diff --git a/ComfyUI/comfy/image_encoders/dino2.py b/ComfyUI/comfy/image_encoders/dino2.py
new file mode 100644
index 0000000000000000000000000000000000000000..976f98c656a6b55eb6ceb8d2c585a2713ac6dddb
--- /dev/null
+++ b/ComfyUI/comfy/image_encoders/dino2.py
@@ -0,0 +1,141 @@
+import torch
+from comfy.text_encoders.bert import BertAttention
+import comfy.model_management
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+
+class Dino2AttentionOutput(torch.nn.Module):
+ def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
+
+ def forward(self, x):
+ return self.dense(x)
+
+
+class Dino2AttentionBlock(torch.nn.Module):
+ def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.attention = BertAttention(embed_dim, heads, dtype, device, operations)
+ self.output = Dino2AttentionOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+ def forward(self, x, mask, optimized_attention):
+ return self.output(self.attention(x, mask, optimized_attention))
+
+
+class LayerScale(torch.nn.Module):
+ def __init__(self, dim, dtype, device, operations):
+ super().__init__()
+ self.lambda1 = torch.nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+
+ def forward(self, x):
+ return x * comfy.model_management.cast_to_device(self.lambda1, x.device, x.dtype)
+
+
+class SwiGLUFFN(torch.nn.Module):
+ def __init__(self, dim, dtype, device, operations):
+ super().__init__()
+ in_features = out_features = dim
+ hidden_features = int(dim * 4)
+ hidden_features = (int(hidden_features * 2 / 3) + 7) // 8 * 8
+
+ self.weights_in = operations.Linear(in_features, 2 * hidden_features, bias=True, device=device, dtype=dtype)
+ self.weights_out = operations.Linear(hidden_features, out_features, bias=True, device=device, dtype=dtype)
+
+ def forward(self, x):
+ x = self.weights_in(x)
+ x1, x2 = x.chunk(2, dim=-1)
+ x = torch.nn.functional.silu(x1) * x2
+ return self.weights_out(x)
+
+
+class Dino2Block(torch.nn.Module):
+ def __init__(self, dim, num_heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.attention = Dino2AttentionBlock(dim, num_heads, layer_norm_eps, dtype, device, operations)
+ self.layer_scale1 = LayerScale(dim, dtype, device, operations)
+ self.layer_scale2 = LayerScale(dim, dtype, device, operations)
+ self.mlp = SwiGLUFFN(dim, dtype, device, operations)
+ self.norm1 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+ self.norm2 = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+ def forward(self, x, optimized_attention):
+ x = x + self.layer_scale1(self.attention(self.norm1(x), None, optimized_attention))
+ x = x + self.layer_scale2(self.mlp(self.norm2(x)))
+ return x
+
+
+class Dino2Encoder(torch.nn.Module):
+ def __init__(self, dim, num_heads, layer_norm_eps, num_layers, dtype, device, operations):
+ super().__init__()
+ self.layer = torch.nn.ModuleList([Dino2Block(dim, num_heads, layer_norm_eps, dtype, device, operations) for _ in range(num_layers)])
+
+ def forward(self, x, intermediate_output=None):
+ optimized_attention = optimized_attention_for_device(x.device, False, small_input=True)
+
+ if intermediate_output is not None:
+ if intermediate_output < 0:
+ intermediate_output = len(self.layer) + intermediate_output
+
+ intermediate = None
+ for i, l in enumerate(self.layer):
+ x = l(x, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ return x, intermediate
+
+
+class Dino2PatchEmbeddings(torch.nn.Module):
+ def __init__(self, dim, num_channels=3, patch_size=14, image_size=518, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.projection = operations.Conv2d(
+ in_channels=num_channels,
+ out_channels=dim,
+ kernel_size=patch_size,
+ stride=patch_size,
+ bias=True,
+ dtype=dtype,
+ device=device
+ )
+
+ def forward(self, pixel_values):
+ return self.projection(pixel_values).flatten(2).transpose(1, 2)
+
+
+class Dino2Embeddings(torch.nn.Module):
+ def __init__(self, dim, dtype, device, operations):
+ super().__init__()
+ patch_size = 14
+ image_size = 518
+
+ self.patch_embeddings = Dino2PatchEmbeddings(dim, patch_size=patch_size, image_size=image_size, dtype=dtype, device=device, operations=operations)
+ self.position_embeddings = torch.nn.Parameter(torch.empty(1, (image_size // patch_size) ** 2 + 1, dim, dtype=dtype, device=device))
+ self.cls_token = torch.nn.Parameter(torch.empty(1, 1, dim, dtype=dtype, device=device))
+ self.mask_token = torch.nn.Parameter(torch.empty(1, dim, dtype=dtype, device=device))
+
+ def forward(self, pixel_values):
+ x = self.patch_embeddings(pixel_values)
+ # TODO: mask_token?
+ x = torch.cat((self.cls_token.to(device=x.device, dtype=x.dtype).expand(x.shape[0], -1, -1), x), dim=1)
+ x = x + comfy.model_management.cast_to_device(self.position_embeddings, x.device, x.dtype)
+ return x
+
+
+class Dinov2Model(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ num_layers = config_dict["num_hidden_layers"]
+ dim = config_dict["hidden_size"]
+ heads = config_dict["num_attention_heads"]
+ layer_norm_eps = config_dict["layer_norm_eps"]
+
+ self.embeddings = Dino2Embeddings(dim, dtype, device, operations)
+ self.encoder = Dino2Encoder(dim, heads, layer_norm_eps, num_layers, dtype, device, operations)
+ self.layernorm = operations.LayerNorm(dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+ def forward(self, pixel_values, attention_mask=None, intermediate_output=None):
+ x = self.embeddings(pixel_values)
+ x, i = self.encoder(x, intermediate_output=intermediate_output)
+ x = self.layernorm(x)
+ pooled_output = x[:, 0, :]
+ return x, i, pooled_output, None
diff --git a/ComfyUI/comfy/k_diffusion/sa_solver.py b/ComfyUI/comfy/k_diffusion/sa_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c6821b605d4b44ac81414f2c635e28ce5d9feb5
--- /dev/null
+++ b/ComfyUI/comfy/k_diffusion/sa_solver.py
@@ -0,0 +1,121 @@
+# SA-Solver: Stochastic Adams Solver (NeurIPS 2023, arXiv:2309.05019)
+# Conference: https://proceedings.neurips.cc/paper_files/paper/2023/file/f4a6806490d31216a3ba667eb240c897-Paper-Conference.pdf
+# Codebase ref: https://github.com/scxue/SA-Solver
+
+import math
+from typing import Union, Callable
+import torch
+
+
+def compute_exponential_coeffs(s: torch.Tensor, t: torch.Tensor, solver_order: int, tau_t: float) -> torch.Tensor:
+ """Compute (1 + tau^2) * integral of exp((1 + tau^2) * x) * x^p dx from s to t with exp((1 + tau^2) * t) factored out, using integration by parts.
+
+ Integral of exp((1 + tau^2) * x) * x^p dx
+ = product_terms[p] - (p / (1 + tau^2)) * integral of exp((1 + tau^2) * x) * x^(p-1) dx,
+ with base case p=0 where integral equals product_terms[0].
+
+ where
+ product_terms[p] = x^p * exp((1 + tau^2) * x) / (1 + tau^2).
+
+ Construct a recursive coefficient matrix following the above recursive relation to compute all integral terms up to p = (solver_order - 1).
+ Return coefficients used by the SA-Solver in data prediction mode.
+
+ Args:
+ s: Start time s.
+ t: End time t.
+ solver_order: Current order of the solver.
+ tau_t: Stochastic strength parameter in the SDE.
+
+ Returns:
+ Exponential coefficients used in data prediction, with exp((1 + tau^2) * t) factored out, ordered from p=0 to p=solver_order−1, shape (solver_order,).
+ """
+ tau_mul = 1 + tau_t ** 2
+ h = t - s
+ p = torch.arange(solver_order, dtype=s.dtype, device=s.device)
+
+ # product_terms after factoring out exp((1 + tau^2) * t)
+ # Includes (1 + tau^2) factor from outside the integral
+ product_terms_factored = (t ** p - s ** p * (-tau_mul * h).exp())
+
+ # Lower triangular recursive coefficient matrix
+ # Accumulates recursive coefficients based on p / (1 + tau^2)
+ recursive_depth_mat = p.unsqueeze(1) - p.unsqueeze(0)
+ log_factorial = (p + 1).lgamma()
+ recursive_coeff_mat = log_factorial.unsqueeze(1) - log_factorial.unsqueeze(0)
+ if tau_t > 0:
+ recursive_coeff_mat = recursive_coeff_mat - (recursive_depth_mat * math.log(tau_mul))
+ signs = torch.where(recursive_depth_mat % 2 == 0, 1.0, -1.0)
+ recursive_coeff_mat = (recursive_coeff_mat.exp() * signs).tril()
+
+ return recursive_coeff_mat @ product_terms_factored
+
+
+def compute_simple_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, is_corrector_step: bool = False) -> torch.Tensor:
+ """Compute simple order-2 b coefficients from SA-Solver paper (Appendix D. Implementation Details)."""
+ tau_mul = 1 + tau_t ** 2
+ h = lambda_t - lambda_s
+ alpha_t = sigma_next * lambda_t.exp()
+ if is_corrector_step:
+ # Simplified 1-step (order-2) corrector
+ b_1 = alpha_t * (0.5 * tau_mul * h)
+ b_2 = alpha_t * (-h * tau_mul).expm1().neg() - b_1
+ else:
+ # Simplified 2-step predictor
+ b_2 = alpha_t * (0.5 * tau_mul * h ** 2) / (curr_lambdas[-2] - lambda_s)
+ b_1 = alpha_t * (-h * tau_mul).expm1().neg() - b_2
+ return torch.stack([b_2, b_1])
+
+
+def compute_stochastic_adams_b_coeffs(sigma_next: torch.Tensor, curr_lambdas: torch.Tensor, lambda_s: torch.Tensor, lambda_t: torch.Tensor, tau_t: float, simple_order_2: bool = False, is_corrector_step: bool = False) -> torch.Tensor:
+ """Compute b_i coefficients for the SA-Solver (see eqs. 15 and 18).
+
+ The solver order corresponds to the number of input lambdas (half-logSNR points).
+
+ Args:
+ sigma_next: Sigma at end time t.
+ curr_lambdas: Lambda time points used to construct the Lagrange basis, shape (N,).
+ lambda_s: Lambda at start time s.
+ lambda_t: Lambda at end time t.
+ tau_t: Stochastic strength parameter in the SDE.
+ simple_order_2: Whether to enable the simple order-2 scheme.
+ is_corrector_step: Flag for corrector step in simple order-2 mode.
+
+ Returns:
+ b_i coefficients for the SA-Solver, shape (N,), where N is the solver order.
+ """
+ num_timesteps = curr_lambdas.shape[0]
+
+ if simple_order_2 and num_timesteps == 2:
+ return compute_simple_stochastic_adams_b_coeffs(sigma_next, curr_lambdas, lambda_s, lambda_t, tau_t, is_corrector_step)
+
+ # Compute coefficients by solving a linear system from Lagrange basis interpolation
+ exp_integral_coeffs = compute_exponential_coeffs(lambda_s, lambda_t, num_timesteps, tau_t)
+ vandermonde_matrix_T = torch.vander(curr_lambdas, num_timesteps, increasing=True).T
+ lagrange_integrals = torch.linalg.solve(vandermonde_matrix_T, exp_integral_coeffs)
+
+ # (sigma_t * exp(-tau^2 * lambda_t)) * exp((1 + tau^2) * lambda_t)
+ # = sigma_t * exp(lambda_t) = alpha_t
+ # exp((1 + tau^2) * lambda_t) is extracted from the integral
+ alpha_t = sigma_next * lambda_t.exp()
+ return alpha_t * lagrange_integrals
+
+
+def get_tau_interval_func(start_sigma: float, end_sigma: float, eta: float = 1.0) -> Callable[[Union[torch.Tensor, float]], float]:
+ """Return a function that controls the stochasticity of SA-Solver.
+
+ When eta = 0, SA-Solver runs as ODE. The official approach uses
+ time t to determine the SDE interval, while here we use sigma instead.
+
+ See:
+ https://github.com/scxue/SA-Solver/blob/main/README.md
+ """
+
+ def tau_func(sigma: Union[torch.Tensor, float]) -> float:
+ if eta <= 0:
+ return 0.0 # ODE
+
+ if isinstance(sigma, torch.Tensor):
+ sigma = sigma.item()
+ return eta if start_sigma >= sigma >= end_sigma else 0.0
+
+ return tau_func
diff --git a/ComfyUI/comfy/k_diffusion/sampling.py b/ComfyUI/comfy/k_diffusion/sampling.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2bc492fd01186e101c6c977b9645ed8bd2e1355
--- /dev/null
+++ b/ComfyUI/comfy/k_diffusion/sampling.py
@@ -0,0 +1,1761 @@
+import math
+from functools import partial
+
+from scipy import integrate
+import torch
+from torch import nn
+import torchsde
+from tqdm.auto import trange, tqdm
+
+from . import utils
+from . import deis
+from . import sa_solver
+import comfy.model_patcher
+import comfy.model_sampling
+
+def append_zero(x):
+ return torch.cat([x, x.new_zeros([1])])
+
+
+def get_sigmas_karras(n, sigma_min, sigma_max, rho=7., device='cpu'):
+ """Constructs the noise schedule of Karras et al. (2022)."""
+ ramp = torch.linspace(0, 1, n, device=device)
+ min_inv_rho = sigma_min ** (1 / rho)
+ max_inv_rho = sigma_max ** (1 / rho)
+ sigmas = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
+ return append_zero(sigmas).to(device)
+
+
+def get_sigmas_exponential(n, sigma_min, sigma_max, device='cpu'):
+ """Constructs an exponential noise schedule."""
+ sigmas = torch.linspace(math.log(sigma_max), math.log(sigma_min), n, device=device).exp()
+ return append_zero(sigmas)
+
+
+def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
+ """Constructs an polynomial in log sigma noise schedule."""
+ ramp = torch.linspace(1, 0, n, device=device) ** rho
+ sigmas = torch.exp(ramp * (math.log(sigma_max) - math.log(sigma_min)) + math.log(sigma_min))
+ return append_zero(sigmas)
+
+
+def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
+ """Constructs a continuous VP noise schedule."""
+ t = torch.linspace(1, eps_s, n, device=device)
+ sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
+ return append_zero(sigmas)
+
+
+def get_sigmas_laplace(n, sigma_min, sigma_max, mu=0., beta=0.5, device='cpu'):
+ """Constructs the noise schedule proposed by Tiankai et al. (2024). """
+ epsilon = 1e-5 # avoid log(0)
+ x = torch.linspace(0, 1, n, device=device)
+ clamp = lambda x: torch.clamp(x, min=sigma_min, max=sigma_max)
+ lmb = mu - beta * torch.sign(0.5-x) * torch.log(1 - 2 * torch.abs(0.5-x) + epsilon)
+ sigmas = clamp(torch.exp(lmb))
+ return sigmas
+
+
+
+def to_d(x, sigma, denoised):
+ """Converts a denoiser output to a Karras ODE derivative."""
+ return (x - denoised) / utils.append_dims(sigma, x.ndim)
+
+
+def get_ancestral_step(sigma_from, sigma_to, eta=1.):
+ """Calculates the noise level (sigma_down) to step down to and the amount
+ of noise to add (sigma_up) when doing an ancestral sampling step."""
+ if not eta:
+ return sigma_to, 0.
+ sigma_up = min(sigma_to, eta * (sigma_to ** 2 * (sigma_from ** 2 - sigma_to ** 2) / sigma_from ** 2) ** 0.5)
+ sigma_down = (sigma_to ** 2 - sigma_up ** 2) ** 0.5
+ return sigma_down, sigma_up
+
+
+def default_noise_sampler(x, seed=None):
+ if seed is not None:
+ generator = torch.Generator(device=x.device)
+ generator.manual_seed(seed)
+ else:
+ generator = None
+
+ return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator)
+
+
+class BatchedBrownianTree:
+ """A wrapper around torchsde.BrownianTree that enables batches of entropy."""
+
+ def __init__(self, x, t0, t1, seed=None, **kwargs):
+ self.cpu_tree = True
+ if "cpu" in kwargs:
+ self.cpu_tree = kwargs.pop("cpu")
+ t0, t1, self.sign = self.sort(t0, t1)
+ w0 = kwargs.get('w0', torch.zeros_like(x))
+ if seed is None:
+ seed = torch.randint(0, 2 ** 63 - 1, []).item()
+ self.batched = True
+ try:
+ assert len(seed) == x.shape[0]
+ w0 = w0[0]
+ except TypeError:
+ seed = [seed]
+ self.batched = False
+ if self.cpu_tree:
+ self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
+ else:
+ self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+
+ @staticmethod
+ def sort(a, b):
+ return (a, b, 1) if a < b else (b, a, -1)
+
+ def __call__(self, t0, t1):
+ t0, t1, sign = self.sort(t0, t1)
+ if self.cpu_tree:
+ w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
+ else:
+ w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
+
+ return w if self.batched else w[0]
+
+
+class BrownianTreeNoiseSampler:
+ """A noise sampler backed by a torchsde.BrownianTree.
+
+ Args:
+ x (Tensor): The tensor whose shape, device and dtype to use to generate
+ random samples.
+ sigma_min (float): The low end of the valid interval.
+ sigma_max (float): The high end of the valid interval.
+ seed (int or List[int]): The random seed. If a list of seeds is
+ supplied instead of a single integer, then the noise sampler will
+ use one BrownianTree per batch item, each with its own seed.
+ transform (callable): A function that maps sigma to the sampler's
+ internal timestep.
+ """
+
+ def __init__(self, x, sigma_min, sigma_max, seed=None, transform=lambda x: x, cpu=False):
+ self.transform = transform
+ t0, t1 = self.transform(torch.as_tensor(sigma_min)), self.transform(torch.as_tensor(sigma_max))
+ self.tree = BatchedBrownianTree(x, t0, t1, seed, cpu=cpu)
+
+ def __call__(self, sigma, sigma_next):
+ t0, t1 = self.transform(torch.as_tensor(sigma)), self.transform(torch.as_tensor(sigma_next))
+ return self.tree(t0, t1) / (t1 - t0).abs().sqrt()
+
+
+def sigma_to_half_log_snr(sigma, model_sampling):
+ """Convert sigma to half-logSNR log(alpha_t / sigma_t)."""
+ if isinstance(model_sampling, comfy.model_sampling.CONST):
+ # log((1 - t) / t) = log((1 - sigma) / sigma)
+ return sigma.logit().neg()
+ return sigma.log().neg()
+
+
+def half_log_snr_to_sigma(half_log_snr, model_sampling):
+ """Convert half-logSNR log(alpha_t / sigma_t) to sigma."""
+ if isinstance(model_sampling, comfy.model_sampling.CONST):
+ # 1 / (1 + exp(half_log_snr))
+ return half_log_snr.neg().sigmoid()
+ return half_log_snr.neg().exp()
+
+
+def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
+ """Adjust the first sigma to avoid invalid logSNR."""
+ if len(sigmas) <= 1:
+ return sigmas
+ if isinstance(model_sampling, comfy.model_sampling.CONST):
+ if sigmas[0] >= 1:
+ sigmas = sigmas.clone()
+ sigmas[0] = model_sampling.percent_to_sigma(percent_offset)
+ return sigmas
+
+
+@torch.no_grad()
+def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """Implements Algorithm 2 (Euler steps) from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ # Euler method
+ x = x + d * dt
+ return x
+
+
+@torch.no_grad()
+def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+ """Ancestral sampling with Euler method steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ if sigma_down == 0:
+ x = denoised
+ else:
+ d = to_d(x, sigmas[i], denoised)
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, s_noise=1., noise_sampler=None):
+ """Ancestral sampling with Euler method steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ if sigmas[i + 1] == 0:
+ x = denoised
+ else:
+ downstep_ratio = 1 + (sigmas[i + 1] / sigmas[i] - 1) * eta
+ sigma_down = sigmas[i + 1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i + 1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i + 1]**2 - sigma_down**2 * alpha_ip1**2 / alpha_down**2)**0.5
+ # Euler method
+ sigma_down_i_ratio = sigma_down / sigmas[i]
+ x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * denoised
+ if eta > 0:
+ x = (alpha_ip1 / alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ return x
+
+@torch.no_grad()
+def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """Implements Algorithm 2 (Heun steps) from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ sigma_hat = sigmas[i] * (gamma + 1)
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ if sigmas[i + 1] == 0:
+ # Euler method
+ x = x + d * dt
+ else:
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+ d_prime = (d + d_2) / 2
+ x = x + d_prime * dt
+ return x
+
+
+@torch.no_grad()
+def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ """A sampler inspired by DPM-Solver-2 and Algorithm 2 from Karras et al. (2022)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ if s_churn > 0:
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ sigma_hat = sigmas[i] * (gamma + 1)
+ else:
+ gamma = 0
+ sigma_hat = sigmas[i]
+
+ if gamma > 0:
+ eps = torch.randn_like(x) * s_noise
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Euler method
+ dt = sigmas[i + 1] - sigma_hat
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigma_hat.log().lerp(sigmas[i + 1].log(), 0.5).exp()
+ dt_1 = sigma_mid - sigma_hat
+ dt_2 = sigmas[i + 1] - sigma_hat
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ return x
+
+
+@torch.no_grad()
+def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+
+ """Ancestral sampling with DPM-Solver second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], denoised)
+ if sigma_down == 0:
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
+ dt_1 = sigma_mid - sigmas[i]
+ dt_2 = sigma_down - sigmas[i]
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+ sigma_down = sigmas[i+1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i+1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ d = to_d(x, sigmas[i], denoised)
+ if sigma_down == 0:
+ # Euler method
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver-2
+ sigma_mid = sigmas[i].log().lerp(sigma_down.log(), 0.5).exp()
+ dt_1 = sigma_mid - sigmas[i]
+ dt_2 = sigma_down - sigmas[i]
+ x_2 = x + d * dt_1
+ denoised_2 = model(x_2, sigma_mid * s_in, **extra_args)
+ d_2 = to_d(x_2, sigma_mid, denoised_2)
+ x = x + d_2 * dt_2
+ x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ return x
+
+def linear_multistep_coeff(order, t, i, j):
+ if order - 1 > i:
+ raise ValueError(f'Order {order} too high for step {i}')
+ def fn(tau):
+ prod = 1.
+ for k in range(order):
+ if j == k:
+ continue
+ prod *= (tau - t[i - k]) / (t[i - j] - t[i - k])
+ return prod
+ return integrate.quad(fn, t[i], t[i + 1], epsrel=1e-4)[0]
+
+
+@torch.no_grad()
+def sample_lms(model, x, sigmas, extra_args=None, callback=None, disable=None, order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigmas_cpu = sigmas.detach().cpu().numpy()
+ ds = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ d = to_d(x, sigmas[i], denoised)
+ ds.append(d)
+ if len(ds) > order:
+ ds.pop(0)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ cur_order = min(i + 1, order)
+ coeffs = [linear_multistep_coeff(cur_order, sigmas_cpu, i, j) for j in range(cur_order)]
+ x = x + sum(coeff * d for coeff, d in zip(coeffs, reversed(ds)))
+ return x
+
+
+class PIDStepSizeController:
+ """A PID controller for ODE adaptive step size control."""
+ def __init__(self, h, pcoeff, icoeff, dcoeff, order=1, accept_safety=0.81, eps=1e-8):
+ self.h = h
+ self.b1 = (pcoeff + icoeff + dcoeff) / order
+ self.b2 = -(pcoeff + 2 * dcoeff) / order
+ self.b3 = dcoeff / order
+ self.accept_safety = accept_safety
+ self.eps = eps
+ self.errs = []
+
+ def limiter(self, x):
+ return 1 + math.atan(x - 1)
+
+ def propose_step(self, error):
+ inv_error = 1 / (float(error) + self.eps)
+ if not self.errs:
+ self.errs = [inv_error, inv_error, inv_error]
+ self.errs[0] = inv_error
+ factor = self.errs[0] ** self.b1 * self.errs[1] ** self.b2 * self.errs[2] ** self.b3
+ factor = self.limiter(factor)
+ accept = factor >= self.accept_safety
+ if accept:
+ self.errs[2] = self.errs[1]
+ self.errs[1] = self.errs[0]
+ self.h *= factor
+ return accept
+
+
+class DPMSolver(nn.Module):
+ """DPM-Solver. See https://arxiv.org/abs/2206.00927."""
+
+ def __init__(self, model, extra_args=None, eps_callback=None, info_callback=None):
+ super().__init__()
+ self.model = model
+ self.extra_args = {} if extra_args is None else extra_args
+ self.eps_callback = eps_callback
+ self.info_callback = info_callback
+
+ def t(self, sigma):
+ return -sigma.log()
+
+ def sigma(self, t):
+ return t.neg().exp()
+
+ def eps(self, eps_cache, key, x, t, *args, **kwargs):
+ if key in eps_cache:
+ return eps_cache[key], eps_cache
+ sigma = self.sigma(t) * x.new_ones([x.shape[0]])
+ eps = (x - self.model(x, sigma, *args, **self.extra_args, **kwargs)) / self.sigma(t)
+ if self.eps_callback is not None:
+ self.eps_callback()
+ return eps, {key: eps, **eps_cache}
+
+ def dpm_solver_1_step(self, x, t, t_next, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ x_1 = x - self.sigma(t_next) * h.expm1() * eps
+ return x_1, eps_cache
+
+ def dpm_solver_2_step(self, x, t, t_next, r1=1 / 2, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ s1 = t + r1 * h
+ u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+ eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+ x_2 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / (2 * r1) * h.expm1() * (eps_r1 - eps)
+ return x_2, eps_cache
+
+ def dpm_solver_3_step(self, x, t, t_next, r1=1 / 3, r2=2 / 3, eps_cache=None):
+ eps_cache = {} if eps_cache is None else eps_cache
+ h = t_next - t
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ s1 = t + r1 * h
+ s2 = t + r2 * h
+ u1 = x - self.sigma(s1) * (r1 * h).expm1() * eps
+ eps_r1, eps_cache = self.eps(eps_cache, 'eps_r1', u1, s1)
+ u2 = x - self.sigma(s2) * (r2 * h).expm1() * eps - self.sigma(s2) * (r2 / r1) * ((r2 * h).expm1() / (r2 * h) - 1) * (eps_r1 - eps)
+ eps_r2, eps_cache = self.eps(eps_cache, 'eps_r2', u2, s2)
+ x_3 = x - self.sigma(t_next) * h.expm1() * eps - self.sigma(t_next) / r2 * (h.expm1() / h - 1) * (eps_r2 - eps)
+ return x_3, eps_cache
+
+ def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
+ noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
+ if not t_end > t_start and eta:
+ raise ValueError('eta must be 0 for reverse sampling')
+
+ m = math.floor(nfe / 3) + 1
+ ts = torch.linspace(t_start, t_end, m + 1, device=x.device)
+
+ if nfe % 3 == 0:
+ orders = [3] * (m - 2) + [2, 1]
+ else:
+ orders = [3] * (m - 1) + [nfe % 3]
+
+ for i in range(len(orders)):
+ eps_cache = {}
+ t, t_next = ts[i], ts[i + 1]
+ if eta:
+ sd, su = get_ancestral_step(self.sigma(t), self.sigma(t_next), eta)
+ t_next_ = torch.minimum(t_end, self.t(sd))
+ su = (self.sigma(t_next) ** 2 - self.sigma(t_next_) ** 2) ** 0.5
+ else:
+ t_next_, su = t_next, 0.
+
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, t)
+ denoised = x - self.sigma(t) * eps
+ if self.info_callback is not None:
+ self.info_callback({'x': x, 'i': i, 't': ts[i], 't_up': t, 'denoised': denoised})
+
+ if orders[i] == 1:
+ x, eps_cache = self.dpm_solver_1_step(x, t, t_next_, eps_cache=eps_cache)
+ elif orders[i] == 2:
+ x, eps_cache = self.dpm_solver_2_step(x, t, t_next_, eps_cache=eps_cache)
+ else:
+ x, eps_cache = self.dpm_solver_3_step(x, t, t_next_, eps_cache=eps_cache)
+
+ x = x + su * s_noise * noise_sampler(self.sigma(t), self.sigma(t_next))
+
+ return x
+
+ def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
+ noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
+ if order not in {2, 3}:
+ raise ValueError('order should be 2 or 3')
+ forward = t_end > t_start
+ if not forward and eta:
+ raise ValueError('eta must be 0 for reverse sampling')
+ h_init = abs(h_init) * (1 if forward else -1)
+ atol = torch.tensor(atol)
+ rtol = torch.tensor(rtol)
+ s = t_start
+ x_prev = x
+ accept = True
+ pid = PIDStepSizeController(h_init, pcoeff, icoeff, dcoeff, 1.5 if eta else order, accept_safety)
+ info = {'steps': 0, 'nfe': 0, 'n_accept': 0, 'n_reject': 0}
+
+ while s < t_end - 1e-5 if forward else s > t_end + 1e-5:
+ eps_cache = {}
+ t = torch.minimum(t_end, s + pid.h) if forward else torch.maximum(t_end, s + pid.h)
+ if eta:
+ sd, su = get_ancestral_step(self.sigma(s), self.sigma(t), eta)
+ t_ = torch.minimum(t_end, self.t(sd))
+ su = (self.sigma(t) ** 2 - self.sigma(t_) ** 2) ** 0.5
+ else:
+ t_, su = t, 0.
+
+ eps, eps_cache = self.eps(eps_cache, 'eps', x, s)
+ denoised = x - self.sigma(s) * eps
+
+ if order == 2:
+ x_low, eps_cache = self.dpm_solver_1_step(x, s, t_, eps_cache=eps_cache)
+ x_high, eps_cache = self.dpm_solver_2_step(x, s, t_, eps_cache=eps_cache)
+ else:
+ x_low, eps_cache = self.dpm_solver_2_step(x, s, t_, r1=1 / 3, eps_cache=eps_cache)
+ x_high, eps_cache = self.dpm_solver_3_step(x, s, t_, eps_cache=eps_cache)
+ delta = torch.maximum(atol, rtol * torch.maximum(x_low.abs(), x_prev.abs()))
+ error = torch.linalg.norm((x_low - x_high) / delta) / x.numel() ** 0.5
+ accept = pid.propose_step(error)
+ if accept:
+ x_prev = x_low
+ x = x_high + su * s_noise * noise_sampler(self.sigma(s), self.sigma(t))
+ s = t
+ info['n_accept'] += 1
+ else:
+ info['n_reject'] += 1
+ info['nfe'] += order
+ info['steps'] += 1
+
+ if self.info_callback is not None:
+ self.info_callback({'x': x, 'i': info['steps'] - 1, 't': s, 't_up': s, 'denoised': denoised, 'error': error, 'h': pid.h, **info})
+
+ return x, info
+
+
+@torch.no_grad()
+def sample_dpm_fast(model, x, sigma_min, sigma_max, n, extra_args=None, callback=None, disable=None, eta=0., s_noise=1., noise_sampler=None):
+ """DPM-Solver-Fast (fixed step size). See https://arxiv.org/abs/2206.00927."""
+ if sigma_min <= 0 or sigma_max <= 0:
+ raise ValueError('sigma_min and sigma_max must not be 0')
+ with tqdm(total=n, disable=disable) as pbar:
+ dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
+ if callback is not None:
+ dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
+ return dpm_solver.dpm_solver_fast(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), n, eta, s_noise, noise_sampler)
+
+
+@torch.no_grad()
+def sample_dpm_adaptive(model, x, sigma_min, sigma_max, extra_args=None, callback=None, disable=None, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None, return_info=False):
+ """DPM-Solver-12 and 23 (adaptive step size). See https://arxiv.org/abs/2206.00927."""
+ if sigma_min <= 0 or sigma_max <= 0:
+ raise ValueError('sigma_min and sigma_max must not be 0')
+ with tqdm(disable=disable) as pbar:
+ dpm_solver = DPMSolver(model, extra_args, eps_callback=pbar.update)
+ if callback is not None:
+ dpm_solver.info_callback = lambda info: callback({'sigma': dpm_solver.sigma(info['t']), 'sigma_hat': dpm_solver.sigma(info['t_up']), **info})
+ x, info = dpm_solver.dpm_solver_adaptive(x, dpm_solver.t(torch.tensor(sigma_max)), dpm_solver.t(torch.tensor(sigma_min)), order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise, noise_sampler)
+ if return_info:
+ return x, info
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if isinstance(model.inner_model.inner_model.model_sampling, comfy.model_sampling.CONST):
+ return sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
+
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigma_down == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver++(2S)
+ t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+ r = 1 / 2
+ h = t_next - t
+ s = t + r * h
+ x_2 = (sigma_fn(s) / sigma_fn(t)) * x - (-h * r).expm1() * denoised
+ denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_2
+ # Noise addition
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
+ lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
+
+ # logged_x = x.unsqueeze(0)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ downstep_ratio = 1 + (sigmas[i+1]/sigmas[i] - 1) * eta
+ sigma_down = sigmas[i+1] * downstep_ratio
+ alpha_ip1 = 1 - sigmas[i+1]
+ alpha_down = 1 - sigma_down
+ renoise_coeff = (sigmas[i+1]**2 - sigma_down**2*alpha_ip1**2/alpha_down**2)**0.5
+ # sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # DPM-Solver++(2S)
+ if sigmas[i] == 1.0:
+ sigma_s = 0.9999
+ else:
+ t_i, t_down = lambda_fn(sigmas[i]), lambda_fn(sigma_down)
+ r = 1 / 2
+ h = t_down - t_i
+ s = t_i + r * h
+ sigma_s = sigma_fn(s)
+ # sigma_s = sigmas[i+1]
+ sigma_s_i_ratio = sigma_s / sigmas[i]
+ u = sigma_s_i_ratio * x + (1 - sigma_s_i_ratio) * denoised
+ D_i = model(u, sigma_s * s_in, **extra_args)
+ sigma_down_i_ratio = sigma_down / sigmas[i]
+ x = sigma_down_i_ratio * x + (1 - sigma_down_i_ratio) * D_i
+ # print("sigma_i", sigmas[i], "sigma_ip1", sigmas[i+1],"sigma_down", sigma_down, "sigma_down_i_ratio", sigma_down_i_ratio, "sigma_s_i_ratio", sigma_s_i_ratio, "renoise_coeff", renoise_coeff)
+ # Noise addition
+ if sigmas[i + 1] > 0 and eta > 0:
+ x = (alpha_ip1/alpha_down) * x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * renoise_coeff
+ # logged_x = torch.cat((logged_x, x.unsqueeze(0)), dim=0)
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+ """DPM-Solver++ (stochastic)."""
+ if len(sigmas) <= 1:
+ return x
+
+ extra_args = {} if extra_args is None else extra_args
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ seed = extra_args.get("seed", None)
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+ sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ # DPM-Solver++
+ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+ h = lambda_t - lambda_s
+ lambda_s_1 = lambda_s + r * h
+ fac = 1 / (2 * r)
+
+ sigma_s_1 = sigma_fn(lambda_s_1)
+
+ alpha_s = sigmas[i] * lambda_s.exp()
+ alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+ alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+ # Step 1
+ sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_s_1.neg().exp(), eta)
+ lambda_s_1_ = sd.log().neg()
+ h_ = lambda_s_1_ - lambda_s
+ x_2 = (alpha_s_1 / alpha_s) * (-h_).exp() * x - alpha_s_1 * (-h_).expm1() * denoised
+ if eta > 0 and s_noise > 0:
+ x_2 = x_2 + alpha_s_1 * noise_sampler(sigmas[i], sigma_s_1) * s_noise * su
+ denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+
+ # Step 2
+ sd, su = get_ancestral_step(lambda_s.neg().exp(), lambda_t.neg().exp(), eta)
+ lambda_t_ = sd.log().neg()
+ h_ = lambda_t_ - lambda_s
+ denoised_d = (1 - fac) * denoised + fac * denoised_2
+ x = (alpha_t / alpha_s) * (-h_).exp() * x - alpha_t * (-h_).expm1() * denoised_d
+ if eta > 0 and s_noise > 0:
+ x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * su
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """DPM-Solver++(2M)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+ old_denoised = None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+ if old_denoised is None or sigmas[i + 1] == 0:
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised
+ else:
+ h_last = t - t_fn(sigmas[i - 1])
+ r = h_last / h
+ denoised_d = (1 + 1 / (2 * r)) * denoised - (1 / (2 * r)) * old_denoised
+ x = (sigma_fn(t_next) / sigma_fn(t)) * x - (-h).expm1() * denoised_d
+ old_denoised = denoised
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+ """DPM-Solver++(2M) SDE."""
+ if len(sigmas) <= 1:
+ return x
+
+ if solver_type not in {'heun', 'midpoint'}:
+ raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
+
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+ old_denoised = None
+ h, h_last = None, None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ # DPM-Solver++(2M) SDE
+ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+ h = lambda_t - lambda_s
+ h_eta = h * (eta + 1)
+
+ alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+ x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
+
+ if old_denoised is not None:
+ r = h_last / h
+ if solver_type == 'heun':
+ x = x + alpha_t * ((-h_eta).expm1().neg() / (-h_eta) + 1) * (1 / r) * (denoised - old_denoised)
+ elif solver_type == 'midpoint':
+ x = x + 0.5 * alpha_t * (-h_eta).expm1().neg() * (1 / r) * (denoised - old_denoised)
+
+ if eta > 0 and s_noise > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+ old_denoised = denoised
+ h_last = h
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """DPM-Solver++(3M) SDE."""
+
+ if len(sigmas) <= 1:
+ return x
+
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+ denoised_1, denoised_2 = None, None
+ h, h_1, h_2 = None, None, None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+ h = lambda_t - lambda_s
+ h_eta = h * (eta + 1)
+
+ alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+ x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x + alpha_t * (-h_eta).expm1().neg() * denoised
+
+ if h_2 is not None:
+ # DPM-Solver++(3M) SDE
+ r0 = h_1 / h
+ r1 = h_2 / h
+ d1_0 = (denoised - denoised_1) / r0
+ d1_1 = (denoised_1 - denoised_2) / r1
+ d1 = d1_0 + (d1_0 - d1_1) * r0 / (r0 + r1)
+ d2 = (d1_0 - d1_1) / (r0 + r1)
+ phi_2 = h_eta.neg().expm1() / h_eta + 1
+ phi_3 = phi_2 / h_eta - 0.5
+ x = x + (alpha_t * phi_2) * d1 - (alpha_t * phi_3) * d2
+ elif h_1 is not None:
+ # DPM-Solver++(2M) SDE
+ r = h_1 / h
+ d = (denoised - denoised_1) / r
+ phi_2 = h_eta.neg().expm1() / h_eta + 1
+ x = x + (alpha_t * phi_2) * d
+
+ if eta > 0 and s_noise > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * h * eta).expm1().neg().sqrt() * s_noise
+
+ denoised_1, denoised_2 = denoised, denoised_1
+ h_1, h_2 = h, h_1
+ return x
+
+
+@torch.no_grad()
+def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ if len(sigmas) <= 1:
+ return x
+ extra_args = {} if extra_args is None else extra_args
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
+
+
+@torch.no_grad()
+def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+ if len(sigmas) <= 1:
+ return x
+ extra_args = {} if extra_args is None else extra_args
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
+
+
+@torch.no_grad()
+def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+ if len(sigmas) <= 1:
+ return x
+ extra_args = {} if extra_args is None else extra_args
+ sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
+ noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
+ return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
+
+
+def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
+ alpha_cumprod = 1 / ((sigma * sigma) + 1)
+ alpha_cumprod_prev = 1 / ((sigma_prev * sigma_prev) + 1)
+ alpha = (alpha_cumprod / alpha_cumprod_prev)
+
+ mu = (1.0 / alpha).sqrt() * (x - (1 - alpha) * noise / (1 - alpha_cumprod).sqrt())
+ if sigma_prev > 0:
+ mu += ((1 - alpha) * (1. - alpha_cumprod_prev) / (1. - alpha_cumprod)).sqrt() * noise_sampler(sigma, sigma_prev)
+ return mu
+
+def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ x = step_function(x / torch.sqrt(1.0 + sigmas[i] ** 2.0), sigmas[i], sigmas[i + 1], (x - denoised) / sigmas[i], noise_sampler)
+ if sigmas[i + 1] != 0:
+ x *= torch.sqrt(1.0 + sigmas[i + 1] ** 2.0)
+ return x
+
+
+@torch.no_grad()
+def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+ return generic_step_sampler(model, x, sigmas, extra_args, callback, disable, noise_sampler, DDPMSampler_step)
+
+@torch.no_grad()
+def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ x = denoised
+ if sigmas[i + 1] > 0:
+ x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
+ return x
+
+
+
+@torch.no_grad()
+def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=None, s_churn=0., s_tmin=0., s_tmax=float('inf'), s_noise=1.):
+ # From MIT licensed: https://github.com/Carzit/sd-webui-samplers-scheduler/
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ s_end = sigmas[-1]
+ for i in trange(len(sigmas) - 1, disable=disable):
+ gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+ eps = torch.randn_like(x) * s_noise
+ sigma_hat = sigmas[i] * (gamma + 1)
+ if gamma > 0:
+ x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x, sigma_hat, denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ if sigmas[i + 1] == s_end:
+ # Euler method
+ x = x + d * dt
+ elif sigmas[i + 2] == s_end:
+
+ # Heun's method
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+
+ w = 2 * sigmas[0]
+ w2 = sigmas[i+1]/w
+ w1 = 1 - w2
+
+ d_prime = d * w1 + d_2 * w2
+
+
+ x = x + d_prime * dt
+
+ else:
+ # Heun++
+ x_2 = x + d * dt
+ denoised_2 = model(x_2, sigmas[i + 1] * s_in, **extra_args)
+ d_2 = to_d(x_2, sigmas[i + 1], denoised_2)
+ dt_2 = sigmas[i + 2] - sigmas[i + 1]
+
+ x_3 = x_2 + d_2 * dt_2
+ denoised_3 = model(x_3, sigmas[i + 2] * s_in, **extra_args)
+ d_3 = to_d(x_3, sigmas[i + 2], denoised_3)
+
+ w = 3 * sigmas[0]
+ w2 = sigmas[i + 1] / w
+ w3 = sigmas[i + 2] / w
+ w1 = 1 - w2 - w3
+
+ d_prime = w1 * d + w2 * d_2 + w3 * d_3
+ x = x + d_prime * dt
+ return x
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if t_next == 0: # Denoising step
+ x_next = denoised
+ elif order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
+ elif order == 3: # Use two history points.
+ x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
+ elif order == 4: # Use three history points.
+ x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur
+ else:
+ buffer_model.append(d_cur)
+
+ return x_next
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+ t_steps = sigmas
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if t_next == 0: # Denoising step
+ x_next = denoised
+ elif order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ coeff1 = (2 + (h_n / h_n_1)) / 2
+ coeff2 = -(h_n / h_n_1) / 2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
+ elif order == 3: # Use two history points.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ h_n_2 = (t_steps[i-1] - t_steps[i-2])
+ temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+ coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
+ coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
+ coeff3 = temp * h_n_1 / h_n_2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
+ elif order == 4: # Use three history points.
+ h_n = (t_next - t_cur)
+ h_n_1 = (t_cur - t_steps[i-1])
+ h_n_2 = (t_steps[i-1] - t_steps[i-2])
+ h_n_3 = (t_steps[i-2] - t_steps[i-3])
+ temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+ temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
+ * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
+ coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
+ coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
+ coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
+ coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
+ x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur.detach()
+ else:
+ buffer_model.append(d_cur.detach())
+
+ return x_next
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+@torch.no_grad()
+def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+
+ x_next = x
+ t_steps = sigmas
+
+ coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
+
+ buffer_model = []
+ for i in trange(len(sigmas) - 1, disable=disable):
+ t_cur = sigmas[i]
+ t_next = sigmas[i + 1]
+
+ x_cur = x_next
+
+ denoised = model(x_cur, t_cur * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ d_cur = (x_cur - denoised) / t_cur
+
+ order = min(max_order, i+1)
+ if t_next <= 0:
+ order = 1
+
+ if order == 1: # First Euler step.
+ x_next = x_cur + (t_next - t_cur) * d_cur
+ elif order == 2: # Use one history point.
+ coeff_cur, coeff_prev1 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
+ elif order == 3: # Use two history points.
+ coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
+ elif order == 4: # Use three history points.
+ coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
+ x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
+
+ if len(buffer_model) == max_order - 1:
+ for k in range(max_order - 2):
+ buffer_model[k] = buffer_model[k+1]
+ buffer_model[-1] = d_cur.detach()
+ else:
+ buffer_model.append(d_cur.detach())
+
+ return x_next
+
+
+@torch.no_grad()
+def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with Euler method steps (CFG++)."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+
+ model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+
+ uncond_denoised = None
+
+ def post_cfg_function(args):
+ nonlocal uncond_denoised
+ uncond_denoised = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ alpha_s = sigmas[i] * lambda_fn(sigmas[i]).exp()
+ alpha_t = sigmas[i + 1] * lambda_fn(sigmas[i + 1]).exp()
+ d = to_d(x, sigmas[i], alpha_s * uncond_denoised) # to noise
+
+ # DDIM stochastic sampling
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i] / alpha_s, sigmas[i + 1] / alpha_t, eta=eta)
+ sigma_down = alpha_t * sigma_down
+
+ # Euler method
+ x = alpha_t * denoised + sigma_down * d
+ if eta > 0 and s_noise > 0:
+ x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+
+@torch.no_grad()
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """Euler method steps (CFG++)."""
+ return sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=0.0, s_noise=0.0, noise_sampler=None)
+
+
+@torch.no_grad()
+def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ """Ancestral sampling with DPM-Solver++(2S) second-order steps."""
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+
+ temp = [0]
+ def post_cfg_function(args):
+ temp[0] = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigma_down == 0:
+ # Euler method
+ d = to_d(x, sigmas[i], temp[0])
+ x = denoised + d * sigma_down
+ else:
+ # DPM-Solver++(2S)
+ t, t_next = t_fn(sigmas[i]), t_fn(sigma_down)
+ # r = torch.sinh(1 + (2 - eta) * (t_next - t) / (t - t_fn(sigma_up))) works only on non-cfgpp, weird
+ r = 1 / 2
+ h = t_next - t
+ s = t + r * h
+ x_2 = (sigma_fn(s) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h * r).expm1() * denoised
+ denoised_2 = model(x_2, sigma_fn(s) * s_in, **extra_args)
+ x = (sigma_fn(t_next) / sigma_fn(t)) * (x + (denoised - temp[0])) - (-h).expm1() * denoised_2
+ # Noise addition
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+ return x
+
+@torch.no_grad()
+def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ """DPM-Solver++(2M)."""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ t_fn = lambda sigma: sigma.log().neg()
+
+ old_uncond_denoised = None
+ uncond_denoised = None
+ def post_cfg_function(args):
+ nonlocal uncond_denoised
+ uncond_denoised = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ t, t_next = t_fn(sigmas[i]), t_fn(sigmas[i + 1])
+ h = t_next - t
+ if old_uncond_denoised is None or sigmas[i + 1] == 0:
+ denoised_mix = -torch.exp(-h) * uncond_denoised
+ else:
+ h_last = t - t_fn(sigmas[i - 1])
+ r = h_last / h
+ denoised_mix = -torch.exp(-h) * uncond_denoised - torch.expm1(-h) * (1 / (2 * r)) * (denoised - old_uncond_denoised)
+ x = denoised + denoised_mix + torch.exp(-h) * x
+ old_uncond_denoised = uncond_denoised
+ return x
+
+@torch.no_grad()
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+ sigma_fn = lambda t: t.neg().exp()
+ t_fn = lambda sigma: sigma.log().neg()
+ phi1_fn = lambda t: torch.expm1(t) / t
+ phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t
+
+ old_sigma_down = None
+ old_denoised = None
+ uncond_denoised = None
+ def post_cfg_function(args):
+ nonlocal uncond_denoised
+ uncond_denoised = args["uncond_denoised"]
+ return args["denoised"]
+
+ if cfg_pp:
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+ if callback is not None:
+ callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+ if sigma_down == 0 or old_denoised is None:
+ # Euler method
+ if cfg_pp:
+ d = to_d(x, sigmas[i], uncond_denoised)
+ x = denoised + d * sigma_down
+ else:
+ d = to_d(x, sigmas[i], denoised)
+ dt = sigma_down - sigmas[i]
+ x = x + d * dt
+ else:
+ # Second order multistep method in https://arxiv.org/pdf/2308.02157
+ t, t_old, t_next, t_prev = t_fn(sigmas[i]), t_fn(old_sigma_down), t_fn(sigma_down), t_fn(sigmas[i - 1])
+ h = t_next - t
+ c2 = (t_prev - t_old) / h
+
+ phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
+ b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+ b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
+
+ if cfg_pp:
+ x = x + (denoised - uncond_denoised)
+ x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
+ else:
+ x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
+
+ # Noise addition
+ if sigmas[i + 1] > 0:
+ x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+
+ if cfg_pp:
+ old_denoised = uncond_denoised
+ else:
+ old_denoised = denoised
+ old_sigma_down = sigma_down
+ return x
+
+@torch.no_grad()
+def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+ return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+ return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+ return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
+
+
+@torch.no_grad()
+def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2., cfg_pp=False):
+ """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
+ extra_args = {} if extra_args is None else extra_args
+ s_in = x.new_ones([x.shape[0]])
+ old_d = None
+
+ uncond_denoised = None
+ def post_cfg_function(args):
+ nonlocal uncond_denoised
+ uncond_denoised = args["uncond_denoised"]
+ return args["denoised"]
+
+ if cfg_pp:
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if cfg_pp:
+ d = to_d(x, sigmas[i], uncond_denoised)
+ else:
+ d = to_d(x, sigmas[i], denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ dt = sigmas[i + 1] - sigmas[i]
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ # Euler method
+ if cfg_pp:
+ x = denoised + d * sigmas[i + 1]
+ else:
+ x = x + d * dt
+
+ if i >= 1:
+ # Gradient estimation
+ d_bar = (ge_gamma - 1) * (d - old_d)
+ x = x + d_bar * dt
+ old_d = d
+ return x
+
+
+@torch.no_grad()
+def sample_gradient_estimation_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
+ return sample_gradient_estimation(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, ge_gamma=ge_gamma, cfg_pp=True)
+
+
+@torch.no_grad()
+def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1.0, noise_sampler=None, noise_scaler=None, max_stage=3):
+ """Extended Reverse-Time SDE solver (VP ER-SDE-Solver-3). arXiv: https://arxiv.org/abs/2309.06169.
+ Code reference: https://github.com/QinpengCui/ER-SDE-Solver/blob/main/er_sde_solver.py.
