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+examples/side_by_side_b.png filter=lfs diff=lfs merge=lfs -text
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controlnet/config.json

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+{
+  "_class_name": "LibreFluxControlNetModel",
+  "_diffusers_version": "0.32.0",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    16,
+    56,
+    56
+  ],
+  "conditioning_embedding_channels": null,
+  "guidance_embeds": true,
+  "in_channels": 64,
+  "joint_attention_dim": 4096,
+  "num_attention_heads": 24,
+  "num_layers": 2,
+  "num_mode": null,
+  "num_single_layers": 4,
+  "patch_size": 1,
+  "pooled_projection_dim": 768
+}

controlnet/diffusion_pytorch_model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06e84cb264fc8bf98cc6c1ed5e53a606d061c4440c5ba9164f941dfce4f054b6
+size 2739920936

controlnet/net.py

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+
+# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This was modied from the control net repo
+
+
+import inspect
+from typing import Any, Callable, Dict, List, Optional, Tuple, Union
+
+from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel
+
+import numpy as np
+import torch
+from transformers import (
+    CLIPTextModel,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+)
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.loaders import FluxLoraLoaderMixin, FromSingleFileMixin
+from diffusers.models.autoencoders import AutoencoderKL
+###  MERGEING THESE ###
+# from src.models.transformer import FluxTransformer2DModel
+# from src.models.controlnet_flux import FluxControlNetModel
+#############
+
+##########################################
+########### ATTENTION MERGE ##############
+##########################################
+
+import torch
+from torch import Tensor, FloatTensor
+from torch.nn import functional as F
+from einops import rearrange
+from diffusers.models.attention_processor import Attention
+from diffusers.models.embeddings import apply_rotary_emb
+
+
+
+def fa3_sdpa(
+    q,
+    k,
+    v,
+):
+    # flash attention 3 sdpa drop-in replacement
+    q, k, v = [x.permute(0, 2, 1, 3) for x in [q, k, v]]
+    out = flash_attn_func(q, k, v)[0]
+    return out.permute(0, 2, 1, 3)
+
+
+class FluxSingleAttnProcessor3_0:
+    r"""
+    Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
+    """
+
+    def __init__(self):
+        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: Tensor,
+        encoder_hidden_states: Tensor = None,
+        attention_mask: FloatTensor = None,
+        image_rotary_emb: Tensor = None,
+    ) -> Tensor:
+        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, _, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        query = attn.to_q(hidden_states)
+        if encoder_hidden_states is None:
+            encoder_hidden_states = 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)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        # 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, dropout_p=0.0, is_causal=False)
+        hidden_states = fa3_sdpa(query, key, value)
+        hidden_states = rearrange(hidden_states, "B H L D -> B L (H D)")
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        return hidden_states
+
+
+class FluxAttnProcessor3_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FluxAttnProcessor3_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: FloatTensor,
+        encoder_hidden_states: FloatTensor = None,
+        attention_mask: FloatTensor = None,
+        image_rotary_emb: Tensor = None,
+    ) -> FloatTensor:
+        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)
+        context_input_ndim = encoder_hidden_states.ndim
+        if context_input_ndim == 4:
+            batch_size, channel, height, width = encoder_hidden_states.shape
+            encoder_hidden_states = encoder_hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size = encoder_hidden_states.shape[0]
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(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)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # `context` projections.
+        encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+        encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+        encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+        encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+        encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+        encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+            batch_size, -1, attn.heads, head_dim
+        ).transpose(1, 2)
+
+        if attn.norm_added_q is not None:
+            encoder_hidden_states_query_proj = attn.norm_added_q(
+                encoder_hidden_states_query_proj
+            )
+        if attn.norm_added_k is not None:
+            encoder_hidden_states_key_proj = attn.norm_added_k(
+                encoder_hidden_states_key_proj
+            )
+
+        # attention
+        query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+        key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+        value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        # hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        hidden_states = fa3_sdpa(query, key, value)
+        hidden_states = rearrange(hidden_states, "B H L D -> B L (H D)")
+
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        encoder_hidden_states, hidden_states = (
+            hidden_states[:, : encoder_hidden_states.shape[1]],
+            hidden_states[:, encoder_hidden_states.shape[1] :],
+        )
+
+        # linear proj
+        hidden_states = attn.to_out[0](hidden_states)
+        # dropout
+        hidden_states = attn.to_out[1](hidden_states)
+        encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+        if context_input_ndim == 4:
+            encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        return hidden_states, encoder_hidden_states
+
+
+
+class FluxFusedSDPAProcessor:
+    """
+    Fused QKV processor using PyTorch's scaled_dot_product_attention.
+    Uses fused projections but splits for attention computation.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FluxFusedSDPAProcessor requires PyTorch 2.0+ for scaled_dot_product_attention"
+            )
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: FloatTensor,
+        encoder_hidden_states: FloatTensor = None,
+        attention_mask: FloatTensor = None,
+        image_rotary_emb: Tensor = None,
+    ) -> FloatTensor:
+        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)
+
+        context_input_ndim = (
+            encoder_hidden_states.ndim if encoder_hidden_states is not None else None
+        )
+        if context_input_ndim == 4:
+            batch_size, channel, height, width = encoder_hidden_states.shape
+            encoder_hidden_states = encoder_hidden_states.view(
+                batch_size, channel, height * width
+            ).transpose(1, 2)
+
+        batch_size = (
+            encoder_hidden_states.shape[0]
+            if encoder_hidden_states is not None
+            else hidden_states.shape[0]
+        )
+
+        # Single attention case (no encoder states)
+        if encoder_hidden_states is None:
+            # Use fused QKV projection
+            qkv = attn.to_qkv(hidden_states)  # (batch, seq_len, 3 * inner_dim)
+            inner_dim = qkv.shape[-1] // 3
+            head_dim = inner_dim // attn.heads
+            seq_len = hidden_states.shape[1]
+
+            # Split and reshape
+            qkv = qkv.view(batch_size, seq_len, 3, attn.heads, head_dim)
+            query, key, value = qkv.unbind(
+                dim=2
+            )  # Each is (batch, seq_len, heads, head_dim)
+
+            # Transpose to (batch, heads, seq_len, head_dim)
+            query = query.transpose(1, 2)
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+
+            # Apply norms if needed
+            if attn.norm_q is not None:
+                query = attn.norm_q(query)
+            if attn.norm_k is not None:
+                key = attn.norm_k(key)
+
+            # Apply RoPE if needed
+            if image_rotary_emb is not None:
+                query = apply_rotary_emb(query, image_rotary_emb)
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+            # SDPA
+            hidden_states = F.scaled_dot_product_attention(
+                query,
+                key,
+                value,
+                attn_mask=attention_mask,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+
+            # Reshape back
+            hidden_states = hidden_states.transpose(1, 2).reshape(
+                batch_size, -1, attn.heads * head_dim
+            )
+            hidden_states = hidden_states.to(query.dtype)
+
+            if input_ndim == 4:
+                hidden_states = hidden_states.transpose(-1, -2).reshape(
+                    batch_size, channel, height, width
+                )
+
+            return hidden_states
+
+        # Joint attention case (with encoder states)
+        else:
+            # Process self-attention QKV
+            qkv = attn.to_qkv(hidden_states)
+            inner_dim = qkv.shape[-1] // 3
+            head_dim = inner_dim // attn.heads
+            seq_len = hidden_states.shape[1]
+
+            qkv = qkv.view(batch_size, seq_len, 3, attn.heads, head_dim)
+            query, key, value = qkv.unbind(dim=2)
+
+            # Transpose to (batch, heads, seq_len, head_dim)
+            query = query.transpose(1, 2)
+            key = key.transpose(1, 2)
+            value = value.transpose(1, 2)
+
+            # Apply norms if needed
+            if attn.norm_q is not None:
+                query = attn.norm_q(query)
+            if attn.norm_k is not None:
+                key = attn.norm_k(key)
+
+            # Process encoder QKV
+            encoder_seq_len = encoder_hidden_states.shape[1]
+            encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
+            encoder_qkv = encoder_qkv.view(
+                batch_size, encoder_seq_len, 3, attn.heads, head_dim
+            )
+            encoder_query, encoder_key, encoder_value = encoder_qkv.unbind(dim=2)
+
+            # Transpose to (batch, heads, seq_len, head_dim)
+            encoder_query = encoder_query.transpose(1, 2)
+            encoder_key = encoder_key.transpose(1, 2)
+            encoder_value = encoder_value.transpose(1, 2)
+
+            # Apply encoder norms if needed
+            if attn.norm_added_q is not None:
+                encoder_query = attn.norm_added_q(encoder_query)
+            if attn.norm_added_k is not None:
+                encoder_key = attn.norm_added_k(encoder_key)
+
+            # Concatenate encoder and self-attention
+            query = torch.cat([encoder_query, query], dim=2)
+            key = torch.cat([encoder_key, key], dim=2)
+            value = torch.cat([encoder_value, value], dim=2)
+
+            # Apply RoPE if needed
+            if image_rotary_emb is not None:
+                query = apply_rotary_emb(query, image_rotary_emb)
+                key = apply_rotary_emb(key, image_rotary_emb)
+
+            # SDPA
+            hidden_states = F.scaled_dot_product_attention(
+                query,
+                key,
+                value,
+                attn_mask=attention_mask,
+                dropout_p=0.0,
+                is_causal=False,
+            )
+
+            # Reshape: (batch, heads, seq_len, head_dim) -> (batch, seq_len, heads * head_dim)
+            hidden_states = hidden_states.transpose(1, 2).reshape(
+                batch_size, -1, attn.heads * head_dim
+            )
+            hidden_states = hidden_states.to(query.dtype)
+
+            # Split encoder and self outputs
+            encoder_hidden_states = hidden_states[:, :encoder_seq_len]
+            hidden_states = hidden_states[:, encoder_seq_len:]
+
+            # Output projections
+            hidden_states = attn.to_out[0](hidden_states)
+            hidden_states = attn.to_out[1](hidden_states)  # dropout
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            # Reshape if needed
+            if input_ndim == 4:
+                hidden_states = hidden_states.transpose(-1, -2).reshape(
+                    batch_size, channel, height, width
+                )
+            if context_input_ndim == 4:
+                encoder_hidden_states = encoder_hidden_states.transpose(-1, -2).reshape(
+                    batch_size, channel, height, width
+                )
+
+            return hidden_states, encoder_hidden_states
+
+
+class FluxSingleFusedSDPAProcessor:
+    """
+    Fused QKV processor for single attention (no encoder states).
+    Simpler version for self-attention only blocks.
+    """
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FluxSingleFusedSDPAProcessor requires PyTorch 2.0+ for scaled_dot_product_attention"
+            )
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: Tensor,
+        encoder_hidden_states: Tensor = None,
+        attention_mask: FloatTensor = None,
+        image_rotary_emb: Tensor = None,
+    ) -> Tensor:
+        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, seq_len, _ = hidden_states.shape
+
+        # Use fused QKV projection
+        qkv = attn.to_qkv(hidden_states)  # (batch, seq_len, 3 * inner_dim)
+        inner_dim = qkv.shape[-1] // 3
+        head_dim = inner_dim // attn.heads
+
+        # Split and reshape in one go
+        qkv = qkv.view(batch_size, seq_len, 3, attn.heads, head_dim)
+        qkv = qkv.permute(2, 0, 3, 1, 4)  # (3, B, H, L, D) – still strided
+        query, key, value = [
+            t.contiguous() for t in qkv.unbind(0)  # make each view dense
+        ]
+        # Now each is (batch, heads, seq_len, head_dim)
+
+        # Apply norms if needed
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # Apply RoPE if needed
+        if image_rotary_emb is not None:
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        # SDPA
+        hidden_states = F.scaled_dot_product_attention(
+            query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
+        )
+
+        # Reshape back
+        hidden_states = rearrange(hidden_states, "B H L D -> B L (H D)")
+        hidden_states = hidden_states.to(query.dtype)
+
+        if input_ndim == 4:
+            hidden_states = hidden_states.transpose(-1, -2).reshape(
+                batch_size, channel, height, width
+            )
+
+        return hidden_states
+
+#################################
+##### TRANSFORMER MERGE #########
+#################################
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (
+    AdaLayerNormContinuous,
+    AdaLayerNormZero,
+    AdaLayerNormZeroSingle,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import (
+    CombinedTimestepGuidanceTextProjEmbeddings,
+    CombinedTimestepTextProjEmbeddings,
+    FluxPosEmbed,
+)
+
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers import FluxTransformer2DModel as OriginalFluxTransformer2DModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+is_flash_attn_available = False
+
+
+
+class FluxAttnProcessor2_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(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)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        if encoder_hidden_states is not None:
+            # `context` projections.
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(
+                    encoder_hidden_states_query_proj
+                )
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(
+                    encoder_hidden_states_key_proj
+                )
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            from diffusers.models.embeddings import apply_rotary_emb
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        if attention_mask is not None:
+            #print ('Attention Used')
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = (attention_mask > 0).bool()
+            # Edit 17 - match attn dtype to query d-type
+            attention_mask = attention_mask.to(
+                device=hidden_states.device, dtype=query.dtype
+            )
+
+        hidden_states = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=0.0,
+            is_causal=False,
+            attn_mask=attention_mask,
+        )
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1] :],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        return hidden_states
+
+
+def expand_flux_attention_mask(
+    hidden_states: torch.Tensor,
+    attn_mask: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Expand a mask so that the image is included.
+    """
+    bsz = attn_mask.shape[0]
+    assert bsz == hidden_states.shape[0]
+    residual_seq_len = hidden_states.shape[1]
+    mask_seq_len = attn_mask.shape[1]
+
+    expanded_mask = torch.ones(bsz, residual_seq_len)
+    expanded_mask[:, :mask_seq_len] = attn_mask
+
+    return expanded_mask
+
+
+@maybe_allow_in_graph
+class FluxSingleTransformerBlock(nn.Module):
+    r"""
+    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+    Reference: https://arxiv.org/abs/2403.03206
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+            processing of `context` conditions.
+    """
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
+        super().__init__()
+        self.mlp_hidden_dim = int(dim * mlp_ratio)
+
+        self.norm = AdaLayerNormZeroSingle(dim)
+        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
+
+        processor = FluxAttnProcessor2_0()
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            bias=True,
+            processor=processor,
+            qk_norm="rms_norm",
+            eps=1e-6,
+            pre_only=True,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        residual = hidden_states
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+        if attention_mask is not None:
+            attention_mask = expand_flux_attention_mask(
+                hidden_states,
+                attention_mask,
+            )
+
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+        )
+
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        gate = gate.unsqueeze(1)
+        hidden_states = gate * self.proj_out(hidden_states)
+        hidden_states = residual + hidden_states
+
+        if hidden_states.dtype == torch.float16:
+            hidden_states = hidden_states.clip(-65504, 65504)
+
+        return hidden_states
+
+
+@maybe_allow_in_graph
+class FluxTransformerBlock(nn.Module):
+    r"""
+    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+    Reference: https://arxiv.org/abs/2403.03206
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+            processing of `context` conditions.
+    """
+
+    def __init__(
+        self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6
+    ):
