Delete ip_adapter

ip_adapter/__init__.py

@@ -1,9 +0,0 @@
-from .ip_adapter import IPAdapter, IPAdapterPlus, IPAdapterPlusXL, IPAdapterXL, IPAdapterFull
-
-__all__ = [
-    "IPAdapter",
-    "IPAdapterPlus",
-    "IPAdapterPlusXL",
-    "IPAdapterXL",
-    "IPAdapterFull",
-]

ip_adapter/attention_processor.py

@@ -1,554 +0,0 @@
-# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class AttnProcessor(nn.Module):
-    r"""
-    Default processor for performing attention-related computations.
-    """
-
-    def __init__(
-        self,
-        hidden_size=None,
-        cross_attention_dim=None,
-    ):
-        super().__init__()
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class IPAttnProcessor(nn.Module):
-    r"""
-    Attention processor for IP-Adapater.
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
-            The context length of the image features.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.scale = scale
-        self.num_tokens = num_tokens
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # for ip-adapter
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-
-        ip_key = attn.head_to_batch_dim(ip_key)
-        ip_value = attn.head_to_batch_dim(ip_value)
-
-        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
-        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
-        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
-
-        hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class AttnProcessor2_0(torch.nn.Module):
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(
-        self,
-        hidden_size=None,
-        cross_attention_dim=None,
-    ):
-        super().__init__()
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class IPAttnProcessor2_0(torch.nn.Module):
-    r"""
-    Attention processor for IP-Adapater for PyTorch 2.0.
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
-            The context length of the image features.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4):
-        super().__init__()
-
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.scale = scale
-        self.num_tokens = num_tokens
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # for ip-adapter
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-
-        ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        ip_hidden_states = F.scaled_dot_product_attention(
-            query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
-        )
-
-        ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        ip_hidden_states = ip_hidden_states.to(query.dtype)
-
-        hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-## for controlnet
-class CNAttnProcessor:
-    r"""
-    Default processor for performing attention-related computations.
-    """
-
-    def __init__(self, num_tokens=4):
-        self.num_tokens = num_tokens
-
-    def __call__(self, attn, hidden_states, encoder_hidden_states=None, attention_mask=None, temb=None):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class CNAttnProcessor2_0:
-    r"""
-    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
-    """
-
-    def __init__(self, num_tokens=4):
-        if not hasattr(F, "scaled_dot_product_attention"):
-            raise ImportError("AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0.")
-        self.num_tokens = num_tokens
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-
-        if attention_mask is not None:
-            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-            # scaled_dot_product_attention expects attention_mask shape to be
-            # (batch, heads, source_length, target_length)
-            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states = encoder_hidden_states[:, :end_pos]  # only use text
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states)
-
-        inner_dim = key.shape[-1]
-        head_dim = inner_dim // attn.heads
-
-        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
-
-        # the output of sdp = (batch, num_heads, seq_len, head_dim)
-        # TODO: add support for attn.scale when we move to Torch 2.1
-        hidden_states = F.scaled_dot_product_attention(
-            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
-        )
-
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states

ip_adapter/attention_processor_faceid.py

@@ -1,204 +0,0 @@
-# modified from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from diffusers.models.lora import LoRALinearLayer
-
-
-class LoRAAttnProcessor(nn.Module):
-    r"""
-    Default processor for performing attention-related computations.
-    """
-
-    def __init__(
-        self,
-        hidden_size=None,
-        cross_attention_dim=None,
-        rank=4,
-        network_alpha=None,
-        lora_scale=1.0,
-    ):
-        super().__init__()
-
-        self.rank = rank
-        self.lora_scale = lora_scale
-        
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        elif attn.norm_cross:
-            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states
-
-
-class LoRAIPAttnProcessor(nn.Module):
-    r"""
-    Attention processor for IP-Adapater.
-    Args:
-        hidden_size (`int`):
-            The hidden size of the attention layer.
-        cross_attention_dim (`int`):
-            The number of channels in the `encoder_hidden_states`.
-        scale (`float`, defaults to 1.0):
-            the weight scale of image prompt.
-        num_tokens (`int`, defaults to 4 when do ip_adapter_plus it should be 16):
-            The context length of the image features.
-    """
-
-    def __init__(self, hidden_size, cross_attention_dim=None, rank=4, network_alpha=None, lora_scale=1.0, scale=1.0, num_tokens=4):
-        super().__init__()
-
-        self.rank = rank
-        self.lora_scale = lora_scale
-        
-        self.to_q_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-        self.to_k_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_v_lora = LoRALinearLayer(cross_attention_dim or hidden_size, hidden_size, rank, network_alpha)
-        self.to_out_lora = LoRALinearLayer(hidden_size, hidden_size, rank, network_alpha)
-
-        self.hidden_size = hidden_size
-        self.cross_attention_dim = cross_attention_dim
-        self.scale = scale
-        self.num_tokens = num_tokens
-
-        self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-        self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
-
-    def __call__(
-        self,
-        attn,
-        hidden_states,
-        encoder_hidden_states=None,
-        attention_mask=None,
-        temb=None,
-    ):
-        residual = hidden_states
-
-        if attn.spatial_norm is not None:
-            hidden_states = attn.spatial_norm(hidden_states, temb)
-
-        input_ndim = hidden_states.ndim
-
-        if input_ndim == 4:
-            batch_size, channel, height, width = hidden_states.shape
-            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
-
-        batch_size, sequence_length, _ = (
-            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
-        )
-        attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
-
-        if attn.group_norm is not None:
-            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
-
-        query = attn.to_q(hidden_states) + self.lora_scale * self.to_q_lora(hidden_states)
-
-        if encoder_hidden_states is None:
-            encoder_hidden_states = hidden_states
-        else:
-            # get encoder_hidden_states, ip_hidden_states
-            end_pos = encoder_hidden_states.shape[1] - self.num_tokens
-            encoder_hidden_states, ip_hidden_states = (
-                encoder_hidden_states[:, :end_pos, :],
-                encoder_hidden_states[:, end_pos:, :],
-            )
-            if attn.norm_cross:
-                encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
-
-        key = attn.to_k(encoder_hidden_states) + self.lora_scale * self.to_k_lora(encoder_hidden_states)
-        value = attn.to_v(encoder_hidden_states) + self.lora_scale * self.to_v_lora(encoder_hidden_states)
-
-        query = attn.head_to_batch_dim(query)
-        key = attn.head_to_batch_dim(key)
-        value = attn.head_to_batch_dim(value)
-
-        attention_probs = attn.get_attention_scores(query, key, attention_mask)
-        hidden_states = torch.bmm(attention_probs, value)
-        hidden_states = attn.batch_to_head_dim(hidden_states)
-
-        # for ip-adapter
-        ip_key = self.to_k_ip(ip_hidden_states)
-        ip_value = self.to_v_ip(ip_hidden_states)
-
-        ip_key = attn.head_to_batch_dim(ip_key)
-        ip_value = attn.head_to_batch_dim(ip_value)
-
-        ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
-        ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
-        ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
-
-        hidden_states = hidden_states + self.scale * ip_hidden_states
-
-        # linear proj
-        hidden_states = attn.to_out[0](hidden_states) + self.lora_scale * self.to_out_lora(hidden_states)
-        # dropout
-        hidden_states = attn.to_out[1](hidden_states)
-
-        if input_ndim == 4:
-            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
-
-        if attn.residual_connection:
-            hidden_states = hidden_states + residual
-
-        hidden_states = hidden_states / attn.rescale_output_factor
-
-        return hidden_states

