import hashlib import math import random import re import torch import torch.nn.functional as F import comfy.sample import comfy.samplers import comfy.model_management import nodes from cstr import cstr def slerp(strength, tensor_from, tensor_to, epsilon=1e-6): """ Perform Spherical Linear Interpolation (Slerp) between two tensors. Parameters: - strength (float): The interpolation factor between tensor_from and tensor_to. - tensor_from (Tensor): The starting tensor. - tensor_to (Tensor): The ending tensor. - epsilon (float): division by zero offset Returns: - Tensor: Interpolated tensor. """ low_norm = F.normalize(tensor_from, p=2, dim=-1, eps=epsilon) high_norm = F.normalize(tensor_to, p=2, dim=-1, eps=epsilon) dot_product = torch.clamp((low_norm * high_norm).sum(dim=-1), -1.0, 1.0) omega = torch.acos(dot_product) so = torch.sin(omega) zero_so_mask = torch.isclose(so, torch.tensor([0.0], device=so.device), atol=epsilon) so = torch.where(zero_so_mask, torch.tensor([1.0], device=so.device), so) sin_omega_minus_strength = torch.sin((1.0 - strength) * omega) / so sin_strength_omega = torch.sin(strength * omega) / so res = sin_omega_minus_strength.unsqueeze(-1) * tensor_from + sin_strength_omega.unsqueeze(-1) * tensor_to res = torch.where(zero_so_mask.unsqueeze(-1), tensor_from if strength < 0.5 else tensor_to, res) return res # from https://discuss.pytorch.org/t/help-regarding-slerp-function-for-generative-model-sampling/32475 def slerp_latents(val, low, high): dims = low.shape #flatten to batches low = low.reshape(dims[0], -1) high = high.reshape(dims[0], -1) low_norm = low/torch.norm(low, dim=1, keepdim=True) high_norm = high/torch.norm(high, dim=1, keepdim=True) # in case we divide by zero low_norm[low_norm != low_norm] = 0.0 high_norm[high_norm != high_norm] = 0.0 omega = torch.acos((low_norm*high_norm).sum(1)) so = torch.sin(omega) res = (torch.sin((1.0-val)*omega)/so).unsqueeze(1)*low + (torch.sin(val*omega)/so).unsqueeze(1) * high return res.reshape(dims) def blend_latents(alpha, latent_1, latent_2): if not isinstance(alpha, torch.Tensor): alpha = torch.tensor([alpha], dtype=latent_1.dtype, device=latent_1.device) blended_latent = (1 - alpha) * latent_1 + alpha * latent_2 return blended_latent def cosine_interp_latents(val, low, high): if not isinstance(val, torch.Tensor): val = torch.tensor([val], dtype=low.dtype, device=low.device) t = (1 - torch.cos(val * math.pi)) / 2 return (1 - t) * low + t * high def unsample(model, seed, cfg, sampler_name, steps, end_at_step, scheduler, normalize, positive, negative, latent_image): device = comfy.model_management.get_torch_device() end_at_step = steps - min(end_at_step, steps - 1) latent = latent_image latent_image = latent["samples"].to(device) noise_shape = latent_image.size() noise = torch.zeros(noise_shape, dtype=latent_image.dtype, layout=latent_image.layout, device=device) noise_mask = comfy.sample.prepare_mask(latent.get("noise_mask"), noise, device) if "noise_mask" in latent else None positive_copy = comfy.sample.broadcast_cond(positive, noise.shape[0], device) negative_copy = comfy.sample.broadcast_cond(negative, noise.shape[0], device) models, inference_memory = comfy.sample.get_additional_models(positive, negative, model.model_dtype()) comfy.model_management.load_models_gpu([model] + models, comfy.model_management.batch_area_memory(noise.numel() // noise.shape[0]) + inference_memory) real_model = model.model sampler = comfy.samplers.KSampler(real_model, steps=steps, device=device, sampler=sampler_name, scheduler=scheduler, denoise=1.0, model_options=model.model_options) sigmas = sampler.sigmas.flip(0) + 0.0001 pbar = comfy.utils.ProgressBar(steps) def callback(step, x0, x, total_steps): pbar.update_absolute(step + 1, total_steps) samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, force_full_denoise=False, denoise_mask=noise_mask, sigmas=sigmas, start_step=0, last_step=end_at_step, callback=callback, seed=seed) if