip_adapter_weights[k_ip],
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cross_attn.get_parameter_name("wv_prime"): ip_adapter_weights[v_ip],
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}
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cross_attn.load_state_dict(state_dict=cross_attn_state_dict, strict=False)
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for k, v in cross_attn_state_dict.items():
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state_dict[f"ip_adapter.{i:03d}.{k}"] = v
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if args.half:
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state_dict = {key: value.half() for key, value in state_dict.items()}
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save_to_safetensors(path=args.output_path, tensors=state_dict)
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if __name__ == "__main__":
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main()
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@ -10,6 +10,7 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1 import (
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StableDiffusion_1_Inpainting,
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SD1UNet,
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SD1ControlnetAdapter,
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SD1IPAdapter,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl import (
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SDXLUNet,
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@ -22,6 +23,7 @@ __all__ = [
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"StableDiffusion_1_Inpainting",
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"SD1UNet",
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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"SDXLUNet",
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"DoubleTextEncoder",
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"DPMSolver",
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@ -4,10 +4,12 @@ from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import (
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StableDiffusion_1_Inpainting,
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)
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.controlnet import SD1ControlnetAdapter
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.image_prompt import SD1IPAdapter
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__all__ = [
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"StableDiffusion_1",
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"StableDiffusion_1_Inpainting",
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"SD1UNet",
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"SD1ControlnetAdapter",
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"SD1IPAdapter",
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]
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from enum import IntEnum
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from pathlib import Path
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from functools import partial
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from torch import Tensor, as_tensor, cat, zeros_like, device as Device, dtype as DType
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from PIL import Image
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from refiners.fluxion.adapters.adapter import Adapter
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from refiners.foundationals.clip.image_encoder import CLIPImageEncoderH
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.unet import SD1UNet
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from refiners.fluxion.layers.attentions import ScaledDotProductAttention
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from refiners.fluxion.utils import image_to_tensor, load_from_safetensors
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import refiners.fluxion.layers as fl
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class ImageProjection(fl.Chain):
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structural_attrs = ["clip_image_embedding_dim", "clip_text_embedding_dim", "sequence_length"]
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def __init__(
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self,
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clip_image_embedding_dim: int = 1024,
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clip_text_embedding_dim: int = 768,
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sequence_length: int = 4,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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self.clip_image_embedding_dim = clip_image_embedding_dim
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self.clip_text_embedding_dim = clip_text_embedding_dim
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self.sequence_length = sequence_length
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super().__init__(
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fl.Linear(
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in_features=clip_image_embedding_dim,
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out_features=clip_text_embedding_dim * sequence_length,
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device=device,
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dtype=dtype,
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),
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fl.Reshape(sequence_length, clip_text_embedding_dim),
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fl.LayerNorm(normalized_shape=clip_text_embedding_dim, device=device, dtype=dtype),
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)
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class _CrossAttnIndex(IntEnum):
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TXT_CROSS_ATTN = 0 # text cross-attention
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IMG_CROSS_ATTN = 1 # image cross-attention
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# Fluxion's Attention layer drop-in replacement implementing Decoupled Cross-Attention
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class IPAttention(fl.Chain):
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structural_attrs = [
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"embedding_dim",
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"text_sequence_length",
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"image_sequence_length",
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"scale",
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"num_heads",
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"heads_dim",
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"key_embedding_dim",
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"value_embedding_dim",
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"inner_dim",
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"use_bias",
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"is_causal",
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]
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def __init__(
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self,
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embedding_dim: int,
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text_sequence_length: int = 77,
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image_sequence_length: int = 4,
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scale: float = 1.0,
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num_heads: int = 1,
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key_embedding_dim: int | None = None,
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value_embedding_dim: int | None = None,
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inner_dim: int | None = None,
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use_bias: bool = True,
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is_causal: bool | None = None,
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device: Device | str | None = None,
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dtype: DType | None = None,
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) -> None:
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assert (
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embedding_dim % num_heads == 0
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), f"embedding_dim {embedding_dim} must be divisible by num_heads {num_heads}"
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self.embedding_dim = embedding_dim
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self.text_sequence_length = text_sequence_length
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self.image_sequence_length = image_sequence_length
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self.scale = scale
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self.num_heads = num_heads
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self.heads_dim = embedding_dim // num_heads
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self.key_embedding_dim = key_embedding_dim or embedding_dim
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self.value_embedding_dim = value_embedding_dim or embedding_dim
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self.inner_dim = inner_dim or embedding_dim
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self.use_bias = use_bias
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self.is_causal = is_causal
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super().__init__(
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fl.Distribute(
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# Note: the same query is used for image cross-attention as for text cross-attention
