Flux pipeline (#9043)

add flux!
Signed-off-by: Adrien <adrien@huggingface.co>
Co-authored-by: Adrien <adrien.69740@gmail.com>
Co-authored-by: Anatoly Belikov <abelikov@singularitynet.io>
Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
Co-authored-by: yiyixuxu <yixu310@gmail.com>

docs/source/en/_toctree.yml

@@ -253,6 +253,8 @@
       title: HunyuanDiT2DModel
     - local: api/models/aura_flow_transformer2d
       title: AuraFlowTransformer2DModel
+    - local: api/models/flux_transformer
+      title: FluxTransformer2DModel
     - local: api/models/latte_transformer3d
       title: LatteTransformer3DModel
     - local: api/models/lumina_nextdit2d
@@ -320,6 +322,8 @@
       title: DiffEdit
     - local: api/pipelines/dit
       title: DiT
+    - local: api/pipelines/flux
+      title: Flux
     - local: api/pipelines/hunyuandit
       title: Hunyuan-DiT
     - local: api/pipelines/i2vgenxl

docs/source/en/api/models/flux_transformer.md

+<!--Copyright 2024 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.
+-->
+
+# FluxTransformer2DModel
+
+A Transformer model for image-like data from [Flux](https://blackforestlabs.ai/announcing-black-forest-labs/).
+
+## FluxTransformer2DModel
+
+[[autodoc]] FluxTransformer2DModel

docs/source/en/api/pipelines/flux.md

+<!--Copyright 2024 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.
+-->
+
+# Flux
+
+Flux is a series of text-to-image generation models based on diffusion transformers. To know more about Flux, check out the original [blog post](https://blackforestlabs.ai/announcing-black-forest-labs/) by the creators of Flux, Black Forest Labs. 
+
+Original model checkpoints for Flux can be found [here](https://huggingface.co/black-forest-labs). Original inference code can be found [here](https://github.com/black-forest-labs/flux).
+
+<Tip>
+
+Flux can be quite expensive to run on consumer hardware devices. However, you can perform a suite of optimizations to run it faster and in a more memory-friendly manner. Check out [this section](https://huggingface.co/blog/sd3#memory-optimizations-for-sd3) for more details. Additionally, Flux can benefit from quantization for memory efficiency with a trade-off in inference latency. Refer to [this blog post](https://huggingface.co/blog/quanto-diffusers) to learn more. 
+
+</Tip>
+
+Flux comes in two variants:
+
+* Timestep-distilled (`black-forest-labs/FLUX.1-schnell`)
+* Guidance-distilled (`black-forest-labs/FLUX.1-dev`)
+
+Both checkpoints have slightly difference usage which we detail below. 
+
+### Timestep-distilled
+
+* `max_sequence_length` cannot be more than 256. 
+* `guidance_scale` needs to be 0.
+* As this is a timestep-distilled model, it benefits from fewer sampling steps.
+
+```python
+import torch
+from diffusers import  FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = "A cat holding a sign that says hello world"
+out = pipe(
+    prompt=prompt, 
+    guidance_scale=0., 
+    height=768, 
+    width=1360, 
+    num_inference_steps=4, 
+    max_sequence_length=256,
+).images[0]
+out.save("image.png")
+```
+
+### Guidance-distilled
+
+* The guidance-distilled variant takes about 50 sampling steps for good-quality generation.
+* It doesn't have any limitations around the `max_sequence_length`. 
+
+```python
+import torch
+from diffusers import  FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload()
+
+prompt = "a tiny astronaut hatching from an egg on the moon"
+out = pipe(
+    prompt=prompt, 
+    guidance_scale=3.5, 
+    height=768, 
+    width=1360, 
+    num_inference_steps=50,
+).images[0]
+out.save("image.png")
+```
+
+## FluxPipeline
+
+[[autodoc]] FluxPipeline
+	- all
+	- __call__
\ No newline at end of file