+ """
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ def default_er_sde_noise_scaler(x):
+ return x * ((x ** 0.3).exp() + 10.0)
+
+ noise_scaler = default_er_sde_noise_scaler if noise_scaler is None else noise_scaler
+ num_integration_points = 200.0
+ point_indice = torch.arange(0, num_integration_points, dtype=torch.float32, device=x.device)
+
+ model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+ half_log_snrs = sigma_to_half_log_snr(sigmas, model_sampling)
+ er_lambdas = half_log_snrs.neg().exp() # er_lambda_t = sigma_t / alpha_t
+
+ old_denoised = None
+ old_denoised_d = None
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ stage_used = min(max_stage, i + 1)
+ if sigmas[i + 1] == 0:
+ x = denoised
+ else:
+ er_lambda_s, er_lambda_t = er_lambdas[i], er_lambdas[i + 1]
+ alpha_s = sigmas[i] / er_lambda_s
+ alpha_t = sigmas[i + 1] / er_lambda_t
+ r_alpha = alpha_t / alpha_s
+ r = noise_scaler(er_lambda_t) / noise_scaler(er_lambda_s)
+
+ # Stage 1 Euler
+ x = r_alpha * r * x + alpha_t * (1 - r) * denoised
+
+ if stage_used >= 2:
+ dt = er_lambda_t - er_lambda_s
+ lambda_step_size = -dt / num_integration_points
+ lambda_pos = er_lambda_t + point_indice * lambda_step_size
+ scaled_pos = noise_scaler(lambda_pos)
+
+ # Stage 2
+ s = torch.sum(1 / scaled_pos) * lambda_step_size
+ denoised_d = (denoised - old_denoised) / (er_lambda_s - er_lambdas[i - 1])
+ x = x + alpha_t * (dt + s * noise_scaler(er_lambda_t)) * denoised_d
+
+ if stage_used >= 3:
+ # Stage 3
+ s_u = torch.sum((lambda_pos - er_lambda_s) / scaled_pos) * lambda_step_size
+ denoised_u = (denoised_d - old_denoised_d) / ((er_lambda_s - er_lambdas[i - 2]) / 2)
+ x = x + alpha_t * ((dt ** 2) / 2 + s_u * noise_scaler(er_lambda_t)) * denoised_u
+ old_denoised_d = denoised_d
+
+ if s_noise > 0:
+ x = x + alpha_t * noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * (er_lambda_t ** 2 - er_lambda_s ** 2 * r ** 2).sqrt().nan_to_num(nan=0.0)
+ old_denoised = denoised
+ return x
+
+
+@torch.no_grad()
+def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=0.5):
+ """SEEDS-2 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 2.
+ arXiv: https://arxiv.org/abs/2305.14267
+ """
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ inject_noise = eta > 0 and s_noise > 0
+
+ model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+ sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ x = denoised
+ else:
+ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+ h = lambda_t - lambda_s
+ h_eta = h * (eta + 1)
+ lambda_s_1 = lambda_s + r * h
+ fac = 1 / (2 * r)
+ sigma_s_1 = sigma_fn(lambda_s_1)
+
+ # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+ alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+ alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+ coeff_1, coeff_2 = (-r * h_eta).expm1(), (-h_eta).expm1()
+ if inject_noise:
+ # 0 < r < 1
+ noise_coeff_1 = (-2 * r * h * eta).expm1().neg().sqrt()
+ noise_coeff_2 = (-r * h * eta).exp() * (-2 * (1 - r) * h * eta).expm1().neg().sqrt()
+ noise_1, noise_2 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigmas[i + 1])
+
+ # Step 1
+ x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
+ if inject_noise:
+ x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+ denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+
+ # Step 2
+ denoised_d = (1 - fac) * denoised + fac * denoised_2
+ x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_2 * denoised_d
+ if inject_noise:
+ x = x + sigmas[i + 1] * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
+ return x
+
+
+@torch.no_grad()
+def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r_1=1./3, r_2=2./3):
+ """SEEDS-3 - Stochastic Explicit Exponential Derivative-free Solvers (VP Data Prediction) stage 3.
+ arXiv: https://arxiv.org/abs/2305.14267
+ """
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ inject_noise = eta > 0 and s_noise > 0
+
+ model_sampling = model.inner_model.model_patcher.get_model_object('model_sampling')
+ sigma_fn = partial(half_log_snr_to_sigma, model_sampling=model_sampling)
+ lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+ if sigmas[i + 1] == 0:
+ x = denoised
+ else:
+ lambda_s, lambda_t = lambda_fn(sigmas[i]), lambda_fn(sigmas[i + 1])
+ h = lambda_t - lambda_s
+ h_eta = h * (eta + 1)
+ lambda_s_1 = lambda_s + r_1 * h
+ lambda_s_2 = lambda_s + r_2 * h
+ sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
+
+ # alpha_t = sigma_t * exp(log(alpha_t / sigma_t)) = sigma_t * exp(lambda_t)
+ alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+ alpha_s_2 = sigma_s_2 * lambda_s_2.exp()
+ alpha_t = sigmas[i + 1] * lambda_t.exp()
+
+ coeff_1, coeff_2, coeff_3 = (-r_1 * h_eta).expm1(), (-r_2 * h_eta).expm1(), (-h_eta).expm1()
+ if inject_noise:
+ # 0 < r_1 < r_2 < 1
+ noise_coeff_1 = (-2 * r_1 * h * eta).expm1().neg().sqrt()
+ noise_coeff_2 = (-r_1 * h * eta).exp() * (-2 * (r_2 - r_1) * h * eta).expm1().neg().sqrt()
+ noise_coeff_3 = (-r_2 * h * eta).exp() * (-2 * (1 - r_2) * h * eta).expm1().neg().sqrt()
+ noise_1, noise_2, noise_3 = noise_sampler(sigmas[i], sigma_s_1), noise_sampler(sigma_s_1, sigma_s_2), noise_sampler(sigma_s_2, sigmas[i + 1])
+
+ # Step 1
+ x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * coeff_1 * denoised
+ if inject_noise:
+ x_2 = x_2 + sigma_s_1 * (noise_coeff_1 * noise_1) * s_noise
+ denoised_2 = model(x_2, sigma_s_1 * s_in, **extra_args)
+
+ # Step 2
+ x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * coeff_2 * denoised + (r_2 / r_1) * alpha_s_2 * (coeff_2 / (r_2 * h_eta) + 1) * (denoised_2 - denoised)
+ if inject_noise:
+ x_3 = x_3 + sigma_s_2 * (noise_coeff_2 * noise_1 + noise_coeff_1 * noise_2) * s_noise
+ denoised_3 = model(x_3, sigma_s_2 * s_in, **extra_args)
+
+ # Step 3
+ x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * coeff_3 * denoised + (1. / r_2) * alpha_t * (coeff_3 / h_eta + 1) * (denoised_3 - denoised)
+ if inject_noise:
+ x = x + sigmas[i + 1] * (noise_coeff_3 * noise_1 + noise_coeff_2 * noise_2 + noise_coeff_1 * noise_3) * s_noise
+ return x
+
+
+@torch.no_grad()
+def sample_sa_solver(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, use_pece=False, simple_order_2=False):
+ """Stochastic Adams Solver with predictor-corrector method (NeurIPS 2023)."""
+ if len(sigmas) <= 1:
+ return x
+ extra_args = {} if extra_args is None else extra_args
+ seed = extra_args.get("seed", None)
+ noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
+ s_in = x.new_ones([x.shape[0]])
+
+ model_sampling = model.inner_model.model_patcher.get_model_object("model_sampling")
+ sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
+ lambdas = sigma_to_half_log_snr(sigmas, model_sampling=model_sampling)
+
+ if tau_func is None:
+ # Use default interval for stochastic sampling
+ start_sigma = model_sampling.percent_to_sigma(0.2)
+ end_sigma = model_sampling.percent_to_sigma(0.8)
+ tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=1.0)
+
+ max_used_order = max(predictor_order, corrector_order)
+ x_pred = x # x: current state, x_pred: predicted next state
+
+ h = 0.0
+ tau_t = 0.0
+ noise = 0.0
+ pred_list = []
+
+ # Lower order near the end to improve stability
+ lower_order_to_end = sigmas[-1].item() == 0
+
+ for i in trange(len(sigmas) - 1, disable=disable):
+ # Evaluation
+ denoised = model(x_pred, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({"x": x_pred, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+ pred_list.append(denoised)
+ pred_list = pred_list[-max_used_order:]
+
+ predictor_order_used = min(predictor_order, len(pred_list))
+ if i == 0 or (sigmas[i + 1] == 0 and not use_pece):
+ corrector_order_used = 0
+ else:
+ corrector_order_used = min(corrector_order, len(pred_list))
+
+ if lower_order_to_end:
+ predictor_order_used = min(predictor_order_used, len(sigmas) - 2 - i)
+ corrector_order_used = min(corrector_order_used, len(sigmas) - 1 - i)
+
+ # Corrector
+ if corrector_order_used == 0:
+ # Update by the predicted state
+ x = x_pred
+ else:
+ curr_lambdas = lambdas[i - corrector_order_used + 1:i + 1]
+ b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
+ sigmas[i],
+ curr_lambdas,
+ lambdas[i - 1],
+ lambdas[i],
+ tau_t,
+ simple_order_2,
+ is_corrector_step=True,
+ )
+ pred_mat = torch.stack(pred_list[-corrector_order_used:], dim=1) # (B, K, ...)
+ corr_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)
+ x = sigmas[i] / sigmas[i - 1] * (-(tau_t ** 2) * h).exp() * x + corr_res
+
+ if tau_t > 0 and s_noise > 0:
+ # The noise from the previous predictor step
+ x = x + noise
+
+ if use_pece:
+ # Evaluate the corrected state
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ pred_list[-1] = denoised
+
+ # Predictor
+ if sigmas[i + 1] == 0:
+ # Denoising step
+ x = denoised
+ else:
+ tau_t = tau_func(sigmas[i + 1])
+ curr_lambdas = lambdas[i - predictor_order_used + 1:i + 1]
+ b_coeffs = sa_solver.compute_stochastic_adams_b_coeffs(
+ sigmas[i + 1],
+ curr_lambdas,
+ lambdas[i],
+ lambdas[i + 1],
+ tau_t,
+ simple_order_2,
+ is_corrector_step=False,
+ )
+ pred_mat = torch.stack(pred_list[-predictor_order_used:], dim=1) # (B, K, ...)
+ pred_res = torch.tensordot(pred_mat, b_coeffs, dims=([1], [0])) # (B, ...)
+ h = lambdas[i + 1] - lambdas[i]
+ x_pred = sigmas[i + 1] / sigmas[i] * (-(tau_t ** 2) * h).exp() * x + pred_res
+
+ if tau_t > 0 and s_noise > 0:
+ noise = noise_sampler(sigmas[i], sigmas[i + 1]) * sigmas[i + 1] * (-2 * tau_t ** 2 * h).expm1().neg().sqrt() * s_noise
+ x_pred = x_pred + noise
+ return x
+
+
+@torch.no_grad()
+def sample_sa_solver_pece(model, x, sigmas, extra_args=None, callback=None, disable=False, tau_func=None, s_noise=1.0, noise_sampler=None, predictor_order=3, corrector_order=4, simple_order_2=False):
+ """Stochastic Adams Solver with PECE (Predict–Evaluate–Correct–Evaluate) mode (NeurIPS 2023)."""
+ return sample_sa_solver(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, tau_func=tau_func, s_noise=s_noise, noise_sampler=noise_sampler, predictor_order=predictor_order, corrector_order=corrector_order, use_pece=True, simple_order_2=simple_order_2)
diff --git a/ComfyUI/comfy/ldm/common_dit.py b/ComfyUI/comfy/ldm/common_dit.py
new file mode 100644
index 0000000000000000000000000000000000000000..f7f56b72ca6fcd3ba2831b8881a7124d84490bee
--- /dev/null
+++ b/ComfyUI/comfy/ldm/common_dit.py
@@ -0,0 +1,16 @@
+import torch
+import comfy.rmsnorm
+
+
+def pad_to_patch_size(img, patch_size=(2, 2), padding_mode="circular"):
+ if padding_mode == "circular" and (torch.jit.is_tracing() or torch.jit.is_scripting()):
+ padding_mode = "reflect"
+
+ pad = ()
+ for i in range(img.ndim - 2):
+ pad = (0, (patch_size[i] - img.shape[i + 2] % patch_size[i]) % patch_size[i]) + pad
+
+ return torch.nn.functional.pad(img, pad, mode=padding_mode)
+
+
+rms_norm = comfy.rmsnorm.rms_norm
diff --git a/ComfyUI/comfy/model_detection.py b/ComfyUI/comfy/model_detection.py
new file mode 100644
index 0000000000000000000000000000000000000000..9fc1f42de4aa21d8e01bcaa0495017775fa28df4
--- /dev/null
+++ b/ComfyUI/comfy/model_detection.py
@@ -0,0 +1,910 @@
+import json
+import comfy.supported_models
+import comfy.supported_models_base
+import comfy.utils
+import math
+import logging
+import torch
+
+def count_blocks(state_dict_keys, prefix_string):
+ count = 0
+ while True:
+ c = False
+ for k in state_dict_keys:
+ if k.startswith(prefix_string.format(count)):
+ c = True
+ break
+ if c == False:
+ break
+ count += 1
+ return count
+
+def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
+ context_dim = None
+ use_linear_in_transformer = False
+
+ transformer_prefix = prefix + "1.transformer_blocks."
+ transformer_keys = sorted(list(filter(lambda a: a.startswith(transformer_prefix), state_dict_keys)))
+ if len(transformer_keys) > 0:
+ last_transformer_depth = count_blocks(state_dict_keys, transformer_prefix + '{}')
+ context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
+ use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
+ time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
+ time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
+ return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
+ return None
+
+def detect_unet_config(state_dict, key_prefix, metadata=None):
+ state_dict_keys = list(state_dict.keys())
+
+ if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
+ unet_config = {}
+ unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
+ patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
+ unet_config["patch_size"] = patch_size
+ final_layer = '{}final_layer.linear.weight'.format(key_prefix)
+ if final_layer in state_dict:
+ unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
+
+ unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
+ unet_config["input_size"] = None
+ y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
+ if y_key in state_dict_keys:
+ unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
+
+ context_key = '{}context_embedder.weight'.format(key_prefix)
+ if context_key in state_dict_keys:
+ in_features = state_dict[context_key].shape[1]
+ out_features = state_dict[context_key].shape[0]
+ unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
+ num_patches_key = '{}pos_embed'.format(key_prefix)
+ if num_patches_key in state_dict_keys:
+ num_patches = state_dict[num_patches_key].shape[1]
+ unet_config["num_patches"] = num_patches
+ unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
+
+ rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
+ if rms_qk in state_dict_keys:
+ unet_config["qk_norm"] = "rms"
+
+ unet_config["pos_embed_scaling_factor"] = None #unused for inference
+ context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
+ if context_processor in state_dict_keys:
+ unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
+ unet_config["x_block_self_attn_layers"] = []
+ for key in state_dict_keys:
+ if key.startswith('{}joint_blocks.'.format(key_prefix)) and key.endswith('.x_block.attn2.qkv.weight'):
+ layer = key[len('{}joint_blocks.'.format(key_prefix)):-len('.x_block.attn2.qkv.weight')]
+ unet_config["x_block_self_attn_layers"].append(int(layer))
+ return unet_config
+
+ if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
+ unet_config = {}
+ text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
+ if text_mapper_name in state_dict_keys:
+ unet_config['stable_cascade_stage'] = 'c'
+ w = state_dict[text_mapper_name]
+ if w.shape[0] == 1536: #stage c lite
+ unet_config['c_cond'] = 1536
+ unet_config['c_hidden'] = [1536, 1536]
+ unet_config['nhead'] = [24, 24]
+ unet_config['blocks'] = [[4, 12], [12, 4]]
+ elif w.shape[0] == 2048: #stage c full
+ unet_config['c_cond'] = 2048
+ elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
+ unet_config['stable_cascade_stage'] = 'b'
+ w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
+ if w.shape[-1] == 640:
+ unet_config['c_hidden'] = [320, 640, 1280, 1280]
+ unet_config['nhead'] = [-1, -1, 20, 20]
+ unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
+ unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
+ elif w.shape[-1] == 576: #stage b lite
+ unet_config['c_hidden'] = [320, 576, 1152, 1152]
+ unet_config['nhead'] = [-1, 9, 18, 18]
+ unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
+ unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
+ return unet_config
+
+ if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
+ unet_config = {}
+ unet_config["audio_model"] = "dit1.0"
+ return unet_config
+
+ if '{}double_layers.0.attn.w1q.weight'.format(key_prefix) in state_dict_keys: #aura flow dit
+ unet_config = {}
+ unet_config["max_seq"] = state_dict['{}positional_encoding'.format(key_prefix)].shape[1]
+ unet_config["cond_seq_dim"] = state_dict['{}cond_seq_linear.weight'.format(key_prefix)].shape[1]
+ double_layers = count_blocks(state_dict_keys, '{}double_layers.'.format(key_prefix) + '{}.')
+ single_layers = count_blocks(state_dict_keys, '{}single_layers.'.format(key_prefix) + '{}.')
+ unet_config["n_double_layers"] = double_layers
+ unet_config["n_layers"] = double_layers + single_layers
+ return unet_config
+
+ if '{}mlp_t5.0.weight'.format(key_prefix) in state_dict_keys: #Hunyuan DiT
+ unet_config = {}
+ unet_config["image_model"] = "hydit"
+ unet_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+ unet_config["hidden_size"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0]
+ if unet_config["hidden_size"] == 1408 and unet_config["depth"] == 40: #DiT-g/2
+ unet_config["mlp_ratio"] = 4.3637
+ if state_dict['{}extra_embedder.0.weight'.format(key_prefix)].shape[1] == 3968:
+ unet_config["size_cond"] = True
+ unet_config["use_style_cond"] = True
+ unet_config["image_model"] = "hydit1"
+ return unet_config
+
+ if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video
+ dit_config = {}
+ dit_config["image_model"] = "hunyuan_video"
+ dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels
+ dit_config["patch_size"] = [1, 2, 2]
+ dit_config["out_channels"] = 16
+ dit_config["vec_in_dim"] = 768
+ dit_config["context_in_dim"] = 4096
+ dit_config["hidden_size"] = 3072
+ dit_config["mlp_ratio"] = 4.0
+ dit_config["num_heads"] = 24
+ dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+ dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
+ dit_config["axes_dim"] = [16, 56, 56]
+ dit_config["theta"] = 256
+ dit_config["qkv_bias"] = True
+ guidance_keys = list(filter(lambda a: a.startswith("{}guidance_in.".format(key_prefix)), state_dict_keys))
+ dit_config["guidance_embed"] = len(guidance_keys) > 0
+ return dit_config
+
+ if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux
+ dit_config = {}
+ dit_config["image_model"] = "flux"
+ dit_config["in_channels"] = 16
+ patch_size = 2
+ dit_config["patch_size"] = patch_size
+ in_key = "{}img_in.weight".format(key_prefix)
+ if in_key in state_dict_keys:
+ dit_config["in_channels"] = state_dict[in_key].shape[1] // (patch_size * patch_size)
+ dit_config["out_channels"] = 16
+ vec_in_key = '{}vector_in.in_layer.weight'.format(key_prefix)
+ if vec_in_key in state_dict_keys:
+ dit_config["vec_in_dim"] = state_dict[vec_in_key].shape[1]
+ dit_config["context_in_dim"] = 4096
+ dit_config["hidden_size"] = 3072
+ dit_config["mlp_ratio"] = 4.0
+ dit_config["num_heads"] = 24
+ dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+ dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
+ dit_config["axes_dim"] = [16, 56, 56]
+ dit_config["theta"] = 10000
+ dit_config["qkv_bias"] = True
+ if '{}distilled_guidance_layer.0.norms.0.scale'.format(key_prefix) in state_dict_keys or '{}distilled_guidance_layer.norms.0.scale'.format(key_prefix) in state_dict_keys: #Chroma
+ dit_config["image_model"] = "chroma"
+ dit_config["in_channels"] = 64
+ dit_config["out_channels"] = 64
+ dit_config["in_dim"] = 64
+ dit_config["out_dim"] = 3072
+ dit_config["hidden_dim"] = 5120
+ dit_config["n_layers"] = 5
+ else:
+ dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+ return dit_config
+
+ if '{}t5_yproj.weight'.format(key_prefix) in state_dict_keys: #Genmo mochi preview
+ dit_config = {}
+ dit_config["image_model"] = "mochi_preview"
+ dit_config["depth"] = 48
+ dit_config["patch_size"] = 2
+ dit_config["num_heads"] = 24
+ dit_config["hidden_size_x"] = 3072
+ dit_config["hidden_size_y"] = 1536
+ dit_config["mlp_ratio_x"] = 4.0
+ dit_config["mlp_ratio_y"] = 4.0
+ dit_config["learn_sigma"] = False
+ dit_config["in_channels"] = 12
+ dit_config["qk_norm"] = True
+ dit_config["qkv_bias"] = False
+ dit_config["out_bias"] = True
+ dit_config["attn_drop"] = 0.0
+ dit_config["patch_embed_bias"] = True
+ dit_config["posenc_preserve_area"] = True
+ dit_config["timestep_mlp_bias"] = True
+ dit_config["attend_to_padding"] = False
+ dit_config["timestep_scale"] = 1000.0
+ dit_config["use_t5"] = True
+ dit_config["t5_feat_dim"] = 4096
+ dit_config["t5_token_length"] = 256
+ dit_config["rope_theta"] = 10000.0
+ return dit_config
+
+ if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys:
+ # PixArt diffusers
+ return None
+
+ if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
+ dit_config = {}
+ dit_config["image_model"] = "ltxv"
+ dit_config["num_layers"] = count_blocks(state_dict_keys, '{}transformer_blocks.'.format(key_prefix) + '{}.')
+ shape = state_dict['{}transformer_blocks.0.attn2.to_k.weight'.format(key_prefix)].shape
+ dit_config["attention_head_dim"] = shape[0] // 32
+ dit_config["cross_attention_dim"] = shape[1]
+ if metadata is not None and "config" in metadata:
+ dit_config.update(json.loads(metadata["config"]).get("transformer", {}))
+ return dit_config
+
+ if '{}genre_embedder.weight'.format(key_prefix) in state_dict_keys: #ACE-Step model
+ dit_config = {}
+ dit_config["audio_model"] = "ace"
+ dit_config["attention_head_dim"] = 128
+ dit_config["in_channels"] = 8
+ dit_config["inner_dim"] = 2560
+ dit_config["max_height"] = 16
+ dit_config["max_position"] = 32768
+ dit_config["max_width"] = 32768
+ dit_config["mlp_ratio"] = 2.5
+ dit_config["num_attention_heads"] = 20
+ dit_config["num_layers"] = 24
+ dit_config["out_channels"] = 8
+ dit_config["patch_size"] = [16, 1]
+ dit_config["rope_theta"] = 1000000.0
+ dit_config["speaker_embedding_dim"] = 512
+ dit_config["text_embedding_dim"] = 768
+
+ dit_config["ssl_encoder_depths"] = [8, 8]
+ dit_config["ssl_latent_dims"] = [1024, 768]
+ dit_config["ssl_names"] = ["mert", "m-hubert"]
+ dit_config["lyric_encoder_vocab_size"] = 6693
+ dit_config["lyric_hidden_size"] = 1024
+ return dit_config
+
+ if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt
+ patch_size = 2
+ dit_config = {}
+ dit_config["num_heads"] = 16
+ dit_config["patch_size"] = patch_size
+ dit_config["hidden_size"] = 1152
+ dit_config["in_channels"] = 4
+ dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+
+ y_key = "{}y_embedder.y_embedding".format(key_prefix)
+ if y_key in state_dict_keys:
+ dit_config["model_max_length"] = state_dict[y_key].shape[0]
+
+ pe_key = "{}pos_embed".format(key_prefix)
+ if pe_key in state_dict_keys:
+ dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size
+ dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess
+
+ ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix)
+ if ar_key in state_dict_keys:
+ dit_config["image_model"] = "pixart_alpha"
+ dit_config["micro_condition"] = True
+ else:
+ dit_config["image_model"] = "pixart_sigma"
+ dit_config["micro_condition"] = False
+ return dit_config
+
+ if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys: # Cosmos
+ dit_config = {}
+ dit_config["image_model"] = "cosmos"
+ dit_config["max_img_h"] = 240
+ dit_config["max_img_w"] = 240
+ dit_config["max_frames"] = 128
+ concat_padding_mask = True
+ dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
+ dit_config["out_channels"] = 16
+ dit_config["patch_spatial"] = 2
+ dit_config["patch_temporal"] = 1
+ dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]
+ dit_config["block_config"] = "FA-CA-MLP"
+ dit_config["concat_padding_mask"] = concat_padding_mask
+ dit_config["pos_emb_cls"] = "rope3d"
+ dit_config["pos_emb_learnable"] = False
+ dit_config["pos_emb_interpolation"] = "crop"
+ dit_config["block_x_format"] = "THWBD"
+ dit_config["affline_emb_norm"] = True
+ dit_config["use_adaln_lora"] = True
+ dit_config["adaln_lora_dim"] = 256
+
+ if dit_config["model_channels"] == 4096:
+ # 7B
+ dit_config["num_blocks"] = 28
+ dit_config["num_heads"] = 32
+ dit_config["extra_per_block_abs_pos_emb"] = True
+ dit_config["rope_h_extrapolation_ratio"] = 1.0
+ dit_config["rope_w_extrapolation_ratio"] = 1.0
+ dit_config["rope_t_extrapolation_ratio"] = 2.0
+ dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
+ else: # 5120
+ # 14B
+ dit_config["num_blocks"] = 36
+ dit_config["num_heads"] = 40
+ dit_config["extra_per_block_abs_pos_emb"] = True
+ dit_config["rope_h_extrapolation_ratio"] = 2.0
+ dit_config["rope_w_extrapolation_ratio"] = 2.0
+ dit_config["rope_t_extrapolation_ratio"] = 2.0
+ dit_config["extra_h_extrapolation_ratio"] = 2.0
+ dit_config["extra_w_extrapolation_ratio"] = 2.0
+ dit_config["extra_t_extrapolation_ratio"] = 2.0
+ dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
+ return dit_config
+
+ if '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys: # Lumina 2
+ dit_config = {}
+ dit_config["image_model"] = "lumina2"
+ dit_config["patch_size"] = 2
+ dit_config["in_channels"] = 16
+ dit_config["dim"] = 2304
+ dit_config["cap_feat_dim"] = 2304
+ dit_config["n_layers"] = 26
+ dit_config["n_heads"] = 24
+ dit_config["n_kv_heads"] = 8
+ dit_config["qk_norm"] = True
+ dit_config["axes_dims"] = [32, 32, 32]
+ dit_config["axes_lens"] = [300, 512, 512]
+ return dit_config
+
+ if '{}head.modulation'.format(key_prefix) in state_dict_keys: # Wan 2.1
+ dit_config = {}
+ dit_config["image_model"] = "wan2.1"
+ dim = state_dict['{}head.modulation'.format(key_prefix)].shape[-1]
+ out_dim = state_dict['{}head.head.weight'.format(key_prefix)].shape[0] // 4
+ dit_config["dim"] = dim
+ dit_config["out_dim"] = out_dim
+ dit_config["num_heads"] = dim // 128
+ dit_config["ffn_dim"] = state_dict['{}blocks.0.ffn.0.weight'.format(key_prefix)].shape[0]
+ dit_config["num_layers"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+ dit_config["patch_size"] = (1, 2, 2)
+ dit_config["freq_dim"] = 256
+ dit_config["window_size"] = (-1, -1)
+ dit_config["qk_norm"] = True
+ dit_config["cross_attn_norm"] = True
+ dit_config["eps"] = 1e-6
+ dit_config["in_dim"] = state_dict['{}patch_embedding.weight'.format(key_prefix)].shape[1]
+ if '{}vace_patch_embedding.weight'.format(key_prefix) in state_dict_keys:
+ dit_config["model_type"] = "vace"
+ dit_config["vace_in_dim"] = state_dict['{}vace_patch_embedding.weight'.format(key_prefix)].shape[1]
+ dit_config["vace_layers"] = count_blocks(state_dict_keys, '{}vace_blocks.'.format(key_prefix) + '{}.')
+ elif '{}control_adapter.conv.weight'.format(key_prefix) in state_dict_keys:
+ dit_config["model_type"] = "camera"
+ else:
+ if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
+ dit_config["model_type"] = "i2v"
+ else:
+ dit_config["model_type"] = "t2v"
+ flf_weight = state_dict.get('{}img_emb.emb_pos'.format(key_prefix))
+ if flf_weight is not None:
+ dit_config["flf_pos_embed_token_number"] = flf_weight.shape[1]
+ return dit_config
+
+ if '{}latent_in.weight'.format(key_prefix) in state_dict_keys: # Hunyuan 3D
+ in_shape = state_dict['{}latent_in.weight'.format(key_prefix)].shape
+ dit_config = {}
+ dit_config["image_model"] = "hunyuan3d2"
+ dit_config["in_channels"] = in_shape[1]
+ dit_config["context_in_dim"] = state_dict['{}cond_in.weight'.format(key_prefix)].shape[1]
+ dit_config["hidden_size"] = in_shape[0]
+ dit_config["mlp_ratio"] = 4.0
+ dit_config["num_heads"] = 16
+ dit_config["depth"] = count_blocks(state_dict_keys, '{}double_blocks.'.format(key_prefix) + '{}.')
+ dit_config["depth_single_blocks"] = count_blocks(state_dict_keys, '{}single_blocks.'.format(key_prefix) + '{}.')
+ dit_config["qkv_bias"] = True
+ dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
+ return dit_config
+
+ if '{}caption_projection.0.linear.weight'.format(key_prefix) in state_dict_keys: # HiDream
+ dit_config = {}
+ dit_config["image_model"] = "hidream"
+ dit_config["attention_head_dim"] = 128
+ dit_config["axes_dims_rope"] = [64, 32, 32]
+ dit_config["caption_channels"] = [4096, 4096]
+ dit_config["max_resolution"] = [128, 128]
+ dit_config["in_channels"] = 16
+ dit_config["llama_layers"] = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31]
+ dit_config["num_attention_heads"] = 20
+ dit_config["num_routed_experts"] = 4
+ dit_config["num_activated_experts"] = 2
+ dit_config["num_layers"] = 16
+ dit_config["num_single_layers"] = 32
+ dit_config["out_channels"] = 16
+ dit_config["patch_size"] = 2
+ dit_config["text_emb_dim"] = 2048
+ return dit_config
+
+ if '{}blocks.0.mlp.layer1.weight'.format(key_prefix) in state_dict_keys: # Cosmos predict2
+ dit_config = {}
+ dit_config["image_model"] = "cosmos_predict2"
+ dit_config["max_img_h"] = 240
+ dit_config["max_img_w"] = 240
+ dit_config["max_frames"] = 128
+ concat_padding_mask = True
+ dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
+ dit_config["out_channels"] = 16
+ dit_config["patch_spatial"] = 2
+ dit_config["patch_temporal"] = 1
+ dit_config["model_channels"] = state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[0]
+ dit_config["concat_padding_mask"] = concat_padding_mask
+ dit_config["crossattn_emb_channels"] = 1024
+ dit_config["pos_emb_cls"] = "rope3d"
+ dit_config["pos_emb_learnable"] = True
+ dit_config["pos_emb_interpolation"] = "crop"
+ dit_config["min_fps"] = 1
+ dit_config["max_fps"] = 30
+
+ dit_config["use_adaln_lora"] = True
+ dit_config["adaln_lora_dim"] = 256
+ if dit_config["model_channels"] == 2048:
+ dit_config["num_blocks"] = 28
+ dit_config["num_heads"] = 16
+ elif dit_config["model_channels"] == 5120:
+ dit_config["num_blocks"] = 36
+ dit_config["num_heads"] = 40
+
+ if dit_config["in_channels"] == 16:
+ dit_config["extra_per_block_abs_pos_emb"] = False
+ dit_config["rope_h_extrapolation_ratio"] = 4.0
+ dit_config["rope_w_extrapolation_ratio"] = 4.0
+ dit_config["rope_t_extrapolation_ratio"] = 1.0
+ elif dit_config["in_channels"] == 17: # img to video
+ if dit_config["model_channels"] == 2048:
+ dit_config["extra_per_block_abs_pos_emb"] = False
+ dit_config["rope_h_extrapolation_ratio"] = 3.0
+ dit_config["rope_w_extrapolation_ratio"] = 3.0
+ dit_config["rope_t_extrapolation_ratio"] = 1.0
+ elif dit_config["model_channels"] == 5120:
+ dit_config["rope_h_extrapolation_ratio"] = 2.0
+ dit_config["rope_w_extrapolation_ratio"] = 2.0
+ dit_config["rope_t_extrapolation_ratio"] = 0.8333333333333334
+
+ dit_config["extra_h_extrapolation_ratio"] = 1.0
+ dit_config["extra_w_extrapolation_ratio"] = 1.0
+ dit_config["extra_t_extrapolation_ratio"] = 1.0
+ dit_config["rope_enable_fps_modulation"] = False
+
+ return dit_config
+
+ if '{}time_caption_embed.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: # Omnigen2
+ dit_config = {}
+ dit_config["image_model"] = "omnigen2"
+ dit_config["axes_dim_rope"] = [40, 40, 40]
+ dit_config["axes_lens"] = [1024, 1664, 1664]
+ dit_config["ffn_dim_multiplier"] = None
+ dit_config["hidden_size"] = 2520
+ dit_config["in_channels"] = 16
+ dit_config["multiple_of"] = 256
+ dit_config["norm_eps"] = 1e-05
+ dit_config["num_attention_heads"] = 21
+ dit_config["num_kv_heads"] = 7
+ dit_config["num_layers"] = 32
+ dit_config["num_refiner_layers"] = 2
+ dit_config["out_channels"] = None
+ dit_config["patch_size"] = 2
+ dit_config["text_feat_dim"] = 2048
+ dit_config["timestep_scale"] = 1000.0
+ return dit_config
+
+ if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
+ return None
+
+ unet_config = {
+ "use_checkpoint": False,
+ "image_size": 32,
+ "use_spatial_transformer": True,
+ "legacy": False
+ }
+
+ y_input = '{}label_emb.0.0.weight'.format(key_prefix)
+ if y_input in state_dict_keys:
+ unet_config["num_classes"] = "sequential"
+ unet_config["adm_in_channels"] = state_dict[y_input].shape[1]
+ else:
+ unet_config["adm_in_channels"] = None
+
+ model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
+ in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
+
+ out_key = '{}out.2.weight'.format(key_prefix)
+ if out_key in state_dict:
+ out_channels = state_dict[out_key].shape[0]
+ else:
+ out_channels = 4
+
+ num_res_blocks = []
+ channel_mult = []
+ transformer_depth = []
+ transformer_depth_output = []
+ context_dim = None
+ use_linear_in_transformer = False
+
+ video_model = False
+ video_model_cross = False
+
+ current_res = 1
+ count = 0
+
+ last_res_blocks = 0
+ last_channel_mult = 0
+
+ input_block_count = count_blocks(state_dict_keys, '{}input_blocks'.format(key_prefix) + '.{}.')
+ for count in range(input_block_count):
+ prefix = '{}input_blocks.{}.'.format(key_prefix, count)
+ prefix_output = '{}output_blocks.{}.'.format(key_prefix, input_block_count - count - 1)
+
+ block_keys = sorted(list(filter(lambda a: a.startswith(prefix), state_dict_keys)))
+ if len(block_keys) == 0:
+ break
+
+ block_keys_output = sorted(list(filter(lambda a: a.startswith(prefix_output), state_dict_keys)))
+
+ if "{}0.op.weight".format(prefix) in block_keys: #new layer
+ num_res_blocks.append(last_res_blocks)
+ channel_mult.append(last_channel_mult)
+
+ current_res *= 2
+ last_res_blocks = 0
+ last_channel_mult = 0
+ out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth_output.append(out[0])
+ else:
+ transformer_depth_output.append(0)
+ else:
+ res_block_prefix = "{}0.in_layers.0.weight".format(prefix)
+ if res_block_prefix in block_keys:
+ last_res_blocks += 1
+ last_channel_mult = state_dict["{}0.out_layers.3.weight".format(prefix)].shape[0] // model_channels
+
+ out = calculate_transformer_depth(prefix, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth.append(out[0])
+ if context_dim is None:
+ context_dim = out[1]
+ use_linear_in_transformer = out[2]
+ video_model = out[3]
+ video_model_cross = out[4]
+ else:
+ transformer_depth.append(0)
+
+ res_block_prefix = "{}0.in_layers.0.weight".format(prefix_output)
+ if res_block_prefix in block_keys_output:
+ out = calculate_transformer_depth(prefix_output, state_dict_keys, state_dict)
+ if out is not None:
+ transformer_depth_output.append(out[0])
+ else:
+ transformer_depth_output.append(0)
+
+
+ num_res_blocks.append(last_res_blocks)
+ channel_mult.append(last_channel_mult)
+ if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
+ transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
+ elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
+ transformer_depth_middle = -1
+ else:
+ transformer_depth_middle = -2
+
+ unet_config["in_channels"] = in_channels
+ unet_config["out_channels"] = out_channels
+ unet_config["model_channels"] = model_channels
+ unet_config["num_res_blocks"] = num_res_blocks
+ unet_config["transformer_depth"] = transformer_depth
+ unet_config["transformer_depth_output"] = transformer_depth_output
+ unet_config["channel_mult"] = channel_mult
+ unet_config["transformer_depth_middle"] = transformer_depth_middle
+ unet_config['use_linear_in_transformer'] = use_linear_in_transformer
+ unet_config["context_dim"] = context_dim
+
+ if video_model:
+ unet_config["extra_ff_mix_layer"] = True
+ unet_config["use_spatial_context"] = True
+ unet_config["merge_strategy"] = "learned_with_images"
+ unet_config["merge_factor"] = 0.0
+ unet_config["video_kernel_size"] = [3, 1, 1]
+ unet_config["use_temporal_resblock"] = True
+ unet_config["use_temporal_attention"] = True
+ unet_config["disable_temporal_crossattention"] = not video_model_cross
+ else:
+ unet_config["use_temporal_resblock"] = False
+ unet_config["use_temporal_attention"] = False
+
+ return unet_config
+
+def model_config_from_unet_config(unet_config, state_dict=None):
+ for model_config in comfy.supported_models.models:
+ if model_config.matches(unet_config, state_dict):
+ return model_config(unet_config)
+
+ logging.error("no match {}".format(unet_config))
+ return None
+
+def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False, metadata=None):
+ unet_config = detect_unet_config(state_dict, unet_key_prefix, metadata=metadata)
+ if unet_config is None:
+ return None
+ model_config = model_config_from_unet_config(unet_config, state_dict)
+ if model_config is None and use_base_if_no_match:
+ model_config = comfy.supported_models_base.BASE(unet_config)
+
+ scaled_fp8_key = "{}scaled_fp8".format(unet_key_prefix)
+ if scaled_fp8_key in state_dict:
+ scaled_fp8_weight = state_dict.pop(scaled_fp8_key)
+ model_config.scaled_fp8 = scaled_fp8_weight.dtype
+ if model_config.scaled_fp8 == torch.float32:
+ model_config.scaled_fp8 = torch.float8_e4m3fn
+ if scaled_fp8_weight.nelement() == 2:
+ model_config.optimizations["fp8"] = False
+ else:
+ model_config.optimizations["fp8"] = True
+
+ return model_config
+
+def unet_prefix_from_state_dict(state_dict):
+ candidates = ["model.diffusion_model.", #ldm/sgm models
+ "model.model.", #audio models
+ "net.", #cosmos
+ ]
+ counts = {k: 0 for k in candidates}
+ for k in state_dict:
+ for c in candidates:
+ if k.startswith(c):
+ counts[c] += 1
+ break
+
+ top = max(counts, key=counts.get)
+ if counts[top] > 5:
+ return top
+ else:
+ return "model." #aura flow and others
+
+
+def convert_config(unet_config):
+ new_config = unet_config.copy()
+ num_res_blocks = new_config.get("num_res_blocks", None)
+ channel_mult = new_config.get("channel_mult", None)
+
+ if isinstance(num_res_blocks, int):
+ num_res_blocks = len(channel_mult) * [num_res_blocks]
+
+ if "attention_resolutions" in new_config:
+ attention_resolutions = new_config.pop("attention_resolutions")
+ transformer_depth = new_config.get("transformer_depth", None)
+ transformer_depth_middle = new_config.get("transformer_depth_middle", None)
+
+ if isinstance(transformer_depth, int):
+ transformer_depth = len(channel_mult) * [transformer_depth]
+ if transformer_depth_middle is None:
+ transformer_depth_middle = transformer_depth[-1]
+ t_in = []
+ t_out = []
+ s = 1
+ for i in range(len(num_res_blocks)):
+ res = num_res_blocks[i]
+ d = 0
+ if s in attention_resolutions:
+ d = transformer_depth[i]
+
+ t_in += [d] * res
+ t_out += [d] * (res + 1)
+ s *= 2
+ transformer_depth = t_in
+ new_config["transformer_depth"] = t_in
+ new_config["transformer_depth_output"] = t_out
+ new_config["transformer_depth_middle"] = transformer_depth_middle
+
+ new_config["num_res_blocks"] = num_res_blocks
+ return new_config
+
+
+def unet_config_from_diffusers_unet(state_dict, dtype=None):
+ if "conv_in.weight" not in state_dict:
+ return None
+
+ match = {}
+ transformer_depth = []
+
+ attn_res = 1
+ down_blocks = count_blocks(state_dict, "down_blocks.{}")
+ for i in range(down_blocks):
+ attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
+ res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
+ for ab in range(attn_blocks):
+ transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
+ transformer_depth.append(transformer_count)
+ if transformer_count > 0:
+ match["context_dim"] = state_dict["down_blocks.{}.attentions.{}.transformer_blocks.0.attn2.to_k.weight".format(i, ab)].shape[1]
+
+ attn_res *= 2
+ if attn_blocks == 0:
+ for i in range(res_blocks):
+ transformer_depth.append(0)
+
+ match["transformer_depth"] = transformer_depth
+
+ match["model_channels"] = state_dict["conv_in.weight"].shape[0]
+ match["in_channels"] = state_dict["conv_in.weight"].shape[1]
+ match["adm_in_channels"] = None
+ if "class_embedding.linear_1.weight" in state_dict:
+ match["adm_in_channels"] = state_dict["class_embedding.linear_1.weight"].shape[1]
+ elif "add_embedding.linear_1.weight" in state_dict:
+ match["adm_in_channels"] = state_dict["add_embedding.linear_1.weight"].shape[1]
+
+ SDXL = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_refiner = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2560, 'dtype': dtype, 'in_channels': 4, 'model_channels': 384,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [0, 0, 4, 4, 4, 4, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 4,
+ 'use_linear_in_transformer': True, 'context_dim': 1280, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 4, 4, 4, 4, 4, 4, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2],
+ 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True,
+ 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21_uncliph = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2048, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD21_unclipl = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 1536, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0], 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD15 = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
+ 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+ 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
+ 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_mid_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 1,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_small_cnet = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 0, 0, 0, 0], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 0,
+ 'use_linear_in_transformer': True, 'num_head_channels': 64, 'context_dim': 1, 'transformer_depth_output': [0, 0, 0, 0, 0, 0, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 9, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+ 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ Segmind_Vega = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 1, 1, 2, 2], 'transformer_depth_output': [0, 0, 0, 1, 1, 1, 2, 2, 2],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+ 'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
+ 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+ 'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
+ 'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
+
+ SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+ 'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+ 'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
+ 'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
+ 'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+ 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
+ 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ LotusD = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': 4,
+ 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
+ 'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': True, 'context_dim': 1024, 'num_heads': 8,
+ 'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
+ 'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+ supported_models = [LotusD, SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]
+
+ for unet_config in supported_models:
+ matches = True
+ for k in match:
+ if match[k] != unet_config[k]:
+ matches = False
+ break
+ if matches:
+ return convert_config(unet_config)
+ return None
+
+def model_config_from_diffusers_unet(state_dict):
+ unet_config = unet_config_from_diffusers_unet(state_dict)
+ if unet_config is not None:
+ return model_config_from_unet_config(unet_config)
+ return None
+
+def convert_diffusers_mmdit(state_dict, output_prefix=""):
+ out_sd = {}
+
+ if 'joint_transformer_blocks.0.attn.add_k_proj.weight' in state_dict: #AuraFlow
+ num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
+ num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+ sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+ elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
+ num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+ sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
+ elif 'x_embedder.weight' in state_dict: #Flux
+ depth = count_blocks(state_dict, 'transformer_blocks.{}.')
+ depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')
+ hidden_size = state_dict["x_embedder.bias"].shape[0]
+ sd_map = comfy.utils.flux_to_diffusers({"depth": depth, "depth_single_blocks": depth_single_blocks, "hidden_size": hidden_size}, output_prefix=output_prefix)
+ elif 'transformer_blocks.0.attn.add_q_proj.weight' in state_dict: #SD3
+ num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+ depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
+ sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
+ else:
+ return None
+
+ for k in sd_map:
+ weight = state_dict.get(k, None)
+ if weight is not None:
+ t = sd_map[k]
+
+ if not isinstance(t, str):
+ if len(t) > 2:
+ fun = t[2]
+ else:
+ fun = lambda a: a
+ offset = t[1]
+ if offset is not None:
+ old_weight = out_sd.get(t[0], None)
+ if old_weight is None:
+ old_weight = torch.empty_like(weight)
+ if old_weight.shape[offset[0]] < offset[1] + offset[2]:
+ exp = list(weight.shape)
+ exp[offset[0]] = offset[1] + offset[2]
+ new = torch.empty(exp, device=weight.device, dtype=weight.dtype)
+ new[:old_weight.shape[0]] = old_weight
+ old_weight = new
+
+ w = old_weight.narrow(offset[0], offset[1], offset[2])
+ else:
+ old_weight = weight
+ w = weight
+ w[:] = fun(weight)
+ t = t[0]
+ out_sd[t] = old_weight
+ else:
+ out_sd[t] = weight
+ state_dict.pop(k)
+
+ return out_sd
diff --git a/ComfyUI/comfy/model_patcher.py b/ComfyUI/comfy/model_patcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..52e76b5f3b999a951818162222c4f6d8647fd955
--- /dev/null
+++ b/ComfyUI/comfy/model_patcher.py
@@ -0,0 +1,1215 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+from __future__ import annotations
+
+import collections
+import copy
+import inspect
+import logging
+import math
+import uuid
+from typing import Callable, Optional
+
+import torch
+
+import comfy.float
+import comfy.hooks
+import comfy.lora
+import comfy.model_management
+import comfy.patcher_extension
+import comfy.utils
+from comfy.comfy_types import UnetWrapperFunction
+from comfy.patcher_extension import CallbacksMP, PatcherInjection, WrappersMP
+
+
+def string_to_seed(data):
+ crc = 0xFFFFFFFF
+ for byte in data:
+ if isinstance(byte, str):
+ byte = ord(byte)
+ crc ^= byte
+ for _ in range(8):
+ if crc & 1:
+ crc = (crc >> 1) ^ 0xEDB88320
+ else:
+ crc >>= 1
+ return crc ^ 0xFFFFFFFF
+
+def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
+ to = model_options["transformer_options"].copy()
+
+ if "patches_replace" not in to:
+ to["patches_replace"] = {}
+ else:
+ to["patches_replace"] = to["patches_replace"].copy()
+
+ if name not in to["patches_replace"]:
+ to["patches_replace"][name] = {}
+ else:
+ to["patches_replace"][name] = to["patches_replace"][name].copy()
+
+ if transformer_index is not None:
+ block = (block_name, number, transformer_index)
+ else:
+ block = (block_name, number)
+ to["patches_replace"][name][block] = patch
+ model_options["transformer_options"] = to
+ return model_options
+
+def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):
+ model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
+ if disable_cfg1_optimization:
+ model_options["disable_cfg1_optimization"] = True
+ return model_options
+
+def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_cfg1_optimization=False):
+ model_options["sampler_pre_cfg_function"] = model_options.get("sampler_pre_cfg_function", []) + [pre_cfg_function]
+ if disable_cfg1_optimization:
+ model_options["disable_cfg1_optimization"] = True
+ return model_options
+
+def create_model_options_clone(orig_model_options: dict):
+ return comfy.patcher_extension.copy_nested_dicts(orig_model_options)
+
+def create_hook_patches_clone(orig_hook_patches):
+ new_hook_patches = {}
+ for hook_ref in orig_hook_patches:
+ new_hook_patches[hook_ref] = {}
+ for k in orig_hook_patches[hook_ref]:
+ new_hook_patches[hook_ref][k] = orig_hook_patches[hook_ref][k][:]
+ return new_hook_patches
+
+def wipe_lowvram_weight(m):
+ if hasattr(m, "prev_comfy_cast_weights"):
+ m.comfy_cast_weights = m.prev_comfy_cast_weights
+ del m.prev_comfy_cast_weights
+
+ if hasattr(m, "weight_function"):
+ m.weight_function = []
+
+ if hasattr(m, "bias_function"):
+ m.bias_function = []
+
+def move_weight_functions(m, device):
+ if device is None:
+ return 0
+
+ memory = 0
+ if hasattr(m, "weight_function"):
+ for f in m.weight_function:
+ if hasattr(f, "move_to"):
+ memory += f.move_to(device=device)
+
+ if hasattr(m, "bias_function"):
+ for f in m.bias_function:
+ if hasattr(f, "move_to"):
+ memory += f.move_to(device=device)
+ return memory
+
+class LowVramPatch:
+ def __init__(self, key, patches):
+ self.key = key
+ self.patches = patches
+ def __call__(self, weight):
+ intermediate_dtype = weight.dtype
+ if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
+ intermediate_dtype = torch.float32
+ return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+
+ return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+
+def get_key_weight(model, key):
+ set_func = None
+ convert_func = None
+ op_keys = key.rsplit('.', 1)
+ if len(op_keys) < 2:
+ weight = comfy.utils.get_attr(model, key)
+ else:
+ op = comfy.utils.get_attr(model, op_keys[0])
+ try:
+ set_func = getattr(op, "set_{}".format(op_keys[1]))
+ except AttributeError:
+ pass
+
+ try:
+ convert_func = getattr(op, "convert_{}".format(op_keys[1]))
+ except AttributeError:
+ pass
+
+ weight = getattr(op, op_keys[1])
+ if convert_func is not None:
+ weight = comfy.utils.get_attr(model, key)
+
+ return weight, set_func, convert_func
+
+class AutoPatcherEjector:
+ def __init__(self, model: 'ModelPatcher', skip_and_inject_on_exit_only=False):
+ self.model = model
+ self.was_injected = False
+ self.prev_skip_injection = False
+ self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only
+
+ def __enter__(self):
+ self.was_injected = False
+ self.prev_skip_injection = self.model.skip_injection
+ if self.skip_and_inject_on_exit_only:
+ self.model.skip_injection = True
+ if self.model.is_injected:
+ self.model.eject_model()
+ self.was_injected = True
+
+ def __exit__(self, *args):
+ if self.skip_and_inject_on_exit_only:
+ self.model.skip_injection = self.prev_skip_injection
+ self.model.inject_model()
+ if self.was_injected and not self.model.skip_injection:
+ self.model.inject_model()
+ self.model.skip_injection = self.prev_skip_injection
+
+class MemoryCounter:
+ def __init__(self, initial: int, minimum=0):
+ self.value = initial
+ self.minimum = minimum
+ # TODO: add a safe limit besides 0
+
+ def use(self, weight: torch.Tensor):
+ weight_size = weight.nelement() * weight.element_size()
+ if self.is_useable(weight_size):
+ self.decrement(weight_size)
+ return True
+ return False
+
+ def is_useable(self, used: int):
+ return self.value - used > self.minimum
+
+ def decrement(self, used: int):
+ self.value -= used
+
+class ModelPatcher:
+ def __init__(self, model, load_device, offload_device, size=0, weight_inplace_update=False):
+ self.size = size
+ self.model = model
+ if not hasattr(self.model, 'device'):
+ logging.debug("Model doesn't have a device attribute.")