normalize == "enable": samples = (samples - samples.mean()) / samples.std() comfy.sample.cleanup_additional_models(models) out = latent.copy() out["samples"] = samples.cpu() return (out,) CLIPTextEncode = nodes.CLIPTextEncode() USE_BLK, BLK_ADV = (False, None) if "BNK_CLIPTextEncodeAdvanced" in nodes.NODE_CLASS_MAPPINGS: BLK_ADV = nodes.NODE_CLASS_MAPPINGS['BNK_CLIPTextEncodeAdvanced'] USE_BLK = True if USE_BLK: cstr(f"Found `{cstr.color.BOLD}ComfyUI_ADV_CLIP_emb{cstr.color.END}`. Using {cstr.color.LIGHTYELLOW}BLK Advanced CLIPTextEncode{cstr.color.END} for Conditioning Sequencing").msg.print() blk_adv = BLK_ADV() class CLIPTextEncodeSequence: @classmethod def INPUT_TYPES(s): return { "required": { "clip": ("CLIP", ), "token_normalization": (["none", "mean", "length", "length+mean"],), "weight_interpretation": (["comfy", "A1111", "compel", "comfy++"],), "text": ("STRING", {"multiline": True, "default": '''0:A portrait of a rosebud 5:A portrait of a blooming rosebud 10:A portrait of a blooming rose 15:A portrait of a rose'''}), } } RETURN_TYPES = ("CONDITIONING_SEQ",) RETURN_NAMES = ("conditioning_sequence",) IS_LIST_OUTPUT = (True,) FUNCTION = "encode" CATEGORY = "conditioning" def encode(self, clip, text, token_normalization, weight_interpretation): text = text.strip() conditionings = [] for l in text.splitlines(): match = re.match(r'(\d+):', l) if match: idx = int(match.group(1)) _, line = l.split(":", 1) line = line.strip() if USE_BLK: encoded = blk_adv.encode(clip=clip, text=line, token_normalization=token_normalization, weight_interpretation=weight_interpretation) else: encoded = CLIPTextEncode.encode(clip=clip, text=line) conditioning = (idx, [encoded[0][0][0], encoded[0][0][1]]) conditionings.append(conditioning) return (conditionings, ) class KSamplerSeq: @classmethod def INPUT_TYPES(s): return {"required": {"model": ("MODEL",), "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}), "seed_mode_seq": (["increment", "decrement", "random", "fixed"],), "steps": ("INT", {"default": 20, "min": 1, "max": 10000}), "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}), "sampler_name": (comfy.samplers.KSampler.SAMPLERS, ), "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), "sequence_loop_count": ("INT", {"default": 20, "min": 1, "max": 100, "step": 1}), "positive_seq": ("CONDITIONING_SEQ", ), "negative_seq": ("CONDITIONING_SEQ", ), "use_conditioning_slerp": ("BOOLEAN", {"default": False}), "cond_slerp_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}), "latent_image": ("LATENT", ), "use_latent_interpolation": ("BOOLEAN", {"default": False}), "latent_interpolation_mode": (["Blend", "Slerp", "Cosine Interp"],), "latent_interp_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.001}), "denoise_start": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}), "denoise_seq": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}), "unsample_latents": ("BOOLEAN", {"default": False}) } } RETURN_TYPES = ("LATENT",) FUNCTION = "sample" CATEGORY = "sampling" def update_seed(self, seed, seed_mode): if seed_mode == "increment": return seed + 1 elif seed_mode == "decrement": return seed - 1 elif seed_mode == "random": return random.randint(0, 0xffffffffffffffff) elif seed_mode == "fixed": return seed def hash_tensor(self, tensor): tensor = tensor.cpu().contiguous() return hashlib.sha256(tensor.numpy().tobytes()).hexdigest() def update_conditioning(self, conditioning_seq, loop_count, last_conditioning): matching_conditioning = None for idx, conditioning, *_ in conditioning_seq: if int(idx) == loop_count: matching_conditioning = conditioning break return matching_conditioning if matching_conditioning else (last_conditioning if last_conditioning else None) def