[Pipeline] AnimateDiff SDXL

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

@@ -101,6 +101,54 @@ AnimateDiff tends to work better with finetuned Stable Diffusion models. If you
 
 </Tip>
 
+### AnimateDiffSDXLPipeline
+
+AnimateDiff can also be used with SDXL models. This is currently an experimental feature as only a beta release of the motion adapter checkpoint is available.
+
+```python
+import torch
+from diffusers.models import MotionAdapter
+from diffusers import AnimateDiffSDXLPipeline, DDIMScheduler
+from diffusers.utils import export_to_gif
+
+# TODO(aryan): ask team to move checkpoint to authors account
+adapter = MotionAdapter.from_pretrained("a-r-r-o-w/animatediff-motion-adapter-sdxl-beta", torch_dtype=torch.float16)
+
+model_id = "stabilityai/stable-diffusion-xl-base-1.0"
+scheduler = DDIMScheduler.from_pretrained(
+    model_id,
+    subfolder="scheduler",
+    clip_sample=False,
+    timestep_spacing="linspace",
+    beta_schedule="linear",
+    steps_offset=1,
+)
+pipe = AnimateDiffSDXLPipeline.from_pretrained(
+    model_id,
+    motion_adapter=adapter,
+    scheduler=scheduler,
+    torch_dtype=torch.float16,
+    variant="fp16",
+).to("cuda")
+
+# enable memory savings
+pipe.enable_vae_slicing()
+pipe.enable_vae_tiling()
+
+output = pipe(
+    prompt="a panda surfing in the ocean, realistic, high quality",
+    negative_prompt="low quality, worst quality",
+    num_inference_steps=20,
+    guidance_scale=8,
+    width=1024,
+    height=1024,
+    num_frames=16,
+)
+
+frames = output.frames[0]
+export_to_gif(frames, "animation.gif")
+```
+
 ### AnimateDiffVideoToVideoPipeline
 
 AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.
@@ -522,6 +570,12 @@ export_to_gif(frames, "animatelcm-motion-lora.gif")
   - all
   - __call__
 
+## AnimateDiffSDXLPipeline
+
+[[autodoc]] AnimateDiffSDXLPipeline
+  - all
+  - __call__
+
 ## AnimateDiffVideoToVideoPipeline
 
 [[autodoc]] AnimateDiffVideoToVideoPipeline

scripts/convert_animatediff_motion_module_to_diffusers.py

@@ -30,6 +30,7 @@ def get_args():
     parser.add_argument("--output_path", type=str, required=True)
     parser.add_argument("--use_motion_mid_block", action="store_true")
     parser.add_argument("--motion_max_seq_length", type=int, default=32)
+    parser.add_argument("--block_out_channels", nargs="+", default=[320, 640, 1280, 1280], type=int)
     parser.add_argument("--save_fp16", action="store_true")
 
     return parser.parse_args()
@@ -44,11 +45,13 @@ def get_args():
 
     conv_state_dict = convert_motion_module(state_dict)
     adapter = MotionAdapter(
-        use_motion_mid_block=args.use_motion_mid_block, motion_max_seq_length=args.motion_max_seq_length
+        block_out_channels=args.block_out_channels,
+        use_motion_mid_block=args.use_motion_mid_block,
+        motion_max_seq_length=args.motion_max_seq_length,
     )
     # skip loading position embeddings
     adapter.load_state_dict(conv_state_dict, strict=False)
     adapter.save_pretrained(args.output_path)
 
     if args.save_fp16:
-        adapter.to(torch.float16).save_pretrained(args.output_path, variant="fp16")
+        adapter.to(dtype=torch.float16).save_pretrained(args.output_path, variant="fp16")

src/diffusers/__init__.py

@@ -216,6 +216,7 @@
             "AmusedInpaintPipeline",
             "AmusedPipeline",
             "AnimateDiffPipeline",
+            "AnimateDiffSDXLPipeline",
             "AnimateDiffVideoToVideoPipeline",
             "AudioLDM2Pipeline",
             "AudioLDM2ProjectionModel",
@@ -594,6 +595,7 @@
             AmusedInpaintPipeline,
             AmusedPipeline,
             AnimateDiffPipeline,
+            AnimateDiffSDXLPipeline,
             AnimateDiffVideoToVideoPipeline,
             AudioLDM2Pipeline,
             AudioLDM2ProjectionModel,