+ self.model.device = offload_device
+ elif self.model.device is None:
+ self.model.device = offload_device
+
+ self.patches = {}
+ self.backup = {}
+ self.object_patches = {}
+ self.object_patches_backup = {}
+ self.weight_wrapper_patches = {}
+ self.model_options = {"transformer_options":{}}
+ self.model_size()
+ self.load_device = load_device
+ self.offload_device = offload_device
+ self.weight_inplace_update = weight_inplace_update
+ self.force_cast_weights = False
+ self.patches_uuid = uuid.uuid4()
+ self.parent = None
+
+ self.attachments: dict[str] = {}
+ self.additional_models: dict[str, list[ModelPatcher]] = {}
+ self.callbacks: dict[str, dict[str, list[Callable]]] = CallbacksMP.init_callbacks()
+ self.wrappers: dict[str, dict[str, list[Callable]]] = WrappersMP.init_wrappers()
+
+ self.is_injected = False
+ self.skip_injection = False
+ self.injections: dict[str, list[PatcherInjection]] = {}
+
+ self.hook_patches: dict[comfy.hooks._HookRef] = {}
+ self.hook_patches_backup: dict[comfy.hooks._HookRef] = None
+ self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {}
+ self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {}
+ self.current_hooks: Optional[comfy.hooks.HookGroup] = None
+ self.forced_hooks: Optional[comfy.hooks.HookGroup] = None # NOTE: only used for CLIP at this time
+ self.is_clip = False
+ self.hook_mode = comfy.hooks.EnumHookMode.MaxSpeed
+
+ if not hasattr(self.model, 'model_loaded_weight_memory'):
+ self.model.model_loaded_weight_memory = 0
+
+ if not hasattr(self.model, 'lowvram_patch_counter'):
+ self.model.lowvram_patch_counter = 0
+
+ if not hasattr(self.model, 'model_lowvram'):
+ self.model.model_lowvram = False
+
+ if not hasattr(self.model, 'current_weight_patches_uuid'):
+ self.model.current_weight_patches_uuid = None
+
+ def model_size(self):
+ if self.size > 0:
+ return self.size
+ self.size = comfy.model_management.module_size(self.model)
+ return self.size
+
+ def loaded_size(self):
+ return self.model.model_loaded_weight_memory
+
+ def lowvram_patch_counter(self):
+ return self.model.lowvram_patch_counter
+
+ def clone(self):
+ n = self.__class__(self.model, self.load_device, self.offload_device, self.size, weight_inplace_update=self.weight_inplace_update)
+ n.patches = {}
+ for k in self.patches:
+ n.patches[k] = self.patches[k][:]
+ n.patches_uuid = self.patches_uuid
+
+ n.object_patches = self.object_patches.copy()
+ n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
+ n.model_options = copy.deepcopy(self.model_options)
+ n.backup = self.backup
+ n.object_patches_backup = self.object_patches_backup
+ n.parent = self
+
+ n.force_cast_weights = self.force_cast_weights
+
+ # attachments
+ n.attachments = {}
+ for k in self.attachments:
+ if hasattr(self.attachments[k], "on_model_patcher_clone"):
+ n.attachments[k] = self.attachments[k].on_model_patcher_clone()
+ else:
+ n.attachments[k] = self.attachments[k]
+ # additional models
+ for k, c in self.additional_models.items():
+ n.additional_models[k] = [x.clone() for x in c]
+ # callbacks
+ for k, c in self.callbacks.items():
+ n.callbacks[k] = {}
+ for k1, c1 in c.items():
+ n.callbacks[k][k1] = c1.copy()
+ # sample wrappers
+ for k, w in self.wrappers.items():
+ n.wrappers[k] = {}
+ for k1, w1 in w.items():
+ n.wrappers[k][k1] = w1.copy()
+ # injection
+ n.is_injected = self.is_injected
+ n.skip_injection = self.skip_injection
+ for k, i in self.injections.items():
+ n.injections[k] = i.copy()
+ # hooks
+ n.hook_patches = create_hook_patches_clone(self.hook_patches)
+ n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup
+ for group in self.cached_hook_patches:
+ n.cached_hook_patches[group] = {}
+ for k in self.cached_hook_patches[group]:
+ n.cached_hook_patches[group][k] = self.cached_hook_patches[group][k]
+ n.hook_backup = self.hook_backup
+ n.current_hooks = self.current_hooks.clone() if self.current_hooks else self.current_hooks
+ n.forced_hooks = self.forced_hooks.clone() if self.forced_hooks else self.forced_hooks
+ n.is_clip = self.is_clip
+ n.hook_mode = self.hook_mode
+
+ for callback in self.get_all_callbacks(CallbacksMP.ON_CLONE):
+ callback(self, n)
+ return n
+
+ def is_clone(self, other):
+ if hasattr(other, 'model') and self.model is other.model:
+ return True
+ return False
+
+ def clone_has_same_weights(self, clone: 'ModelPatcher'):
+ if not self.is_clone(clone):
+ return False
+
+ if self.current_hooks != clone.current_hooks:
+ return False
+ if self.forced_hooks != clone.forced_hooks:
+ return False
+ if self.hook_patches.keys() != clone.hook_patches.keys():
+ return False
+ if self.attachments.keys() != clone.attachments.keys():
+ return False
+ if self.additional_models.keys() != clone.additional_models.keys():
+ return False
+ for key in self.callbacks:
+ if len(self.callbacks[key]) != len(clone.callbacks[key]):
+ return False
+ for key in self.wrappers:
+ if len(self.wrappers[key]) != len(clone.wrappers[key]):
+ return False
+ if self.injections.keys() != clone.injections.keys():
+ return False
+
+ if len(self.patches) == 0 and len(clone.patches) == 0:
+ return True
+
+ if self.patches_uuid == clone.patches_uuid:
+ if len(self.patches) != len(clone.patches):
+ logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.")
+ else:
+ return True
+
+ def memory_required(self, input_shape):
+ return self.model.memory_required(input_shape=input_shape)
+
+ def set_model_sampler_cfg_function(self, sampler_cfg_function, disable_cfg1_optimization=False):
+ if len(inspect.signature(sampler_cfg_function).parameters) == 3:
+ self.model_options["sampler_cfg_function"] = lambda args: sampler_cfg_function(args["cond"], args["uncond"], args["cond_scale"]) #Old way
+ else:
+ self.model_options["sampler_cfg_function"] = sampler_cfg_function
+ if disable_cfg1_optimization:
+ self.model_options["disable_cfg1_optimization"] = True
+
+ def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
+ self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
+
+ def set_model_sampler_pre_cfg_function(self, pre_cfg_function, disable_cfg1_optimization=False):
+ self.model_options = set_model_options_pre_cfg_function(self.model_options, pre_cfg_function, disable_cfg1_optimization)
+
+ def set_model_sampler_calc_cond_batch_function(self, sampler_calc_cond_batch_function):
+ self.model_options["sampler_calc_cond_batch_function"] = sampler_calc_cond_batch_function
+
+ def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
+ self.model_options["model_function_wrapper"] = unet_wrapper_function
+
+ def set_model_denoise_mask_function(self, denoise_mask_function):
+ self.model_options["denoise_mask_function"] = denoise_mask_function
+
+ def set_model_patch(self, patch, name):
+ to = self.model_options["transformer_options"]
+ if "patches" not in to:
+ to["patches"] = {}
+ to["patches"][name] = to["patches"].get(name, []) + [patch]
+
+ def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
+ self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)
+
+ def set_model_attn1_patch(self, patch):
+ self.set_model_patch(patch, "attn1_patch")
+
+ def set_model_attn2_patch(self, patch):
+ self.set_model_patch(patch, "attn2_patch")
+
+ def set_model_attn1_replace(self, patch, block_name, number, transformer_index=None):
+ self.set_model_patch_replace(patch, "attn1", block_name, number, transformer_index)
+
+ def set_model_attn2_replace(self, patch, block_name, number, transformer_index=None):
+ self.set_model_patch_replace(patch, "attn2", block_name, number, transformer_index)
+
+ def set_model_attn1_output_patch(self, patch):
+ self.set_model_patch(patch, "attn1_output_patch")
+
+ def set_model_attn2_output_patch(self, patch):
+ self.set_model_patch(patch, "attn2_output_patch")
+
+ def set_model_input_block_patch(self, patch):
+ self.set_model_patch(patch, "input_block_patch")
+
+ def set_model_input_block_patch_after_skip(self, patch):
+ self.set_model_patch(patch, "input_block_patch_after_skip")
+
+ def set_model_output_block_patch(self, patch):
+ self.set_model_patch(patch, "output_block_patch")
+
+ def set_model_emb_patch(self, patch):
+ self.set_model_patch(patch, "emb_patch")
+
+ def set_model_forward_timestep_embed_patch(self, patch):
+ self.set_model_patch(patch, "forward_timestep_embed_patch")
+
+ def add_object_patch(self, name, obj):
+ self.object_patches[name] = obj
+
+ def set_model_compute_dtype(self, dtype):
+ self.add_object_patch("manual_cast_dtype", dtype)
+ if dtype is not None:
+ self.force_cast_weights = True
+ self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this
+
+ def add_weight_wrapper(self, name, function):
+ self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]
+ self.patches_uuid = uuid.uuid4()
+
+ def get_model_object(self, name: str) -> torch.nn.Module:
+ """Retrieves a nested attribute from an object using dot notation considering
+ object patches.
+
+ Args:
+ name (str): The attribute path using dot notation (e.g. "model.layer.weight")
+
+ Returns:
+ The value of the requested attribute
+
+ Example:
+ patcher = ModelPatcher()
+ weight = patcher.get_model_object("layer1.conv.weight")
+ """
+ if name in self.object_patches:
+ return self.object_patches[name]
+ else:
+ if name in self.object_patches_backup:
+ return self.object_patches_backup[name]
+ else:
+ return comfy.utils.get_attr(self.model, name)
+
+ def model_patches_to(self, device):
+ to = self.model_options["transformer_options"]
+ if "patches" in to:
+ patches = to["patches"]
+ for name in patches:
+ patch_list = patches[name]
+ for i in range(len(patch_list)):
+ if hasattr(patch_list[i], "to"):
+ patch_list[i] = patch_list[i].to(device)
+ if "patches_replace" in to:
+ patches = to["patches_replace"]
+ for name in patches:
+ patch_list = patches[name]
+ for k in patch_list:
+ if hasattr(patch_list[k], "to"):
+ patch_list[k] = patch_list[k].to(device)
+ if "model_function_wrapper" in self.model_options:
+ wrap_func = self.model_options["model_function_wrapper"]
+ if hasattr(wrap_func, "to"):
+ self.model_options["model_function_wrapper"] = wrap_func.to(device)
+
+ def model_dtype(self):
+ if hasattr(self.model, "get_dtype"):
+ return self.model.get_dtype()
+
+ def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
+ with self.use_ejected():
+ p = set()
+ model_sd = self.model.state_dict()
+ for k in patches:
+ offset = None
+ function = None
+ if isinstance(k, str):
+ key = k
+ else:
+ offset = k[1]
+ key = k[0]
+ if len(k) > 2:
+ function = k[2]
+
+ if key in model_sd:
+ p.add(k)
+ current_patches = self.patches.get(key, [])
+ current_patches.append((strength_patch, patches[k], strength_model, offset, function))
+ self.patches[key] = current_patches
+
+ self.patches_uuid = uuid.uuid4()
+ return list(p)
+
+ def get_key_patches(self, filter_prefix=None):
+ model_sd = self.model_state_dict()
+ p = {}
+ for k in model_sd:
+ if filter_prefix is not None:
+ if not k.startswith(filter_prefix):
+ continue
+ bk = self.backup.get(k, None)
+ hbk = self.hook_backup.get(k, None)
+ weight, set_func, convert_func = get_key_weight(self.model, k)
+ if bk is not None:
+ weight = bk.weight
+ if hbk is not None:
+ weight = hbk[0]
+ if convert_func is None:
+ convert_func = lambda a, **kwargs: a
+
+ if k in self.patches:
+ p[k] = [(weight, convert_func)] + self.patches[k]
+ else:
+ p[k] = [(weight, convert_func)]
+ return p
+
+ def model_state_dict(self, filter_prefix=None):
+ with self.use_ejected():
+ sd = self.model.state_dict()
+ keys = list(sd.keys())
+ if filter_prefix is not None:
+ for k in keys:
+ if not k.startswith(filter_prefix):
+ sd.pop(k)
+ return sd
+
+ def patch_weight_to_device(self, key, device_to=None, inplace_update=False):
+ if key not in self.patches:
+ return
+
+ weight, set_func, convert_func = get_key_weight(self.model, key)
+ inplace_update = self.weight_inplace_update or inplace_update
+
+ if key not in self.backup:
+ self.backup[key] = collections.namedtuple('Dimension', ['weight', 'inplace_update'])(weight.to(device=self.offload_device, copy=inplace_update), inplace_update)
+
+ if device_to is not None:
+ temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+ else:
+ temp_weight = weight.to(torch.float32, copy=True)
+ if convert_func is not None:
+ temp_weight = convert_func(temp_weight, inplace=True)
+
+ out_weight = comfy.lora.calculate_weight(self.patches[key], temp_weight, key)
+ if set_func is None:
+ out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
+ if inplace_update:
+ comfy.utils.copy_to_param(self.model, key, out_weight)
+ else:
+ comfy.utils.set_attr_param(self.model, key, out_weight)
+ else:
+ set_func(out_weight, inplace_update=inplace_update, seed=string_to_seed(key))
+
+ def _load_list(self):
+ loading = []
+ for n, m in self.model.named_modules():
+ params = []
+ skip = False
+ for name, param in m.named_parameters(recurse=False):
+ params.append(name)
+ for name, param in m.named_parameters(recurse=True):
+ if name not in params:
+ skip = True # skip random weights in non leaf modules
+ break
+ if not skip and (hasattr(m, "comfy_cast_weights") or len(params) > 0):
+ loading.append((comfy.model_management.module_size(m), n, m, params))
+ return loading
+
+ def load(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False, full_load=False):
+ with self.use_ejected():
+ self.unpatch_hooks()
+ mem_counter = 0
+ patch_counter = 0
+ lowvram_counter = 0
+ loading = self._load_list()
+
+ load_completely = []
+ loading.sort(reverse=True)
+ for x in loading:
+ n = x[1]
+ m = x[2]
+ params = x[3]
+ module_mem = x[0]
+
+ lowvram_weight = False
+
+ weight_key = "{}.weight".format(n)
+ bias_key = "{}.bias".format(n)
+
+ if not full_load and hasattr(m, "comfy_cast_weights"):
+ if mem_counter + module_mem >= lowvram_model_memory:
+ lowvram_weight = True
+ lowvram_counter += 1
+ if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
+ continue
+
+ cast_weight = self.force_cast_weights
+ if lowvram_weight:
+ if hasattr(m, "comfy_cast_weights"):
+ m.weight_function = []
+ m.bias_function = []
+
+ if weight_key in self.patches:
+ if force_patch_weights:
+ self.patch_weight_to_device(weight_key)
+ else:
+ m.weight_function = [LowVramPatch(weight_key, self.patches)]
+ patch_counter += 1
+ if bias_key in self.patches:
+ if force_patch_weights:
+ self.patch_weight_to_device(bias_key)
+ else:
+ m.bias_function = [LowVramPatch(bias_key, self.patches)]
+ patch_counter += 1
+
+ cast_weight = True
+ else:
+ if hasattr(m, "comfy_cast_weights"):
+ wipe_lowvram_weight(m)
+
+ if full_load or mem_counter + module_mem < lowvram_model_memory:
+ mem_counter += module_mem
+ load_completely.append((module_mem, n, m, params))
+
+ if cast_weight and hasattr(m, "comfy_cast_weights"):
+ m.prev_comfy_cast_weights = m.comfy_cast_weights
+ m.comfy_cast_weights = True
+
+ if weight_key in self.weight_wrapper_patches:
+ m.weight_function.extend(self.weight_wrapper_patches[weight_key])
+
+ if bias_key in self.weight_wrapper_patches:
+ m.bias_function.extend(self.weight_wrapper_patches[bias_key])
+
+ mem_counter += move_weight_functions(m, device_to)
+
+ load_completely.sort(reverse=True)
+ for x in load_completely:
+ n = x[1]
+ m = x[2]
+ params = x[3]
+ if hasattr(m, "comfy_patched_weights"):
+ if m.comfy_patched_weights == True:
+ continue
+
+ for param in params:
+ self.patch_weight_to_device("{}.{}".format(n, param), device_to=device_to)
+
+ logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
+ m.comfy_patched_weights = True
+
+ for x in load_completely:
+ x[2].to(device_to)
+
+ if lowvram_counter > 0:
+ logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+ self.model.model_lowvram = True
+ else:
+ logging.info("loaded completely {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), full_load))
+ self.model.model_lowvram = False
+ if full_load:
+ self.model.to(device_to)
+ mem_counter = self.model_size()
+
+ self.model.lowvram_patch_counter += patch_counter
+ self.model.device = device_to
+ self.model.model_loaded_weight_memory = mem_counter
+ self.model.current_weight_patches_uuid = self.patches_uuid
+
+ for callback in self.get_all_callbacks(CallbacksMP.ON_LOAD):
+ callback(self, device_to, lowvram_model_memory, force_patch_weights, full_load)
+
+ self.apply_hooks(self.forced_hooks, force_apply=True)
+
+ def patch_model(self, device_to=None, lowvram_model_memory=0, load_weights=True, force_patch_weights=False):
+ with self.use_ejected():
+ for k in self.object_patches:
+ old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
+ if k not in self.object_patches_backup:
+ self.object_patches_backup[k] = old
+
+ if lowvram_model_memory == 0:
+ full_load = True
+ else:
+ full_load = False
+
+ if load_weights:
+ self.load(device_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights, full_load=full_load)
+ self.inject_model()
+ return self.model
+
+ def unpatch_model(self, device_to=None, unpatch_weights=True):
+ self.eject_model()
+ if unpatch_weights:
+ self.unpatch_hooks()
+ if self.model.model_lowvram:
+ for m in self.model.modules():
+ move_weight_functions(m, device_to)
+ wipe_lowvram_weight(m)
+
+ self.model.model_lowvram = False
+ self.model.lowvram_patch_counter = 0
+
+ keys = list(self.backup.keys())
+
+ for k in keys:
+ bk = self.backup[k]
+ if bk.inplace_update:
+ comfy.utils.copy_to_param(self.model, k, bk.weight)
+ else:
+ comfy.utils.set_attr_param(self.model, k, bk.weight)
+
+ self.model.current_weight_patches_uuid = None
+ self.backup.clear()
+
+ if device_to is not None:
+ self.model.to(device_to)
+ self.model.device = device_to
+ self.model.model_loaded_weight_memory = 0
+
+ for m in self.model.modules():
+ if hasattr(m, "comfy_patched_weights"):
+ del m.comfy_patched_weights
+
+ keys = list(self.object_patches_backup.keys())
+ for k in keys:
+ comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
+
+ self.object_patches_backup.clear()
+
+ def partially_unload(self, device_to, memory_to_free=0):
+ with self.use_ejected():
+ hooks_unpatched = False
+ memory_freed = 0
+ patch_counter = 0
+ unload_list = self._load_list()
+ unload_list.sort()
+ for unload in unload_list:
+ if memory_to_free < memory_freed:
+ break
+ module_mem = unload[0]
+ n = unload[1]
+ m = unload[2]
+ params = unload[3]
+
+ lowvram_possible = hasattr(m, "comfy_cast_weights")
+ if hasattr(m, "comfy_patched_weights") and m.comfy_patched_weights == True:
+ move_weight = True
+ for param in params:
+ key = "{}.{}".format(n, param)
+ bk = self.backup.get(key, None)
+ if bk is not None:
+ if not lowvram_possible:
+ move_weight = False
+ break
+
+ if not hooks_unpatched:
+ self.unpatch_hooks()
+ hooks_unpatched = True
+
+ if bk.inplace_update:
+ comfy.utils.copy_to_param(self.model, key, bk.weight)
+ else:
+ comfy.utils.set_attr_param(self.model, key, bk.weight)
+ self.backup.pop(key)
+
+ weight_key = "{}.weight".format(n)
+ bias_key = "{}.bias".format(n)
+ if move_weight:
+ cast_weight = self.force_cast_weights
+ m.to(device_to)
+ module_mem += move_weight_functions(m, device_to)
+ if lowvram_possible:
+ if weight_key in self.patches:
+ m.weight_function.append(LowVramPatch(weight_key, self.patches))
+ patch_counter += 1
+ if bias_key in self.patches:
+ m.bias_function.append(LowVramPatch(bias_key, self.patches))
+ patch_counter += 1
+ cast_weight = True
+
+ if cast_weight:
+ m.prev_comfy_cast_weights = m.comfy_cast_weights
+ m.comfy_cast_weights = True
+ m.comfy_patched_weights = False
+ memory_freed += module_mem
+ logging.debug("freed {}".format(n))
+
+ self.model.model_lowvram = True
+ self.model.lowvram_patch_counter += patch_counter
+ self.model.model_loaded_weight_memory -= memory_freed
+ return memory_freed
+
+ def partially_load(self, device_to, extra_memory=0, force_patch_weights=False):
+ with self.use_ejected(skip_and_inject_on_exit_only=True):
+ unpatch_weights = self.model.current_weight_patches_uuid is not None and (self.model.current_weight_patches_uuid != self.patches_uuid or force_patch_weights)
+ # TODO: force_patch_weights should not unload + reload full model
+ used = self.model.model_loaded_weight_memory
+ self.unpatch_model(self.offload_device, unpatch_weights=unpatch_weights)
+ if unpatch_weights:
+ extra_memory += (used - self.model.model_loaded_weight_memory)
+
+ self.patch_model(load_weights=False)
+ full_load = False
+ if self.model.model_lowvram == False and self.model.model_loaded_weight_memory > 0:
+ self.apply_hooks(self.forced_hooks, force_apply=True)
+ return 0
+ if self.model.model_loaded_weight_memory + extra_memory > self.model_size():
+ full_load = True
+ current_used = self.model.model_loaded_weight_memory
+ try:
+ self.load(device_to, lowvram_model_memory=current_used + extra_memory, force_patch_weights=force_patch_weights, full_load=full_load)
+ except Exception as e:
+ self.detach()
+ raise e
+
+ return self.model.model_loaded_weight_memory - current_used
+
+ def detach(self, unpatch_all=True):
+ self.eject_model()
+ self.model_patches_to(self.offload_device)
+ if unpatch_all:
+ self.unpatch_model(self.offload_device, unpatch_weights=unpatch_all)
+ for callback in self.get_all_callbacks(CallbacksMP.ON_DETACH):
+ callback(self, unpatch_all)
+ return self.model
+
+ def current_loaded_device(self):
+ return self.model.device
+
+ def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
+ logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
+ return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
+
+ def cleanup(self):
+ self.clean_hooks()
+ if hasattr(self.model, "current_patcher"):
+ self.model.current_patcher = None
+ for callback in self.get_all_callbacks(CallbacksMP.ON_CLEANUP):
+ callback(self)
+
+ def add_callback(self, call_type: str, callback: Callable):
+ self.add_callback_with_key(call_type, None, callback)
+
+ def add_callback_with_key(self, call_type: str, key: str, callback: Callable):
+ c = self.callbacks.setdefault(call_type, {}).setdefault(key, [])
+ c.append(callback)
+
+ def remove_callbacks_with_key(self, call_type: str, key: str):
+ c = self.callbacks.get(call_type, {})
+ if key in c:
+ c.pop(key)
+
+ def get_callbacks(self, call_type: str, key: str):
+ return self.callbacks.get(call_type, {}).get(key, [])
+
+ def get_all_callbacks(self, call_type: str):
+ c_list = []
+ for c in self.callbacks.get(call_type, {}).values():
+ c_list.extend(c)
+ return c_list
+
+ def add_wrapper(self, wrapper_type: str, wrapper: Callable):
+ self.add_wrapper_with_key(wrapper_type, None, wrapper)
+
+ def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable):
+ w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, [])
+ w.append(wrapper)
+
+ def remove_wrappers_with_key(self, wrapper_type: str, key: str):
+ w = self.wrappers.get(wrapper_type, {})
+ if key in w:
+ w.pop(key)
+
+ def get_wrappers(self, wrapper_type: str, key: str):
+ return self.wrappers.get(wrapper_type, {}).get(key, [])
+
+ def get_all_wrappers(self, wrapper_type: str):
+ w_list = []
+ for w in self.wrappers.get(wrapper_type, {}).values():
+ w_list.extend(w)
+ return w_list
+
+ def set_attachments(self, key: str, attachment):
+ self.attachments[key] = attachment
+
+ def remove_attachments(self, key: str):
+ if key in self.attachments:
+ self.attachments.pop(key)
+
+ def get_attachment(self, key: str):
+ return self.attachments.get(key, None)
+
+ def set_injections(self, key: str, injections: list[PatcherInjection]):
+ self.injections[key] = injections
+
+ def remove_injections(self, key: str):
+ if key in self.injections:
+ self.injections.pop(key)
+
+ def get_injections(self, key: str):
+ return self.injections.get(key, None)
+
+ def set_additional_models(self, key: str, models: list['ModelPatcher']):
+ self.additional_models[key] = models
+
+ def remove_additional_models(self, key: str):
+ if key in self.additional_models:
+ self.additional_models.pop(key)
+
+ def get_additional_models_with_key(self, key: str):
+ return self.additional_models.get(key, [])
+
+ def get_additional_models(self):
+ all_models = []
+ for models in self.additional_models.values():
+ all_models.extend(models)
+ return all_models
+
+ def get_nested_additional_models(self):
+ def _evaluate_sub_additional_models(prev_models: list[ModelPatcher], cache_set: set[ModelPatcher]):
+ '''Make sure circular references do not cause infinite recursion.'''
+ next_models = []
+ for model in prev_models:
+ candidates = model.get_additional_models()
+ for c in candidates:
+ if c not in cache_set:
+ next_models.append(c)
+ cache_set.add(c)
+ if len(next_models) == 0:
+ return prev_models
+ return prev_models + _evaluate_sub_additional_models(next_models, cache_set)
+
+ all_models = self.get_additional_models()
+ models_set = set(all_models)
+ real_all_models = _evaluate_sub_additional_models(prev_models=all_models, cache_set=models_set)
+ return real_all_models
+
+ def use_ejected(self, skip_and_inject_on_exit_only=False):
+ return AutoPatcherEjector(self, skip_and_inject_on_exit_only=skip_and_inject_on_exit_only)
+
+ def inject_model(self):
+ if self.is_injected or self.skip_injection:
+ return
+ for injections in self.injections.values():
+ for inj in injections:
+ inj.inject(self)
+ self.is_injected = True
+ if self.is_injected:
+ for callback in self.get_all_callbacks(CallbacksMP.ON_INJECT_MODEL):
+ callback(self)
+
+ def eject_model(self):
+ if not self.is_injected:
+ return
+ for injections in self.injections.values():
+ for inj in injections:
+ inj.eject(self)
+ self.is_injected = False
+ for callback in self.get_all_callbacks(CallbacksMP.ON_EJECT_MODEL):
+ callback(self)
+
+ def pre_run(self):
+ if hasattr(self.model, "current_patcher"):
+ self.model.current_patcher = self
+ for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):
+ callback(self)
+
+ def prepare_state(self, timestep):
+ for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):
+ callback(self, timestep)
+
+ def restore_hook_patches(self):
+ if self.hook_patches_backup is not None:
+ self.hook_patches = self.hook_patches_backup
+ self.hook_patches_backup = None
+
+ def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode):
+ self.hook_mode = hook_mode
+
+ def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):
+ curr_t = t[0]
+ reset_current_hooks = False
+ transformer_options = model_options.get("transformer_options", {})
+ for hook in hook_group.hooks:
+ changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)
+ # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;
+ # this will cause the weights to be recalculated when sampling
+ if changed:
+ # reset current_hooks if contains hook that changed
+ if self.current_hooks is not None:
+ for current_hook in self.current_hooks.hooks:
+ if current_hook == hook:
+ reset_current_hooks = True
+ break
+ for cached_group in list(self.cached_hook_patches.keys()):
+ if cached_group.contains(hook):
+ self.cached_hook_patches.pop(cached_group)
+ if reset_current_hooks:
+ self.patch_hooks(None)
+
+ def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,
+ registered: comfy.hooks.HookGroup = None):
+ self.restore_hook_patches()
+ if registered is None:
+ registered = comfy.hooks.HookGroup()
+ # handle WeightHooks
+ weight_hooks_to_register: list[comfy.hooks.WeightHook] = []
+ for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight):
+ if hook.hook_ref not in self.hook_patches:
+ weight_hooks_to_register.append(hook)
+ else:
+ registered.add(hook)
+ if len(weight_hooks_to_register) > 0:
+ # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state
+ self.hook_patches_backup = create_hook_patches_clone(self.hook_patches)
+ for hook in weight_hooks_to_register:
+ hook.add_hook_patches(self, model_options, target_dict, registered)
+ for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES):
+ callback(self, hooks, target_dict, model_options, registered)
+ return registered
+
+ def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0):
+ with self.use_ejected():
+ # NOTE: this mirrors behavior of add_patches func
+ current_hook_patches: dict[str,list] = self.hook_patches.get(hook.hook_ref, {})
+ p = set()
+ model_sd = self.model.state_dict()
+ for k in patches:
+ offset = None
+ function = None
+ if isinstance(k, str):
+ key = k
+ else:
+ offset = k[1]
+ key = k[0]
+ if len(k) > 2:
+ function = k[2]
+
+ if key in model_sd:
+ p.add(k)
+ current_patches: list[tuple] = current_hook_patches.get(key, [])
+ current_patches.append((strength_patch, patches[k], strength_model, offset, function))
+ current_hook_patches[key] = current_patches
+ self.hook_patches[hook.hook_ref] = current_hook_patches
+ # since should care about these patches too to determine if same model, reroll patches_uuid
+ self.patches_uuid = uuid.uuid4()
+ return list(p)
+
+ def get_combined_hook_patches(self, hooks: comfy.hooks.HookGroup):
+ # combined_patches will contain weights of all relevant hooks, per key
+ combined_patches = {}
+ if hooks is not None:
+ for hook in hooks.hooks:
+ hook_patches: dict = self.hook_patches.get(hook.hook_ref, {})
+ for key in hook_patches.keys():
+ current_patches: list[tuple] = combined_patches.get(key, [])
+ if math.isclose(hook.strength, 1.0):
+ current_patches.extend(hook_patches[key])
+ else:
+ # patches are stored as tuples: (strength_patch, (tuple_with_weights,), strength_model)
+ for patch in hook_patches[key]:
+ new_patch = list(patch)
+ new_patch[0] *= hook.strength
+ current_patches.append(tuple(new_patch))
+ combined_patches[key] = current_patches
+ return combined_patches
+
+ def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False):
+ # TODO: return transformer_options dict with any additions from hooks
+ if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)):
+ return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)
+ self.patch_hooks(hooks=hooks)
+ for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS):
+ callback(self, hooks)
+ return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)
+
+ def patch_hooks(self, hooks: comfy.hooks.HookGroup):
+ with self.use_ejected():
+ if hooks is not None:
+ model_sd_keys = list(self.model_state_dict().keys())
+ memory_counter = None
+ if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:
+ # TODO: minimum_counter should have a minimum that conforms to loaded model requirements
+ memory_counter = MemoryCounter(initial=comfy.model_management.get_free_memory(self.load_device),
+ minimum=comfy.model_management.minimum_inference_memory()*2)
+ # if have cached weights for hooks, use it
+ cached_weights = self.cached_hook_patches.get(hooks, None)
+ if cached_weights is not None:
+ model_sd_keys_set = set(model_sd_keys)
+ for key in cached_weights:
+ if key not in model_sd_keys:
+ logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
+ continue
+ self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)
+ model_sd_keys_set.remove(key)
+ self.unpatch_hooks(model_sd_keys_set)
+ else:
+ self.unpatch_hooks()
+ relevant_patches = self.get_combined_hook_patches(hooks=hooks)
+ original_weights = None
+ if len(relevant_patches) > 0:
+ original_weights = self.get_key_patches()
+ for key in relevant_patches:
+ if key not in model_sd_keys:
+ logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}")
+ continue
+ self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,
+ memory_counter=memory_counter)
+ else:
+ self.unpatch_hooks()
+ self.current_hooks = hooks
+
+ def patch_cached_hook_weights(self, cached_weights: dict, key: str, memory_counter: MemoryCounter):
+ if key not in self.hook_backup:
+ weight: torch.Tensor = comfy.utils.get_attr(self.model, key)
+ target_device = self.offload_device
+ if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:
+ used = memory_counter.use(weight)
+ if used:
+ target_device = weight.device
+ self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device)
+ comfy.utils.copy_to_param(self.model, key, cached_weights[key][0].to(device=cached_weights[key][1]))
+
+ def clear_cached_hook_weights(self):
+ self.cached_hook_patches.clear()
+ self.patch_hooks(None)
+
+ def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter):
+ if key not in combined_patches:
+ return
+
+ weight, set_func, convert_func = get_key_weight(self.model, key)
+ weight: torch.Tensor
+ if key not in self.hook_backup:
+ target_device = self.offload_device
+ if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:
+ used = memory_counter.use(weight)
+ if used:
+ target_device = weight.device
+ self.hook_backup[key] = (weight.to(device=target_device, copy=True), weight.device)
+ # TODO: properly handle LowVramPatch, if it ends up an issue
+ temp_weight = comfy.model_management.cast_to_device(weight, weight.device, torch.float32, copy=True)
+ if convert_func is not None:
+ temp_weight = convert_func(temp_weight, inplace=True)
+
+ out_weight = comfy.lora.calculate_weight(combined_patches[key],
+ temp_weight,
+ key, original_weights=original_weights)
+ del original_weights[key]
+ if set_func is None:
+ out_weight = comfy.float.stochastic_rounding(out_weight, weight.dtype, seed=string_to_seed(key))
+ comfy.utils.copy_to_param(self.model, key, out_weight)
+ else:
+ set_func(out_weight, inplace_update=True, seed=string_to_seed(key))
+ if self.hook_mode == comfy.hooks.EnumHookMode.MaxSpeed:
+ # TODO: disable caching if not enough system RAM to do so
+ target_device = self.offload_device
+ used = memory_counter.use(weight)
+ if used:
+ target_device = weight.device
+ self.cached_hook_patches.setdefault(hooks, {})
+ self.cached_hook_patches[hooks][key] = (out_weight.to(device=target_device, copy=False), weight.device)
+ del temp_weight
+ del out_weight
+ del weight
+
+ def unpatch_hooks(self, whitelist_keys_set: set[str]=None) -> None:
+ with self.use_ejected():
+ if len(self.hook_backup) == 0:
+ self.current_hooks = None
+ return
+ keys = list(self.hook_backup.keys())
+ if whitelist_keys_set:
+ for k in keys:
+ if k in whitelist_keys_set:
+ comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
+ self.hook_backup.pop(k)
+ else:
+ for k in keys:
+ comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
+
+ self.hook_backup.clear()
+ self.current_hooks = None
+
+ def clean_hooks(self):
+ self.unpatch_hooks()
+ self.clear_cached_hook_weights()
+
+ def __del__(self):
+ self.detach(unpatch_all=False)
+
diff --git a/ComfyUI/comfy/ops.py b/ComfyUI/comfy/ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cc9bbc27e76d1fe016a4150cc3e66471547b987
--- /dev/null
+++ b/ComfyUI/comfy/ops.py
@@ -0,0 +1,441 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Stability AI
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+import logging
+import comfy.model_management
+from comfy.cli_args import args, PerformanceFeature
+import comfy.float
+import comfy.rmsnorm
+import contextlib
+
+cast_to = comfy.model_management.cast_to #TODO: remove once no more references
+
+def cast_to_input(weight, input, non_blocking=False, copy=True):
+ return comfy.model_management.cast_to(weight, input.dtype, input.device, non_blocking=non_blocking, copy=copy)
+
+def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
+ if input is not None:
+ if dtype is None:
+ dtype = input.dtype
+ if bias_dtype is None:
+ bias_dtype = dtype
+ if device is None:
+ device = input.device
+
+ offload_stream = comfy.model_management.get_offload_stream(device)
+ if offload_stream is not None:
+ wf_context = offload_stream
+ else:
+ wf_context = contextlib.nullcontext()
+
+ bias = None
+ non_blocking = comfy.model_management.device_supports_non_blocking(device)
+ if s.bias is not None:
+ has_function = len(s.bias_function) > 0
+ bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
+
+ if has_function:
+ with wf_context:
+ for f in s.bias_function:
+ bias = f(bias)
+
+ has_function = len(s.weight_function) > 0
+ weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function, stream=offload_stream)
+ if has_function:
+ with wf_context:
+ for f in s.weight_function:
+ weight = f(weight)
+
+ comfy.model_management.sync_stream(device, offload_stream)
+ return weight, bias
+
+class CastWeightBiasOp:
+ comfy_cast_weights = False
+ weight_function = []
+ bias_function = []
+
+class disable_weight_init:
+ class Linear(torch.nn.Linear, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.linear(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return self._conv_forward(input, weight, bias)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ if self.weight is not None:
+ weight, bias = cast_bias_weight(self, input)
+ else:
+ weight = None
+ bias = None
+ return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class RMSNorm(comfy.rmsnorm.RMSNorm, CastWeightBiasOp):
+ def reset_parameters(self):
+ self.bias = None
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ if self.weight is not None:
+ weight, bias = cast_bias_weight(self, input)
+ else:
+ weight = None
+ return comfy.rmsnorm.rms_norm(input, weight, self.eps) # TODO: switch to commented out line when old torch is deprecated
+ # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input, output_size=None):
+ num_spatial_dims = 2
+ output_padding = self._output_padding(
+ input, output_size, self.stride, self.padding, self.kernel_size,
+ num_spatial_dims, self.dilation)
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.conv_transpose2d(
+ input, weight, bias, self.stride, self.padding,
+ output_padding, self.groups, self.dilation)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input, output_size=None):
+ num_spatial_dims = 1
+ output_padding = self._output_padding(
+ input, output_size, self.stride, self.padding, self.kernel_size,
+ num_spatial_dims, self.dilation)
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.conv_transpose1d(
+ input, weight, bias, self.stride, self.padding,
+ output_padding, self.groups, self.dilation)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ return super().forward(*args, **kwargs)
+
+ class Embedding(torch.nn.Embedding, CastWeightBiasOp):
+ def reset_parameters(self):
+ self.bias = None
+ return None
+
+ def forward_comfy_cast_weights(self, input, out_dtype=None):
+ output_dtype = out_dtype
+ if self.weight.dtype == torch.float16 or self.weight.dtype == torch.bfloat16:
+ out_dtype = None
+ weight, bias = cast_bias_weight(self, device=input.device, dtype=out_dtype)
+ return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
+
+ def forward(self, *args, **kwargs):
+ if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
+ return self.forward_comfy_cast_weights(*args, **kwargs)
+ else:
+ if "out_dtype" in kwargs:
+ kwargs.pop("out_dtype")
+ return super().forward(*args, **kwargs)
+
+ @classmethod
+ def conv_nd(s, dims, *args, **kwargs):
+ if dims == 2:
+ return s.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return s.Conv3d(*args, **kwargs)
+ else:
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class manual_cast(disable_weight_init):
+ class Linear(disable_weight_init.Linear):
+ comfy_cast_weights = True
+
+ class Conv1d(disable_weight_init.Conv1d):
+ comfy_cast_weights = True
+
+ class Conv2d(disable_weight_init.Conv2d):
+ comfy_cast_weights = True
+
+ class Conv3d(disable_weight_init.Conv3d):
+ comfy_cast_weights = True
+
+ class GroupNorm(disable_weight_init.GroupNorm):
+ comfy_cast_weights = True
+
+ class LayerNorm(disable_weight_init.LayerNorm):
+ comfy_cast_weights = True
+
+ class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
+ comfy_cast_weights = True
+
+ class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
+ comfy_cast_weights = True
+
+ class RMSNorm(disable_weight_init.RMSNorm):
+ comfy_cast_weights = True
+
+ class Embedding(disable_weight_init.Embedding):
+ comfy_cast_weights = True
+
+
+def fp8_linear(self, input):
+ dtype = self.weight.dtype
+ if dtype not in [torch.float8_e4m3fn]:
+ return None
+
+ tensor_2d = False
+ if len(input.shape) == 2:
+ tensor_2d = True
+ input = input.unsqueeze(1)
+
+ input_shape = input.shape
+ input_dtype = input.dtype
+ if len(input.shape) == 3:
+ w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
+ w = w.t()
+
+ scale_weight = self.scale_weight
+ scale_input = self.scale_input
+ if scale_weight is None:
+ scale_weight = torch.ones((), device=input.device, dtype=torch.float32)
+ else:
+ scale_weight = scale_weight.to(input.device)
+
+ if scale_input is None:
+ scale_input = torch.ones((), device=input.device, dtype=torch.float32)
+ input = torch.clamp(input, min=-448, max=448, out=input)
+ input = input.reshape(-1, input_shape[2]).to(dtype).contiguous()
+ else:
+ scale_input = scale_input.to(input.device)
+ input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype).contiguous()
+
+ if bias is not None:
+ o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
+ else:
+ o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight)
+
+ if isinstance(o, tuple):
+ o = o[0]
+
+ if tensor_2d:
+ return o.reshape(input_shape[0], -1)
+
+ return o.reshape((-1, input_shape[1], self.weight.shape[0]))
+
+ return None
+
+class fp8_ops(manual_cast):
+ class Linear(manual_cast.Linear):
+ def reset_parameters(self):
+ self.scale_weight = None
+ self.scale_input = None
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ try:
+ out = fp8_linear(self, input)
+ if out is not None:
+ return out
+ except Exception as e:
+ logging.info("Exception during fp8 op: {}".format(e))
+
+ weight, bias = cast_bias_weight(self, input)
+ return torch.nn.functional.linear(input, weight, bias)
+
+def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None):
+ logging.info("Using scaled fp8: fp8 matrix mult: {}, scale input: {}".format(fp8_matrix_mult, scale_input))
+ class scaled_fp8_op(manual_cast):
+ class Linear(manual_cast.Linear):
+ def __init__(self, *args, **kwargs):
+ if override_dtype is not None:
+ kwargs['dtype'] = override_dtype
+ super().__init__(*args, **kwargs)
+
+ def reset_parameters(self):
+ if not hasattr(self, 'scale_weight'):
+ self.scale_weight = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+
+ if not scale_input:
+ self.scale_input = None
+
+ if not hasattr(self, 'scale_input'):
+ self.scale_input = torch.nn.parameter.Parameter(data=torch.ones((), device=self.weight.device, dtype=torch.float32), requires_grad=False)
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ if fp8_matrix_mult:
+ out = fp8_linear(self, input)
+ if out is not None:
+ return out
+
+ weight, bias = cast_bias_weight(self, input)
+
+ if weight.numel() < input.numel(): #TODO: optimize
+ return torch.nn.functional.linear(input, weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype), bias)
+ else:
+ return torch.nn.functional.linear(input * self.scale_weight.to(device=weight.device, dtype=weight.dtype), weight, bias)
+
+ def convert_weight(self, weight, inplace=False, **kwargs):
+ if inplace:
+ weight *= self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+ return weight
+ else:
+ return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
+
+ def set_weight(self, weight, inplace_update=False, seed=None, **kwargs):
+ weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+ if inplace_update:
+ self.weight.data.copy_(weight)
+ else:
+ self.weight = torch.nn.Parameter(weight, requires_grad=False)
+
+ return scaled_fp8_op
+
+CUBLAS_IS_AVAILABLE = False
+try:
+ from cublas_ops import CublasLinear
+ CUBLAS_IS_AVAILABLE = True
+except ImportError:
+ pass
+
+if CUBLAS_IS_AVAILABLE:
+ class cublas_ops(disable_weight_init):
+ class Linear(CublasLinear, disable_weight_init.Linear):
+ def reset_parameters(self):
+ return None
+
+ def forward_comfy_cast_weights(self, input):
+ return super().forward(input)
+
+ def forward(self, *args, **kwargs):
+ return super().forward(*args, **kwargs)
+
+def pick_operations(weight_dtype, compute_dtype, load_device=None, disable_fast_fp8=False, fp8_optimizations=False, scaled_fp8=None):
+ fp8_compute = comfy.model_management.supports_fp8_compute(load_device)
+ if scaled_fp8 is not None:
+ return scaled_fp8_ops(fp8_matrix_mult=fp8_compute and fp8_optimizations, scale_input=fp8_optimizations, override_dtype=scaled_fp8)
+
+ if (
+ fp8_compute and
+ (fp8_optimizations or PerformanceFeature.Fp8MatrixMultiplication in args.fast) and
+ not disable_fast_fp8
+ ):
+ return fp8_ops
+
+ if (
+ PerformanceFeature.CublasOps in args.fast and
+ CUBLAS_IS_AVAILABLE and
+ weight_dtype == torch.float16 and
+ (compute_dtype == torch.float16 or compute_dtype is None)
+ ):
+ logging.info("Using cublas ops")
+ return cublas_ops
+
+ if compute_dtype is None or weight_dtype == compute_dtype:
+ return disable_weight_init
+
+ return manual_cast
diff --git a/ComfyUI/comfy/patcher_extension.py b/ComfyUI/comfy/patcher_extension.py
new file mode 100644
index 0000000000000000000000000000000000000000..965958f4c994a3c656c1fe695f1c3ba55c6c0389
--- /dev/null
+++ b/ComfyUI/comfy/patcher_extension.py
@@ -0,0 +1,157 @@
+from __future__ import annotations
+from typing import Callable
+
+class CallbacksMP:
+ ON_CLONE = "on_clone"
+ ON_LOAD = "on_load_after"
+ ON_DETACH = "on_detach_after"
+ ON_CLEANUP = "on_cleanup"
+ ON_PRE_RUN = "on_pre_run"
+ ON_PREPARE_STATE = "on_prepare_state"
+ ON_APPLY_HOOKS = "on_apply_hooks"
+ ON_REGISTER_ALL_HOOK_PATCHES = "on_register_all_hook_patches"
+ ON_INJECT_MODEL = "on_inject_model"
+ ON_EJECT_MODEL = "on_eject_model"
+
+ # callbacks dict is in the format:
+ # {"call_type": {"key": [Callable1, Callable2, ...]} }
+ @classmethod
+ def init_callbacks(cls) -> dict[str, dict[str, list[Callable]]]:
+ return {}
+
+def add_callback(call_type: str, callback: Callable, transformer_options: dict, is_model_options=False):
+ add_callback_with_key(call_type, None, callback, transformer_options, is_model_options)
+
+def add_callback_with_key(call_type: str, key: str, callback: Callable, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.setdefault("transformer_options", {})
+ callbacks: dict[str, dict[str, list]] = transformer_options.setdefault("callbacks", {})
+ c = callbacks.setdefault(call_type, {}).setdefault(key, [])
+ c.append(callback)
+
+def get_callbacks_with_key(call_type: str, key: str, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.get("transformer_options", {})
+ c_list = []
+ callbacks: dict[str, list] = transformer_options.get("callbacks", {})
+ c_list.extend(callbacks.get(call_type, {}).get(key, []))
+ return c_list
+
+def get_all_callbacks(call_type: str, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.get("transformer_options", {})
+ c_list = []
+ callbacks: dict[str, list] = transformer_options.get("callbacks", {})
+ for c in callbacks.get(call_type, {}).values():
+ c_list.extend(c)
+ return c_list
+
+class WrappersMP:
+ OUTER_SAMPLE = "outer_sample"
+ PREPARE_SAMPLING = "prepare_sampling"
+ SAMPLER_SAMPLE = "sampler_sample"
+ CALC_COND_BATCH = "calc_cond_batch"
+ APPLY_MODEL = "apply_model"
+ DIFFUSION_MODEL = "diffusion_model"
+
+ # wrappers dict is in the format:
+ # {"wrapper_type": {"key": [Callable1, Callable2, ...]} }
+ @classmethod
+ def init_wrappers(cls) -> dict[str, dict[str, list[Callable]]]:
+ return {}
+
+def add_wrapper(wrapper_type: str, wrapper: Callable, transformer_options: dict, is_model_options=False):
+ add_wrapper_with_key(wrapper_type, None, wrapper, transformer_options, is_model_options)
+
+def add_wrapper_with_key(wrapper_type: str, key: str, wrapper: Callable, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.setdefault("transformer_options", {})
+ wrappers: dict[str, dict[str, list]] = transformer_options.setdefault("wrappers", {})
+ w = wrappers.setdefault(wrapper_type, {}).setdefault(key, [])
+ w.append(wrapper)
+
+def get_wrappers_with_key(wrapper_type: str, key: str, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.get("transformer_options", {})
+ w_list = []
+ wrappers: dict[str, list] = transformer_options.get("wrappers", {})
+ w_list.extend(wrappers.get(wrapper_type, {}).get(key, []))
+ return w_list
+
+def get_all_wrappers(wrapper_type: str, transformer_options: dict, is_model_options=False):
+ if is_model_options:
+ transformer_options = transformer_options.get("transformer_options", {})
+ w_list = []
+ wrappers: dict[str, list] = transformer_options.get("wrappers", {})
+ for w in wrappers.get(wrapper_type, {}).values():
+ w_list.extend(w)
+ return w_list
+
+class WrapperExecutor:
+ """Handles call stack of wrappers around a function in an ordered manner."""
+ def __init__(self, original: Callable, class_obj: object, wrappers: list[Callable], idx: int):
+ # NOTE: class_obj exists so that wrappers surrounding a class method can access
+ # the class instance at runtime via executor.class_obj
+ self.original = original
+ self.class_obj = class_obj
+ self.wrappers = wrappers.copy()
+ self.idx = idx
+ self.is_last = idx == len(wrappers)
+
+ def __call__(self, *args, **kwargs):
+ """Calls the next wrapper or original function, whichever is appropriate."""
+ new_executor = self._create_next_executor()
+ return new_executor.execute(*args, **kwargs)
+
+ def execute(self, *args, **kwargs):
+ """Used to initiate executor internally - DO NOT use this if you received executor in wrapper."""
+ args = list(args)
+ kwargs = dict(kwargs)
+ if self.is_last:
+ return self.original(*args, **kwargs)
+ return self.wrappers[self.idx](self, *args, **kwargs)
+
+ def _create_next_executor(self) -> 'WrapperExecutor':
+ new_idx = self.idx + 1
+ if new_idx > len(self.wrappers):
+ raise Exception("Wrapper idx exceeded available wrappers; something went very wrong.")