scripts/convert_flux_to_diffusers.py

+import argparse
+from contextlib import nullcontext
+
+import safetensors.torch
+import torch
+from accelerate import init_empty_weights
+from huggingface_hub import hf_hub_download
+
+from diffusers import AutoencoderKL, FluxTransformer2DModel
+from diffusers.loaders.single_file_utils import convert_ldm_vae_checkpoint
+from diffusers.utils.import_utils import is_accelerate_available
+
+
+"""
+# Transformer
+
+python scripts/convert_flux_to_diffusers.py  \
+--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \
+--filename "flux1-schnell.sft"
+--output_path "flux-schnell" \
+--transformer
+"""
+
+"""
+# VAE
+
+python scripts/convert_flux_to_diffusers.py  \
+--original_state_dict_repo_id "black-forest-labs/FLUX.1-schnell" \
+--filename "ae.sft"
+--output_path "flux-schnell" \
+--vae
+"""
+
+CTX = init_empty_weights if is_accelerate_available else nullcontext
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--original_state_dict_repo_id", default=None, type=str)
+parser.add_argument("--filename", default="flux.safetensors", type=str)
+parser.add_argument("--checkpoint_path", default=None, type=str)
+parser.add_argument("--vae", action="store_true")
+parser.add_argument("--transformer", action="store_true")
+parser.add_argument("--output_path", type=str)
+parser.add_argument("--dtype", type=str, default="bf16")
+
+args = parser.parse_args()
+dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float32
+
+
+def load_original_checkpoint(args):
+    if args.original_state_dict_repo_id is not None:
+        ckpt_path = hf_hub_download(repo_id=args.original_state_dict_repo_id, filename=args.filename)
+    elif args.checkpoint_path is not None:
+        ckpt_path = args.checkpoint_path
+    else:
+        raise ValueError(" please provide either `original_state_dict_repo_id` or a local `checkpoint_path`")
+
+    original_state_dict = safetensors.torch.load_file(ckpt_path)
+    return original_state_dict
+
+
+# in SD3 original implementation of AdaLayerNormContinuous, it split linear projection output into shift, scale;
+# while in diffusers it split into scale, shift. Here we swap the linear projection weights in order to be able to use diffusers implementation
+def swap_scale_shift(weight):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+
+def convert_flux_transformer_checkpoint_to_diffusers(
+    original_state_dict, num_layers, num_single_layers, inner_dim, mlp_ratio=4.0
+):
+    converted_state_dict = {}
+
+    ## time_text_embed.timestep_embedder <-  time_in
+    converted_state_dict["time_text_embed.timestep_embedder.linear_1.weight"] = original_state_dict.pop(
+        "time_in.in_layer.weight"
+    )
+    converted_state_dict["time_text_embed.timestep_embedder.linear_1.bias"] = original_state_dict.pop(
+        "time_in.in_layer.bias"
+    )
+    converted_state_dict["time_text_embed.timestep_embedder.linear_2.weight"] = original_state_dict.pop(
+        "time_in.out_layer.weight"
+    )
+    converted_state_dict["time_text_embed.timestep_embedder.linear_2.bias"] = original_state_dict.pop(
+        "time_in.out_layer.bias"
+    )
+
+    ## time_text_embed.text_embedder <- vector_in
+    converted_state_dict["time_text_embed.text_embedder.linear_1.weight"] = original_state_dict.pop(
+        "vector_in.in_layer.weight"
+    )
+    converted_state_dict["time_text_embed.text_embedder.linear_1.bias"] = original_state_dict.pop(
+        "vector_in.in_layer.bias"
+    )
+    converted_state_dict["time_text_embed.text_embedder.linear_2.weight"] = original_state_dict.pop(
+        "vector_in.out_layer.weight"
+    )
+    converted_state_dict["time_text_embed.text_embedder.linear_2.bias"] = original_state_dict.pop(
+        "vector_in.out_layer.bias"
+    )
+
+    # guidance
+    has_guidance = any("guidance" in k for k in original_state_dict)
+    if has_guidance:
+        converted_state_dict["time_text_embed.guidance_embedder.linear_1.weight"] = original_state_dict.pop(
+            "guidance_in.in_layer.weight"
+        )
+        converted_state_dict["time_text_embed.guidance_embedder.linear_1.bias"] = original_state_dict.pop(
+            "guidance_in.in_layer.bias"
+        )
+        converted_state_dict["time_text_embed.guidance_embedder.linear_2.weight"] = original_state_dict.pop(
+            "guidance_in.out_layer.weight"
+        )
+        converted_state_dict["time_text_embed.guidance_embedder.linear_2.bias"] = original_state_dict.pop(
+            "guidance_in.out_layer.bias"
+        )
+
+    # context_embedder
+    converted_state_dict["context_embedder.weight"] = original_state_dict.pop("txt_in.weight")
+    converted_state_dict["context_embedder.bias"] = original_state_dict.pop("txt_in.bias")
+
+    # x_embedder
+    converted_state_dict["x_embedder.weight"] = original_state_dict.pop("img_in.weight")
+    converted_state_dict["x_embedder.bias"] = original_state_dict.pop("img_in.bias")
+
+    # double transformer blocks
+    for i in range(num_layers):
+        block_prefix = f"transformer_blocks.{i}."
+        # norms.
+        ## norm1
+        converted_state_dict[f"{block_prefix}norm1.linear.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mod.lin.weight"
+        )
+        converted_state_dict[f"{block_prefix}norm1.linear.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mod.lin.bias"
+        )
+        ## norm1_context
+        converted_state_dict[f"{block_prefix}norm1_context.linear.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mod.lin.weight"
+        )
+        converted_state_dict[f"{block_prefix}norm1_context.linear.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mod.lin.bias"
+        )
+        # Q, K, V
+        sample_q, sample_k, sample_v = torch.chunk(
+            original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.weight"), 3, dim=0
+        )
+        context_q, context_k, context_v = torch.chunk(
+            original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.weight"), 3, dim=0
+        )
+        sample_q_bias, sample_k_bias, sample_v_bias = torch.chunk(
+            original_state_dict.pop(f"double_blocks.{i}.img_attn.qkv.bias"), 3, dim=0
+        )
+        context_q_bias, context_k_bias, context_v_bias = torch.chunk(
+            original_state_dict.pop(f"double_blocks.{i}.txt_attn.qkv.bias"), 3, dim=0
+        )
+        converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([sample_q])
+        converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([sample_q_bias])
+        converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([sample_k])
+        converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([sample_k_bias])
+        converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([sample_v])
+        converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([sample_v_bias])
+        converted_state_dict[f"{block_prefix}attn.add_q_proj.weight"] = torch.cat([context_q])
