295 lines
12 KiB
Python
295 lines
12 KiB
Python
from __future__ import annotations
|
|
|
|
import importlib.util
|
|
import json
|
|
import logging
|
|
import os
|
|
from typing import Any
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image, ImageOps
|
|
|
|
import comfy.hooks
|
|
import comfy.utils
|
|
import folder_paths
|
|
import node_helpers
|
|
from nodes import (
|
|
CLIPTextEncode,
|
|
ControlNetApplyAdvanced,
|
|
ControlNetLoader,
|
|
EmptyLatentImage,
|
|
LoraLoader,
|
|
NODE_CLASS_MAPPINGS,
|
|
)
|
|
|
|
from . import flake_io
|
|
|
|
|
|
def _resolve_lora_name(stem_or_name: str) -> str:
|
|
result = flake_io._resolve_model_name("loras", stem_or_name)
|
|
available = folder_paths.get_filename_list("loras")
|
|
if result.replace("\\", "/") in {p.replace("\\", "/") for p in available}:
|
|
return result
|
|
raise FileNotFoundError(
|
|
f"LoRA '{stem_or_name}' not found in models/loras/. "
|
|
f"Provide the stem or full filename of an existing LoRA."
|
|
)
|
|
|
|
|
|
def _load_cn_image(image_name: str) -> torch.Tensor:
|
|
"""Load an image tensor shaped [1, H, W, 3] (ComfyUI's IMAGE format) from ComfyUI/input/.
|
|
|
|
`image_name` may be a stem ('standing_openpose') or a filename with extension.
|
|
Subdirectories under input/ are allowed ('cnet/standing_openpose').
|
|
"""
|
|
input_dir = folder_paths.get_input_directory()
|
|
norm = image_name.replace("\\", "/")
|
|
|
|
candidates: list[str] = []
|
|
direct = os.path.join(input_dir, norm)
|
|
if os.path.isfile(direct):
|
|
candidates.append(direct)
|
|
else:
|
|
for ext in (".png", ".jpg", ".jpeg", ".webp"):
|
|
alt = os.path.join(input_dir, f"{norm}{ext}")
|
|
if os.path.isfile(alt):
|
|
candidates.append(alt)
|
|
break
|
|
|
|
if not candidates:
|
|
raise FileNotFoundError(
|
|
f"ControlNet image '{image_name}' not found in {input_dir}. "
|
|
f"Place the image there (any of .png/.jpg/.jpeg/.webp) or use a full filename."
|
|
)
|
|
|
|
img = Image.open(candidates[0])
|
|
img = ImageOps.exif_transpose(img).convert("RGB")
|
|
arr = np.array(img).astype(np.float32) / 255.0
|
|
return torch.from_numpy(arr)[None,]
|
|
|
|
|
|
def _fit_cn_image(image: torch.Tensor, target_w: int, target_h: int) -> torch.Tensor:
|
|
"""Resize a ControlNet hint image (IMAGE tensor [B, H, W, 3]) to the
|
|
generation resolution, cropping to the target aspect first (#255).
|
|
|
|
ComfyUI otherwise stretches the hint to the latent size at sample time,
|
|
which distorts the pose whenever the control image's aspect ratio differs
|
|
from the generation resolution. Crop-and-resize (matching A1111's default)
|
|
keeps the pose aligned with the generated frame.
|
|
"""
|
|
if target_w <= 0 or target_h <= 0:
|
|
return image
|
|
h, w = int(image.shape[1]), int(image.shape[2])
|
|
if (w, h) == (target_w, target_h):
|
|
return image
|
|
samples = image.movedim(-1, 1) # [B, 3, H, W]
|
|
samples = comfy.utils.common_upscale(samples, target_w, target_h, "lanczos", "center")
|
|
return samples.movedim(1, -1) # back to [B, H, W, 3]
