support sd3.5 for controlnet example

examples/controlnet/README_sd3.md

@@ -1,6 +1,6 @@
-# ControlNet training example for Stable Diffusion 3 (SD3)
+# ControlNet training example for Stable Diffusion 3/3.5 (SD3/3.5)
 
-The `train_controlnet_sd3.py` script shows how to implement the ControlNet training procedure and adapt it for [Stable Diffusion 3](https://arxiv.org/abs/2403.03206).
+The `train_controlnet_sd3.py` script shows how to implement the ControlNet training procedure and adapt it for [Stable Diffusion 3](https://arxiv.org/abs/2403.03206) and [Stable Diffusion 3.5](https://stability.ai/news/introducing-stable-diffusion-3-5).
 
 ## Running locally with PyTorch
 
@@ -51,9 +51,9 @@ Please download the dataset and unzip it in the directory `fill50k` in the `exam
 
 ## Training
 
-First download the SD3 model from [Hugging Face Hub](https://huggingface.co/stabilityai/stable-diffusion-3-medium). We will use it as a base model for the ControlNet training.
+First download the SD3 model from [Hugging Face Hub](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) or the SD3.5 model from [Hugging Face Hub](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium). We will use it as a base model for the ControlNet training.
 > [!NOTE]
-> As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
+> As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers) or [Stable Diffusion 3.5 Large Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3.5-medium), fill in the form and accept the gate. Once you are in, you need to log in so that your system knows you’ve accepted the gate. Use the command below to log in:
 
 ```bash
 huggingface-cli login
@@ -90,6 +90,8 @@ accelerate launch train_controlnet_sd3.py \
     --gradient_accumulation_steps=4
 ```
 
+To train a ControlNet model for Stable Diffusion 3.5, replace the `MODEL_DIR` with `stabilityai/stable-diffusion-3.5-medium`.
+
 To better track our training experiments, we're using flags `validation_image`, `validation_prompt`, and `validation_steps` to allow the script to do a few validation inference runs. This allows us to qualitatively check if the training is progressing as expected.
 