ip_adapter/custom_pipelines.py

@@ -1,394 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional, Tuple, Union
-
-import torch
-from diffusers import StableDiffusionXLPipeline
-from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput
-from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl import rescale_noise_cfg
-
-from .utils import is_torch2_available
-
-if is_torch2_available():
-    from .attention_processor import IPAttnProcessor2_0 as IPAttnProcessor
-else:
-    from .attention_processor import IPAttnProcessor
-
-
-class StableDiffusionXLCustomPipeline(StableDiffusionXLPipeline):
-    def set_scale(self, scale):
-        for attn_processor in self.unet.attn_processors.values():
-            if isinstance(attn_processor, IPAttnProcessor):
-                attn_processor.scale = scale
-
-    @torch.no_grad()
-    def __call__(  # noqa: C901
-        self,
-        prompt: Optional[Union[str, List[str]]] = None,
-        prompt_2: Optional[Union[str, List[str]]] = None,
-        height: Optional[int] = None,
-        width: Optional[int] = None,
-        num_inference_steps: int = 50,
-        denoising_end: Optional[float] = None,
-        guidance_scale: float = 5.0,
-        negative_prompt: Optional[Union[str, List[str]]] = None,
-        negative_prompt_2: Optional[Union[str, List[str]]] = None,
-        num_images_per_prompt: Optional[int] = 1,
-        eta: float = 0.0,
-        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
-        latents: Optional[torch.FloatTensor] = None,
-        prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
-        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
-        output_type: Optional[str] = "pil",
-        return_dict: bool = True,
-        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
-        callback_steps: int = 1,
-        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
-        guidance_rescale: float = 0.0,
-        original_size: Optional[Tuple[int, int]] = None,
-        crops_coords_top_left: Tuple[int, int] = (0, 0),
-        target_size: Optional[Tuple[int, int]] = None,
-        negative_original_size: Optional[Tuple[int, int]] = None,
-        negative_crops_coords_top_left: Tuple[int, int] = (0, 0),
-        negative_target_size: Optional[Tuple[int, int]] = None,
-        control_guidance_start: float = 0.0,
-        control_guidance_end: float = 1.0,
-    ):
-        r"""
-        Function invoked when calling the pipeline for generation.
-
-        Args:
-            prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
-                instead.
-            prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
-                used in both text-encoders
-            height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The height in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor):
-                The width in pixels of the generated image. This is set to 1024 by default for the best results.
-                Anything below 512 pixels won't work well for
-                [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
-                and checkpoints that are not specifically fine-tuned on low resolutions.
-            num_inference_steps (`int`, *optional*, defaults to 50):
-                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
-                expense of slower inference.
-            denoising_end (`float`, *optional*):
-                When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
-                completed before it is intentionally prematurely terminated. As a result, the returned sample will
-                still retain a substantial amount of noise as determined by the discrete timesteps selected by the
-                scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
-                "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image
-                Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output)
-            guidance_scale (`float`, *optional*, defaults to 5.0):
-                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
-                `guidance_scale` is defined as `w` of equation 2. of [Imagen
-                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
-                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
-                usually at the expense of lower image quality.
-            negative_prompt (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation. If not defined, one has to pass
-                `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
-                less than `1`).
-            negative_prompt_2 (`str` or `List[str]`, *optional*):
-                The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and
-                `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders
-            num_images_per_prompt (`int`, *optional*, defaults to 1):
-                The number of images to generate per prompt.
-            eta (`float`, *optional*, defaults to 0.0):
-                Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
-                [`schedulers.DDIMScheduler`], will be ignored for others.
-            generator (`torch.Generator` or `List[torch.Generator]`, *optional*):
-                One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html)
-                to make generation deterministic.
-            latents (`torch.FloatTensor`, *optional*):
-                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
-                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
-                tensor will ge generated by sampling using the supplied random `generator`.
-            prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
-                provided, text embeddings will be generated from `prompt` input argument.
-            negative_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
-                argument.
-            pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
-                If not provided, pooled text embeddings will be generated from `prompt` input argument.
-            negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*):
-                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
-                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
-                input argument.
-            output_type (`str`, *optional*, defaults to `"pil"`):
-                The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
-            return_dict (`bool`, *optional*, defaults to `True`):
-                Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead
-                of a plain tuple.
-            callback (`Callable`, *optional*):
-                A function that will be called every `callback_steps` steps during inference. The function will be
-                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
-            callback_steps (`int`, *optional*, defaults to 1):
-                The frequency at which the `callback` function will be called. If not specified, the callback will be
-                called at every step.
-            cross_attention_kwargs (`dict`, *optional*):
-                A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
-                `self.processor` in
-                [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
-            guidance_rescale (`float`, *optional*, defaults to 0.7):
-                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
-                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
-                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
-                Guidance rescale factor should fix overexposure when using zero terminal SNR.
-            original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled.
-                `original_size` defaults to `(width, height)` if not specified. Part of SDXL's micro-conditioning as
-                explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position
-                `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting
-                `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                For most cases, `target_size` should be set to the desired height and width of the generated image. If
-                not specified it will default to `(width, height)`. Part of SDXL's micro-conditioning as explained in
-                section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952).
-            negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a specific image resolution. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)):
-                To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's