|
|
|
|
|
|
# Model-patch ControlNets (#365), most recently used last: {name: (mtime, MODEL_PATCH)}.
|
|
# Reusing the same MODEL_PATCH object across runs lets a Flake Combo batch skip
|
|
# re-reading the file and lets ComfyUI keep it resident. Bounded so a multi-GB
|
|
# Z-Image union patch isn't pinned once workflows stop using it.
|
|
_CN_PATCH_CACHE: dict[str, tuple[float, Any]] = {}
|
|
_CN_PATCH_CACHE_SIZE = 4
|
|
|
|
|
|
def load_cn_patch(name: str) -> Any:
|
|
"""Load a ControlNet model patch from models/model_patches/ (#365)."""
|
|
from comfy_extras.nodes_model_patch import ModelPatchLoader
|
|
|
|
mtime = os.path.getmtime(folder_paths.get_full_path_or_raise("model_patches", name))
|
|
hit = _CN_PATCH_CACHE.pop(name, None)
|
|
if hit is None or hit[0] != mtime:
|
|
hit = (mtime, ModelPatchLoader().load_model_patch(name)[0])
|
|
_CN_PATCH_CACHE[name] = hit
|
|
while len(_CN_PATCH_CACHE) > _CN_PATCH_CACHE_SIZE:
|
|
del _CN_PATCH_CACHE[next(iter(_CN_PATCH_CACHE))]
|
|
return hit[1]
|
|
|
|
|
|
def apply_model_patch_cn(cn_kind: str, model: Any, vae: Any, model_patch: Any, image: torch.Tensor,
|
|
strength: float, start_percent: float, end_percent: float) -> Any:
|
|
"""Apply an Anima LLLite or Z-Image Fun ControlNet by patching the model (#365).
|
|
|
|
These hook the diffusion model's blocks rather than the conditioning. The
|
|
returned model is a clone sharing the base weights, so swapping the patch
|
|
between runs does not reload the diffusion model.
|
|
"""
|
|
from comfy_extras.nodes_model_patch import AnimaLLLiteApply, ZImageFunControlnet
|
|
|
|
if cn_kind == "anima_lllite":
|
|
return AnimaLLLiteApply().apply_patch(model, model_patch, image, strength, start_percent, end_percent)[0]
|
|
if (start_percent, end_percent) != (0.0, 1.0):
|
|
logging.warning("[flakes] Z-Image Fun ControlNet has no start/end; applying it to every step")
|
|
return ZImageFunControlnet().diffsynth_controlnet(model, model_patch, vae, image=image, strength=strength)[0]
|
|
|
|
|
|
def anima_vace_name(model_name: str) -> str | None:
|
|
"""The models/controlnet/anima/ file ``model_name`` resolves to when it is
|
|
an Anima VACE ControlNet (``control_blocks.*.after_proj`` keys), else None (#378)."""
|
|
name = flake_io.resolve_cn_model_name(model_name, "anima", "controlnet")
|
|
if not name.replace("\\", "/").lower().startswith("anima/") or name not in folder_paths.get_filename_list("controlnet"):
|
|
return None
|
|
header = comfy.utils.safetensors_header(folder_paths.get_full_path_or_raise("controlnet", name))
|
|
if header and any("control_blocks" in k and "after_proj" in k for k in json.loads(header)):
|
|
return name
|
|
return None
|
|
|
|
|
|
def apply_anima_vace(positive: Any, negative: Any, name: str, vae: Any, image: torch.Tensor,
|
|
strength: float, start_percent: float, end_percent: float) -> tuple[Any, Any]:
|
|
"""Apply an Anima VACE ControlNet through the nodes of the
|
|
ComfyUI-Advanced-ControlNet-Anima fork (#378); core ComfyUI can't load it."""