+ if self.class_obj is None:
+ return WrapperExecutor.new_executor(self.original, self.wrappers, new_idx)
+ return WrapperExecutor.new_class_executor(self.original, self.class_obj, self.wrappers, new_idx)
+
+ @classmethod
+ def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0):
+ return cls(original, class_obj=None, wrappers=wrappers, idx=idx)
+
+ @classmethod
+ def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0):
+ return cls(original, class_obj, wrappers, idx=idx)
+
+class PatcherInjection:
+ def __init__(self, inject: Callable, eject: Callable):
+ self.inject = inject
+ self.eject = eject
+
+def copy_nested_dicts(input_dict: dict):
+ new_dict = input_dict.copy()
+ for key, value in input_dict.items():
+ if isinstance(value, dict):
+ new_dict[key] = copy_nested_dicts(value)
+ elif isinstance(value, list):
+ new_dict[key] = value.copy()
+ return new_dict
+
+def merge_nested_dicts(dict1: dict, dict2: dict, copy_dict1=True):
+ if copy_dict1:
+ merged_dict = copy_nested_dicts(dict1)
+ else:
+ merged_dict = dict1
+ for key, value in dict2.items():
+ if isinstance(value, dict):
+ curr_value = merged_dict.setdefault(key, {})
+ merged_dict[key] = merge_nested_dicts(value, curr_value)
+ elif isinstance(value, list):
+ merged_dict.setdefault(key, []).extend(value)
+ else:
+ merged_dict[key] = value
+ return merged_dict
diff --git a/ComfyUI/comfy/sample.py b/ComfyUI/comfy/sample.py
new file mode 100644
index 0000000000000000000000000000000000000000..be5a7e246fdf6168cd74d1bb1b550c6852a83212
--- /dev/null
+++ b/ComfyUI/comfy/sample.py
@@ -0,0 +1,52 @@
+import torch
+import comfy.model_management
+import comfy.samplers
+import comfy.utils
+import numpy as np
+import logging
+
+def prepare_noise(latent_image, seed, noise_inds=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
+ """
+ generator = torch.manual_seed(seed)
+ if noise_inds is None:
+ return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
+
+ 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="cpu")
+ if i in unique_inds:
+ noises.append(noise)
+ noises = [noises[i] for i in inverse]
+ noises = torch.cat(noises, axis=0)
+ return noises
+
+def fix_empty_latent_channels(model, latent_image):
+ latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
+ if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
+ latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
+ if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
+ latent_image = latent_image.unsqueeze(2)
+ return latent_image
+
+def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
+ logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
+ return model, positive, negative, noise_mask, []
+
+def cleanup_additional_models(models):
+ logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
+
+def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
+ sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
+
+ samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+ return samples
+
+def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
+ samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+ samples = samples.to(comfy.model_management.intermediate_device())
+ return samples
diff --git a/ComfyUI/comfy/samplers.py b/ComfyUI/comfy/samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..e93d2a315c8e45161c964d08f54e94f78c4a9b6e
--- /dev/null
+++ b/ComfyUI/comfy/samplers.py
@@ -0,0 +1,1143 @@
+from __future__ import annotations
+from .k_diffusion import sampling as k_diffusion_sampling
+from .extra_samplers import uni_pc
+from typing import TYPE_CHECKING, Callable, NamedTuple
+if TYPE_CHECKING:
+ from comfy.model_patcher import ModelPatcher
+ from comfy.model_base import BaseModel
+ from comfy.controlnet import ControlBase
+import torch
+from functools import partial
+import collections
+from comfy import model_management
+import math
+import logging
+import comfy.sampler_helpers
+import comfy.model_patcher
+import comfy.patcher_extension
+import comfy.hooks
+import scipy.stats
+import numpy
+
+
+def add_area_dims(area, num_dims):
+ while (len(area) // 2) < num_dims:
+ area = [2147483648] + area[:len(area) // 2] + [0] + area[len(area) // 2:]
+ return area
+
+def get_area_and_mult(conds, x_in, timestep_in):
+ dims = tuple(x_in.shape[2:])
+ area = None
+ strength = 1.0
+
+ if 'timestep_start' in conds:
+ timestep_start = conds['timestep_start']
+ if timestep_in[0] > timestep_start:
+ return None
+ if 'timestep_end' in conds:
+ timestep_end = conds['timestep_end']
+ if timestep_in[0] < timestep_end:
+ return None
+ if 'area' in conds:
+ area = list(conds['area'])
+ area = add_area_dims(area, len(dims))
+ if (len(area) // 2) > len(dims):
+ area = area[:len(dims)] + area[len(area) // 2:(len(area) // 2) + len(dims)]
+
+ if 'strength' in conds:
+ strength = conds['strength']
+
+ input_x = x_in
+ if area is not None:
+ for i in range(len(dims)):
+ area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
+ input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
+
+ if 'mask' in conds:
+ # Scale the mask to the size of the input
+ # The mask should have been resized as we began the sampling process
+ mask_strength = 1.0
+ if "mask_strength" in conds:
+ mask_strength = conds["mask_strength"]
+ mask = conds['mask']
+ assert (mask.shape[1:] == x_in.shape[2:])
+
+ mask = mask[:input_x.shape[0]]
+ if area is not None:
+ for i in range(len(dims)):
+ mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
+
+ mask = mask * mask_strength
+ mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
+ else:
+ mask = torch.ones_like(input_x)
+ mult = mask * strength
+
+ if 'mask' not in conds and area is not None:
+ fuzz = 8
+ for i in range(len(dims)):
+ rr = min(fuzz, mult.shape[2 + i] // 4)
+ if area[len(dims) + i] != 0:
+ for t in range(rr):
+ m = mult.narrow(i + 2, t, 1)
+ m *= ((1.0 / rr) * (t + 1))
+ if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
+ for t in range(rr):
+ m = mult.narrow(i + 2, area[i] - 1 - t, 1)
+ m *= ((1.0 / rr) * (t + 1))
+
+ conditioning = {}
+ model_conds = conds["model_conds"]
+ for c in model_conds:
+ conditioning[c] = model_conds[c].process_cond(batch_size=x_in.shape[0], device=x_in.device, area=area)
+
+ hooks = conds.get('hooks', None)
+ control = conds.get('control', None)
+
+ patches = None
+ if 'gligen' in conds:
+ gligen = conds['gligen']
+ patches = {}
+ gligen_type = gligen[0]
+ gligen_model = gligen[1]
+ if gligen_type == "position":
+ gligen_patch = gligen_model.model.set_position(input_x.shape, gligen[2], input_x.device)
+ else:
+ gligen_patch = gligen_model.model.set_empty(input_x.shape, input_x.device)
+
+ patches['middle_patch'] = [gligen_patch]
+
+ cond_obj = collections.namedtuple('cond_obj', ['input_x', 'mult', 'conditioning', 'area', 'control', 'patches', 'uuid', 'hooks'])
+ return cond_obj(input_x, mult, conditioning, area, control, patches, conds['uuid'], hooks)
+
+def cond_equal_size(c1, c2):
+ if c1 is c2:
+ return True
+ if c1.keys() != c2.keys():
+ return False
+ for k in c1:
+ if not c1[k].can_concat(c2[k]):
+ return False
+ return True
+
+def can_concat_cond(c1, c2):
+ if c1.input_x.shape != c2.input_x.shape:
+ return False
+
+ def objects_concatable(obj1, obj2):
+ if (obj1 is None) != (obj2 is None):
+ return False
+ if obj1 is not None:
+ if obj1 is not obj2:
+ return False
+ return True
+
+ if not objects_concatable(c1.control, c2.control):
+ return False
+
+ if not objects_concatable(c1.patches, c2.patches):
+ return False
+
+ return cond_equal_size(c1.conditioning, c2.conditioning)
+
+def cond_cat(c_list):
+ temp = {}
+ for x in c_list:
+ for k in x:
+ cur = temp.get(k, [])
+ cur.append(x[k])
+ temp[k] = cur
+
+ out = {}
+ for k in temp:
+ conds = temp[k]
+ out[k] = conds[0].concat(conds[1:])
+
+ return out
+
+def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options):
+ # need to figure out remaining unmasked area for conds
+ default_mults = []
+ for _ in default_conds:
+ default_mults.append(torch.ones_like(x_in))
+ # look through each finalized cond in hooked_to_run for 'mult' and subtract it from each cond
+ for lora_hooks, to_run in hooked_to_run.items():
+ for cond_obj, i in to_run:
+ # if no default_cond for cond_type, do nothing
+ if len(default_conds[i]) == 0:
+ continue
+ area: list[int] = cond_obj.area
+ if area is not None:
+ curr_default_mult: torch.Tensor = default_mults[i]
+ dims = len(area) // 2
+ for i in range(dims):
+ curr_default_mult = curr_default_mult.narrow(i + 2, area[i + dims], area[i])
+ curr_default_mult -= cond_obj.mult
+ else:
+ default_mults[i] -= cond_obj.mult
+ # for each default_mult, ReLU to make negatives=0, and then check for any nonzeros
+ for i, mult in enumerate(default_mults):
+ # if no default_cond for cond type, do nothing
+ if len(default_conds[i]) == 0:
+ continue
+ torch.nn.functional.relu(mult, inplace=True)
+ # if mult is all zeros, then don't add default_cond
+ if torch.max(mult) == 0.0:
+ continue
+
+ cond = default_conds[i]
+ for x in cond:
+ # do get_area_and_mult to get all the expected values
+ p = get_area_and_mult(x, x_in, timestep)
+ if p is None:
+ continue
+ # replace p's mult with calculated mult
+ p = p._replace(mult=mult)
+ if p.hooks is not None:
+ model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
+ hooked_to_run.setdefault(p.hooks, list())
+ hooked_to_run[p.hooks] += [(p, i)]
+
+def calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
+ executor = comfy.patcher_extension.WrapperExecutor.new_executor(
+ _calc_cond_batch,
+ comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.CALC_COND_BATCH, model_options, is_model_options=True)
+ )
+ return executor.execute(model, conds, x_in, timestep, model_options)
+
+def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Tensor, timestep, model_options):
+ out_conds = []
+ out_counts = []
+ # separate conds by matching hooks
+ hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]] = {}
+ default_conds = []
+ has_default_conds = False
+
+ for i in range(len(conds)):
+ out_conds.append(torch.zeros_like(x_in))
+ out_counts.append(torch.ones_like(x_in) * 1e-37)
+
+ cond = conds[i]
+ default_c = []
+ if cond is not None:
+ for x in cond:
+ if 'default' in x:
+ default_c.append(x)
+ has_default_conds = True
+ continue
+ p = get_area_and_mult(x, x_in, timestep)
+ if p is None:
+ continue
+ if p.hooks is not None:
+ model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
+ hooked_to_run.setdefault(p.hooks, list())
+ hooked_to_run[p.hooks] += [(p, i)]
+ default_conds.append(default_c)
+
+ if has_default_conds:
+ finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
+
+ model.current_patcher.prepare_state(timestep)
+
+ # run every hooked_to_run separately
+ for hooks, to_run in hooked_to_run.items():
+ while len(to_run) > 0:
+ first = to_run[0]
+ first_shape = first[0][0].shape
+ to_batch_temp = []
+ for x in range(len(to_run)):
+ if can_concat_cond(to_run[x][0], first[0]):
+ to_batch_temp += [x]
+
+ to_batch_temp.reverse()
+ to_batch = to_batch_temp[:1]
+
+ free_memory = model_management.get_free_memory(x_in.device)
+ for i in range(1, len(to_batch_temp) + 1):
+ batch_amount = to_batch_temp[:len(to_batch_temp)//i]
+ input_shape = [len(batch_amount) * first_shape[0]] + list(first_shape)[1:]
+ cond_shapes = collections.defaultdict(list)
+ for tt in batch_amount:
+ cond = {k: v.size() for k, v in to_run[tt][0].conditioning.items()}
+ for k, v in to_run[tt][0].conditioning.items():
+ cond_shapes[k].append(v.size())
+
+ if model.memory_required(input_shape, cond_shapes=cond_shapes) * 1.5 < free_memory:
+ to_batch = batch_amount
+ break
+
+ input_x = []
+ mult = []
+ c = []
+ cond_or_uncond = []
+ uuids = []
+ area = []
+ control = None
+ patches = None
+ for x in to_batch:
+ o = to_run.pop(x)
+ p = o[0]
+ input_x.append(p.input_x)
+ mult.append(p.mult)
+ c.append(p.conditioning)
+ area.append(p.area)
+ cond_or_uncond.append(o[1])
+ uuids.append(p.uuid)
+ control = p.control
+ patches = p.patches
+
+ batch_chunks = len(cond_or_uncond)
+ input_x = torch.cat(input_x)
+ c = cond_cat(c)
+ timestep_ = torch.cat([timestep] * batch_chunks)
+
+ transformer_options = model.current_patcher.apply_hooks(hooks=hooks)
+ if 'transformer_options' in model_options:
+ transformer_options = comfy.patcher_extension.merge_nested_dicts(transformer_options,
+ model_options['transformer_options'],
+ copy_dict1=False)
+
+ if patches is not None:
+ # TODO: replace with merge_nested_dicts function
+ if "patches" in transformer_options:
+ cur_patches = transformer_options["patches"].copy()
+ for p in patches:
+ if p in cur_patches:
+ cur_patches[p] = cur_patches[p] + patches[p]
+ else:
+ cur_patches[p] = patches[p]
+ transformer_options["patches"] = cur_patches
+ else:
+ transformer_options["patches"] = patches
+
+ transformer_options["cond_or_uncond"] = cond_or_uncond[:]
+ transformer_options["uuids"] = uuids[:]
+ transformer_options["sigmas"] = timestep
+
+ c['transformer_options'] = transformer_options
+
+ if control is not None:
+ c['control'] = control.get_control(input_x, timestep_, c, len(cond_or_uncond), transformer_options)
+
+ if 'model_function_wrapper' in model_options:
+ output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
+ else:
+ output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
+
+ for o in range(batch_chunks):
+ cond_index = cond_or_uncond[o]
+ a = area[o]
+ if a is None:
+ out_conds[cond_index] += output[o] * mult[o]
+ out_counts[cond_index] += mult[o]
+ else:
+ out_c = out_conds[cond_index]
+ out_cts = out_counts[cond_index]
+ dims = len(a) // 2
+ for i in range(dims):
+ out_c = out_c.narrow(i + 2, a[i + dims], a[i])
+ out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
+ out_c += output[o] * mult[o]
+ out_cts += mult[o]
+
+ for i in range(len(out_conds)):
+ out_conds[i] /= out_counts[i]
+
+ return out_conds
+
+def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
+ logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
+ return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
+
+def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
+ if "sampler_cfg_function" in model_options:
+ args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+ "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+ cfg_result = x - model_options["sampler_cfg_function"](args)
+ else:
+ cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+ for fn in model_options.get("sampler_post_cfg_function", []):
+ args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "cond_scale": cond_scale, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+ "sigma": timestep, "model_options": model_options, "input": x}
+ cfg_result = fn(args)
+
+ return cfg_result
+
+#The main sampling function shared by all the samplers
+#Returns denoised
+def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
+ if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+ uncond_ = None
+ else:
+ uncond_ = uncond
+
+ conds = [cond, uncond_]
+ if "sampler_calc_cond_batch_function" in model_options:
+ args = {"conds": conds, "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+ out = model_options["sampler_calc_cond_batch_function"](args)
+ else:
+ out = calc_cond_batch(model, conds, x, timestep, model_options)
+
+ for fn in model_options.get("sampler_pre_cfg_function", []):
+ args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
+ "input": x, "sigma": timestep, "model": model, "model_options": model_options}
+ out = fn(args)
+
+ return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
+
+
+class KSamplerX0Inpaint:
+ def __init__(self, model, sigmas):
+ self.inner_model = model
+ self.sigmas = sigmas
+ def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
+ if denoise_mask is not None:
+ if "denoise_mask_function" in model_options:
+ denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
+ latent_mask = 1. - denoise_mask
+ x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask
+ out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
+ if denoise_mask is not None:
+ out = out * denoise_mask + self.latent_image * latent_mask
+ return out
+
+def simple_scheduler(model_sampling, steps):
+ s = model_sampling
+ sigs = []
+ ss = len(s.sigmas) / steps
+ for x in range(steps):
+ sigs += [float(s.sigmas[-(1 + int(x * ss))])]
+ sigs += [0.0]
+ return torch.FloatTensor(sigs)
+
+def ddim_scheduler(model_sampling, steps):
+ s = model_sampling
+ sigs = []
+ x = 1
+ if math.isclose(float(s.sigmas[x]), 0, abs_tol=0.00001):
+ steps += 1
+ sigs = []
+ else:
+ sigs = [0.0]
+
+ ss = max(len(s.sigmas) // steps, 1)
+ while x < len(s.sigmas):
+ sigs += [float(s.sigmas[x])]
+ x += ss
+ sigs = sigs[::-1]
+ return torch.FloatTensor(sigs)
+
+def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
+ s = model_sampling
+ start = s.timestep(s.sigma_max)
+ end = s.timestep(s.sigma_min)
+
+ append_zero = True
+ if sgm:
+ timesteps = torch.linspace(start, end, steps + 1)[:-1]
+ else:
+ if math.isclose(float(s.sigma(end)), 0, abs_tol=0.00001):
+ steps += 1
+ append_zero = False
+ timesteps = torch.linspace(start, end, steps)
+
+ sigs = []
+ for x in range(len(timesteps)):
+ ts = timesteps[x]
+ sigs.append(float(s.sigma(ts)))
+
+ if append_zero:
+ sigs += [0.0]
+
+ return torch.FloatTensor(sigs)
+
+# Implemented based on: https://arxiv.org/abs/2407.12173
+def beta_scheduler(model_sampling, steps, alpha=0.6, beta=0.6):
+ total_timesteps = (len(model_sampling.sigmas) - 1)
+ ts = 1 - numpy.linspace(0, 1, steps, endpoint=False)
+ ts = numpy.rint(scipy.stats.beta.ppf(ts, alpha, beta) * total_timesteps)
+
+ sigs = []
+ last_t = -1
+ for t in ts:
+ if t != last_t:
+ sigs += [float(model_sampling.sigmas[int(t)])]
+ last_t = t
+ sigs += [0.0]
+ return torch.FloatTensor(sigs)
+
+# from: https://github.com/genmoai/models/blob/main/src/mochi_preview/infer.py#L41
+def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, linear_steps=None):
+ if steps == 1:
+ sigma_schedule = [1.0, 0.0]
+ else:
+ if linear_steps is None:
+ linear_steps = steps // 2
+ linear_sigma_schedule = [i * threshold_noise / linear_steps for i in range(linear_steps)]
+ threshold_noise_step_diff = linear_steps - threshold_noise * steps
+ quadratic_steps = steps - linear_steps
+ quadratic_coef = threshold_noise_step_diff / (linear_steps * quadratic_steps ** 2)
+ linear_coef = threshold_noise / linear_steps - 2 * threshold_noise_step_diff / (quadratic_steps ** 2)
+ const = quadratic_coef * (linear_steps ** 2)
+ quadratic_sigma_schedule = [
+ quadratic_coef * (i ** 2) + linear_coef * i + const
+ for i in range(linear_steps, steps)
+ ]
+ sigma_schedule = linear_sigma_schedule + quadratic_sigma_schedule + [1.0]
+ sigma_schedule = [1.0 - x for x in sigma_schedule]
+ return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()
+
+# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
+def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor:
+ adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1)
+ sigmas = adj_idxs.new_zeros(n + 1)
+ sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_()
+ return sigmas
+
+def get_mask_aabb(masks):
+ if masks.numel() == 0:
+ return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
+
+ b = masks.shape[0]
+
+ bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
+ is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
+ for i in range(b):
+ mask = masks[i]
+ if mask.numel() == 0:
+ continue
+ if torch.max(mask != 0) == False:
+ is_empty[i] = True
+ continue
+ y, x = torch.where(mask)
+ bounding_boxes[i, 0] = torch.min(x)
+ bounding_boxes[i, 1] = torch.min(y)
+ bounding_boxes[i, 2] = torch.max(x)
+ bounding_boxes[i, 3] = torch.max(y)
+
+ return bounding_boxes, is_empty
+
+def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
+ # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
+ # While we're doing this, we can also resolve the mask device and scaling for performance reasons
+ for i in range(len(conditions)):
+ c = conditions[i]
+ if 'area' in c:
+ area = c['area']
+ if area[0] == "percentage":
+ modified = c.copy()
+ a = area[1:]
+ a_len = len(a) // 2
+ area = ()
+ for d in range(len(dims)):
+ area += (max(1, round(a[d] * dims[d])),)
+ for d in range(len(dims)):
+ area += (round(a[d + a_len] * dims[d]),)
+
+ modified['area'] = area
+ c = modified
+ conditions[i] = c
+
+ if 'mask' in c:
+ mask = c['mask']
+ mask = mask.to(device=device)
+ modified = c.copy()
+ if len(mask.shape) == len(dims):
+ mask = mask.unsqueeze(0)
+ if mask.shape[1:] != dims:
+ mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
+
+ if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
+ bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
+ boxes, is_empty = get_mask_aabb(bounds)
+ if is_empty[0]:
+ # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
+ modified['area'] = (8, 8, 0, 0)
+ else:
+ box = boxes[0]
+ H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
+ H = max(8, H)
+ W = max(8, W)
+ area = (int(H), int(W), int(Y), int(X))
+ modified['area'] = area
+
+ modified['mask'] = mask
+ conditions[i] = modified
+
+def resolve_areas_and_cond_masks(conditions, h, w, device):
+ logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
+ return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
+
+def create_cond_with_same_area_if_none(conds, c):
+ if 'area' not in c:
+ return
+
+ def area_inside(a, area_cmp):
+ a = add_area_dims(a, len(area_cmp) // 2)
+ area_cmp = add_area_dims(area_cmp, len(a) // 2)
+
+ a_l = len(a) // 2
+ area_cmp_l = len(area_cmp) // 2
+ for i in range(min(a_l, area_cmp_l)):
+ if a[a_l + i] < area_cmp[area_cmp_l + i]:
+ return False
+ for i in range(min(a_l, area_cmp_l)):
+ if (a[i] + a[a_l + i]) > (area_cmp[i] + area_cmp[area_cmp_l + i]):
+ return False
+ return True
+
+ c_area = c['area']
+ smallest = None
+ for x in conds:
+ if 'area' in x:
+ a = x['area']
+ if area_inside(c_area, a):
+ if smallest is None:
+ smallest = x
+ elif 'area' not in smallest:
+ smallest = x
+ else:
+ if math.prod(smallest['area'][:len(smallest['area']) // 2]) > math.prod(a[:len(a) // 2]):
+ smallest = x
+ else:
+ if smallest is None:
+ smallest = x
+ if smallest is None:
+ return
+ if 'area' in smallest:
+ if smallest['area'] == c_area:
+ return
+
+ out = c.copy()
+ out['model_conds'] = smallest['model_conds'].copy() #TODO: which fields should be copied?
+ conds += [out]
+
+def calculate_start_end_timesteps(model, conds):
+ s = model.model_sampling
+ for t in range(len(conds)):
+ x = conds[t]
+
+ timestep_start = None
+ timestep_end = None
+ # handle clip hook schedule, if needed
+ if 'clip_start_percent' in x:
+ timestep_start = s.percent_to_sigma(max(x['clip_start_percent'], x.get('start_percent', 0.0)))
+ timestep_end = s.percent_to_sigma(min(x['clip_end_percent'], x.get('end_percent', 1.0)))
+ else:
+ if 'start_percent' in x:
+ timestep_start = s.percent_to_sigma(x['start_percent'])
+ if 'end_percent' in x:
+ timestep_end = s.percent_to_sigma(x['end_percent'])
+
+ if (timestep_start is not None) or (timestep_end is not None):
+ n = x.copy()
+ if (timestep_start is not None):
+ n['timestep_start'] = timestep_start
+ if (timestep_end is not None):
+ n['timestep_end'] = timestep_end
+ conds[t] = n
+
+def pre_run_control(model, conds):
+ s = model.model_sampling
+ for t in range(len(conds)):
+ x = conds[t]
+
+ percent_to_timestep_function = lambda a: s.percent_to_sigma(a)
+ if 'control' in x:
+ x['control'].pre_run(model, percent_to_timestep_function)
+
+def apply_empty_x_to_equal_area(conds, uncond, name, uncond_fill_func):
+ cond_cnets = []
+ cond_other = []
+ uncond_cnets = []
+ uncond_other = []
+ for t in range(len(conds)):
+ x = conds[t]
+ if 'area' not in x:
+ if name in x and x[name] is not None:
+ cond_cnets.append(x[name])
+ else:
+ cond_other.append((x, t))
+ for t in range(len(uncond)):
+ x = uncond[t]
+ if 'area' not in x:
+ if name in x and x[name] is not None:
+ uncond_cnets.append(x[name])
+ else:
+ uncond_other.append((x, t))
+
+ if len(uncond_cnets) > 0:
+ return
+
+ for x in range(len(cond_cnets)):
+ temp = uncond_other[x % len(uncond_other)]
+ o = temp[0]
+ if name in o and o[name] is not None:
+ n = o.copy()
+ n[name] = uncond_fill_func(cond_cnets, x)
+ uncond += [n]
+ else:
+ n = o.copy()
+ n[name] = uncond_fill_func(cond_cnets, x)
+ uncond[temp[1]] = n
+
+def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwargs):
+ for t in range(len(conds)):
+ x = conds[t]
+ params = x.copy()
+ params["device"] = device
+ params["noise"] = noise
+ default_width = None
+ if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
+ default_width = noise.shape[3] * 8
+ params["width"] = params.get("width", default_width)
+ params["height"] = params.get("height", noise.shape[2] * 8)
+ params["prompt_type"] = params.get("prompt_type", prompt_type)
+ for k in kwargs:
+ if k not in params:
+ params[k] = kwargs[k]
+
+ out = model_function(**params)
+ x = x.copy()
+ model_conds = x['model_conds'].copy()
+ for k in out:
+ model_conds[k] = out[k]
+ x['model_conds'] = model_conds
+ conds[t] = x
+ return conds
+
+class Sampler:
+ def sample(self):
+ pass
+
+ def max_denoise(self, model_wrap, sigmas):
+ max_sigma = float(model_wrap.inner_model.model_sampling.sigma_max)
+ sigma = float(sigmas[0])
+ return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
+
+KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+ "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
+ "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+ "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
+ "gradient_estimation", "gradient_estimation_cfg_pp", "er_sde", "seeds_2", "seeds_3", "sa_solver", "sa_solver_pece"]
+
+class KSAMPLER(Sampler):
+ def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
+ self.sampler_function = sampler_function
+ self.extra_options = extra_options
+ self.inpaint_options = inpaint_options
+
+ def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
+ extra_args["denoise_mask"] = denoise_mask
+ model_k = KSamplerX0Inpaint(model_wrap, sigmas)
+ model_k.latent_image = latent_image
+ if self.inpaint_options.get("random", False): #TODO: Should this be the default?
+ generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
+ model_k.noise = torch.randn(noise.shape, generator=generator, device="cpu").to(noise.dtype).to(noise.device)
+ else:
+ model_k.noise = noise
+
+ noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
+
+ k_callback = None
+ total_steps = len(sigmas) - 1
+ if callback is not None:
+ k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
+
+ samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
+ samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
+ return samples
+
+
+def ksampler(sampler_name, extra_options={}, inpaint_options={}):
+ if sampler_name == "dpm_fast":
+ def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
+ if len(sigmas) <= 1:
+ return noise
+
+ sigma_min = sigmas[-1]
+ if sigma_min == 0:
+ sigma_min = sigmas[-2]
+ total_steps = len(sigmas) - 1
+ return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
+ sampler_function = dpm_fast_function
+ elif sampler_name == "dpm_adaptive":
+ def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options):
+ if len(sigmas) <= 1:
+ return noise
+
+ sigma_min = sigmas[-1]
+ if sigma_min == 0:
+ sigma_min = sigmas[-2]
+ return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options)
+ sampler_function = dpm_adaptive_function
+ else:
+ sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
+
+ return KSAMPLER(sampler_function, extra_options, inpaint_options)
+
+
+def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
+ for k in conds:
+ conds[k] = conds[k][:]
+ resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
+
+ for k in conds:
+ calculate_start_end_timesteps(model, conds[k])
+
+ if hasattr(model, 'extra_conds'):
+ for k in conds:
+ conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
+
+ #make sure each cond area has an opposite one with the same area
+ for k in conds:
+ for c in conds[k]:
+ for kk in conds:
+ if k != kk:
+ create_cond_with_same_area_if_none(conds[kk], c)
+
+ for k in conds:
+ for c in conds[k]:
+ if 'hooks' in c:
+ for hook in c['hooks'].hooks:
+ hook.initialize_timesteps(model)
+
+ for k in conds:
+ pre_run_control(model, conds[k])
+
+ if "positive" in conds:
+ positive = conds["positive"]
+ for k in conds:
+ if k != "positive":
+ apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])
+ apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])
+
+ return conds
+
+
+def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]):
+ # determine which ControlNets have extra_hooks that should be combined with normal hooks
+ hook_replacement: dict[tuple[ControlBase, comfy.hooks.HookGroup], list[dict]] = {}
+ for k in conds:
+ for kk in conds[k]:
+ if 'control' in kk:
+ control: 'ControlBase' = kk['control']
+ extra_hooks = control.get_extra_hooks()
+ if len(extra_hooks) > 0:
+ hooks: comfy.hooks.HookGroup = kk.get('hooks', None)
+ to_replace = hook_replacement.setdefault((control, hooks), [])
+ to_replace.append(kk)
+ # if nothing to replace, do nothing
+ if len(hook_replacement) == 0:
+ return
+
+ # for optimal sampling performance, common ControlNets + hook combos should have identical hooks
+ # on the cond dicts
+ for key, conds_to_modify in hook_replacement.items():
+ control = key[0]
+ hooks = key[1]
+ hooks = comfy.hooks.HookGroup.combine_all_hooks(control.get_extra_hooks() + [hooks])
+ # if combined hooks are not None, set as new hooks for all relevant conds
+ if hooks is not None:
+ for cond in conds_to_modify:
+ cond['hooks'] = hooks
+
+def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]):
+ '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for
+ HookGroups that have the same reference.'''
+ registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None)
+ # if None were registered, make sure all hooks are cleaned from conds
+ if registered is None:
+ for k in conds:
+ for kk in conds[k]:
+ kk.pop('hooks', None)
+ return
+ # find conds that contain hooks to be replaced - group by common HookGroup refs
+ hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {}
+ for k in conds:
+ for kk in conds[k]:
+ hooks: comfy.hooks.HookGroup = kk.get('hooks', None)
+ if hooks is not None:
+ if not hooks.is_subset_of(registered):
+ to_replace = hook_replacement.setdefault(hooks, [])
+ to_replace.append(kk)
+ # for each hook to replace, create a new proper HookGroup and assign to all common conds
+ for hooks, conds_to_modify in hook_replacement.items():
+ new_hooks = hooks.new_with_common_hooks(registered)
+ if len(new_hooks) == 0:
+ new_hooks = None
+ for kk in conds_to_modify:
+ kk['hooks'] = new_hooks
+
+
+def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]):
+ hooks_set = set()
+ for k in conds:
+ for kk in conds[k]:
+ hooks_set.add(kk.get('hooks', None))
+ return len(hooks_set)
+
+
+def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
+ '''
+ If any patches from hooks, wrappers, or callbacks have .to to be called, call it.
+ '''
+ if model_options is None:
+ return
+ to_load_options = model_options.get("to_load_options", None)
+ if to_load_options is None:
+ return
+
+ casts = []
+ if device is not None:
+ casts.append(device)
+ if dtype is not None:
+ casts.append(dtype)
+ # if nothing to apply, do nothing
+ if len(casts) == 0:
+ return
+
+ # try to call .to on patches
+ if "patches" in to_load_options:
+ patches = to_load_options["patches"]
+ for name in patches:
+ patch_list = patches[name]
+ for i in range(len(patch_list)):
+ if hasattr(patch_list[i], "to"):
+ for cast in casts:
+ patch_list[i] = patch_list[i].to(cast)
+ if "patches_replace" in to_load_options:
+ patches = to_load_options["patches_replace"]
+ for name in patches:
+ patch_list = patches[name]
+ for k in patch_list:
+ if hasattr(patch_list[k], "to"):
+ for cast in casts:
+ patch_list[k] = patch_list[k].to(cast)
+ # try to call .to on any wrappers/callbacks
+ wrappers_and_callbacks = ["wrappers", "callbacks"]
+ for wc_name in wrappers_and_callbacks:
+ if wc_name in to_load_options:
+ wc: dict[str, list] = to_load_options[wc_name]
+ for wc_dict in wc.values():
+ for wc_list in wc_dict.values():
+ for i in range(len(wc_list)):
+ if hasattr(wc_list[i], "to"):
+ for cast in casts:
+ wc_list[i] = wc_list[i].to(cast)
+
+
+class CFGGuider:
+ def __init__(self, model_patcher: ModelPatcher):
+ self.model_patcher = model_patcher
+ self.model_options = model_patcher.model_options
+ self.original_conds = {}
+ self.cfg = 1.0
+
+ def set_conds(self, positive, negative):
+ self.inner_set_conds({"positive": positive, "negative": negative})
+
+ def set_cfg(self, cfg):
+ self.cfg = cfg
+
+ def inner_set_conds(self, conds):
+ for k in conds:
+ self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
+
+ def __call__(self, *args, **kwargs):
+ return self.predict_noise(*args, **kwargs)
+
+ def predict_noise(self, x, timestep, model_options={}, seed=None):
+ return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
+
+ def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
+ if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+ latent_image = self.inner_model.process_latent_in(latent_image)
+
+ self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
+
+ extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
+ extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas
+ extra_args = {"model_options": extra_model_options, "seed": seed}
+
+ executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
+ sampler.sample,
+ sampler,
+ comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.SAMPLER_SAMPLE, extra_args["model_options"], is_model_options=True)
+ )
+ samples = executor.execute(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
+ return self.inner_model.process_latent_out(samples.to(torch.float32))
+
+ def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+ self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
+ device = self.model_patcher.load_device
+
+ if denoise_mask is not None:
+ denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
+
+ noise = noise.to(device)
+ latent_image = latent_image.to(device)
+ sigmas = sigmas.to(device)
+ cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
+
+ try:
+ self.model_patcher.pre_run()
+ output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+ finally:
+ self.model_patcher.cleanup()
+
+ comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+ del self.inner_model
+ del self.loaded_models
+ return output
+
+ def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+ if sigmas.shape[-1] == 0:
+ return latent_image
+
+ self.conds = {}
+ for k in self.original_conds:
+ self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
+ preprocess_conds_hooks(self.conds)
+
+ try:
+ orig_model_options = self.model_options
+ self.model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
+ # if one hook type (or just None), then don't bother caching weights for hooks (will never change after first step)
+ orig_hook_mode = self.model_patcher.hook_mode
+ if get_total_hook_groups_in_conds(self.conds) <= 1:
+ self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
+ comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options)
+ filter_registered_hooks_on_conds(self.conds, self.model_options)
+ executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
+ self.outer_sample,
+ self,
+ comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, self.model_options, is_model_options=True)
+ )
+ output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+ finally:
+ cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)
+ self.model_options = orig_model_options
+ self.model_patcher.hook_mode = orig_hook_mode
+ self.model_patcher.restore_hook_patches()
+
+ del self.conds
+ return output
+
+
+def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+ cfg_guider = CFGGuider(model)
+ cfg_guider.set_conds(positive, negative)
+ cfg_guider.set_cfg(cfg)
+ return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+
+
+SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
+
+class SchedulerHandler(NamedTuple):
+ handler: Callable[..., torch.Tensor]
+ # Boolean indicates whether to call the handler like:
+ # scheduler_function(model_sampling, steps) or
+ # scheduler_function(n, sigma_min: float, sigma_max: float)
+ use_ms: bool = True
+
+SCHEDULER_HANDLERS = {
+ "simple": SchedulerHandler(simple_scheduler),
+ "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)),
+ "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False),
+ "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False),
+ "ddim_uniform": SchedulerHandler(ddim_scheduler),
+ "beta": SchedulerHandler(beta_scheduler),
+ "normal": SchedulerHandler(normal_scheduler),
+ "linear_quadratic": SchedulerHandler(linear_quadratic_schedule),
+ "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False),
+}
+SCHEDULER_NAMES = list(SCHEDULER_HANDLERS)
+
+def calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor:
+ handler = SCHEDULER_HANDLERS.get(scheduler_name)
+ if handler is None:
+ err = f"error invalid scheduler {scheduler_name}"
+ logging.error(err)
+ raise ValueError(err)
+ if handler.use_ms:
+ return handler.handler(model_sampling, steps)
+ return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
+
+def sampler_object(name):
+ if name == "uni_pc":
+ sampler = KSAMPLER(uni_pc.sample_unipc)
+ elif name == "uni_pc_bh2":
+ sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
+ elif name == "ddim":
+ sampler = ksampler("euler", inpaint_options={"random": True})
+ else:
+ sampler = ksampler(name)
+ return sampler
+
+class KSampler:
+ SCHEDULERS = SCHEDULER_NAMES
+ SAMPLERS = SAMPLER_NAMES
+ DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))
+
+ def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
+ self.model = model
+ self.device = device
+ if scheduler not in self.SCHEDULERS:
+ scheduler = self.SCHEDULERS[0]
+ if sampler not in self.SAMPLERS:
+ sampler = self.SAMPLERS[0]
+ self.scheduler = scheduler
+ self.sampler = sampler
+ self.set_steps(steps, denoise)
+ self.denoise = denoise
+ self.model_options = model_options
+
+ def calculate_sigmas(self, steps):
+ sigmas = None
+
+ discard_penultimate_sigma = False
+ if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:
+ steps += 1
+ discard_penultimate_sigma = True
+
+ sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps)
+
+ if discard_penultimate_sigma:
+ sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
+ return sigmas
+
+ def set_steps(self, steps, denoise=None):
+ self.steps = steps
+ if denoise is None or denoise > 0.9999:
+ self.sigmas = self.calculate_sigmas(steps).to(self.device)
+ else:
+ if denoise <= 0.0:
+ self.sigmas = torch.FloatTensor([])
+ else:
+ new_steps = int(steps/denoise)
+ sigmas = self.calculate_sigmas(new_steps).to(self.device)
+ self.sigmas = sigmas[-(steps + 1):]
+
+ def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
+ if sigmas is None:
+ sigmas = self.sigmas
+
+ if last_step is not None and last_step < (len(sigmas) - 1):
+ sigmas = sigmas[:last_step + 1]
+ if force_full_denoise:
+ sigmas[-1] = 0
+
+ if start_step is not None:
+ if start_step < (len(sigmas) - 1):
+ sigmas = sigmas[start_step:]
+ else:
+ if latent_image is not None:
+ return latent_image
+ else:
+ return torch.zeros_like(noise)
+
+ sampler = sampler_object(self.sampler)
+
+ return sample(self.model, noise, positive, negative, cfg, self.device, sampler, sigmas, self.model_options, latent_image=latent_image, denoise_mask=denoise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
diff --git a/ComfyUI/comfy/sd1_clip.py b/ComfyUI/comfy/sd1_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..ade340fd190106e5bf388a5ac35b6e68c1e52292
--- /dev/null
+++ b/ComfyUI/comfy/sd1_clip.py
@@ -0,0 +1,687 @@
+import os
+
+from transformers import CLIPTokenizer
+import comfy.ops
+import torch
+import traceback
+import zipfile
+from . import model_management
+import comfy.clip_model
+import json
+import logging
+import numbers
+import re
+
+def gen_empty_tokens(special_tokens, length):
+ start_token = special_tokens.get("start", None)
+ end_token = special_tokens.get("end", None)
+ pad_token = special_tokens.get("pad")
+ output = []
+ if start_token is not None:
+ output.append(start_token)
+ if end_token is not None:
+ output.append(end_token)
+ output += [pad_token] * (length - len(output))
+ return output
+
+class ClipTokenWeightEncoder:
+ def encode_token_weights(self, token_weight_pairs):
+ to_encode = list()
+ max_token_len = 0
+ has_weights = False
+ for x in token_weight_pairs:
+ tokens = list(map(lambda a: a[0], x))
+ max_token_len = max(len(tokens), max_token_len)
+ has_weights = has_weights or not all(map(lambda a: a[1] == 1.0, x))
+ to_encode.append(tokens)
+
+ sections = len(to_encode)
+ if has_weights or sections == 0:
+ if hasattr(self, "gen_empty_tokens"):
+ to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))
+ else:
+ to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
+
+ o = self.encode(to_encode)
+ out, pooled = o[:2]
+
+ if pooled is not None:
+ first_pooled = pooled[0:1].to(model_management.intermediate_device())
+ else:
+ first_pooled = pooled
+
+ output = []
+ for k in range(0, sections):
+ z = out[k:k+1]
+ if has_weights:
+ z_empty = out[-1]
+ for i in range(len(z)):
+ for j in range(len(z[i])):
+ weight = token_weight_pairs[k][j][1]
+ if weight != 1.0:
+ z[i][j] = (z[i][j] - z_empty[j]) * weight + z_empty[j]
+ output.append(z)
+
+ if (len(output) == 0):
+ r = (out[-1:].to(model_management.intermediate_device()), first_pooled)
+ else:
+ r = (torch.cat(output, dim=-2).to(model_management.intermediate_device()), first_pooled)
+
+ if len(o) > 2:
+ extra = {}
+ for k in o[2]:
+ v = o[2][k]
+ if k == "attention_mask":
+ v = v[:sections].flatten().unsqueeze(dim=0).to(model_management.intermediate_device())
+ extra[k] = v
+
+ r = r + (extra,)
+ return r
+
+class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden",
+ "all"
+ ]
+ def __init__(self, device="cpu", max_length=77,
+ freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
+ special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
+ return_projected_pooled=True, return_attention_masks=False, model_options={}): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+
+ if textmodel_json_config is None:
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_clip_config.json")
+ if "model_name" not in model_options:
+ model_options = {**model_options, "model_name": "clip_l"}
+
+ if isinstance(textmodel_json_config, dict):
+ config = textmodel_json_config
+ else:
+ with open(textmodel_json_config) as f:
+ config = json.load(f)
+
+ te_model_options = model_options.get("{}_model_config".format(model_options.get("model_name", "")), {})
+ for k, v in te_model_options.items():
+ config[k] = v
+
+ operations = model_options.get("custom_operations", None)
+ scaled_fp8 = None
+
+ if operations is None:
+ scaled_fp8 = model_options.get("scaled_fp8", None)
+ if scaled_fp8 is not None:
+ operations = comfy.ops.scaled_fp8_ops(fp8_matrix_mult=False, override_dtype=scaled_fp8)
+ else:
+ operations = comfy.ops.manual_cast
+
+ self.operations = operations
+ self.transformer = model_class(config, dtype, device, self.operations)
+ if scaled_fp8 is not None:
+ self.transformer.scaled_fp8 = torch.nn.Parameter(torch.tensor([], dtype=scaled_fp8))
+
+ self.num_layers = self.transformer.num_layers
+
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = None
+ self.special_tokens = special_tokens
+
+ self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
+ self.enable_attention_masks = enable_attention_masks
+ self.zero_out_masked = zero_out_masked
+
+ self.layer_norm_hidden_state = layer_norm_hidden_state
+ self.return_projected_pooled = return_projected_pooled
+ self.return_attention_masks = return_attention_masks
+
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert abs(layer_idx) < self.num_layers
+ self.set_clip_options({"layer": layer_idx})
+ self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def set_clip_options(self, options):
+ layer_idx = options.get("layer", self.layer_idx)
+ self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+ if self.layer == "all":
+ pass
+ elif layer_idx is None or abs(layer_idx) > self.num_layers:
+ self.layer = "last"
+ else:
+ self.layer = "hidden"
+ self.layer_idx = layer_idx
+
+ def reset_clip_options(self):
+ self.layer = self.options_default[0]
+ self.layer_idx = self.options_default[1]
+ self.return_projected_pooled = self.options_default[2]
+
+ def process_tokens(self, tokens, device):
+ end_token = self.special_tokens.get("end", None)
+ if end_token is None:
+ cmp_token = self.special_tokens.get("pad", -1)
+ else:
+ cmp_token = end_token
+
+ embeds_out = []
+ attention_masks = []
+ num_tokens = []
+
+ for x in tokens:
+ attention_mask = []
+ tokens_temp = []
+ other_embeds = []
+ eos = False
+ index = 0
+ for y in x:
+ if isinstance(y, numbers.Integral):
+ if eos:
+ attention_mask.append(0)
+ else:
+ attention_mask.append(1)
+ token = int(y)
+ tokens_temp += [token]
+ if not eos and token == cmp_token:
+ if end_token is None:
+ attention_mask[-1] = 0
+ eos = True
+ else:
+ other_embeds.append((index, y))
+ index += 1
+
+ tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long)
+ tokens_embed = self.transformer.get_input_embeddings()(tokens_embed, out_dtype=torch.float32)
+ index = 0
+ pad_extra = 0
+ for o in other_embeds:
+ emb = o[1]
+ if torch.is_tensor(emb):
+ emb = {"type": "embedding", "data": emb}
+
+ emb_type = emb.get("type", None)
+ if emb_type == "embedding":
+ emb = emb.get("data", None)
+ else:
+ if hasattr(self.transformer, "preprocess_embed"):
+ emb = self.transformer.preprocess_embed(emb, device=device)
+ else:
+ emb = None
+
+ if emb is None:
+ index += -1
+ continue
+
+ ind = index + o[0]
+ emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32)
+ emb_shape = emb.shape[1]
+ if emb.shape[-1] == tokens_embed.shape[-1]:
+ tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1)
+ attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:]
+ index += emb_shape - 1
+ else:
+ index += -1
+ pad_extra += emb_shape
+ logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(emb.shape[-1], tokens_embed.shape[-1]))
+
+ if pad_extra > 0:
+ padd_embed = self.transformer.get_input_embeddings()(torch.tensor([[self.special_tokens["pad"]] * pad_extra], device=device, dtype=torch.long), out_dtype=torch.float32)
+ tokens_embed = torch.cat([tokens_embed, padd_embed], dim=1)
+ attention_mask = attention_mask + [0] * pad_extra
+
+ embeds_out.append(tokens_embed)
+ attention_masks.append(attention_mask)
+ num_tokens.append(sum(attention_mask))
+
+ return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens
+
+ def forward(self, tokens):
+ device = self.transformer.get_input_embeddings().weight.device
+ embeds, attention_mask, num_tokens = self.process_tokens(tokens, device)
+
+ attention_mask_model = None
+ if self.enable_attention_masks:
+ attention_mask_model = attention_mask
+
+ if self.layer == "all":
+ intermediate_output = "all"
+ else:
+ intermediate_output = self.layer_idx
+
+ outputs = self.transformer(None, attention_mask_model, embeds=embeds, num_tokens=num_tokens, intermediate_output=intermediate_output, final_layer_norm_intermediate=self.layer_norm_hidden_state, dtype=torch.float32)
+
+ if self.layer == "last":
+ z = outputs[0].float()
+ else:
+ z = outputs[1].float()
+
+ if self.zero_out_masked:
+ z *= attention_mask.unsqueeze(-1).float()
+
+ pooled_output = None
+ if len(outputs) >= 3:
+ if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
+ pooled_output = outputs[3].float()
+ elif outputs[2] is not None:
+ pooled_output = outputs[2].float()
+
+ extra = {}
+ if self.return_attention_masks:
+ extra["attention_mask"] = attention_mask
+
+ if len(extra) > 0:
+ return z, pooled_output, extra
+
+ return z, pooled_output
+
+ def encode(self, tokens):
+ return self(tokens)
+
+ def load_sd(self, sd):
+ return self.transformer.load_state_dict(sd, strict=False)
+
+def parse_parentheses(string):
+ result = []
+ current_item = ""
+ nesting_level = 0
+ for char in string:
+ if char == "(":
+ if nesting_level == 0:
+ if current_item:
+ result.append(current_item)
+ current_item = "("
+ else:
+ current_item = "("
+ else:
+ current_item += char
+ nesting_level += 1
+ elif char == ")":
+ nesting_level -= 1
+ if nesting_level == 0:
+ result.append(current_item + ")")
+ current_item = ""
+ else:
+ current_item += char
+ else:
+ current_item += char
+ if current_item:
+ result.append(current_item)
+ return result
+
+def token_weights(string, current_weight):
+ a = parse_parentheses(string)
+ out = []
+ for x in a:
+ weight = current_weight
+ if len(x) >= 2 and x[-1] == ')' and x[0] == '(':
+ x = x[1:-1]
+ xx = x.rfind(":")
+ weight *= 1.1
+ if xx > 0:
+ try:
+ weight = float(x[xx+1:])
+ x = x[:xx]
+ except:
+ pass
+ out += token_weights(x, weight)
+ else:
+ out += [(x, current_weight)]
+ return out
+
+def escape_important(text):
+ text = text.replace("\\)", "\0\1")
+ text = text.replace("\\(", "\0\2")
+ return text
+
+def unescape_important(text):
+ text = text.replace("\0\1", ")")
+ text = text.replace("\0\2", "(")
+ return text
+
+def safe_load_embed_zip(embed_path):
+ with zipfile.ZipFile(embed_path) as myzip:
+ names = list(filter(lambda a: "data/" in a, myzip.namelist()))
+ names.reverse()
+ for n in names:
+ with myzip.open(n) as myfile:
+ data = myfile.read()
+ number = len(data) // 4
+ length_embed = 1024 #sd2.x
+ if number < 768:
+ continue
+ if number % 768 == 0:
+ length_embed = 768 #sd1.x
+ num_embeds = number // length_embed
+ embed = torch.frombuffer(data, dtype=torch.float)
+ out = embed.reshape((num_embeds, length_embed)).clone()
+ del embed
+ return out
+
+def expand_directory_list(directories):
+ dirs = set()
+ for x in directories:
+ dirs.add(x)
+ for root, subdir, file in os.walk(x, followlinks=True):
+ dirs.add(root)
+ return list(dirs)
+
+def bundled_embed(embed, prefix, suffix): #bundled embedding in lora format
+ out_list = []
+ for k in embed:
+ if k.startswith(prefix) and k.endswith(suffix):
+ out_list.append(embed[k])
+ if len(out_list) == 0:
+ return None
+
+ return torch.cat(out_list, dim=0)
+
+def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
+ if isinstance(embedding_directory, str):
+ embedding_directory = [embedding_directory]
+
+ embedding_directory = expand_directory_list(embedding_directory)
+
+ valid_file = None
+ for embed_dir in embedding_directory:
+ embed_path = os.path.abspath(os.path.join(embed_dir, embedding_name))
+ embed_dir = os.path.abspath(embed_dir)
+ try:
+ if os.path.commonpath((embed_dir, embed_path)) != embed_dir:
+ continue
+ except:
+ continue
+ if not os.path.isfile(embed_path):
+ extensions = ['.safetensors', '.pt', '.bin']
+ for x in extensions:
+ t = embed_path + x
+ if os.path.isfile(t):
+ valid_file = t
+ break
+ else:
+ valid_file = embed_path
+ if valid_file is not None:
+ break
+
+ if valid_file is None:
+ return None
+
+ embed_path = valid_file
+
+ embed_out = None
+
+ try:
+ if embed_path.lower().endswith(".safetensors"):
+ import safetensors.torch
+ embed = safetensors.torch.load_file(embed_path, device="cpu")
+ else:
+ try:
+ embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+ except:
+ embed_out = safe_load_embed_zip(embed_path)
+ except Exception:
+ logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
+ return None
+
+ if embed_out is None:
+ if 'string_to_param' in embed:
+ values = embed['string_to_param'].values()
+ embed_out = next(iter(values))
+ elif isinstance(embed, list):
+ out_list = []
+ for x in range(len(embed)):
+ for k in embed[x]:
+ t = embed[x][k]
+ if t.shape[-1] != embedding_size:
+ continue
+ out_list.append(t.reshape(-1, t.shape[-1]))
+ embed_out = torch.cat(out_list, dim=0)
+ elif embed_key is not None and embed_key in embed:
+ embed_out = embed[embed_key]
+ else:
+ embed_out = bundled_embed(embed, 'bundle_emb.', '.string_to_param.*')
+ if embed_out is None:
+ embed_out = bundled_embed(embed, 'bundle_emb.', '.{}'.format(embed_key))
+ if embed_out is None:
+ values = embed.values()
+ embed_out = next(iter(values))
+ return embed_out
+
+class SDTokenizer:
+ def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, min_padding=None, tokenizer_data={}, tokenizer_args={}):
+ if tokenizer_path is None:
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
+ self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
+ self.max_length = tokenizer_data.get("{}_max_length".format(embedding_key), max_length)
+ self.min_length = tokenizer_data.get("{}_min_length".format(embedding_key), min_length)
+ self.end_token = None
+ self.min_padding = min_padding
+
+ empty = self.tokenizer('')["input_ids"]
+ self.tokenizer_adds_end_token = has_end_token
+ if has_start_token:
+ self.tokens_start = 1
+ self.start_token = empty[0]
+ if end_token is not None:
+ self.end_token = end_token
+ else:
+ if has_end_token:
+ self.end_token = empty[1]
+ else:
+ self.tokens_start = 0
+ self.start_token = None
+ if end_token is not None:
+ self.end_token = end_token
+ else:
+ if has_end_token:
+ self.end_token = empty[0]
+
+ if pad_token is not None:
+ self.pad_token = pad_token
+ elif pad_with_end:
+ self.pad_token = self.end_token
+ else:
+ self.pad_token = 0
+
+ self.pad_with_end = pad_with_end
+ self.pad_to_max_length = pad_to_max_length
+
+ vocab = self.tokenizer.get_vocab()
+ self.inv_vocab = {v: k for k, v in vocab.items()}
+ self.embedding_directory = embedding_directory
+ self.max_word_length = 8
+ self.embedding_identifier = "embedding:"
+ self.embedding_size = embedding_size
+ self.embedding_key = embedding_key
+
+ def _try_get_embedding(self, embedding_name:str):
+ '''
+ Takes a potential embedding name and tries to retrieve it.
+ Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
+ '''
+ split_embed = embedding_name.split()
+ embedding_name = split_embed[0]
+ leftover = ' '.join(split_embed[1:])
+ embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
+ if embed is None:
+ stripped = embedding_name.strip(',')
+ if len(stripped) < len(embedding_name):
+ embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
+ return (embed, "{} {}".format(embedding_name[len(stripped):], leftover))
+ return (embed, leftover)
+
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, tokenizer_options={}, **kwargs):
+ '''
+ Takes a prompt and converts it to a list of (token, weight, word id) elements.
+ Tokens can both be integer tokens and pre computed CLIP tensors.
+ Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
+ Returned list has the dimensions NxM where M is the input size of CLIP
+ '''
+ min_length = tokenizer_options.get("{}_min_length".format(self.embedding_key), self.min_length)
+ min_padding = tokenizer_options.get("{}_min_padding".format(self.embedding_key), self.min_padding)
+
+ text = escape_important(text)
+ parsed_weights = token_weights(text, 1.0)
+
+ # tokenize words
+ tokens = []
+ for weighted_segment, weight in parsed_weights:
+ to_tokenize = unescape_important(weighted_segment)
+ split = re.split(' {0}|\n{0}'.format(self.embedding_identifier), to_tokenize)
+ to_tokenize = [split[0]]
+ for i in range(1, len(split)):
+ to_tokenize.append("{}{}".format(self.embedding_identifier, split[i]))
+
+ to_tokenize = [x for x in to_tokenize if x != ""]
+ for word in to_tokenize:
+ # if we find an embedding, deal with the embedding
+ if word.startswith(self.embedding_identifier) and self.embedding_directory is not None:
+ embedding_name = word[len(self.embedding_identifier):].strip('\n')
+ embed, leftover = self._try_get_embedding(embedding_name)
+ if embed is None:
+ logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
+ else:
+ if len(embed.shape) == 1:
+ tokens.append([(embed, weight)])
+ else:
+ tokens.append([(embed[x], weight) for x in range(embed.shape[0])])
+ #if we accidentally have leftover text, continue parsing using leftover, else move on to next word
+ if leftover != "":
+ word = leftover
+ else:
+ continue
+ end = 999999999999
+ if self.tokenizer_adds_end_token:
+ end = -1
+ #parse word
+ tokens.append([(t, weight) for t in self.tokenizer(word)["input_ids"][self.tokens_start:end]])
+
+ #reshape token array to CLIP input size
+ batched_tokens = []
+ batch = []
+ if self.start_token is not None:
+ batch.append((self.start_token, 1.0, 0))
+ batched_tokens.append(batch)
+ for i, t_group in enumerate(tokens):
+ #determine if we're going to try and keep the tokens in a single batch
+ is_large = len(t_group) >= self.max_word_length
+ if self.end_token is not None:
+ has_end_token = 1
+ else:
+ has_end_token = 0
+
+ while len(t_group) > 0:
+ if len(t_group) + len(batch) > self.max_length - has_end_token:
+ remaining_length = self.max_length - len(batch) - has_end_token
+ #break word in two and add end token
+ if is_large:
+ batch.extend([(t,w,i+1) for t,w in t_group[:remaining_length]])
+ if self.end_token is not None:
+ batch.append((self.end_token, 1.0, 0))
+ t_group = t_group[remaining_length:]
+ #add end token and pad
+ else:
+ if self.end_token is not None:
+ batch.append((self.end_token, 1.0, 0))
+ if self.pad_to_max_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
+ #start new batch
+ batch = []
+ if self.start_token is not None:
+ batch.append((self.start_token, 1.0, 0))
+ batched_tokens.append(batch)
+ else:
+ batch.extend([(t,w,i+1) for t,w in t_group])
+ t_group = []
+
+ #fill last batch
+ if self.end_token is not None:
+ batch.append((self.end_token, 1.0, 0))
+ if min_padding is not None:
+ batch.extend([(self.pad_token, 1.0, 0)] * min_padding)
+ if self.pad_to_max_length and len(batch) < self.max_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+ if min_length is not None and len(batch) < min_length:
+ batch.extend([(self.pad_token, 1.0, 0)] * (min_length - len(batch)))
+
+ if not return_word_ids:
+ batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
+
+ return batched_tokens
+
+
+ def untokenize(self, token_weight_pair):
+ return list(map(lambda a: (a, self.inv_vocab[a[0]]), token_weight_pair))
+
+ def state_dict(self):
+ return {}
+
+class SD1Tokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
+ if name is not None:
+ self.clip_name = name
+ self.clip = "{}".format(self.clip_name)
+ else:
+ self.clip_name = clip_name
+ self.clip = "clip_{}".format(self.clip_name)
+
+ tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
+ setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out[self.clip_name] = getattr(self, self.clip).tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return getattr(self, self.clip).untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return getattr(self, self.clip).state_dict()
+
+class SD1CheckpointClipModel(SDClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, return_projected_pooled=False, dtype=dtype, model_options=model_options)
+
+class SD1ClipModel(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}, clip_name="l", clip_model=SD1CheckpointClipModel, name=None, **kwargs):
+ super().__init__()
+
+ if name is not None:
+ self.clip_name = name
+ self.clip = "{}".format(self.clip_name)
+ else:
+ self.clip_name = clip_name
+ self.clip = "clip_{}".format(self.clip_name)
+
+ clip_model = model_options.get("{}_class".format(self.clip), clip_model)
+ model_options = {**model_options, "model_name": self.clip}
+ setattr(self, self.clip, clip_model(device=device, dtype=dtype, model_options=model_options, **kwargs))
+
+ self.dtypes = set()
+ if dtype is not None:
+ self.dtypes.add(dtype)
+
+ def set_clip_options(self, options):
+ getattr(self, self.clip).set_clip_options(options)
+
+ def reset_clip_options(self):
+ getattr(self, self.clip).reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs = token_weight_pairs[self.clip_name]
+ out = getattr(self, self.clip).encode_token_weights(token_weight_pairs)
+ return out
+
+ def load_sd(self, sd):
+ return getattr(self, self.clip).load_sd(sd)
diff --git a/ComfyUI/comfy/sd1_clip_config.json b/ComfyUI/comfy/sd1_clip_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..3ba8c6b5bc3d6389fb6c9e2c8231729ad9d663a4
--- /dev/null
+++ b/ComfyUI/comfy/sd1_clip_config.json
@@ -0,0 +1,25 @@
+{
+ "_name_or_path": "openai/clip-vit-large-patch14",
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "quick_gelu",
+ "hidden_size": 768,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 3072,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 12,
+ "num_hidden_layers": 12,
+ "pad_token_id": 1,
+ "projection_dim": 768,
+ "torch_dtype": "float32",
+ "transformers_version": "4.24.0",
+ "vocab_size": 49408
+}
diff --git a/ComfyUI/comfy/sd1_tokenizer/merges.txt b/ComfyUI/comfy/sd1_tokenizer/merges.txt
new file mode 100644
index 0000000000000000000000000000000000000000..76e821f1b6f0a9709293c3b6b51ed90980b3166b
--- /dev/null
+++ b/ComfyUI/comfy/sd1_tokenizer/merges.txt
@@ -0,0 +1,48895 @@
+#version: 0.2
+i n
+t h
+a n
+r e
+a r
+e r
+th e
+in g
+o u
+o n
+s t
+o r
+e n
+o n
+a l
+a t
+e r
+i t
+i n
+t o
+r o
+i s
+l e
+i c
+a t
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+e d
+o f
+c h
+o r
+e s
+i l
+e l
+s t
+a c
+o m
+a m
+l o
+a n
+a y
+s h
+r i
+l i
+t i
+f or
+n e
+ð Ł
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+h a
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+o l
+v e
+s i
+u r
+a l
+s e
+' s
+u n
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+a s
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+v i
+p p
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+m y
+. .
+b u
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+c on
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+.. .
+p re
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+! !
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++ /-
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index 0000000000000000000000000000000000000000..8f7b3151d008bdee5739e317b887be0fc3b805c3
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diff --git a/ComfyUI/comfy/supported_models.py b/ComfyUI/comfy/supported_models.py
new file mode 100644
index 0000000000000000000000000000000000000000..8f3f4652d0f79005e38b177b254d7b2240e793e4
--- /dev/null
+++ b/ComfyUI/comfy/supported_models.py
@@ -0,0 +1,1235 @@
+import torch
+from . import model_base
+from . import utils
+
+from . import sd1_clip
+from . import sdxl_clip
+import comfy.text_encoders.sd2_clip
+import comfy.text_encoders.sd3_clip
+import comfy.text_encoders.sa_t5
+import comfy.text_encoders.aura_t5
+import comfy.text_encoders.pixart_t5
+import comfy.text_encoders.hydit
+import comfy.text_encoders.flux
+import comfy.text_encoders.genmo
+import comfy.text_encoders.lt
+import comfy.text_encoders.hunyuan_video
+import comfy.text_encoders.cosmos
+import comfy.text_encoders.lumina2
+import comfy.text_encoders.wan
+import comfy.text_encoders.ace
+import comfy.text_encoders.omnigen2
+
+from . import supported_models_base
+from . import latent_formats
+
+from . import diffusers_convert
+
+class SD15(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ latent_format = latent_formats.SD15
+ memory_usage_factor = 1.0
+
+ def process_clip_state_dict(self, state_dict):
+ k = list(state_dict.keys())
+ for x in k:
+ if x.startswith("cond_stage_model.transformer.") and not x.startswith("cond_stage_model.transformer.text_model."):
+ y = x.replace("cond_stage_model.transformer.", "cond_stage_model.transformer.text_model.")
+ state_dict[y] = state_dict.pop(x)
+
+ if 'cond_stage_model.transformer.text_model.embeddings.position_ids' in state_dict:
+ ids = state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids']
+ if ids.dtype == torch.float32:
+ state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
+
+ replace_prefix = {}
+ replace_prefix["cond_stage_model."] = "clip_l."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+ for p in pop_keys:
+ if p in state_dict:
+ state_dict.pop(p)
+
+ replace_prefix = {"clip_l.": "cond_stage_model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
+
+class SD20(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ "attn_precision": torch.float32,
+ }
+
+ latent_format = latent_formats.SD15
+ memory_usage_factor = 1.0
+
+ def model_type(self, state_dict, prefix=""):
+ if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
+ k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
+ out = state_dict.get(k, None)
+ if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
+ return model_base.ModelType.V_PREDICTION
+ return model_base.ModelType.EPS
+
+ def process_clip_state_dict(self, state_dict):
+ replace_prefix = {}
+ replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
+ replace_prefix["cond_stage_model.model."] = "clip_h."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ replace_prefix["clip_h"] = "cond_stage_model.model"
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
+ state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
+ return state_dict
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.sd2_clip.SD2Tokenizer, comfy.text_encoders.sd2_clip.SD2ClipModel)
+
+class SD21UnclipL(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": 1536,
+ "use_temporal_attention": False,
+ }
+
+ clip_vision_prefix = "embedder.model.visual."
+ noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 768}
+
+
+class SD21UnclipH(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": 2048,
+ "use_temporal_attention": False,
+ }
+
+ clip_vision_prefix = "embedder.model.visual."
+ noise_aug_config = {"noise_schedule_config": {"timesteps": 1000, "beta_schedule": "squaredcos_cap_v2"}, "timestep_dim": 1024}
+
+class SDXLRefiner(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 384,
+ "use_linear_in_transformer": True,
+ "context_dim": 1280,
+ "adm_in_channels": 2560,
+ "transformer_depth": [0, 0, 4, 4, 4, 4, 0, 0],
+ "use_temporal_attention": False,
+ }
+
+ latent_format = latent_formats.SDXL
+ memory_usage_factor = 1.0
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SDXLRefiner(self, device=device)
+
+ def process_clip_state_dict(self, state_dict):
+ keys_to_replace = {}
+ replace_prefix = {}
+ replace_prefix["conditioner.embedders.0.model."] = "clip_g."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
+ state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+ if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
+ state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
+ replace_prefix["clip_g"] = "conditioner.embedders.0.model"
+ state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
+ return state_dict_g
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
+
+class SDXL(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 10, 10],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+ latent_format = latent_formats.SDXL
+
+ memory_usage_factor = 0.8
+
+ def model_type(self, state_dict, prefix=""):
+ if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
+ self.latent_format = latent_formats.SDXL_Playground_2_5()
+ self.sampling_settings["sigma_data"] = 0.5
+ self.sampling_settings["sigma_max"] = 80.0
+ self.sampling_settings["sigma_min"] = 0.002
+ return model_base.ModelType.EDM
+ elif "edm_vpred.sigma_max" in state_dict:
+ self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
+ if "edm_vpred.sigma_min" in state_dict:
+ self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
+ return model_base.ModelType.V_PREDICTION_EDM
+ elif "v_pred" in state_dict:
+ if "ztsnr" in state_dict: #Some zsnr anime checkpoints
+ self.sampling_settings["zsnr"] = True
+ return model_base.ModelType.V_PREDICTION
+ else:
+ return model_base.ModelType.EPS
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SDXL(self, model_type=self.model_type(state_dict, prefix), device=device)
+ if self.inpaint_model():
+ out.set_inpaint()
+ return out
+
+ def process_clip_state_dict(self, state_dict):
+ keys_to_replace = {}
+ replace_prefix = {}
+
+ replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
+ replace_prefix["conditioner.embedders.1.model."] = "clip_g."
+ state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+
+ state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+ state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
+ for k in state_dict:
+ if k.startswith("clip_l"):
+ state_dict_g[k] = state_dict[k]
+
+ state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
+ pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+ for p in pop_keys:
+ if p in state_dict_g:
+ state_dict_g.pop(p)
+
+ replace_prefix["clip_g"] = "conditioner.embedders.1.model"
+ replace_prefix["clip_l"] = "conditioner.embedders.0"
+ state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
+ return state_dict_g
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
+
+class SSD1B(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 4, 4],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class Segmind_Vega(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 1, 1, 2, 2],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class KOALA_700M(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 2, 5],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class KOALA_1B(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 2, 6],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ }
+
+class SVD_img2vid(supported_models_base.BASE):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 768,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ "attn_precision": torch.float32,
+ }
+
+ clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
+
+ latent_format = latent_formats.SD15
+
+ sampling_settings = {"sigma_max": 700.0, "sigma_min": 0.002}
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SVD_img2vid(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None
+
+class SV3D_u(SVD_img2vid):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 256,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+ vae_key_prefix = ["conditioner.embedders.1.encoder."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SV3D_u(self, device=device)
+ return out
+
+class SV3D_p(SV3D_u):
+ unet_config = {
+ "model_channels": 320,
+ "in_channels": 8,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+ "context_dim": 1024,
+ "adm_in_channels": 1280,
+ "use_temporal_attention": True,
+ "use_temporal_resblock": True
+ }
+
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SV3D_p(self, device=device)
+ return out
+
+class Stable_Zero123(supported_models_base.BASE):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ unet_extra_config = {
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ required_keys = {
+ "cc_projection.weight": None,
+ "cc_projection.bias": None,
+ }
+
+ clip_vision_prefix = "cond_stage_model.model.visual."
+
+ latent_format = latent_formats.SD15
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None
+
+class SD_X4Upscaler(SD20):
+ unet_config = {
+ "context_dim": 1024,
+ "model_channels": 256,
+ 'in_channels': 7,
+ "use_linear_in_transformer": True,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ }
+
+ unet_extra_config = {
+ "disable_self_attentions": [True, True, True, False],
+ "num_classes": 1000,
+ "num_heads": 8,
+ "num_head_channels": -1,
+ }
+
+ latent_format = latent_formats.SD_X4
+
+ sampling_settings = {
+ "linear_start": 0.0001,
+ "linear_end": 0.02,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SD_X4Upscaler(self, device=device)
+ return out
+
+class Stable_Cascade_C(supported_models_base.BASE):
+ unet_config = {
+ "stable_cascade_stage": 'c',
+ }
+
+ unet_extra_config = {}
+
+ latent_format = latent_formats.SC_Prior
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ sampling_settings = {
+ "shift": 2.0,
+ }
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoder."]
+ clip_vision_prefix = "clip_l_vision."
+
+ def process_unet_state_dict(self, state_dict):
+ key_list = list(state_dict.keys())
+ for y in ["weight", "bias"]:
+ suffix = "in_proj_{}".format(y)
+ keys = filter(lambda a: a.endswith(suffix), key_list)
+ for k_from in keys:
+ weights = state_dict.pop(k_from)
+ prefix = k_from[:-(len(suffix) + 1)]
+ shape_from = weights.shape[0] // 3
+ for x in range(3):
+ p = ["to_q", "to_k", "to_v"]
+ k_to = "{}.{}.{}".format(prefix, p[x], y)
+ state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+ return state_dict
+
+ def process_clip_state_dict(self, state_dict):
+ state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+ if "clip_g.text_projection" in state_dict:
+ state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
+ return state_dict
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.StableCascade_C(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
+
+class Stable_Cascade_B(Stable_Cascade_C):
+ unet_config = {
+ "stable_cascade_stage": 'b',
+ }
+
+ unet_extra_config = {}
+
+ latent_format = latent_formats.SC_B
+ supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+ sampling_settings = {
+ "shift": 1.0,
+ }
+
+ clip_vision_prefix = None
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.StableCascade_B(self, device=device)
+ return out
+
+class SD15_instructpix2pix(SD15):
+ unet_config = {
+ "context_dim": 768,
+ "model_channels": 320,
+ "use_linear_in_transformer": False,
+ "adm_in_channels": None,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SD15_instructpix2pix(self, device=device)
+
+class SDXL_instructpix2pix(SDXL):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "transformer_depth": [0, 0, 2, 2, 10, 10],
+ "context_dim": 2048,
+ "adm_in_channels": 2816,
+ "use_temporal_attention": False,
+ "in_channels": 8,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
+
+class LotusD(SD20):
+ unet_config = {
+ "model_channels": 320,
+ "use_linear_in_transformer": True,
+ "use_temporal_attention": False,
+ "adm_in_channels": 4,
+ "in_channels": 4,
+ }
+
+ unet_extra_config = {
+ "num_classes": 'sequential'
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ return model_base.Lotus(self, device=device)
+
+class SD3(supported_models_base.BASE):
+ unet_config = {
+ "in_channels": 16,
+ "pos_embed_scaling_factor": None,
+ }
+
+ sampling_settings = {
+ "shift": 3.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.SD3
+
+ memory_usage_factor = 1.2
+
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.SD3(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ clip_l = False
+ clip_g = False
+ t5 = False
+ pref = self.text_encoder_key_prefix[0]
+ if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+ clip_l = True
+ if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+ clip_g = True
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ if "dtype_t5" in t5_detect:
+ t5 = True
+
+ return supported_models_base.ClipTarget(comfy.text_encoders.sd3_clip.SD3Tokenizer, comfy.text_encoders.sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, **t5_detect))
+
+class StableAudio(supported_models_base.BASE):
+ unet_config = {
+ "audio_model": "dit1.0",
+ }
+
+ sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
+
+ unet_extra_config = {}
+ latent_format = latent_formats.StableAudio1
+
+ text_encoder_key_prefix = ["text_encoders."]
+ vae_key_prefix = ["pretransform.model."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
+ seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
+ return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
+
+ def process_unet_state_dict(self, state_dict):
+ for k in list(state_dict.keys()):
+ if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
+ state_dict.pop(k)
+ return state_dict
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model.model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.sa_t5.SAT5Tokenizer, comfy.text_encoders.sa_t5.SAT5Model)
+
+class AuraFlow(supported_models_base.BASE):
+ unet_config = {
+ "cond_seq_dim": 2048,
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 1.73,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.SDXL
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.AuraFlow(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
+
+class PixArtAlpha(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "pixart_alpha",
+ }
+
+ sampling_settings = {
+ "beta_schedule" : "sqrt_linear",
+ "linear_start" : 0.0001,
+ "linear_end" : 0.02,
+ "timesteps" : 1000,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.SD15
+
+ memory_usage_factor = 0.5
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.PixArt(self, device=device)
+ return out.eval()
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL)
+
+class PixArtSigma(PixArtAlpha):
+ unet_config = {
+ "image_model": "pixart_sigma",
+ }
+ latent_format = latent_formats.SDXL
+
+class HunyuanDiT(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "hydit",
+ }
+
+ unet_extra_config = {
+ "attn_precision": torch.float32,
+ }
+
+ sampling_settings = {
+ "linear_start": 0.00085,
+ "linear_end": 0.018,
+ }
+
+ latent_format = latent_formats.SDXL
+
+ memory_usage_factor = 1.3
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HunyuanDiT(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.hydit.HyditTokenizer, comfy.text_encoders.hydit.HyditModel)
+
+class HunyuanDiT1(HunyuanDiT):
+ unet_config = {
+ "image_model": "hydit1",
+ }
+
+ unet_extra_config = {}
+
+ sampling_settings = {
+ "linear_start" : 0.00085,
+ "linear_end" : 0.03,
+ }
+
+class Flux(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": True,
+ }
+
+ sampling_settings = {
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Flux
+
+ memory_usage_factor = 2.8
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Flux(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.flux.FluxTokenizer, comfy.text_encoders.flux.flux_clip(**t5_detect))
+
+class FluxInpaint(Flux):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": True,
+ "in_channels": 96,
+ }
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+class FluxSchnell(Flux):
+ unet_config = {
+ "image_model": "flux",
+ "guidance_embed": False,
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 1.0,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Flux(self, model_type=model_base.ModelType.FLOW, device=device)
+ return out
+
+class GenmoMochi(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "mochi_preview",
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 6.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Mochi
+
+ memory_usage_factor = 2.0 #TODO
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.GenmoMochi(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.genmo.MochiT5Tokenizer, comfy.text_encoders.genmo.mochi_te(**t5_detect))
+
+class LTXV(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "ltxv",
+ }
+
+ sampling_settings = {
+ "shift": 2.37,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.LTXV
+
+ memory_usage_factor = 5.5 # TODO: img2vid is about 2x vs txt2vid
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def __init__(self, unet_config):
+ super().__init__(unet_config)
+ self.memory_usage_factor = (unet_config.get("cross_attention_dim", 2048) / 2048) * 5.5
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.LTXV(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.lt.LTXVT5Tokenizer, comfy.text_encoders.lt.ltxv_te(**t5_detect))
+
+class HunyuanVideo(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "hunyuan_video",
+ }
+
+ sampling_settings = {
+ "shift": 7.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.HunyuanVideo
+
+ memory_usage_factor = 1.8 #TODO
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HunyuanVideo(self, device=device)
+ return out
+
+ def process_unet_state_dict(self, state_dict):
+ out_sd = {}
+ for k in list(state_dict.keys()):
+ key_out = k
+ key_out = key_out.replace("txt_in.t_embedder.mlp.0.", "txt_in.t_embedder.in_layer.").replace("txt_in.t_embedder.mlp.2.", "txt_in.t_embedder.out_layer.")
+ key_out = key_out.replace("txt_in.c_embedder.linear_1.", "txt_in.c_embedder.in_layer.").replace("txt_in.c_embedder.linear_2.", "txt_in.c_embedder.out_layer.")
+ key_out = key_out.replace("_mod.linear.", "_mod.lin.").replace("_attn_qkv.", "_attn.qkv.")
+ key_out = key_out.replace("mlp.fc1.", "mlp.0.").replace("mlp.fc2.", "mlp.2.")
+ key_out = key_out.replace("_attn_q_norm.weight", "_attn.norm.query_norm.scale").replace("_attn_k_norm.weight", "_attn.norm.key_norm.scale")
+ key_out = key_out.replace(".q_norm.weight", ".norm.query_norm.scale").replace(".k_norm.weight", ".norm.key_norm.scale")
+ key_out = key_out.replace("_attn_proj.", "_attn.proj.")
+ key_out = key_out.replace(".modulation.linear.", ".modulation.lin.")
+ key_out = key_out.replace("_in.mlp.2.", "_in.out_layer.").replace("_in.mlp.0.", "_in.in_layer.")
+ out_sd[key_out] = state_dict[k]
+ return out_sd
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model.model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))
+
+class HunyuanVideoI2V(HunyuanVideo):
+ unet_config = {
+ "image_model": "hunyuan_video",
+ "in_channels": 33,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HunyuanVideoI2V(self, device=device)
+ return out
+
+class HunyuanVideoSkyreelsI2V(HunyuanVideo):
+ unet_config = {
+ "image_model": "hunyuan_video",
+ "in_channels": 32,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HunyuanVideoSkyreelsI2V(self, device=device)
+ return out
+
+class CosmosT2V(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "cosmos",
+ "in_channels": 16,
+ }
+
+ sampling_settings = {
+ "sigma_data": 0.5,
+ "sigma_max": 80.0,
+ "sigma_min": 0.002,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Cosmos1CV8x8x8
+
+ memory_usage_factor = 1.6 #TODO
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.CosmosVideo(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
+
+class CosmosI2V(CosmosT2V):
+ unet_config = {
+ "image_model": "cosmos",
+ "in_channels": 17,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.CosmosVideo(self, image_to_video=True, device=device)
+ return out
+
+class CosmosT2IPredict2(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "cosmos_predict2",
+ "in_channels": 16,
+ }
+
+ sampling_settings = {
+ "sigma_data": 1.0,
+ "sigma_max": 80.0,
+ "sigma_min": 0.002,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Wan21
+
+ memory_usage_factor = 1.0
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ def __init__(self, unet_config):
+ super().__init__(unet_config)
+ self.memory_usage_factor = (unet_config.get("model_channels", 2048) / 2048) * 0.9
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.CosmosPredict2(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
+
+class CosmosI2VPredict2(CosmosT2IPredict2):
+ unet_config = {
+ "image_model": "cosmos_predict2",
+ "in_channels": 17,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.CosmosPredict2(self, image_to_video=True, device=device)
+ return out
+
+class Lumina2(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "lumina2",
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 6.0,
+ }
+
+ memory_usage_factor = 1.2
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Flux
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Lumina2(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**hunyuan_detect))
+
+class WAN21_T2V(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "t2v",
+ }
+
+ sampling_settings = {
+ "shift": 8.0,
+ }
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Wan21
+
+ memory_usage_factor = 1.0
+
+ supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def __init__(self, unet_config):
+ super().__init__(unet_config)
+ self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2000
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN21(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}umt5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.wan.WanT5Tokenizer, comfy.text_encoders.wan.te(**t5_detect))
+
+class WAN21_I2V(WAN21_T2V):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "i2v",
+ "in_dim": 36,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN21(self, image_to_video=True, device=device)
+ return out
+
+class WAN21_FunControl2V(WAN21_T2V):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "i2v",
+ "in_dim": 48,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN21(self, image_to_video=False, device=device)
+ return out
+
+class WAN21_Camera(WAN21_T2V):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "camera",
+ "in_dim": 32,
+ }
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN21_Camera(self, image_to_video=False, device=device)
+ return out
+class WAN21_Vace(WAN21_T2V):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "vace",
+ }
+
+ def __init__(self, unet_config):
+ super().__init__(unet_config)
+ self.memory_usage_factor = 1.2 * self.memory_usage_factor
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
+ return out
+
+class WAN22_T2V(WAN21_T2V):
+ unet_config = {
+ "image_model": "wan2.1",
+ "model_type": "t2v",
+ "out_dim": 48,
+ }
+
+ latent_format = latent_formats.Wan22
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.WAN22(self, image_to_video=True, device=device)
+ return out
+
+class Hunyuan3Dv2(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "hunyuan3d2",
+ }
+
+ unet_extra_config = {}
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 1.0,
+ }
+
+ memory_usage_factor = 3.5
+
+ clip_vision_prefix = "conditioner.main_image_encoder.model."
+ vae_key_prefix = ["vae."]
+
+ latent_format = latent_formats.Hunyuan3Dv2
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Hunyuan3Dv2(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None
+
+class Hunyuan3Dv2mini(Hunyuan3Dv2):
+ unet_config = {
+ "image_model": "hunyuan3d2",
+ "depth": 8,
+ }
+
+ latent_format = latent_formats.Hunyuan3Dv2mini
+
+class HiDream(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "hidream",
+ }
+
+ sampling_settings = {
+ "shift": 3.0,
+ }
+
+ sampling_settings = {
+ }
+
+ # memory_usage_factor = 1.2 # TODO
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Flux
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.HiDream(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return None # TODO
+
+class Chroma(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "chroma",
+ }
+
+ unet_extra_config = {
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ }
+
+ latent_format = comfy.latent_formats.Flux
+
+ memory_usage_factor = 3.2
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Chroma(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))
+
+class ACEStep(supported_models_base.BASE):
+ unet_config = {
+ "audio_model": "ace",
+ }
+
+ unet_extra_config = {
+ }
+
+ sampling_settings = {
+ "shift": 3.0,
+ }
+
+ latent_format = comfy.latent_formats.ACEAudio
+
+ memory_usage_factor = 0.5
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.ACEStep(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ return supported_models_base.ClipTarget(comfy.text_encoders.ace.AceT5Tokenizer, comfy.text_encoders.ace.AceT5Model)
+
+class Omnigen2(supported_models_base.BASE):
+ unet_config = {
+ "image_model": "omnigen2",
+ }
+
+ sampling_settings = {
+ "multiplier": 1.0,
+ "shift": 2.6,
+ }
+
+ memory_usage_factor = 1.65 #TODO
+
+ unet_extra_config = {}
+ latent_format = latent_formats.Flux
+
+ supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+ vae_key_prefix = ["vae."]
+ text_encoder_key_prefix = ["text_encoders."]
+
+ def __init__(self, unet_config):
+ super().__init__(unet_config)
+ if comfy.model_management.extended_fp16_support():
+ self.supported_inference_dtypes = [torch.float16] + self.supported_inference_dtypes
+
+ def get_model(self, state_dict, prefix="", device=None):
+ out = model_base.Omnigen2(self, device=device)
+ return out
+
+ def clip_target(self, state_dict={}):
+ pref = self.text_encoder_key_prefix[0]
+ hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_3b.transformer.".format(pref))
+ return supported_models_base.ClipTarget(comfy.text_encoders.omnigen2.Omnigen2Tokenizer, comfy.text_encoders.omnigen2.te(**hunyuan_detect))
+
+
+models = [LotusD, Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, Hunyuan3Dv2mini, Hunyuan3Dv2, HiDream, Chroma, ACEStep, Omnigen2]
+
+models += [SVD_img2vid]
diff --git a/ComfyUI/comfy/supported_models_base.py b/ComfyUI/comfy/supported_models_base.py
new file mode 100644
index 0000000000000000000000000000000000000000..54573abb110d8cc5e190ecefa0f9aecf95da0b99
--- /dev/null
+++ b/ComfyUI/comfy/supported_models_base.py
@@ -0,0 +1,119 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+import torch
+from . import model_base
+from . import utils
+from . import latent_formats
+
+class ClipTarget:
+ def __init__(self, tokenizer, clip):
+ self.clip = clip
+ self.tokenizer = tokenizer
+ self.params = {}
+
+class BASE:
+ unet_config = {}
+ unet_extra_config = {
+ "num_heads": -1,
+ "num_head_channels": 64,
+ }
+
+ required_keys = {}
+
+ clip_prefix = []
+ clip_vision_prefix = None
+ noise_aug_config = None
+ sampling_settings = {}
+ latent_format = latent_formats.LatentFormat
+ vae_key_prefix = ["first_stage_model."]
+ text_encoder_key_prefix = ["cond_stage_model."]
+ supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+ memory_usage_factor = 2.0
+
+ manual_cast_dtype = None
+ custom_operations = None
+ scaled_fp8 = None
+ optimizations = {"fp8": False}
+
+ @classmethod
+ def matches(s, unet_config, state_dict=None):
+ for k in s.unet_config:
+ if k not in unet_config or s.unet_config[k] != unet_config[k]:
+ return False
+ if state_dict is not None:
+ for k in s.required_keys:
+ if k not in state_dict:
+ return False
+ return True
+
+ def model_type(self, state_dict, prefix=""):
+ return model_base.ModelType.EPS
+
+ def inpaint_model(self):
+ return self.unet_config["in_channels"] > 4
+
+ def __init__(self, unet_config):
+ self.unet_config = unet_config.copy()
+ self.sampling_settings = self.sampling_settings.copy()
+ self.latent_format = self.latent_format()
+ self.optimizations = self.optimizations.copy()
+ for x in self.unet_extra_config:
+ self.unet_config[x] = self.unet_extra_config[x]
+
+ def get_model(self, state_dict, prefix="", device=None):
+ if self.noise_aug_config is not None:
+ out = model_base.SD21UNCLIP(self, self.noise_aug_config, model_type=self.model_type(state_dict, prefix), device=device)
+ else:
+ out = model_base.BaseModel(self, model_type=self.model_type(state_dict, prefix), device=device)
+ if self.inpaint_model():
+ out.set_inpaint()
+ return out
+
+ def process_clip_state_dict(self, state_dict):
+ state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+ return state_dict
+
+ def process_unet_state_dict(self, state_dict):
+ return state_dict
+
+ def process_vae_state_dict(self, state_dict):
+ return state_dict
+
+ def process_clip_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": self.text_encoder_key_prefix[0]}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_clip_vision_state_dict_for_saving(self, state_dict):
+ replace_prefix = {}
+ if self.clip_vision_prefix is not None:
+ replace_prefix[""] = self.clip_vision_prefix
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_unet_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": "model.diffusion_model."}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def process_vae_state_dict_for_saving(self, state_dict):
+ replace_prefix = {"": self.vae_key_prefix[0]}
+ return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+ def set_inference_dtype(self, dtype, manual_cast_dtype):
+ self.unet_config['dtype'] = dtype
+ self.manual_cast_dtype = manual_cast_dtype
diff --git a/ComfyUI/comfy/t2i_adapter/adapter.py b/ComfyUI/comfy/t2i_adapter/adapter.py
new file mode 100644
index 0000000000000000000000000000000000000000..10ea18e326693f237b3b219970c86e3808f6d334
--- /dev/null
+++ b/ComfyUI/comfy/t2i_adapter/adapter.py
@@ -0,0 +1,299 @@
+#taken from https://github.com/TencentARC/T2I-Adapter
+import torch
+import torch.nn as nn
+from collections import OrderedDict
+
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if not self.use_conv:
+ padding = [x.shape[2] % 2, x.shape[3] % 2]
+ self.op.padding = padding
+
+ x = self.op(x)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, in_c, out_c, down, ksize=3, sk=False, use_conv=True):
+ super().__init__()
+ ps = ksize // 2
+ if in_c != out_c or sk == False:
+ self.in_conv = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+ else:
+ # print('n_in')
+ self.in_conv = None
+ self.block1 = nn.Conv2d(out_c, out_c, 3, 1, 1)
+ self.act = nn.ReLU()
+ self.block2 = nn.Conv2d(out_c, out_c, ksize, 1, ps)
+ if sk == False:
+ self.skep = nn.Conv2d(in_c, out_c, ksize, 1, ps)
+ else:
+ self.skep = None
+
+ self.down = down
+ if self.down == True:
+ self.down_opt = Downsample(in_c, use_conv=use_conv)
+
+ def forward(self, x):
+ if self.down == True:
+ x = self.down_opt(x)
+ if self.in_conv is not None: # edit
+ x = self.in_conv(x)
+
+ h = self.block1(x)
+ h = self.act(h)
+ h = self.block2(h)
+ if self.skep is not None:
+ return h + self.skep(x)
+ else:
+ return h + x
+
+
+class Adapter(nn.Module):
+ def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64, ksize=3, sk=False, use_conv=True, xl=True):
+ super(Adapter, self).__init__()
+ self.unshuffle_amount = 8
+ resblock_no_downsample = []
+ resblock_downsample = [3, 2, 1]
+ self.xl = xl
+ if self.xl:
+ self.unshuffle_amount = 16
+ resblock_no_downsample = [1]
+ resblock_downsample = [2]
+
+ self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
+ self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
+ self.channels = channels
+ self.nums_rb = nums_rb
+ self.body = []
+ for i in range(len(channels)):
+ for j in range(nums_rb):
+ if (i in resblock_downsample) and (j == 0):
+ self.body.append(
+ ResnetBlock(channels[i - 1], channels[i], down=True, ksize=ksize, sk=sk, use_conv=use_conv))
+ elif (i in resblock_no_downsample) and (j == 0):
+ self.body.append(
+ ResnetBlock(channels[i - 1], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
+ else:
+ self.body.append(
+ ResnetBlock(channels[i], channels[i], down=False, ksize=ksize, sk=sk, use_conv=use_conv))
+ self.body = nn.ModuleList(self.body)
+ self.conv_in = nn.Conv2d(cin, channels[0], 3, 1, 1)
+
+ def forward(self, x):
+ # unshuffle
+ x = self.unshuffle(x)
+ # extract features
+ features = []
+ x = self.conv_in(x)
+ for i in range(len(self.channels)):
+ for j in range(self.nums_rb):
+ idx = i * self.nums_rb + j
+ x = self.body[idx](x)
+ if self.xl:
+ features.append(None)
+ if i == 0:
+ features.append(None)
+ features.append(None)
+ if i == 2:
+ features.append(None)
+ else:
+ features.append(None)
+ features.append(None)
+ features.append(x)
+
+ features = features[::-1]
+
+ if self.xl:
+ return {"input": features[1:], "middle": features[:1]}
+ else:
+ return {"input": features}
+
+
+
+class LayerNorm(nn.LayerNorm):
+ """Subclass torch's LayerNorm to handle fp16."""
+
+ def forward(self, x: torch.Tensor):
+ orig_type = x.dtype
+ ret = super().forward(x.type(torch.float32))
+ return ret.type(orig_type)
+
+
+class QuickGELU(nn.Module):
+
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+
+ def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+ super().__init__()
+
+ self.attn = nn.MultiheadAttention(d_model, n_head)
+ self.ln_1 = LayerNorm(d_model)
+ self.mlp = nn.Sequential(
+ OrderedDict([("c_fc", nn.Linear(d_model, d_model * 4)), ("gelu", QuickGELU()),
+ ("c_proj", nn.Linear(d_model * 4, d_model))]))
+ self.ln_2 = LayerNorm(d_model)
+ self.attn_mask = attn_mask
+
+ def attention(self, x: torch.Tensor):
+ self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+ return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
+
+ def forward(self, x: torch.Tensor):
+ x = x + self.attention(self.ln_1(x))
+ x = x + self.mlp(self.ln_2(x))
+ return x
+
+
+class StyleAdapter(nn.Module):
+
+ def __init__(self, width=1024, context_dim=768, num_head=8, n_layes=3, num_token=4):
+ super().__init__()
+
+ scale = width ** -0.5
+ self.transformer_layes = nn.Sequential(*[ResidualAttentionBlock(width, num_head) for _ in range(n_layes)])
+ self.num_token = num_token
+ self.style_embedding = nn.Parameter(torch.randn(1, num_token, width) * scale)
+ self.ln_post = LayerNorm(width)
+ self.ln_pre = LayerNorm(width)
+ self.proj = nn.Parameter(scale * torch.randn(width, context_dim))
+
+ def forward(self, x):
+ # x shape [N, HW+1, C]
+ style_embedding = self.style_embedding + torch.zeros(
+ (x.shape[0], self.num_token, self.style_embedding.shape[-1]), device=x.device)
+ x = torch.cat([x, style_embedding], dim=1)
+ x = self.ln_pre(x)
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer_layes(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+
+ x = self.ln_post(x[:, -self.num_token:, :])
+ x = x @ self.proj
+
+ return x
+
+
+class ResnetBlock_light(nn.Module):
+ def __init__(self, in_c):
+ super().__init__()
+ self.block1 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+ self.act = nn.ReLU()
+ self.block2 = nn.Conv2d(in_c, in_c, 3, 1, 1)
+
+ def forward(self, x):
+ h = self.block1(x)
+ h = self.act(h)
+ h = self.block2(h)
+
+ return h + x
+
+
+class extractor(nn.Module):
+ def __init__(self, in_c, inter_c, out_c, nums_rb, down=False):
+ super().__init__()
+ self.in_conv = nn.Conv2d(in_c, inter_c, 1, 1, 0)
+ self.body = []
+ for _ in range(nums_rb):
+ self.body.append(ResnetBlock_light(inter_c))
+ self.body = nn.Sequential(*self.body)
+ self.out_conv = nn.Conv2d(inter_c, out_c, 1, 1, 0)
+ self.down = down
+ if self.down == True:
+ self.down_opt = Downsample(in_c, use_conv=False)
+
+ def forward(self, x):
+ if self.down == True:
+ x = self.down_opt(x)
+ x = self.in_conv(x)
+ x = self.body(x)
+ x = self.out_conv(x)
+
+ return x
+
+
+class Adapter_light(nn.Module):
+ def __init__(self, channels=[320, 640, 1280, 1280], nums_rb=3, cin=64):
+ super(Adapter_light, self).__init__()
+ self.unshuffle_amount = 8
+ self.unshuffle = nn.PixelUnshuffle(self.unshuffle_amount)
+ self.input_channels = cin // (self.unshuffle_amount * self.unshuffle_amount)
+ self.channels = channels
+ self.nums_rb = nums_rb
+ self.body = []
+ self.xl = False
+
+ for i in range(len(channels)):
+ if i == 0:
+ self.body.append(extractor(in_c=cin, inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=False))
+ else:
+ self.body.append(extractor(in_c=channels[i-1], inter_c=channels[i]//4, out_c=channels[i], nums_rb=nums_rb, down=True))
+ self.body = nn.ModuleList(self.body)
+
+ def forward(self, x):
+ # unshuffle
+ x = self.unshuffle(x)
+ # extract features
+ features = []
+ for i in range(len(self.channels)):
+ x = self.body[i](x)
+ features.append(None)
+ features.append(None)
+ features.append(x)
+
+ return {"input": features[::-1]}
diff --git a/ComfyUI/comfy/taesd/taesd.py b/ComfyUI/comfy/taesd/taesd.py
new file mode 100644
index 0000000000000000000000000000000000000000..ce36f1a84dae599a35e84a8da3462408c0f0ccc6
--- /dev/null
+++ b/ComfyUI/comfy/taesd/taesd.py
@@ -0,0 +1,79 @@
+#!/usr/bin/env python3
+"""
+Tiny AutoEncoder for Stable Diffusion
+(DNN for encoding / decoding SD's latent space)
+"""
+import torch
+import torch.nn as nn
+
+import comfy.utils
+import comfy.ops
+
+def conv(n_in, n_out, **kwargs):
+ return comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 3, padding=1, **kwargs)
+
+class Clamp(nn.Module):
+ def forward(self, x):
+ return torch.tanh(x / 3) * 3
+
+class Block(nn.Module):
+ def __init__(self, n_in, n_out):
+ super().__init__()
+ self.conv = nn.Sequential(conv(n_in, n_out), nn.ReLU(), conv(n_out, n_out), nn.ReLU(), conv(n_out, n_out))
+ self.skip = comfy.ops.disable_weight_init.Conv2d(n_in, n_out, 1, bias=False) if n_in != n_out else nn.Identity()
+ self.fuse = nn.ReLU()
+ def forward(self, x):
+ return self.fuse(self.conv(x) + self.skip(x))
+
+def Encoder(latent_channels=4):
+ return nn.Sequential(
+ conv(3, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
+ conv(64, latent_channels),
+ )
+
+
+def Decoder(latent_channels=4):
+ return nn.Sequential(
+ Clamp(), conv(latent_channels, 64), nn.ReLU(),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
+ Block(64, 64), conv(64, 3),
+ )
+
+class TAESD(nn.Module):
+ latent_magnitude = 3
+ latent_shift = 0.5
+
+ def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
+ """Initialize pretrained TAESD on the given device from the given checkpoints."""
+ super().__init__()
+ self.taesd_encoder = Encoder(latent_channels=latent_channels)
+ self.taesd_decoder = Decoder(latent_channels=latent_channels)
+ self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
+ self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
+ if encoder_path is not None:
+ self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
+ if decoder_path is not None:
+ self.taesd_decoder.load_state_dict(comfy.utils.load_torch_file(decoder_path, safe_load=True))
+
+ @staticmethod
+ def scale_latents(x):
+ """raw latents -> [0, 1]"""
+ return x.div(2 * TAESD.latent_magnitude).add(TAESD.latent_shift).clamp(0, 1)
+
+ @staticmethod
+ def unscale_latents(x):
+ """[0, 1] -> raw latents"""
+ return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
+
+ def decode(self, x):
+ x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
+ x_sample = x_sample.sub(0.5).mul(2)
+ return x_sample
+
+ def encode(self, x):
+ return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
diff --git a/ComfyUI/comfy/text_encoders/ace.py b/ComfyUI/comfy/text_encoders/ace.py
new file mode 100644
index 0000000000000000000000000000000000000000..d650bb10d5d5bfdd485f18095e4a99ed7b06b6b7
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/ace.py
@@ -0,0 +1,153 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+import re
+import torch
+import logging
+
+from tokenizers import Tokenizer
+from .ace_text_cleaners import multilingual_cleaners, japanese_to_romaji
+
+SUPPORT_LANGUAGES = {
+ "en": 259, "de": 260, "fr": 262, "es": 284, "it": 285,
+ "pt": 286, "pl": 294, "tr": 295, "ru": 267, "cs": 293,
+ "nl": 297, "ar": 5022, "zh": 5023, "ja": 5412, "hu": 5753,
+ "ko": 6152, "hi": 6680
+}
+
+structure_pattern = re.compile(r"\[.*?\]")
+
+DEFAULT_VOCAB_FILE = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json")
+
+
+class VoiceBpeTokenizer:
+ def __init__(self, vocab_file=DEFAULT_VOCAB_FILE):
+ self.tokenizer = None
+ if vocab_file is not None:
+ self.tokenizer = Tokenizer.from_file(vocab_file)
+
+ def preprocess_text(self, txt, lang):
+ txt = multilingual_cleaners(txt, lang)
+ return txt
+
+ def encode(self, txt, lang='en'):
+ # lang = lang.split("-")[0] # remove the region
+ # self.check_input_length(txt, lang)
+ txt = self.preprocess_text(txt, lang)
+ lang = "zh-cn" if lang == "zh" else lang
+ txt = f"[{lang}]{txt}"
+ txt = txt.replace(" ", "[SPACE]")
+ return self.tokenizer.encode(txt).ids
+
+ def get_lang(self, line):
+ if line.startswith("[") and line[3:4] == ']':
+ lang = line[1:3].lower()
+ if lang in SUPPORT_LANGUAGES:
+ return lang, line[4:]
+ return "en", line
+
+ def __call__(self, string):
+ lines = string.split("\n")
+ lyric_token_idx = [261]
+ for line in lines:
+ line = line.strip()
+ if not line:
+ lyric_token_idx += [2]
+ continue
+
+ lang, line = self.get_lang(line)
+
+ if lang not in SUPPORT_LANGUAGES:
+ lang = "en"
+ if "zh" in lang:
+ lang = "zh"
+ if "spa" in lang:
+ lang = "es"
+
+ try:
+ line_out = japanese_to_romaji(line)
+ if line_out != line:
+ lang = "ja"
+ line = line_out
+ except:
+ pass
+
+ try:
+ if structure_pattern.match(line):
+ token_idx = self.encode(line, "en")
+ else:
+ token_idx = self.encode(line, lang)
+ lyric_token_idx = lyric_token_idx + token_idx + [2]
+ except Exception as e:
+ logging.warning("tokenize error {} for line {} major_language {}".format(e, line, lang))
+ return {"input_ids": lyric_token_idx}
+
+ @staticmethod
+ def from_pretrained(path, **kwargs):
+ return VoiceBpeTokenizer(path, **kwargs)
+
+ def get_vocab(self):
+ return {}
+
+
+class UMT5BaseModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "umt5_config_base.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=False, model_options=model_options)
+
+class UMT5BaseTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer = tokenizer_data.get("spiece_model", None)
+ super().__init__(tokenizer, pad_with_end=False, embedding_size=768, embedding_key='umt5base', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=0, tokenizer_data=tokenizer_data)
+
+ def state_dict(self):
+ return {"spiece_model": self.tokenizer.serialize_model()}
+
+class LyricsTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "ace_lyrics_tokenizer"), "vocab.json")
+ super().__init__(tokenizer, pad_with_end=False, embedding_size=1024, embedding_key='lyrics', tokenizer_class=VoiceBpeTokenizer, has_start_token=True, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=2, has_end_token=False, tokenizer_data=tokenizer_data)
+
+class AceT5Tokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ self.voicebpe = LyricsTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.umt5base = UMT5BaseTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out["lyrics"] = self.voicebpe.tokenize_with_weights(kwargs.get("lyrics", ""), return_word_ids, **kwargs)
+ out["umt5base"] = self.umt5base.tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.umt5base.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return self.umt5base.state_dict()
+
+class AceT5Model(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__()
+ self.umt5base = UMT5BaseModel(device=device, dtype=dtype, model_options=model_options)
+ self.dtypes = set()
+ if dtype is not None:
+ self.dtypes.add(dtype)
+
+ def set_clip_options(self, options):
+ self.umt5base.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.umt5base.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_umt5base = token_weight_pairs["umt5base"]
+ token_weight_pairs_lyrics = token_weight_pairs["lyrics"]
+
+ t5_out, t5_pooled = self.umt5base.encode_token_weights(token_weight_pairs_umt5base)
+
+ lyrics_embeds = torch.tensor(list(map(lambda a: a[0], token_weight_pairs_lyrics[0]))).unsqueeze(0)
+ return t5_out, None, {"conditioning_lyrics": lyrics_embeds}
+
+ def load_sd(self, sd):
+ return self.umt5base.load_sd(sd)
diff --git a/ComfyUI/comfy/text_encoders/ace_text_cleaners.py b/ComfyUI/comfy/text_encoders/ace_text_cleaners.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd31d8d8cdffcda51cc26405c3f6f2bcee410d48
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/ace_text_cleaners.py
@@ -0,0 +1,395 @@
+# basic text cleaners for the ACE step model
+# I didn't copy the ones from the reference code because I didn't want to deal with the dependencies
+# TODO: more languages than english?