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fl.Linear(
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in_features=self.embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wq
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fl.Parallel(
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fl.Chain(
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fl.Slicing(dim=1, start=0, length=text_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wk
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),
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fl.Chain(
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fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wk'
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),
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),
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fl.Parallel(
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fl.Chain(
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fl.Slicing(dim=1, start=0, length=text_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wv
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),
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fl.Chain(
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fl.Slicing(dim=1, start=text_sequence_length, length=image_sequence_length),
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fl.Linear(
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in_features=self.key_embedding_dim,
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out_features=self.inner_dim,
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bias=self.use_bias,
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device=device,
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dtype=dtype,
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), # Wv'
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),
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),
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),
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fl.Sum(
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fl.Chain(
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fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.TXT_CROSS_ATTN)),
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ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
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),
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fl.Chain(
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fl.Lambda(func=partial(self.select_qkv, index=_CrossAttnIndex.IMG_CROSS_ATTN)),
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ScaledDotProductAttention(num_heads=num_heads, is_causal=is_causal),
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fl.Lambda(func=self.scale_outputs),
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),
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),
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fl.Linear(
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in_features=self.inner_dim,
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out_features=self.embedding_dim,
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bias=True,
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device=device,
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dtype=dtype,
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),
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)
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def select_qkv(
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self, query: Tensor, keys: tuple[Tensor, Tensor], values: tuple[Tensor, Tensor], index: _CrossAttnIndex
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) -> tuple[Tensor, Tensor, Tensor]:
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return (query, keys[index.value], values[index.value])
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def scale_outputs(self, x: Tensor) -> Tensor:
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return x * self.scale
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class CrossAttentionAdapter(fl.Chain, Adapter[fl.Attention]):
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structural_attrs = ["text_sequence_length", "image_sequence_length", "scale"]
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def __init__(
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self,
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target: fl.Attention,
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text_sequence_length: int = 77,
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image_sequence_length: int = 4,
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scale: float = 1.0,
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) -> None:
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self.text_sequence_length = text_sequence_length
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self.image_sequence_length = image_sequence_length
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self.scale = scale
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with self.setup_adapter(target):
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super().__init__(
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IPAttention(
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embedding_dim=target.embedding_dim,
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text_sequence_length=text_sequence_length,
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image_sequence_length=image_sequence_length,
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scale=scale,
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num_heads=target.num_heads,
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key_embedding_dim=target.key_embedding_dim,
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value_embedding_dim=target.value_embedding_dim,
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inner_dim=target.inner_dim,
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use_bias=target.use_bias,
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is_causal=target.is_causal,
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device=target.device,
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dtype=target.dtype,
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)
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)
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def get_parameter_name(self, matrix: str, bias: bool = False) -> str:
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match matrix:
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case "wq":
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index = 0
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case "wk":
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index = 1
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case "wk_prime":
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index = 2
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case "wv":
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index = 3
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case "wv_prime":
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index = 4
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case "proj":
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index = 5
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case _:
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raise ValueError(f"Unexpected matrix name {matrix}")
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linear = list(self.IPAttention.layers(fl.Linear))[index]
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param = getattr(linear, "bias" if bias else "weight")
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name = next((n for n, p in self.named_parameters() if id(p) == id(param)), None)
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assert name is not None
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return name
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class SD1IPAdapter(fl.Chain, Adapter[SD1UNet]):
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def __init__(
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self,
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target: SD1UNet,
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clip_image_encoder: CLIPImageEncoderH | None = None,
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scale: float = 1.0,
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weights: dict[str, Tensor] | None = None,
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) -> None:
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with self.setup_adapter(target):
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super().__init__(target)
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self.clip_image_encoder = clip_image_encoder or CLIPImageEncoderH(device=target.device, dtype=target.dtype)
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self.image_proj = ImageProjection(device=target.device, dtype=target.dtype)
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self.sub_adapters = [
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CrossAttentionAdapter(target=cross_attn, scale=scale)
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for cross_attn in filter(lambda attn: type(attn) != fl.SelfAttention, target.layers(fl.Attention))
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]
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if weights is not None:
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image_proj_state_dict: dict[str, Tensor] = {
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k.removeprefix("image_proj."): v for k, v in weights.items() if k.startswith("image_proj.")