|
|
loader = NODE_CLASS_MAPPINGS.get("ACN_ControlNetLoaderAdvanced")
|
|
apply = NODE_CLASS_MAPPINGS.get("ACN_AdvancedControlNetApply_v2")
|
|
# Upstream Advanced-ControlNet registers the same nodes without VACE support.
|
|
if loader is None or apply is None or importlib.util.find_spec(
|
|
loader.__module__.rsplit(".", 1)[0] + ".control_anima_vace") is None:
|
|
raise RuntimeError(
|
|
f"Anima VACE ControlNet '{name}' needs the ComfyUI-Advanced-ControlNet-Anima custom nodes "
|
|
f"(github.com/TaihoC/ComfyUI-Advanced-ControlNet-Anima, branch feat/anima-vace-controlnet)."
|
|
)
|
|
control_net = loader().load_controlnet(name)[0]
|
|
return apply().apply_controlnet(positive, negative, control_net, image, strength, start_percent, end_percent,
|
|
vae_optional=vae)
|
|
|
|
|
|
def maybe_zero_out_negative(negative: Any, cfg: float) -> Any:
|
|
"""Zero out the negative conditioning when sampling at cfg <= 1 (#359).
|
|
|
|
Distilled/turbo models (Z-Image Turbo, Krea2 Turbo, Anima with the turbo
|
|
LoRA) run at cfg 1.0, where the negative branch is never evaluated — the
|
|
workflow templates ComfyUI ships all feed those samplers a
|
|
``ConditioningZeroOut`` instead of encoded text.
|
|
|
|
Keying this off the *effective* cfg rather than the model family is what
|
|
makes it correct for free: Anima base at cfg 4 keeps its real negative,
|
|
Anima + turbo LoRA at cfg 1 does not, and no per-family flag can get out of
|
|
step with the value the user actually sampled at.
|
|
"""
|
|
if negative is None or cfg is None or cfg > 1.0:
|
|
return negative
|
|
from nodes import ConditioningZeroOut
|
|
|
|
return ConditioningZeroOut().zero_out(negative)[0]
|
|
|
|
|
|
def schedule_segments(schedule: list[flake_io.ScheduleKeyframe]) -> list[tuple[float, float, float]]:
|
|
"""``(start, end, strength)`` spans covering [0, 1] for a hook schedule (#370)."""
|
|
segments = []
|
|
start, strength = 0.0, 1.0
|
|
for kf in schedule:
|
|
if kf.start > start:
|
|
segments.append((start, kf.start, strength))
|
|
start, strength = kf.start, kf.strength
|
|
if start < 1.0:
|
|
segments.append((start, 1.0, strength))
|
|
return segments
|
|
|
|
|
|
def scheduled_lora_hooks(lora_name: str, strength: float, schedule: list[flake_io.ScheduleKeyframe]) -> Any:
|
|
"""Hook group applying a LoRA's model weights under ``schedule`` (#370).
|
|
|
|
Hooked weights only act on conds that carry the hooks, so the caller
|
|
attaches the group to both positive and negative.
|
|
"""
|
|
lora = comfy.utils.load_torch_file(folder_paths.get_full_path_or_raise("loras", lora_name), safe_load=True)
|
|
hooks = comfy.hooks.create_hook_lora(lora, strength, 0.0)
|
|
keyframes = comfy.hooks.HookKeyframeGroup()
|
|
for kf in schedule:
|
|
keyframes.add(comfy.hooks.HookKeyframe(strength=kf.strength, start_percent=kf.start))
|
|
hooks.set_keyframes_on_hooks(keyframes)
|
|
return hooks
|
|
|
|
|
|
def encode_scheduled(clip: Any, text: str, schedule: list[flake_io.ScheduleKeyframe]) -> list:
|
|
"""Encode ``text`` as one cond entry per schedule segment with strength > 0,
|
|
to be appended after the base cond (#370)."""