-                micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)):
-                To negatively condition the generation process based on a target image resolution. It should be as same
-                as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of
-                [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more
-                information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208.
-            control_guidance_start (`float`, *optional*, defaults to 0.0):
-                The percentage of total steps at which the ControlNet starts applying.
-            control_guidance_end (`float`, *optional*, defaults to 1.0):
-                The percentage of total steps at which the ControlNet stops applying.
-
-        Examples:
-
-        Returns:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`:
-            [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a
-            `tuple`. When returning a tuple, the first element is a list with the generated images.
-        """
-        # 0. Default height and width to unet
-        height = height or self.default_sample_size * self.vae_scale_factor
-        width = width or self.default_sample_size * self.vae_scale_factor
-
-        original_size = original_size or (height, width)
-        target_size = target_size or (height, width)
-
-        # 1. Check inputs. Raise error if not correct
-        self.check_inputs(
-            prompt,
-            prompt_2,
-            height,
-            width,
-            callback_steps,
-            negative_prompt,
-            negative_prompt_2,
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        )
-
-        # 2. Define call parameters
-        if prompt is not None and isinstance(prompt, str):
-            batch_size = 1
-        elif prompt is not None and isinstance(prompt, list):
-            batch_size = len(prompt)
-        else:
-            batch_size = prompt_embeds.shape[0]
-
-        device = self._execution_device
-
-        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
-        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
-        # corresponds to doing no classifier free guidance.
-        do_classifier_free_guidance = guidance_scale > 1.0
-
-        # 3. Encode input prompt
-        text_encoder_lora_scale = (
-            cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
-        )
-        (
-            prompt_embeds,
-            negative_prompt_embeds,
-            pooled_prompt_embeds,
-            negative_pooled_prompt_embeds,
-        ) = self.encode_prompt(
-            prompt=prompt,
-            prompt_2=prompt_2,
-            device=device,
-            num_images_per_prompt=num_images_per_prompt,
-            do_classifier_free_guidance=do_classifier_free_guidance,
-            negative_prompt=negative_prompt,
-            negative_prompt_2=negative_prompt_2,
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            lora_scale=text_encoder_lora_scale,
-        )
-
-        # 4. Prepare timesteps
-        self.scheduler.set_timesteps(num_inference_steps, device=device)
-
-        timesteps = self.scheduler.timesteps
-
-        # 5. Prepare latent variables
-        num_channels_latents = self.unet.config.in_channels
-        latents = self.prepare_latents(
-            batch_size * num_images_per_prompt,
-            num_channels_latents,
-            height,
-            width,
-            prompt_embeds.dtype,
-            device,
-            generator,
-            latents,
-        )
-
-        # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
-        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
-
-        # 7. Prepare added time ids & embeddings
-        add_text_embeds = pooled_prompt_embeds
-        if self.text_encoder_2 is None:
-            text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1])
-        else:
-            text_encoder_projection_dim = self.text_encoder_2.config.projection_dim
-
-        add_time_ids = self._get_add_time_ids(
-            original_size,
-            crops_coords_top_left,
-            target_size,
-            dtype=prompt_embeds.dtype,
-            text_encoder_projection_dim=text_encoder_projection_dim,
-        )
-        if negative_original_size is not None and negative_target_size is not None:
-            negative_add_time_ids = self._get_add_time_ids(
-                negative_original_size,
-                negative_crops_coords_top_left,
-                negative_target_size,
-                dtype=prompt_embeds.dtype,
-                text_encoder_projection_dim=text_encoder_projection_dim,
-            )
-        else:
-            negative_add_time_ids = add_time_ids
-
-        if do_classifier_free_guidance:
-            prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            add_text_embeds = torch.cat([negative_pooled_prompt_embeds, add_text_embeds], dim=0)
-            add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], dim=0)
-
-        prompt_embeds = prompt_embeds.to(device)
-        add_text_embeds = add_text_embeds.to(device)
-        add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
-
-        # 8. Denoising loop
-        num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
-
-        # 7.1 Apply denoising_end
-        if denoising_end is not None and isinstance(denoising_end, float) and denoising_end > 0 and denoising_end < 1:
-            discrete_timestep_cutoff = int(
-                round(
-                    self.scheduler.config.num_train_timesteps
-                    - (denoising_end * self.scheduler.config.num_train_timesteps)
-                )
-            )
-            num_inference_steps = len(list(filter(lambda ts: ts >= discrete_timestep_cutoff, timesteps)))
-            timesteps = timesteps[:num_inference_steps]
-
-        # get init conditioning scale
-        for attn_processor in self.unet.attn_processors.values():
-            if isinstance(attn_processor, IPAttnProcessor):
-                conditioning_scale = attn_processor.scale
-                break
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                if (i / len(timesteps) < control_guidance_start) or ((i + 1) / len(timesteps) > control_guidance_end):
-                    self.set_scale(0.0)
-                else:
-                    self.set_scale(conditioning_scale)
-
-                # expand the latents if we are doing classifier free guidance
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # predict the noise residual
-                added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
-                noise_pred = self.unet(
-                    latent_model_input,
-                    t,
-                    encoder_hidden_states=prompt_embeds,
-                    cross_attention_kwargs=cross_attention_kwargs,
-                    added_cond_kwargs=added_cond_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # perform guidance
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
-                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
-
-                if do_classifier_free_guidance and guidance_rescale > 0.0:
-                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
-                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
-
-                # compute the previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
-
-                # call the callback, if provided
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-                    if callback is not None and i % callback_steps == 0:
-                        callback(i, t, latents)
-
-        if not output_type == "latent":
-            # make sure the VAE is in float32 mode, as it overflows in float16
-            needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast
-
-            if needs_upcasting:
-                self.upcast_vae()
-                latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
-
-            image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
-
-            # cast back to fp16 if needed
-            if needs_upcasting:
-                self.vae.to(dtype=torch.float16)
-        else:
-            image = latents
-
-        if output_type != "latent":
-            # apply watermark if available
-            if self.watermark is not None:
-                image = self.watermark.apply_watermark(image)
-
-            image = self.image_processor.postprocess(image, output_type=output_type)
-
-        # Offload all models
-        self.maybe_free_model_hooks()
-
-        if not return_dict:
-            return (image,)
-
-        return StableDiffusionXLPipelineOutput(images=image)