+        converted_state_dict[f"{block_prefix}attn.add_q_proj.bias"] = torch.cat([context_q_bias])
+        converted_state_dict[f"{block_prefix}attn.add_k_proj.weight"] = torch.cat([context_k])
+        converted_state_dict[f"{block_prefix}attn.add_k_proj.bias"] = torch.cat([context_k_bias])
+        converted_state_dict[f"{block_prefix}attn.add_v_proj.weight"] = torch.cat([context_v])
+        converted_state_dict[f"{block_prefix}attn.add_v_proj.bias"] = torch.cat([context_v_bias])
+        # qk_norm
+        converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_attn.norm.query_norm.scale"
+        )
+        converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_attn.norm.key_norm.scale"
+        )
+        converted_state_dict[f"{block_prefix}attn.norm_added_q.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_attn.norm.query_norm.scale"
+        )
+        converted_state_dict[f"{block_prefix}attn.norm_added_k.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_attn.norm.key_norm.scale"
+        )
+        # ff img_mlp
+        converted_state_dict[f"{block_prefix}ff.net.0.proj.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mlp.0.weight"
+        )
+        converted_state_dict[f"{block_prefix}ff.net.0.proj.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mlp.0.bias"
+        )
+        converted_state_dict[f"{block_prefix}ff.net.2.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mlp.2.weight"
+        )
+        converted_state_dict[f"{block_prefix}ff.net.2.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_mlp.2.bias"
+        )
+        converted_state_dict[f"{block_prefix}ff_context.net.0.proj.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mlp.0.weight"
+        )
+        converted_state_dict[f"{block_prefix}ff_context.net.0.proj.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mlp.0.bias"
+        )
+        converted_state_dict[f"{block_prefix}ff_context.net.2.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mlp.2.weight"
+        )
+        converted_state_dict[f"{block_prefix}ff_context.net.2.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_mlp.2.bias"
+        )
+        # output projections.
+        converted_state_dict[f"{block_prefix}attn.to_out.0.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_attn.proj.weight"
+        )
+        converted_state_dict[f"{block_prefix}attn.to_out.0.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.img_attn.proj.bias"
+        )
+        converted_state_dict[f"{block_prefix}attn.to_add_out.weight"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_attn.proj.weight"
+        )
+        converted_state_dict[f"{block_prefix}attn.to_add_out.bias"] = original_state_dict.pop(
+            f"double_blocks.{i}.txt_attn.proj.bias"
+        )
+
+    # single transfomer blocks
+    for i in range(num_single_layers):
+        block_prefix = f"single_transformer_blocks.{i}."
+        # norm.linear  <- single_blocks.0.modulation.lin
+        converted_state_dict[f"{block_prefix}norm.linear.weight"] = original_state_dict.pop(
+            f"single_blocks.{i}.modulation.lin.weight"
+        )
+        converted_state_dict[f"{block_prefix}norm.linear.bias"] = original_state_dict.pop(
+            f"single_blocks.{i}.modulation.lin.bias"
+        )
+        # Q, K, V, mlp
+        mlp_hidden_dim = int(inner_dim * mlp_ratio)
+        split_size = (inner_dim, inner_dim, inner_dim, mlp_hidden_dim)
+        q, k, v, mlp = torch.split(original_state_dict.pop(f"single_blocks.{i}.linear1.weight"), split_size, dim=0)
+        q_bias, k_bias, v_bias, mlp_bias = torch.split(
+            original_state_dict.pop(f"single_blocks.{i}.linear1.bias"), split_size, dim=0
+        )
+        converted_state_dict[f"{block_prefix}attn.to_q.weight"] = torch.cat([q])
+        converted_state_dict[f"{block_prefix}attn.to_q.bias"] = torch.cat([q_bias])
+        converted_state_dict[f"{block_prefix}attn.to_k.weight"] = torch.cat([k])
+        converted_state_dict[f"{block_prefix}attn.to_k.bias"] = torch.cat([k_bias])
+        converted_state_dict[f"{block_prefix}attn.to_v.weight"] = torch.cat([v])
+        converted_state_dict[f"{block_prefix}attn.to_v.bias"] = torch.cat([v_bias])
+        converted_state_dict[f"{block_prefix}proj_mlp.weight"] = torch.cat([mlp])
+        converted_state_dict[f"{block_prefix}proj_mlp.bias"] = torch.cat([mlp_bias])
+        # qk norm
+        converted_state_dict[f"{block_prefix}attn.norm_q.weight"] = original_state_dict.pop(
+            f"single_blocks.{i}.norm.query_norm.scale"
+        )
+        converted_state_dict[f"{block_prefix}attn.norm_k.weight"] = original_state_dict.pop(
+            f"single_blocks.{i}.norm.key_norm.scale"
+        )
+        # output projections.
+        converted_state_dict[f"{block_prefix}proj_out.weight"] = original_state_dict.pop(
+            f"single_blocks.{i}.linear2.weight"
+        )
+        converted_state_dict[f"{block_prefix}proj_out.bias"] = original_state_dict.pop(
+            f"single_blocks.{i}.linear2.bias"
+        )
+
+    converted_state_dict["proj_out.weight"] = original_state_dict.pop("final_layer.linear.weight")
+    converted_state_dict["proj_out.bias"] = original_state_dict.pop("final_layer.linear.bias")
+    converted_state_dict["norm_out.linear.weight"] = swap_scale_shift(
+        original_state_dict.pop("final_layer.adaLN_modulation.1.weight")
+    )
+    converted_state_dict["norm_out.linear.bias"] = swap_scale_shift(
+        original_state_dict.pop("final_layer.adaLN_modulation.1.bias")
+    )
+
+    return converted_state_dict
+
+
+def main(args):
+    original_ckpt = load_original_checkpoint(args)
+    has_guidance = any("guidance" in k for k in original_ckpt)
+
+    if args.transformer:
+        num_layers = 19
+        num_single_layers = 38
+        inner_dim = 3072
+        mlp_ratio = 4.0
+        converted_transformer_state_dict = convert_flux_transformer_checkpoint_to_diffusers(
+            original_ckpt, num_layers, num_single_layers, inner_dim, mlp_ratio=mlp_ratio
+        )
+        transformer = FluxTransformer2DModel(guidance_embeds=has_guidance)
+        transformer.load_state_dict(converted_transformer_state_dict, strict=True)
+
+        print(
+            f"Saving Flux Transformer in Diffusers format. Variant: {'guidance-distilled' if has_guidance else 'timestep-distilled'}"
+        )
+        transformer.to(dtype).save_pretrained(f"{args.output_path}/transformer")
+
+    if args.vae:
+        config = AutoencoderKL.load_config("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="vae")
+        vae = AutoencoderKL.from_config(config, scaling_factor=0.3611, shift_factor=0.1159).to(torch.bfloat16)
+
+        converted_vae_state_dict = convert_ldm_vae_checkpoint(original_ckpt, vae.config)
+        vae.load_state_dict(converted_vae_state_dict, strict=True)
+        vae.to(dtype).save_pretrained(f"{args.output_path}/vae")
+
+
+if __name__ == "__main__":
+    main(args)

