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1e3ce4c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 | """AnimateDiff backend adapter (Apache 2.0).
Wraps `diffusers.AnimateDiffSparseControlNetPipeline` so a `PixelCursor` over a
single Image can be animated through one of the 8 verified MotionLoRA presets
(zoom, pan, tilt, roll). SparseCtrl is the I2V path: frame 0 is pinned to the
cursor's source image, frames 1..N-1 are diffused with motion-LoRA conditioning.
Light surface (works without torch):
- `register()` wires bind_motion_exemplar + write_motion into the op registry
- `bind_motion_exemplar(cursor, ["zoom_in"])` packs the resolved MotionLoRA
id into an IdentityLock — no model load
- `dry_run(cursor)` returns a diagnostic dict (what would happen, what it costs)
Heavy surface (requires `pip install -e '.[backends]'`):
- `write_motion(cursor, motion_spec)` lazy-imports torch + diffusers, loads
the pipeline, runs SparseCtrl-conditioned generation, returns FrameStack
References:
diffusers AnimateDiffSparseControlNetPipeline docs (Apache 2.0)
guoyww/animatediff-motion-adapter-v1-5-2 (Apache 2.0)
guoyww/animatediff-sparsectrl-rgb (Apache 2.0)
"""
from __future__ import annotations
from dataclasses import dataclass
from typing import Sequence
import numpy as np
from pixel_cursor import PixelCursor, Image, IdentityLock, FrameStack, ops
from pixel_cursor.artifact import _new_framestack
# Verified by A5 research (background subagent, confidence: high)
MOTION_LORA_MAP: dict[str, str] = {
"zoom_in": "guoyww/animatediff-motion-lora-zoom-in",
"zoom_out": "guoyww/animatediff-motion-lora-zoom-out",
"pan_left": "guoyww/animatediff-motion-lora-pan-left",
"pan_right": "guoyww/animatediff-motion-lora-pan-right",
"tilt_up": "guoyww/animatediff-motion-lora-tilt-up",
"tilt_down": "guoyww/animatediff-motion-lora-tilt-down",
"roll_anticlockwise": "guoyww/animatediff-motion-lora-rolling-anticlockwise",
"roll_clockwise": "guoyww/animatediff-motion-lora-rolling-clockwise",
}
DEFAULT_MOTION_ADAPTER = "guoyww/animatediff-motion-adapter-v1-5-2"
DEFAULT_SPARSECTRL = "guoyww/animatediff-sparsectrl-rgb"
DEFAULT_BASE_MODEL = "SG161222/Realistic_Vision_V5.1_noVAE"
INSTALL_HINT = (
"AnimateDiff inference requires torch + diffusers. Install with:\n"
" pip install -e '.[backends]'\n"
"(or: pip install torch diffusers transformers)"
)
class AnimateDiffAdapter:
"""Adapter exposing AnimateDiff primitives to the PixelCursor op registry."""
SOURCE_TAG = "animatediff"
def __init__(
self,
motion_adapter_id: str = DEFAULT_MOTION_ADAPTER,
base_model_id: str = DEFAULT_BASE_MODEL,
sparsectrl_id: str = DEFAULT_SPARSECTRL,
) -> None:
self.motion_adapter_id = motion_adapter_id
self.base_model_id = base_model_id
self.sparsectrl_id = sparsectrl_id
def register(self) -> None:
ops.register_backend("bind_motion_exemplar", "animatediff", self.bind_motion_exemplar)
ops.register_backend("write_motion", "animatediff", self.write_motion)
def bind_motion_exemplar(
self, cursor: PixelCursor, exemplar: Sequence[str]
) -> PixelCursor:
"""Resolve a preset name (e.g. ["zoom_in"]) to a MotionLoRA id and pack into
an IdentityLock on the cursor. No model load happens here.
Accepts a sequence to keep the signature uniform with MotionDirector
(which takes K reference clips). For AnimateDiff a single preset is expected;
a list of clip paths is rejected with a helpful error pointing to session 3.
"""
if len(exemplar) != 1:
raise ValueError(
f"animatediff.bind_motion_exemplar expects exactly one preset name; "
f"got {len(exemplar)} items. For few-shot training from K reference "
f"clips, use backend='motiondirector' (session 3)."
)
preset = exemplar[0]
if preset.endswith((".mp4", ".webm", ".mkv", ".gif", ".avi", ".mov")):
raise ValueError(
f"animatediff backend takes preset names from MOTION_LORA_MAP "
f"(e.g. 'zoom_in'); got what looks like a video path: {preset!r}. "
f"For exemplar-based few-shot, use backend='motiondirector'."
)
if preset not in MOTION_LORA_MAP:
valid = sorted(MOTION_LORA_MAP)
raise ValueError(
f"Unknown AnimateDiff preset {preset!r}. Valid presets: {valid}"
)
lora_id = MOTION_LORA_MAP[preset]
lock = IdentityLock(
embedding=np.zeros(0, dtype=np.float32),
source=f"{self.SOURCE_TAG}:{preset}:{lora_id}",
)
return cursor.lock_identity(lock)
def dry_run(self, cursor: PixelCursor, motion_spec: dict | None = None) -> dict:
"""Return what `write_motion` WOULD do without running it.