ip_adapter/ip_adapter.py

@@ -1,413 +0,0 @@
-import os
-from typing import List
-
-import torch
-from diffusers import StableDiffusionPipeline
-from diffusers.pipelines.controlnet import MultiControlNetModel
-from PIL import Image
-from safetensors import safe_open
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from .utils import is_torch2_available
-
-if is_torch2_available():
-    from .attention_processor import (
-        AttnProcessor2_0 as AttnProcessor,
-    )
-    from .attention_processor import (
-        CNAttnProcessor2_0 as CNAttnProcessor,
-    )
-    from .attention_processor import (
-        IPAttnProcessor2_0 as IPAttnProcessor,
-    )
-else:
-    from .attention_processor import AttnProcessor, CNAttnProcessor, IPAttnProcessor
-from .resampler import Resampler
-
-
-class ImageProjModel(torch.nn.Module):
-    """Projection Model"""
-
-    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024, clip_extra_context_tokens=4):
-        super().__init__()
-
-        self.cross_attention_dim = cross_attention_dim
-        self.clip_extra_context_tokens = clip_extra_context_tokens
-        self.proj = torch.nn.Linear(clip_embeddings_dim, self.clip_extra_context_tokens * cross_attention_dim)
-        self.norm = torch.nn.LayerNorm(cross_attention_dim)
-
-    def forward(self, image_embeds):
-        embeds = image_embeds
-        clip_extra_context_tokens = self.proj(embeds).reshape(
-            -1, self.clip_extra_context_tokens, self.cross_attention_dim
-        )
-        clip_extra_context_tokens = self.norm(clip_extra_context_tokens)
-        return clip_extra_context_tokens
-
-
-class MLPProjModel(torch.nn.Module):
-    """SD model with image prompt"""
-    def __init__(self, cross_attention_dim=1024, clip_embeddings_dim=1024):
-        super().__init__()
-        
-        self.proj = torch.nn.Sequential(
-            torch.nn.Linear(clip_embeddings_dim, clip_embeddings_dim),
-            torch.nn.GELU(),
-            torch.nn.Linear(clip_embeddings_dim, cross_attention_dim),
-            torch.nn.LayerNorm(cross_attention_dim)
-        )
-        
-    def forward(self, image_embeds):
-        clip_extra_context_tokens = self.proj(image_embeds)
-        return clip_extra_context_tokens
-
-
-class IPAdapter:
-    def __init__(self, sd_pipe, image_encoder_path, ip_ckpt, device, num_tokens=4):
-        self.device = device
-        self.image_encoder_path = image_encoder_path
-        self.ip_ckpt = ip_ckpt
-        self.num_tokens = num_tokens
-
-        self.pipe = sd_pipe.to(self.device)
-        self.set_ip_adapter()
-
-        # load image encoder
-        self.image_encoder = CLIPVisionModelWithProjection.from_pretrained(self.image_encoder_path).to(
-            self.device, dtype=torch.float16
-        )
-        self.clip_image_processor = CLIPImageProcessor()
-        # image proj model
-        self.image_proj_model = self.init_proj()
-
-        self.load_ip_adapter()
-
-    def init_proj(self):
-        image_proj_model = ImageProjModel(
-            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-            clip_embeddings_dim=self.image_encoder.config.projection_dim,
-            clip_extra_context_tokens=self.num_tokens,
-        ).to(self.device, dtype=torch.float16)
-        return image_proj_model
-
-    def set_ip_adapter(self):
-        unet = self.pipe.unet
-        attn_procs = {}
-        for name in unet.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = unet.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = unet.config.block_out_channels[block_id]
-            if cross_attention_dim is None:
-                attn_procs[name] = AttnProcessor()
-            else:
-                attn_procs[name] = IPAttnProcessor(
-                    hidden_size=hidden_size,
-                    cross_attention_dim=cross_attention_dim,
-                    scale=1.0,
-                    num_tokens=self.num_tokens,
-                ).to(self.device, dtype=torch.float16)
-        unet.set_attn_processor(attn_procs)
-        if hasattr(self.pipe, "controlnet"):
-            if isinstance(self.pipe.controlnet, MultiControlNetModel):
-                for controlnet in self.pipe.controlnet.nets:
-                    controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
-            else:
-                self.pipe.controlnet.set_attn_processor(CNAttnProcessor(num_tokens=self.num_tokens))
-
-    def load_ip_adapter(self):
-        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
-            state_dict = {"image_proj": {}, "ip_adapter": {}}
-            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
-                for key in f.keys():
-                    if key.startswith("image_proj."):
-                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
-                    elif key.startswith("ip_adapter."):
-                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
-        else:
-            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
-        self.image_proj_model.load_state_dict(state_dict["image_proj"])
-        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
-        ip_layers.load_state_dict(state_dict["ip_adapter"])
-
-    @torch.inference_mode()
-    def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
-        if pil_image is not None:
-            if isinstance(pil_image, Image.Image):
-                pil_image = [pil_image]
-            clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-            clip_image_embeds = self.image_encoder(clip_image.to(self.device, dtype=torch.float16)).image_embeds
-        else:
-            clip_image_embeds = clip_image_embeds.to(self.device, dtype=torch.float16)
-        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
-        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(clip_image_embeds))
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-    def set_scale(self, scale):
-        for attn_processor in self.pipe.unet.attn_processors.values():
-            if isinstance(attn_processor, IPAttnProcessor):
-                attn_processor.scale = scale
-
-    def generate(
-        self,
-        pil_image=None,
-        clip_image_embeds=None,
-        prompt=None,
-        negative_prompt=None,
-        scale=1.0,
-        num_samples=4,
-        seed=None,
-        guidance_scale=7.5,
-        num_inference_steps=30,
-        **kwargs,
-    ):
-        self.set_scale(scale)
-
-        if pil_image is not None:
-            num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
-        else:
-            num_prompts = clip_image_embeds.size(0)
-
-        if prompt is None:
-            prompt = "best quality, high quality"
-        if negative_prompt is None:
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-        if not isinstance(prompt, List):
-            prompt = [prompt] * num_prompts
-        if not isinstance(negative_prompt, List):
-            negative_prompt = [negative_prompt] * num_prompts
-
-        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(
-            pil_image=pil_image, clip_image_embeds=clip_image_embeds
-        )
-        bs_embed, seq_len, _ = image_prompt_embeds.shape