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}
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self.image_proj.load_state_dict(image_proj_state_dict)
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for i, cross_attn in enumerate(self.sub_adapters):
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cross_attn_state_dict: dict[str, Tensor] = {}
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for k, v in weights.items():
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prefix = f"ip_adapter.{i:03d}."
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if not k.startswith(prefix):
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continue
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cross_attn_state_dict[k.removeprefix(prefix)] = v
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# Retrieve original (frozen) cross-attention weights
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# Note: this assumes the target UNet has already loaded weights
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cross_attn_linears = list(cross_attn.target.layers(fl.Linear))
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assert len(cross_attn_linears) == 4 # Wq, Wk, Wv and Proj
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cross_attn_state_dict[cross_attn.get_parameter_name("wq")] = cross_attn_linears[0].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("wk")] = cross_attn_linears[1].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("wv")] = cross_attn_linears[2].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("proj")] = cross_attn_linears[3].weight
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cross_attn_state_dict[cross_attn.get_parameter_name("proj", bias=True)] = cross_attn_linears[3].bias
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cross_attn.load_state_dict(state_dict=cross_attn_state_dict)
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@classmethod
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def from_safetensors(
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cls,
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target: SD1UNet,
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checkpoint_path: Path | str,
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clip_image_encoder: CLIPImageEncoderH | None = None,
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scale: float = 1.0,
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||||
):
|
||||
weights = load_from_safetensors(checkpoint_path, device=target.device if target.device is not None else "cpu")
|
||||
|
||||
return cls(
|
||||
target=target,
|
||||
clip_image_encoder=clip_image_encoder,
|
||||
scale=scale,
|
||||
weights=weights,
|
||||
)
|
||||
|
||||
def inject(self: "SD1IPAdapter", parent: fl.Chain | None = None) -> "SD1IPAdapter":
|
||||
for adapter in self.sub_adapters:
|
||||
adapter.inject()
|
||||
return super().inject(parent)
|
||||
|
||||
def eject(self) -> None:
|
||||
for adapter in self.sub_adapters:
|
||||
adapter.eject()
|
||||
super().eject()
|
||||
|
||||
# These should be concatenated to the CLIP text embedding before setting the UNet context
|
||||
def compute_clip_image_embedding(self, image_prompt: Tensor | None) -> Tensor:
|
||||
clip_embedding = self.clip_image_encoder(image_prompt)
|
||||
conditional_embedding = self.image_proj(clip_embedding)
|
||||
negative_embedding = self.image_proj(zeros_like(clip_embedding))
|
||||
return cat((negative_embedding, conditional_embedding))
|
||||
|
||||
def preprocess_image(
|
||||
self,
|
||||
image: Image.Image,
|
||||
size: tuple[int, int] = (224, 224),
|
||||
mean: list[float] | None = None,
|
||||
std: list[float] | None = None,
|
||||
) -> Tensor:
|
||||
# Default mean and std are parameters from https://github.com/openai/CLIP
|
||||
return self._normalize(
|
||||
image_to_tensor(image.resize(size), device=self.target.device, dtype=self.target.dtype),
|
||||
mean=[0.48145466, 0.4578275, 0.40821073] if mean is None else mean,
|
||||
std=[0.26862954, 0.26130258, 0.27577711] if std is None else std,
|
||||
)
|
||||
|
||||
# Adapted from https://github.com/pytorch/vision/blob/main/torchvision/transforms/_functional_tensor.py
|
||||
@staticmethod
|
||||
def _normalize(tensor: Tensor, mean: list[float], std: list[float], inplace: bool = False) -> Tensor:
|
||||
assert tensor.is_floating_point()
|
||||
assert tensor.ndim >= 3
|
||||
|
||||
if not inplace:
|
||||
tensor = tensor.clone()
|
||||
|
||||
dtype = tensor.dtype
|
||||
|
||||
mean_tensor = as_tensor(mean, dtype=tensor.dtype, device=tensor.device)
|
||||
std_tensor = as_tensor(std, dtype=tensor.dtype, device=tensor.device)
|
||||
|
||||
if (std_tensor == 0).any():
|
||||
raise ValueError(f"std evaluated to zero after conversion to {dtype}, leading to division by zero.")