src/diffusers/__init__.py

@@ -85,6 +85,7 @@ else:
             "ControlNetModel",
             "ControlNetXSAdapter",
             "DiTTransformer2DModel",
+            "FluxTransformer2DModel",
             "HunyuanDiT2DControlNetModel",
             "HunyuanDiT2DModel",
             "HunyuanDiT2DMultiControlNetModel",
@@ -249,6 +250,7 @@ else:
             "ChatGLMTokenizer",
             "CLIPImageProjection",
             "CycleDiffusionPipeline",
+            "FluxPipeline",
             "HunyuanDiTControlNetPipeline",
             "HunyuanDiTPipeline",
             "I2VGenXLPipeline",
@@ -527,6 +529,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ControlNetModel,
             ControlNetXSAdapter,
             DiTTransformer2DModel,
+            FluxTransformer2DModel,
             HunyuanDiT2DControlNetModel,
             HunyuanDiT2DModel,
             HunyuanDiT2DMultiControlNetModel,
@@ -669,6 +672,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             ChatGLMTokenizer,
             CLIPImageProjection,
             CycleDiffusionPipeline,
+            FluxPipeline,
             HunyuanDiTControlNetPipeline,
             HunyuanDiTPipeline,
             I2VGenXLPipeline,

src/diffusers/models/__init__.py

@@ -51,6 +51,7 @@ if is_torch_available():
     _import_structure["transformers.stable_audio_transformer"] = ["StableAudioDiTModel"]
     _import_structure["transformers.t5_film_transformer"] = ["T5FilmDecoder"]
     _import_structure["transformers.transformer_2d"] = ["Transformer2DModel"]
+    _import_structure["transformers.transformer_flux"] = ["FluxTransformer2DModel"]
     _import_structure["transformers.transformer_sd3"] = ["SD3Transformer2DModel"]
     _import_structure["transformers.transformer_temporal"] = ["TransformerTemporalModel"]
     _import_structure["unets.unet_1d"] = ["UNet1DModel"]
@@ -93,6 +94,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             AuraFlowTransformer2DModel,
             DiTTransformer2DModel,
             DualTransformer2DModel,
+            FluxTransformer2DModel,
             HunyuanDiT2DModel,
             LatteTransformer3DModel,
             LuminaNextDiT2DModel,

src/diffusers/models/attention_processor.py

@@ -121,11 +121,12 @@ class Attention(nn.Module):
         processor: Optional["AttnProcessor"] = None,
         out_dim: int = None,
         context_pre_only=None,
+        pre_only=False,
     ):
         super().__init__()
 
