208 lines
12 KiB
Python
208 lines
12 KiB
Python
"""PromptForge Sampler: KSampler / KSampler (Advanced) / custom sigmas in one node.
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It runs ComfyUI's own `comfy.sample.sample`, so every sampler, scheduler, model patch and preview works as in
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the stock nodes. On top of that:
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* `scheduler = auto` picks the family's schedule (LTX Video's token-count dependent one, otherwise simple),
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* sigmas can come from a SIGMAS input, a typed list, or the LTXV schedule with its parameters,
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* `runs` samples several seeds in one go and returns them as one batch,
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* settings can be saved as named presets, and recommended settings come from the model's sidecar and family
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(see sampler_defaults.py).
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"""
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import json
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from pathlib import Path
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import torch
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from aiohttp import web
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from server import PromptServer
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import comfy.model_management
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import comfy.sample
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import comfy.samplers
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import comfy.utils
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import folder_paths
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import latent_preview
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from comfy_extras import nodes_easycache, nodes_lt
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from . import families, sampler_defaults
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AUTO = "auto"
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LTXV = "ltxv"
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PRESET_FIELDS = ("steps", "cfg", "sampler_name", "scheduler", "denoise", "runs", "step_cache", "cache_threshold")
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# step caching skips whole model calls, which only pays off (and only stays clean) with enough steps
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AUTO_CACHE_MIN_STEPS = 15
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CACHES = {"easycache": nodes_easycache.EasyCacheNode, "lazycache": nodes_easycache.LazyCacheNode}
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def _preset_file():
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return Path(folder_paths.get_user_directory()) / "promptforge" / "sampler_presets.json"
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def load_presets():
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try:
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return json.loads(_preset_file().read_text(encoding="utf-8"))
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except (OSError, ValueError):
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return {}
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@PromptServer.instance.routes.get("/promptforge/sampler_presets")
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async def _presets_route(request):
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return web.json_response(load_presets())
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@PromptServer.instance.routes.post("/promptforge/sampler_presets")
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async def _save_preset_route(request):
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body = await request.json()
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name = str(body.get("name", "")).strip()
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if not name:
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return web.json_response({"error": "a preset needs a name"}, status=400)
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presets = load_presets()
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if body.get("delete"):
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presets.pop(name, None)
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else:
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presets[name] = {field: body["settings"][field] for field in PRESET_FIELDS if field in body.get("settings", {})}
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_preset_file().parent.mkdir(parents=True, exist_ok=True)
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_preset_file().write_text(json.dumps(presets, indent=1), encoding="utf-8")
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return web.json_response(presets)
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@PromptServer.instance.routes.get("/promptforge/recommend")
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async def _recommend_route(request):
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family, name, folder = (request.query.get(key, "") for key in ("family", "model", "folder"))
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path = folder_paths.get_full_path(folder, name) if folder and name else None
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settings, source = sampler_defaults.recommend(family, name, path)
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return web.json_response({"settings": settings, "source": source})
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def parse_sigmas(text):
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"""'1, 0.8, 0.5' -> descending sigma tensor ending in 0."""
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values = [float(part) for part in text.replace("\n", ",").split(",") if part.strip()]
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if len(values) < 2 or any(a < b for a, b in zip(values, values[1:])):
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raise ValueError("[PromptForge] custom_sigmas must be a descending list of at least two numbers, for example 1, 0.8, 0.5, 0.")
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if values[-1] > 0:
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values.append(0.0)
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return torch.tensor(values, dtype=torch.float32)
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def trim_for_denoise(sigmas, steps):
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"""Keep the last `steps` intervals, which is what denoise < 1 means for a schedule computed at steps / denoise."""