 Our experiments were conducted on a single 40GB A100 GPU.
@@ -124,6 +126,8 @@ image = pipe(
 image.save("./output.png")
 ```
 
+Similarly, for SD3.5, replace the `base_model_path` with `stabilityai/stable-diffusion-3.5-medium` and controlnet_path `DavyMorgan/sd35-controlnet-out'.
+
 ## Notes
 
 ### GPU usage
@@ -135,6 +139,8 @@ Make sure to use the right GPU when configuring the [accelerator](https://huggin
 
 ## Example results
 
+### SD3
+
 #### After 500 steps with batch size 8
 
 | |  |
@@ -150,3 +156,20 @@ Make sure to use the right GPU when configuring the [accelerator](https://huggin
 || pale golden rod circle with old lace background |
  ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![pale golden rod circle with old lace background](https://huggingface.co/datasets/DavyMorgan/sd3-controlnet-results/resolve/main/step-6500.png) |
 
+### SD3.5
+
+#### After 500 steps with batch size 8
+
+| |                                                                                                                                                     |
+|-------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------:|
+||                                                   pale golden rod circle with old lace background                                                   |
+ ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![pale golden rod circle with old lace background](https://huggingface.co/datasets/DavyMorgan/sd3-controlnet-results/resolve/main/step-500-3.5.png) |
+
+
+#### After 3000 steps with batch size 8:
+
+| |                                                                                                                                                      |
+|-------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------:|
+||                                                   pale golden rod circle with old lace background                                                    |
+ ![conditioning image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet_training/conditioning_image_1.png) | ![pale golden rod circle with old lace background](https://huggingface.co/datasets/DavyMorgan/sd3-controlnet-results/resolve/main/step-3000-3.5.png) |
+

examples/controlnet/test_controlnet.py

@@ -138,6 +138,27 @@ def test_controlnet_sd3(self):
             self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
 
 
+class ControlNetSD35(ExamplesTestsAccelerate):
+    def test_controlnet_sd3(self):
+        with tempfile.TemporaryDirectory() as tmpdir:
+            test_args = f"""
+            examples/controlnet/train_controlnet_sd3.py
+            --pretrained_model_name_or_path=DavyMorgan/tiny-sd35-pipe
+            --dataset_name=hf-internal-testing/fill10
+            --output_dir={tmpdir}
+            --resolution=64
+            --train_batch_size=1
+            --gradient_accumulation_steps=1
+            --controlnet_model_name_or_path=DavyMorgan/tiny-controlnet-sd35
+            --max_train_steps=4
+            --checkpointing_steps=2
+            """.split()
+
+            run_command(self._launch_args + test_args)
+
+            self.assertTrue(os.path.isfile(os.path.join(tmpdir, "diffusion_pytorch_model.safetensors")))
+
+
 class ControlNetflux(ExamplesTestsAccelerate):
     def test_controlnet_flux(self):
         with tempfile.TemporaryDirectory() as tmpdir:

examples/controlnet/train_controlnet_sd3.py

@@ -539,6 +539,9 @@ def parse_args(input_args=None):
         default=77,
         help="Maximum sequence length to use with with the T5 text encoder",
     )
+    parser.add_argument(
+        "--dataset_preprocess_batch_size", type=int, default=1000, help="Batch size for preprocessing dataset."
+    )
     parser.add_argument(
         "--validation_prompt",
         type=str,
@@ -986,7 +989,7 @@ def main(args):
         controlnet = SD3ControlNetModel.from_pretrained(args.controlnet_model_name_or_path)
     else:
         logger.info("Initializing controlnet weights from transformer")
-        controlnet = SD3ControlNetModel.from_transformer(transformer)
+        controlnet = SD3ControlNetModel.from_transformer(transformer, num_extra_conditioning_channels=0)
 
     transformer.requires_grad_(False)
     vae.requires_grad_(False)
@@ -1123,7 +1126,12 @@ def compute_text_embeddings(batch, text_encoders, tokenizers):
         # fingerprint used by the cache for the other processes to load the result
         # details: https://github.com/huggingface/diffusers/pull/4038#discussion_r1266078401
         new_fingerprint = Hasher.hash(args)
-        train_dataset = train_dataset.map(compute_embeddings_fn, batched=True, new_fingerprint=new_fingerprint)
+        train_dataset = train_dataset.map(
+            compute_embeddings_fn,
+            batched=True,
+            batch_size=args.dataset_preprocess_batch_size,
+            new_fingerprint=new_fingerprint,
+        )
 
     del text_encoder_one, text_encoder_two, text_encoder_three
     del tokenizer_one, tokenizer_two, tokenizer_three

src/diffusers/models/controlnet_sd3.py

@@ -55,6 +55,10 @@ def __init__(
         pooled_projection_dim: int = 2048,
         out_channels: int = 16,
         pos_embed_max_size: int = 96,
+        dual_attention_layers: Tuple[
+            int, ...
+        ] = (),  # () for sd3.0; (0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12) for sd3.5
+        qk_norm: Optional[str] = None,
         extra_conditioning_channels: int = 0,
     ):
         super().__init__()
@@ -84,6 +88,8 @@ def __init__(
                     num_attention_heads=num_attention_heads,
                     attention_head_dim=self.config.attention_head_dim,
                     context_pre_only=False,
+                    qk_norm=qk_norm,
+                    use_dual_attention=True if i in dual_attention_layers else False,
                 )
                 for i in range(num_layers)
             ]

src/diffusers/models/transformers/transformer_sd3.py

@@ -11,8 +11,7 @@
 # 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.
-
-
+import math
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import torch
@@ -344,7 +343,7 @@ def custom_forward(*inputs):
 
             # controlnet residual
             if block_controlnet_hidden_states is not None and block.context_pre_only is False:
-                interval_control = len(self.transformer_blocks) // len(block_controlnet_hidden_states)
+                interval_control = int(math.ceil(len(self.transformer_blocks) / len(block_controlnet_hidden_states)))
                 hidden_states = hidden_states + block_controlnet_hidden_states[index_block // interval_control]
 
         hidden_states = self.norm_out(hidden_states, temb)

tests/pipelines/controlnet_sd3/test_controlnet_sd3.py

@@ -59,7 +59,7 @@ class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTes
     )
     batch_params = frozenset(["prompt", "negative_prompt"])
 
-    def get_dummy_components(self):
+    def get_dummy_components(self, qk_norm=None, use_dual_attention=False):
         torch.manual_seed(0)
         transformer = SD3Transformer2DModel(
             sample_size=32,
@@ -72,6 +72,8 @@ def get_dummy_components(self):
             caption_projection_dim=32,
             pooled_projection_dim=64,
             out_channels=8,
+            qk_norm=qk_norm,
+            dual_attention_layers=() if not use_dual_attention else (0, 1),
         )
 
         torch.manual_seed(0)
@@ -86,7 +88,10 @@ def get_dummy_components(self):
             caption_projection_dim=32,
             pooled_projection_dim=64,
             out_channels=8,
+            qk_norm=qk_norm,
+            dual_attention_layers=() if not use_dual_attention else (0,),
         )
+
         clip_text_encoder_config = CLIPTextConfig(
             bos_token_id=0,
             eos_token_id=2,
@@ -171,8 +176,7 @@ def get_dummy_inputs(self, device, seed=0):
 
         return inputs
 
-    def test_controlnet_sd3(self):
-        components = self.get_dummy_components()
+    def run_pipe(self, components):
         sd_pipe = StableDiffusion3ControlNetPipeline(**components)
         sd_pipe = sd_pipe.to(torch_device, dtype=torch.float16)
         sd_pipe.set_progress_bar_config(disable=None)
@@ -191,6 +195,14 @@ def test_controlnet_sd3(self):
             np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
         ), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
 
+    def test_controlnet_sd3(self):
+        components = self.get_dummy_components()
+        self.run_pipe(components)
+
+    def test_controlnet_sd35(self):
+        components = self.get_dummy_components(qk_norm="rms_norm", use_dual_attention=True)
+        self.run_pipe(components)
+
     @unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
     def test_xformers_attention_forwardGenerator_pass(self):
         pass