Mehdi Chaid
f6b97619b7
FlakeStack.execute() was appending to generation_data[7]['stems'] in place. When ComfyUI re-used the cached tuple from a prior run, each queued generation compounded the output_stem, producing paths like frieren/frieren/frieren/... after a few runs. Build a fresh stems list every execution; preserve only the preset name from any upstream filename state.
323 lines
12 KiB
Python
323 lines
12 KiB
Python
from __future__ import annotations
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import json
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import logging
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import os
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from datetime import datetime
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import folder_paths
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import comfy.sd
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import comfy.utils
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from nodes import CLIPTextEncode, EmptyLatentImage, ControlNetApplyAdvanced, ControlNetLoader, LoraLoader
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from . import flake_io
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def _build_filename_prefix(preset_name: str, stems: list[str]) -> str:
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"""Build a filename prefix from preset name and flake stems."""
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folder_parts = []
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file_parts = []
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for stem in stems:
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if "/" in stem:
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folder_parts.append(stem)
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else:
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file_parts.append(stem)
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path = (preset_name + "/") if preset_name else ""
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if folder_parts:
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path += "".join(folder_parts)
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now = datetime.now()
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path += now.strftime("%Y-%m-%d") + "/"
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filename = now.strftime("%H-%M-%S")
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if file_parts:
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filename += "_" + "_".join(file_parts)
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return path + filename
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def _resolve_lora_name(stem_or_name: str) -> str:
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available = folder_paths.get_filename_list("loras")
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# Map normalized (forward-slash) paths back to original paths
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available_norm = {p.replace("\\", "/"): p for p in available}
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norm = stem_or_name.replace("\\", "/")
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if norm in available_norm:
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return available_norm[norm]
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norm_stem, _ = os.path.splitext(norm)
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for cand_norm, candidate in available_norm.items():
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stem, _ = os.path.splitext(cand_norm)
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if stem == norm_stem:
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return candidate
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raise FileNotFoundError(f"LoRA '{stem_or_name}' not found in models/loras/")
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def _load_preset_bundle(preset_name: str):
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"""Load a model preset and return (model_bundle, generation_data, sampling_preset)."""
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preset_data = flake_io.load_preset(preset_name)
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# --- Load checkpoint ----------------------------------------------------
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ckpt_path = folder_paths.get_full_path("checkpoints", preset_data.checkpoint)
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if not ckpt_path or not os.path.isfile(ckpt_path):
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raise FileNotFoundError(
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f"Checkpoint '{preset_data.checkpoint}' not found in models/checkpoints/"
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)
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embedding_dir = folder_paths.get_folder_paths("embeddings")
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model, clip, vae, _ = comfy.sd.load_checkpoint_guess_config(
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ckpt_path,
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output_vae=True,
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output_clip=True,
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embedding_directory=embedding_dir,
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)
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# --- Clip skip ----------------------------------------------------------
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if preset_data.clip_skip:
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clip = clip.clone()
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clip.clip_layer(preset_data.clip_skip)
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# --- Optional VAE override ----------------------------------------------
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if preset_data.vae:
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vae_path = folder_paths.get_full_path("vae", preset_data.vae)
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if vae_path and os.path.isfile(vae_path):
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vae_sd = comfy.utils.load_torch_file(vae_path)
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vae = comfy.sd.VAE(sd=vae_sd)
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# --- Encode prompts -----------------------------------------------------
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encoder = CLIPTextEncode()
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pos_text = preset_data.positive.strip()
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neg_text = preset_data.negative.strip()
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positive = encoder.encode(clip, pos_text)[0] if pos_text else encoder.encode(clip, "")[0]
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negative = encoder.encode(clip, neg_text)[0] if neg_text else encoder.encode(clip, "")[0]
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# --- Latent -------------------------------------------------------------
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width, height = preset_data.width, preset_data.height
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latent = EmptyLatentImage().generate(width, height, 1)[0]
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model_bundle = (model, clip, vae)
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filename_state = {"preset": preset_name, "stems": []}
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generation_data = (positive, negative, latent, width, height, pos_text, neg_text, filename_state)
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sampling_preset = (preset_data.steps, preset_data.cfg, preset_data.sampler, preset_data.scheduler)
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return model_bundle, generation_data, sampling_preset
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_MODEL_FAMILIES = ["SDXL/Base", "SDXL/Illustrious", "SDXL/Pony", "ZImage/Base", "ZImage/Turbo"]
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class FlakeModelPreset:
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@classmethod
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def INPUT_TYPES(cls):
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try:
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preset_names = flake_io.list_presets()
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except Exception:
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preset_names = []
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if not preset_names:
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preset_names = ["No model preset is selected"]
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return {
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"required": {
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"model_family": (_MODEL_FAMILIES, {"default": "SDXL/Base"}),
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"preset": (["Select a preset..."] + preset_names,),
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},
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}
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RETURN_TYPES = (
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"FLAKES_MODEL", "FLAKES_COND", "FLAKES_SAMPLER",
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)
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RETURN_NAMES = (
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"model_bundle", "generation_data", "sampling_preset",
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)
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FUNCTION = "execute"
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CATEGORY = "flakes"
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DESCRIPTION = (
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"Load a model preset (checkpoint, VAE, prompts, resolution, sampler settings). "
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"Outputs are bundled for wiring into FlakeStack / FlakeCombo nodes."
