[@cene555][Kandinsky 3.0] Add Kandinsky 3.0

docs/source/en/_toctree.yml

@@ -278,6 +278,8 @@
       title: Kandinsky 2.1
     - local: api/pipelines/kandinsky_v22
       title: Kandinsky 2.2
+    - local: api/pipelines/kandinsky3
+      title: Kandinsky 3
     - local: api/pipelines/latent_consistency_models
       title: Latent Consistency Models
     - local: api/pipelines/latent_diffusion

docs/source/en/api/pipelines/kandinsky3.md

@@ -0,0 +1,24 @@
+<!--Copyright 2023 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.
+-->
+
+# Kandinsky 3
+
+TODO
+
+## Kandinsky3Pipeline
+
+[[autodoc]] Kandinsky3Pipeline
+	- all
+	- __call__
+
+## Kandinsky3Img2ImgPipeline
+
+[[autodoc]] Kandinsky3Img2ImgPipeline
+	- all
+	- __call__

scripts/convert_kandinsky3_unet.py

@@ -0,0 +1,98 @@
+#!/usr/bin/env python3
+import argparse
+import fnmatch
+
+from safetensors.torch import load_file
+
+from diffusers import Kandinsky3UNet
+
+
+MAPPING = {
+    "to_time_embed.1": "time_embedding.linear_1",
+    "to_time_embed.3": "time_embedding.linear_2",
+    "in_layer": "conv_in",
+    "out_layer.0": "conv_norm_out",
+    "out_layer.2": "conv_out",
+    "down_samples": "down_blocks",
+    "up_samples": "up_blocks",
+    "projection_lin": "encoder_hid_proj.projection_linear",
+    "projection_ln": "encoder_hid_proj.projection_norm",
+    "feature_pooling": "add_time_condition",
+    "to_query": "to_q",
+    "to_key": "to_k",
+    "to_value": "to_v",
+    "output_layer": "to_out.0",
+    "self_attention_block": "attentions.0",
+}
+
+DYNAMIC_MAP = {
+    "resnet_attn_blocks.*.0": "resnets_in.*",
+    "resnet_attn_blocks.*.1": ("attentions.*", 1),
+    "resnet_attn_blocks.*.2": "resnets_out.*",
+}
+# MAPPING = {}
+
+
+def convert_state_dict(unet_state_dict):
+    """
+    Convert the state dict of a U-Net model to match the key format expected by Kandinsky3UNet model.
+    Args:
+        unet_model (torch.nn.Module): The original U-Net model.
+        unet_kandi3_model (torch.nn.Module): The Kandinsky3UNet model to match keys with.
+
+    Returns:
+        OrderedDict: The converted state dictionary.
+    """
+    # Example of renaming logic (this will vary based on your model's architecture)
+    converted_state_dict = {}
+    for key in unet_state_dict:
+        new_key = key
+        for pattern, new_pattern in MAPPING.items():
+            new_key = new_key.replace(pattern, new_pattern)
+
+        for dyn_pattern, dyn_new_pattern in DYNAMIC_MAP.items():
+            has_matched = False
+            if fnmatch.fnmatch(new_key, f"*.{dyn_pattern}.*") and not has_matched:
+                star = int(new_key.split(dyn_pattern.split(".")[0])[-1].split(".")[1])
+
+                if isinstance(dyn_new_pattern, tuple):
+                    new_star = star + dyn_new_pattern[-1]
+                    dyn_new_pattern = dyn_new_pattern[0]
+                else:
+                    new_star = star
+
+                pattern = dyn_pattern.replace("*", str(star))
+                new_pattern = dyn_new_pattern.replace("*", str(new_star))
+
+                new_key = new_key.replace(pattern, new_pattern)
+                has_matched = True
+
+        converted_state_dict[new_key] = unet_state_dict[key]
+
+    return converted_state_dict
+
+
+def main(model_path, output_path):
+    # Load your original U-Net model
+    unet_state_dict = load_file(model_path)
+
+    # Initialize your Kandinsky3UNet model
+    config = {}
+
+    # Convert the state dict
+    converted_state_dict = convert_state_dict(unet_state_dict)
+
+    unet = Kandinsky3UNet(config)
+    unet.load_state_dict(converted_state_dict)
+
+    unet.save_pretrained(output_path)
+    print(f"Converted model saved to {output_path}")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Convert U-Net PyTorch model to Kandinsky3UNet format")
+    parser.add_argument("--model_path", type=str, required=True, help="Path to the original U-Net PyTorch model")
+    parser.add_argument("--output_path", type=str, required=True, help="Path to save the converted model")
+
+    args = parser.parse_args()
+    main(args.model_path, args.output_path)

