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Configuration error
Configuration error
| 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 ..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() | |
| 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)) | |
| 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) | |
| 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) | |
| 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, sd3=False): | |
| 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 sd3: | |
| latent = torch.ones([batch_size, 16, empty_latent_height // 8, empty_latent_width // 8], device=self.device) * 0.0609 | |
| 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 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): | |
| device = comfy.model_management.get_torch_device() | |
| latent_image = latent["samples"] | |
| 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="cpu") | |
| else: | |
| batch_inds = latent["batch_index"] if "batch_index" in latent else None | |
| noise = comfy.sample.prepare_noise(latent_image, seed, batch_inds) | |
| ####################################################################################### | |
| # 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): | |
| device = comfy.model_management.get_torch_device() | |
| 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"] | |
| 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, noise, guider, sampler, sigmas, latent_image): | |
| 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 | |
| noise_mask = None | |
| if "noise_mask" in latent: | |
| noise_mask = latent["noise_mask"] | |
| x0_output = {} | |
| callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output) | |
| disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED | |
| samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, | |
| callback=callback, disable_pbar=disable_pbar, seed=noise.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, | |
| 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, | |
| 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, | |
| 7.49001646, 6.14220476, 4.65472794, 3.07277966, 1.84880662, 0.803307, 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, | |
| 7.49001646, 6.14220476, 4.86714602, 3.60512662, 2.6383388, 1.56271636, 0.72133851, 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, | |
| 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, | |
| 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.75849152, | |
| 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, 0.72133851, | |
| 0.02916753], | |
| [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, | |
| 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.46139455, 2.45070267, 1.56271636, | |
| 0.72133851, 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, 3.46139455, 2.45070267, | |
| 1.56271636, 0.72133851, 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, 3.46139455, | |
| 2.45070267, 1.56271636, 0.72133851, 0.02916753], | |
| ], | |
| 0.90: [ | |
| [14.61464119, 6.77309084, 0.02916753], | |
| [14.61464119, 7.49001646, 1.56271636, 0.02916753], | |
| [14.61464119, 7.49001646, 3.07277966, 0.95350921, 0.02916753], | |
| [14.61464119, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753], | |
| [14.61464119, 11.54541874, 7.49001646, 4.86714602, 2.54230714, 0.89115214, 0.02916753], | |
| [14.61464119, 11.54541874, 7.49001646, 5.09240818, 3.07277966, 1.61558151, 0.69515091, 0.02916753], | |
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| [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 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], | |
| ], | |
| 1.40: [ | |
| [14.61464119, 0.59516323, 0.02916753], | |
| [14.61464119, 0.95350921, 0.34370604, 0.02916753], | |
| [14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753], | |
| [14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753], | |
| [14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753], | |
| [14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, | |
| 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, | |
| 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, | |
| 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, | |
| 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, | |
| 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, | |
| 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, | |
| 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, | |
| 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, | |
| 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, | |
| 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 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, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 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], | |
| ], | |
| 1.45: [ | |
| [14.61464119, 0.59516323, 0.02916753], | |
| [14.61464119, 0.803307, 0.25053367, 0.02916753], | |
| [14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753], | |
| [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753], | |
| [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753], | |
| [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753], | |
| [14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, | |
| 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, | |
| 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, | |
| 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, | |
| 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, | |
| 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, | |
| 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, | |
| 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, | |
| 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, | |
| 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 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, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 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, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 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], | |
| ], | |
| 1.50: [ | |
| [14.61464119, 0.54755926, 0.02916753], | |
| [14.61464119, 0.803307, 0.25053367, 0.02916753], | |
| [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753], | |
| [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753], | |
| [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753], | |
| [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753], | |
| [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, | |
| 0.02916753], | |
| [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, | |
| 0.09824532, 0.02916753], | |
| [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, | |
| 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, | |
| 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, | |
| 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, | |
| 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, | |
| 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753], | |
| [14.61464119, 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), ) |