-        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-
-        with torch.inference_mode():
-            prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
-                prompt,
-                device=self.device,
-                num_images_per_prompt=num_samples,
-                do_classifier_free_guidance=True,
-                negative_prompt=negative_prompt,
-            )
-            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
-            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
-
-        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
-        images = self.pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            **kwargs,
-        ).images
-
-        return images
-
-
-class IPAdapterXL(IPAdapter):
-    """SDXL"""
-
-    def generate(
-        self,
-        pil_image,
-        prompt=None,
-        negative_prompt=None,
-        scale=1.0,
-        num_samples=4,
-        seed=None,
-        num_inference_steps=30,
-        **kwargs,
-    ):
-        self.set_scale(scale)
-
-        num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
-
-        if prompt is None:
-            prompt = "best quality, high quality"
-        if negative_prompt is None:
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-        if not isinstance(prompt, List):
-            prompt = [prompt] * num_prompts
-        if not isinstance(negative_prompt, List):
-            negative_prompt = [negative_prompt] * num_prompts
-
-        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
-        bs_embed, seq_len, _ = image_prompt_embeds.shape
-        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-
-        with torch.inference_mode():
-            (
-                prompt_embeds,
-                negative_prompt_embeds,
-                pooled_prompt_embeds,
-                negative_pooled_prompt_embeds,
-            ) = self.pipe.encode_prompt(
-                prompt,
-                num_images_per_prompt=num_samples,
-                do_classifier_free_guidance=True,
-                negative_prompt=negative_prompt,
-            )
-            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
-            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
-
-        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
-        images = self.pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            **kwargs,
-        ).images
-
-        return images
-
-
-class IPAdapterPlus(IPAdapter):
-    """IP-Adapter with fine-grained features"""
-
-    def init_proj(self):
-        image_proj_model = Resampler(
-            dim=self.pipe.unet.config.cross_attention_dim,
-            depth=4,
-            dim_head=64,
-            heads=12,
-            num_queries=self.num_tokens,
-            embedding_dim=self.image_encoder.config.hidden_size,
-            output_dim=self.pipe.unet.config.cross_attention_dim,
-            ff_mult=4,
-        ).to(self.device, dtype=torch.float16)
-        return image_proj_model
-
-    @torch.inference_mode()
-    def get_image_embeds(self, pil_image=None, clip_image_embeds=None):
-        if isinstance(pil_image, Image.Image):
-            pil_image = [pil_image]
-        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(self.device, dtype=torch.float16)
-        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
-        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
-        uncond_clip_image_embeds = self.image_encoder(
-            torch.zeros_like(clip_image), output_hidden_states=True
-        ).hidden_states[-2]
-        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-
-class IPAdapterFull(IPAdapterPlus):
-    """IP-Adapter with full features"""
-
-    def init_proj(self):
-        image_proj_model = MLPProjModel(
-            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-            clip_embeddings_dim=self.image_encoder.config.hidden_size,
-        ).to(self.device, dtype=torch.float16)
-        return image_proj_model
-
-
-class IPAdapterPlusXL(IPAdapter):
-    """SDXL"""
-
-    def init_proj(self):
-        image_proj_model = Resampler(
-            dim=1280,
-            depth=4,
-            dim_head=64,
-            heads=20,
-            num_queries=self.num_tokens,
-            embedding_dim=self.image_encoder.config.hidden_size,
-            output_dim=self.pipe.unet.config.cross_attention_dim,
-            ff_mult=4,
-        ).to(self.device, dtype=torch.float16)
-        return image_proj_model
-
-    @torch.inference_mode()
-    def get_image_embeds(self, pil_image):
-        if isinstance(pil_image, Image.Image):
-            pil_image = [pil_image]
-        clip_image = self.clip_image_processor(images=pil_image, return_tensors="pt").pixel_values
-        clip_image = clip_image.to(self.device, dtype=torch.float16)
-        clip_image_embeds = self.image_encoder(clip_image, output_hidden_states=True).hidden_states[-2]
-        image_prompt_embeds = self.image_proj_model(clip_image_embeds)
-        uncond_clip_image_embeds = self.image_encoder(
-            torch.zeros_like(clip_image), output_hidden_states=True
-        ).hidden_states[-2]
-        uncond_image_prompt_embeds = self.image_proj_model(uncond_clip_image_embeds)
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-    def generate(
-        self,
-        pil_image,
-        prompt=None,
-        negative_prompt=None,
-        scale=1.0,
-        num_samples=4,
-        seed=None,
-        num_inference_steps=30,
-        **kwargs,
-    ):
-        self.set_scale(scale)
-
-        num_prompts = 1 if isinstance(pil_image, Image.Image) else len(pil_image)
-
-        if prompt is None:
-            prompt = "best quality, high quality"
-        if negative_prompt is None:
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-        if not isinstance(prompt, List):
-            prompt = [prompt] * num_prompts
-        if not isinstance(negative_prompt, List):
-            negative_prompt = [negative_prompt] * num_prompts
-
-        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(pil_image)
-        bs_embed, seq_len, _ = image_prompt_embeds.shape
-        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-
-        with torch.inference_mode():
-            (
-                prompt_embeds,
-                negative_prompt_embeds,
-                pooled_prompt_embeds,
-                negative_pooled_prompt_embeds,
-            ) = self.pipe.encode_prompt(
-                prompt,
-                num_images_per_prompt=num_samples,
-                do_classifier_free_guidance=True,
-                negative_prompt=negative_prompt,
-            )
-            prompt_embeds = torch.cat([prompt_embeds, image_prompt_embeds], dim=1)
-            negative_prompt_embeds = torch.cat([negative_prompt_embeds, uncond_image_prompt_embeds], dim=1)
-
-        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
-        images = self.pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            pooled_prompt_embeds=pooled_prompt_embeds,
-            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            **kwargs,
-        ).images
-
-        return images