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return sigmas[-(steps + 1):]
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class PROMPTFORGE_Sampler:
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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": ("MODEL",),
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"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",),
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"latent_image": ("LATENT",),
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"seed": ("INT", {"default": 0, "min": 0, "max": 2 ** 64 - 1}),
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"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
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"cfg": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
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"sampler_name": (comfy.samplers.KSampler.SAMPLERS, {"default": "euler"}),
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"scheduler": ([AUTO, LTXV] + comfy.samplers.KSampler.SCHEDULERS, {"default": AUTO, "tooltip": "auto: the family's schedule (ltxv for LTX Video, otherwise simple). ltxv: LTX Video's token-count dependent schedule."}),
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"denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
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"runs": ("INT", {"default": 1, "min": 1, "max": 64, "tooltip": "Samples this many seeds (seed, seed+1 ...) and returns them as one batch."}),
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},
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"optional": {
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"pipe": ("PROMPTFORGE_PIPE", {"tooltip": "From a PromptForge Loader: sets the family and fills recommended settings when connected."}),
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"sigmas": ("SIGMAS", {"tooltip": "Overrides the scheduler."}),
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"custom_sigmas": ("STRING", {"default": "", "tooltip": "Comma separated descending sigmas, for example 1, 0.8, 0.5, 0. Overrides the scheduler."}),
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"step_cache": (["off", "auto", "easycache", "lazycache"], {"default": "off", "tooltip": "Skips model calls on steps where the output barely changes. easycache is general and also handles audio+video models; lazycache is cheaper but rougher. auto = easycache when steps >= 15, else off. Useful for many-step runs; distilled 4-8 step models should stay off."}),
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"cache_threshold": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 3.0, "step": 0.01, "tooltip": "Higher skips more steps (faster, softer). 0.1-0.2 is conservative."}),
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"cache_start": ("FLOAT", {"default": 0.15, "min": 0.0, "max": 1.0, "step": 0.01, "advanced": True, "tooltip": "Fraction of the run before which nothing is skipped."}),
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"cache_end": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 1.0, "step": 0.01, "advanced": True}),
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"start_at_step": ("INT", {"default": 0, "min": 0, "max": 10000, "advanced": True}),
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"end_at_step": ("INT", {"default": 10000, "min": 0, "max": 10000, "advanced": True}),
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"add_noise": ("BOOLEAN", {"default": True, "advanced": True}),
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"ltxv_max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step": 0.01, "advanced": True}),
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"ltxv_base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step": 0.01, "advanced": True}),
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"ltxv_terminal": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01, "advanced": True}),
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"preview": (["default", "taesd", "latent2rgb", "none"], {"default": "default", "tooltip": "Live preview while sampling. default follows ComfyUI's --preview-method; taesd uses the model's tiny decoder when it is in models/vae_approx (taeh3 for MiniMax H3) and latent2rgb otherwise."}),
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},
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}
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RETURN_TYPES = ("LATENT", "SIGMAS", "STRING")
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RETURN_NAMES = ("LATENT", "SIGMAS", "INFO")
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FUNCTION = "sample"
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CATEGORY = "🔮 PromptForge/Sampling"
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DESCRIPTION = "KSampler with family-aware schedules, custom sigmas, multiple seeds per run and saved presets."
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@staticmethod
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def _family(model, pipe):
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if pipe and pipe.get("family"):
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return pipe["family"]
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config = getattr(getattr(model, "model", None), "model_config", None)
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family = families.family_from_config(type(config).__name__) if config is not None else None
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return family.key if family else ""
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def _sigmas(self, model, sampler_name, scheduler, steps, denoise, latent_image, sigmas, custom_sigmas, shifts):
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"""The sigmas to sample with: an input, a typed list, the LTXV schedule, or the named scheduler's."""