+        super().__init__()
+
+        self.norm1 = AdaLayerNormZero(dim)
+
+        self.norm1_context = AdaLayerNormZero(dim)
+
+        if hasattr(F, "scaled_dot_product_attention"):
+            processor = FluxAttnProcessor2_0()
+        else:
+            raise ValueError(
+                "The current PyTorch version does not support the `scaled_dot_product_attention` function."
+            )
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            added_kv_proj_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            context_pre_only=False,
+            bias=True,
+            processor=processor,
+            qk_norm=qk_norm,
+            eps=eps,
+        )
+
+        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_context = FeedForward(
+            dim=dim, dim_out=dim, activation_fn="gelu-approximate"
+        )
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        attention_mask: Optional[torch.Tensor] = None,
+    ):
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+            hidden_states, emb=temb
+        )
+
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (
+            self.norm1_context(encoder_hidden_states, emb=temb)
+        )
+
+        if attention_mask is not None:
+            attention_mask = expand_flux_attention_mask(
+                torch.cat([encoder_hidden_states, hidden_states], dim=1),
+                attention_mask,
+            )
+
+        # Attention.
+        attention_outputs = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+        )
+        if len(attention_outputs) == 2:
+            attn_output, context_attn_output = attention_outputs
+        elif len(attention_outputs) == 3:
+            attn_output, context_attn_output, ip_attn_output = attention_outputs
+
+        # Process attention outputs for the `hidden_states`.
+        attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = hidden_states + attn_output
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = (
+            norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        )
+
+        ff_output = self.ff(norm_hidden_states)
+        ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = hidden_states + ff_output
+        if len(attention_outputs) == 3:
+            hidden_states = hidden_states + ip_attn_output
+
+        # Process attention outputs for the `encoder_hidden_states`.
+        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
+        encoder_hidden_states = encoder_hidden_states + context_attn_output
+
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+        norm_encoder_hidden_states = (
+            norm_encoder_hidden_states * (1 + c_scale_mlp[:, None])
+            + c_shift_mlp[:, None]
+        )
+
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+        encoder_hidden_states = (
+            encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
+        )
+
+        if encoder_hidden_states.dtype == torch.float16:
+            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
+
+        return encoder_hidden_states, hidden_states
+
+
+class LibreFluxTransformer2DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin
+):
+    """
+    The Transformer model introduced in Flux.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Parameters:
+        patch_size (`int`): Patch size to turn the input data into small patches.
+        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
+        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
+        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
+        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
+        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
+        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
+        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Tuple[int] = (16, 56, 56),
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = (
+            self.config.num_attention_heads * self.config.attention_head_dim
+        )
+
+        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+        text_time_guidance_cls = (
+            CombinedTimestepGuidanceTextProjEmbeddings  ### 3 input forward (timestep, guidance, pooled_projection)
+            if guidance_embeds
+            else CombinedTimestepTextProjEmbeddings  #### 2 input forward (timestep, pooled_projection)
+        )
+        self.time_text_embed = text_time_guidance_cls(
+            embedding_dim=self.inner_dim,
+            pooled_projection_dim=self.config.pooled_projection_dim,
+        )
+
+        self.context_embedder = nn.Linear(
+            self.config.joint_attention_dim, self.inner_dim
+        )
+        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_single_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(
+            self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6
+        )
+        self.proj_out = nn.Linear(
+            self.inner_dim, patch_size * patch_size * self.out_channels, bias=True
+        )
+
+        self.gradient_checkpointing = False
+        # added for users to disable checkpointing every nth step
+        self.gradient_checkpointing_interval = None
+
+    def set_gradient_checkpointing_interval(self, value: int):
+        self.gradient_checkpointing_interval = value
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        attention_mask: Optional[torch.Tensor] = None,
+        controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_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).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if (
+                joint_attention_kwargs is not None
+                and joint_attention_kwargs.get("scale", None) is not None
+            ):
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        #print( self.time_text_embed)
+        temb = (
+            self.time_text_embed(timestep,pooled_projections)
+            # Edit 1 # Charlie   NOT NEEDED - UNDONE
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+
+        image_rotary_emb = self.pos_embed(ids)
+
+        # IP adapter
+        if (
+            joint_attention_kwargs is not None
+            and "ip_adapter_image_embeds" in joint_attention_kwargs
+        ):
+            ip_adapter_image_embeds = joint_attention_kwargs.pop(
+                "ip_adapter_image_embeds"
+            )
+            ip_hidden_states = self.encoder_hid_proj(ip_adapter_image_embeds)
+            joint_attention_kwargs.update({"ip_hidden_states": ip_hidden_states})
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if (
+                self.training
+                and self.gradient_checkpointing
+                and (
+                    self.gradient_checkpointing_interval is None
+                    or index_block % self.gradient_checkpointing_interval == 0
+                )
+            ):
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                encoder_hidden_states, hidden_states = (
+                    torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        image_rotary_emb,
+                        attention_mask,
+                        **ckpt_kwargs,
+                    )
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask,
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(
+                    controlnet_block_samples
+                )
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states
+                        + controlnet_block_samples[
+                            index_block % len(controlnet_block_samples)
+                        ]
+                    )
+                else:
+                    hidden_states = (
+                        hidden_states
+                        + controlnet_block_samples[index_block // interval_control]
+                    )
+
+        # Flux places the text tokens in front of the image tokens in the
+        # sequence.
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if (
+                self.training
+                and self.gradient_checkpointing
+                or (
+                    self.gradient_checkpointing_interval is not None
+                    and index_block % self.gradient_checkpointing_interval == 0
+                )
+            ):
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask,
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(
+                    controlnet_single_block_samples
+                )
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+
+###################################
+# END TRANS MERGE
+####################################
+
+# Copyright 2024 Black Forest Labs, The HuggingFace Team and The InstantX Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This was modied from the control net repo
+
+
+####################################
+##### CONTROL NET MODEL MERGE ######
+####################################
+
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import PeftAdapterMixin
+from diffusers.models.attention_processor import AttentionProcessor
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.models.controlnets.controlnet import ControlNetConditioningEmbedding, zero_module
+from diffusers.models.embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings, FluxPosEmbed
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+@dataclass
+class FluxControlNetOutput(BaseOutput):
+    controlnet_block_samples: Tuple[torch.Tensor]
+    controlnet_single_block_samples: Tuple[torch.Tensor]
+
+
+class LibreFluxControlNetModel(ModelMixin, ConfigMixin, PeftAdapterMixin):
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: List[int] = [16, 56, 56],
+        num_mode: int = None,
+        conditioning_embedding_channels: int = None,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = num_attention_heads * attention_head_dim
+
+        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+
+        # edit 19
+        #text_time_guidance_cls = (
+        #    CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+        #)
+
+        text_time_guidance_cls = CombinedTimestepGuidanceTextProjEmbeddings
+        text_time_cls = CombinedTimestepTextProjEmbeddings
+
+        self.time_text_embed = text_time_cls(
+            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
+        )
+        self.time_text_guidance_embed = text_time_guidance_cls(
+            embedding_dim=self.inner_dim, pooled_projection_dim=pooled_projection_dim
+        )
+
+        self.context_embedder = nn.Linear(joint_attention_dim, self.inner_dim)
+        self.x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=num_attention_heads,
+                    attention_head_dim=attention_head_dim,
+                )
+                for i in range(num_single_layers)
+            ]
+        )
+
+        # controlnet_blocks
+        self.controlnet_blocks = nn.ModuleList([])
+        for _ in range(len(self.transformer_blocks)):
+            self.controlnet_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.controlnet_single_blocks = nn.ModuleList([])
+        for _ in range(len(self.single_transformer_blocks)):
+            self.controlnet_single_blocks.append(zero_module(nn.Linear(self.inner_dim, self.inner_dim)))
+
+        self.union = num_mode is not None
+        if self.union:
+            self.controlnet_mode_embedder = nn.Embedding(num_mode, self.inner_dim)
+
+        if conditioning_embedding_channels is not None:
+            self.input_hint_block = ControlNetConditioningEmbedding(
+                conditioning_embedding_channels=conditioning_embedding_channels, block_out_channels=(16, 16, 16, 16)
+            )
+            self.controlnet_x_embedder = torch.nn.Linear(in_channels, self.inner_dim)
+        else:
+            self.input_hint_block = None
+            self.controlnet_x_embedder = zero_module(torch.nn.Linear(in_channels, self.inner_dim))
+
+        self.gradient_checkpointing = False
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self):
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(self, processor):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if hasattr(module, "gradient_checkpointing"):
+            module.gradient_checkpointing = value
+
+    @classmethod
+    def from_transformer(
+        cls,
+        transformer,
+        num_layers: int = 4,
+        num_single_layers: int = 10,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        load_weights_from_transformer=True,
+    ):
+        config = dict(transformer.config)
+        config["num_layers"] = num_layers
+        config["num_single_layers"] = num_single_layers
+        config["attention_head_dim"] = attention_head_dim
+        config["num_attention_heads"] = num_attention_heads
+
+        controlnet = cls.from_config(config)
+
+        if load_weights_from_transformer:
+            controlnet.pos_embed.load_state_dict(transformer.pos_embed.state_dict())
+            controlnet.time_text_embed.load_state_dict(transformer.time_text_embed.state_dict())
+            controlnet.context_embedder.load_state_dict(transformer.context_embedder.state_dict())
+            controlnet.x_embedder.load_state_dict(transformer.x_embedder.state_dict())
+            controlnet.transformer_blocks.load_state_dict(transformer.transformer_blocks.state_dict(), strict=False)
+            controlnet.single_transformer_blocks.load_state_dict(
+                transformer.single_transformer_blocks.state_dict(), strict=False
+            )
+
+            controlnet.controlnet_x_embedder = zero_module(controlnet.controlnet_x_embedder)
+
+        return controlnet
+
+    # Edit 13 Adding attention masking to forward
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        controlnet_cond: torch.Tensor,
+        controlnet_mode: torch.Tensor = None,
+        conditioning_scale: float = 1.0,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        return_dict: bool = True,
+        attention_mask: Optional[torch.Tensor] = None,  # <-- 1. ADD ARGUMENT HERE
+
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            controlnet_cond (`torch.Tensor`):
+                The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`.
+            controlnet_mode (`torch.Tensor`):
+                The mode tensor of shape `(batch_size, 1)`.
+            conditioning_scale (`float`, defaults to `1.0`):
+                The scale factor for ControlNet outputs.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_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).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if joint_attention_kwargs is not None and joint_attention_kwargs.get("scale", None) is not None:
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        if self.input_hint_block is not None:
+            controlnet_cond = self.input_hint_block(controlnet_cond)
+            batch_size, channels, height_pw, width_pw = controlnet_cond.shape
+            height = height_pw // self.config.patch_size
+            width = width_pw // self.config.patch_size
+            controlnet_cond = controlnet_cond.reshape(
+                batch_size, channels, height, self.config.patch_size, width, self.config.patch_size
+            )
+            controlnet_cond = controlnet_cond.permute(0, 2, 4, 1, 3, 5)
+            controlnet_cond = controlnet_cond.reshape(batch_size, height * width, -1)
+        # add
+        hidden_states = hidden_states + self.controlnet_x_embedder(controlnet_cond)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        #print ('Guidance:', guidance)
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            # edit 19
+            else self.time_text_guidance_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if self.union:
+            # union mode
+            if controlnet_mode is None:
+                raise ValueError("`controlnet_mode` cannot be `None` when applying ControlNet-Union")
+            # union mode emb
+            controlnet_mode_emb = self.controlnet_mode_embedder(controlnet_mode)
+            encoder_hidden_states = torch.cat([controlnet_mode_emb, encoder_hidden_states], dim=1)
+            txt_ids = torch.cat([txt_ids[:1], txt_ids], dim=0)
+
+        if txt_ids.ndim == 3:
+            logger.warning(
+                "Passing `txt_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            logger.warning(
+                "Passing `img_ids` 3d torch.Tensor is deprecated."
+                "Please remove the batch dimension and pass it as a 2d torch Tensor"
+            )
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+        image_rotary_emb = self.pos_embed(ids)
+
+        block_samples = ()
+        for index_block, block in enumerate(self.transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                encoder_hidden_states, hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    encoder_hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,  # Edit 13
+                    **ckpt_kwargs,
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask, #  Edit 13
+
+                )
+            block_samples = block_samples + (hidden_states,)
+
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        single_block_samples = ()
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,  # <-- 2. PASS MASK TO GRADIENT CHECKPOINTING  #  Edit 13
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask,  # <-- 2. PASS MASK TO BLOCK  Edit 13
+
+                )
+            single_block_samples = single_block_samples + (hidden_states[:, encoder_hidden_states.shape[1] :],)
+
+        # controlnet block
+        controlnet_block_samples = ()
+        for block_sample, controlnet_block in zip(block_samples, self.controlnet_blocks):
+            block_sample = controlnet_block(block_sample)
+            controlnet_block_samples = controlnet_block_samples + (block_sample,)
+
+        controlnet_single_block_samples = ()
+        for single_block_sample, controlnet_block in zip(single_block_samples, self.controlnet_single_blocks):
+            single_block_sample = controlnet_block(single_block_sample)
+            controlnet_single_block_samples = controlnet_single_block_samples + (single_block_sample,)
+
+        # scaling
+        controlnet_block_samples = [sample * conditioning_scale for sample in controlnet_block_samples]
+        controlnet_single_block_samples = [sample * conditioning_scale for sample in controlnet_single_block_samples]
+
+        controlnet_block_samples = None if len(controlnet_block_samples) == 0 else controlnet_block_samples
+        controlnet_single_block_samples = (
+            None if len(controlnet_single_block_samples) == 0 else controlnet_single_block_samples
+        )
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (controlnet_block_samples, controlnet_single_block_samples)
+
+        return FluxControlNetOutput(
+            controlnet_block_samples=controlnet_block_samples,
+            controlnet_single_block_samples=controlnet_single_block_samples,
+        )
+