|
||||
|
||||
if mean_tensor.ndim == 1:
|
||||
mean_tensor = mean_tensor.view(-1, 1, 1)
|
||||
|
||||
if std_tensor.ndim == 1:
|
||||
std_tensor = std_tensor.view(-1, 1, 1)
|
||||
|
||||
return tensor.sub_(mean_tensor).div_(std_tensor)
|
|
@ -13,6 +13,7 @@ from refiners.foundationals.latent_diffusion import (
|
|||
StableDiffusion_1_Inpainting,
|
||||
SD1UNet,
|
||||
SD1ControlnetAdapter,
|
||||
SD1IPAdapter,
|
||||
)
|
||||
from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter
|
||||
from refiners.foundationals.latent_diffusion.schedulers import DDIM
|
||||
|
@ -42,6 +43,11 @@ def kitchen_dog_mask(ref_path: Path) -> Image.Image:
|
|||
return Image.open(ref_path / "kitchen_dog_mask.png").convert("RGB")
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def woman_image(ref_path: Path) -> Image.Image:
|
||||
return Image.open(ref_path / "woman.png").convert("RGB")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def expected_image_std_random_init(ref_path: Path) -> Image.Image:
|
||||
return Image.open(ref_path / "expected_std_random_init.png").convert("RGB")
|
||||
|
@ -62,6 +68,11 @@ def expected_image_controlnet_stack(ref_path: Path) -> Image.Image:
|
|||
return Image.open(ref_path / "expected_controlnet_stack.png").convert("RGB")
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def expected_image_ip_adapter_woman(ref_path: Path) -> Image.Image:
|
||||
return Image.open(ref_path / "expected_image_ip_adapter_woman.png").convert("RGB")
|
||||
|
||||
|
||||
@pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"])
|
||||
def controlnet_data(
|
||||
ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest
|
||||
|
@ -182,6 +193,33 @@ def unet_weights_inpainting(test_weights_path: Path) -> Path:
|
|||
return unet_weights_inpainting
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def lda_ft_mse_weights(test_weights_path: Path) -> Path:
|
||||
lda_weights = test_weights_path / "lda_ft_mse.safetensors"
|
||||
if not lda_weights.is_file():
|
||||
warn(f"could not find weights at {lda_weights}, skipping")
|
||||
pytest.skip(allow_module_level=True)
|
||||
return lda_weights
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def ip_adapter_weights(test_weights_path: Path) -> Path:
|
||||
ip_adapter_weights = test_weights_path / "ip-adapter_sd15.safetensors"
|
||||
if not ip_adapter_weights.is_file():
|
||||
warn(f"could not find weights at {ip_adapter_weights}, skipping")
|
||||
pytest.skip(allow_module_level=True)
|
||||
return ip_adapter_weights
|
||||
|
||||
|
||||
@pytest.fixture(scope="module")
|
||||
def image_encoder_weights(test_weights_path: Path) -> Path:
|
||||
image_encoder_weights = test_weights_path / "CLIPImageEncoderH.safetensors"
|
||||
if not image_encoder_weights.is_file():
|
||||
warn(f"could not find weights at {image_encoder_weights}, skipping")
|
||||
pytest.skip(allow_module_level=True)
|
||||
return image_encoder_weights
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sd15_std(
|
||||
text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device
|
||||
|
@ -270,6 +308,24 @@ def sd15_ddim(
|
|||
return sd15
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def sd15_ddim_lda_ft_mse(
|
||||
text_encoder_weights: Path, lda_ft_mse_weights: Path, unet_weights_std: Path, test_device: torch.device
|
||||
) -> StableDiffusion_1:
|
||||
if test_device.type == "cpu":
|
||||
warn("not running on CPU, skipping")
|
||||
pytest.skip()
|
||||
|
||||
ddim_scheduler = DDIM(num_inference_steps=20)
|
||||
sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device)
|
||||
|
||||
sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights))
|
||||
sd15.lda.load_state_dict(load_from_safetensors(lda_ft_mse_weights))
|
||||
sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std))
|
||||
|
||||
return sd15
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def test_diffusion_std_random_init(
|
||||
sd15_std: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device
|
||||
|
@ -844,3 +900,60 @@ def test_diffusion_textual_inversion_random_init(
|
|||
predicted_image = sd15.lda.decode_latents(x)
|
||||
|
||||
ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98)
|
||||
|
||||
|
||||
@torch.no_grad()
|
||||
def test_diffusion_ip_adapter(
|
||||
sd15_ddim_lda_ft_mse: StableDiffusion_1,
|
||||
ip_adapter_weights: Path,
|
||||
image_encoder_weights: Path,
|
||||
woman_image: Image.Image,
|
||||
expected_image_ip_adapter_woman: Image.Image,
|
||||
test_device: torch.device,
|
||||
):
|
||||
sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16)