Useful for verifying the preset mapping and resource estimate before
paying the install + model-download cost. Safe to call without torch.
"""
if cursor.identity_lock is None or not cursor.identity_lock.source.startswith(
f"{self.SOURCE_TAG}:"
):
raise ValueError(
"dry_run requires bind_motion_exemplar(..., backend='animatediff') first"
)
if not isinstance(cursor.artifact, Image):
raise TypeError(
f"AnimateDiff dry_run expects an Image artifact; got {type(cursor.artifact).__name__}"
)
_, preset, lora_id = cursor.identity_lock.source.split(":", 2)
ms = motion_spec or {}
return {
"backend": "animatediff",
"pipeline_class": "diffusers.AnimateDiffSparseControlNetPipeline",
"base_model": self.base_model_id,
"motion_adapter": self.motion_adapter_id,
"sparsectrl": self.sparsectrl_id,
"preset": preset,
"motion_lora_id": lora_id,
"input_image_shape": tuple(cursor.artifact.pixels.shape),
"num_frames": ms.get("num_frames", 16),
"native_fps": 8,
"target_fps": 24,
"post_interpolation": "RIFE 3x (8fps -> 24fps), MIT licensed",
"expected_output_shape": (ms.get("num_frames", 16), *cursor.artifact.pixels.shape),
"hardware_floor_vram_gb": 13,
"expected_wall_time_s_4090": 45,
"prompt": ms.get("prompt", "(none — cursor has no text prompt yet)"),
"guidance_scale": ms.get("guidance_scale", 7.5),
"num_inference_steps": ms.get("num_inference_steps", 25),
"torch_installed": _torch_available(),
"note": (
"If torch_installed is False, `write_motion` will raise ImportError. "
"Install with: pip install -e '.[backends]'"
),
}
def write_motion(self, cursor: PixelCursor, motion_spec: dict | None = None) -> FrameStack:
if cursor.identity_lock is None or not cursor.identity_lock.source.startswith(
f"{self.SOURCE_TAG}:"
):
raise ValueError(
"write_motion(animatediff) requires bind_motion_exemplar(..., backend='animatediff') first"
)
if not isinstance(cursor.artifact, Image):
raise TypeError(
f"AnimateDiff write_motion expects an Image artifact; got {type(cursor.artifact).__name__}"
)
try:
import torch # noqa: F401
from diffusers import ( # noqa: F401
AnimateDiffSparseControlNetPipeline,
MotionAdapter,
SparseControlNetModel,
DDIMScheduler,
)
except ImportError as e:
raise ImportError(INSTALL_HINT) from e
return self._run_inference(cursor, motion_spec or {})
def _run_inference(self, cursor: PixelCursor, motion_spec: dict) -> FrameStack:
import torch
from PIL import Image as PILImage
from diffusers import (
AnimateDiffSparseControlNetPipeline,
MotionAdapter,
SparseControlNetModel,
DDIMScheduler,
)
_, preset, lora_id = cursor.identity_lock.source.split(":", 2)
dtype = torch.float16
motion_adapter = MotionAdapter.from_pretrained(self.motion_adapter_id, torch_dtype=dtype)
controlnet = SparseControlNetModel.from_pretrained(self.sparsectrl_id, torch_dtype=dtype)
pipe = AnimateDiffSparseControlNetPipeline.from_pretrained(
self.base_model_id,
motion_adapter=motion_adapter,
controlnet=controlnet,
torch_dtype=dtype,
)
pipe.scheduler = DDIMScheduler.from_config(
pipe.scheduler.config,
clip_sample=False,
timestep_spacing="linspace",
beta_schedule="linear",
steps_offset=1,
)
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
pipe.load_lora_weights(lora_id, adapter_name=preset)
pipe.set_adapters([preset], adapter_weights=[motion_spec.get("lora_weight", 1.0)])
conditioning_pil = PILImage.fromarray(cursor.artifact.pixels)
output = pipe(
prompt=motion_spec.get("prompt", "high quality, detailed"),
negative_prompt=motion_spec.get("negative_prompt", "bad quality, blurry"),
num_frames=motion_spec.get("num_frames", 16),
guidance_scale=motion_spec.get("guidance_scale", 7.5),
num_inference_steps=motion_spec.get("num_inference_steps", 25),
conditioning_frames=[conditioning_pil],
controlnet_conditioning_scale=motion_spec.get("controlnet_scale", 1.0),
controlnet_frame_indices=[0],
generator=torch.Generator("cpu").manual_seed(motion_spec.get("seed", 42)),
)
frames_pil = output.frames[0]
frames_np = np.stack([np.array(f) for f in frames_pil], axis=0).astype(np.uint8)
return _new_framestack(frames_np, fps=8, name=f"animatediff:{preset}")
@dataclass(frozen=True)
class _ProbeResult:
torch_installed: bool
diffusers_installed: bool
def _torch_available() -> bool:
try:
import torch # noqa: F401
return True
except ImportError:
return False
def probe() -> _ProbeResult:
"""Diagnostic: are the heavy deps installed?"""
try:
import torch # noqa: F401
t = True
except ImportError:
t = False
try:
import diffusers # noqa: F401
d = True
except ImportError:
d = False
return _ProbeResult(torch_installed=t, diffusers_installed=d)
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