flux_ip_adapter.py

@@ -0,0 +1,332 @@
+from itertools import chain
+import torch
+from torch import nn
+from diffusers.models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+)
+
+from diffusers.models.transformers.transformer_sd3 import SD3Transformer2DModel
+import torch.nn.functional as F
+from diffusers.utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from diffusers.models.attention_processor import Attention
+import inspect
+from functools import partial
+from diffusers.models.normalization import RMSNorm
+from typing import Any, Dict, List, Optional, Union
+import torch
+import torch.nn as nn
+
+
+class IPFluxAttnProcessor2_0(nn.Module):
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self, hidden_size, cross_attention_dim=None, scale=1.0, num_tokens=4, num_heads=0):
+        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)
+
+        self.norm_added_k = RMSNorm(128, eps=1e-5, elementwise_affine=False)
+
+    def __call__(
+        self,
+        attn,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        ip_encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        layer_scale: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
+        
+        ip_hidden_states = ip_encoder_hidden_states
+        
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(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)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # handle IP attention FIRST
+
+
+        # for ip-adapter
+        if ip_hidden_states != None:
+            ip_key = self.to_k_ip(ip_hidden_states)
+            ip_value = self.to_v_ip(ip_hidden_states)
+
+            # reshaping to match query shape
+            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)
+
+            ip_key = self.norm_added_k(ip_key)
+
+
+            # Using flux stype attention here
+            ip_hidden_states = F.scaled_dot_product_attention(
+                query,
+                ip_key,
+                ip_value,
+                dropout_p=0.0,
+                is_causal=False,
+                attn_mask=None,
+            )
+
+            # reshaping ip_hidden_states in the same way as hidden_states
+            ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(
+                batch_size, -1, attn.heads * head_dim
+            )
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        if encoder_hidden_states is not None:
+            # `context` projections.
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(
+                    encoder_hidden_states_query_proj
+                )
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(
+                    encoder_hidden_states_key_proj
+                )
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            from diffusers.models.embeddings import apply_rotary_emb
+            query = apply_rotary_emb(query, image_rotary_emb)
+
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        if attention_mask is not None:
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = (attention_mask > 0).bool()
+            attention_mask = attention_mask.to(
+                device=hidden_states.device, dtype=query.dtype
+            )
+        original_hidden_states = hidden_states
+
+        hidden_states = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=0.0,
+            is_causal=False,
+            attn_mask=attention_mask,
+        )
+        
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+
+        layer_scale = layer_scale.view(-1, 1, 1)
+
+        if encoder_hidden_states is not None:
+
+                    encoder_hidden_states, hidden_states = (
+                        hidden_states[:, : encoder_hidden_states.shape[1]],
+                        hidden_states[:, encoder_hidden_states.shape[1] :],
+                    )
+                
+                    # Final injection of ip addapter hidden_states
+                    if ip_hidden_states != None:
+                      hidden_states = hidden_states + (self.scale * layer_scale) * ip_hidden_states
+
+                    # linear proj
+                    hidden_states = attn.to_out[0](hidden_states)
+                    # dropout
+                    hidden_states = attn.to_out[1](hidden_states)
+                    encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+                    return hidden_states, encoder_hidden_states
+
+        else:
+            
+                    # Final injection of ip addapter hidden_states
+                    if ip_hidden_states != None:
+                      hidden_states = hidden_states + (self.scale * layer_scale) * ip_hidden_states
+
+                    if attn.to_out is not None:
+                        hidden_states = attn.to_out[0](hidden_states)
+                        hidden_states = attn.to_out[1](hidden_states)
+
+                    return hidden_states
+
+
+class ImageProjModel(nn.Module):
+    def __init__(self, clip_dim=768, cross_attention_dim=4096, num_tokens=16):
+        super().__init__()
+
+        self.num_tokens = num_tokens
+        self.cross_attention_dim = cross_attention_dim
+        self.clip_dim = clip_dim
+
+        self.proj = torch.nn.Sequential(
+            torch.nn.Linear(clip_dim,clip_dim*2),
+            torch.nn.GELU(),
+            torch.nn.Linear(clip_dim*2, cross_attention_dim*num_tokens),
+        )        
+        self.norm = torch.nn.LayerNorm(cross_attention_dim)
+    
+    def forward(self,input):
+        
+        raw_proj = self.proj(input)
+        reshaped_proj = raw_proj.reshape(input.shape[0],self.num_tokens,self.cross_attention_dim)
+        reshaped_proj = self.norm( reshaped_proj )
+
+        return reshaped_proj
+
+
+class LibreFluxIPAdapter(nn.Module):
+    def __init__(self, transformer, image_proj_model, checkpoint=None):
+        super().__init__()
+        self.transformer = transformer
+        self.image_proj_model = image_proj_model
+
+        # Using startswith uses only double transformer blocks, and skips the single transformer blocks
+        self.culled_transformer_blocks = {}
+        for name, module in self.transformer.named_modules():
+            if isinstance(module, Attention):
+                if name.startswith('transformer_blocks') or name.startswith('single_transformer_blocks'):
+                    #print (f"Using Transformer: {name}")
+                    self.culled_transformer_blocks[name] = module
+                #else:
+                    #print (f"Ignoring Transformer: {name}")
+        # Apply the adapter to the culled blocks
+        self.wrap_attention_blocks()
+        
+        if checkpoint:
+            self.load_from_checkpoint(checkpoint)
+
+    def wrap_attention_blocks(self,scale=1.0, num_tokens=16):
+        """ Inject the IP-Adapter modules into the Transformer model """
+        sample_attn = self.transformer.transformer_blocks[0].attn
+
+        hidden_size = sample_attn.inner_dim
+        cross_attention_dim = sample_attn.cross_attention_dim
+        num_heads = sample_attn.heads
+        scale = 1.0
+        num_tokens = 16
+ 
+        processor_list = []
+        for name in self.culled_transformer_blocks:
+            module = self.culled_transformer_blocks[name]
+            module.processor = IPFluxAttnProcessor2_0(
+                    hidden_size= hidden_size,
+                    cross_attention_dim=4096,
+                    num_heads=num_heads,
+                    scale=1.0,
+                    num_tokens=16,
+                )
+            processor_list.append(module.processor )
+        lay_count = len(processor_list)
+        print (f"Added Attention IP Wrapper to {lay_count} layers")
+
+        # Store adapters as a module list for saving/loading
+        self.adapter_modules = torch.nn.ModuleList(processor_list)
+        
+    def parameters(self):
+        """ Easy way to return all params """
+        # Apply adapter
+        adapter_param_list = []
+        for name in self.culled_transformer_blocks:
+            module = self.culled_transformer_blocks[name]            
+            adapter_param_list.append(module.processor.parameters())
+                    
+        all_params = chain(*adapter_param_list,self.image_proj_model.parameters())
+        return all_params
+
+    def forward(self, ref_image, *args, layer_scale= torch.Tensor([1.0]), **kwargs):
+        """ Run projection and run forward """
+        mod_dtype = next(self.image_proj_model.parameters()).dtype
+        mod_device = next(self.image_proj_model.parameters()).device
+
+        ip_encoder_hidden_states = None
+        if ref_image != None:
+          ip_encoder_hidden_states = self.image_proj_model(ref_image)
+
+        # Add ip hidden states to kwargs
+        if 'joint_attention_kwargs' not in kwargs:
+            kwargs['joint_attention_kwargs'] = {}
+        layer_scale = layer_scale.to(dtype=mod_dtype,
+        device=mod_device)   
+
+        kwargs['joint_attention_kwargs']['ip_layer_scale'] = layer_scale
+        kwargs['joint_attention_kwargs']['ip_hidden_states'] = ip_encoder_hidden_states
+
+        output = self.transformer(*args,
+                **kwargs)
+
+        return output
+
+    def save_pretrained(self,ckpt_path):
+        """ Save model weights """
+        state_dict = {}
+
+        state_dict["image_proj"] = self.image_proj_model.state_dict()
+        state_dict["ip_adapter"] = self.adapter_modules.state_dict()
+        torch.save(state_dict, ckpt_path)
+
+    def load_from_checkpoint(self, ckpt_path):
+        """ Loader ripped from tencent repo """
+        # Calculate original checksums
+        orig_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        orig_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        state_dict = torch.load(ckpt_path, map_location="cpu")
+
+        # Load state dict for image_proj_model and adapter_modules
+        self.image_proj_model.load_state_dict(state_dict["image_proj"], strict=True)
+        self.adapter_modules.load_state_dict(state_dict["ip_adapter"], strict=True)
+
+        # Calculate new checksums
+        new_ip_proj_sum = torch.sum(torch.stack([torch.sum(p) for p in self.image_proj_model.parameters()]))
+        new_adapter_sum = torch.sum(torch.stack([torch.sum(p) for p in self.adapter_modules.parameters()]))
+
+        # Verify if the weights have changed
+        assert orig_ip_proj_sum != new_ip_proj_sum, "Weights of image_proj_model did not change!"
+        assert orig_adapter_sum != new_adapter_sum, "Weights of adapter_modules did not change!"
+
+        print(f"Successfully loaded weights from checkpoint {ckpt_path}")
+
+
+    @property
+    def dtype(self):
+        return next(self.image_proj_model.parameters()).dtype

image_encoder/config.json

@@ -0,0 +1,18 @@
+{
+  "architectures": [
+    "SiglipVisionModel"
+  ],
+  "attention_dropout": 0.0,
+  "dtype": "float32",
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "image_size": 384,
+  "intermediate_size": 4304,
+  "layer_norm_eps": 1e-06,
+  "model_type": "siglip_vision_model",
+  "num_attention_heads": 16,
+  "num_channels": 3,
+  "num_hidden_layers": 27,
+  "patch_size": 14,
+  "transformers_version": "4.57.1"
+}

image_encoder/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:979cc297d21bc2634d7fc0e3fdc2589b0c891acf380708ffdeb1f988e3a2d817
+size 1712957296

image_encoder/preprocessor_config.json

@@ -0,0 +1,24 @@
+{
+  "do_convert_rgb": null,
+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "image_processor_type": "SiglipImageProcessor",
+  "image_std": [
+    0.5,
+    0.5,
+    0.5
+  ],
+  "processor_class": "SiglipProcessor",
+  "resample": 3,
+  "rescale_factor": 0.00392156862745098,
+  "size": {
+    "height": 384,
+    "width": 384
+  }
+}

image_encoder/special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": true,
+    "single_word": false
+  }
+}

image_encoder/spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e5036bed065526c3c212dfbe288752391797c4bb1a284aa18c9a0b23fcaf8ec
+size 798330

image_encoder/tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "added_tokens_decoder": {
+    "1": {
+      "content": "</s>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "<unk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_input_names": [
+    "input_ids"
+  ],
+  "model_max_length": 64,
+  "pad_token": "</s>",
+  "processor_class": "SiglipProcessor",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "SiglipTokenizer",
+  "unk_token": "<unk>"
+}