|
|
cond = CLIPTextEncode().encode(clip, text)[0]
|
|
out = []
|
|
for start, end, strength in schedule_segments(schedule):
|
|
if strength > 0:
|
|
out += node_helpers.conditioning_set_values(
|
|
cond, {"start_percent": start, "end_percent": end, "strength": strength})
|
|
return out
|
|
|
|
|
|
def carry_schedules(src_cond: list, dst_cond: list, ranged: list, hook_groups: list) -> list:
|
|
"""Append upstream and new time-ranged prompt entries to freshly encoded
|
|
``dst_cond`` and attach upstream and new LoRA hooks to every entry (#370).
|
|
|
|
A chained Flake node re-encodes the accumulated text, which drops what
|
|
upstream nodes attached (like ``control``, see ``_carry_controlnets``).
|
|
"""
|
|
upstream_ranged = [t for t in src_cond if "start_percent" in t[1]]
|
|
hooks = comfy.hooks.HookGroup.combine_all_hooks([src_cond[0][1].get("hooks")] + hook_groups)
|
|
return comfy.hooks.set_hooks_for_conditioning(dst_cond + upstream_ranged + ranged, hooks, append_hooks=False)
|
|
|
|
|
|
def compose(
|
|
model: Any,
|
|
clip: Any,
|
|
entries: list[dict[str, Any]],
|
|
) -> tuple:
|
|
flakes = [flake_io.resolve(e) for e in entries]
|
|
|
|
lora_loader = LoraLoader()
|
|
for f in flakes:
|
|
if not f.lora_path:
|
|
continue
|
|
lora_name = _resolve_lora_name(f.lora_path)
|
|
model, clip = lora_loader.load_lora(model, clip, lora_name, f.strength, f.strength)
|
|
|
|
pos_text = " BREAK ".join(f.positive.strip() for f in flakes if f.positive and f.positive.strip())
|
|
neg_text = ", ".join(f.negative.strip() for f in flakes if f.negative and f.negative.strip())
|
|
|
|
encoder = CLIPTextEncode()
|
|
positive = encoder.encode(clip, pos_text)[0]
|
|
negative = encoder.encode(clip, neg_text)[0]
|
|
|
|
cn_model_cache: dict[str, Any] = {}
|
|
cn_loader = ControlNetLoader()
|
|
cn_apply = ControlNetApplyAdvanced()
|
|
for f in flakes:
|
|
for cn in f.controlnets:
|
|
if cn.strength == 0:
|
|
continue
|
|
if not cn.model_name.strip():
|
|
print(f"[flakes] skipping controlnet entry with empty model_name")
|
|
continue
|
|
if not cn.image_name.strip():
|
|
print(f"[flakes] skipping controlnet entry with empty image_name (type={cn.type})")
|
|
continue
|
|
cn_resolved = flake_io._resolve_model_name("controlnet", cn.model_name)
|
|
if cn_resolved not in cn_model_cache:
|
|
cn_model_cache[cn_resolved] = cn_loader.load_controlnet(cn_resolved)[0]
|
|
cn_model = cn_model_cache[cn_resolved]
|
|
image = _load_cn_image(cn.image_name)
|
|
positive, negative = cn_apply.apply_controlnet(
|
|
positive, negative, cn_model, image,
|
|
cn.strength, cn.start_percent, cn.end_percent,
|
|
)
|
|
logging.info(
|
|
"[flakes] applied CN model=%s image=%s strength=%.2f start=%.2f end=%.2f",
|
|
cn_resolved, cn.image_name, cn.strength, cn.start_percent, cn.end_percent,
|
|
)
|
|
|
|
width, height = 1024, 1024
|
|
for f in flakes:
|
|
if f.resolution is not None:
|
|
width, height = f.resolution
|
|
break
|
|
|
|
latent = EmptyLatentImage().generate(width, height, 1)[0]
|
|
|
|
logging.info(
|
|
"[FlakeStack] composed %d flake(s), resolution %dx%d, %d controlnet(s)",
|
|
len(flakes), width, height,
|
|
sum(len(f.controlnets) for f in flakes),
|
|
)
|
|
|
|
return model, clip, positive, negative, latent, width, height
|