         # To prevent circular import.
-        from .normalization import FP32LayerNorm
+        from .normalization import FP32LayerNorm, RMSNorm
 
         self.inner_dim = out_dim if out_dim is not None else dim_head * heads
         self.inner_kv_dim = self.inner_dim if kv_heads is None else dim_head * kv_heads
@@ -141,6 +142,7 @@ class Attention(nn.Module):
         self.fused_projections = False
         self.out_dim = out_dim if out_dim is not None else query_dim
         self.context_pre_only = context_pre_only
+        self.pre_only = pre_only
 
         # we make use of this private variable to know whether this class is loaded
         # with an deprecated state dict so that we can convert it on the fly
@@ -186,6 +188,9 @@ class Attention(nn.Module):
             # Lumina applys qk norm across all heads
             self.norm_q = nn.LayerNorm(dim_head * heads, eps=eps)
             self.norm_k = nn.LayerNorm(dim_head * kv_heads, eps=eps)
+        elif qk_norm == "rms_norm":
+            self.norm_q = RMSNorm(dim_head, eps=eps)
+            self.norm_k = RMSNorm(dim_head, eps=eps)
         else:
             raise ValueError(f"unknown qk_norm: {qk_norm}. Should be None or 'layer_norm'")
 
@@ -228,9 +233,10 @@ class Attention(nn.Module):
             if self.context_pre_only is not None:
                 self.add_q_proj = nn.Linear(added_kv_proj_dim, self.inner_dim, bias=added_proj_bias)
 
-        self.to_out = nn.ModuleList([])
-        self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
-        self.to_out.append(nn.Dropout(dropout))
+        if not self.pre_only:
+            self.to_out = nn.ModuleList([])
+            self.to_out.append(nn.Linear(self.inner_dim, self.out_dim, bias=out_bias))
+            self.to_out.append(nn.Dropout(dropout))
 
         if self.context_pre_only is not None and not self.context_pre_only:
             self.to_add_out = nn.Linear(self.inner_dim, self.out_dim, bias=out_bias)
@@ -239,6 +245,9 @@ class Attention(nn.Module):
             if qk_norm == "fp32_layer_norm":
                 self.norm_added_q = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
                 self.norm_added_k = FP32LayerNorm(dim_head, elementwise_affine=False, bias=False, eps=eps)
+            elif qk_norm == "rms_norm":
+                self.norm_added_q = RMSNorm(dim_head, eps=eps)
+                self.norm_added_k = RMSNorm(dim_head, eps=eps)
         else:
             self.norm_added_q = None
             self.norm_added_k = None
@@ -1265,6 +1274,179 @@ class AuraFlowAttnProcessor2_0:
             return hidden_states
 
 
+# YiYi to-do: refactor rope related functions/classes
+def apply_rope(xq, xk, freqs_cis):
+    xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+    xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
+
+
+class FluxSingleAttnProcessor2_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: Attention,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        image_rotary_emb: Optional[torch.Tensor] = None,
+    ) -> torch.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:
+            # YiYi to-do: update uising apply_rotary_emb
+            # from ..embeddings import apply_rotary_emb
+            # query = apply_rotary_emb(query, image_rotary_emb)
+            # key = apply_rotary_emb(key, image_rotary_emb)
+            query, key = apply_rope(query, 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 = 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 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:
+        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:
+            # YiYi to-do: update uising apply_rotary_emb
+            # from ..embeddings import apply_rotary_emb
+            # query = apply_rotary_emb(query, image_rotary_emb)
+            # key = apply_rotary_emb(key, image_rotary_emb)
+            query, key = apply_rope(query, key, image_rotary_emb)
+
+        hidden_states = F.scaled_dot_product_attention(query, key, value, dropout_p=0.0, is_causal=False)
+        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
+        hidden_states = hidden_states.to(query.dtype)
+
+        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 XFormersAttnAddedKVProcessor:
     r"""
     Processor for implementing memory efficient attention using xFormers.

src/diffusers/models/embeddings.py

@@ -795,6 +795,30 @@ class CombinedTimestepTextProjEmbeddings(nn.Module):
         return conditioning
 