ip_adapter.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2a1fcb0c5d436335ae332704585105d547dd4908832cfd361d7a5a8101eefcd0
+size 5291247917

model_index.json

@@ -0,0 +1,40 @@
+{
+  "_class_name": "LibreFluxIPAdapterPipeline",
+  "_diffusers_version": "0.35.2",
+  "controlnet": [
+    "net",
+    "LibreFluxControlNetModel"
+  ],
+  "image_encoder": [
+    "transformers",
+    "SiglipVisionModel"
+  ],
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "trans",
+    "LibreFluxTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

pipeline.py

@@ -0,0 +1,1227 @@
+# @title
+# Copyright 2024 Stability AI, The HuggingFace Team, The InstantX Team, and Terminus Research Group. All rights reserved.
+#
+# Copyright 2024 Black Forest Labs and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# Originally licensed under the Apache License, Version 2.0 (the "License");
+# Updated to "Affero GENERAL PUBLIC LICENSE Version 3, 19 November 2007" via extensive updates to attn_mask usage.
+
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import Attention
+
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (
+    AdaLayerNormContinuous,
+    AdaLayerNormZero,
+    AdaLayerNormZeroSingle,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import (
+    CombinedTimestepGuidanceTextProjEmbeddings,
+    CombinedTimestepTextProjEmbeddings,
+)
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+
+from dataclasses import dataclass
+from typing import List, Union
+import PIL.Image
+from diffusers.utils import BaseOutput
+
+import inspect
+from functools import lru_cache
+from typing import Any, Callable, Dict, List, Optional, Union
+
+import numpy as np
+import torch
+from transformers import (
+    CLIPTextModel,
+    CLIPTokenizer,
+    T5EncoderModel,
+    T5TokenizerFast,
+    CLIPVisionModelWithProjection,
+    CLIPTextModelWithProjection,
+    CLIPImageProcessor
+)
+
+from diffusers.image_processor import VaeImageProcessor
+from diffusers.loaders import SD3LoraLoaderMixin
+from diffusers.models.autoencoders import AutoencoderKL
+from diffusers.schedulers import FlowMatchEulerDiscreteScheduler
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_xla_available,
+    logging,
+    replace_example_docstring,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import randn_tensor
+from diffusers.pipelines.pipeline_utils import DiffusionPipeline
+
+from PIL import Image
+
+from .transformer.trans import *
+from .flux_ip_adapter import *
+from .controlnet.net import LibreFluxControlNetModel
+
+if is_torch_xla_available():
+    import torch_xla.core.xla_model as xm
+
+    XLA_AVAILABLE = True
+else:
+    XLA_AVAILABLE = False
+
+
+@dataclass
+class FluxPipelineOutput(BaseOutput):
+    """
+    Output class for Stable Diffusion pipelines.
+    Args:
+        images (`List[PIL.Image.Image]` or `np.ndarray`)
+            List of denoised PIL images of length `batch_size` or numpy array of shape `(batch_size, height, width,
+            num_channels)`. PIL images or numpy array present the denoised images of the diffusion pipeline.
+    """
+
+    images: Union[List[PIL.Image.Image], np.ndarray]
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+EXAMPLE_DOC_STRING = """
+    Examples:
+        ```py
+        >>> import torch
+        >>> from diffusers import FluxPipeline
+        >>> pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+        >>> pipe.to("cuda")
+        >>> prompt = "A cat holding a sign that says hello world"
+        >>> # Depending on the variant being used, the pipeline call will slightly vary.
+        >>> # Refer to the pipeline documentation for more details.
+        >>> image = pipe(prompt, num_inference_steps=4, guidance_scale=0.0).images[0]
+        >>> image.save("flux.png")
+        ```
+"""
+
+
+def calculate_shift(
+    image_seq_len,
+    base_seq_len: int = 256,
+    max_seq_len: int = 4096,
+    base_shift: float = 0.5,
+    max_shift: float = 1.16,
+):
+    m = (max_shift - base_shift) / (max_seq_len - base_seq_len)
+    b = base_shift - m * base_seq_len
+    mu = image_seq_len * m + b
+    return mu
+
+
+# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps
+def retrieve_timesteps(
+    scheduler,
+    num_inference_steps: Optional[int] = None,
+    device: Optional[Union[str, torch.device]] = None,
+    timesteps: Optional[List[int]] = None,
+    sigmas: Optional[List[float]] = None,
+    **kwargs,
+):
+    """
+    Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles
+    custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`.
+    Args:
+        scheduler (`SchedulerMixin`):
+            The scheduler to get timesteps from.
+        num_inference_steps (`int`):
+            The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps`
+            must be `None`.
+        device (`str` or `torch.device`, *optional*):
+            The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
+        timesteps (`List[int]`, *optional*):
+            Custom timesteps used to override the timestep spacing strategy of the scheduler. If `timesteps` is passed,
+            `num_inference_steps` and `sigmas` must be `None`.
+        sigmas (`List[float]`, *optional*):
+            Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed,
+            `num_inference_steps` and `timesteps` must be `None`.
+    Returns:
+        `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the
+        second element is the number of inference steps.
+    """
+    if timesteps is not None and sigmas is not None:
+        raise ValueError(
+            "Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values"
+        )
+    if timesteps is not None:
+        accepts_timesteps = "timesteps" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accepts_timesteps:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" timestep schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    elif sigmas is not None:
+        accept_sigmas = "sigmas" in set(
+            inspect.signature(scheduler.set_timesteps).parameters.keys()
+        )
+        if not accept_sigmas:
+            raise ValueError(
+                f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom"
+                f" sigmas schedules. Please check whether you are using the correct scheduler."
+            )
+        scheduler.set_timesteps(sigmas=sigmas, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+        num_inference_steps = len(timesteps)
+    else:
+        scheduler.set_timesteps(num_inference_steps, device=device, **kwargs)
+        timesteps = scheduler.timesteps
+    return timesteps, num_inference_steps
+
+
+class LibreFluxIpAdapterPipeline(DiffusionPipeline, SD3LoraLoaderMixin):
+    r"""
+    The Flux pipeline for text-to-image generation.
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+    Args:
+        transformer ([`LibreFluxTransformer2DModel`]):
+            Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
+        scheduler ([`FlowMatchEulerDiscreteScheduler`]):
+            A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
+        vae ([`AutoencoderKL`]):
+            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
+        text_encoder ([`CLIPTextModelWithProjection`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant,
+            with an additional added projection layer that is initialized with a diagonal matrix with the `hidden_size`
+            as its dimension.
+        text_encoder_2 ([`CLIPTextModelWithProjection`]):
+            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection),
+            specifically the
+            [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k)
+            variant.
+        tokenizer (`CLIPTokenizer`):
+            Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+        tokenizer_2 (`CLIPTokenizer`):
+            Second Tokenizer of class
+            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
+    """
+
+    model_cpu_offload_seq = "text_encoder->text_encoder_2->transformer->vae"
+    _optional_components = ["ip_adapter"]
+    _callback_tensor_inputs = ["latents", "prompt_embeds"]
+
+    def __init__(
+        self,
+        scheduler: FlowMatchEulerDiscreteScheduler,
+        vae: AutoencoderKL,
+        text_encoder: CLIPTextModel,
+        tokenizer: CLIPTokenizer,
+        text_encoder_2: T5EncoderModel,
+        tokenizer_2: T5TokenizerFast,
+        transformer: LibreFluxTransformer2DModel,
+        image_encoder:  CLIPVisionModelWithProjection,
+        controlnet: Union[
+            LibreFluxControlNetModel, List[LibreFluxControlNetModel], Tuple[LibreFluxControlNetModel],
+        ],
+        ip_adapter: Optional[LibreFluxIPAdapter] = None
+    ):
+        super().__init__()
+
+        image_proj_model = ImageProjModel( clip_dim = image_encoder.config.hidden_size,
+                                        cross_attention_dim=4096,
+                                        num_tokens=128)
+
+        ip_adapter = LibreFluxIPAdapter(transformer,
+                                          image_proj_model)
+
+        self.ip_loaded = False
+
+        self.register_modules(
+            vae=vae,
+            text_encoder=text_encoder,
+            text_encoder_2=text_encoder_2,
+            tokenizer=tokenizer,
+            tokenizer_2=tokenizer_2,
+            transformer=transformer,
+            scheduler=scheduler,
+            image_encoder=image_encoder,
+            controlnet=controlnet,
+            ip_adapter=ip_adapter  # <-- Now registered
+        )
+        self.vae_scale_factor = (
+            2 ** (len(self.vae.config.block_out_channels))
+            if hasattr(self, "vae") and self.vae is not None
+            else 16
+        )
+        self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
+        self.tokenizer_max_length = (
+            self.tokenizer.model_max_length
+            if hasattr(self, "tokenizer") and self.tokenizer is not None
+            else 77
+        )
+        self.default_sample_size = 64
+
+        #self.clip_image_processor = CLIPImageProcessor()
+        from transformers import AutoProcessor, SiglipVisionModel
+        self.clip_image_processor = AutoProcessor.from_pretrained("google/siglip-so400m-patch14-384")
+
+    def _get_t5_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]] = None,
+        num_images_per_prompt: int = 1,
+        max_sequence_length: int = 512,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ):
+        device = device or self._execution_device
+        dtype = dtype or self.text_encoder.dtype
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer_2(
+            prompt,
+            padding="max_length",
+            max_length=max_sequence_length,
+            truncation=True,
+            return_length=False,
+            return_overflowing_tokens=False,
+            return_tensors="pt",
+        )
+        prompt_attention_mask = text_inputs.attention_mask
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer_2(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids
+
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
+            removed_text = self.tokenizer_2.batch_decode(untruncated_ids[:, self.tokenizer_max_length - 1 : -1])
+            logger.warning(
+                "The following part of your input was truncated because `max_sequence_length` is set to "
+                f" {max_sequence_length} tokens: {removed_text}"
+            )
+
+        prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
+
+        dtype = self.text_encoder_2.dtype
+        prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)
+
+        _, seq_len, _ = prompt_embeds.shape
+
+        # duplicate text embeddings and attention mask for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
+
+        return prompt_embeds, prompt_attention_mask
+
+    def prepare_image(
+        self,
+        image,
+        width,
+        height,
+        batch_size,
+        num_images_per_prompt,
+        device,
+        dtype,
+        do_classifier_free_guidance=False,
+        guess_mode=False,
+    ):
+        if isinstance(image, torch.Tensor):
+            pass
+        else:
+            image = self.image_processor.preprocess(image, height=height, width=width)
+
+        image_batch_size = image.shape[0]
+
+        if image_batch_size == 1:
+            repeat_by = batch_size
+        else:
+            # image batch size is the same as prompt batch size
+            repeat_by = num_images_per_prompt
+
+        image = image.repeat_interleave(repeat_by, dim=0)
+
+        image = image.to(device=device, dtype=dtype)
+
+        if do_classifier_free_guidance and not guess_mode:
+            image = torch.cat([image] * 2)
+
+        return image
+
+    def _get_clip_prompt_embeds(
+        self,
+        prompt: Union[str, List[str]],
+        num_images_per_prompt: int = 1,
+        device: Optional[torch.device] = None,
+    ):
+        device = device or self._execution_device
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        batch_size = len(prompt)
+
+        text_inputs = self.tokenizer(
+            prompt,
+            padding="max_length",
+            max_length=self.tokenizer_max_length,
+            truncation=True,
+            return_overflowing_tokens=False,
+            return_length=False,
+            return_tensors="pt",
+        )
+
+        text_input_ids = text_inputs.input_ids
+        untruncated_ids = self.tokenizer(
+            prompt, padding="longest", return_tensors="pt"
+        ).input_ids
+        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
+            text_input_ids, untruncated_ids
+        ):
+            removed_text = self.tokenizer.batch_decode(
+                untruncated_ids[:, self.tokenizer_max_length - 1 : -1]
+            )
+            logger.warning(
+                "The following part of your input was truncated because CLIP can only handle sequences up to"
+                f" {self.tokenizer_max_length} tokens: {removed_text}"
+            )
+        prompt_embeds = self.text_encoder(
+            text_input_ids.to(device), output_hidden_states=False
+        )
+
+        # Use pooled output of CLIPTextModel
+        prompt_embeds = prompt_embeds.pooler_output
+        prompt_embeds = prompt_embeds.to(dtype=self.text_encoder.dtype, device=device)
+
+        # duplicate text embeddings for each generation per prompt, using mps friendly method
+        prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
+        prompt_embeds = prompt_embeds.view(batch_size * num_images_per_prompt, -1)
+
+        return prompt_embeds
+
+    @lru_cache(maxsize=128)
+    def encode_prompt(
+        self,
+        prompt: Union[str, List[str]],
+        prompt_2: Union[str, List[str]],
+        device: Optional[torch.device] = None,
+        num_images_per_prompt: int = 1,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        max_sequence_length: int = 512,
+        lora_scale: Optional[float] = None,
+    ):
+        r"""
+        Args:
+            prompt (`str` or `List[str]`, *optional*):
+                prompt to be encoded
+            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 all text-encoders
+            device: (`torch.device`):
+                torch device
+            num_images_per_prompt (`int`):
+                number of images that should be generated per prompt
+            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.
+            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.
+            clip_skip (`int`, *optional*):
+                Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
+                the output of the pre-final layer will be used for computing the prompt embeddings.
+            lora_scale (`float`, *optional*):
+                A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
+        """
+        device = device or self._execution_device
+
+        # set lora scale so that monkey patched LoRA
+        # function of text encoder can correctly access it
+        if lora_scale is not None and isinstance(self, SD3LoraLoaderMixin):
+            self._lora_scale = lora_scale
+
+            # dynamically adjust the LoRA scale
+            if self.text_encoder is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder, lora_scale)
+            if self.text_encoder_2 is not None and USE_PEFT_BACKEND:
+                scale_lora_layers(self.text_encoder_2, lora_scale)
+
+        prompt = [prompt] if isinstance(prompt, str) else prompt
+        if prompt is not None:
+            batch_size = len(prompt)
+        else:
+            batch_size = prompt_embeds.shape[0]
+
+        prompt_attention_mask = None
+        if prompt_embeds is None:
+            prompt_2 = prompt_2 or prompt
+            prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2
+
+            # We only use the pooled prompt output from the CLIPTextModel
+            pooled_prompt_embeds = self._get_clip_prompt_embeds(
+                prompt=prompt,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+            )
+            prompt_embeds, prompt_attention_mask = self._get_t5_prompt_embeds(
+                prompt=prompt_2,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                device=device,
+            )
+
+        if self.text_encoder is not None:
+            if isinstance(self, SD3LoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder, lora_scale)
+
+        if self.text_encoder_2 is not None:
+            if isinstance(self, SD3LoraLoaderMixin) and USE_PEFT_BACKEND:
+                # Retrieve the original scale by scaling back the LoRA layers
+                unscale_lora_layers(self.text_encoder_2, lora_scale)
+
+        dtype = self.text_encoder.dtype if self.text_encoder is not None else self.transformer.dtype
+        text_ids = torch.zeros(batch_size, prompt_embeds.shape[1], 3).to(device=device, dtype=dtype)
+        text_ids = text_ids.repeat(num_images_per_prompt, 1, 1)
+
+        return prompt_embeds, pooled_prompt_embeds, text_ids, prompt_attention_mask
+
+    def check_inputs(
+        self,
+        prompt,
+        prompt_2,
+        height,
+        width,
+        prompt_embeds=None,
+        pooled_prompt_embeds=None,
+        callback_on_step_end_tensor_inputs=None,
+        max_sequence_length=None,
+    ):
+        if height % 8 != 0 or width % 8 != 0:
+            raise ValueError(
+                f"`height` and `width` have to be divisible by 8 but are {height} and {width}."
+            )
+
+        if callback_on_step_end_tensor_inputs is not None and not all(
+            k in self._callback_tensor_inputs
+            for k in callback_on_step_end_tensor_inputs
+        ):
+            raise ValueError(
+                f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
+            )
+
+        if prompt is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt_2 is not None and prompt_embeds is not None:
+            raise ValueError(
+                f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
+                " only forward one of the two."
+            )
+        elif prompt is None and prompt_embeds is None:
+            raise ValueError(
+                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
+            )
+        elif prompt is not None and (
+            not isinstance(prompt, str) and not isinstance(prompt, list)
+        ):
+            raise ValueError(
+                f"`prompt` has to be of type `str` or `list` but is {type(prompt)}"
+            )
+        elif prompt_2 is not None and (
+            not isinstance(prompt_2, str) and not isinstance(prompt_2, list)
+        ):
+            raise ValueError(
+                f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}"
+            )
+
+        if prompt_embeds is not None and pooled_prompt_embeds is None:
+            raise ValueError(
+                "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`."
+            )
+
+        if max_sequence_length is not None and max_sequence_length > 512:
+            raise ValueError(
+                f"`max_sequence_length` cannot be greater than 512 but is {max_sequence_length}"
+            )
+
+    @staticmethod
+    def _prepare_latent_image_ids(batch_size, height, width, device, dtype):
+        latent_image_ids = torch.zeros(height // 2, width // 2, 3)
+        latent_image_ids[..., 1] = (
+            latent_image_ids[..., 1] + torch.arange(height // 2)[:, None]
+        )
+        latent_image_ids[..., 2] = (
+            latent_image_ids[..., 2] + torch.arange(width // 2)[None, :]
+        )
+
+        latent_image_id_height, latent_image_id_width, latent_image_id_channels = (
+            latent_image_ids.shape
+        )
+
+        latent_image_ids = latent_image_ids[None, :].repeat(batch_size, 1, 1, 1)
+        latent_image_ids = latent_image_ids.reshape(
+            batch_size,
+            latent_image_id_height * latent_image_id_width,
+            latent_image_id_channels,
+        )
+
+        return latent_image_ids.to(dtype=dtype, device=device)
+
+    @staticmethod
+    def _pack_latents(latents, batch_size, num_channels_latents, height, width):
+        latents = latents.view(
+            batch_size, num_channels_latents, height // 2, 2, width // 2, 2
+        )
+        latents = latents.permute(0, 2, 4, 1, 3, 5)
+        latents = latents.reshape(
+            batch_size, (height // 2) * (width // 2), num_channels_latents * 4
+        )
+
+        return latents
+
+    @staticmethod
+    def _unpack_latents(latents, height, width, vae_scale_factor):
+        batch_size, num_patches, channels = latents.shape
+
+        height = height // vae_scale_factor
+        width = width // vae_scale_factor
+
+        latents = latents.view(batch_size, height, width, channels // 4, 2, 2)
+        latents = latents.permute(0, 3, 1, 4, 2, 5)
+
+        latents = latents.reshape(
+            batch_size, channels // (2 * 2), height * 2, width * 2
+        )
+
+        return latents
+
+    def prepare_latents(
+        self,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        generator,
+        latents=None,
+    ):
+        height = 2 * (int(height) // self.vae_scale_factor)
+        width = 2 * (int(width) // self.vae_scale_factor)
+
+        shape = (batch_size, num_channels_latents, height, width)
+
+        if latents is not None:
+            latent_image_ids = self._prepare_latent_image_ids(
+                batch_size, height, width, device, dtype
+            )
+            return latents, latent_image_ids
+
+        if isinstance(generator, list) and len(generator) != batch_size:
+            raise ValueError(
+                f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
+                f" size of {batch_size}. Make sure the batch size matches the length of the generators."
+            )
+
+        latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
+        latents = self._pack_latents(
+            latents, batch_size, num_channels_latents, height, width
+        )
+
+        latent_image_ids = self._prepare_latent_image_ids(
+            batch_size, height, width, device, dtype
+        )
+
+        return latents, latent_image_ids
+
+    @property
+    def guidance_scale(self):
+        return self._guidance_scale
+
+    @property
+    def joint_attention_kwargs(self):
+        return self._joint_attention_kwargs
+
