From bb9de2bbe4b287821062b4dfc885465e4dddcffd Mon Sep 17 00:00:00 2001 From: braintacles <131351878+braintacles@users.noreply.github.com> Date: Thu, 23 May 2024 15:03:16 +0900 Subject: [PATCH] Update braintacles_nodes.py --- braintacles_nodes.py | 130 ++++++++++++++++++++----------------------- 1 file changed, 60 insertions(+), 70 deletions(-) diff --git a/braintacles_nodes.py b/braintacles_nodes.py index 1fcf53f..75bf772 100644 --- a/braintacles_nodes.py +++ b/braintacles_nodes.py @@ -1,6 +1,8 @@ import torch import random - +import comfy.samplers +import comfy.sample +import latent_preview class CLIPTextEncodeSDXL_Multi_IO: @classmethod @@ -191,84 +193,72 @@ class RandomFindAndReplace: return (prompt, choice, seed,) -class VAEDecodePipe: +def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False): + latent_image = latent["samples"] + if disable_noise: + noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") + else: + batch_inds = latent["batch_index"] if "batch_index" in latent else None + noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) + + noise_mask = None + if "noise_mask" in latent: + noise_mask = latent["noise_mask"] + + callback = latent_preview.prepare_callback(model, steps) + disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED + samples = comfy.sample.sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, + denoise=denoise, disable_noise=disable_noise, start_step=start_step, last_step=last_step, + force_full_denoise=force_full_denoise, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) + out = latent.copy() + out["samples"] = samples + return (out, ) + +class IntervalSampler: @classmethod def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT", ), "vae": ("VAE", )}} - RETURN_TYPES = ("IMAGE","VAE",) - FUNCTION = "decode" + return {"required": + {"modelA": ("MODEL",), + "modelB": ("MODEL",), + "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), + "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), + "interval": ("INT", {"default": 1, "min": 1, "max": 1000}), + "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}), + "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), + "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), + "positiveA": ("CONDITIONING", ), + "negativeA": ("CONDITIONING", ), + "positiveB": ("CONDITIONING", ), + "negativeB": ("CONDITIONING", ), + "latent_image": ("LATENT", ) + } + } - CATEGORY = "braintacles/latent" + RETURN_TYPES = ("LATENT",) + FUNCTION = "sample" - def decode(self, vae, samples): - return (vae.decode(samples["samples"]), vae, ) - - -class VAEDecodeTiledPipe: - @classmethod - def INPUT_TYPES(s): - return {"required": {"samples": ("LATENT", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 1024, "min": 320, "max": 4096, "step": 64}) - }} - RETURN_TYPES = ("IMAGE","VAE",) - FUNCTION = "decode" - - CATEGORY = "braintacles/latent" - - def decode(self, vae, samples, tile_size): - return (vae.decode_tiled(samples["samples"], tile_x=tile_size // 8, tile_y=tile_size // 8, ), vae, ) - - -class VAEEncodePipe: - @classmethod - def INPUT_TYPES(s): - return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", )}} - RETURN_TYPES = ("LATENT","VAE",) - FUNCTION = "encode" - - CATEGORY = "braintacles/latent" - - @staticmethod - def vae_encode_crop_pixels(pixels): - x = (pixels.shape[1] // 8) * 8 - y = (pixels.shape[2] // 8) * 8 - if pixels.shape[1] != x or pixels.shape[2] != y: - x_offset = (pixels.shape[1] % 8) // 2 - y_offset = (pixels.shape[2] % 8) // 2 - pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :] - return pixels - - def encode(self, vae, pixels): - pixels = self.vae_encode_crop_pixels(pixels) - t = vae.encode(pixels[:, :, :, :3]) - return ({"samples": t}, vae, ) - - -class VAEEncodeTiledPipe: - @classmethod - def INPUT_TYPES(s): - return {"required": {"pixels": ("IMAGE", ), "vae": ("VAE", ), - "tile_size": ("INT", {"default": 1024, "min": 320, "max": 4096, "step": 64}) - }} - RETURN_TYPES = ("LATENT","VAE",) - FUNCTION = "encode" - - CATEGORY = "braintacles/latent" - - def encode(self, vae, pixels, tile_size): - pixels = VAEEncodePipe.vae_encode_crop_pixels(pixels) - t = vae.encode_tiled(pixels[:, :, :, :3], - tile_x=tile_size, tile_y=tile_size, ) - return ({"samples": t}, vae, ) + CATEGORY = "braintacles/sampling" + def sample(self, modelA, modelB, noise_seed, steps, interval, cfg, sampler_name, scheduler, positiveA, negativeA, positiveB, negativeB, latent_image, denoise=1.0): + force_full_denoise = False + disable_noise = False + latest_latent = latent_image + latest_model = "B" + for i in range(0, steps, interval): + if i>0: + disable_noise = True + print(f"Sampling Steps {i} to {i+interval} out of {steps} with noise {'enabled' if not disable_noise else 'disabled'} on model {latest_model}") + latest_model = "A" if latest_model == "B" else "B" + model = modelA if latest_model == "A" else modelB + latest_positive = positiveA if latest_model == "A" else positiveB + latest_negative = negativeA if latest_model == "A" else negativeB + latest_latent = common_ksampler(model, noise_seed, steps, cfg, sampler_name, scheduler, latest_positive, latest_negative, latest_latent, denoise=denoise, disable_noise=disable_noise, start_step=i, last_step=i+interval, force_full_denoise=force_full_denoise)[0] + return (latest_latent, ) NODE_CLASS_MAPPINGS = { "CLIPTextEncodeSDXL-Multi-IO": CLIPTextEncodeSDXL_Multi_IO, "CLIPTextEncodeSDXL-Pipe": CLIPTextEncodeSDXL_Pipe, "Empty Latent Image from Aspect-Ratio": EmptyLatentImageFromAspectRatio, "Random Find and Replace": RandomFindAndReplace, - "VAE Decode Pipe": VAEDecodePipe, - "VAE Decode Tiled Pipe": VAEDecodeTiledPipe, - "VAE Encode Pipe": VAEEncodePipe, - "VAE Encode Tiled Pipe": VAEEncodeTiledPipe, + "Interval Sampler": IntervalSampler }