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)
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def execute(self, model_family: str, preset: str):
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preset_name = preset.strip() if preset else ""
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if not preset_name or preset_name in ("Select a preset...", "No model preset is selected"):
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raise ValueError("No model preset is selected — pick one from the dropdown.")
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return _load_preset_bundle(preset_name)
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class FlakeStack:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"model_family": (_MODEL_FAMILIES, {"default": "SDXL/Base"}),
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"model_bundle": ("FLAKES_MODEL",),
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"generation_data": ("FLAKES_COND",),
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"sampling_preset": ("FLAKES_SAMPLER",),
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"flakes_json": ("STRING", {
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"multiline": True,
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"default": "[]",
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"tooltip": "JSON-encoded list of flake entries. Managed by the Full Flakes widget.",
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}),
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},
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}
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RETURN_TYPES = (
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"FLAKES_MODEL", "FLAKES_COND", "FLAKES_SAMPLER",
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)
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RETURN_NAMES = (
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"model_bundle", "generation_data", "sampling_preset",
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)
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FUNCTION = "execute"
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CATEGORY = "flakes"
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DESCRIPTION = (
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"Compose flakes on top of an incoming model/generation/sampler bundle. "
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"Applies LoRAs, merges prompts, resolution overrides and ControlNets. "
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"Outputs updated bundles for chaining into downstream nodes."
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)
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def execute(self, model_family: str, model_bundle, generation_data, sampling_preset, flakes_json: str):
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model, clip, vae = model_bundle
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positive_cond, negative_cond, latent, width, height, pos_text, neg_text = generation_data[:7]
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steps, cfg, sampler, scheduler = sampling_preset
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# --- Filename prefix state ----------------------------------------------
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# Always start with a fresh stems list so we don't accumulate them
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# across cached executions — ComfyUI may hand us back the same
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# generation_data tuple from a previous run, and mutating its dict
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# in place would compound the output_stem path on every queue.