|
||||
n_steps = 50
|
||||
|
||||
# See tencent-ailab/IP-Adapter best practices section:
|
||||
#
|
||||
# If you only use the image prompt, you can set the scale=1.0 and text_prompt="" (or some generic text
|
||||
# prompts, e.g. "best quality", you can also use any negative text prompt).
|
||||
#
|
||||
# The prompts below are the ones used by default by IPAdapter's generate method if none are specified
|
||||
prompt = "best quality, high quality"
|
||||
negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
|
||||
|
||||
ip_adapter = SD1IPAdapter.from_safetensors(target=sd15.unet, checkpoint_path=ip_adapter_weights)
|
||||
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
|
||||
ip_adapter.inject()
|
||||
|
||||
with torch.no_grad():
|
||||
clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt)
|
||||
clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image))
|
||||
|
||||
negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2)
|
||||
negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2)
|
||||
|
||||
clip_text_embedding = torch.cat(
|
||||
(
|
||||
torch.cat([negative_text_embedding, negative_image_embedding], dim=1),
|
||||
torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1),
|
||||
)
|
||||
)
|
||||
|
||||
sd15.set_num_inference_steps(n_steps)
|
||||
|
||||
manual_seed(2)
|
||||
x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16)
|
||||
|
||||
with torch.no_grad():
|
||||
for step in sd15.steps:
|
||||
x = sd15(
|
||||
x,
|
||||
step=step,
|
||||
clip_text_embedding=clip_text_embedding,
|
||||
condition_scale=7.5,
|
||||
)
|
||||
predicted_image = sd15.lda.decode_latents(x)
|
||||
|
||||
ensure_similar_images(predicted_image, expected_image_ip_adapter_woman)
|
||||
|
|
|
@ -35,7 +35,7 @@ output.images[0].save("std_random_init_expected.png")
|
|||
Special cases:
|
||||
|
||||
- `expected_refonly.png` has been generated [with Stable Diffusion web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui).
|
||||
- `expected_inpainting_refonly.png` has been generated with refiners itself (and inspected so that it looks reasonable).
|
||||
- `expected_inpainting_refonly.png`, `expected_image_ip_adapter_woman.png` have been generated with refiners itself (and inspected so that it looks reasonable).
|
||||
|
||||
## Other images
|
||||
|
||||
|
@ -59,6 +59,8 @@ Special cases:
|
|||
- `inpainting-scene.png`: cropped-to-square-and-resized version of https://unsplash.com/photos/RCz6eSVPGYU by @jannerboy62
|
||||
- `inpainting-target.png`: computed with `convert <(convert -size 512x512 xc:white png:-) kitchen_dog.png <(convert inpainting-mask.png -negate png:-) -compose Over -composite inpainting-target.png`
|
||||
|
||||
- `woman.png` [comes from tencent-ailab/IP-Adapter](https://github.com/tencent-ailab/IP-Adapter/blob/8b96670cc5c8ef00278b42c0c7b62fe8a74510b9/assets/images/woman.png).
|
||||
|
||||
## VAE without randomness
|
||||
|
||||
```diff
|
||||
|
|
BIN
tests/e2e/test_diffusion_ref/expected_image_ip_adapter_woman.png
Normal file
BIN
tests/e2e/test_diffusion_ref/expected_image_ip_adapter_woman.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 317 KiB |
BIN
tests/e2e/test_diffusion_ref/woman.png
Normal file
BIN
tests/e2e/test_diffusion_ref/woman.png
Normal file
Binary file not shown.
After Width: | Height: | Size: 1.2 MiB |
Loading…
Reference in a new issue