 
+class CombinedTimestepGuidanceTextProjEmbeddings(nn.Module):
+    def __init__(self, embedding_dim, pooled_projection_dim):
+        super().__init__()
+
+        self.time_proj = Timesteps(num_channels=256, flip_sin_to_cos=True, downscale_freq_shift=0)
+        self.timestep_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.guidance_embedder = TimestepEmbedding(in_channels=256, time_embed_dim=embedding_dim)
+        self.text_embedder = PixArtAlphaTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")
+
+    def forward(self, timestep, guidance, pooled_projection):
+        timesteps_proj = self.time_proj(timestep)
+        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=pooled_projection.dtype))  # (N, D)
+
+        guidance_proj = self.time_proj(guidance)
+        guidance_emb = self.guidance_embedder(guidance_proj.to(dtype=pooled_projection.dtype))  # (N, D)
+
+        time_guidance_emb = timesteps_emb + guidance_emb
+
+        pooled_projections = self.text_embedder(pooled_projection)
+        conditioning = time_guidance_emb + pooled_projections
+
+        return conditioning
+
+
 class HunyuanDiTAttentionPool(nn.Module):
     # Copied from https://github.com/Tencent/HunyuanDiT/blob/cb709308d92e6c7e8d59d0dff41b74d35088db6a/hydit/modules/poolers.py#L6
 

src/diffusers/models/normalization.py

@@ -106,6 +106,38 @@ class AdaLayerNormZero(nn.Module):
         return x, gate_msa, shift_mlp, scale_mlp, gate_mlp
 
 
+class AdaLayerNormZeroSingle(nn.Module):
+    r"""
+    Norm layer adaptive layer norm zero (adaLN-Zero).
+
+    Parameters:
+        embedding_dim (`int`): The size of each embedding vector.
+        num_embeddings (`int`): The size of the embeddings dictionary.
+    """
+
+    def __init__(self, embedding_dim: int, norm_type="layer_norm", bias=True):
+        super().__init__()
+
+        self.silu = nn.SiLU()
+        self.linear = nn.Linear(embedding_dim, 3 * embedding_dim, bias=bias)
+        if norm_type == "layer_norm":
+            self.norm = nn.LayerNorm(embedding_dim, elementwise_affine=False, eps=1e-6)
+        else:
+            raise ValueError(
+                f"Unsupported `norm_type` ({norm_type}) provided. Supported ones are: 'layer_norm', 'fp32_layer_norm'."
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+        emb = self.linear(self.silu(emb))
+        shift_msa, scale_msa, gate_msa = emb.chunk(3, dim=1)
+        x = self.norm(x) * (1 + scale_msa[:, None]) + shift_msa[:, None]
+        return x, gate_msa
+
+
 class LuminaRMSNormZero(nn.Module):
     """
     Norm layer adaptive RMS normalization zero.

src/diffusers/models/transformers/__init__.py

@@ -13,5 +13,6 @@ if is_torch_available():
     from .stable_audio_transformer import StableAudioDiTModel
     from .t5_film_transformer import T5FilmDecoder
     from .transformer_2d import Transformer2DModel
+    from .transformer_flux import FluxTransformer2DModel
     from .transformer_sd3 import SD3Transformer2DModel
     from .transformer_temporal import TransformerTemporalModel