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if len(generation_data) > 7 and isinstance(generation_data[7], dict):
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filename_state = {
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"preset": generation_data[7].get("preset", ""),
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"stems": [],
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}
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else:
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filename_state = {"preset": "", "stems": []}
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# --- Parse flakes_json --------------------------------------------------
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try:
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entries = json.loads(flakes_json) if flakes_json else []
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except json.JSONDecodeError as exc:
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raise ValueError(f"flakes_json is not valid JSON: {exc}") from exc
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if not isinstance(entries, list):
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raise ValueError("flakes_json must be a JSON list")
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normalized: list[dict] = []
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for i, entry in enumerate(entries):
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if not isinstance(entry, dict):
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logging.warning("[FlakeStack] skipping non-object entry %d: %r", i, entry)
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continue
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if entry.get("inline") or entry.get("name"):
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normalized.append(entry)
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else:
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logging.warning("[FlakeStack] skipping entry %d (needs 'name' or 'inline')", i)
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# --- Resolve flakes -----------------------------------------------------
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flakes = [flake_io.resolve(e) for e in normalized]
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# --- Apply LoRAs --------------------------------------------------------
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lora_loader = LoraLoader()
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for f in flakes:
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# New multi-LoRA format
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for lr in f.loras:
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if not lr.path:
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continue
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lora_name = _resolve_lora_name(lr.path)
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model, clip = lora_loader.load_lora(model, clip, lora_name, lr.strength, lr.strength)
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# Legacy single LoRA fallback
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if f.lora_path:
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lora_name = _resolve_lora_name(f.lora_path)
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model, clip = lora_loader.load_lora(model, clip, lora_name, f.strength, f.strength)
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# --- Build prompt text (existing → flakes) ------------------------------
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pos_parts: list[str] = []
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neg_parts: list[str] = []
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if pos_text and pos_text.strip():
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pos_parts.append(pos_text.strip())
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if neg_text and neg_text.strip():
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neg_parts.append(neg_text.strip())
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for f in flakes:
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if f.positive and f.positive.strip():
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pos_parts.append(f.positive.strip())
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if f.negative and f.negative.strip():
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neg_parts.append(f.negative.strip())
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new_pos_text = " BREAK ".join(pos_parts) if pos_parts else ""
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new_neg_text = ", ".join(neg_parts) if neg_parts else ""
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# --- Re-encode prompts --------------------------------------------------
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encoder = CLIPTextEncode()
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positive_cond = encoder.encode(clip, new_pos_text)[0] if new_pos_text else encoder.encode(clip, "")[0]
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negative_cond = encoder.encode(clip, new_neg_text)[0] if new_neg_text else encoder.encode(clip, "")[0]
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# --- Apply ControlNets (from flakes) ------------------------------------
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from .flake_compose import _load_cn_image
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cn_model_cache = {}
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cn_loader = ControlNetLoader()
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cn_apply = ControlNetApplyAdvanced()
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for f in flakes:
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for cn in f.controlnets:
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if cn.strength == 0:
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continue
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if cn.model_name not in cn_model_cache:
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cn_model_cache[cn.model_name] = cn_loader.load_controlnet(cn.model_name)[0]
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cn_model = cn_model_cache[cn.model_name]
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image = _load_cn_image(cn.image_name)
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positive_cond, negative_cond = cn_apply.apply_controlnet(
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positive_cond, negative_cond, cn_model, image,
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cn.strength, cn.start_percent, cn.end_percent,
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)
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# --- Resolution ---------------------------------------------------------
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new_width, new_height = width, height
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for f in flakes:
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if f.resolution is not None:
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new_width, new_height = f.resolution
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break
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if (new_width, new_height) != (width, height):
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latent = EmptyLatentImage().generate(new_width, new_height, 1)[0]
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# --- Filename prefix ----------------------------------------------------
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for f in flakes:
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if f.output_stem:
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filename_state["stems"].append(f.output_stem)
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logging.info(
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"[FlakeStack] %dx%s steps=%s cfg=%s flakes=%d",
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new_width, new_height, steps, cfg, len(flakes),
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)
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out_model_bundle = (model, clip, vae)
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out_generation_data = (positive_cond, negative_cond, latent, new_width, new_height, new_pos_text, new_neg_text, filename_state)
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out_sampling_preset = (steps, cfg, sampler, scheduler)
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return (
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out_model_bundle, out_generation_data, out_sampling_preset,
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)
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class FlakeCombo(FlakeStack):
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"""Mutually-exclusive flake stack.
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The backend behavior is identical to FlakeStack — it receives a
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``flakes_json`` list and applies whatever entries are inside.
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The frontend widget ensures that ``flakes_json`` contains exactly
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one active flake at a time, and the JavaScript extension hooks
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``app.queuePrompt`` to queue every combination across all
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FlakeCombo / FlakeModelCombo nodes in the graph.
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"""
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pass
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class FlakeModelCombo(FlakeModelPreset):
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"""Multiple model preset selector.
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The backend behavior is identical to FlakeModelPreset — it loads
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a single preset from the ``preset`` dropdown. The frontend widget
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manages a list of selected presets in ``node.properties`` and the
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JavaScript extension cycles the ``preset`` widget value for each
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combination when queueing.
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"""
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pass
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