import comfy import comfy.model_management import comfy.samplers import torch import numpy as np import latent_preview from nodes import MAX_RESOLUTION from PIL import Image from typing import Dict, List, Optional, Tuple, Union, Any from ..modules.brushnet.model_patch import add_model_patch class easySampler: def __init__(self): self.last_helds: dict[str, list] = { "results": [], "pipe_line": [], } self.device = comfy.model_management.intermediate_device() @staticmethod def tensor2pil(image: torch.Tensor) -> Image.Image: """Convert a torch tensor to a PIL image.""" return Image.fromarray(np.clip(255. * image.cpu().numpy().squeeze(), 0, 255).astype(np.uint8)) @staticmethod def pil2tensor(image: Image.Image) -> torch.Tensor: """Convert a PIL image to a torch tensor.""" return torch.from_numpy(np.array(image).astype(np.float32) / 255.0).unsqueeze(0) @staticmethod def enforce_mul_of_64(d): d = int(d) if d <= 7: d = 8 leftover = d % 8 # 8 is the number of pixels per byte if leftover != 0: # if the number of pixels is not a multiple of 8 if (leftover < 4): # if the number of pixels is less than 4 d -= leftover # remove the leftover pixels else: # if the number of pixels is more than 4 d += 8 - leftover # add the leftover pixels return int(d) @staticmethod def safe_split(to_split: str, delimiter: str) -> List[str]: """Split the input string and return a list of non-empty parts.""" parts = to_split.split(delimiter) parts = [part for part in parts if part not in ('', ' ', ' ')] while len(parts) < 2: parts.append('None') return parts def emptyLatent(self, resolution, empty_latent_width, empty_latent_height, batch_size=1, compression=0, model_type='sd', video_length=25): if resolution not in ["自定义 x 自定义", 'width x height (custom)']: try: width, height = map(int, resolution.split(' x ')) empty_latent_width = width empty_latent_height = height except ValueError: raise ValueError("Invalid base_resolution format.") if model_type == 'sd3': latent = torch.ones([batch_size, 16, empty_latent_height // 8, empty_latent_width // 8], device=self.device) * 0.0609 samples = {"samples": latent} elif model_type == 'mochi': latent = torch.zeros([batch_size, 12, ((video_length - 1) // 6) + 1, empty_latent_height // 8, empty_latent_width // 8], device=self.device) samples = {"samples": latent} elif compression == 0: latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8], device=self.device) samples = {"samples": latent} else: latent_c = torch.zeros( [batch_size, 16, empty_latent_height // compression, empty_latent_width // compression]) latent_b = torch.zeros([batch_size, 4, empty_latent_height // 4, empty_latent_width // 4]) samples = ({"samples": latent_c}, {"samples": latent_b}) return samples def prepare_noise(self, latent_image, seed, noise_inds=None, noise_device="cpu", incremental_seed_mode="comfy", variation_seed=None, variation_strength=None): """ creates random noise given a latent image and a seed. optional arg skip can be used to skip and discard x number of noise generations for a given seed """ latent_size = latent_image.size() latent_size_1batch = [1, latent_size[1], latent_size[2], latent_size[3]] if variation_strength is not None and variation_strength > 0 or incremental_seed_mode.startswith( "variation str inc"): if noise_device == "cpu": variation_generator = torch.manual_seed(variation_seed) else: torch.cuda.manual_seed(variation_seed) variation_generator = None variation_latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=variation_generator, device=noise_device) else: variation_latent = None def apply_variation(input_latent, strength_up=None): if variation_latent is None: return input_latent else: strength = variation_strength if strength_up is not None: strength += strength_up variation_noise = variation_latent.expand(input_latent.size()[0], -1, -1, -1) result = (1 - strength) * input_latent + strength * variation_noise return result # method: incremental seed batch noise if noise_inds is None and incremental_seed_mode == "incremental": batch_cnt = latent_size[0] latents = None for i in range(batch_cnt): if noise_device == "cpu": generator = torch.manual_seed(seed + i) else: torch.cuda.manual_seed(seed + i) generator = None latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device) latent = apply_variation(latent) if latents is None: latents = latent else: latents = torch.cat((latents, latent), dim=0) return latents # method: incremental variation batch noise elif noise_inds is None and incremental_seed_mode.startswith("variation str inc"): batch_cnt = latent_size[0] latents = None for i in range(batch_cnt): if noise_device == "cpu": generator = torch.manual_seed(seed) else: torch.cuda.manual_seed(seed) generator = None latent = torch.randn(latent_size_1batch, dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device) step = float(incremental_seed_mode[18:]) latent = apply_variation(latent, step * i) if latents is None: latents = latent else: latents = torch.cat((latents, latent), dim=0) return latents # method: comfy batch noise if noise_device == "cpu": generator = torch.manual_seed(seed) else: torch.cuda.manual_seed(seed) generator = None if noise_inds is None: latents = torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device) latents = apply_variation(latents) return latents unique_inds, inverse = np.unique(noise_inds, return_inverse=True) noises = [] for i in range(unique_inds[-1] + 1): noise = torch.randn([1] + list(latent_image.size())[1:], dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device=noise_device) if i in unique_inds: noises.append(noise) noises = [noises[i] for i in inverse] noises = torch.cat(noises, axis=0) return noises def common_ksampler(self, 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, preview_latent=True, disable_pbar=False, noise_device='CPU'): device = comfy.model_management.get_torch_device() noise_device = 'cpu' if noise_device == 'CPU' else device latent_image = latent["samples"] latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] preview_format = "JPEG" if preview_format not in ["JPEG", "PNG"]: preview_format = "JPEG" previewer = False if preview_latent: previewer = latent_preview.get_previewer(device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): preview_bytes = None if previewer: preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) pbar.update_absolute(step + 1, total_steps, preview_bytes) if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device) else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) ####################################################################################### # add model patch # brushnet add_model_patch(model) ####################################################################################### 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 def custom_ksampler(self, model, seed, steps, cfg, _sampler, sigmas, positive, negative, latent, disable_noise=False, preview_latent=True, disable_pbar=False, noise_device='CPU'): device = comfy.model_management.get_torch_device() noise_device = 'cpu' if noise_device == 'CPU' else device latent_image = latent["samples"] if disable_noise: noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device=noise_device) else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] preview_format = "JPEG" if preview_format not in ["JPEG", "PNG"]: preview_format = "JPEG" previewer = False if preview_latent: previewer = latent_preview.get_previewer(device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): preview_bytes = None if previewer: preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) pbar.update_absolute(step + 1, total_steps, preview_bytes) samples = comfy.samplers.sample(model, noise, positive, negative, cfg, device, _sampler, sigmas, latent_image=latent_image, model_options=model.model_options, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) out = latent.copy() out["samples"] = samples return out def custom_advanced_ksampler(self, guider, sampler, sigmas, latent_image, add_noise='enable', seed=0, preview_latent=False): latent = latent_image latent_image = latent["samples"] latent = latent.copy() latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image) latent["samples"] = latent_image device = comfy.model_management.get_torch_device() noise_device = device if add_noise == 'enable (GPU=A1111)' else 'cpu' if add_noise == 'disable': noise = torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu") else: batch_inds = latent["batch_index"] if "batch_index" in latent else None noise = self.prepare_noise(latent_image, seed, batch_inds, noise_device=noise_device) noise_mask = None if "noise_mask" in latent: noise_mask = latent["noise_mask"] x0_output = {} previewer = False model = guider.model_patcher steps = sigmas.shape[-1] - 1 if preview_latent: previewer = latent_preview.get_previewer(model.load_device, model.model.latent_format) pbar = comfy.utils.ProgressBar(steps) preview_format = "JPEG" if preview_format not in ["JPEG", "PNG"]: preview_format = "JPEG" def callback(step, x0, x, total_steps): if x0_output is not None: x0_output["x0"] = x0 preview_bytes = None if previewer: preview_bytes = previewer.decode_latent_to_preview_image(preview_format, x0) pbar.update_absolute(step + 1, total_steps, preview_bytes) disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED samples = guider.sample(noise, latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed) samples = samples.to(comfy.model_management.intermediate_device()) out = latent.copy() out["samples"] = samples if "x0" in x0_output: out_denoised = latent.copy() out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu()) else: out_denoised = out return (out, out_denoised) def get_value_by_id(self, key: str, my_unique_id: Any) -> Optional[Any]: """Retrieve value by its associated ID.""" try: for value, id_ in self.last_helds[key]: if id_ == my_unique_id: return value except KeyError: return None def update_value_by_id(self, key: str, my_unique_id: Any, new_value: Any) -> Union[bool, None]: """Update the value associated with a given ID. Return True if updated, False if appended, None if key doesn't exist.""" try: for i, (value, id_) in enumerate(self.last_helds[key]): if id_ == my_unique_id: self.last_helds[key][i] = (new_value, id_) return True self.last_helds[key].append((new_value, my_unique_id)) return False except KeyError: return False def upscale(self, samples, upscale_method, scale_by, crop): s = samples.copy() width = self.enforce_mul_of_64(round(samples["samples"].shape[3] * scale_by)) height = self.enforce_mul_of_64(round(samples["samples"].shape[2] * scale_by)) if (width > MAX_RESOLUTION): width = MAX_RESOLUTION if (height > MAX_RESOLUTION): height = MAX_RESOLUTION s["samples"] = comfy.utils.common_upscale(samples["samples"], width, height, upscale_method, crop) return (s,) def handle_upscale(self, samples: dict, upscale_method: str, factor: float, crop: bool) -> dict: """Upscale the samples if the upscale_method is not set to 'None'.""" if upscale_method != "None": samples = self.upscale(samples, upscale_method, factor, crop)[0] return samples def init_state(self, my_unique_id: Any, key: str, default: Any) -> Any: """Initialize the state by either fetching the stored value or setting a default.""" value = self.get_value_by_id(key, my_unique_id) if value is not None: return value return default def get_output(self, pipe: dict,) -> Tuple: """Return a tuple of various elements fetched from the input pipe dictionary.""" return ( pipe, pipe.get("images"), pipe.get("model"), pipe.get("positive"), pipe.get("negative"), pipe.get("samples"), pipe.get("vae"), pipe.get("clip"), pipe.get("seed"), ) def get_output_sdxl(self, sdxl_pipe: dict) -> Tuple: """Return a tuple of various elements fetched from the input sdxl_pipe dictionary.""" return ( sdxl_pipe, sdxl_pipe.get("model"), sdxl_pipe.get("positive"), sdxl_pipe.get("negative"), sdxl_pipe.get("vae"), sdxl_pipe.get("refiner_model"), sdxl_pipe.get("refiner_positive"), sdxl_pipe.get("refiner_negative"), sdxl_pipe.get("refiner_vae"), sdxl_pipe.get("samples"), sdxl_pipe.get("clip"), sdxl_pipe.get("images"), sdxl_pipe.get("seed") ) def loglinear_interp(t_steps, num_steps): """ Performs log-linear interpolation of a given array of decreasing numbers. """ xs = np.linspace(0, 1, len(t_steps)) ys = np.log(t_steps[::-1]) new_xs = np.linspace(0, 1, num_steps) new_ys = np.interp(new_xs, xs, ys) interped_ys = np.exp(new_ys)[::-1].copy() return interped_ys class alignYourStepsScheduler: NOISE_LEVELS = { "SD1": [14.6146412293, 6.4745760956, 3.8636745985, 2.6946151520, 1.8841921177, 1.3943805092, 0.9642583904, 0.6523686016, 0.3977456272, 0.1515232662, 0.0291671582], "SDXL": [14.6146412293, 6.3184485287, 3.7681790315, 2.1811480769, 1.3405244945, 0.8620721141, 0.5550693289, 0.3798540708, 0.2332364134, 0.1114188177, 0.0291671582], "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]} def get_sigmas(self, model_type, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) sigmas = self.NOISE_LEVELS[model_type][:] if (steps + 1) != len(sigmas): sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas),) class gitsScheduler: NOISE_LEVELS = { 0.80: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753], [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753], ], 0.85: [ [14.61464119, 7.49001646, 0.02916753], [14.61464119, 7.49001646, 1.84880662, 0.02916753], [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753], [14.61464119, 11.54541874, 7.11996698, 3.07277966, 1.24153244, 0.02916753], [14.61464119, 11.54541874, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.09240818, 2.84484982, 0.95350921, 0.02916753], [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.58536053, 3.1956799, 1.84880662, 0.803307, 0.02916753], [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], [14.61464119, 13.76078796, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 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2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], ], } def get_sigmas(self, coeff, steps, denoise): total_steps = steps if denoise < 1.0: if denoise <= 0.0: return (torch.FloatTensor([]),) total_steps = round(steps * denoise) if steps <= 20: sigmas = self.NOISE_LEVELS[round(coeff, 2)][steps-2][:] else: sigmas = self.NOISE_LEVELS[round(coeff, 2)][-1][:] sigmas = loglinear_interp(sigmas, steps + 1) sigmas = sigmas[-(total_steps + 1):] sigmas[-1] = 0 return (torch.FloatTensor(sigmas), )