src/diffusers/models/unets/unet_3d_blocks.py

@@ -121,6 +121,7 @@ def get_down_block(
             raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion")
         return CrossAttnDownBlockMotion(
             num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
             in_channels=in_channels,
             out_channels=out_channels,
             temb_channels=temb_channels,
@@ -255,6 +256,7 @@ def get_up_block(
             raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion")
         return CrossAttnUpBlockMotion(
             num_layers=num_layers,
+            transformer_layers_per_block=transformer_layers_per_block,
             in_channels=in_channels,
             out_channels=out_channels,
             prev_output_channel=prev_output_channel,

src/diffusers/models/unets/unet_motion_model.py

@@ -211,13 +211,18 @@ def __init__(
         norm_num_groups: int = 32,
         norm_eps: float = 1e-5,
         cross_attention_dim: int = 1280,
+        transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1,
+        reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None,
         use_linear_projection: bool = False,
         num_attention_heads: Union[int, Tuple[int, ...]] = 8,
         motion_max_seq_length: int = 32,
         motion_num_attention_heads: int = 8,
         use_motion_mid_block: int = True,
         encoder_hid_dim: Optional[int] = None,
         encoder_hid_dim_type: Optional[str] = None,
+        addition_embed_type: Optional[str] = None,
+        addition_time_embed_dim: Optional[int] = None,
+        projection_class_embeddings_input_dim: Optional[int] = None,
         time_cond_proj_dim: Optional[int] = None,
     ):
         super().__init__()
@@ -240,6 +245,21 @@ def __init__(
                 f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}."
             )
 
+        if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}."
+            )
+
+        if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types):
+            raise ValueError(
+                f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}."
+            )
+
+        if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None:
+            for layer_number_per_block in transformer_layers_per_block:
+                if isinstance(layer_number_per_block, list):
+                    raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.")
+
         # input
         conv_in_kernel = 3
         conv_out_kernel = 3
@@ -260,13 +280,26 @@ def __init__(
         if encoder_hid_dim_type is None:
             self.encoder_hid_proj = None
 
+        if addition_embed_type == "text_time":
+            self.add_time_proj = Timesteps(addition_time_embed_dim, True, 0)
+            self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim)
+
         # class embedding
         self.down_blocks = nn.ModuleList([])
         self.up_blocks = nn.ModuleList([])
 
         if isinstance(num_attention_heads, int):
             num_attention_heads = (num_attention_heads,) * len(down_block_types)
 
+        if isinstance(cross_attention_dim, int):
+            cross_attention_dim = (cross_attention_dim,) * len(down_block_types)
+
+        if isinstance(layers_per_block, int):
+            layers_per_block = [layers_per_block] * len(down_block_types)
+
+        if isinstance(transformer_layers_per_block, int):
+            transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types)
+
         # down
         output_channel = block_out_channels[0]
         for i, down_block_type in enumerate(down_block_types):
@@ -276,21 +309,22 @@ def __init__(
 
             down_block = get_down_block(
                 down_block_type,
-                num_layers=layers_per_block,
+                num_layers=layers_per_block[i],
                 in_channels=input_channel,
                 out_channels=output_channel,
                 temb_channels=time_embed_dim,
                 add_downsample=not is_final_block,
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[i],
                 num_attention_heads=num_attention_heads[i],
                 downsample_padding=downsample_padding,
                 use_linear_projection=use_linear_projection,
                 dual_cross_attention=False,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=transformer_layers_per_block[i],
             )
             self.down_blocks.append(down_block)
 
@@ -302,13 +336,14 @@ def __init__(
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[-1],
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
                 use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=transformer_layers_per_block[-1],
             )
 
         else:
@@ -318,11 +353,12 @@ def __init__(
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 output_scale_factor=mid_block_scale_factor,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=cross_attention_dim[-1],
                 num_attention_heads=num_attention_heads[-1],
                 resnet_groups=norm_num_groups,
                 dual_cross_attention=False,
                 use_linear_projection=use_linear_projection,
+                transformer_layers_per_block=transformer_layers_per_block[-1],
             )
 