src/diffusers/models/transformers/transformer_flux.py

+# Copyright 2024 Stability AI, 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.
+
+
+from typing import Any, Dict, List, Optional, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ...configuration_utils import ConfigMixin, register_to_config
+from ...loaders import FromOriginalModelMixin, PeftAdapterMixin
+from ...models.attention import FeedForward
+from ...models.attention_processor import Attention, FluxAttnProcessor2_0, FluxSingleAttnProcessor2_0
+from ...models.modeling_utils import ModelMixin
+from ...models.normalization import AdaLayerNormContinuous, AdaLayerNormZero, AdaLayerNormZeroSingle
+from ...utils import USE_PEFT_BACKEND, is_torch_version, logging, scale_lora_layers, unscale_lora_layers
+from ...utils.torch_utils import maybe_allow_in_graph
+from ..embeddings import CombinedTimestepGuidanceTextProjEmbeddings, CombinedTimestepTextProjEmbeddings
+from ..modeling_outputs import Transformer2DModelOutput
+
+
+logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
+
+
+# YiYi to-do: refactor rope related functions/classes
+def rope(pos: torch.Tensor, dim: int, theta: int) -> torch.Tensor:
+    assert dim % 2 == 0, "The dimension must be even."
+
+    scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+    omega = 1.0 / (theta**scale)
+
+    batch_size, seq_length = pos.shape
+    out = torch.einsum("...n,d->...nd", pos, omega)
+    cos_out = torch.cos(out)
+    sin_out = torch.sin(out)
+
+    stacked_out = torch.stack([cos_out, -sin_out, sin_out, cos_out], dim=-1)
+    out = stacked_out.view(batch_size, -1, dim // 2, 2, 2)
+    return out.float()
+
+
+# YiYi to-do: refactor rope related functions/classes
+class EmbedND(nn.Module):
+    def __init__(self, dim: int, theta: int, axes_dim: List[int]):
+        super().__init__()
+        self.dim = dim
+        self.theta = theta
+        self.axes_dim = axes_dim
+
+    def forward(self, ids: torch.Tensor) -> torch.Tensor:
+        n_axes = ids.shape[-1]
+        emb = torch.cat(
+            [rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+            dim=-3,
+        )
+
+        return emb.unsqueeze(1)
+
+
+@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 = FluxSingleAttnProcessor2_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,
+    ):
+        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))
+
+        attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        hidden_states = torch.cat([attn_output, mlp_hidden_states], dim=2)
+        hidden_states = gate * self.proj_out(hidden_states)
+        hidden_states = residual + hidden_states
+
+        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,
+    ):
+        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
+        )
+
+        # Attention.
+        attn_output, context_attn_output = self.attn(
+            hidden_states=norm_hidden_states,
+            encoder_hidden_states=norm_encoder_hidden_states,
+            image_rotary_emb=image_rotary_emb,
+        )
+
+        # 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
+
+        # 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
+
+        return encoder_hidden_states, hidden_states
+
+
+class FluxTransformer2DModel(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,
+    ):
+        super().__init__()
+        self.out_channels = in_channels
+        self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim
+
+        self.pos_embed = EmbedND(dim=self.inner_dim, theta=10000, axes_dim=[16, 56, 56])
+        text_time_guidance_cls = (
+            CombinedTimestepGuidanceTextProjEmbeddings if guidance_embeds else CombinedTimestepTextProjEmbeddings
+        )
+        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
+
+    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,
+        return_dict: bool = True,
+    ) -> 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
+        temb = (
+            self.time_text_embed(timestep, pooled_projections)
+            if guidance is None
+            else self.time_text_embed(timestep, guidance, pooled_projections)
+        )
+        encoder_hidden_states = self.context_embedder(encoder_hidden_states)
+
+        ids = torch.cat((txt_ids, img_ids), dim=1)
+        image_rotary_emb = self.pos_embed(ids)
+
+        for index_block, block in enumerate(self.transformer_blocks):
+            if self.training 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,
+                    **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,
+                )
+
+        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:
+
+                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,
+                    **ckpt_kwargs,
+                )
+
+            else:
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    temb=temb,
+                    image_rotary_emb=image_rotary_emb,
+                )
+
+        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)

src/diffusers/pipelines/__init__.py

@@ -123,6 +123,7 @@ else:
         "AnimateDiffSparseControlNetPipeline",
         "AnimateDiffVideoToVideoPipeline",
     ]
+    _import_structure["flux"] = ["FluxPipeline"]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
     _import_structure["audioldm2"] = [
         "AudioLDM2Pipeline",
@@ -476,6 +477,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
             VersatileDiffusionTextToImagePipeline,
             VQDiffusionPipeline,
         )
+        from .flux import FluxPipeline
         from .hunyuandit import HunyuanDiTPipeline
         from .i2vgen_xl import I2VGenXLPipeline
         from .kandinsky import (

src/diffusers/pipelines/auto_pipeline.py

@@ -28,6 +28,7 @@ from .controlnet import (
     StableDiffusionXLControlNetPipeline,
 )
 from .deepfloyd_if import IFImg2ImgPipeline, IFInpaintingPipeline, IFPipeline
+from .flux import FluxPipeline
 from .hunyuandit import HunyuanDiTPipeline
 from .kandinsky import (
     KandinskyCombinedPipeline,
@@ -99,6 +100,7 @@ AUTO_TEXT2IMAGE_PIPELINES_MAPPING = OrderedDict(
         ("stable-diffusion-xl-controlnet-pag", StableDiffusionXLControlNetPAGPipeline),
         ("auraflow", AuraFlowPipeline),
         ("kolors", KolorsPipeline),
+        ("flux", FluxPipeline),
     ]
 )
 

src/diffusers/pipelines/flux/__init__.py

+from typing import TYPE_CHECKING
+
+from ...utils import (
+    DIFFUSERS_SLOW_IMPORT,
+    OptionalDependencyNotAvailable,
+    _LazyModule,
+    get_objects_from_module,
+    is_torch_available,
+    is_transformers_available,
+)
+
+
+_dummy_objects = {}
+_additional_imports = {}
+_import_structure = {"pipeline_output": ["FluxPipelineOutput"]}
+
+try:
+    if not (is_transformers_available() and is_torch_available()):
+        raise OptionalDependencyNotAvailable()
+except OptionalDependencyNotAvailable:
+    from ...utils import dummy_torch_and_transformers_objects  # noqa F403
+
+    _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
+else:
+    _import_structure["pipeline_flux"] = ["FluxPipeline"]
+if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
+    try:
+        if not (is_transformers_available() and is_torch_available()):
+            raise OptionalDependencyNotAvailable()
+    except OptionalDependencyNotAvailable:
+        from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
+    else:
+        from .pipeline_flux import FluxPipeline
+else:
+    import sys
+
+    sys.modules[__name__] = _LazyModule(
+        __name__,
+        globals()["__file__"],
+        _import_structure,
+        module_spec=__spec__,
+    )
+
+    for name, value in _dummy_objects.items():
+        setattr(sys.modules[__name__], name, value)
+    for name, value in _additional_imports.items():
+        setattr(sys.modules[__name__], name, value)

