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