+    @property
+    def num_timesteps(self):
+        return self._num_timesteps
+
+    @property
+    def interrupt(self):
+        return self._interrupt
+
+    @torch.no_grad()
+    @replace_example_docstring(EXAMPLE_DOC_STRING)
+    def __call__(
+        self,
+        prompt: Union[str, List[str]] = None,
+        prompt_mask: Optional[Union[torch.FloatTensor, List[torch.FloatTensor]]] = None,
+        negative_mask: Optional[
+            Union[torch.FloatTensor, List[torch.FloatTensor]]
+        ] = None,
+        prompt_2: Optional[Union[str, List[str]]] = None,
+        height: Optional[int] = None,
+        width: Optional[int] = None,
+        num_inference_steps: int = 28,
+        timesteps: List[int] = None,
+        guidance_scale: float = 3.5,
+        control_image: PipelineImageInput = None,
+        control_mode: Optional[Union[int, List[int]]] = None,
+        control_image_undo_centering: bool = False,
+        controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
+        num_images_per_prompt: Optional[int] = 1,
+        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
+        latents: Optional[torch.FloatTensor] = None,
+        prompt_embeds: Optional[torch.FloatTensor] = None,
+        pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        output_type: Optional[str] = "pil",
+        return_dict: bool = True,
+        joint_attention_kwargs: Optional[Dict[str, Any]] = None,
+        callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
+        callback_on_step_end_tensor_inputs: List[str] = ["latents"],
+        max_sequence_length: int = 512,
+        guidance_scale_real: float = 1.0,
+        negative_prompt: Union[str, List[str]] = "",
+        negative_prompt_2: Union[str, List[str]] = "",
+        negative_prompt_embeds: Optional[torch.FloatTensor] = None,
+        negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
+        no_cfg_until_timestep: int = 0,
+        do_batch_cfg: bool=True,
+        ip_adapter_image: PipelineImageInput=None,
+        ip_adapter_scale: float=1.0,
+        device=torch.device('cuda'), # TODO let this work with non-cuda stuff? Might if you set this to None
+    ):
+        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_mask (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be used as a mask for the image generation. If not defined, `prompt` is used
+                instead.
+            prompt_2 (`str` or `List[str]`, *optional*):
+                The prompt or prompts to be sent to `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is
+                will be used instead
+            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.
+            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.
+            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.
+            timesteps (`List[int]`, *optional*):
+                Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument
+                in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is
+                passed will be used. Must be in descending order.
+            guidance_scale (`float`, *optional*, defaults to 7.0):
+                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.
+            num_images_per_prompt (`int`, *optional*, defaults to 1):
+                The number of images to generate per prompt.
+            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.
+            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.
+            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.flux.FluxPipelineOutput`] instead of a plain tuple.
+            joint_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).
+            callback_on_step_end (`Callable`, *optional*):
+                A function that calls at the end of each denoising steps during the inference. The function is called
+                with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int,
+                callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by
+                `callback_on_step_end_tensor_inputs`.
+            callback_on_step_end_tensor_inputs (`List`, *optional*):
+                The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list
+                will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the
+                `._callback_tensor_inputs` attribute of your pipeline class.
+            max_sequence_length (`int` defaults to 512): Maximum sequence length to use with the `prompt`.
+        Examples:
+        Returns:
+            [`~pipelines.flux.FluxPipelineOutput`] or `tuple`: [`~pipelines.flux.FluxPipelineOutput`] if `return_dict`
+            is True, otherwise a `tuple`. When returning a tuple, the first element is a list with the generated
+            images.
+        """
+
+        height = height or self.default_sample_size * self.vae_scale_factor
+        width = width or self.default_sample_size * self.vae_scale_factor
+
+        # 1. Check inputs. Raise error if not correct
+        self.check_inputs(
+            prompt,
+            prompt_2,
+            height,
+            width,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
+            max_sequence_length=max_sequence_length,
+        )
+
+        # guidance_scale_real is redundant because this pipeline was originally
+        # made to be backwards compatible, but to make it the default just set
+        # guidance scale to be the same things.
+        guidance_scale_real = guidance_scale
+
+        self._guidance_scale = guidance_scale
+        self._guidance_scale_real = guidance_scale_real
+        self._joint_attention_kwargs = joint_attention_kwargs
+        self._interrupt = False
+
+        # 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 = device or self._execution_device
+        dtype = self.transformer.dtype
+
+        lora_scale = (
+            self.joint_attention_kwargs.get("scale", None)
+            if self.joint_attention_kwargs is not None
+            else None
+        )
+        (
+            prompt_embeds,
+            pooled_prompt_embeds,
+            text_ids,
+            _prompt_mask,
+        ) = self.encode_prompt(
+            prompt=prompt,
+            prompt_2=prompt_2,
+            prompt_embeds=prompt_embeds,
+            pooled_prompt_embeds=pooled_prompt_embeds,
+            device=device,
+            num_images_per_prompt=num_images_per_prompt,
+            max_sequence_length=max_sequence_length,
+            lora_scale=lora_scale,
+        )
+        if _prompt_mask is not None:
+            prompt_mask = _prompt_mask
+        assert prompt_mask is not None
+
+        if negative_prompt_2 == "" and negative_prompt != "":
+            negative_prompt_2 = negative_prompt
+
+        negative_text_ids = text_ids
+        if self._guidance_scale_real > 1.0 and (
+            negative_prompt_embeds is None or negative_pooled_prompt_embeds is None
+        ):
+            (
+                negative_prompt_embeds,
+                negative_pooled_prompt_embeds,
+                negative_text_ids,
+                _neg_prompt_mask,
+            ) = self.encode_prompt(
+                prompt=negative_prompt,
+                prompt_2=negative_prompt_2,
+                prompt_embeds=None,
+                pooled_prompt_embeds=None,
+                device=device,
+                num_images_per_prompt=num_images_per_prompt,
+                max_sequence_length=max_sequence_length,
+                lora_scale=lora_scale,
+            )
+
+            if _neg_prompt_mask is not None:
+                negative_mask = _neg_prompt_mask
+
+        assert negative_mask is not None
+
+
+        ##################################
+        # CONTROL NET - VARIALBE PREP
+        ##################################
+        if control_image != None:
+          # 3. Prepare control image
+          num_channels_latents = self.transformer.config.in_channels // 4
+
+
+          inner_module = self.controlnet
+
+          control_image = self.prepare_image(
+              image=control_image,
+              width=width,
+              height=height,
+              batch_size=batch_size * num_images_per_prompt,
+              num_images_per_prompt=num_images_per_prompt,
+              device=device,
+              dtype=dtype,
+          )
+
+          if control_image_undo_centering:
+              if not self.image_processor.do_normalize:
+                  raise ValueError(
+                      "`control_image_undo_centering` only makes sense if `do_normalize==True` in the image processor"
+                  )
+              control_image = control_image*0.5 + 0.5
+
+          height, width = control_image.shape[-2:]
+
+          # vae encode
+          control_image = self.vae.encode(control_image).latent_dist.sample()
+          control_image = (control_image - self.vae.config.shift_factor) * self.vae.config.scaling_factor
+          # pack
+          height_control_image, width_control_image = control_image.shape[2:]
+          control_image = self._pack_latents(
+              control_image,
+              batch_size * num_images_per_prompt,
+              num_channels_latents,
+              height_control_image,
+              width_control_image,
+          )
+
+          # set control mode
+          if control_mode is not None:
+              control_mode = torch.tensor(control_mode).to(device, dtype=torch.long)
+              control_mode = control_mode.reshape([-1, 1])
+
+
+          # set control mode
+          control_mode_ = []
+          if isinstance(control_mode, list):
+              for cmode in control_mode:
+                  if cmode is None:
+                      control_mode_.append(-1)
+                  else:
+                      control_mode_.append(cmode)
+          control_mode = torch.tensor(control_mode_).to(device, dtype=torch.long)
+          control_mode = control_mode.reshape([-1, 1])
+        else:
+          control_image = None
+          control_mode = None
+        ##################################
+        # END CONTROL NET - VARIALBE PREP
+        ##################################
+
+        # 4. Prepare latent variables
+        num_channels_latents = self.transformer.config.in_channels // 4
+        latents, latent_image_ids = self.prepare_latents(
+            batch_size * num_images_per_prompt,
+            num_channels_latents,
+            height,
+            width,
+            prompt_embeds.dtype,
+            device,
+            generator,
+            latents,
+        )
+
+        # 5. Prepare timesteps
+        sigmas = np.linspace(1.0, 1 / num_inference_steps, num_inference_steps)
+        image_seq_len = latents.shape[1]
+        mu = calculate_shift(
+            image_seq_len,
+            self.scheduler.config.base_image_seq_len,
+            self.scheduler.config.max_image_seq_len,
+            self.scheduler.config.base_shift,
+            self.scheduler.config.max_shift,
+        )
+        timesteps, num_inference_steps = retrieve_timesteps(
+            self.scheduler,
+            num_inference_steps,
+            device,
+            timesteps,
+            sigmas,
+            mu=mu,
+        )
+        num_warmup_steps = max(
+            len(timesteps) - num_inference_steps * self.scheduler.order, 0
+        )
+        self._num_timesteps = len(timesteps)
+
+        latents = latents
+        latent_image_ids = latent_image_ids
+        timesteps = timesteps
+        text_ids = text_ids.to(device=device)
+
+        # Denoising loop
+        with self.progress_bar(total=num_inference_steps) as progress_bar:
+            for i, t in enumerate(timesteps):
+                if self.interrupt:
+                    continue
+
+                # Prepare the latent model input
+                prompt_embeds_input = prompt_embeds
+                pooled_prompt_embeds_input = pooled_prompt_embeds
+                text_ids_input = text_ids
+                latent_image_ids_input = latent_image_ids
+                prompt_mask_input = prompt_mask
+                latent_model_input = latents
+
+                if guidance_scale_real > 1.0 and i >= no_cfg_until_timestep:
+                    progress_bar.set_postfix(
+                        {
+                            'ts': t.detach().item() / 1000.0,
+                            'cfg': self._guidance_scale_real,
+                        },
+                    )
+                else:
+                    progress_bar.set_postfix(
+                        {
+                            'ts': t.detach().item() / 1000.0,
+                            'cfg': 'N/A',
+                        },
+                    )
+
+                # Forward pass through the transformer
+                with torch.no_grad():
+                    if ip_adapter_image != None and self.ip_loaded:
+
+                      clip_image = self.clip_image_processor(images=ip_adapter_image,
+                                                            return_tensors="pt").pixel_values
+                      clip_image = clip_image.to(device=self.image_encoder.device,
+                                                dtype=self.image_encoder.dtype)
+                      image_embeds = self.image_encoder(clip_image).pooler_output
+                      image_embeds_input = image_embeds
+                    else:
+                      image_embeds = None
+                      image_embeds_input = None
+
+                    layer_scale = torch.Tensor([ip_adapter_scale])
+                    layer_scale_input = layer_scale
+                    neg_layer_scale = torch.Tensor([0.0])
+                    current_control_image = control_image
+
+
+
+                    if do_batch_cfg and guidance_scale_real > 1.0 and i >= no_cfg_until_timestep:
+                        # Concatenate prompt embeddings
+                        prompt_embeds_input = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
+                        pooled_prompt_embeds_input = torch.cat([negative_pooled_prompt_embeds, pooled_prompt_embeds], dim=0)
+
+                        image_embeds_input = None
+                        if image_embeds != None:
+                          image_embeds_input =  torch.cat([image_embeds]*2, dim=0)
+
+                        layer_scale_input = torch.cat([neg_layer_scale , layer_scale], dim=0)
+                        # Concatenate text IDs if they are used
+                        # if text_ids is not None and negative_text_ids is not None:
+                        #     text_ids_input = torch.cat([negative_text_ids, text_ids], dim=0)
+
+                        # Concatenate latent image IDs if they are used
+                        # if latent_image_ids is not None:
+                        #     latent_image_ids_input = torch.cat([latent_image_ids, latent_image_ids], dim=0)
+
+                        # Concatenate prompt masks if they are used
+                        if prompt_mask is not None and negative_mask is not None:
+                            prompt_mask_input = torch.cat([negative_mask, prompt_mask], dim=0)
+                        # Duplicate latents for unconditional and conditional inputs
+                        latent_model_input = torch.cat([latents] * 2)
+                        if control_image != None:
+                          current_control_image = torch.cat([control_image] * 2)
+                        else:
+                          current_control_image = None
+
+                # Expand timestep to match batch size
+                timestep = t.expand(latent_model_input.shape[0]).to(latents.dtype)
+
+                guidance = None
+
+                div_timestep = (timestep / 1000.0)
+                text_ids = [ t for t in text_ids ]
+
+                ######################################
+                # ADD CONTROLNET - FORWARD
+                ######################################
+                if control_image != None:
+                  controlnet_block_samples, controlnet_single_block_samples = self.controlnet(
+                      hidden_states=latent_model_input,
+                      controlnet_cond=current_control_image,
+                      controlnet_mode=control_mode,
+                      conditioning_scale=controlnet_conditioning_scale,
+                      timestep=div_timestep,
+                      guidance=None,
+                      pooled_projections=pooled_prompt_embeds_input,
+                      encoder_hidden_states=prompt_embeds_input,
+                      attention_mask=prompt_mask_input,
+                      txt_ids=text_ids_input[0],
+                      img_ids=latent_image_ids_input[0],
+                      joint_attention_kwargs=self.joint_attention_kwargs,
+                      return_dict=False
+                  )
+                else:
+                  controlnet_block_samples = None
+                  controlnet_single_block_samples = None
+                ######################################
+                # END - ADD CONTROLNET - FORWARD
+                ######################################
+
+
+                noise_pred = self.ip_adapter(
+                  image_embeds_input,
+                  latent_model_input.to(device=self.transformer.device),
+                  layer_scale=layer_scale_input,
+                  timestep=div_timestep.to(device=self.transformer.device),
+                  guidance=None,
+                  pooled_projections=pooled_prompt_embeds_input.to(device=self.transformer.device),
+                  encoder_hidden_states=prompt_embeds_input.to(device=self.transformer.device),
+                  attention_mask=prompt_mask_input.to(device=self.transformer.device),
+                  controlnet_block_samples=controlnet_block_samples,  ### A CONTROL NET INPUT
+                  controlnet_single_block_samples=controlnet_single_block_samples,  ### A CONTROL NET INPUT
+                  txt_ids=text_ids_input[0],
+                  img_ids=latent_image_ids_input[0].to(device=self.transformer.device),
+                  return_dict=False,
+                )[0]
+
+                # Apply real CFG
+                if guidance_scale_real > 1.0 and i >= no_cfg_until_timestep:
+                    if do_batch_cfg:
+                        # Batched CFG: Split the noise prediction into unconditional and conditional parts
+                        noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
+                        noise_pred = noise_pred_uncond + guidance_scale_real * (noise_pred_cond - noise_pred_uncond)
+                    else:
+                        # Sequential CFG: Compute unconditional noise prediction separately
+                        if control_image != None:
+                          controlnet_block_samples_uncond, controlnet_single_block_samples_uncond = self.controlnet(
+                              hidden_states=latent_model_input,
+                              controlnet_cond=control_image,
+                              controlnet_mode=control_mode,
+                              conditioning_scale=controlnet_conditioning_scale,
+                              timestep=div_timestep,
+                              guidance=None,
+                              pooled_projections=negative_pooled_prompt_embeds.to(device=self.transformer.device),
+                              encoder_hidden_states=negative_prompt_embeds.to(device=self.transformer.device),
+                              attention_mask=negative_mask,
+                              txt_ids=negative_text_ids.to(device=self.transformer.device) if negative_text_ids is not None else None,
+                              img_ids=latent_image_ids[0].to(device=self.transformer.device),
+                              joint_attention_kwargs=self.joint_attention_kwargs,
+                              return_dict=False
+                          )
+                        else:
+                          controlnet_block_samples_uncond = None
+                          controlnet_single_block_samples_uncond = None
+
+                        noise_pred_uncond = self.ip_adapter(
+                        image_embeds,
+                        latents.to(device=self.transformer.device),
+                        layer_scale=neg_layer_scale,
+                        timestep=div_timestep,
+                        guidance=None,
+                        pooled_projections=negative_pooled_prompt_embeds.to(device=self.transformer.device),
+                        encoder_hidden_states=negative_prompt_embeds.to(device=self.transformer.device),
+                        attention_mask=negative_mask,
+                        controlnet_block_samples=controlnet_block_samples_uncond,  ### A CONTROL NET INPUT
+                        controlnet_single_block_samples=controlnet_single_block_samples_uncond,  ### A CONTROL NET INPUT
+                        txt_ids=negative_text_ids.to(device=self.transformer.device) if negative_text_ids is not None else None,
+                        img_ids=latent_image_ids[0].to(device=self.transformer.device),
+                        return_dict=False,
+                        )[0]
+
+                        # Combine conditional and unconditional predictions
+                        noise_pred = noise_pred_uncond + guidance_scale_real * (noise_pred - noise_pred_uncond)
+
+                # Compute the previous noisy sample x_t -> x_t-1
+                latents_dtype = latents.dtype
+                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
+
+                # Ensure latents have the correct dtype
+                if latents.dtype != latents_dtype:
+                    if torch.backends.mps.is_available():
+                        latents = latents.to(latents_dtype)
+
+                # Callback at the end of the step, if provided
+                if callback_on_step_end is not None:
+                    callback_kwargs = {k: locals()[k] for k in callback_on_step_end_tensor_inputs}
+                    callback_outputs = callback_on_step_end(self, i, t, callback_kwargs)
+                    latents = callback_outputs.get("latents", latents)
+                    prompt_embeds = callback_outputs.get("prompt_embeds", prompt_embeds)
+
+                # Update the progress bar
+                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
+                    progress_bar.update()
+
+                # Mark step for XLA devices
+                if XLA_AVAILABLE:
+                    xm.mark_step()
+
+        if output_type == "latent":
+            image = latents
+
+        else:
+            latents = self._unpack_latents(
+                latents, height, width, self.vae_scale_factor
+            )
+            latents = (
+                latents / self.vae.config.scaling_factor
+            ) + self.vae.config.shift_factor
+
+            latents = latents.to(dtype=self.vae.dtype)
+
+            image = self.vae.decode(
+                latents,
+                return_dict=False,
+            )[0]
+            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 FluxPipelineOutput(images=image)
+
+    def load_ip_adapter(self, checkpoint_path):
+        """ Init model and load weights, or just load weights"""
+
+        self.ip_adapter.load_from_checkpoint(checkpoint_path)
+        self.ip_adapter.to(self.transformer.device,dtype=self.dtype)
+        self.ip_loaded = True
+
+        return self