+
+import re
+
+def japanese_to_romaji(japanese_text):
+ """
+ Convert Japanese hiragana and katakana to romaji (Latin alphabet representation).
+
+ Args:
+ japanese_text (str): Text containing hiragana and/or katakana characters
+
+ Returns:
+ str: The romaji (Latin alphabet) equivalent
+ """
+ # Dictionary mapping kana characters to their romaji equivalents
+ kana_map = {
+ # Katakana characters
+ 'ア': 'a', 'イ': 'i', 'ウ': 'u', 'エ': 'e', 'オ': 'o',
+ 'カ': 'ka', 'キ': 'ki', 'ク': 'ku', 'ケ': 'ke', 'コ': 'ko',
+ 'サ': 'sa', 'シ': 'shi', 'ス': 'su', 'セ': 'se', 'ソ': 'so',
+ 'タ': 'ta', 'チ': 'chi', 'ツ': 'tsu', 'テ': 'te', 'ト': 'to',
+ 'ナ': 'na', 'ニ': 'ni', 'ヌ': 'nu', 'ネ': 'ne', 'ノ': 'no',
+ 'ハ': 'ha', 'ヒ': 'hi', 'フ': 'fu', 'ヘ': 'he', 'ホ': 'ho',
+ 'マ': 'ma', 'ミ': 'mi', 'ム': 'mu', 'メ': 'me', 'モ': 'mo',
+ 'ヤ': 'ya', 'ユ': 'yu', 'ヨ': 'yo',
+ 'ラ': 'ra', 'リ': 'ri', 'ル': 'ru', 'レ': 're', 'ロ': 'ro',
+ 'ワ': 'wa', 'ヲ': 'wo', 'ン': 'n',
+
+ # Katakana voiced consonants
+ 'ガ': 'ga', 'ギ': 'gi', 'グ': 'gu', 'ゲ': 'ge', 'ゴ': 'go',
+ 'ザ': 'za', 'ジ': 'ji', 'ズ': 'zu', 'ゼ': 'ze', 'ゾ': 'zo',
+ 'ダ': 'da', 'ヂ': 'ji', 'ヅ': 'zu', 'デ': 'de', 'ド': 'do',
+ 'バ': 'ba', 'ビ': 'bi', 'ブ': 'bu', 'ベ': 'be', 'ボ': 'bo',
+ 'パ': 'pa', 'ピ': 'pi', 'プ': 'pu', 'ペ': 'pe', 'ポ': 'po',
+
+ # Katakana combinations
+ 'キャ': 'kya', 'キュ': 'kyu', 'キョ': 'kyo',
+ 'シャ': 'sha', 'シュ': 'shu', 'ショ': 'sho',
+ 'チャ': 'cha', 'チュ': 'chu', 'チョ': 'cho',
+ 'ニャ': 'nya', 'ニュ': 'nyu', 'ニョ': 'nyo',
+ 'ヒャ': 'hya', 'ヒュ': 'hyu', 'ヒョ': 'hyo',
+ 'ミャ': 'mya', 'ミュ': 'myu', 'ミョ': 'myo',
+ 'リャ': 'rya', 'リュ': 'ryu', 'リョ': 'ryo',
+ 'ギャ': 'gya', 'ギュ': 'gyu', 'ギョ': 'gyo',
+ 'ジャ': 'ja', 'ジュ': 'ju', 'ジョ': 'jo',
+ 'ビャ': 'bya', 'ビュ': 'byu', 'ビョ': 'byo',
+ 'ピャ': 'pya', 'ピュ': 'pyu', 'ピョ': 'pyo',
+
+ # Katakana small characters and special cases
+ 'ッ': '', # Small tsu (doubles the following consonant)
+ 'ャ': 'ya', 'ュ': 'yu', 'ョ': 'yo',
+
+ # Katakana extras
+ 'ヴ': 'vu', 'ファ': 'fa', 'フィ': 'fi', 'フェ': 'fe', 'フォ': 'fo',
+ 'ウィ': 'wi', 'ウェ': 'we', 'ウォ': 'wo',
+
+ # Hiragana characters
+ 'あ': 'a', 'い': 'i', 'う': 'u', 'え': 'e', 'お': 'o',
+ 'か': 'ka', 'き': 'ki', 'く': 'ku', 'け': 'ke', 'こ': 'ko',
+ 'さ': 'sa', 'し': 'shi', 'す': 'su', 'せ': 'se', 'そ': 'so',
+ 'た': 'ta', 'ち': 'chi', 'つ': 'tsu', 'て': 'te', 'と': 'to',
+ 'な': 'na', 'に': 'ni', 'ぬ': 'nu', 'ね': 'ne', 'の': 'no',
+ 'は': 'ha', 'ひ': 'hi', 'ふ': 'fu', 'へ': 'he', 'ほ': 'ho',
+ 'ま': 'ma', 'み': 'mi', 'む': 'mu', 'め': 'me', 'も': 'mo',
+ 'や': 'ya', 'ゆ': 'yu', 'よ': 'yo',
+ 'ら': 'ra', 'り': 'ri', 'る': 'ru', 'れ': 're', 'ろ': 'ro',
+ 'わ': 'wa', 'を': 'wo', 'ん': 'n',
+
+ # Hiragana voiced consonants
+ 'が': 'ga', 'ぎ': 'gi', 'ぐ': 'gu', 'げ': 'ge', 'ご': 'go',
+ 'ざ': 'za', 'じ': 'ji', 'ず': 'zu', 'ぜ': 'ze', 'ぞ': 'zo',
+ 'だ': 'da', 'ぢ': 'ji', 'づ': 'zu', 'で': 'de', 'ど': 'do',
+ 'ば': 'ba', 'び': 'bi', 'ぶ': 'bu', 'べ': 'be', 'ぼ': 'bo',
+ 'ぱ': 'pa', 'ぴ': 'pi', 'ぷ': 'pu', 'ぺ': 'pe', 'ぽ': 'po',
+
+ # Hiragana combinations
+ 'きゃ': 'kya', 'きゅ': 'kyu', 'きょ': 'kyo',
+ 'しゃ': 'sha', 'しゅ': 'shu', 'しょ': 'sho',
+ 'ちゃ': 'cha', 'ちゅ': 'chu', 'ちょ': 'cho',
+ 'にゃ': 'nya', 'にゅ': 'nyu', 'にょ': 'nyo',
+ 'ひゃ': 'hya', 'ひゅ': 'hyu', 'ひょ': 'hyo',
+ 'みゃ': 'mya', 'みゅ': 'myu', 'みょ': 'myo',
+ 'りゃ': 'rya', 'りゅ': 'ryu', 'りょ': 'ryo',
+ 'ぎゃ': 'gya', 'ぎゅ': 'gyu', 'ぎょ': 'gyo',
+ 'じゃ': 'ja', 'じゅ': 'ju', 'じょ': 'jo',
+ 'びゃ': 'bya', 'びゅ': 'byu', 'びょ': 'byo',
+ 'ぴゃ': 'pya', 'ぴゅ': 'pyu', 'ぴょ': 'pyo',
+
+ # Hiragana small characters and special cases
+ 'っ': '', # Small tsu (doubles the following consonant)
+ 'ゃ': 'ya', 'ゅ': 'yu', 'ょ': 'yo',
+
+ # Common punctuation and spaces
+ ' ': ' ', # Japanese space
+ '、': ', ', '。': '. ',
+ }
+
+ result = []
+ i = 0
+
+ while i < len(japanese_text):
+ # Check for small tsu (doubling the following consonant)
+ if i < len(japanese_text) - 1 and (japanese_text[i] == 'っ' or japanese_text[i] == 'ッ'):
+ if i < len(japanese_text) - 1 and japanese_text[i+1] in kana_map:
+ next_romaji = kana_map[japanese_text[i+1]]
+ if next_romaji and next_romaji[0] not in 'aiueon':
+ result.append(next_romaji[0]) # Double the consonant
+ i += 1
+ continue
+
+ # Check for combinations with small ya, yu, yo
+ if i < len(japanese_text) - 1 and japanese_text[i+1] in ('ゃ', 'ゅ', 'ょ', 'ャ', 'ュ', 'ョ'):
+ combo = japanese_text[i:i+2]
+ if combo in kana_map:
+ result.append(kana_map[combo])
+ i += 2
+ continue
+
+ # Regular character
+ if japanese_text[i] in kana_map:
+ result.append(kana_map[japanese_text[i]])
+ else:
+ # If it's not in our map, keep it as is (might be kanji, romaji, etc.)
+ result.append(japanese_text[i])
+
+ i += 1
+
+ return ''.join(result)
+
+def number_to_text(num, ordinal=False):
+ """
+ Convert a number (int or float) to its text representation.
+
+ Args:
+ num: The number to convert
+
+ Returns:
+ str: Text representation of the number
+ """
+
+ if not isinstance(num, (int, float)):
+ return "Input must be a number"
+
+ # Handle special case of zero
+ if num == 0:
+ return "zero"
+
+ # Handle negative numbers
+ negative = num < 0
+ num = abs(num)
+
+ # Handle floats
+ if isinstance(num, float):
+ # Split into integer and decimal parts
+ int_part = int(num)
+
+ # Convert both parts
+ int_text = _int_to_text(int_part)
+
+ # Handle decimal part (convert to string and remove '0.')
+ decimal_str = str(num).split('.')[1]
+ decimal_text = " point " + " ".join(_digit_to_text(int(digit)) for digit in decimal_str)
+
+ result = int_text + decimal_text
+ else:
+ # Handle integers
+ result = _int_to_text(num)
+
+ # Add 'negative' prefix for negative numbers
+ if negative:
+ result = "negative " + result
+
+ return result
+
+
+def _int_to_text(num):
+ """Helper function to convert an integer to text"""
+
+ ones = ["", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine",
+ "ten", "eleven", "twelve", "thirteen", "fourteen", "fifteen", "sixteen",
+ "seventeen", "eighteen", "nineteen"]
+
+ tens = ["", "", "twenty", "thirty", "forty", "fifty", "sixty", "seventy", "eighty", "ninety"]
+
+ if num < 20:
+ return ones[num]
+
+ if num < 100:
+ return tens[num // 10] + (" " + ones[num % 10] if num % 10 != 0 else "")
+
+ if num < 1000:
+ return ones[num // 100] + " hundred" + (" " + _int_to_text(num % 100) if num % 100 != 0 else "")
+
+ if num < 1000000:
+ return _int_to_text(num // 1000) + " thousand" + (" " + _int_to_text(num % 1000) if num % 1000 != 0 else "")
+
+ if num < 1000000000:
+ return _int_to_text(num // 1000000) + " million" + (" " + _int_to_text(num % 1000000) if num % 1000000 != 0 else "")
+
+ return _int_to_text(num // 1000000000) + " billion" + (" " + _int_to_text(num % 1000000000) if num % 1000000000 != 0 else "")
+
+
+def _digit_to_text(digit):
+ """Convert a single digit to text"""
+ digits = ["zero", "one", "two", "three", "four", "five", "six", "seven", "eight", "nine"]
+ return digits[digit]
+
+
+_whitespace_re = re.compile(r"\s+")
+
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = {
+ "en": [
+ (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+ for x in [
+ ("mrs", "misess"),
+ ("mr", "mister"),
+ ("dr", "doctor"),
+ ("st", "saint"),
+ ("co", "company"),
+ ("jr", "junior"),
+ ("maj", "major"),
+ ("gen", "general"),
+ ("drs", "doctors"),
+ ("rev", "reverend"),
+ ("lt", "lieutenant"),
+ ("hon", "honorable"),
+ ("sgt", "sergeant"),
+ ("capt", "captain"),
+ ("esq", "esquire"),
+ ("ltd", "limited"),
+ ("col", "colonel"),
+ ("ft", "fort"),
+ ]
+ ],
+}
+
+
+def expand_abbreviations_multilingual(text, lang="en"):
+ for regex, replacement in _abbreviations[lang]:
+ text = re.sub(regex, replacement, text)
+ return text
+
+
+_symbols_multilingual = {
+ "en": [
+ (re.compile(r"%s" % re.escape(x[0]), re.IGNORECASE), x[1])
+ for x in [
+ ("&", " and "),
+ ("@", " at "),
+ ("%", " percent "),
+ ("#", " hash "),
+ ("$", " dollar "),
+ ("£", " pound "),
+ ("°", " degree "),
+ ]
+ ],
+}
+
+
+def expand_symbols_multilingual(text, lang="en"):
+ for regex, replacement in _symbols_multilingual[lang]:
+ text = re.sub(regex, replacement, text)
+ text = text.replace(" ", " ") # Ensure there are no double spaces
+ return text.strip()
+
+
+_ordinal_re = {
+ "en": re.compile(r"([0-9]+)(st|nd|rd|th)"),
+}
+_number_re = re.compile(r"[0-9]+")
+_currency_re = {
+ "USD": re.compile(r"((\$[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+\$))"),
+ "GBP": re.compile(r"((£[0-9\.\,]*[0-9]+)|([0-9\.\,]*[0-9]+£))"),
+ "EUR": re.compile(r"(([0-9\.\,]*[0-9]+€)|((€[0-9\.\,]*[0-9]+)))"),
+}
+
+_comma_number_re = re.compile(r"\b\d{1,3}(,\d{3})*(\.\d+)?\b")
+_dot_number_re = re.compile(r"\b\d{1,3}(.\d{3})*(\,\d+)?\b")
+_decimal_number_re = re.compile(r"([0-9]+[.,][0-9]+)")
+
+
+def _remove_commas(m):
+ text = m.group(0)
+ if "," in text:
+ text = text.replace(",", "")
+ return text
+
+
+def _remove_dots(m):
+ text = m.group(0)
+ if "." in text:
+ text = text.replace(".", "")
+ return text
+
+
+def _expand_decimal_point(m, lang="en"):
+ amount = m.group(1).replace(",", ".")
+ return number_to_text(float(amount))
+
+
+def _expand_currency(m, lang="en", currency="USD"):
+ amount = float((re.sub(r"[^\d.]", "", m.group(0).replace(",", "."))))
+ full_amount = number_to_text(amount)
+
+ and_equivalents = {
+ "en": ", ",
+ "es": " con ",
+ "fr": " et ",
+ "de": " und ",
+ "pt": " e ",
+ "it": " e ",
+ "pl": ", ",
+ "cs": ", ",
+ "ru": ", ",
+ "nl": ", ",
+ "ar": ", ",
+ "tr": ", ",
+ "hu": ", ",
+ "ko": ", ",
+ }
+
+ if amount.is_integer():
+ last_and = full_amount.rfind(and_equivalents[lang])
+ if last_and != -1:
+ full_amount = full_amount[:last_and]
+
+ return full_amount
+
+
+def _expand_ordinal(m, lang="en"):
+ return number_to_text(int(m.group(1)), ordinal=True)
+
+
+def _expand_number(m, lang="en"):
+ return number_to_text(int(m.group(0)))
+
+
+def expand_numbers_multilingual(text, lang="en"):
+ if lang in ["en", "ru"]:
+ text = re.sub(_comma_number_re, _remove_commas, text)
+ else:
+ text = re.sub(_dot_number_re, _remove_dots, text)
+ try:
+ text = re.sub(_currency_re["GBP"], lambda m: _expand_currency(m, lang, "GBP"), text)
+ text = re.sub(_currency_re["USD"], lambda m: _expand_currency(m, lang, "USD"), text)
+ text = re.sub(_currency_re["EUR"], lambda m: _expand_currency(m, lang, "EUR"), text)
+ except:
+ pass
+
+ text = re.sub(_decimal_number_re, lambda m: _expand_decimal_point(m, lang), text)
+ text = re.sub(_ordinal_re[lang], lambda m: _expand_ordinal(m, lang), text)
+ text = re.sub(_number_re, lambda m: _expand_number(m, lang), text)
+ return text
+
+
+def lowercase(text):
+ return text.lower()
+
+
+def collapse_whitespace(text):
+ return re.sub(_whitespace_re, " ", text)
+
+
+def multilingual_cleaners(text, lang):
+ text = text.replace('"', "")
+ if lang == "tr":
+ text = text.replace("İ", "i")
+ text = text.replace("Ö", "ö")
+ text = text.replace("Ü", "ü")
+ text = lowercase(text)
+ try:
+ text = expand_numbers_multilingual(text, lang)
+ except:
+ pass
+ try:
+ text = expand_abbreviations_multilingual(text, lang)
+ except:
+ pass
+ try:
+ text = expand_symbols_multilingual(text, lang=lang)
+ except:
+ pass
+ text = collapse_whitespace(text)
+ return text
+
+
+def basic_cleaners(text):
+ """Basic pipeline that lowercases and collapses whitespace without transliteration."""
+ text = lowercase(text)
+ text = collapse_whitespace(text)
+ return text
diff --git a/ComfyUI/comfy/text_encoders/aura_t5.py b/ComfyUI/comfy/text_encoders/aura_t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf4252eea3ad6ed02e0a8ee4c13385c78909cbd8
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/aura_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.t5
+import os
+
+class PT5XlModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_config_xl.json")
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 2, "pad": 1}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, zero_out_masked=True, model_options=model_options)
+
+class PT5XlTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_pile_tokenizer"), "tokenizer.model")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='pile_t5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, pad_token=1, tokenizer_data=tokenizer_data)
+
+class AuraT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="pile_t5xl", tokenizer=PT5XlTokenizer)
+
+class AuraT5Model(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, name="pile_t5xl", clip_model=PT5XlModel, **kwargs)
diff --git a/ComfyUI/comfy/text_encoders/bert.py b/ComfyUI/comfy/text_encoders/bert.py
new file mode 100644
index 0000000000000000000000000000000000000000..551b031626911717d237f85e48ec9b1d74a6baa9
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/bert.py
@@ -0,0 +1,143 @@
+import torch
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class BertAttention(torch.nn.Module):
+ def __init__(self, embed_dim, heads, dtype, device, operations):
+ super().__init__()
+
+ self.heads = heads
+ self.query = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.key = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+ self.value = operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device)
+
+
+ def forward(self, x, mask=None, optimized_attention=None):
+ q = self.query(x)
+ k = self.key(x)
+ v = self.value(x)
+
+ out = optimized_attention(q, k, v, self.heads, mask)
+ return out
+
+class BertOutput(torch.nn.Module):
+ def __init__(self, input_dim, output_dim, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.dense = operations.Linear(input_dim, output_dim, dtype=dtype, device=device)
+ self.LayerNorm = operations.LayerNorm(output_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(0.0)
+
+ def forward(self, x, y):
+ x = self.dense(x)
+ # hidden_states = self.dropout(hidden_states)
+ x = self.LayerNorm(x + y)
+ return x
+
+class BertAttentionBlock(torch.nn.Module):
+ def __init__(self, embed_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.self = BertAttention(embed_dim, heads, dtype, device, operations)
+ self.output = BertOutput(embed_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+ def forward(self, x, mask, optimized_attention):
+ y = self.self(x, mask, optimized_attention)
+ return self.output(y, x)
+
+class BertIntermediate(torch.nn.Module):
+ def __init__(self, embed_dim, intermediate_dim, dtype, device, operations):
+ super().__init__()
+ self.dense = operations.Linear(embed_dim, intermediate_dim, dtype=dtype, device=device)
+
+ def forward(self, x):
+ x = self.dense(x)
+ return torch.nn.functional.gelu(x)
+
+
+class BertBlock(torch.nn.Module):
+ def __init__(self, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.attention = BertAttentionBlock(embed_dim, heads, layer_norm_eps, dtype, device, operations)
+ self.intermediate = BertIntermediate(embed_dim, intermediate_dim, dtype, device, operations)
+ self.output = BertOutput(intermediate_dim, embed_dim, layer_norm_eps, dtype, device, operations)
+
+ def forward(self, x, mask, optimized_attention):
+ x = self.attention(x, mask, optimized_attention)
+ y = self.intermediate(x)
+ return self.output(y, x)
+
+class BertEncoder(torch.nn.Module):
+ def __init__(self, num_layers, embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.layer = torch.nn.ModuleList([BertBlock(embed_dim, intermediate_dim, heads, layer_norm_eps, dtype, device, operations) for i in range(num_layers)])
+
+ def forward(self, x, mask=None, intermediate_output=None):
+ optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+
+ if intermediate_output is not None:
+ if intermediate_output < 0:
+ intermediate_output = len(self.layer) + intermediate_output
+
+ intermediate = None
+ for i, l in enumerate(self.layer):
+ x = l(x, mask, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ return x, intermediate
+
+class BertEmbeddings(torch.nn.Module):
+ def __init__(self, vocab_size, max_position_embeddings, type_vocab_size, pad_token_id, embed_dim, layer_norm_eps, dtype, device, operations):
+ super().__init__()
+ self.word_embeddings = operations.Embedding(vocab_size, embed_dim, padding_idx=pad_token_id, dtype=dtype, device=device)
+ self.position_embeddings = operations.Embedding(max_position_embeddings, embed_dim, dtype=dtype, device=device)
+ self.token_type_embeddings = operations.Embedding(type_vocab_size, embed_dim, dtype=dtype, device=device)
+
+ self.LayerNorm = operations.LayerNorm(embed_dim, eps=layer_norm_eps, dtype=dtype, device=device)
+
+ def forward(self, input_tokens, embeds=None, token_type_ids=None, dtype=None):
+ if embeds is not None:
+ x = embeds
+ else:
+ x = self.word_embeddings(input_tokens, out_dtype=dtype)
+ x += comfy.ops.cast_to_input(self.position_embeddings.weight[:x.shape[1]], x)
+ if token_type_ids is not None:
+ x += self.token_type_embeddings(token_type_ids, out_dtype=x.dtype)
+ else:
+ x += comfy.ops.cast_to_input(self.token_type_embeddings.weight[0], x)
+ x = self.LayerNorm(x)
+ return x
+
+
+class BertModel_(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ embed_dim = config_dict["hidden_size"]
+ layer_norm_eps = config_dict["layer_norm_eps"]
+
+ self.embeddings = BertEmbeddings(config_dict["vocab_size"], config_dict["max_position_embeddings"], config_dict["type_vocab_size"], config_dict["pad_token_id"], embed_dim, layer_norm_eps, dtype, device, operations)
+ self.encoder = BertEncoder(config_dict["num_hidden_layers"], embed_dim, config_dict["intermediate_size"], config_dict["num_attention_heads"], layer_norm_eps, dtype, device, operations)
+
+ def forward(self, input_tokens, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+ x = self.embeddings(input_tokens, embeds=embeds, dtype=dtype)
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+ x, i = self.encoder(x, mask, intermediate_output)
+ return x, i
+
+
+class BertModel(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.bert = BertModel_(config_dict, dtype, device, operations)
+ self.num_layers = config_dict["num_hidden_layers"]
+
+ def get_input_embeddings(self):
+ return self.bert.embeddings.word_embeddings
+
+ def set_input_embeddings(self, embeddings):
+ self.bert.embeddings.word_embeddings = embeddings
+
+ def forward(self, *args, **kwargs):
+ return self.bert(*args, **kwargs)
diff --git a/ComfyUI/comfy/text_encoders/cosmos.py b/ComfyUI/comfy/text_encoders/cosmos.py
new file mode 100644
index 0000000000000000000000000000000000000000..a1adb5242bc9d625bbb32b9d55ca426a0f61e419
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/cosmos.py
@@ -0,0 +1,42 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import os
+from transformers import T5TokenizerFast
+
+
+class T5XXLModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json")
+ t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+ if t5xxl_scaled_fp8 is not None:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)
+
+class CosmosT5XXL(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512, tokenizer_data=tokenizer_data)
+
+
+class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class CosmosTEModel_(CosmosT5XXL):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ if dtype is None:
+ dtype = dtype_t5
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return CosmosTEModel_
diff --git a/ComfyUI/comfy/text_encoders/flux.py b/ComfyUI/comfy/text_encoders/flux.py
new file mode 100644
index 0000000000000000000000000000000000000000..d61ef66689b32e52d15e962d41793a489aa44eab
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/flux.py
@@ -0,0 +1,70 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+import comfy.model_management
+from transformers import T5TokenizerFast
+import torch
+import os
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)
+
+
+class FluxTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_l.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+
+class FluxClipModel(torch.nn.Module):
+ def __init__(self, dtype_t5=None, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+ self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+ self.t5xxl = comfy.text_encoders.sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options)
+ self.dtypes = set([dtype, dtype_t5])
+
+ def set_clip_options(self, options):
+ self.clip_l.set_clip_options(options)
+ self.t5xxl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.clip_l.reset_clip_options()
+ self.t5xxl.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+
+ t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+ l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ return t5_out, l_pooled
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return self.t5xxl.load_sd(sd)
+
+def flux_clip(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class FluxClipModel_(FluxClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ super().__init__(dtype_t5=dtype_t5, device=device, dtype=dtype, model_options=model_options)
+ return FluxClipModel_
diff --git a/ComfyUI/comfy/text_encoders/genmo.py b/ComfyUI/comfy/text_encoders/genmo.py
new file mode 100644
index 0000000000000000000000000000000000000000..9dcf190a232550c9e678e819c88092a5bc518058
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/genmo.py
@@ -0,0 +1,38 @@
+from comfy import sd1_clip
+import comfy.text_encoders.sd3_clip
+import os
+from transformers import T5TokenizerFast
+
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+ def __init__(self, **kwargs):
+ kwargs["attention_mask"] = True
+ super().__init__(**kwargs)
+
+
+class MochiT5XXL(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)
+
+
+class MochiT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def mochi_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class MochiTEModel_(MochiT5XXL):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ if dtype is None:
+ dtype = dtype_t5
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return MochiTEModel_
diff --git a/ComfyUI/comfy/text_encoders/hidream.py b/ComfyUI/comfy/text_encoders/hidream.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbcf52784d63b6fc07c289d1e4e27a9fd7376b4c
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/hidream.py
@@ -0,0 +1,155 @@
+from . import hunyuan_video
+from . import sd3_clip
+from comfy import sd1_clip
+from comfy import sdxl_clip
+import comfy.model_management
+import torch
+import logging
+
+
+class HiDreamTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.t5xxl = sd3_clip.T5XXLTokenizer(embedding_directory=embedding_directory, min_length=128, max_length=128, tokenizer_data=tokenizer_data)
+ self.llama = hunyuan_video.LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=128, pad_token=128009, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+ t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["t5xxl"] = [t5xxl[0]] # Use only first 128 tokens
+ out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_g.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+
+class HiDreamTEModel(torch.nn.Module):
+ def __init__(self, clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ self.dtypes = set()
+ if clip_l:
+ self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=True, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_l = None
+
+ if clip_g:
+ self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_g = None
+
+ if t5:
+ dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+ self.t5xxl = sd3_clip.T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=True)
+ self.dtypes.add(dtype_t5)
+ else:
+ self.t5xxl = None
+
+ if llama:
+ dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device)
+ if "vocab_size" not in model_options:
+ model_options["vocab_size"] = 128256
+ self.llama = hunyuan_video.LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options, layer="all", layer_idx=None, special_tokens={"start": 128000, "pad": 128009})
+ self.dtypes.add(dtype_llama)
+ else:
+ self.llama = None
+
+ logging.debug("Created HiDream text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}, llama {}:{}".format(clip_l, clip_g, t5, dtype_t5, llama, dtype_llama))
+
+ def set_clip_options(self, options):
+ if self.clip_l is not None:
+ self.clip_l.set_clip_options(options)
+ if self.clip_g is not None:
+ self.clip_g.set_clip_options(options)
+ if self.t5xxl is not None:
+ self.t5xxl.set_clip_options(options)
+ if self.llama is not None:
+ self.llama.set_clip_options(options)
+
+ def reset_clip_options(self):
+ if self.clip_l is not None:
+ self.clip_l.reset_clip_options()
+ if self.clip_g is not None:
+ self.clip_g.reset_clip_options()
+ if self.t5xxl is not None:
+ self.t5xxl.reset_clip_options()
+ if self.llama is not None:
+ self.llama.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_g = token_weight_pairs["g"]
+ token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+ token_weight_pairs_llama = token_weight_pairs["llama"]
+ lg_out = None
+ pooled = None
+ extra = {}
+
+ if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+ if self.clip_l is not None:
+ lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ else:
+ l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+ if self.clip_g is not None:
+ g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+ else:
+ g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+ pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+ if self.t5xxl is not None:
+ t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+ t5_out, t5_pooled = t5_output[:2]
+ else:
+ t5_out = None
+
+ if self.llama is not None:
+ ll_output = self.llama.encode_token_weights(token_weight_pairs_llama)
+ ll_out, ll_pooled = ll_output[:2]
+ ll_out = ll_out[:, 1:]
+ else:
+ ll_out = None
+
+ if t5_out is None:
+ t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device())
+
+ if ll_out is None:
+ ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device())
+
+ if pooled is None:
+ pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+
+ extra["conditioning_llama3"] = ll_out
+ return t5_out, pooled, extra
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ return self.clip_g.load_sd(sd)
+ elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in sd:
+ return self.t5xxl.load_sd(sd)
+ else:
+ return self.llama.load_sd(sd)
+
+
+def hidream_clip(clip_l=True, clip_g=True, t5=True, llama=True, dtype_t5=None, dtype_llama=None, t5xxl_scaled_fp8=None, llama_scaled_fp8=None):
+ class HiDreamTEModel_(HiDreamTEModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["llama_scaled_fp8"] = llama_scaled_fp8
+ super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, llama=llama, dtype_t5=dtype_t5, dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+ return HiDreamTEModel_
diff --git a/ComfyUI/comfy/text_encoders/hunyuan_video.py b/ComfyUI/comfy/text_encoders/hunyuan_video.py
new file mode 100644
index 0000000000000000000000000000000000000000..b02148b3346db8463b471d83542a59071f7d15e4
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/hunyuan_video.py
@@ -0,0 +1,159 @@
+from comfy import sd1_clip
+import comfy.model_management
+import comfy.text_encoders.llama
+from transformers import LlamaTokenizerFast
+import torch
+import os
+import numbers
+
+
+def llama_detect(state_dict, prefix=""):
+ out = {}
+ t5_key = "{}model.norm.weight".format(prefix)
+ if t5_key in state_dict:
+ out["dtype_llama"] = state_dict[t5_key].dtype
+
+ scaled_fp8_key = "{}scaled_fp8".format(prefix)
+ if scaled_fp8_key in state_dict:
+ out["llama_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+
+ return out
+
+
+class LLAMA3Tokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=256, pad_token=128258):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "llama_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='llama', tokenizer_class=LlamaTokenizerFast, has_start_token=True, has_end_token=False, pad_to_max_length=False, max_length=99999999, pad_token=pad_token, min_length=min_length, tokenizer_data=tokenizer_data)
+
+class LLAMAModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="hidden", layer_idx=-3, dtype=None, attention_mask=True, model_options={}, special_tokens={"start": 128000, "pad": 128258}):
+ llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
+ if llama_scaled_fp8 is not None:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = llama_scaled_fp8
+
+ textmodel_json_config = {}
+ vocab_size = model_options.get("vocab_size", None)
+ if vocab_size is not None:
+ textmodel_json_config["vocab_size"] = vocab_size
+
+ model_options = {**model_options, "model_name": "llama"}
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens=special_tokens, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Llama2, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class HunyuanVideoTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.llama_template = """<|start_header_id|>system<|end_header_id|>\n\nDescribe the video by detailing the following aspects: 1. The main content and theme of the video.2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects.3. Actions, events, behaviors temporal relationships, physical movement changes of the objects.4. background environment, light, style and atmosphere.5. camera angles, movements, and transitions used in the video:<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{}<|eot_id|>""" # 95 tokens
+ self.llama = LLAMA3Tokenizer(embedding_directory=embedding_directory, min_length=1, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, image_embeds=None, image_interleave=1, **kwargs):
+ out = {}
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+
+ if llama_template is None:
+ llama_text = self.llama_template.format(text)
+ else:
+ llama_text = llama_template.format(text)
+ llama_text_tokens = self.llama.tokenize_with_weights(llama_text, return_word_ids, **kwargs)
+ embed_count = 0
+ for r in llama_text_tokens:
+ for i in range(len(r)):
+ if r[i][0] == 128257:
+ if image_embeds is not None and embed_count < image_embeds.shape[0]:
+ r[i] = ({"type": "embedding", "data": image_embeds[embed_count], "original_type": "image", "image_interleave": image_interleave},) + r[i][1:]
+ embed_count += 1
+ out["llama"] = llama_text_tokens
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_l.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+
+class HunyuanVideoClipModel(torch.nn.Module):
+ def __init__(self, dtype_llama=None, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ dtype_llama = comfy.model_management.pick_weight_dtype(dtype_llama, dtype, device)
+ self.clip_l = sd1_clip.SDClipModel(device=device, dtype=dtype, return_projected_pooled=False, model_options=model_options)
+ self.llama = LLAMAModel(device=device, dtype=dtype_llama, model_options=model_options)
+ self.dtypes = set([dtype, dtype_llama])
+
+ def set_clip_options(self, options):
+ self.clip_l.set_clip_options(options)
+ self.llama.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.clip_l.reset_clip_options()
+ self.llama.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_llama = token_weight_pairs["llama"]
+
+ llama_out, llama_pooled, llama_extra_out = self.llama.encode_token_weights(token_weight_pairs_llama)
+
+ template_end = 0
+ extra_template_end = 0
+ extra_sizes = 0
+ user_end = 9999999999999
+ images = []
+
+ tok_pairs = token_weight_pairs_llama[0]
+ for i, v in enumerate(tok_pairs):
+ elem = v[0]
+ if not torch.is_tensor(elem):
+ if isinstance(elem, numbers.Integral):
+ if elem == 128006:
+ if tok_pairs[i + 1][0] == 882:
+ if tok_pairs[i + 2][0] == 128007:
+ template_end = i + 2
+ user_end = -1
+ if elem == 128009 and user_end == -1:
+ user_end = i + 1
+ else:
+ if elem.get("original_type") == "image":
+ elem_size = elem.get("data").shape[0]
+ if template_end > 0:
+ if user_end == -1:
+ extra_template_end += elem_size - 1
+ else:
+ image_start = i + extra_sizes
+ image_end = i + elem_size + extra_sizes
+ images.append((image_start, image_end, elem.get("image_interleave", 1)))
+ extra_sizes += elem_size - 1
+
+ if llama_out.shape[1] > (template_end + 2):
+ if tok_pairs[template_end + 1][0] == 271:
+ template_end += 2
+ llama_output = llama_out[:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
+ llama_extra_out["attention_mask"] = llama_extra_out["attention_mask"][:, template_end + extra_sizes:user_end + extra_sizes + extra_template_end]
+ if llama_extra_out["attention_mask"].sum() == torch.numel(llama_extra_out["attention_mask"]):
+ llama_extra_out.pop("attention_mask") # attention mask is useless if no masked elements
+
+ if len(images) > 0:
+ out = []
+ for i in images:
+ out.append(llama_out[:, i[0]: i[1]: i[2]])
+ llama_output = torch.cat(out + [llama_output], dim=1)
+
+ l_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ return llama_output, l_pooled, llama_extra_out
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return self.llama.load_sd(sd)
+
+
+def hunyuan_video_clip(dtype_llama=None, llama_scaled_fp8=None):
+ class HunyuanVideoClipModel_(HunyuanVideoClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["llama_scaled_fp8"] = llama_scaled_fp8
+ super().__init__(dtype_llama=dtype_llama, device=device, dtype=dtype, model_options=model_options)
+ return HunyuanVideoClipModel_
diff --git a/ComfyUI/comfy/text_encoders/hydit.py b/ComfyUI/comfy/text_encoders/hydit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac6994529acd8493e6ec65a975f8984fd06749e8
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/hydit.py
@@ -0,0 +1,81 @@
+from comfy import sd1_clip
+from transformers import BertTokenizer
+from .spiece_tokenizer import SPieceTokenizer
+from .bert import BertModel
+import comfy.text_encoders.t5
+import os
+import torch
+
+class HyditBertModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip.json")
+ model_options = {**model_options, "model_name": "hydit_clip"}
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 101, "end": 102, "pad": 0}, model_class=BertModel, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class HyditBertTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "hydit_clip_tokenizer")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=1024, embedding_key='chinese_roberta', tokenizer_class=BertTokenizer, pad_to_max_length=False, max_length=512, min_length=77, tokenizer_data=tokenizer_data)
+
+
+class MT5XLModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_config_xl.json")
+ model_options = {**model_options, "model_name": "mt5xl"}
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=True, return_attention_masks=True, model_options=model_options)
+
+class MT5XLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ #tokenizer_path = os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "mt5_tokenizer"), "spiece.model")
+ tokenizer = tokenizer_data.get("spiece_model", None)
+ super().__init__(tokenizer, pad_with_end=False, embedding_size=2048, embedding_key='mt5xl', tokenizer_class=SPieceTokenizer, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=256, tokenizer_data=tokenizer_data)
+
+ def state_dict(self):
+ return {"spiece_model": self.tokenizer.serialize_model()}
+
+class HyditTokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ mt5_tokenizer_data = tokenizer_data.get("mt5xl.spiece_model", None)
+ self.hydit_clip = HyditBertTokenizer(embedding_directory=embedding_directory)
+ self.mt5xl = MT5XLTokenizer(tokenizer_data={**tokenizer_data, "spiece_model": mt5_tokenizer_data}, embedding_directory=embedding_directory)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out["hydit_clip"] = self.hydit_clip.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["mt5xl"] = self.mt5xl.tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.hydit_clip.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
+
+class HyditModel(torch.nn.Module):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ self.hydit_clip = HyditBertModel(dtype=dtype, model_options=model_options)
+ self.mt5xl = MT5XLModel(dtype=dtype, model_options=model_options)
+
+ self.dtypes = set()
+ if dtype is not None:
+ self.dtypes.add(dtype)
+
+ def encode_token_weights(self, token_weight_pairs):
+ hydit_out = self.hydit_clip.encode_token_weights(token_weight_pairs["hydit_clip"])
+ mt5_out = self.mt5xl.encode_token_weights(token_weight_pairs["mt5xl"])
+ return hydit_out[0], hydit_out[1], {"attention_mask": hydit_out[2]["attention_mask"], "conditioning_mt5xl": mt5_out[0], "attention_mask_mt5xl": mt5_out[2]["attention_mask"]}
+
+ def load_sd(self, sd):
+ if "bert.encoder.layer.0.attention.self.query.weight" in sd:
+ return self.hydit_clip.load_sd(sd)
+ else:
+ return self.mt5xl.load_sd(sd)
+
+ def set_clip_options(self, options):
+ self.hydit_clip.set_clip_options(options)
+ self.mt5xl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ self.hydit_clip.reset_clip_options()
+ self.mt5xl.reset_clip_options()
diff --git a/ComfyUI/comfy/text_encoders/hydit_clip.json b/ComfyUI/comfy/text_encoders/hydit_clip.json
new file mode 100644
index 0000000000000000000000000000000000000000..c41c7c1ff376407f42e3ff20ab26faaa98bb5e65
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/hydit_clip.json
@@ -0,0 +1,35 @@
+{
+ "_name_or_path": "hfl/chinese-roberta-wwm-ext-large",
+ "architectures": [
+ "BertModel"
+ ],
+ "attention_probs_dropout_prob": 0.1,
+ "bos_token_id": 0,
+ "classifier_dropout": null,
+ "directionality": "bidi",
+ "eos_token_id": 2,
+ "hidden_act": "gelu",
+ "hidden_dropout_prob": 0.1,
+ "hidden_size": 1024,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-12,
+ "max_position_embeddings": 512,
+ "model_type": "bert",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "pooler_fc_size": 768,
+ "pooler_num_attention_heads": 12,
+ "pooler_num_fc_layers": 3,
+ "pooler_size_per_head": 128,
+ "pooler_type": "first_token_transform",
+ "position_embedding_type": "absolute",
+ "torch_dtype": "float32",
+ "transformers_version": "4.22.1",
+ "type_vocab_size": 2,
+ "use_cache": true,
+ "vocab_size": 47020
+}
+
diff --git a/ComfyUI/comfy/text_encoders/llama.py b/ComfyUI/comfy/text_encoders/llama.py
new file mode 100644
index 0000000000000000000000000000000000000000..7fbd0f6047f8a582c79e07439a5613c7170b3c0d
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/llama.py
@@ -0,0 +1,358 @@
+import torch
+import torch.nn as nn
+from dataclasses import dataclass
+from typing import Optional, Any
+
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.model_management
+import comfy.ldm.common_dit
+
+import comfy.model_management
+
+@dataclass
+class Llama2Config:
+ vocab_size: int = 128320
+ hidden_size: int = 4096
+ intermediate_size: int = 14336
+ num_hidden_layers: int = 32
+ num_attention_heads: int = 32
+ num_key_value_heads: int = 8
+ max_position_embeddings: int = 8192
+ rms_norm_eps: float = 1e-5
+ rope_theta: float = 500000.0
+ transformer_type: str = "llama"
+ head_dim = 128
+ rms_norm_add = False
+ mlp_activation = "silu"
+ qkv_bias = False
+
+@dataclass
+class Qwen25_3BConfig:
+ vocab_size: int = 151936
+ hidden_size: int = 2048
+ intermediate_size: int = 11008
+ num_hidden_layers: int = 36
+ num_attention_heads: int = 16
+ num_key_value_heads: int = 2
+ max_position_embeddings: int = 128000
+ rms_norm_eps: float = 1e-6
+ rope_theta: float = 1000000.0
+ transformer_type: str = "llama"
+ head_dim = 128
+ rms_norm_add = False
+ mlp_activation = "silu"
+ qkv_bias = True
+
+@dataclass
+class Gemma2_2B_Config:
+ vocab_size: int = 256000
+ hidden_size: int = 2304
+ intermediate_size: int = 9216
+ num_hidden_layers: int = 26
+ num_attention_heads: int = 8
+ num_key_value_heads: int = 4
+ max_position_embeddings: int = 8192
+ rms_norm_eps: float = 1e-6
+ rope_theta: float = 10000.0
+ transformer_type: str = "gemma2"
+ head_dim = 256
+ rms_norm_add = True
+ mlp_activation = "gelu_pytorch_tanh"
+ qkv_bias = False
+
+class RMSNorm(nn.Module):
+ def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
+ super().__init__()
+ self.eps = eps
+ self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+ self.add = add
+
+ def forward(self, x: torch.Tensor):
+ w = self.weight
+ if self.add:
+ w = w + 1.0
+
+ return comfy.ldm.common_dit.rms_norm(x, w, self.eps)
+
+
+
+def rotate_half(x):
+ """Rotates half the hidden dims of the input."""
+ x1 = x[..., : x.shape[-1] // 2]
+ x2 = x[..., x.shape[-1] // 2 :]
+ return torch.cat((-x2, x1), dim=-1)
+
+
+def precompute_freqs_cis(head_dim, seq_len, theta, device=None):
+ theta_numerator = torch.arange(0, head_dim, 2, device=device).float()
+ inv_freq = 1.0 / (theta ** (theta_numerator / head_dim))
+
+ position_ids = torch.arange(0, seq_len, device=device).unsqueeze(0)
+
+ inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+ position_ids_expanded = position_ids[:, None, :].float()
+ freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+ emb = torch.cat((freqs, freqs), dim=-1)
+ cos = emb.cos()
+ sin = emb.sin()
+ return (cos, sin)
+
+
+def apply_rope(xq, xk, freqs_cis):
+ cos = freqs_cis[0].unsqueeze(1)
+ sin = freqs_cis[1].unsqueeze(1)
+ q_embed = (xq * cos) + (rotate_half(xq) * sin)
+ k_embed = (xk * cos) + (rotate_half(xk) * sin)
+ return q_embed, k_embed
+
+
+class Attention(nn.Module):
+ def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+ super().__init__()
+ self.num_heads = config.num_attention_heads
+ self.num_kv_heads = config.num_key_value_heads
+ self.hidden_size = config.hidden_size
+
+ self.head_dim = config.head_dim
+ self.inner_size = self.num_heads * self.head_dim
+
+ ops = ops or nn
+ self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=config.qkv_bias, device=device, dtype=dtype)
+ self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+ self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
+ self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ freqs_cis: Optional[torch.Tensor] = None,
+ optimized_attention=None,
+ ):
+ batch_size, seq_length, _ = hidden_states.shape
+ xq = self.q_proj(hidden_states)
+ xk = self.k_proj(hidden_states)
+ xv = self.v_proj(hidden_states)
+
+ xq = xq.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
+ xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
+ xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+ xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
+
+ xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+ xv = xv.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
+
+ output = optimized_attention(xq, xk, xv, self.num_heads, mask=attention_mask, skip_reshape=True)
+ return self.o_proj(output)
+
+class MLP(nn.Module):
+ def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+ super().__init__()
+ ops = ops or nn
+ self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
+ self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
+ self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+ if config.mlp_activation == "silu":
+ self.activation = torch.nn.functional.silu
+ elif config.mlp_activation == "gelu_pytorch_tanh":
+ self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh")
+
+ def forward(self, x):
+ return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
+
+class TransformerBlock(nn.Module):
+ def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+ super().__init__()
+ self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
+ self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+ self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+ self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ freqs_cis: Optional[torch.Tensor] = None,
+ optimized_attention=None,
+ ):
+ # Self Attention
+ residual = x
+ x = self.input_layernorm(x)
+ x = self.self_attn(
+ hidden_states=x,
+ attention_mask=attention_mask,
+ freqs_cis=freqs_cis,
+ optimized_attention=optimized_attention,
+ )
+ x = residual + x
+
+ # MLP
+ residual = x
+ x = self.post_attention_layernorm(x)
+ x = self.mlp(x)
+ x = residual + x
+
+ return x
+
+class TransformerBlockGemma2(nn.Module):
+ def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+ super().__init__()
+ self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
+ self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+ self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+ self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+ self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+ self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+ def forward(
+ self,
+ x: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ freqs_cis: Optional[torch.Tensor] = None,
+ optimized_attention=None,
+ ):
+ # Self Attention
+ residual = x
+ x = self.input_layernorm(x)
+ x = self.self_attn(
+ hidden_states=x,
+ attention_mask=attention_mask,
+ freqs_cis=freqs_cis,
+ optimized_attention=optimized_attention,
+ )
+
+ x = self.post_attention_layernorm(x)
+ x = residual + x
+
+ # MLP
+ residual = x
+ x = self.pre_feedforward_layernorm(x)
+ x = self.mlp(x)
+ x = self.post_feedforward_layernorm(x)
+ x = residual + x
+
+ return x
+
+class Llama2_(nn.Module):
+ def __init__(self, config, device=None, dtype=None, ops=None):
+ super().__init__()
+ self.config = config
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = ops.Embedding(
+ config.vocab_size,
+ config.hidden_size,
+ device=device,
+ dtype=dtype
+ )
+ if self.config.transformer_type == "gemma2":
+ transformer = TransformerBlockGemma2
+ self.normalize_in = True
+ else:
+ transformer = TransformerBlock
+ self.normalize_in = False
+
+ self.layers = nn.ModuleList([
+ transformer(config, device=device, dtype=dtype, ops=ops)
+ for _ in range(config.num_hidden_layers)
+ ])
+ self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+ # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
+
+ def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+ if embeds is not None:
+ x = embeds
+ else:
+ x = self.embed_tokens(x, out_dtype=dtype)
+
+ if self.normalize_in:
+ x *= self.config.hidden_size ** 0.5
+
+ freqs_cis = precompute_freqs_cis(self.config.head_dim,
+ x.shape[1],
+ self.config.rope_theta,
+ device=x.device)
+
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+ causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
+ if mask is not None:
+ mask += causal_mask
+ else:
+ mask = causal_mask
+ optimized_attention = optimized_attention_for_device(x.device, mask=mask is not None, small_input=True)
+
+ intermediate = None
+ all_intermediate = None
+ if intermediate_output is not None:
+ if intermediate_output == "all":
+ all_intermediate = []
+ intermediate_output = None
+ elif intermediate_output < 0:
+ intermediate_output = len(self.layers) + intermediate_output
+
+ for i, layer in enumerate(self.layers):
+ if all_intermediate is not None:
+ all_intermediate.append(x.unsqueeze(1).clone())
+ x = layer(
+ x=x,
+ attention_mask=mask,
+ freqs_cis=freqs_cis,
+ optimized_attention=optimized_attention,
+ )
+ if i == intermediate_output:
+ intermediate = x.clone()
+
+ x = self.norm(x)
+ if all_intermediate is not None:
+ all_intermediate.append(x.unsqueeze(1).clone())
+
+ if all_intermediate is not None:
+ intermediate = torch.cat(all_intermediate, dim=1)
+
+ if intermediate is not None and final_layer_norm_intermediate:
+ intermediate = self.norm(intermediate)
+
+ return x, intermediate
+
+class BaseLlama:
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, embeddings):
+ self.model.embed_tokens = embeddings
+
+ def forward(self, input_ids, *args, **kwargs):
+ return self.model(input_ids, *args, **kwargs)
+
+
+class Llama2(BaseLlama, torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ config = Llama2Config(**config_dict)
+ self.num_layers = config.num_hidden_layers
+
+ self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+ self.dtype = dtype
+
+class Qwen25_3B(BaseLlama, torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ config = Qwen25_3BConfig(**config_dict)
+ self.num_layers = config.num_hidden_layers
+
+ self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+ self.dtype = dtype
+
+class Gemma2_2B(BaseLlama, torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ config = Gemma2_2B_Config(**config_dict)
+ self.num_layers = config.num_hidden_layers
+
+ self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+ self.dtype = dtype
diff --git a/ComfyUI/comfy/text_encoders/long_clipl.py b/ComfyUI/comfy/text_encoders/long_clipl.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d4c7619d4b2007462cf14721bcdae093a5b4a7b
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/long_clipl.py
@@ -0,0 +1,27 @@
+
+
+def model_options_long_clip(sd, tokenizer_data, model_options):
+ w = sd.get("clip_l.text_model.embeddings.position_embedding.weight", None)
+ if w is None:
+ w = sd.get("clip_g.text_model.embeddings.position_embedding.weight", None)
+ else:
+ model_name = "clip_g"
+
+ if w is None:
+ w = sd.get("text_model.embeddings.position_embedding.weight", None)
+ if w is not None:
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ model_name = "clip_g"
+ elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ model_name = "clip_l"
+ else:
+ model_name = "clip_l"
+
+ if w is not None:
+ tokenizer_data = tokenizer_data.copy()
+ model_options = model_options.copy()
+ model_config = model_options.get("model_config", {})
+ model_config["max_position_embeddings"] = w.shape[0]
+ model_options["{}_model_config".format(model_name)] = model_config
+ tokenizer_data["{}_max_length".format(model_name)] = w.shape[0]
+ return tokenizer_data, model_options
diff --git a/ComfyUI/comfy/text_encoders/lt.py b/ComfyUI/comfy/text_encoders/lt.py
new file mode 100644
index 0000000000000000000000000000000000000000..48ea67e6782388637dbe55b3e6366cda051ec519
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/lt.py
@@ -0,0 +1,18 @@
+from comfy import sd1_clip
+import os
+from transformers import T5TokenizerFast
+import comfy.text_encoders.genmo
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128, tokenizer_data=tokenizer_data) #pad to 128?