src/diffusers/__init__.py

@@ -79,6 +79,7 @@
             "AutoencoderTiny",
             "ConsistencyDecoderVAE",
             "ControlNetModel",
+            "Kandinsky3UNet",
             "ModelMixin",
             "MotionAdapter",
             "MultiAdapter",
@@ -214,6 +215,8 @@
             "IFPipeline",
             "IFSuperResolutionPipeline",
             "ImageTextPipelineOutput",
+            "Kandinsky3Img2ImgPipeline",
+            "Kandinsky3Pipeline",
             "KandinskyCombinedPipeline",
             "KandinskyImg2ImgCombinedPipeline",
             "KandinskyImg2ImgPipeline",
@@ -446,6 +449,7 @@
             AutoencoderTiny,
             ConsistencyDecoderVAE,
             ControlNetModel,
+            Kandinsky3UNet,
             ModelMixin,
             MotionAdapter,
             MultiAdapter,
@@ -560,6 +564,8 @@
             IFPipeline,
             IFSuperResolutionPipeline,
             ImageTextPipelineOutput,
+            Kandinsky3Img2ImgPipeline,
+            Kandinsky3Pipeline,
             KandinskyCombinedPipeline,
             KandinskyImg2ImgCombinedPipeline,
             KandinskyImg2ImgPipeline,

src/diffusers/models/__init__.py

@@ -36,6 +36,7 @@
     _import_structure["unet_2d"] = ["UNet2DModel"]
     _import_structure["unet_2d_condition"] = ["UNet2DConditionModel"]
     _import_structure["unet_3d_condition"] = ["UNet3DConditionModel"]
+    _import_structure["unet_kandi3"] = ["Kandinsky3UNet"]
     _import_structure["unet_motion_model"] = ["MotionAdapter", "UNetMotionModel"]
     _import_structure["vq_model"] = ["VQModel"]
 
@@ -63,6 +64,7 @@
         from .unet_2d import UNet2DModel
         from .unet_2d_condition import UNet2DConditionModel
         from .unet_3d_condition import UNet3DConditionModel
+        from .unet_kandi3 import Kandinsky3UNet
         from .unet_motion_model import MotionAdapter, UNetMotionModel
         from .vq_model import VQModel
 

src/diffusers/models/attention_processor.py

@@ -16,7 +16,7 @@
 
 import torch
 import torch.nn.functional as F
-from torch import nn
+from torch import einsum, nn
 
 from ..utils import USE_PEFT_BACKEND, deprecate, logging
 from ..utils.import_utils import is_xformers_available
@@ -2219,6 +2219,44 @@ def __call__(
         return hidden_states
 
 
+# TODO(Yiyi): This class should not exist, we can replace it with a normal attention processor I believe
+# this way torch.compile and co. will work as well
+class Kandi3AttnProcessor:
+    r"""
+    Default kandinsky3 proccesor for performing attention-related computations.
+    """
+
+    @staticmethod
+    def _reshape(hid_states, h):
+        b, n, f = hid_states.shape
+        d = f // h
+        return hid_states.unsqueeze(-1).reshape(b, n, h, d).permute(0, 2, 1, 3)
+
+    def __call__(
+        self,
+        attn,
+        x,
+        context,
+        context_mask=None,
+    ):
+        query = self._reshape(attn.to_q(x), h=attn.num_heads)
+        key = self._reshape(attn.to_k(context), h=attn.num_heads)
+        value = self._reshape(attn.to_v(context), h=attn.num_heads)
+
+        attention_matrix = einsum("b h i d, b h j d -> b h i j", query, key)
+
+        if context_mask is not None:
+            max_neg_value = -torch.finfo(attention_matrix.dtype).max
+            context_mask = context_mask.unsqueeze(1).unsqueeze(1)
+            attention_matrix = attention_matrix.masked_fill(~(context_mask != 0), max_neg_value)
+        attention_matrix = (attention_matrix * attn.scale).softmax(dim=-1)
+
+        out = einsum("b h i j, b h j d -> b h i d", attention_matrix, value)
+        out = out.permute(0, 2, 1, 3).reshape(out.shape[0], out.shape[2], -1)
+        out = attn.to_out[0](out)
+        return out
+
+
 LORA_ATTENTION_PROCESSORS = (
     LoRAAttnProcessor,
     LoRAAttnProcessor2_0,
@@ -2244,6 +2282,7 @@ def __call__(
     LoRAXFormersAttnProcessor,
     IPAdapterAttnProcessor,
     IPAdapterAttnProcessor2_0,
+    Kandi3AttnProcessor,
 )
 
 AttentionProcessor = Union[