         # count how many layers upsample the images
@@ -331,6 +367,9 @@ def __init__(
         # up
         reversed_block_out_channels = list(reversed(block_out_channels))
         reversed_num_attention_heads = list(reversed(num_attention_heads))
+        reversed_layers_per_block = list(reversed(layers_per_block))
+        reversed_cross_attention_dim = list(reversed(cross_attention_dim))
+        reversed_transformer_layers_per_block = list(reversed(transformer_layers_per_block))
 
         output_channel = reversed_block_out_channels[0]
         for i, up_block_type in enumerate(up_block_types):
@@ -349,7 +388,7 @@ def __init__(
 
             up_block = get_up_block(
                 up_block_type,
-                num_layers=layers_per_block + 1,
+                num_layers=reversed_layers_per_block[i] + 1,
                 in_channels=input_channel,
                 out_channels=output_channel,
                 prev_output_channel=prev_output_channel,
@@ -358,13 +397,14 @@ def __init__(
                 resnet_eps=norm_eps,
                 resnet_act_fn=act_fn,
                 resnet_groups=norm_num_groups,
-                cross_attention_dim=cross_attention_dim,
+                cross_attention_dim=reversed_cross_attention_dim[i],
                 num_attention_heads=reversed_num_attention_heads[i],
                 dual_cross_attention=False,
                 resolution_idx=i,
                 use_linear_projection=use_linear_projection,
                 temporal_num_attention_heads=motion_num_attention_heads,
                 temporal_max_seq_length=motion_max_seq_length,
+                transformer_layers_per_block=reversed_transformer_layers_per_block[i],
             )
             self.up_blocks.append(up_block)
             prev_output_channel = output_channel
@@ -835,6 +875,28 @@ def forward(
         t_emb = t_emb.to(dtype=self.dtype)
 
         emb = self.time_embedding(t_emb, timestep_cond)
+        aug_emb = None
+
+        if self.config.addition_embed_type == "text_time":
+            if "text_embeds" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`"
+                )
+
+            text_embeds = added_cond_kwargs.get("text_embeds")
+            if "time_ids" not in added_cond_kwargs:
+                raise ValueError(
+                    f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`"
+                )
+            time_ids = added_cond_kwargs.get("time_ids")
+            time_embeds = self.add_time_proj(time_ids.flatten())
+            time_embeds = time_embeds.reshape((text_embeds.shape[0], -1))
+
+            add_embeds = torch.concat([text_embeds, time_embeds], dim=-1)
+            add_embeds = add_embeds.to(emb.dtype)
+            aug_emb = self.add_embedding(add_embeds)
+
+        emb = emb if aug_emb is None else emb + aug_emb
         emb = emb.repeat_interleave(repeats=num_frames, dim=0)
         encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
 

src/diffusers/pipelines/__init__.py

@@ -114,6 +114,7 @@
     _import_structure["amused"] = ["AmusedImg2ImgPipeline", "AmusedInpaintPipeline", "AmusedPipeline"]
     _import_structure["animatediff"] = [
         "AnimateDiffPipeline",
+        "AnimateDiffSDXLPipeline",
         "AnimateDiffVideoToVideoPipeline",
     ]
     _import_structure["audioldm"] = ["AudioLDMPipeline"]
@@ -367,7 +368,7 @@
         from ..utils.dummy_torch_and_transformers_objects import *
     else:
         from .amused import AmusedImg2ImgPipeline, AmusedInpaintPipeline, AmusedPipeline
-        from .animatediff import AnimateDiffPipeline, AnimateDiffVideoToVideoPipeline
+        from .animatediff import AnimateDiffPipeline, AnimateDiffSDXLPipeline, AnimateDiffVideoToVideoPipeline
         from .audioldm import AudioLDMPipeline
         from .audioldm2 import (
             AudioLDM2Pipeline,

src/diffusers/pipelines/animatediff/__init__.py

@@ -22,6 +22,7 @@
     _dummy_objects.update(get_objects_from_module(dummy_torch_and_transformers_objects))
 else:
     _import_structure["pipeline_animatediff"] = ["AnimateDiffPipeline"]
+    _import_structure["pipeline_animatediff_sdxl"] = ["AnimateDiffSDXLPipeline"]
     _import_structure["pipeline_animatediff_video2video"] = ["AnimateDiffVideoToVideoPipeline"]
 
 if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
@@ -33,6 +34,7 @@
 
     else:
         from .pipeline_animatediff import AnimateDiffPipeline
+        from .pipeline_animatediff_sdxl import AnimateDiffSDXLPipeline
         from .pipeline_animatediff_video2video import AnimateDiffVideoToVideoPipeline
         from .pipeline_output import AnimateDiffPipelineOutput