+
+
+class LTXVT5Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def ltxv_te(*args, **kwargs):
+ return comfy.text_encoders.genmo.mochi_te(*args, **kwargs)
diff --git a/ComfyUI/comfy/text_encoders/lumina2.py b/ComfyUI/comfy/text_encoders/lumina2.py
new file mode 100644
index 0000000000000000000000000000000000000000..674461b75077ac21d92667d96a6787b1277367fe
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/lumina2.py
@@ -0,0 +1,39 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.llama
+
+
+class Gemma2BTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer = tokenizer_data.get("spiece_model", None)
+ super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+
+ def state_dict(self):
+ return {"spiece_model": self.tokenizer.serialize_model()}
+
+
+class LuminaTokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
+
+
+class Gemma2_2BModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class LuminaModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_scaled_fp8=None):
+ class LuminaTEModel_(LuminaModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = llama_scaled_fp8
+ if dtype_llama is not None:
+ dtype = dtype_llama
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return LuminaTEModel_
diff --git a/ComfyUI/comfy/text_encoders/mt5_config_xl.json b/ComfyUI/comfy/text_encoders/mt5_config_xl.json
new file mode 100644
index 0000000000000000000000000000000000000000..092fefd6e32dac566e443fc03eae53f6a8b57400
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/mt5_config_xl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 5120,
+ "d_kv": 64,
+ "d_model": 2048,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "mt5",
+ "num_decoder_layers": 24,
+ "num_heads": 32,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 250112
+}
diff --git a/ComfyUI/comfy/text_encoders/omnigen2.py b/ComfyUI/comfy/text_encoders/omnigen2.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a01b2dd430461a20af92b75e9a1930b8d153fea
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/omnigen2.py
@@ -0,0 +1,44 @@
+from transformers import Qwen2Tokenizer
+from comfy import sd1_clip
+import comfy.text_encoders.llama
+import os
+
+
+class Qwen25_3BTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "qwen25_tokenizer")
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_size=2048, embedding_key='qwen25_3b', tokenizer_class=Qwen2Tokenizer, has_start_token=False, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, pad_token=151643, tokenizer_data=tokenizer_data)
+
+
+class Omnigen2Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="qwen25_3b", tokenizer=Qwen25_3BTokenizer)
+ self.llama_template = '<|im_start|>system\nYou are a helpful assistant that generates high-quality images based on user instructions.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n'
+
+ def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None,**kwargs):
+ if llama_template is None:
+ llama_text = self.llama_template.format(text)
+ else:
+ llama_text = llama_template.format(text)
+ return super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, **kwargs)
+
+class Qwen25_3BModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"pad": 151643}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Qwen25_3B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class Omnigen2Model(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="qwen25_3b", clip_model=Qwen25_3BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_scaled_fp8=None):
+ class Omnigen2TEModel_(Omnigen2Model):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = llama_scaled_fp8
+ if dtype_llama is not None:
+ dtype = dtype_llama
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return Omnigen2TEModel_
diff --git a/ComfyUI/comfy/text_encoders/pixart_t5.py b/ComfyUI/comfy/text_encoders/pixart_t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f383de076fad52fb934c9747b3f95ff453d8e6a
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/pixart_t5.py
@@ -0,0 +1,42 @@
+import os
+
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+from comfy.sd1_clip import gen_empty_tokens
+
+from transformers import T5TokenizerFast
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+ def __init__(self, **kwargs):
+ super().__init__(**kwargs)
+
+ def gen_empty_tokens(self, special_tokens, *args, **kwargs):
+ # PixArt expects the negative to be all pad tokens
+ special_tokens = special_tokens.copy()
+ special_tokens.pop("end")
+ return gen_empty_tokens(special_tokens, *args, **kwargs)
+
+class PixArtT5XXL(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_data=tokenizer_data) # no padding
+
+class PixArtTokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+def pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+ class PixArtTEModel_(PixArtT5XXL):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ if dtype is None:
+ dtype = dtype_t5
+ super().__init__(device=device, dtype=dtype, model_options=model_options)
+ return PixArtTEModel_
diff --git a/ComfyUI/comfy/text_encoders/sd2_clip.py b/ComfyUI/comfy/text_encoders/sd2_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..700a23bf09f03f09290f677092bd25f29d6f065a
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/sd2_clip.py
@@ -0,0 +1,23 @@
+from comfy import sd1_clip
+import os
+
+class SD2ClipHModel(sd1_clip.SDClipModel):
+ def __init__(self, arch="ViT-H-14", device="cpu", max_length=77, freeze=True, layer="penultimate", layer_idx=None, dtype=None, model_options={}):
+ if layer == "penultimate":
+ layer="hidden"
+ layer_idx=-2
+
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd2_clip_config.json")
+ super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"start": 49406, "end": 49407, "pad": 0}, return_projected_pooled=True, model_options=model_options)
+
+class SD2ClipHTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, tokenizer_path=None, embedding_directory=None, tokenizer_data={}):
+ super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1024, embedding_key='clip_h', tokenizer_data=tokenizer_data)
+
+class SD2Tokenizer(sd1_clip.SD1Tokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="h", tokenizer=SD2ClipHTokenizer)
+
+class SD2ClipModel(sd1_clip.SD1ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}, **kwargs):
+ super().__init__(device=device, dtype=dtype, model_options=model_options, clip_name="h", clip_model=SD2ClipHModel, **kwargs)
diff --git a/ComfyUI/comfy/text_encoders/sd2_clip_config.json b/ComfyUI/comfy/text_encoders/sd2_clip_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..00893cfdc9b00f8eb7cf5aaa9c343e7fcd298d82
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/sd2_clip_config.json
@@ -0,0 +1,23 @@
+{
+ "architectures": [
+ "CLIPTextModel"
+ ],
+ "attention_dropout": 0.0,
+ "bos_token_id": 0,
+ "dropout": 0.0,
+ "eos_token_id": 49407,
+ "hidden_act": "gelu",
+ "hidden_size": 1024,
+ "initializer_factor": 1.0,
+ "initializer_range": 0.02,
+ "intermediate_size": 4096,
+ "layer_norm_eps": 1e-05,
+ "max_position_embeddings": 77,
+ "model_type": "clip_text_model",
+ "num_attention_heads": 16,
+ "num_hidden_layers": 24,
+ "pad_token_id": 1,
+ "projection_dim": 1024,
+ "torch_dtype": "float32",
+ "vocab_size": 49408
+}
diff --git a/ComfyUI/comfy/text_encoders/sd3_clip.py b/ComfyUI/comfy/text_encoders/sd3_clip.py
new file mode 100644
index 0000000000000000000000000000000000000000..ff5d412db1481fee09cf7742b9e32e90f6ed74d9
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/sd3_clip.py
@@ -0,0 +1,166 @@
+from comfy import sd1_clip
+from comfy import sdxl_clip
+from transformers import T5TokenizerFast
+import comfy.text_encoders.t5
+import torch
+import os
+import comfy.model_management
+import logging
+
+class T5XXLModel(sd1_clip.SDClipModel):
+ def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=False, model_options={}):
+ textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+ t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+ if t5xxl_scaled_fp8 is not None:
+ model_options = model_options.copy()
+ model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+ model_options = {**model_options, "model_name": "t5xxl"}
+ super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+def t5_xxl_detect(state_dict, prefix=""):
+ out = {}
+ t5_key = "{}encoder.final_layer_norm.weight".format(prefix)
+ if t5_key in state_dict:
+ out["dtype_t5"] = state_dict[t5_key].dtype
+
+ scaled_fp8_key = "{}scaled_fp8".format(prefix)
+ if scaled_fp8_key in state_dict:
+ out["t5xxl_scaled_fp8"] = state_dict[scaled_fp8_key].dtype
+
+ return out
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+ def __init__(self, embedding_directory=None, tokenizer_data={}, min_length=77, max_length=99999999):
+ tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+ super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=max_length, min_length=min_length, tokenizer_data=tokenizer_data)
+
+
+class SD3Tokenizer:
+ def __init__(self, embedding_directory=None, tokenizer_data={}):
+ self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+ self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data)
+
+ def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
+ out = {}
+ out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+ out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
+ return out
+
+ def untokenize(self, token_weight_pair):
+ return self.clip_g.untokenize(token_weight_pair)
+
+ def state_dict(self):
+ return {}
+
+class SD3ClipModel(torch.nn.Module):
+ def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5_attention_mask=False, device="cpu", dtype=None, model_options={}):
+ super().__init__()
+ self.dtypes = set()
+ if clip_l:
+ self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_l = None
+
+ if clip_g:
+ self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype, model_options=model_options)
+ self.dtypes.add(dtype)
+ else:
+ self.clip_g = None
+
+ if t5:
+ dtype_t5 = comfy.model_management.pick_weight_dtype(dtype_t5, dtype, device)
+ self.t5_attention_mask = t5_attention_mask
+ self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5, model_options=model_options, attention_mask=self.t5_attention_mask)
+ self.dtypes.add(dtype_t5)
+ else:
+ self.t5xxl = None
+
+ logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
+
+ def set_clip_options(self, options):
+ if self.clip_l is not None:
+ self.clip_l.set_clip_options(options)
+ if self.clip_g is not None:
+ self.clip_g.set_clip_options(options)
+ if self.t5xxl is not None:
+ self.t5xxl.set_clip_options(options)
+
+ def reset_clip_options(self):
+ if self.clip_l is not None:
+ self.clip_l.reset_clip_options()
+ if self.clip_g is not None:
+ self.clip_g.reset_clip_options()
+ if self.t5xxl is not None:
+ self.t5xxl.reset_clip_options()
+
+ def encode_token_weights(self, token_weight_pairs):
+ token_weight_pairs_l = token_weight_pairs["l"]
+ token_weight_pairs_g = token_weight_pairs["g"]
+ token_weight_pairs_t5 = token_weight_pairs["t5xxl"]
+ lg_out = None
+ pooled = None
+ out = None
+ extra = {}
+
+ if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+ if self.clip_l is not None:
+ lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+ else:
+ l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+ if self.clip_g is not None:
+ g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+ if lg_out is not None:
+ cut_to = min(lg_out.shape[1], g_out.shape[1])
+ lg_out = torch.cat([lg_out[:,:cut_to], g_out[:,:cut_to]], dim=-1)
+ else:
+ lg_out = torch.nn.functional.pad(g_out, (768, 0))
+ else:
+ g_out = None
+ g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+ if lg_out is not None:
+ lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+ out = lg_out
+ pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+ if self.t5xxl is not None:
+ t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
+ t5_out, t5_pooled = t5_output[:2]
+ if self.t5_attention_mask:
+ extra["attention_mask"] = t5_output[2]["attention_mask"]
+
+ if lg_out is not None:
+ out = torch.cat([lg_out, t5_out], dim=-2)
+ else:
+ out = t5_out
+
+ if out is None:
+ out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+
+ if pooled is None:
+ pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+
+ return out, pooled, extra
+
+ def load_sd(self, sd):
+ if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+ return self.clip_g.load_sd(sd)
+ elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+ return self.clip_l.load_sd(sd)
+ else:
+ return self.t5xxl.load_sd(sd)
+
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None, t5xxl_scaled_fp8=None, t5_attention_mask=False):
+ class SD3ClipModel_(SD3ClipModel):
+ def __init__(self, device="cpu", dtype=None, model_options={}):
+ if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+ model_options = model_options.copy()
+ model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+ super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, t5_attention_mask=t5_attention_mask, device=device, dtype=dtype, model_options=model_options)
+ return SD3ClipModel_
diff --git a/ComfyUI/comfy/text_encoders/t5.py b/ComfyUI/comfy/text_encoders/t5.py
new file mode 100644
index 0000000000000000000000000000000000000000..36bf353095b6d9756f694cb6dd2172e5e5dbfcbd
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/t5.py
@@ -0,0 +1,249 @@
+import torch
+import math
+from comfy.ldm.modules.attention import optimized_attention_for_device
+import comfy.ops
+
+class T5LayerNorm(torch.nn.Module):
+ def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
+ super().__init__()
+ self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+ self.variance_epsilon = eps
+
+ def forward(self, x):
+ variance = x.pow(2).mean(-1, keepdim=True)
+ x = x * torch.rsqrt(variance + self.variance_epsilon)
+ return comfy.ops.cast_to_input(self.weight, x) * x
+
+activations = {
+ "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+ "relu": torch.nn.functional.relu,
+}
+
+class T5DenseActDense(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+ super().__init__()
+ self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = activations[ff_activation]
+
+ def forward(self, x):
+ x = self.act(self.wi(x))
+ # x = self.dropout(x)
+ x = self.wo(x)
+ return x
+
+class T5DenseGatedActDense(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+ super().__init__()
+ self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+ self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+ self.act = activations[ff_activation]
+
+ def forward(self, x):
+ hidden_gelu = self.act(self.wi_0(x))
+ hidden_linear = self.wi_1(x)
+ x = hidden_gelu * hidden_linear
+ # x = self.dropout(x)
+ x = self.wo(x)
+ return x
+
+class T5LayerFF(torch.nn.Module):
+ def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
+ super().__init__()
+ if gated_act:
+ self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+ else:
+ self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+
+ self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x):
+ forwarded_states = self.layer_norm(x)
+ forwarded_states = self.DenseReluDense(forwarded_states)
+ # x = x + self.dropout(forwarded_states)
+ x += forwarded_states
+ return x
+
+class T5Attention(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+
+ # Mesh TensorFlow initialization to avoid scaling before softmax
+ self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+ self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
+ self.num_heads = num_heads
+
+ self.relative_attention_bias = None
+ if relative_attention_bias:
+ self.relative_attention_num_buckets = 32
+ self.relative_attention_max_distance = 128
+ self.relative_attention_bias = operations.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device, dtype=dtype)
+
+ @staticmethod
+ def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+ """
+ Adapted from Mesh Tensorflow:
+ https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+ Translate relative position to a bucket number for relative attention. The relative position is defined as
+ memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+ position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+ small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+ positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+ This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+ Args:
+ relative_position: an int32 Tensor
+ bidirectional: a boolean - whether the attention is bidirectional
+ num_buckets: an integer
+ max_distance: an integer
+
+ Returns:
+ a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ """
+ relative_buckets = 0
+ if bidirectional:
+ num_buckets //= 2
+ relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+ relative_position = torch.abs(relative_position)
+ else:
+ relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+ # now relative_position is in the range [0, inf)
+
+ # half of the buckets are for exact increments in positions
+ max_exact = num_buckets // 2
+ is_small = relative_position < max_exact
+
+ # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+ relative_position_if_large = max_exact + (
+ torch.log(relative_position.float() / max_exact)
+ / math.log(max_distance / max_exact)
+ * (num_buckets - max_exact)
+ ).to(torch.long)
+ relative_position_if_large = torch.min(
+ relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+ )
+
+ relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+ return relative_buckets
+
+ def compute_bias(self, query_length, key_length, device, dtype):
+ """Compute binned relative position bias"""
+ context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+ memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+ relative_position = memory_position - context_position # shape (query_length, key_length)
+ relative_position_bucket = self._relative_position_bucket(
+ relative_position, # shape (query_length, key_length)
+ bidirectional=True,
+ num_buckets=self.relative_attention_num_buckets,
+ max_distance=self.relative_attention_max_distance,
+ )
+ values = self.relative_attention_bias(relative_position_bucket, out_dtype=dtype) # shape (query_length, key_length, num_heads)
+ values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
+ return values.contiguous()
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ q = self.q(x)
+ k = self.k(x)
+ v = self.v(x)
+ if self.relative_attention_bias is not None:
+ past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device, x.dtype)
+
+ if past_bias is not None:
+ if mask is not None:
+ mask = mask + past_bias
+ else:
+ mask = past_bias
+
+ out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
+ return self.o(out), past_bias
+
+class T5LayerSelfAttention(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+ self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
+ self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
+ # x = x + self.dropout(attention_output)
+ x += output
+ return x, past_bias
+
+class T5Block(torch.nn.Module):
+ def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
+ super().__init__()
+ self.layer = torch.nn.ModuleList()
+ self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
+ self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
+
+ def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+ x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
+ x = self.layer[-1](x)
+ return x, past_bias
+
+class T5Stack(torch.nn.Module):
+ def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
+ super().__init__()
+
+ self.block = torch.nn.ModuleList(
+ [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+ )
+ self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+ # self.dropout = nn.Dropout(config.dropout_rate)
+
+ def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
+ mask = None
+ if attention_mask is not None:
+ mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+ mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+ intermediate = None
+ optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
+ past_bias = None
+
+ if intermediate_output is not None:
+ if intermediate_output < 0:
+ intermediate_output = len(self.block) + intermediate_output
+
+ for i, l in enumerate(self.block):
+ x, past_bias = l(x, mask, past_bias, optimized_attention)
+ if i == intermediate_output:
+ intermediate = x.clone()
+ x = self.final_layer_norm(x)
+ if intermediate is not None and final_layer_norm_intermediate:
+ intermediate = self.final_layer_norm(intermediate)
+ return x, intermediate
+
+class T5(torch.nn.Module):
+ def __init__(self, config_dict, dtype, device, operations):
+ super().__init__()
+ self.num_layers = config_dict["num_layers"]
+ model_dim = config_dict["d_model"]
+ inner_dim = config_dict["d_kv"] * config_dict["num_heads"]
+
+ self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
+ self.dtype = dtype
+ self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
+
+ def get_input_embeddings(self):
+ return self.shared
+
+ def set_input_embeddings(self, embeddings):
+ self.shared = embeddings
+
+ def forward(self, input_ids, attention_mask, embeds=None, num_tokens=None, **kwargs):
+ if input_ids is None:
+ x = embeds
+ else:
+ x = self.shared(input_ids, out_dtype=kwargs.get("dtype", torch.float32))
+ if self.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+ x = torch.nan_to_num(x) #Fix for fp8 T5 base
+ return self.encoder(x, attention_mask=attention_mask, **kwargs)
diff --git a/ComfyUI/comfy/text_encoders/t5_config_base.json b/ComfyUI/comfy/text_encoders/t5_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..71f68327c27280ce150d0c8e92fd61eca0b52a63
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/t5_config_base.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 3072,
+ "d_kv": 64,
+ "d_model": 768,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "relu",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "num_decoder_layers": 12,
+ "num_heads": 12,
+ "num_layers": 12,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/ComfyUI/comfy/text_encoders/t5_config_xxl.json b/ComfyUI/comfy/text_encoders/t5_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..28283b51a11bed6a874499f82d411c16cc646eb1
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/t5_config_xxl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 10240,
+ "d_kv": 64,
+ "d_model": 4096,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "num_decoder_layers": 24,
+ "num_heads": 64,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/ComfyUI/comfy/text_encoders/t5_old_config_xxl.json b/ComfyUI/comfy/text_encoders/t5_old_config_xxl.json
new file mode 100644
index 0000000000000000000000000000000000000000..c9fdd7782197ca0523ee82133e9b417fa947a5c5
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/t5_old_config_xxl.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 65536,
+ "d_kv": 128,
+ "d_model": 1024,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "relu",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": false,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "t5",
+ "num_decoder_layers": 24,
+ "num_heads": 128,
+ "num_layers": 24,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 32128
+}
diff --git a/ComfyUI/comfy/text_encoders/umt5_config_base.json b/ComfyUI/comfy/text_encoders/umt5_config_base.json
new file mode 100644
index 0000000000000000000000000000000000000000..6b3618f075eb4503d0666bd50f2259cc34b653c4
--- /dev/null
+++ b/ComfyUI/comfy/text_encoders/umt5_config_base.json
@@ -0,0 +1,22 @@
+{
+ "d_ff": 2048,
+ "d_kv": 64,
+ "d_model": 768,
+ "decoder_start_token_id": 0,
+ "dropout_rate": 0.1,
+ "eos_token_id": 1,
+ "dense_act_fn": "gelu_pytorch_tanh",
+ "initializer_factor": 1.0,
+ "is_encoder_decoder": true,
+ "is_gated_act": true,
+ "layer_norm_epsilon": 1e-06,
+ "model_type": "umt5",
+ "num_decoder_layers": 12,
+ "num_heads": 12,
+ "num_layers": 12,
+ "output_past": true,
+ "pad_token_id": 0,
+ "relative_attention_num_buckets": 32,
+ "tie_word_embeddings": false,
+ "vocab_size": 256384
+}
diff --git a/ComfyUI/comfy/utils.py b/ComfyUI/comfy/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fab28cf088c0049de71527268d0bb4dadc9cfe7e
--- /dev/null
+++ b/ComfyUI/comfy/utils.py
@@ -0,0 +1,1104 @@
+"""
+ This file is part of ComfyUI.
+ Copyright (C) 2024 Comfy
+
+ This program is free software: you can redistribute it and/or modify
+ it under the terms of the GNU General Public License as published by
+ the Free Software Foundation, either version 3 of the License, or
+ (at your option) any later version.
+
+ This program is distributed in the hope that it will be useful,
+ but WITHOUT ANY WARRANTY; without even the implied warranty of
+ MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+ GNU General Public License for more details.
+
+ You should have received a copy of the GNU General Public License
+ along with this program. If not, see .
+"""
+
+
+import torch
+import math
+import struct
+import comfy.checkpoint_pickle
+import safetensors.torch
+import numpy as np
+from PIL import Image
+import logging
+import itertools
+from torch.nn.functional import interpolate
+from einops import rearrange
+from comfy.cli_args import args
+
+MMAP_TORCH_FILES = args.mmap_torch_files
+DISABLE_MMAP = args.disable_mmap
+
+ALWAYS_SAFE_LOAD = False
+if hasattr(torch.serialization, "add_safe_globals"): # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated
+ class ModelCheckpoint:
+ pass
+ ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
+
+ from numpy.core.multiarray import scalar
+ from numpy import dtype
+ from numpy.dtypes import Float64DType
+ from _codecs import encode
+
+ torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
+ ALWAYS_SAFE_LOAD = True
+ logging.info("Checkpoint files will always be loaded safely.")
+else:
+ logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
+
+def load_torch_file(ckpt, safe_load=False, device=None, return_metadata=False):
+ if device is None:
+ device = torch.device("cpu")
+ metadata = None
+ if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
+ try:
+ with safetensors.safe_open(ckpt, framework="pt", device=device.type) as f:
+ sd = {}
+ for k in f.keys():
+ tensor = f.get_tensor(k)
+ if DISABLE_MMAP: # TODO: Not sure if this is the best way to bypass the mmap issues
+ tensor = tensor.to(device=device, copy=True)
+ sd[k] = tensor
+ if return_metadata:
+ metadata = f.metadata()
+ except Exception as e:
+ if len(e.args) > 0:
+ message = e.args[0]
+ if "HeaderTooLarge" in message:
+ raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
+ if "MetadataIncompleteBuffer" in message:
+ raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
+ raise e
+ else:
+ torch_args = {}
+ if MMAP_TORCH_FILES:
+ torch_args["mmap"] = True
+
+ if safe_load or ALWAYS_SAFE_LOAD:
+ pl_sd = torch.load(ckpt, map_location=device, weights_only=True, **torch_args)
+ else:
+ logging.warning("WARNING: loading {} unsafely, upgrade your pytorch to 2.4 or newer to load this file safely.".format(ckpt))
+ pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
+ if "state_dict" in pl_sd:
+ sd = pl_sd["state_dict"]
+ else:
+ if len(pl_sd) == 1:
+ key = list(pl_sd.keys())[0]
+ sd = pl_sd[key]
+ if not isinstance(sd, dict):
+ sd = pl_sd
+ else:
+ sd = pl_sd
+ return (sd, metadata) if return_metadata else sd
+
+def save_torch_file(sd, ckpt, metadata=None):
+ if metadata is not None:
+ safetensors.torch.save_file(sd, ckpt, metadata=metadata)
+ else:
+ safetensors.torch.save_file(sd, ckpt)
+
+def calculate_parameters(sd, prefix=""):
+ params = 0
+ for k in sd.keys():
+ if k.startswith(prefix):
+ w = sd[k]
+ params += w.nelement()
+ return params
+
+def weight_dtype(sd, prefix=""):
+ dtypes = {}
+ for k in sd.keys():
+ if k.startswith(prefix):
+ w = sd[k]
+ dtypes[w.dtype] = dtypes.get(w.dtype, 0) + w.numel()
+
+ if len(dtypes) == 0:
+ return None
+
+ return max(dtypes, key=dtypes.get)
+
+def state_dict_key_replace(state_dict, keys_to_replace):
+ for x in keys_to_replace:
+ if x in state_dict:
+ state_dict[keys_to_replace[x]] = state_dict.pop(x)
+ return state_dict
+
+def state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=False):
+ if filter_keys:
+ out = {}
+ else:
+ out = state_dict
+ for rp in replace_prefix:
+ replace = list(map(lambda a: (a, "{}{}".format(replace_prefix[rp], a[len(rp):])), filter(lambda a: a.startswith(rp), state_dict.keys())))
+ for x in replace:
+ w = state_dict.pop(x[0])
+ out[x[1]] = w
+ return out
+
+
+def transformers_convert(sd, prefix_from, prefix_to, number):
+ keys_to_replace = {
+ "{}positional_embedding": "{}embeddings.position_embedding.weight",
+ "{}token_embedding.weight": "{}embeddings.token_embedding.weight",
+ "{}ln_final.weight": "{}final_layer_norm.weight",
+ "{}ln_final.bias": "{}final_layer_norm.bias",
+ }
+
+ for k in keys_to_replace:
+ x = k.format(prefix_from)
+ if x in sd:
+ sd[keys_to_replace[k].format(prefix_to)] = sd.pop(x)
+
+ resblock_to_replace = {
+ "ln_1": "layer_norm1",
+ "ln_2": "layer_norm2",
+ "mlp.c_fc": "mlp.fc1",
+ "mlp.c_proj": "mlp.fc2",
+ "attn.out_proj": "self_attn.out_proj",
+ }
+
+ for resblock in range(number):
+ for x in resblock_to_replace:
+ for y in ["weight", "bias"]:
+ k = "{}transformer.resblocks.{}.{}.{}".format(prefix_from, resblock, x, y)
+ k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, resblock_to_replace[x], y)
+ if k in sd:
+ sd[k_to] = sd.pop(k)
+
+ for y in ["weight", "bias"]:
+ k_from = "{}transformer.resblocks.{}.attn.in_proj_{}".format(prefix_from, resblock, y)
+ if k_from in sd:
+ weights = sd.pop(k_from)
+ shape_from = weights.shape[0] // 3
+ for x in range(3):
+ p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
+ k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
+ sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+
+ return sd
+
+def clip_text_transformers_convert(sd, prefix_from, prefix_to):
+ sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
+
+ tp = "{}text_projection.weight".format(prefix_from)
+ if tp in sd:
+ sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
+
+ tp = "{}text_projection".format(prefix_from)
+ if tp in sd:
+ sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
+ return sd
+
+
+UNET_MAP_ATTENTIONS = {
+ "proj_in.weight",
+ "proj_in.bias",
+ "proj_out.weight",
+ "proj_out.bias",
+ "norm.weight",
+ "norm.bias",
+}
+
+TRANSFORMER_BLOCKS = {
+ "norm1.weight",
+ "norm1.bias",
+ "norm2.weight",
+ "norm2.bias",
+ "norm3.weight",
+ "norm3.bias",
+ "attn1.to_q.weight",
+ "attn1.to_k.weight",
+ "attn1.to_v.weight",
+ "attn1.to_out.0.weight",
+ "attn1.to_out.0.bias",
+ "attn2.to_q.weight",
+ "attn2.to_k.weight",
+ "attn2.to_v.weight",
+ "attn2.to_out.0.weight",
+ "attn2.to_out.0.bias",
+ "ff.net.0.proj.weight",
+ "ff.net.0.proj.bias",
+ "ff.net.2.weight",
+ "ff.net.2.bias",
+}
+
+UNET_MAP_RESNET = {
+ "in_layers.2.weight": "conv1.weight",
+ "in_layers.2.bias": "conv1.bias",
+ "emb_layers.1.weight": "time_emb_proj.weight",
+ "emb_layers.1.bias": "time_emb_proj.bias",
+ "out_layers.3.weight": "conv2.weight",
+ "out_layers.3.bias": "conv2.bias",
+ "skip_connection.weight": "conv_shortcut.weight",
+ "skip_connection.bias": "conv_shortcut.bias",
+ "in_layers.0.weight": "norm1.weight",
+ "in_layers.0.bias": "norm1.bias",
+ "out_layers.0.weight": "norm2.weight",
+ "out_layers.0.bias": "norm2.bias",
+}
+
+UNET_MAP_BASIC = {
+ ("label_emb.0.0.weight", "class_embedding.linear_1.weight"),
+ ("label_emb.0.0.bias", "class_embedding.linear_1.bias"),
+ ("label_emb.0.2.weight", "class_embedding.linear_2.weight"),
+ ("label_emb.0.2.bias", "class_embedding.linear_2.bias"),
+ ("label_emb.0.0.weight", "add_embedding.linear_1.weight"),
+ ("label_emb.0.0.bias", "add_embedding.linear_1.bias"),
+ ("label_emb.0.2.weight", "add_embedding.linear_2.weight"),
+ ("label_emb.0.2.bias", "add_embedding.linear_2.bias"),
+ ("input_blocks.0.0.weight", "conv_in.weight"),
+ ("input_blocks.0.0.bias", "conv_in.bias"),
+ ("out.0.weight", "conv_norm_out.weight"),
+ ("out.0.bias", "conv_norm_out.bias"),
+ ("out.2.weight", "conv_out.weight"),
+ ("out.2.bias", "conv_out.bias"),
+ ("time_embed.0.weight", "time_embedding.linear_1.weight"),
+ ("time_embed.0.bias", "time_embedding.linear_1.bias"),
+ ("time_embed.2.weight", "time_embedding.linear_2.weight"),
+ ("time_embed.2.bias", "time_embedding.linear_2.bias")
+}
+
+def unet_to_diffusers(unet_config):
+ if "num_res_blocks" not in unet_config:
+ return {}
+ num_res_blocks = unet_config["num_res_blocks"]
+ channel_mult = unet_config["channel_mult"]
+ transformer_depth = unet_config["transformer_depth"][:]
+ transformer_depth_output = unet_config["transformer_depth_output"][:]
+ num_blocks = len(channel_mult)
+
+ transformers_mid = unet_config.get("transformer_depth_middle", None)
+
+ diffusers_unet_map = {}
+ for x in range(num_blocks):
+ n = 1 + (num_res_blocks[x] + 1) * x
+ for i in range(num_res_blocks[x]):
+ for b in UNET_MAP_RESNET:
+ diffusers_unet_map["down_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "input_blocks.{}.0.{}".format(n, b)
+ num_transformers = transformer_depth.pop(0)
+ if num_transformers > 0:
+ for b in UNET_MAP_ATTENTIONS:
+ diffusers_unet_map["down_blocks.{}.attentions.{}.{}".format(x, i, b)] = "input_blocks.{}.1.{}".format(n, b)
+ for t in range(num_transformers):
+ for b in TRANSFORMER_BLOCKS:
+ diffusers_unet_map["down_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "input_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
+ n += 1
+ for k in ["weight", "bias"]:
+ diffusers_unet_map["down_blocks.{}.downsamplers.0.conv.{}".format(x, k)] = "input_blocks.{}.0.op.{}".format(n, k)
+
+ i = 0
+ for b in UNET_MAP_ATTENTIONS:
+ diffusers_unet_map["mid_block.attentions.{}.{}".format(i, b)] = "middle_block.1.{}".format(b)
+ for t in range(transformers_mid):
+ for b in TRANSFORMER_BLOCKS:
+ diffusers_unet_map["mid_block.attentions.{}.transformer_blocks.{}.{}".format(i, t, b)] = "middle_block.1.transformer_blocks.{}.{}".format(t, b)
+
+ for i, n in enumerate([0, 2]):
+ for b in UNET_MAP_RESNET:
+ diffusers_unet_map["mid_block.resnets.{}.{}".format(i, UNET_MAP_RESNET[b])] = "middle_block.{}.{}".format(n, b)
+
+ num_res_blocks = list(reversed(num_res_blocks))
+ for x in range(num_blocks):
+ n = (num_res_blocks[x] + 1) * x
+ l = num_res_blocks[x] + 1
+ for i in range(l):
+ c = 0
+ for b in UNET_MAP_RESNET:
+ diffusers_unet_map["up_blocks.{}.resnets.{}.{}".format(x, i, UNET_MAP_RESNET[b])] = "output_blocks.{}.0.{}".format(n, b)
+ c += 1
+ num_transformers = transformer_depth_output.pop()
+ if num_transformers > 0:
+ c += 1
+ for b in UNET_MAP_ATTENTIONS:
+ diffusers_unet_map["up_blocks.{}.attentions.{}.{}".format(x, i, b)] = "output_blocks.{}.1.{}".format(n, b)
+ for t in range(num_transformers):
+ for b in TRANSFORMER_BLOCKS:
+ diffusers_unet_map["up_blocks.{}.attentions.{}.transformer_blocks.{}.{}".format(x, i, t, b)] = "output_blocks.{}.1.transformer_blocks.{}.{}".format(n, t, b)
+ if i == l - 1:
+ for k in ["weight", "bias"]:
+ diffusers_unet_map["up_blocks.{}.upsamplers.0.conv.{}".format(x, k)] = "output_blocks.{}.{}.conv.{}".format(n, c, k)
+ n += 1
+
+ for k in UNET_MAP_BASIC:
+ diffusers_unet_map[k[1]] = k[0]
+
+ return diffusers_unet_map
+
+def swap_scale_shift(weight):
+ shift, scale = weight.chunk(2, dim=0)
+ new_weight = torch.cat([scale, shift], dim=0)
+ return new_weight
+
+MMDIT_MAP_BASIC = {
+ ("context_embedder.bias", "context_embedder.bias"),
+ ("context_embedder.weight", "context_embedder.weight"),
+ ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+ ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+ ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+ ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+ ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+ ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+ ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
+ ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
+ ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
+ ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
+ ("pos_embed", "pos_embed.pos_embed"),
+ ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+ ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+ ("final_layer.linear.bias", "proj_out.bias"),
+ ("final_layer.linear.weight", "proj_out.weight"),
+}
+
+MMDIT_MAP_BLOCK = {
+ ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
+ ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
+ ("context_block.attn.proj.bias", "attn.to_add_out.bias"),
+ ("context_block.attn.proj.weight", "attn.to_add_out.weight"),
+ ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
+ ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
+ ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
+ ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+ ("context_block.attn.ln_q.weight", "attn.norm_added_q.weight"),
+ ("context_block.attn.ln_k.weight", "attn.norm_added_k.weight"),
+ ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
+ ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
+ ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
+ ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+ ("x_block.attn.ln_q.weight", "attn.norm_q.weight"),
+ ("x_block.attn.ln_k.weight", "attn.norm_k.weight"),
+ ("x_block.attn2.proj.bias", "attn2.to_out.0.bias"),
+ ("x_block.attn2.proj.weight", "attn2.to_out.0.weight"),
+ ("x_block.attn2.ln_q.weight", "attn2.norm_q.weight"),
+ ("x_block.attn2.ln_k.weight", "attn2.norm_k.weight"),
+ ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
+ ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
+ ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
+ ("x_block.mlp.fc2.weight", "ff.net.2.weight"),
+}
+
+def mmdit_to_diffusers(mmdit_config, output_prefix=""):
+ key_map = {}
+
+ depth = mmdit_config.get("depth", 0)
+ num_blocks = mmdit_config.get("num_blocks", depth)
+ for i in range(num_blocks):
+ block_from = "transformer_blocks.{}".format(i)
+ block_to = "{}joint_blocks.{}".format(output_prefix, i)
+
+ offset = depth * 64
+
+ for end in ("weight", "bias"):
+ k = "{}.attn.".format(block_from)
+ qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
+ key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+ key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+ key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+ qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
+ key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
+ key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
+ key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+ k = "{}.attn2.".format(block_from)
+ qkv = "{}.x_block.attn2.qkv.{}".format(block_to, end)
+ key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+ key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+ key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+ for k in MMDIT_MAP_BLOCK:
+ key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+ map_basic = MMDIT_MAP_BASIC.copy()
+ map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
+ map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
+
+ for k in map_basic:
+ if len(k) > 2:
+ key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+ else:
+ key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+ return key_map
+
+PIXART_MAP_BASIC = {
+ ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+ ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
+ ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+ ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
+ ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+ ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+ ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+ ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+ ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+ ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+ ("y_embedder.y_embedding", "caption_projection.y_embedding"),
+ ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+ ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
+ ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+ ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
+ ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+ ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
+ ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+ ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
+ ("t_block.1.weight", "adaln_single.linear.weight"),
+ ("t_block.1.bias", "adaln_single.linear.bias"),
+ ("final_layer.linear.weight", "proj_out.weight"),
+ ("final_layer.linear.bias", "proj_out.bias"),
+ ("final_layer.scale_shift_table", "scale_shift_table"),
+}
+
+PIXART_MAP_BLOCK = {
+ ("scale_shift_table", "scale_shift_table"),
+ ("attn.proj.weight", "attn1.to_out.0.weight"),
+ ("attn.proj.bias", "attn1.to_out.0.bias"),
+ ("mlp.fc1.weight", "ff.net.0.proj.weight"),
+ ("mlp.fc1.bias", "ff.net.0.proj.bias"),
+ ("mlp.fc2.weight", "ff.net.2.weight"),
+ ("mlp.fc2.bias", "ff.net.2.bias"),
+ ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"),
+ ("cross_attn.proj.bias" ,"attn2.to_out.0.bias"),
+}
+
+def pixart_to_diffusers(mmdit_config, output_prefix=""):
+ key_map = {}
+
+ depth = mmdit_config.get("depth", 0)
+ offset = mmdit_config.get("hidden_size", 1152)
+
+ for i in range(depth):
+ block_from = "transformer_blocks.{}".format(i)
+ block_to = "{}blocks.{}".format(output_prefix, i)
+
+ for end in ("weight", "bias"):
+ s = "{}.attn1.".format(block_from)
+ qkv = "{}.attn.qkv.{}".format(block_to, end)
+ key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset))
+ key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset))
+ key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset))
+
+ s = "{}.attn2.".format(block_from)
+ q = "{}.cross_attn.q_linear.{}".format(block_to, end)
+ kv = "{}.cross_attn.kv_linear.{}".format(block_to, end)
+
+ key_map["{}to_q.{}".format(s, end)] = q
+ key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset))
+ key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset))
+
+ for k in PIXART_MAP_BLOCK:
+ key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+ for k in PIXART_MAP_BASIC:
+ key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+ return key_map
+
+def auraflow_to_diffusers(mmdit_config, output_prefix=""):
+ n_double_layers = mmdit_config.get("n_double_layers", 0)
+ n_layers = mmdit_config.get("n_layers", 0)
+
+ key_map = {}
+ for i in range(n_layers):
+ if i < n_double_layers:
+ index = i
+ prefix_from = "joint_transformer_blocks"
+ prefix_to = "{}double_layers".format(output_prefix)
+ block_map = {
+ "attn.to_q.weight": "attn.w2q.weight",
+ "attn.to_k.weight": "attn.w2k.weight",
+ "attn.to_v.weight": "attn.w2v.weight",
+ "attn.to_out.0.weight": "attn.w2o.weight",
+ "attn.add_q_proj.weight": "attn.w1q.weight",
+ "attn.add_k_proj.weight": "attn.w1k.weight",
+ "attn.add_v_proj.weight": "attn.w1v.weight",
+ "attn.to_add_out.weight": "attn.w1o.weight",
+ "ff.linear_1.weight": "mlpX.c_fc1.weight",
+ "ff.linear_2.weight": "mlpX.c_fc2.weight",
+ "ff.out_projection.weight": "mlpX.c_proj.weight",
+ "ff_context.linear_1.weight": "mlpC.c_fc1.weight",
+ "ff_context.linear_2.weight": "mlpC.c_fc2.weight",
+ "ff_context.out_projection.weight": "mlpC.c_proj.weight",
+ "norm1.linear.weight": "modX.1.weight",
+ "norm1_context.linear.weight": "modC.1.weight",
+ }
+ else:
+ index = i - n_double_layers
+ prefix_from = "single_transformer_blocks"
+ prefix_to = "{}single_layers".format(output_prefix)
+
+ block_map = {
+ "attn.to_q.weight": "attn.w1q.weight",
+ "attn.to_k.weight": "attn.w1k.weight",
+ "attn.to_v.weight": "attn.w1v.weight",
+ "attn.to_out.0.weight": "attn.w1o.weight",
+ "norm1.linear.weight": "modCX.1.weight",
+ "ff.linear_1.weight": "mlp.c_fc1.weight",
+ "ff.linear_2.weight": "mlp.c_fc2.weight",
+ "ff.out_projection.weight": "mlp.c_proj.weight"
+ }
+
+ for k in block_map:
+ key_map["{}.{}.{}".format(prefix_from, index, k)] = "{}.{}.{}".format(prefix_to, index, block_map[k])
+
+ MAP_BASIC = {
+ ("positional_encoding", "pos_embed.pos_embed"),
+ ("register_tokens", "register_tokens"),
+ ("t_embedder.mlp.0.weight", "time_step_proj.linear_1.weight"),
+ ("t_embedder.mlp.0.bias", "time_step_proj.linear_1.bias"),
+ ("t_embedder.mlp.2.weight", "time_step_proj.linear_2.weight"),
+ ("t_embedder.mlp.2.bias", "time_step_proj.linear_2.bias"),
+ ("cond_seq_linear.weight", "context_embedder.weight"),
+ ("init_x_linear.weight", "pos_embed.proj.weight"),
+ ("init_x_linear.bias", "pos_embed.proj.bias"),
+ ("final_linear.weight", "proj_out.weight"),
+ ("modF.1.weight", "norm_out.linear.weight", swap_scale_shift),
+ }
+
+ for k in MAP_BASIC:
+ if len(k) > 2:
+ key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+ else:
+ key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+ return key_map
+
+def flux_to_diffusers(mmdit_config, output_prefix=""):
+ n_double_layers = mmdit_config.get("depth", 0)
+ n_single_layers = mmdit_config.get("depth_single_blocks", 0)
+ hidden_size = mmdit_config.get("hidden_size", 0)
+
+ key_map = {}
+ for index in range(n_double_layers):
+ prefix_from = "transformer_blocks.{}".format(index)
+ prefix_to = "{}double_blocks.{}".format(output_prefix, index)
+
+ for end in ("weight", "bias"):
+ k = "{}.attn.".format(prefix_from)
+ qkv = "{}.img_attn.qkv.{}".format(prefix_to, end)
+ key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+ key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+ key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+ k = "{}.attn.".format(prefix_from)
+ qkv = "{}.txt_attn.qkv.{}".format(prefix_to, end)
+ key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+ key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+ key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+
+ block_map = {
+ "attn.to_out.0.weight": "img_attn.proj.weight",
+ "attn.to_out.0.bias": "img_attn.proj.bias",
+ "norm1.linear.weight": "img_mod.lin.weight",
+ "norm1.linear.bias": "img_mod.lin.bias",
+ "norm1_context.linear.weight": "txt_mod.lin.weight",
+ "norm1_context.linear.bias": "txt_mod.lin.bias",
+ "attn.to_add_out.weight": "txt_attn.proj.weight",
+ "attn.to_add_out.bias": "txt_attn.proj.bias",
+ "ff.net.0.proj.weight": "img_mlp.0.weight",
+ "ff.net.0.proj.bias": "img_mlp.0.bias",
+ "ff.net.2.weight": "img_mlp.2.weight",
+ "ff.net.2.bias": "img_mlp.2.bias",
+ "ff_context.net.0.proj.weight": "txt_mlp.0.weight",
+ "ff_context.net.0.proj.bias": "txt_mlp.0.bias",
+ "ff_context.net.2.weight": "txt_mlp.2.weight",
+ "ff_context.net.2.bias": "txt_mlp.2.bias",
+ "attn.norm_q.weight": "img_attn.norm.query_norm.scale",
+ "attn.norm_k.weight": "img_attn.norm.key_norm.scale",
+ "attn.norm_added_q.weight": "txt_attn.norm.query_norm.scale",
+ "attn.norm_added_k.weight": "txt_attn.norm.key_norm.scale",
+ }
+
+ for k in block_map:
+ key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+ for index in range(n_single_layers):
+ prefix_from = "single_transformer_blocks.{}".format(index)
+ prefix_to = "{}single_blocks.{}".format(output_prefix, index)
+
+ for end in ("weight", "bias"):
+ k = "{}.attn.".format(prefix_from)
+ qkv = "{}.linear1.{}".format(prefix_to, end)
+ key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, hidden_size))
+ key_map["{}to_k.{}".format(k, end)] = (qkv, (0, hidden_size, hidden_size))
+ key_map["{}to_v.{}".format(k, end)] = (qkv, (0, hidden_size * 2, hidden_size))
+ key_map["{}.proj_mlp.{}".format(prefix_from, end)] = (qkv, (0, hidden_size * 3, hidden_size * 4))
+
+ block_map = {
+ "norm.linear.weight": "modulation.lin.weight",
+ "norm.linear.bias": "modulation.lin.bias",
+ "proj_out.weight": "linear2.weight",
+ "proj_out.bias": "linear2.bias",
+ "attn.norm_q.weight": "norm.query_norm.scale",
+ "attn.norm_k.weight": "norm.key_norm.scale",
+ }
+
+ for k in block_map:
+ key_map["{}.{}".format(prefix_from, k)] = "{}.{}".format(prefix_to, block_map[k])
+
+ MAP_BASIC = {
+ ("final_layer.linear.bias", "proj_out.bias"),
+ ("final_layer.linear.weight", "proj_out.weight"),
+ ("img_in.bias", "x_embedder.bias"),
+ ("img_in.weight", "x_embedder.weight"),
+ ("time_in.in_layer.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+ ("time_in.in_layer.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+ ("time_in.out_layer.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+ ("time_in.out_layer.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+ ("txt_in.bias", "context_embedder.bias"),
+ ("txt_in.weight", "context_embedder.weight"),
+ ("vector_in.in_layer.bias", "time_text_embed.text_embedder.linear_1.bias"),
+ ("vector_in.in_layer.weight", "time_text_embed.text_embedder.linear_1.weight"),
+ ("vector_in.out_layer.bias", "time_text_embed.text_embedder.linear_2.bias"),
+ ("vector_in.out_layer.weight", "time_text_embed.text_embedder.linear_2.weight"),
+ ("guidance_in.in_layer.bias", "time_text_embed.guidance_embedder.linear_1.bias"),
+ ("guidance_in.in_layer.weight", "time_text_embed.guidance_embedder.linear_1.weight"),
+ ("guidance_in.out_layer.bias", "time_text_embed.guidance_embedder.linear_2.bias"),
+ ("guidance_in.out_layer.weight", "time_text_embed.guidance_embedder.linear_2.weight"),
+ ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+ ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+ ("pos_embed_input.bias", "controlnet_x_embedder.bias"),
+ ("pos_embed_input.weight", "controlnet_x_embedder.weight"),
+ }
+
+ for k in MAP_BASIC:
+ if len(k) > 2:
+ key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+ else:
+ key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+ return key_map
+
+def repeat_to_batch_size(tensor, batch_size, dim=0):
+ if tensor.shape[dim] > batch_size:
+ return tensor.narrow(dim, 0, batch_size)
+ elif tensor.shape[dim] < batch_size:
+ return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
+ return tensor
+
+def resize_to_batch_size(tensor, batch_size):
+ in_batch_size = tensor.shape[0]
+ if in_batch_size == batch_size:
+ return tensor
+
+ if batch_size <= 1:
+ return tensor[:batch_size]
+
+ output = torch.empty([batch_size] + list(tensor.shape)[1:], dtype=tensor.dtype, device=tensor.device)
+ if batch_size < in_batch_size:
+ scale = (in_batch_size - 1) / (batch_size - 1)
+ for i in range(batch_size):
+ output[i] = tensor[min(round(i * scale), in_batch_size - 1)]
+ else:
+ scale = in_batch_size / batch_size
+ for i in range(batch_size):
+ output[i] = tensor[min(math.floor((i + 0.5) * scale), in_batch_size - 1)]
+
+ return output
+
+def resize_list_to_batch_size(l, batch_size):
+ in_batch_size = len(l)
+ if in_batch_size == batch_size or in_batch_size == 0:
+ return l
+
+ if batch_size <= 1:
+ return l[:batch_size]
+
+ output = []
+ if batch_size < in_batch_size:
+ scale = (in_batch_size - 1) / (batch_size - 1)
+ for i in range(batch_size):
+ output.append(l[min(round(i * scale), in_batch_size - 1)])
+ else:
+ scale = in_batch_size / batch_size
+ for i in range(batch_size):
+ output.append(l[min(math.floor((i + 0.5) * scale), in_batch_size - 1)])
+
+ return output
+
+def convert_sd_to(state_dict, dtype):
+ keys = list(state_dict.keys())
+ for k in keys:
+ state_dict[k] = state_dict[k].to(dtype)
+ return state_dict
+
+def safetensors_header(safetensors_path, max_size=100*1024*1024):
+ with open(safetensors_path, "rb") as f:
+ header = f.read(8)
+ length_of_header = struct.unpack(' max_size:
+ return None
+ return f.read(length_of_header)
+
+def set_attr(obj, attr, value):
+ attrs = attr.split(".")
+ for name in attrs[:-1]:
+ obj = getattr(obj, name)
+ prev = getattr(obj, attrs[-1])
+ setattr(obj, attrs[-1], value)
+ return prev
+
+def set_attr_param(obj, attr, value):
+ return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
+
+def copy_to_param(obj, attr, value):
+ # inplace update tensor instead of replacing it
+ attrs = attr.split(".")
+ for name in attrs[:-1]:
+ obj = getattr(obj, name)
+ prev = getattr(obj, attrs[-1])
+ prev.data.copy_(value)
+
+def get_attr(obj, attr: str):
+ """Retrieves a nested attribute from an object using dot notation.
+
+ Args:
+ obj: The object to get the attribute from
+ attr (str): The attribute path using dot notation (e.g. "model.layer.weight")
+
+ Returns:
+ The value of the requested attribute
+
+ Example:
+ model = MyModel()
+ weight = get_attr(model, "layer1.conv.weight")
+ # Equivalent to: model.layer1.conv.weight
+
+ Important:
+ Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when
+ accessing nested model objects under `ModelPatcher.model`.
+ """
+ attrs = attr.split(".")
+ for name in attrs:
+ obj = getattr(obj, name)
+ return obj
+
+def bislerp(samples, width, height):
+ def slerp(b1, b2, r):
+ '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
+
+ c = b1.shape[-1]
+
+ #norms
+ b1_norms = torch.norm(b1, dim=-1, keepdim=True)
+ b2_norms = torch.norm(b2, dim=-1, keepdim=True)
+
+ #normalize
+ b1_normalized = b1 / b1_norms
+ b2_normalized = b2 / b2_norms
+
+ #zero when norms are zero
+ b1_normalized[b1_norms.expand(-1,c) == 0.0] = 0.0
+ b2_normalized[b2_norms.expand(-1,c) == 0.0] = 0.0
+
+ #slerp
+ dot = (b1_normalized*b2_normalized).sum(1)
+ omega = torch.acos(dot)
+ so = torch.sin(omega)
+
+ #technically not mathematically correct, but more pleasing?