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if sigmas is not None:
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return sigmas
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if custom_sigmas.strip():
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return parse_sigmas(custom_sigmas)
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if scheduler == LTXV:
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total = max(steps, int(steps / denoise)) if denoise > 0 else steps
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schedule = nodes_lt.LTXVScheduler.execute(total, shifts[0], shifts[1], True, shifts[2], {"samples": latent_image}).args[0]
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return trim_for_denoise(schedule, steps)
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return comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options).sigmas
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def sample(self, model, positive, negative, latent_image, seed, steps, cfg, sampler_name, scheduler, denoise, runs,
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pipe=None, sigmas=None, custom_sigmas="", step_cache="off", cache_threshold=0.2, cache_start=0.15, cache_end=0.95,
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start_at_step=0, end_at_step=10000, add_noise=True, ltxv_max_shift=2.05, ltxv_base_shift=0.95, ltxv_terminal=0.1, preview="default"):
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family = self._family(model, pipe)
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if step_cache == "auto":
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step_cache = "easycache" if steps >= AUTO_CACHE_MIN_STEPS else "off"
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if step_cache != "off":
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model = CACHES[step_cache].execute(model, cache_threshold, cache_start, cache_end, False).args[0]
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if scheduler == AUTO:
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scheduler = LTXV if family == "ltxv" else "simple"
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samples = comfy.sample.fix_empty_latent_channels(model, latent_image["samples"], latent_image.get("downscale_ratio_spacial"), latent_image.get("downscale_ratio_temporal"))
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used = self._sigmas(model, sampler_name, scheduler, steps, denoise, samples, sigmas, custom_sigmas, (ltxv_max_shift, ltxv_base_shift, ltxv_terminal))
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if runs > 1 and not isinstance(samples, torch.Tensor):
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raise ValueError("[PromptForge] runs > 1 needs a plain latent; this model uses a combined audio+video latent.")
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device = comfy.model_management.get_torch_device()
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if device.type == "cuda":
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torch.cuda.reset_peak_memory_stats(device)
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latent_preview.set_preview_method(None if preview == "default" else preview)
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try:
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try:
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callback = latent_preview.prepare_callback(model, steps)
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except AttributeError:
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# the tiny decoder file is not a loadable VAE; the colour preview needs no file
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print("[PromptForge] the tiny preview decoder could not be loaded; using latent2rgb previews.")
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latent_preview.set_preview_method("latent2rgb")
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callback = latent_preview.prepare_callback(model, steps)
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progress = comfy.utils.PROGRESS_BAR_ENABLED
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results = []
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for index in range(runs):
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current = seed + index
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noise = comfy.sample.prepare_empty_noise(samples) if not add_noise else comfy.sample.prepare_noise(samples, current, latent_image.get("batch_index"))
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results.append(comfy.sample.sample(
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model, noise, steps, cfg, sampler_name, scheduler, positive, negative, samples,
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denoise=denoise, disable_noise=not add_noise, start_step=start_at_step or None,
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last_step=end_at_step if end_at_step < steps else None, force_full_denoise=end_at_step >= steps,
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noise_mask=latent_image.get("noise_mask"), sigmas=used, callback=callback, disable_pbar=not progress, seed=current))
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finally:
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latent_preview.set_preview_method()
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merged = results[0] if runs == 1 else torch.cat(results)
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out = latent_image.copy()
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out.pop("downscale_ratio_spacial", None)
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out.pop("downscale_ratio_temporal", None)
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out["samples"] = merged
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schedule = "custom sigmas" if sigmas is not None or custom_sigmas.strip() else scheduler
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info = f"{sampler_name} / {schedule}, {steps} steps, cfg {cfg:g}, denoise {denoise:g}, seed {seed}" + (f" x{runs}" if runs > 1 else "") + (f" ({family})" if family else "") + (f", {step_cache} {cache_threshold:g}" if step_cache != "off" else "")
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if device.type == "cuda":
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info += f", peak VRAM {torch.cuda.max_memory_allocated(device) / 1024 ** 3:.1f} GB"
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return (out, used, info)
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NODE_CLASS_MAPPINGS = {"PROMPTFORGE_Sampler": PROMPTFORGE_Sampler}
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NODE_DISPLAY_NAME_MAPPINGS = {"PROMPTFORGE_Sampler": "PromptForge Sampler"}
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