scheduler/scheduler_config.json

@@ -0,0 +1,11 @@
+{
+  "_class_name": "FlowMatchEulerDiscreteScheduler",
+  "_diffusers_version":  "0.35.2",
+  "base_image_seq_len": 256,
+  "base_shift": 0.5,
+  "max_image_seq_len": 4096,
+  "max_shift": 1.15,
+  "num_train_timesteps": 1000,
+  "shift": 1.0,
+  "use_dynamic_shifting": false
+}

text_encoder/config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "openai/clip-vit-large-patch14",
+  "architectures": [
+    "CLIPTextModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "dropout": 0.0,
+  "eos_token_id": 2,
+  "hidden_act": "quick_gelu",
+  "hidden_size": 768,
+  "initializer_factor": 1.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 77,
+  "model_type": "clip_text_model",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "vocab_size": 49408
+}

text_encoder/model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:893d67a23f4693ed42cdab4cbad7fe3e727cf59609c40da28a46b5470f9ed082
+size 246144352

text_encoder_2/config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "google/t5-v1_1-xxl",
+  "architectures": [
+    "T5EncoderModel"
+  ],
+  "classifier_dropout": 0.0,
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dense_act_fn": "gelu_new",
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "feed_forward_proj": "gated-gelu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_max_distance": 128,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "use_cache": true,
+  "vocab_size": 32128
+}

text_encoder_2/model-00001-of-00002.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ec87bffd1923e8b2774a6d240c922a41f6143081d52cf83b8fe39e9d838c893e
+size 4994582224