src/diffusers/pipelines/flux/pipeline_output.py

+from dataclasses import dataclass
+from typing import List, Union
+
+import numpy as np
+import PIL.Image
+
+from ...utils import BaseOutput
+
+
+@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]

src/diffusers/schedulers/scheduling_flow_match_euler_discrete.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import math
 from dataclasses import dataclass
 from typing import List, Optional, Tuple, Union
 
@@ -20,7 +21,6 @@ import torch
 
 from ..configuration_utils import ConfigMixin, register_to_config
 from ..utils import BaseOutput, logging
-from ..utils.torch_utils import randn_tensor
 from .scheduling_utils import SchedulerMixin
 
 
@@ -66,12 +66,19 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         self,
         num_train_timesteps: int = 1000,
         shift: float = 1.0,
+        use_dynamic_shifting=False,
+        base_shift: Optional[float] = 0.5,
+        max_shift: Optional[float] = 1.15,
+        base_image_seq_len: Optional[int] = 256,
+        max_image_seq_len: Optional[int] = 4096,
     ):
         timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32)[::-1].copy()
         timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32)
 
         sigmas = timesteps / num_train_timesteps
-        sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
+        if not use_dynamic_shifting:
+            # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution
+            sigmas = shift * sigmas / (1 + (shift - 1) * sigmas)
 
         self.timesteps = sigmas * num_train_timesteps
 
@@ -158,11 +165,15 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
     def _sigma_to_t(self, sigma):
         return sigma * self.config.num_train_timesteps
 
+    def time_shift(self, mu: float, sigma: float, t: torch.Tensor):
+        return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma)
+
     def set_timesteps(
         self,
         num_inference_steps: int = None,
         device: Union[str, torch.device] = None,
         sigmas: Optional[List[float]] = None,
+        mu: Optional[float] = None,
     ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
@@ -174,6 +185,9 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
                 The device to which the timesteps should be moved to. If `None`, the timesteps are not moved.
         """
 
+        if self.config.use_dynamic_shifting and mu is None:
+            raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`")
+
         if sigmas is None:
             self.num_inference_steps = num_inference_steps
             timesteps = np.linspace(
@@ -181,6 +195,10 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             )
 
             sigmas = timesteps / self.config.num_train_timesteps
+
+        if self.config.use_dynamic_shifting:
+            sigmas = self.time_shift(mu, 1.0, sigmas)
+        else:
             sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas)
 
         sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device)
@@ -274,32 +292,10 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
         sample = sample.to(torch.float32)
 
         sigma = self.sigmas[self.step_index]
+        sigma_next = self.sigmas[self.step_index + 1]
 
-        gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0
-
-        noise = randn_tensor(
-            model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator
-        )
-
-        eps = noise * s_noise
-        sigma_hat = sigma * (gamma + 1)
-
-        if gamma > 0:
-            sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5
-
-        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
-        # NOTE: "original_sample" should not be an expected prediction_type but is left in for
-        # backwards compatibility
-
-        # if self.config.prediction_type == "vector_field":
-
-        denoised = sample - model_output * sigma
-        # 2. Convert to an ODE derivative
-        derivative = (sample - denoised) / sigma_hat
-
-        dt = self.sigmas[self.step_index + 1] - sigma_hat
+        prev_sample = sample + (sigma_next - sigma) * model_output
 
-        prev_sample = sample + derivative * dt
         # Cast sample back to model compatible dtype
         prev_sample = prev_sample.to(model_output.dtype)
 

src/diffusers/utils/dummy_pt_objects.py

@@ -152,6 +152,21 @@ class DiTTransformer2DModel(metaclass=DummyObject):
         requires_backends(cls, ["torch"])
 
 
+class FluxTransformer2DModel(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch"])
+
+
 class HunyuanDiT2DControlNetModel(metaclass=DummyObject):
     _backends = ["torch"]
 

src/diffusers/utils/dummy_torch_and_transformers_objects.py

@@ -302,6 +302,21 @@ class CycleDiffusionPipeline(metaclass=DummyObject):
         requires_backends(cls, ["torch", "transformers"])
 
 
+class FluxPipeline(metaclass=DummyObject):
+    _backends = ["torch", "transformers"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch", "transformers"])
+
+    @classmethod
+    def from_config(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        requires_backends(cls, ["torch", "transformers"])
+
+
 class HunyuanDiTControlNetPipeline(metaclass=DummyObject):
     _backends = ["torch", "transformers"]