ip_adapter/ip_adapter_faceid.py

@@ -1,166 +0,0 @@
-import os
-from typing import List
-
-import torch
-from diffusers import StableDiffusionPipeline
-from diffusers.pipelines.controlnet import MultiControlNetModel
-from PIL import Image
-from safetensors import safe_open
-from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
-
-from .attention_processor_faceid import LoRAAttnProcessor, LoRAIPAttnProcessor
-
-
-class MLPProjModel(torch.nn.Module):
-    """SD model with image prompt"""
-    def __init__(self, cross_attention_dim=768, id_embeddings_dim=512, num_tokens=4):
-        super().__init__()
-        
-        self.cross_attention_dim = cross_attention_dim
-        self.num_tokens = num_tokens
-        
-        self.proj = torch.nn.Sequential(
-            torch.nn.Linear(id_embeddings_dim, id_embeddings_dim*2),
-            torch.nn.GELU(),
-            torch.nn.Linear(id_embeddings_dim*2, cross_attention_dim*num_tokens),
-        )
-        self.norm = torch.nn.LayerNorm(cross_attention_dim)
-        
-    def forward(self, id_embeds):
-        x = self.proj(id_embeds)
-        x = x.reshape(-1, self.num_tokens, self.cross_attention_dim)
-        x = self.norm(x)
-        return x
-
-
-class IPAdapterFaceID:
-    def __init__(self, sd_pipe, ip_ckpt, device, lora_rank=128, num_tokens=4):
-        self.device = device
-        self.ip_ckpt = ip_ckpt
-        self.lora_rank = lora_rank
-        self.num_tokens = num_tokens
-
-        self.pipe = sd_pipe.to(self.device)
-        self.set_ip_adapter()
-
-        # image proj model
-        self.image_proj_model = self.init_proj()
-
-        self.load_ip_adapter()
-
-    def init_proj(self):
-        image_proj_model = MLPProjModel(
-            cross_attention_dim=self.pipe.unet.config.cross_attention_dim,
-            id_embeddings_dim=512,
-            num_tokens=self.num_tokens,
-        ).to(self.device, dtype=torch.float16)
-        return image_proj_model
-
-    def set_ip_adapter(self):
-        unet = self.pipe.unet
-        attn_procs = {}
-        for name in unet.attn_processors.keys():
-            cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
-            if name.startswith("mid_block"):
-                hidden_size = unet.config.block_out_channels[-1]
-            elif name.startswith("up_blocks"):
-                block_id = int(name[len("up_blocks.")])
-                hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
-            elif name.startswith("down_blocks"):
-                block_id = int(name[len("down_blocks.")])
-                hidden_size = unet.config.block_out_channels[block_id]
-            if cross_attention_dim is None:
-                attn_procs[name] = LoRAAttnProcessor(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, rank=self.lora_rank,
-                ).to(self.device, dtype=torch.float16)
-            else:
-                attn_procs[name] = LoRAIPAttnProcessor(
-                    hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0, rank=self.lora_rank, num_tokens=self.num_tokens,
-                ).to(self.device, dtype=torch.float16)
-        unet.set_attn_processor(attn_procs)
-
-    def load_ip_adapter(self):
-        if os.path.splitext(self.ip_ckpt)[-1] == ".safetensors":
-            state_dict = {"image_proj": {}, "ip_adapter": {}}
-            with safe_open(self.ip_ckpt, framework="pt", device="cpu") as f:
-                for key in f.keys():
-                    if key.startswith("image_proj."):
-                        state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
-                    elif key.startswith("ip_adapter."):
-                        state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
-        else:
-            state_dict = torch.load(self.ip_ckpt, map_location="cpu")
-        self.image_proj_model.load_state_dict(state_dict["image_proj"])
-        ip_layers = torch.nn.ModuleList(self.pipe.unet.attn_processors.values())
-        ip_layers.load_state_dict(state_dict["ip_adapter"])
-
-    @torch.inference_mode()
-    def get_image_embeds(self, faceid_embeds):
-        
-        faceid_embeds = faceid_embeds.to(self.device, dtype=torch.float16)
-        image_prompt_embeds = self.image_proj_model(faceid_embeds)
-        uncond_image_prompt_embeds = self.image_proj_model(torch.zeros_like(faceid_embeds))
-        return image_prompt_embeds, uncond_image_prompt_embeds
-
-    def set_scale(self, scale):
-        for attn_processor in self.pipe.unet.attn_processors.values():
-            if isinstance(attn_processor, LoRAIPAttnProcessor):
-                attn_processor.scale = scale
-
-    def generate(
-        self,
-        faceid_embeds=None,
-        prompt=None,
-        negative_prompt=None,
-        scale=1.0,
-        num_samples=4,
-        seed=None,
-        guidance_scale=7.5,
-        num_inference_steps=30,
-        **kwargs,
-    ):
-        self.set_scale(scale)
-
-       
-        num_prompts = faceid_embeds.size(0)
-
-        if prompt is None:
-            prompt = "best quality, high quality"
-        if negative_prompt is None:
-            negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality"
-
-        if not isinstance(prompt, List):
-            prompt = [prompt] * num_prompts
-        if not isinstance(negative_prompt, List):
-            negative_prompt = [negative_prompt] * num_prompts
-
-        image_prompt_embeds, uncond_image_prompt_embeds = self.get_image_embeds(faceid_embeds)
-
-        bs_embed, seq_len, _ = image_prompt_embeds.shape
-        image_prompt_embeds = image_prompt_embeds.repeat(1, num_samples, 1)
-        image_prompt_embeds = image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.repeat(1, num_samples, 1)
-        uncond_image_prompt_embeds = uncond_image_prompt_embeds.view(bs_embed * num_samples, seq_len, -1)
-
-        with torch.inference_mode():
-            prompt_embeds_, negative_prompt_embeds_ = self.pipe.encode_prompt(
-                prompt,
-                device=self.device,
-                num_images_per_prompt=num_samples,
-                do_classifier_free_guidance=True,
-                negative_prompt=negative_prompt,
-            )
-            prompt_embeds = torch.cat([prompt_embeds_, image_prompt_embeds], dim=1)
-            negative_prompt_embeds = torch.cat([negative_prompt_embeds_, uncond_image_prompt_embeds], dim=1)
-
-        generator = torch.Generator(self.device).manual_seed(seed) if seed is not None else None
-        images = self.pipe(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_inference_steps,
-            generator=generator,
-            **kwargs,
-        ).images
-
-        return images