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tokenizer/special_tokens_map.json

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+  "legacy": true,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}

transformer/config.json

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+{
+  "_class_name": "LibreFluxTransformer2DModel",
+  "_diffusers_version": "0.35.2",
+  "_name_or_path": "/path/to/transformer",
+  "attention_head_dim": 128,
+  "axes_dims_rope": [
+    16,
+    56,
+    56
+  ],
+  "guidance_embeds": false,
+  "in_channels": 64,
+  "joint_attention_dim": 4096,
+  "num_attention_heads": 24,
+  "num_layers": 19,
+  "num_single_layers": 38,
+  "patch_size": 1,
+  "pooled_projection_dim": 768
+}

transformer/diffusion_pytorch_model-00001-of-00003.safetensors

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transformer/diffusion_pytorch_model-00003-of-00003.safetensors

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transformer/trans.py

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+# Copyright 2024 Stability AI, The HuggingFace Team, The InstantX Team, and Terminus Research Group. All rights reserved.
+#
+# Originally licensed under the Apache License, Version 2.0 (the "License");
+# Updated to "Affero GENERAL PUBLIC LICENSE Version 3, 19 November 2007" via extensive updates to attn_mask usage.
+# This was taken from SimpleTuner and modified as needed
+
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from diffusers.configuration_utils import ConfigMixin, register_to_config
+from diffusers.loaders import FromOriginalModelMixin, PeftAdapterMixin
+from diffusers.models.attention import FeedForward
+from diffusers.models.attention_processor import (
+    Attention,
+    AttentionProcessor,
+)
+from diffusers.models.modeling_utils import ModelMixin
+from diffusers.models.normalization import (
+    AdaLayerNormContinuous,
+    AdaLayerNormZero,
+    AdaLayerNormZeroSingle,
+)
+from diffusers.utils import (
+    USE_PEFT_BACKEND,
+    is_torch_version,
+    logging,
+    scale_lora_layers,
+    unscale_lora_layers,
+)
+from diffusers.utils.torch_utils import maybe_allow_in_graph
+from diffusers.models.embeddings import (
+    CombinedTimestepGuidanceTextProjEmbeddings,
+    CombinedTimestepTextProjEmbeddings,
+    FluxPosEmbed,
+)
+
+from diffusers.models.modeling_outputs import Transformer2DModelOutput
+from diffusers import FluxTransformer2DModel as OriginalFluxTransformer2DModel
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+
+class FluxAttnProcessor2_0:
+    """Attention processor used typically in processing the SD3-like self-attention projections."""
+
+    def __init__(self):
+        if not hasattr(F, "scaled_dot_product_attention"):
+            raise ImportError(
+                "FluxAttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
+            )
+
+    def __call__(
+        self,
+        attn: Attention,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+        ip_encoder_hidden_states:Optional[torch.Tensor] = None,
+        layer_scale:Optional[torch.Tensor] = None,  
+    ) -> torch.FloatTensor:
+        batch_size, _, _ = (
+            hidden_states.shape
+            if encoder_hidden_states is None
+            else encoder_hidden_states.shape
+        )
+
+        # `sample` projections.
+        query = attn.to_q(hidden_states)
+        key = attn.to_k(hidden_states)
+        value = attn.to_v(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)
+
+        if attn.norm_q is not None:
+            query = attn.norm_q(query)
+        if attn.norm_k is not None:
+            key = attn.norm_k(key)
+
+        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
+        if encoder_hidden_states is not None:
+            # `context` projections.
+            encoder_hidden_states_query_proj = attn.add_q_proj(encoder_hidden_states)
+            encoder_hidden_states_key_proj = attn.add_k_proj(encoder_hidden_states)
+            encoder_hidden_states_value_proj = attn.add_v_proj(encoder_hidden_states)
+
+            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
+                batch_size, -1, attn.heads, head_dim
+            ).transpose(1, 2)
+
+            if attn.norm_added_q is not None:
+                encoder_hidden_states_query_proj = attn.norm_added_q(
+                    encoder_hidden_states_query_proj
+                )
+            if attn.norm_added_k is not None:
+                encoder_hidden_states_key_proj = attn.norm_added_k(
+                    encoder_hidden_states_key_proj
+                )
+
+            # attention
+            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
+            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
+            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)
+
+        if image_rotary_emb is not None:
+            from diffusers.models.embeddings import apply_rotary_emb
+
+            query = apply_rotary_emb(query, image_rotary_emb)
+            key = apply_rotary_emb(key, image_rotary_emb)
+
+        if attention_mask is not None:
+            #print ('Attention Used')
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_mask = (attention_mask > 0).bool()
+            # Edit 17 - match attn dtype to query d-type
+            attention_mask = attention_mask.to(
+                device=hidden_states.device, dtype=query.dtype
+            )
+
+        hidden_states = F.scaled_dot_product_attention(
+            query,
+            key,
+            value,
+            dropout_p=0.0,
+            is_causal=False,
+            attn_mask=attention_mask,
+        )
+        hidden_states = hidden_states.transpose(1, 2).reshape(
+            batch_size, -1, attn.heads * head_dim
+        )
+        hidden_states = hidden_states.to(query.dtype)
+
+        if encoder_hidden_states is not None:
+            encoder_hidden_states, hidden_states = (
+                hidden_states[:, : encoder_hidden_states.shape[1]],
+                hidden_states[:, encoder_hidden_states.shape[1] :],
+            )
+
+            # linear proj
+            hidden_states = attn.to_out[0](hidden_states)
+            # dropout
+            hidden_states = attn.to_out[1](hidden_states)
+            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)
+
+            return hidden_states, encoder_hidden_states
+        return hidden_states
+
+
+def expand_flux_attention_mask(
+    hidden_states: torch.Tensor,
+    attn_mask: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Expand a mask so that the image is included.
+    """
+    bsz = attn_mask.shape[0]
+    assert bsz == hidden_states.shape[0]
+    residual_seq_len = hidden_states.shape[1]
+    mask_seq_len = attn_mask.shape[1]
+
+    expanded_mask = torch.ones(bsz, residual_seq_len)
+    expanded_mask[:, :mask_seq_len] = attn_mask
+
+    return expanded_mask
+
+
+@maybe_allow_in_graph
+class FluxSingleTransformerBlock(nn.Module):
+    r"""
+    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+    Reference: https://arxiv.org/abs/2403.03206
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+            processing of `context` conditions.
+    """
+
+    def __init__(self, dim, num_attention_heads, attention_head_dim, mlp_ratio=4.0):
+        super().__init__()
+        self.mlp_hidden_dim = int(dim * mlp_ratio)
+
+        self.norm = AdaLayerNormZeroSingle(dim)
+        self.proj_mlp = nn.Linear(dim, self.mlp_hidden_dim)
+        self.act_mlp = nn.GELU(approximate="tanh")
+        self.proj_out = nn.Linear(dim + self.mlp_hidden_dim, dim)
+
+        processor = FluxAttnProcessor2_0()
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            bias=True,
+            processor=processor,
+            qk_norm="rms_norm",
+            eps=1e-6,
+            pre_only=True,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        attention_mask: Optional[torch.Tensor] = None,
+        joint_attention_kwargs: dict = {},
+    ):
+        residual = hidden_states
+        norm_hidden_states, gate = self.norm(hidden_states, emb=temb)
+        mlp_hidden_states = self.act_mlp(self.proj_mlp(norm_hidden_states))
+
+        if attention_mask is not None:
+            attention_mask = expand_flux_attention_mask(
+                hidden_states,
+                attention_mask,
+            )
+
+        # Adding ability to pass hidden state info to IP Adapter
+        ip_encoder_hidden_states = None
+        ip_layer_scale = 1.0
+        if 'ip_hidden_states' in joint_attention_kwargs:
+            ip_encoder_hidden_states = joint_attention_kwargs['ip_hidden_states']
+        if 'ip_layer_scale' in joint_attention_kwargs:
+            ip_layer_scale = joint_attention_kwargs['ip_layer_scale']
+            
+        # Attention.
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+            ip_encoder_hidden_states=ip_encoder_hidden_states,
+            layer_scale=ip_layer_scale,
+        )    
+        #attn_output = self.attn(
+        #    hidden_states=norm_hidden_states,
+        #    image_rotary_emb=image_rotary_emb,
+        #    attention_mask=attention_mask,
+        #)
+
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        gate = gate.unsqueeze(1)
+        hidden_states = gate * self.proj_out(hidden_states)
+        hidden_states = residual + hidden_states
+
+        if hidden_states.dtype == torch.float16:
+            hidden_states = hidden_states.clip(-65504, 65504)
+
+        return hidden_states
+
+
+@maybe_allow_in_graph
+class FluxTransformerBlock(nn.Module):
+    r"""
+    A Transformer block following the MMDiT architecture, introduced in Stable Diffusion 3.
+
+    Reference: https://arxiv.org/abs/2403.03206
+
+    Parameters:
+        dim (`int`): The number of channels in the input and output.
+        num_attention_heads (`int`): The number of heads to use for multi-head attention.
+        attention_head_dim (`int`): The number of channels in each head.
+        context_pre_only (`bool`): Boolean to determine if we should add some blocks associated with the
+            processing of `context` conditions.
+    """
+
+    def __init__(
+        self, dim, num_attention_heads, attention_head_dim, qk_norm="rms_norm", eps=1e-6
+    ):
+        super().__init__()
+
+        self.norm1 = AdaLayerNormZero(dim)
+
+        self.norm1_context = AdaLayerNormZero(dim)
+
+        if hasattr(F, "scaled_dot_product_attention"):
+            processor = FluxAttnProcessor2_0()
+        else:
+            raise ValueError(
+                "The current PyTorch version does not support the `scaled_dot_product_attention` function."
+            )
+        self.attn = Attention(
+            query_dim=dim,
+            cross_attention_dim=None,
+            added_kv_proj_dim=dim,
+            dim_head=attention_head_dim,
+            heads=num_attention_heads,
+            out_dim=dim,
+            context_pre_only=False,
+            bias=True,
+            processor=processor,
+            qk_norm=qk_norm,
+            eps=eps,
+        )
+
+        self.norm2 = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff = FeedForward(dim=dim, dim_out=dim, activation_fn="gelu-approximate")
+
+        self.norm2_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-6)
+        self.ff_context = FeedForward(
+            dim=dim, dim_out=dim, activation_fn="gelu-approximate"
+        )
+
+        # let chunk size default to None
+        self._chunk_size = None
+        self._chunk_dim = 0
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        encoder_hidden_states: torch.FloatTensor,
+        temb: torch.FloatTensor,
+        image_rotary_emb=None,
+        attention_mask: Optional[torch.Tensor] = None,
+        joint_attention_kwargs: dict = {},
+    ):
+        norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1(
+            hidden_states, emb=temb
+        )
+
+        norm_encoder_hidden_states, c_gate_msa, c_shift_mlp, c_scale_mlp, c_gate_mlp = (
+            self.norm1_context(encoder_hidden_states, emb=temb)
+        )
+
+        if attention_mask is not None:
+            attention_mask = expand_flux_attention_mask(
+                torch.cat([encoder_hidden_states, hidden_states], dim=1),
+                attention_mask,
+            )
+
+        # Adding ability to pass hidden state info to IP Adapter
+        ip_encoder_hidden_states = None
+        ip_layer_scale = 1.0
+        if 'ip_hidden_states' in joint_attention_kwargs:
+            ip_encoder_hidden_states = joint_attention_kwargs['ip_hidden_states']
+        if 'ip_layer_scale' in joint_attention_kwargs:
+            ip_layer_scale = joint_attention_kwargs['ip_layer_scale']
+
+        # Attention.
+        attention_outputs = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+            attention_mask=attention_mask,
+            ip_encoder_hidden_states=ip_encoder_hidden_states,
+            layer_scale=ip_layer_scale,
+        )
+        if len(attention_outputs) == 2:
+            attn_output, context_attn_output = attention_outputs
+        elif len(attention_outputs) == 3:
+            attn_output, context_attn_output, ip_attn_output = attention_outputs
+
+        # Process attention outputs for the `hidden_states`.
+        attn_output = gate_msa.unsqueeze(1) * attn_output
+        hidden_states = hidden_states + attn_output
+
+        norm_hidden_states = self.norm2(hidden_states)
+        norm_hidden_states = (
+            norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None]
+        )
+
+        ff_output = self.ff(norm_hidden_states)
+        ff_output = gate_mlp.unsqueeze(1) * ff_output
+
+        hidden_states = hidden_states + ff_output
+
+        # Removing this, was casuing error after adding ip_adapter
+        #if len(attention_outputs) == 3:
+        #    hidden_states = hidden_states + ip_attn_output
+
+        # Process attention outputs for the `encoder_hidden_states`.
+        context_attn_output = c_gate_msa.unsqueeze(1) * context_attn_output
+        encoder_hidden_states = encoder_hidden_states + context_attn_output
+
+        norm_encoder_hidden_states = self.norm2_context(encoder_hidden_states)
+        norm_encoder_hidden_states = (
+            norm_encoder_hidden_states * (1 + c_scale_mlp[:, None])
+            + c_shift_mlp[:, None]
+        )
+
+        context_ff_output = self.ff_context(norm_encoder_hidden_states)
+        encoder_hidden_states = (
+            encoder_hidden_states + c_gate_mlp.unsqueeze(1) * context_ff_output
+        )
+
+        if encoder_hidden_states.dtype == torch.float16:
+            encoder_hidden_states = encoder_hidden_states.clip(-65504, 65504)
+
+        return encoder_hidden_states, hidden_states
+
+class LibreFluxTransformer2DModel(
+    ModelMixin, ConfigMixin, PeftAdapterMixin, FromOriginalModelMixin
+):
+    """
+    The Transformer model introduced in Flux.
+
+    Reference: https://blackforestlabs.ai/announcing-black-forest-labs/
+
+    Parameters:
+        patch_size (`int`): Patch size to turn the input data into small patches.
+        in_channels (`int`, *optional*, defaults to 16): The number of channels in the input.
+        num_layers (`int`, *optional*, defaults to 18): The number of layers of MMDiT blocks to use.
+        num_single_layers (`int`, *optional*, defaults to 18): The number of layers of single DiT blocks to use.
+        attention_head_dim (`int`, *optional*, defaults to 64): The number of channels in each head.
+        num_attention_heads (`int`, *optional*, defaults to 18): The number of heads to use for multi-head attention.
+        joint_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
+        pooled_projection_dim (`int`): Number of dimensions to use when projecting the `pooled_projections`.
+        guidance_embeds (`bool`, defaults to False): Whether to use guidance embeddings.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    @register_to_config
+    def __init__(
+        self,
+        patch_size: int = 1,
+        in_channels: int = 64,
+        num_layers: int = 19,
+        num_single_layers: int = 38,
+        attention_head_dim: int = 128,
+        num_attention_heads: int = 24,
+        joint_attention_dim: int = 4096,
+        pooled_projection_dim: int = 768,
+        guidance_embeds: bool = False,
+        axes_dims_rope: Tuple[int] = (16, 56, 56),
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = (
+            self.config.num_attention_heads * self.config.attention_head_dim
+        )
+
+        self.pos_embed = FluxPosEmbed(theta=10000, axes_dim=axes_dims_rope)
+        text_time_guidance_cls = (
+            CombinedTimestepGuidanceTextProjEmbeddings  ### 3 input forward (timestep, guidance, pooled_projection)
+            if guidance_embeds
+            else CombinedTimestepTextProjEmbeddings  #### 2 input forward (timestep, pooled_projection)
+        )
+        self.time_text_embed = text_time_guidance_cls(
+            embedding_dim=self.inner_dim,
+            pooled_projection_dim=self.config.pooled_projection_dim,
+        )
+
+        self.context_embedder = nn.Linear(
+            self.config.joint_attention_dim, self.inner_dim
+        )
+        self.x_embedder = torch.nn.Linear(self.config.in_channels, self.inner_dim)
+
+        self.transformer_blocks = nn.ModuleList(
+            [
+                FluxTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_layers)
+            ]
+        )
+
+        self.single_transformer_blocks = nn.ModuleList(
+            [
+                FluxSingleTransformerBlock(
+                    dim=self.inner_dim,
+                    num_attention_heads=self.config.num_attention_heads,
+                    attention_head_dim=self.config.attention_head_dim,
+                )
+                for i in range(self.config.num_single_layers)
+            ]
+        )
+
+        self.norm_out = AdaLayerNormContinuous(
+            self.inner_dim, self.inner_dim, elementwise_affine=False, eps=1e-6
+        )
+        self.proj_out = nn.Linear(
+            self.inner_dim, patch_size * patch_size * self.out_channels, bias=True
+        )
+
+        self.gradient_checkpointing = False
+        # added for users to disable checkpointing every nth step
+        self.gradient_checkpointing_interval = None
+
+    def set_gradient_checkpointing_interval(self, value: int):
+        self.gradient_checkpointing_interval = value
+
+    @property
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
+    def attn_processors(self) -> Dict[str, AttentionProcessor]:
+        r"""
+        Returns:
+            `dict` of attention processors: A dictionary containing all attention processors used in the model with
+            indexed by its weight name.
+        """
+        # set recursively
+        processors = {}
+
+        def fn_recursive_add_processors(
+            name: str,
+            module: torch.nn.Module,
+            processors: Dict[str, AttentionProcessor],
+        ):
+            if hasattr(module, "get_processor"):
+                processors[f"{name}.processor"] = module.get_processor()
+
+            for sub_name, child in module.named_children():
+                fn_recursive_add_processors(f"{name}.{sub_name}", child, processors)
+
+            return processors
+
+        for name, module in self.named_children():
+            fn_recursive_add_processors(name, module, processors)
+
+        return processors
+
+    # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor
+    def set_attn_processor(
+        self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]
+    ):
+        r"""
+        Sets the attention processor to use to compute attention.
+
+        Parameters:
+            processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`):
+                The instantiated processor class or a dictionary of processor classes that will be set as the processor
+                for **all** `Attention` layers.
+
+                If `processor` is a dict, the key needs to define the path to the corresponding cross attention
+                processor. This is strongly recommended when setting trainable attention processors.
+
+        """
+        count = len(self.attn_processors.keys())
+
+        if isinstance(processor, dict) and len(processor) != count:
+            raise ValueError(
+                f"A dict of processors was passed, but the number of processors {len(processor)} does not match the"
+                f" number of attention layers: {count}. Please make sure to pass {count} processor classes."
+            )
+
+        def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor):
+            if hasattr(module, "set_processor"):
+                if not isinstance(processor, dict):
+                    module.set_processor(processor)
+                else:
+                    module.set_processor(processor.pop(f"{name}.processor"))
+
+            for sub_name, child in module.named_children():
+                fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor)
+
+        for name, module in self.named_children():
+            fn_recursive_attn_processor(name, module, processor)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor = None,
+        pooled_projections: torch.Tensor = None,
+        timestep: torch.LongTensor = None,
+        img_ids: torch.Tensor = None,
+        txt_ids: torch.Tensor = None,
+        guidance: torch.Tensor = None,
+        joint_attention_kwargs: dict = {},
+        controlnet_block_samples=None,
+        controlnet_single_block_samples=None,
+        return_dict: bool = True,
+        attention_mask: Optional[torch.Tensor] = None,
+        controlnet_blocks_repeat: bool = False,
+    ) -> Union[torch.FloatTensor, Transformer2DModelOutput]:
+        """
+        The [`FluxTransformer2DModel`] forward method.
+
+        Args:
+            hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`):
+                Input `hidden_states`.
+            encoder_hidden_states (`torch.FloatTensor` of shape `(batch size, sequence_len, embed_dims)`):
+                Conditional embeddings (embeddings computed from the input conditions such as prompts) to use.
+            pooled_projections (`torch.FloatTensor` of shape `(batch_size, projection_dim)`): Embeddings projected
+                from the embeddings of input conditions.
+            timestep ( `torch.LongTensor`):
+                Used to indicate denoising step.
+            block_controlnet_hidden_states: (`list` of `torch.Tensor`):
+                A list of tensors that if specified are added to the residuals of transformer blocks.
+            joint_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).
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`~models.transformer_2d.Transformer2DModelOutput`] instead of a plain
+                tuple.
+
+        Returns:
+            If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
+            `tuple` where the first element is the sample tensor.
+        """
+        if joint_attention_kwargs is not None:
+            joint_attention_kwargs = joint_attention_kwargs.copy()
+            lora_scale = joint_attention_kwargs.pop("scale", 1.0)
+        else:
+            lora_scale = 1.0
+
+        if USE_PEFT_BACKEND:
+            # weight the lora layers by setting `lora_scale` for each PEFT layer
+            scale_lora_layers(self, lora_scale)
+        else:
+            if (
+                joint_attention_kwargs is not None
+                and joint_attention_kwargs.get("scale", None) is not None
+            ):
+                logger.warning(
+                    "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
+                )
+        hidden_states = self.x_embedder(hidden_states)
+
+        timestep = timestep.to(hidden_states.dtype) * 1000
+        if guidance is not None:
+            guidance = guidance.to(hidden_states.dtype) * 1000
+        else:
+            guidance = None
+
+        #print( self.time_text_embed)
+        temb = (
+            self.time_text_embed(timestep,pooled_projections)
+            # Edit 1 # Charlie   NOT NEEDED - UNDONE
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        if txt_ids.ndim == 3:
+            txt_ids = txt_ids[0]
+        if img_ids.ndim == 3:
+            img_ids = img_ids[0]
+
+        ids = torch.cat((txt_ids, img_ids), dim=0)
+
+        image_rotary_emb = self.pos_embed(ids)
+
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if (
+                self.training
+                and self.gradient_checkpointing
+                and (
+                    self.gradient_checkpointing_interval is None
+                    or index_block % self.gradient_checkpointing_interval == 0
+                )
+            ):
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                encoder_hidden_states, hidden_states = (
+                    torch.utils.checkpoint.checkpoint(
+                        create_custom_forward(block),
+                        hidden_states,
+                        encoder_hidden_states,
+                        temb,
+                        image_rotary_emb,
+                        attention_mask,
+                        joint_attention_kwargs,  # Add this line
+                        **ckpt_kwargs,
+                    )
+                )
+
+            else:
+                encoder_hidden_states, hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask,
+                    joint_attention_kwargs=joint_attention_kwargs  # Add this line
+                )
+
+            # controlnet residual
+            if controlnet_block_samples is not None:
+                interval_control = len(self.transformer_blocks) / len(
+                    controlnet_block_samples
+                )
+                interval_control = int(np.ceil(interval_control))
+                # For Xlabs ControlNet.
+                if controlnet_blocks_repeat:
+                    hidden_states = (
+                        hidden_states
+                        + controlnet_block_samples[
+                            index_block % len(controlnet_block_samples)
+                        ]
+                    )
+                else:
+                    hidden_states = (
+                        hidden_states
+                        + controlnet_block_samples[index_block // interval_control]
+                    )
+
+        # Flux places the text tokens in front of the image tokens in the
+        # sequence.
+        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
+
+        for index_block, block in enumerate(self.single_transformer_blocks):
+            if (
+                self.training
+                and self.gradient_checkpointing
+                or (
+                    self.gradient_checkpointing_interval is not None
+                    and index_block % self.gradient_checkpointing_interval == 0
+                )
+            ):
+
+                def create_custom_forward(module, return_dict=None):
+                    def custom_forward(*inputs):
+                        if return_dict is not None:
+                            return module(*inputs, return_dict=return_dict)
+                        else:
+                            return module(*inputs)
+
+                    return custom_forward
+
+                ckpt_kwargs: Dict[str, Any] = (
+                    {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {}
+                )
+                hidden_states = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(block),
+                    hidden_states,
+                    temb,
+                    image_rotary_emb,
+                    attention_mask,
+                    joint_attention_kwargs,  # Add this line
+
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                    attention_mask=attention_mask,
+                    joint_attention_kwargs= joint_attention_kwargs,  # Add this line
+
+                )
+
+            # controlnet residual
+            if controlnet_single_block_samples is not None:
+                interval_control = len(self.single_transformer_blocks) / len(
+                    controlnet_single_block_samples
+                )
+                interval_control = int(np.ceil(interval_control))
+                hidden_states[:, encoder_hidden_states.shape[1] :, ...] = (
+                    hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+                    + controlnet_single_block_samples[index_block // interval_control]
+                )
+
+        hidden_states = hidden_states[:, encoder_hidden_states.shape[1] :, ...]
+
+        hidden_states = self.norm_out(hidden_states, temb)
+        output = self.proj_out(hidden_states)
+
+        if USE_PEFT_BACKEND:
+            # remove `lora_scale` from each PEFT layer
+            unscale_lora_layers(self, lora_scale)
+
+        if not return_dict:
+            return (output,)
+
+        return Transformer2DModelOutput(sample=output)
+

vae/config.json

@@ -0,0 +1,37 @@
+{
+  "_class_name": "AutoencoderKL",
+  "_diffusers_version": "0.30.0.dev0",
+  "act_fn": "silu",
+  "block_out_channels": [
+    128,
+    256,
+    512,
+    512
+  ],
+  "down_block_types": [
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D",
+    "DownEncoderBlock2D"
+  ],
+  "force_upcast": true,
+  "in_channels": 3,
+  "latent_channels": 16,
+  "latents_mean": null,
+  "latents_std": null,
+  "layers_per_block": 2,
+  "mid_block_add_attention": true,
+  "norm_num_groups": 32,
+  "out_channels": 3,
+  "sample_size": 1024,
+  "scaling_factor": 0.3611,
+  "shift_factor": 0.1159,
+  "up_block_types": [
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D",
+    "UpDecoderBlock2D"
+  ],
+  "use_post_quant_conv": false,
+  "use_quant_conv": false
+}

vae/diffusion_pytorch_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f5b59a26851551b67ae1fe58d32e76486e1e812def4696a4bea97f16604d40a3
+size 167666902