sample(self, model, seed, seed_mode_seq, steps, cfg, sampler_name, scheduler, sequence_loop_count, positive_seq, negative_seq, cond_slerp_strength, latent_image, use_latent_interpolation, latent_interpolation_mode, latent_interp_strength, denoise_start=1.0, denoise_seq=0.5, use_conditioning_slerp=False, unsample_latents=False): positive_seq = positive_seq negative_seq = negative_seq results = [] positive_conditioning = None negative_conditioning = None for loop_count in range(sequence_loop_count): seq_seed = seed if loop_count <= 0 else self.update_seed(seq_seed, seed_mode_seq) cstr(f"Loop count: {loop_count}, Seed: {seq_seed}").msg.print() last_positive_conditioning = positive_conditioning[0] if positive_conditioning else None last_negative_conditioning = negative_conditioning[0] if negative_conditioning else None positive_conditioning = self.update_conditioning(positive_seq, loop_count, last_positive_conditioning) negative_conditioning = self.update_conditioning(negative_seq, loop_count, last_negative_conditioning) if use_conditioning_slerp and (last_positive_conditioning and last_negative_conditioning): a = last_positive_conditioning[0].clone() b = positive_conditioning[0].clone() na = last_negative_conditioning[0].clone() nb = negative_conditioning[0].clone() pa = last_positive_conditioning[1]["pooled_output"].clone() pb = positive_conditioning[1]["pooled_output"].clone() npa = last_negative_conditioning[1]["pooled_output"].clone() npb = negative_conditioning[1]["pooled_output"].clone() pos_cond = slerp(cond_slerp_strength, a, b) pos_pooled = slerp(cond_slerp_strength, pa, pb) neg_cond = slerp(cond_slerp_strength, na, nb) neg_pooled = slerp(cond_slerp_strength, npa, npb) positive_conditioning = [pos_cond, {"pooled_output": pos_pooled}] negative_conditioning = [neg_cond, {"pooled_output": neg_pooled}] positive_conditioning = [positive_conditioning] negative_conditioning = [negative_conditioning] if positive_conditioning is not None or negative_conditioning is not None: end_at_step = steps if results is not None and len(results) > 0: latent_input = {'samples': results[-1]} denoise = denoise_seq start_at_step = round((1 - denoise) * steps) end_at_step = steps else: latent_input = latent_image denoise = denoise_start if unsample_latents and loop_count > 0: force_full_denoise = False if loop_count > 0 or loop_count <= steps - 1 else True disable_noise = False unsampled_latent = unsample(model=model, seed=seq_seed, cfg=cfg, sampler_name=sampler_name, steps=steps, end_at_step=end_at_step, scheduler=scheduler, normalize=False, positive=positive_conditioning, negative=negative_conditioning, latent_image=latent_input)[0] sample = nodes.common_ksampler(model, seq_seed, steps, cfg, sampler_name, scheduler, positive_conditioning, negative_conditioning, unsampled_latent, denoise=denoise, disable_noise=disable_noise, start_step=start_at_step, last_step=end_at_step, force_full_denoise=force_full_denoise)[0]['samples'] else: sample = nodes.common_ksampler(model, seq_seed, steps, cfg, sampler_name, scheduler, positive_conditioning, negative_conditioning, latent_input, denoise=denoise)[0]['samples'] if use_latent_interpolation and results and loop_count > 0: if latent_interpolation_mode == "Blend": sample = blend_latents(latent_interp_strength, results[-1], sample) elif latent_interpolation_mode == "Slerp": sample = slerp_latents(latent_interp_strength, results[-1], sample) elif latent_interpolation_mode == "Cosine Interp": sample = cosine_interp_latents(latent_interp_strength, results[-1], sample) else: sample = sample results.append(sample) results = torch.cat(results, dim=0) results = {'samples': results} return (results,) NODE_CLASS_MAPPINGS = { "CLIPTextEncodeList": CLIPTextEncodeSequence, "KSamplerSeq": KSamplerSeq, } NODE_DISPLAY_NAME_MAPPINGS = { "CLIPTextEncodeList": "CLIP Text Encode Sequence (Advanced)", "KSamplerSeq": "KSampler Sequence", }