ip_adapter/resampler.py

@@ -1,158 +0,0 @@
-# modified from https://github.com/mlfoundations/open_flamingo/blob/main/open_flamingo/src/helpers.py
-# and https://github.com/lucidrains/imagen-pytorch/blob/main/imagen_pytorch/imagen_pytorch.py
-
-import math
-
-import torch
-import torch.nn as nn
-from einops import rearrange
-from einops.layers.torch import Rearrange
-
-
-# FFN
-def FeedForward(dim, mult=4):
-    inner_dim = int(dim * mult)
-    return nn.Sequential(
-        nn.LayerNorm(dim),
-        nn.Linear(dim, inner_dim, bias=False),
-        nn.GELU(),
-        nn.Linear(inner_dim, dim, bias=False),
-    )
-
-
-def reshape_tensor(x, heads):
-    bs, length, width = x.shape
-    # (bs, length, width) --> (bs, length, n_heads, dim_per_head)
-    x = x.view(bs, length, heads, -1)
-    # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)
-    x = x.transpose(1, 2)
-    # (bs, n_heads, length, dim_per_head) --> (bs*n_heads, length, dim_per_head)
-    x = x.reshape(bs, heads, length, -1)
-    return x
-
-
-class PerceiverAttention(nn.Module):
-    def __init__(self, *, dim, dim_head=64, heads=8):
-        super().__init__()
-        self.scale = dim_head**-0.5
-        self.dim_head = dim_head
-        self.heads = heads
-        inner_dim = dim_head * heads
-
-        self.norm1 = nn.LayerNorm(dim)
-        self.norm2 = nn.LayerNorm(dim)
-
-        self.to_q = nn.Linear(dim, inner_dim, bias=False)
-        self.to_kv = nn.Linear(dim, inner_dim * 2, bias=False)
-        self.to_out = nn.Linear(inner_dim, dim, bias=False)
-
-    def forward(self, x, latents):
-        """
-        Args:
-            x (torch.Tensor): image features
-                shape (b, n1, D)
-            latent (torch.Tensor): latent features
-                shape (b, n2, D)
-        """
-        x = self.norm1(x)
-        latents = self.norm2(latents)
-
-        b, l, _ = latents.shape
-
-        q = self.to_q(latents)
-        kv_input = torch.cat((x, latents), dim=-2)
-        k, v = self.to_kv(kv_input).chunk(2, dim=-1)
-
-        q = reshape_tensor(q, self.heads)
-        k = reshape_tensor(k, self.heads)
-        v = reshape_tensor(v, self.heads)
-
-        # attention
-        scale = 1 / math.sqrt(math.sqrt(self.dim_head))
-        weight = (q * scale) @ (k * scale).transpose(-2, -1)  # More stable with f16 than dividing afterwards
-        weight = torch.softmax(weight.float(), dim=-1).type(weight.dtype)
-        out = weight @ v
-
-        out = out.permute(0, 2, 1, 3).reshape(b, l, -1)
-
-        return self.to_out(out)
-
-
-class Resampler(nn.Module):
-    def __init__(
-        self,
-        dim=1024,
-        depth=8,
-        dim_head=64,
-        heads=16,
-        num_queries=8,
-        embedding_dim=768,
-        output_dim=1024,
-        ff_mult=4,
-        max_seq_len: int = 257,  # CLIP tokens + CLS token
-        apply_pos_emb: bool = False,
-        num_latents_mean_pooled: int = 0,  # number of latents derived from mean pooled representation of the sequence
-    ):
-        super().__init__()
-        self.pos_emb = nn.Embedding(max_seq_len, embedding_dim) if apply_pos_emb else None
-
-        self.latents = nn.Parameter(torch.randn(1, num_queries, dim) / dim**0.5)
-
-        self.proj_in = nn.Linear(embedding_dim, dim)
-
-        self.proj_out = nn.Linear(dim, output_dim)
-        self.norm_out = nn.LayerNorm(output_dim)
-
-        self.to_latents_from_mean_pooled_seq = (
-            nn.Sequential(
-                nn.LayerNorm(dim),
-                nn.Linear(dim, dim * num_latents_mean_pooled),
-                Rearrange("b (n d) -> b n d", n=num_latents_mean_pooled),
-            )
-            if num_latents_mean_pooled > 0
-            else None
-        )
-
-        self.layers = nn.ModuleList([])
-        for _ in range(depth):
-            self.layers.append(
-                nn.ModuleList(
-                    [
-                        PerceiverAttention(dim=dim, dim_head=dim_head, heads=heads),
-                        FeedForward(dim=dim, mult=ff_mult),
-                    ]
-                )
-            )
-
-    def forward(self, x):
-        if self.pos_emb is not None:
-            n, device = x.shape[1], x.device
-            pos_emb = self.pos_emb(torch.arange(n, device=device))
-            x = x + pos_emb
-
-        latents = self.latents.repeat(x.size(0), 1, 1)
-
-        x = self.proj_in(x)
-
-        if self.to_latents_from_mean_pooled_seq:
-            meanpooled_seq = masked_mean(x, dim=1, mask=torch.ones(x.shape[:2], device=x.device, dtype=torch.bool))
-            meanpooled_latents = self.to_latents_from_mean_pooled_seq(meanpooled_seq)
-            latents = torch.cat((meanpooled_latents, latents), dim=-2)
-
-        for attn, ff in self.layers:
-            latents = attn(x, latents) + latents
-            latents = ff(latents) + latents
-
-        latents = self.proj_out(latents)
-        return self.norm_out(latents)
-
-
-def masked_mean(t, *, dim, mask=None):
-    if mask is None:
-        return t.mean(dim=dim)
-
-    denom = mask.sum(dim=dim, keepdim=True)
-    mask = rearrange(mask, "b n -> b n 1")
-    masked_t = t.masked_fill(~mask, 0.0)
-
-    return masked_t.sum(dim=dim) / denom.clamp(min=1e-5)

ip_adapter/test_resampler.py

@@ -1,44 +0,0 @@
-import torch
-from resampler import Resampler
-from transformers import CLIPVisionModel
-
-BATCH_SIZE = 2
-OUTPUT_DIM = 1280
-NUM_QUERIES = 8
-NUM_LATENTS_MEAN_POOLED = 4  # 0 for no mean pooling (previous behavior)
-APPLY_POS_EMB = True  # False for no positional embeddings (previous behavior)
-IMAGE_ENCODER_NAME_OR_PATH = "laion/CLIP-ViT-H-14-laion2B-s32B-b79K"
-
-
-def main():
-    image_encoder = CLIPVisionModel.from_pretrained(IMAGE_ENCODER_NAME_OR_PATH)
-    embedding_dim = image_encoder.config.hidden_size
-    print(f"image_encoder hidden size: ", embedding_dim)
-
-    image_proj_model = Resampler(
-        dim=1024,
-        depth=2,
-        dim_head=64,
-        heads=16,
-        num_queries=NUM_QUERIES,
-        embedding_dim=embedding_dim,
-        output_dim=OUTPUT_DIM,
-        ff_mult=2,
-        max_seq_len=257,
-        apply_pos_emb=APPLY_POS_EMB,
-        num_latents_mean_pooled=NUM_LATENTS_MEAN_POOLED,
-    )
-
-    dummy_images = torch.randn(BATCH_SIZE, 3, 224, 224)
-    with torch.no_grad():
-        image_embeds = image_encoder(dummy_images, output_hidden_states=True).hidden_states[-2]
-    print("image_embds shape: ", image_embeds.shape)
-
-    with torch.no_grad():
-        ip_tokens = image_proj_model(image_embeds)
-    print("ip_tokens shape:", ip_tokens.shape)
-    assert ip_tokens.shape == (BATCH_SIZE, NUM_QUERIES + NUM_LATENTS_MEAN_POOLED, OUTPUT_DIM)
-
-
-if __name__ == "__main__":
-    main()

ip_adapter/utils.py

@@ -1,5 +0,0 @@
-import torch.nn.functional as F
-
-
-def is_torch2_available():
-    return hasattr(F, "scaled_dot_product_attention")