+ res = (torch.sin((1.0-r.squeeze(1))*omega)/so).unsqueeze(1)*b1_normalized + (torch.sin(r.squeeze(1)*omega)/so).unsqueeze(1) * b2_normalized
+ res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
+
+ #edge cases for same or polar opposites
+ res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
+ res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
+ return res
+
+ def generate_bilinear_data(length_old, length_new, device):
+ coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
+ coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
+ ratios = coords_1 - coords_1.floor()
+ coords_1 = coords_1.to(torch.int64)
+
+ coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
+ coords_2[:,:,:,-1] -= 1
+ coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
+ coords_2 = coords_2.to(torch.int64)
+ return ratios, coords_1, coords_2
+
+ orig_dtype = samples.dtype
+ samples = samples.float()
+ n,c,h,w = samples.shape
+ h_new, w_new = (height, width)
+
+ #linear w
+ ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
+ coords_1 = coords_1.expand((n, c, h, -1))
+ coords_2 = coords_2.expand((n, c, h, -1))
+ ratios = ratios.expand((n, 1, h, -1))
+
+ pass_1 = samples.gather(-1,coords_1).movedim(1, -1).reshape((-1,c))
+ pass_2 = samples.gather(-1,coords_2).movedim(1, -1).reshape((-1,c))
+ ratios = ratios.movedim(1, -1).reshape((-1,1))
+
+ result = slerp(pass_1, pass_2, ratios)
+ result = result.reshape(n, h, w_new, c).movedim(-1, 1)
+
+ #linear h
+ ratios, coords_1, coords_2 = generate_bilinear_data(h, h_new, samples.device)
+ coords_1 = coords_1.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
+ coords_2 = coords_2.reshape((1,1,-1,1)).expand((n, c, -1, w_new))
+ ratios = ratios.reshape((1,1,-1,1)).expand((n, 1, -1, w_new))
+
+ pass_1 = result.gather(-2,coords_1).movedim(1, -1).reshape((-1,c))
+ pass_2 = result.gather(-2,coords_2).movedim(1, -1).reshape((-1,c))
+ ratios = ratios.movedim(1, -1).reshape((-1,1))
+
+ result = slerp(pass_1, pass_2, ratios)
+ result = result.reshape(n, h_new, w_new, c).movedim(-1, 1)
+ return result.to(orig_dtype)
+
+def lanczos(samples, width, height):
+ images = [Image.fromarray(np.clip(255. * image.movedim(0, -1).cpu().numpy(), 0, 255).astype(np.uint8)) for image in samples]
+ images = [image.resize((width, height), resample=Image.Resampling.LANCZOS) for image in images]
+ images = [torch.from_numpy(np.array(image).astype(np.float32) / 255.0).movedim(-1, 0) for image in images]
+ result = torch.stack(images)
+ return result.to(samples.device, samples.dtype)
+
+def common_upscale(samples, width, height, upscale_method, crop):
+ orig_shape = tuple(samples.shape)
+ if len(orig_shape) > 4:
+ samples = samples.reshape(samples.shape[0], samples.shape[1], -1, samples.shape[-2], samples.shape[-1])
+ samples = samples.movedim(2, 1)
+ samples = samples.reshape(-1, orig_shape[1], orig_shape[-2], orig_shape[-1])
+ if crop == "center":
+ old_width = samples.shape[-1]
+ old_height = samples.shape[-2]
+ old_aspect = old_width / old_height
+ new_aspect = width / height
+ x = 0
+ y = 0
+ if old_aspect > new_aspect:
+ x = round((old_width - old_width * (new_aspect / old_aspect)) / 2)
+ elif old_aspect < new_aspect:
+ y = round((old_height - old_height * (old_aspect / new_aspect)) / 2)
+ s = samples.narrow(-2, y, old_height - y * 2).narrow(-1, x, old_width - x * 2)
+ else:
+ s = samples
+
+ if upscale_method == "bislerp":
+ out = bislerp(s, width, height)
+ elif upscale_method == "lanczos":
+ out = lanczos(s, width, height)
+ else:
+ out = torch.nn.functional.interpolate(s, size=(height, width), mode=upscale_method)
+
+ if len(orig_shape) == 4:
+ return out
+
+ out = out.reshape((orig_shape[0], -1, orig_shape[1]) + (height, width))
+ return out.movedim(2, 1).reshape(orig_shape[:-2] + (height, width))
+
+def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
+ rows = 1 if height <= tile_y else math.ceil((height - overlap) / (tile_y - overlap))
+ cols = 1 if width <= tile_x else math.ceil((width - overlap) / (tile_x - overlap))
+ return rows * cols
+
+@torch.inference_mode()
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None):
+ dims = len(tile)
+
+ if not (isinstance(upscale_amount, (tuple, list))):
+ upscale_amount = [upscale_amount] * dims
+
+ if not (isinstance(overlap, (tuple, list))):
+ overlap = [overlap] * dims
+
+ if index_formulas is None:
+ index_formulas = upscale_amount
+
+ if not (isinstance(index_formulas, (tuple, list))):
+ index_formulas = [index_formulas] * dims
+
+ def get_upscale(dim, val):
+ up = upscale_amount[dim]
+ if callable(up):
+ return up(val)
+ else:
+ return up * val
+
+ def get_downscale(dim, val):
+ up = upscale_amount[dim]
+ if callable(up):
+ return up(val)
+ else:
+ return val / up
+
+ def get_upscale_pos(dim, val):
+ up = index_formulas[dim]
+ if callable(up):
+ return up(val)
+ else:
+ return up * val
+
+ def get_downscale_pos(dim, val):
+ up = index_formulas[dim]
+ if callable(up):
+ return up(val)
+ else:
+ return val / up
+
+ if downscale:
+ get_scale = get_downscale
+ get_pos = get_downscale_pos
+ else:
+ get_scale = get_upscale
+ get_pos = get_upscale_pos
+
+ def mult_list_upscale(a):
+ out = []
+ for i in range(len(a)):
+ out.append(round(get_scale(i, a[i])))
+ return out
+
+ output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device)
+
+ for b in range(samples.shape[0]):
+ s = samples[b:b+1]
+
+ # handle entire input fitting in a single tile
+ if all(s.shape[d+2] <= tile[d] for d in range(dims)):
+ output[b:b+1] = function(s).to(output_device)
+ if pbar is not None:
+ pbar.update(1)
+ continue
+
+ out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+ out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
+
+ positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
+
+ for it in itertools.product(*positions):
+ s_in = s
+ upscaled = []
+
+ for d in range(dims):
+ pos = max(0, min(s.shape[d + 2] - overlap[d], it[d]))
+ l = min(tile[d], s.shape[d + 2] - pos)
+ s_in = s_in.narrow(d + 2, pos, l)
+ upscaled.append(round(get_pos(d, pos)))
+
+ ps = function(s_in).to(output_device)
+ mask = torch.ones_like(ps)
+
+ for d in range(2, dims + 2):
+ feather = round(get_scale(d - 2, overlap[d - 2]))
+ if feather >= mask.shape[d]:
+ continue
+ for t in range(feather):
+ a = (t + 1) / feather
+ mask.narrow(d, t, 1).mul_(a)
+ mask.narrow(d, mask.shape[d] - 1 - t, 1).mul_(a)
+
+ o = out
+ o_d = out_div
+ for d in range(dims):
+ o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+ o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+
+ o.add_(ps * mask)
+ o_d.add_(mask)
+
+ if pbar is not None:
+ pbar.update(1)
+
+ output[b:b+1] = out/out_div
+ return output
+
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+ return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
+
+PROGRESS_BAR_ENABLED = True
+def set_progress_bar_enabled(enabled):
+ global PROGRESS_BAR_ENABLED
+ PROGRESS_BAR_ENABLED = enabled
+
+PROGRESS_BAR_HOOK = None
+def set_progress_bar_global_hook(function):
+ global PROGRESS_BAR_HOOK
+ PROGRESS_BAR_HOOK = function
+
+class ProgressBar:
+ def __init__(self, total, node_id=None):
+ global PROGRESS_BAR_HOOK
+ self.total = total
+ self.current = 0
+ self.hook = PROGRESS_BAR_HOOK
+ self.node_id = node_id
+
+ def update_absolute(self, value, total=None, preview=None):
+ if total is not None:
+ self.total = total
+ if value > self.total:
+ value = self.total
+ self.current = value
+ if self.hook is not None:
+ self.hook(self.current, self.total, preview, node_id=self.node_id)
+
+ def update(self, value):
+ self.update_absolute(self.current + value)
+
+def reshape_mask(input_mask, output_shape):
+ dims = len(output_shape) - 2
+
+ if dims == 1:
+ scale_mode = "linear"
+
+ if dims == 2:
+ input_mask = input_mask.reshape((-1, 1, input_mask.shape[-2], input_mask.shape[-1]))
+ scale_mode = "bilinear"
+
+ if dims == 3:
+ if len(input_mask.shape) < 5:
+ input_mask = input_mask.reshape((1, 1, -1, input_mask.shape[-2], input_mask.shape[-1]))
+ scale_mode = "trilinear"
+
+ mask = torch.nn.functional.interpolate(input_mask, size=output_shape[2:], mode=scale_mode)
+ if mask.shape[1] < output_shape[1]:
+ mask = mask.repeat((1, output_shape[1]) + (1,) * dims)[:,:output_shape[1]]
+ mask = repeat_to_batch_size(mask, output_shape[0])
+ return mask
+
+def upscale_dit_mask(mask: torch.Tensor, img_size_in, img_size_out):
+ hi, wi = img_size_in
+ ho, wo = img_size_out
+ # if it's already the correct size, no need to do anything
+ if (hi, wi) == (ho, wo):
+ return mask
+ if mask.ndim == 2:
+ mask = mask.unsqueeze(0)
+ if mask.ndim != 3:
+ raise ValueError(f"Got a mask of shape {list(mask.shape)}, expected [b, q, k] or [q, k]")
+ txt_tokens = mask.shape[1] - (hi * wi)
+ # quadrants of the mask
+ txt_to_txt = mask[:, :txt_tokens, :txt_tokens]
+ txt_to_img = mask[:, :txt_tokens, txt_tokens:]
+ img_to_img = mask[:, txt_tokens:, txt_tokens:]
+ img_to_txt = mask[:, txt_tokens:, :txt_tokens]
+
+ # convert to 1d x 2d, interpolate, then back to 1d x 1d
+ txt_to_img = rearrange (txt_to_img, "b t (h w) -> b t h w", h=hi, w=wi)
+ txt_to_img = interpolate(txt_to_img, size=img_size_out, mode="bilinear")
+ txt_to_img = rearrange (txt_to_img, "b t h w -> b t (h w)")
+ # this one is hard because we have to do it twice
+ # convert to 1d x 2d, interpolate, then to 2d x 1d, interpolate, then 1d x 1d
+ img_to_img = rearrange (img_to_img, "b hw (h w) -> b hw h w", h=hi, w=wi)
+ img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear")
+ img_to_img = rearrange (img_to_img, "b (hk wk) hq wq -> b (hq wq) hk wk", hk=hi, wk=wi)
+ img_to_img = interpolate(img_to_img, size=img_size_out, mode="bilinear")
+ img_to_img = rearrange (img_to_img, "b (hq wq) hk wk -> b (hk wk) (hq wq)", hq=ho, wq=wo)
+ # convert to 2d x 1d, interpolate, then back to 1d x 1d
+ img_to_txt = rearrange (img_to_txt, "b (h w) t -> b t h w", h=hi, w=wi)
+ img_to_txt = interpolate(img_to_txt, size=img_size_out, mode="bilinear")
+ img_to_txt = rearrange (img_to_txt, "b t h w -> b (h w) t")
+
+ # reassemble the mask from blocks
+ out = torch.cat([
+ torch.cat([txt_to_txt, txt_to_img], dim=2),
+ torch.cat([img_to_txt, img_to_img], dim=2)],
+ dim=1
+ )
+ return out
diff --git a/ComfyUI/comfy/weight_adapter/__init__.py b/ComfyUI/comfy/weight_adapter/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b40f920e400fc895443d4016076d1ded406ae258
--- /dev/null
+++ b/ComfyUI/comfy/weight_adapter/__init__.py
@@ -0,0 +1,34 @@
+from .base import WeightAdapterBase, WeightAdapterTrainBase
+from .lora import LoRAAdapter
+from .loha import LoHaAdapter
+from .lokr import LoKrAdapter
+from .glora import GLoRAAdapter
+from .oft import OFTAdapter
+from .boft import BOFTAdapter
+
+
+adapters: list[type[WeightAdapterBase]] = [
+ LoRAAdapter,
+ LoHaAdapter,
+ LoKrAdapter,
+ GLoRAAdapter,
+ OFTAdapter,
+ BOFTAdapter,
+]
+adapter_maps: dict[str, type[WeightAdapterBase]] = {
+ "LoRA": LoRAAdapter,
+ "LoHa": LoHaAdapter,
+ "LoKr": LoKrAdapter,
+ "OFT": OFTAdapter,
+ ## We disable not implemented algo for now
+ # "GLoRA": GLoRAAdapter,
+ # "BOFT": BOFTAdapter,
+}
+
+
+__all__ = [
+ "WeightAdapterBase",
+ "WeightAdapterTrainBase",
+ "adapters",
+ "adapter_maps",
+] + [a.__name__ for a in adapters]
diff --git a/ComfyUI/comfy/weight_adapter/boft.py b/ComfyUI/comfy/weight_adapter/boft.py
new file mode 100644
index 0000000000000000000000000000000000000000..b2a2f1bd46bed5b177f9c583e8d329dc167af1c3
--- /dev/null
+++ b/ComfyUI/comfy/weight_adapter/boft.py
@@ -0,0 +1,115 @@
+import logging
+from typing import Optional
+
+import torch
+import comfy.model_management
+from .base import WeightAdapterBase, weight_decompose
+
+
+class BOFTAdapter(WeightAdapterBase):
+ name = "boft"
+
+ def __init__(self, loaded_keys, weights):
+ self.loaded_keys = loaded_keys
+ self.weights = weights
+
+ @classmethod
+ def load(
+ cls,
+ x: str,
+ lora: dict[str, torch.Tensor],
+ alpha: float,
+ dora_scale: torch.Tensor,
+ loaded_keys: set[str] = None,
+ ) -> Optional["BOFTAdapter"]:
+ if loaded_keys is None:
+ loaded_keys = set()
+ blocks_name = "{}.oft_blocks".format(x)
+ rescale_name = "{}.rescale".format(x)
+
+ blocks = None
+ if blocks_name in lora.keys():
+ blocks = lora[blocks_name]
+ if blocks.ndim == 4:
+ loaded_keys.add(blocks_name)
+ else:
+ blocks = None
+ if blocks is None:
+ return None
+
+ rescale = None
+ if rescale_name in lora.keys():
+ rescale = lora[rescale_name]
+ loaded_keys.add(rescale_name)
+
+ weights = (blocks, rescale, alpha, dora_scale)
+ return cls(loaded_keys, weights)
+
+ def calculate_weight(
+ self,
+ weight,
+ key,
+ strength,
+ strength_model,
+ offset,
+ function,
+ intermediate_dtype=torch.float32,
+ original_weight=None,
+ ):
+ v = self.weights
+ blocks = v[0]
+ rescale = v[1]
+ alpha = v[2]
+ dora_scale = v[3]
+
+ blocks = comfy.model_management.cast_to_device(blocks, weight.device, intermediate_dtype)
+ if rescale is not None:
+ rescale = comfy.model_management.cast_to_device(rescale, weight.device, intermediate_dtype)
+
+ boft_m, block_num, boft_b, *_ = blocks.shape
+
+ try:
+ # Get r
+ I = torch.eye(boft_b, device=blocks.device, dtype=blocks.dtype)
+ # for Q = -Q^T
+ q = blocks - blocks.transpose(-1, -2)
+ normed_q = q
+ if alpha > 0: # alpha in boft/bboft is for constraint
+ q_norm = torch.norm(q) + 1e-8
+ if q_norm > alpha:
+ normed_q = q * alpha / q_norm
+ # use float() to prevent unsupported type in .inverse()
+ r = (I + normed_q) @ (I - normed_q).float().inverse()
+ r = r.to(weight)
+ inp = org = weight
+
+ r_b = boft_b//2
+ for i in range(boft_m):
+ bi = r[i]
+ g = 2
+ k = 2**i * r_b
+ if strength != 1:
+ bi = bi * strength + (1-strength) * I
+ inp = (
+ inp.unflatten(0, (-1, g, k))
+ .transpose(1, 2)
+ .flatten(0, 2)
+ .unflatten(0, (-1, boft_b))
+ )
+ inp = torch.einsum("b i j, b j ...-> b i ...", bi, inp)
+ inp = (
+ inp.flatten(0, 1).unflatten(0, (-1, k, g)).transpose(1, 2).flatten(0, 2)
+ )
+
+ if rescale is not None:
+ inp = inp * rescale
+
+ lora_diff = inp - org
+ lora_diff = comfy.model_management.cast_to_device(lora_diff, weight.device, intermediate_dtype)
+ if dora_scale is not None:
+ weight = weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype, function)
+ else:
+ weight += function((strength * lora_diff).type(weight.dtype))
+ except Exception as e:
+ logging.error("ERROR {} {} {}".format(self.name, key, e))
+ return weight
diff --git a/ComfyUI/comfy_api/feature_flags.py b/ComfyUI/comfy_api/feature_flags.py
new file mode 100644
index 0000000000000000000000000000000000000000..0d4389a6e9e731a8b9e0f994584ab494981eacbc
--- /dev/null
+++ b/ComfyUI/comfy_api/feature_flags.py
@@ -0,0 +1,69 @@
+"""
+Feature flags module for ComfyUI WebSocket protocol negotiation.
+
+This module handles capability negotiation between frontend and backend,
+allowing graceful protocol evolution while maintaining backward compatibility.
+"""
+
+from typing import Any, Dict
+
+from comfy.cli_args import args
+
+# Default server capabilities
+SERVER_FEATURE_FLAGS: Dict[str, Any] = {
+ "supports_preview_metadata": True,
+ "max_upload_size": args.max_upload_size * 1024 * 1024, # Convert MB to bytes
+}
+
+
+def get_connection_feature(
+ sockets_metadata: Dict[str, Dict[str, Any]],
+ sid: str,
+ feature_name: str,
+ default: Any = False
+) -> Any:
+ """
+ Get a feature flag value for a specific connection.
+
+ Args:
+ sockets_metadata: Dictionary of socket metadata
+ sid: Session ID of the connection
+ feature_name: Name of the feature to check
+ default: Default value if feature not found
+
+ Returns:
+ Feature value or default if not found
+ """
+ if sid not in sockets_metadata:
+ return default
+
+ return sockets_metadata[sid].get("feature_flags", {}).get(feature_name, default)
+
+
+def supports_feature(
+ sockets_metadata: Dict[str, Dict[str, Any]],
+ sid: str,
+ feature_name: str
+) -> bool:
+ """
+ Check if a connection supports a specific feature.
+
+ Args:
+ sockets_metadata: Dictionary of socket metadata
+ sid: Session ID of the connection
+ feature_name: Name of the feature to check
+
+ Returns:
+ Boolean indicating if feature is supported
+ """
+ return get_connection_feature(sockets_metadata, sid, feature_name, False) is True
+
+
+def get_server_features() -> Dict[str, Any]:
+ """
+ Get the server's feature flags.
+
+ Returns:
+ Dictionary of server feature flags
+ """
+ return SERVER_FEATURE_FLAGS.copy()
diff --git a/ComfyUI/comfy_api_nodes/README.md b/ComfyUI/comfy_api_nodes/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..f56d6c8606346dc4d6c8d688cc8ca4f52cb5c9cd
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/README.md
@@ -0,0 +1,65 @@
+# ComfyUI API Nodes
+
+## Introduction
+
+Below are a collection of nodes that work by calling external APIs. More information available in our [docs](https://docs.comfy.org/tutorials/api-nodes/overview).
+
+## Development
+
+While developing, you should be testing against the Staging environment. To test against staging:
+
+**Install ComfyUI_frontend**
+
+Follow the instructions [here](https://github.com/Comfy-Org/ComfyUI_frontend) to start the frontend server. By default, it will connect to Staging authentication.
+
+> **Hint:** If you use --front-end-version argument for ComfyUI, it will use production authentication.
+
+```bash
+python run main.py --comfy-api-base https://stagingapi.comfy.org
+```
+
+To authenticate to staging, please login and then ask one of Comfy Org team to whitelist you for access to staging.
+
+API stubs are generated through automatic codegen tools from OpenAPI definitions. Since the Comfy Org OpenAPI definition contains many things from the Comfy Registry as well, we use redocly/cli to filter out only the paths relevant for API nodes.
+
+### Redocly Instructions
+
+**Tip**
+When developing locally, use the `redocly-dev.yaml` file to generate pydantic models. This lets you use stubs for APIs that are not marked `Released` yet.
+
+Before your API node PR merges, make sure to add the `Released` tag to the `openapi.yaml` file and test in staging.
+
+```bash
+# Download the OpenAPI file from staging server.
+curl -o openapi.yaml https://stagingapi.comfy.org/openapi
+
+# Filter out unneeded API definitions.
+npm install -g @redocly/cli
+redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly-dev.yaml --remove-unused-components
+
+# Generate the pydantic datamodels for validation.
+datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
+
+```
+
+
+# Merging to Master
+
+Before merging to comfyanonymous/ComfyUI master, follow these steps:
+
+1. Add the "Released" tag to the ComfyUI OpenAPI yaml file for each endpoint you are using in the nodes.
+1. Make sure the ComfyUI API is deployed to prod with your changes.
+1. Run the code generation again with `redocly.yaml` and the production OpenAPI yaml file.
+
+```bash
+# Download the OpenAPI file from prod server.
+curl -o openapi.yaml https://api.comfy.org/openapi
+
+# Filter out unneeded API definitions.
+npm install -g @redocly/cli
+redocly bundle openapi.yaml --output filtered-openapi.yaml --config comfy_api_nodes/redocly.yaml --remove-unused-components
+
+# Generate the pydantic datamodels for validation.
+datamodel-codegen --use-subclass-enum --field-constraints --strict-types bytes --input filtered-openapi.yaml --output comfy_api_nodes/apis/__init__.py --output-model-type pydantic_v2.BaseModel
+
+```
diff --git a/ComfyUI/comfy_api_nodes/__init__.py b/ComfyUI/comfy_api_nodes/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ComfyUI/comfy_api_nodes/apinode_utils.py b/ComfyUI/comfy_api_nodes/apinode_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..788e2803f5d645ad03b8285349a11d4d160a3903
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/apinode_utils.py
@@ -0,0 +1,678 @@
+from __future__ import annotations
+import io
+import logging
+import mimetypes
+from typing import Optional, Union
+from comfy.utils import common_upscale
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.util import VideoContainer, VideoCodec
+from comfy_api.input.video_types import VideoInput
+from comfy_api.input.basic_types import AudioInput
+from comfy_api_nodes.apis.client import (
+ ApiClient,
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ UploadRequest,
+ UploadResponse,
+)
+from server import PromptServer
+
+
+import numpy as np
+from PIL import Image
+import requests
+import torch
+import math
+import base64
+import uuid
+from io import BytesIO
+import av
+
+
+def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile:
+ """Downloads a video from a URL and returns a `VIDEO` output.
+
+ Args:
+ video_url: The URL of the video to download.
+
+ Returns:
+ A Comfy node `VIDEO` output.
+ """
+ video_io = download_url_to_bytesio(video_url, timeout)
+ if video_io is None:
+ error_msg = f"Failed to download video from {video_url}"
+ logging.error(error_msg)
+ raise ValueError(error_msg)
+ return VideoFromFile(video_io)
+
+
+def downscale_image_tensor(image, total_pixels=1536 * 1024) -> torch.Tensor:
+ """Downscale input image tensor to roughly the specified total pixels."""
+ samples = image.movedim(-1, 1)
+ total = int(total_pixels)
+ scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+ if scale_by >= 1:
+ return image
+ width = round(samples.shape[3] * scale_by)
+ height = round(samples.shape[2] * scale_by)
+
+ s = common_upscale(samples, width, height, "lanczos", "disabled")
+ s = s.movedim(1, -1)
+ return s
+
+
+def validate_and_cast_response(
+ response, timeout: int = None, node_id: Union[str, None] = None
+) -> torch.Tensor:
+ """Validates and casts a response to a torch.Tensor.
+
+ Args:
+ response: The response to validate and cast.
+ timeout: Request timeout in seconds. Defaults to None (no timeout).
+
+ Returns:
+ A torch.Tensor representing the image (1, H, W, C).
+
+ Raises:
+ ValueError: If the response is not valid.
+ """
+ # validate raw JSON response
+ data = response.data
+ if not data or len(data) == 0:
+ raise ValueError("No images returned from API endpoint")
+
+ # Initialize list to store image tensors
+ image_tensors: list[torch.Tensor] = []
+
+ # Process each image in the data array
+ for image_data in data:
+ image_url = image_data.url
+ b64_data = image_data.b64_json
+
+ if not image_url and not b64_data:
+ raise ValueError("No image was generated in the response")
+
+ if b64_data:
+ img_data = base64.b64decode(b64_data)
+ img = Image.open(io.BytesIO(img_data))
+
+ elif image_url:
+ if node_id:
+ PromptServer.instance.send_progress_text(
+ f"Result URL: {image_url}", node_id
+ )
+ img_response = requests.get(image_url, timeout=timeout)
+ if img_response.status_code != 200:
+ raise ValueError("Failed to download the image")
+ img = Image.open(io.BytesIO(img_response.content))
+
+ img = img.convert("RGBA")
+
+ # Convert to numpy array, normalize to float32 between 0 and 1
+ img_array = np.array(img).astype(np.float32) / 255.0
+ img_tensor = torch.from_numpy(img_array)
+
+ # Add to list of tensors
+ image_tensors.append(img_tensor)
+
+ return torch.stack(image_tensors, dim=0)
+
+
+def validate_aspect_ratio(
+ aspect_ratio: str,
+ minimum_ratio: float,
+ maximum_ratio: float,
+ minimum_ratio_str: str,
+ maximum_ratio_str: str,
+) -> float:
+ """Validates and casts an aspect ratio string to a float.
+
+ Args:
+ aspect_ratio: The aspect ratio string to validate.
+ minimum_ratio: The minimum aspect ratio.
+ maximum_ratio: The maximum aspect ratio.
+ minimum_ratio_str: The minimum aspect ratio string.
+ maximum_ratio_str: The maximum aspect ratio string.
+
+ Returns:
+ The validated and cast aspect ratio.
+
+ Raises:
+ Exception: If the aspect ratio is not valid.
+ """
+ # get ratio values
+ numbers = aspect_ratio.split(":")
+ if len(numbers) != 2:
+ raise TypeError(
+ f"Aspect ratio must be in the format X:Y, such as 16:9, but was {aspect_ratio}."
+ )
+ try:
+ numerator = int(numbers[0])
+ denominator = int(numbers[1])
+ except ValueError as exc:
+ raise TypeError(
+ f"Aspect ratio must contain numbers separated by ':', such as 16:9, but was {aspect_ratio}."
+ ) from exc
+ calculated_ratio = numerator / denominator
+ # if not close to minimum and maximum, check bounds
+ if not math.isclose(calculated_ratio, minimum_ratio) or not math.isclose(
+ calculated_ratio, maximum_ratio
+ ):
+ if calculated_ratio < minimum_ratio:
+ raise TypeError(
+ f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
+ )
+ elif calculated_ratio > maximum_ratio:
+ raise TypeError(
+ f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
+ )
+ return aspect_ratio
+
+
+def mimetype_to_extension(mime_type: str) -> str:
+ """Converts a MIME type to a file extension."""
+ return mime_type.split("/")[-1].lower()
+
+
+def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
+ """Downloads content from a URL using requests and returns it as BytesIO.
+
+ Args:
+ url: The URL to download.
+ timeout: Request timeout in seconds. Defaults to None (no timeout).
+
+ Returns:
+ BytesIO object containing the downloaded content.
+ """
+ response = requests.get(url, stream=True, timeout=timeout)
+ response.raise_for_status() # Raises HTTPError for bad responses (4XX or 5XX)
+ return BytesIO(response.content)
+
+
+def bytesio_to_image_tensor(image_bytesio: BytesIO, mode: str = "RGBA") -> torch.Tensor:
+ """Converts image data from BytesIO to a torch.Tensor.
+
+ Args:
+ image_bytesio: BytesIO object containing the image data.
+ mode: The PIL mode to convert the image to (e.g., "RGB", "RGBA").
+
+ Returns:
+ A torch.Tensor representing the image (1, H, W, C).
+
+ Raises:
+ PIL.UnidentifiedImageError: If the image data cannot be identified.
+ ValueError: If the specified mode is invalid.
+ """
+ image = Image.open(image_bytesio)
+ image = image.convert(mode)
+ image_array = np.array(image).astype(np.float32) / 255.0
+ return torch.from_numpy(image_array).unsqueeze(0)
+
+
+def download_url_to_image_tensor(url: str, timeout: int = None) -> torch.Tensor:
+ """Downloads an image from a URL and returns a [B, H, W, C] tensor."""
+ image_bytesio = download_url_to_bytesio(url, timeout)
+ return bytesio_to_image_tensor(image_bytesio)
+
+
+def process_image_response(response: requests.Response) -> torch.Tensor:
+ """Uses content from a Response object and converts it to a torch.Tensor"""
+ return bytesio_to_image_tensor(BytesIO(response.content))
+
+
+def _tensor_to_pil(image: torch.Tensor, total_pixels: int = 2048 * 2048) -> Image.Image:
+ """Converts a single torch.Tensor image [H, W, C] to a PIL Image, optionally downscaling."""
+ if len(image.shape) > 3:
+ image = image[0]
+ # TODO: remove alpha if not allowed and present
+ input_tensor = image.cpu()
+ input_tensor = downscale_image_tensor(
+ input_tensor.unsqueeze(0), total_pixels=total_pixels
+ ).squeeze()
+ image_np = (input_tensor.numpy() * 255).astype(np.uint8)
+ img = Image.fromarray(image_np)
+ return img
+
+
+def _pil_to_bytesio(img: Image.Image, mime_type: str = "image/png") -> BytesIO:
+ """Converts a PIL Image to a BytesIO object."""
+ if not mime_type:
+ mime_type = "image/png"
+
+ img_byte_arr = io.BytesIO()
+ # Derive PIL format from MIME type (e.g., 'image/png' -> 'PNG')
+ pil_format = mime_type.split("/")[-1].upper()
+ if pil_format == "JPG":
+ pil_format = "JPEG"
+ img.save(img_byte_arr, format=pil_format)
+ img_byte_arr.seek(0)
+ return img_byte_arr
+
+
+def tensor_to_bytesio(
+ image: torch.Tensor,
+ name: Optional[str] = None,
+ total_pixels: int = 2048 * 2048,
+ mime_type: str = "image/png",
+) -> BytesIO:
+ """Converts a torch.Tensor image to a named BytesIO object.
+
+ Args:
+ image: Input torch.Tensor image.
+ name: Optional filename for the BytesIO object.
+ total_pixels: Maximum total pixels for potential downscaling.
+ mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
+
+ Returns:
+ Named BytesIO object containing the image data.
+ """
+ if not mime_type:
+ mime_type = "image/png"
+
+ pil_image = _tensor_to_pil(image, total_pixels=total_pixels)
+ img_binary = _pil_to_bytesio(pil_image, mime_type=mime_type)
+ img_binary.name = (
+ f"{name if name else uuid.uuid4()}.{mimetype_to_extension(mime_type)}"
+ )
+ return img_binary
+
+
+def tensor_to_base64_string(
+ image_tensor: torch.Tensor,
+ total_pixels: int = 2048 * 2048,
+ mime_type: str = "image/png",
+) -> str:
+ """Convert [B, H, W, C] or [H, W, C] tensor to a base64 string.
+
+ Args:
+ image_tensor: Input torch.Tensor image.
+ total_pixels: Maximum total pixels for potential downscaling.
+ mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
+
+ Returns:
+ Base64 encoded string of the image.
+ """
+ pil_image = _tensor_to_pil(image_tensor, total_pixels=total_pixels)
+ img_byte_arr = _pil_to_bytesio(pil_image, mime_type=mime_type)
+ img_bytes = img_byte_arr.getvalue()
+ # Encode bytes to base64 string
+ base64_encoded_string = base64.b64encode(img_bytes).decode("utf-8")
+ return base64_encoded_string
+
+
+def tensor_to_data_uri(
+ image_tensor: torch.Tensor,
+ total_pixels: int = 2048 * 2048,
+ mime_type: str = "image/png",
+) -> str:
+ """Converts a tensor image to a Data URI string.
+
+ Args:
+ image_tensor: Input torch.Tensor image.
+ total_pixels: Maximum total pixels for potential downscaling.
+ mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp').
+
+ Returns:
+ Data URI string (e.g., 'data:image/png;base64,...').
+ """
+ base64_string = tensor_to_base64_string(image_tensor, total_pixels, mime_type)
+ return f"data:{mime_type};base64,{base64_string}"
+
+
+def text_filepath_to_base64_string(filepath: str) -> str:
+ """Converts a text file to a base64 string."""
+ with open(filepath, "rb") as f:
+ file_content = f.read()
+ return base64.b64encode(file_content).decode("utf-8")
+
+
+def text_filepath_to_data_uri(filepath: str) -> str:
+ """Converts a text file to a data URI."""
+ base64_string = text_filepath_to_base64_string(filepath)
+ mime_type, _ = mimetypes.guess_type(filepath)
+ if mime_type is None:
+ mime_type = "application/octet-stream"
+ return f"data:{mime_type};base64,{base64_string}"
+
+
+def upload_file_to_comfyapi(
+ file_bytes_io: BytesIO,
+ filename: str,
+ upload_mime_type: str,
+ auth_kwargs: Optional[dict[str, str]] = None,
+) -> str:
+ """
+ Uploads a single file to ComfyUI API and returns its download URL.
+
+ Args:
+ file_bytes_io: BytesIO object containing the file data.
+ filename: The filename of the file.
+ upload_mime_type: MIME type of the file.
+ auth_kwargs: Optional authentication token(s).
+
+ Returns:
+ The download URL for the uploaded file.
+ """
+ request_object = UploadRequest(file_name=filename, content_type=upload_mime_type)
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/customers/storage",
+ method=HttpMethod.POST,
+ request_model=UploadRequest,
+ response_model=UploadResponse,
+ ),
+ request=request_object,
+ auth_kwargs=auth_kwargs,
+ )
+
+ response: UploadResponse = operation.execute()
+ upload_response = ApiClient.upload_file(
+ response.upload_url, file_bytes_io, content_type=upload_mime_type
+ )
+ upload_response.raise_for_status()
+
+ return response.download_url
+
+
+def video_to_base64_string(
+ video: VideoInput,
+ container_format: VideoContainer = None,
+ codec: VideoCodec = None
+) -> str:
+ """
+ Converts a video input to a base64 string.
+
+ Args:
+ video: The video input to convert
+ container_format: Optional container format to use (defaults to video.container if available)
+ codec: Optional codec to use (defaults to video.codec if available)
+ """
+ video_bytes_io = io.BytesIO()
+
+ # Use provided format/codec if specified, otherwise use video's own if available
+ format_to_use = container_format if container_format is not None else getattr(video, 'container', VideoContainer.MP4)
+ codec_to_use = codec if codec is not None else getattr(video, 'codec', VideoCodec.H264)
+
+ video.save_to(video_bytes_io, format=format_to_use, codec=codec_to_use)
+ video_bytes_io.seek(0)
+ return base64.b64encode(video_bytes_io.getvalue()).decode("utf-8")
+
+
+def upload_video_to_comfyapi(
+ video: VideoInput,
+ auth_kwargs: Optional[dict[str, str]] = None,
+ container: VideoContainer = VideoContainer.MP4,
+ codec: VideoCodec = VideoCodec.H264,
+ max_duration: Optional[int] = None,
+) -> str:
+ """
+ Uploads a single video to ComfyUI API and returns its download URL.
+ Uses the specified container and codec for saving the video before upload.
+
+ Args:
+ video: VideoInput object (Comfy VIDEO type).
+ auth_kwargs: Optional authentication token(s).
+ container: The video container format to use (default: MP4).
+ codec: The video codec to use (default: H264).
+ max_duration: Optional maximum duration of the video in seconds. If the video is longer than this, an error will be raised.
+
+ Returns:
+ The download URL for the uploaded video file.
+ """
+ if max_duration is not None:
+ try:
+ actual_duration = video.duration_seconds
+ if actual_duration is not None and actual_duration > max_duration:
+ raise ValueError(
+ f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)."
+ )
+ except Exception as e:
+ logging.error(f"Error getting video duration: {e}")
+ raise ValueError(f"Could not verify video duration from source: {e}") from e
+
+ upload_mime_type = f"video/{container.value.lower()}"
+ filename = f"uploaded_video.{container.value.lower()}"
+
+ # Convert VideoInput to BytesIO using specified container/codec
+ video_bytes_io = io.BytesIO()
+ video.save_to(video_bytes_io, format=container, codec=codec)
+ video_bytes_io.seek(0)
+
+ return upload_file_to_comfyapi(
+ video_bytes_io, filename, upload_mime_type, auth_kwargs
+ )
+
+
+def audio_tensor_to_contiguous_ndarray(waveform: torch.Tensor) -> np.ndarray:
+ """
+ Prepares audio waveform for av library by converting to a contiguous numpy array.
+
+ Args:
+ waveform: a tensor of shape (1, channels, samples) derived from a Comfy `AUDIO` type.
+
+ Returns:
+ Contiguous numpy array of the audio waveform. If the audio was batched,
+ the first item is taken.
+ """
+ if waveform.ndim != 3 or waveform.shape[0] != 1:
+ raise ValueError("Expected waveform tensor shape (1, channels, samples)")
+
+ # If batch is > 1, take first item
+ if waveform.shape[0] > 1:
+ waveform = waveform[0]
+
+ # Prepare for av: remove batch dim, move to CPU, make contiguous, convert to numpy array
+ audio_data_np = waveform.squeeze(0).cpu().contiguous().numpy()
+ if audio_data_np.dtype != np.float32:
+ audio_data_np = audio_data_np.astype(np.float32)
+
+ return audio_data_np
+
+
+def audio_ndarray_to_bytesio(
+ audio_data_np: np.ndarray,
+ sample_rate: int,
+ container_format: str = "mp4",
+ codec_name: str = "aac",
+) -> BytesIO:
+ """
+ Encodes a numpy array of audio data into a BytesIO object.
+ """
+ audio_bytes_io = io.BytesIO()
+ with av.open(audio_bytes_io, mode="w", format=container_format) as output_container:
+ audio_stream = output_container.add_stream(codec_name, rate=sample_rate)
+ frame = av.AudioFrame.from_ndarray(
+ audio_data_np,
+ format="fltp",
+ layout="stereo" if audio_data_np.shape[0] > 1 else "mono",
+ )
+ frame.sample_rate = sample_rate
+ frame.pts = 0
+
+ for packet in audio_stream.encode(frame):
+ output_container.mux(packet)
+
+ # Flush stream
+ for packet in audio_stream.encode(None):
+ output_container.mux(packet)
+
+ audio_bytes_io.seek(0)
+ return audio_bytes_io
+
+
+def upload_audio_to_comfyapi(
+ audio: AudioInput,
+ auth_kwargs: Optional[dict[str, str]] = None,
+ container_format: str = "mp4",
+ codec_name: str = "aac",
+ mime_type: str = "audio/mp4",
+ filename: str = "uploaded_audio.mp4",
+) -> str:
+ """
+ Uploads a single audio input to ComfyUI API and returns its download URL.
+ Encodes the raw waveform into the specified format before uploading.
+
+ Args:
+ audio: a Comfy `AUDIO` type (contains waveform tensor and sample_rate)
+ auth_kwargs: Optional authentication token(s).
+
+ Returns:
+ The download URL for the uploaded audio file.
+ """
+ sample_rate: int = audio["sample_rate"]
+ waveform: torch.Tensor = audio["waveform"]
+ audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+ audio_bytes_io = audio_ndarray_to_bytesio(
+ audio_data_np, sample_rate, container_format, codec_name
+ )
+
+ return upload_file_to_comfyapi(audio_bytes_io, filename, mime_type, auth_kwargs)
+
+
+def audio_to_base64_string(
+ audio: AudioInput, container_format: str = "mp4", codec_name: str = "aac"
+) -> str:
+ """Converts an audio input to a base64 string."""
+ sample_rate: int = audio["sample_rate"]
+ waveform: torch.Tensor = audio["waveform"]
+ audio_data_np = audio_tensor_to_contiguous_ndarray(waveform)
+ audio_bytes_io = audio_ndarray_to_bytesio(
+ audio_data_np, sample_rate, container_format, codec_name
+ )
+ audio_bytes = audio_bytes_io.getvalue()
+ return base64.b64encode(audio_bytes).decode("utf-8")
+
+
+def upload_images_to_comfyapi(
+ image: torch.Tensor,
+ max_images=8,
+ auth_kwargs: Optional[dict[str, str]] = None,
+ mime_type: Optional[str] = None,
+) -> list[str]:
+ """
+ Uploads images to ComfyUI API and returns download URLs.
+ To upload multiple images, stack them in the batch dimension first.
+
+ Args:
+ image: Input torch.Tensor image.
+ max_images: Maximum number of images to upload.
+ auth_kwargs: Optional authentication token(s).
+ mime_type: Optional MIME type for the image.
+ """
+ # if batch, try to upload each file if max_images is greater than 0
+ idx_image = 0
+ download_urls: list[str] = []
+ is_batch = len(image.shape) > 3
+ batch_length = 1
+ if is_batch:
+ batch_length = image.shape[0]
+ while True:
+ curr_image = image
+ if len(image.shape) > 3:
+ curr_image = image[idx_image]
+ # get BytesIO version of image
+ img_binary = tensor_to_bytesio(curr_image, mime_type=mime_type)
+ # first, request upload/download urls from comfy API
+ if not mime_type:
+ request_object = UploadRequest(file_name=img_binary.name)
+ else:
+ request_object = UploadRequest(
+ file_name=img_binary.name, content_type=mime_type
+ )
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/customers/storage",
+ method=HttpMethod.POST,
+ request_model=UploadRequest,
+ response_model=UploadResponse,
+ ),
+ request=request_object,
+ auth_kwargs=auth_kwargs,
+ )
+ response = operation.execute()
+
+ upload_response = ApiClient.upload_file(
+ response.upload_url, img_binary, content_type=mime_type
+ )
+ # verify success
+ try:
+ upload_response.raise_for_status()
+ except requests.exceptions.HTTPError as e:
+ raise ValueError(f"Could not upload one or more images: {e}") from e
+ # add download_url to list
+ download_urls.append(response.download_url)
+
+ idx_image += 1
+ # stop uploading additional files if done
+ if is_batch and max_images > 0:
+ if idx_image >= max_images:
+ break
+ if idx_image >= batch_length:
+ break
+ return download_urls
+
+
+def resize_mask_to_image(
+ mask: torch.Tensor,
+ image: torch.Tensor,
+ upscale_method="nearest-exact",
+ crop="disabled",
+ allow_gradient=True,
+ add_channel_dim=False,
+):
+ """
+ Resize mask to be the same dimensions as an image, while maintaining proper format for API calls.
+ """
+ _, H, W, _ = image.shape
+ mask = mask.unsqueeze(-1)
+ mask = mask.movedim(-1, 1)
+ mask = common_upscale(
+ mask, width=W, height=H, upscale_method=upscale_method, crop=crop
+ )
+ mask = mask.movedim(1, -1)
+ if not add_channel_dim:
+ mask = mask.squeeze(-1)
+ if not allow_gradient:
+ mask = (mask > 0.5).float()
+ return mask
+
+
+def validate_string(
+ string: str,
+ strip_whitespace=True,
+ field_name="prompt",
+ min_length=None,
+ max_length=None,
+):
+ if string is None:
+ raise Exception(f"Field '{field_name}' cannot be empty.")
+ if strip_whitespace:
+ string = string.strip()
+ if min_length and len(string) < min_length:
+ raise Exception(
+ f"Field '{field_name}' cannot be shorter than {min_length} characters; was {len(string)} characters long."
+ )
+ if max_length and len(string) > max_length:
+ raise Exception(
+ f" Field '{field_name} cannot be longer than {max_length} characters; was {len(string)} characters long."
+ )
+
+
+def image_tensor_pair_to_batch(
+ image1: torch.Tensor, image2: torch.Tensor
+) -> torch.Tensor:
+ """
+ Converts a pair of image tensors to a batch tensor.
+ If the images are not the same size, the smaller image is resized to
+ match the larger image.
+ """
+ if image1.shape[1:] != image2.shape[1:]:
+ image2 = common_upscale(
+ image2.movedim(-1, 1),
+ image1.shape[2],
+ image1.shape[1],
+ "bilinear",
+ "center",
+ ).movedim(1, -1)
+ return torch.cat((image1, image2), dim=0)
diff --git a/ComfyUI/comfy_api_nodes/canary.py b/ComfyUI/comfy_api_nodes/canary.py
new file mode 100644
index 0000000000000000000000000000000000000000..4df7590b64fea8f6f3f77fde912cb2f64fdad357
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/canary.py
@@ -0,0 +1,10 @@
+import av
+
+ver = av.__version__.split(".")
+if int(ver[0]) < 14:
+ raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.")
+
+if int(ver[0]) == 14 and int(ver[1]) < 2:
+ raise Exception("INSTALL NEW VERSION OF PYAV TO USE API NODES.")
+
+NODE_CLASS_MAPPINGS = {}
diff --git a/ComfyUI/comfy_api_nodes/mapper_utils.py b/ComfyUI/comfy_api_nodes/mapper_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..6fab8f4bbc6ce4495ce41588f2fba8103abeffeb
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/mapper_utils.py
@@ -0,0 +1,116 @@
+from enum import Enum
+
+from pydantic.fields import FieldInfo
+from pydantic import BaseModel
+from pydantic_core import PydanticUndefined
+
+from comfy.comfy_types.node_typing import IO, InputTypeOptions
+
+NodeInput = tuple[IO, InputTypeOptions]
+
+
+def _create_base_config(field_info: FieldInfo) -> InputTypeOptions:
+ config = {}
+ if hasattr(field_info, "default") and field_info.default is not PydanticUndefined:
+ config["default"] = field_info.default
+ if hasattr(field_info, "description") and field_info.description is not None:
+ config["tooltip"] = field_info.description
+ return config
+
+
+def _get_number_constraints_config(field_info: FieldInfo) -> dict:
+ config = {}
+ if hasattr(field_info, "metadata"):
+ metadata = field_info.metadata
+ for constraint in metadata:
+ if hasattr(constraint, "ge"):
+ config["min"] = constraint.ge
+ if hasattr(constraint, "le"):
+ config["max"] = constraint.le
+ if hasattr(constraint, "multiple_of"):
+ config["step"] = constraint.multiple_of
+ return config
+
+
+def _model_field_to_image_input(field_info: FieldInfo, **kwargs) -> NodeInput:
+ return IO.IMAGE, {
+ **_create_base_config(field_info),
+ **kwargs,
+ }
+
+
+def _model_field_to_string_input(field_info: FieldInfo, **kwargs) -> NodeInput:
+ return IO.STRING, {
+ **_create_base_config(field_info),
+ **kwargs,
+ }
+
+
+def _model_field_to_float_input(field_info: FieldInfo, **kwargs) -> NodeInput:
+ return IO.FLOAT, {
+ **_create_base_config(field_info),
+ **_get_number_constraints_config(field_info),
+ **kwargs,
+ }
+
+
+def _model_field_to_int_input(field_info: FieldInfo, **kwargs) -> NodeInput:
+ return IO.INT, {
+ **_create_base_config(field_info),
+ **_get_number_constraints_config(field_info),
+ **kwargs,
+ }
+
+
+def _model_field_to_combo_input(
+ field_info: FieldInfo, enum_type: type[Enum] = None, **kwargs
+) -> NodeInput:
+ combo_config = {}
+ if enum_type is not None:
+ combo_config["options"] = [option.value for option in enum_type]
+ combo_config = {
+ **combo_config,
+ **_create_base_config(field_info),
+ **kwargs,
+ }
+ return IO.COMBO, combo_config
+
+
+def model_field_to_node_input(
+ input_type: IO, base_model: type[BaseModel], field_name: str, **kwargs
+) -> NodeInput:
+ """
+ Maps a field from a Pydantic model to a Comfy node input.
+
+ Args:
+ input_type: The type of the input.
+ base_model: The Pydantic model to map the field from.
+ field_name: The name of the field to map.
+ **kwargs: Additional key/values to include in the input options.
+
+ Note:
+ For combo inputs, pass an `Enum` to the `enum_type` keyword argument to populate the options automatically.
+
+ Example:
+ >>> model_field_to_node_input(IO.STRING, MyModel, "my_field", multiline=True)
+ >>> model_field_to_node_input(IO.COMBO, MyModel, "my_field", enum_type=MyEnum)
+ >>> model_field_to_node_input(IO.FLOAT, MyModel, "my_field", slider=True)
+ """
+ field_info: FieldInfo = base_model.model_fields[field_name]
+ result: NodeInput
+
+ if input_type == IO.IMAGE:
+ result = _model_field_to_image_input(field_info, **kwargs)
+ elif input_type == IO.STRING:
+ result = _model_field_to_string_input(field_info, **kwargs)
+ elif input_type == IO.FLOAT:
+ result = _model_field_to_float_input(field_info, **kwargs)
+ elif input_type == IO.INT:
+ result = _model_field_to_int_input(field_info, **kwargs)
+ elif input_type == IO.COMBO:
+ result = _model_field_to_combo_input(field_info, **kwargs)
+ else:
+ message = f"Invalid input type: {input_type}"
+ raise ValueError(message)
+
+ return result
diff --git a/ComfyUI/comfy_api_nodes/nodes_bfl.py b/ComfyUI/comfy_api_nodes/nodes_bfl.py
new file mode 100644
index 0000000000000000000000000000000000000000..d93fbd77822beddc596830dccfde1de51e8dbded
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_bfl.py
@@ -0,0 +1,1075 @@
+import io
+from inspect import cleandoc
+from typing import Union, Optional
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+from comfy_api_nodes.apis.bfl_api import (
+ BFLStatus,
+ BFLFluxExpandImageRequest,
+ BFLFluxFillImageRequest,
+ BFLFluxCannyImageRequest,
+ BFLFluxDepthImageRequest,
+ BFLFluxProGenerateRequest,
+ BFLFluxKontextProGenerateRequest,
+ BFLFluxProUltraGenerateRequest,
+ BFLFluxProGenerateResponse,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+)
+from comfy_api_nodes.apinode_utils import (
+ downscale_image_tensor,
+ validate_aspect_ratio,
+ process_image_response,
+ resize_mask_to_image,
+ validate_string,
+)
+
+import numpy as np
+from PIL import Image
+import requests
+import torch
+import base64
+import time
+from server import PromptServer
+
+
+def convert_mask_to_image(mask: torch.Tensor):
+ """
+ Make mask have the expected amount of dims (4) and channels (3) to be recognized as an image.
+ """
+ mask = mask.unsqueeze(-1)
+ mask = torch.cat([mask]*3, dim=-1)
+ return mask
+
+
+def handle_bfl_synchronous_operation(
+ operation: SynchronousOperation,
+ timeout_bfl_calls=360,
+ node_id: Union[str, None] = None,
+):
+ response_api: BFLFluxProGenerateResponse = operation.execute()
+ return _poll_until_generated(
+ response_api.polling_url, timeout=timeout_bfl_calls, node_id=node_id
+ )
+
+
+def _poll_until_generated(
+ polling_url: str, timeout=360, node_id: Union[str, None] = None
+):
+ # used bfl-comfy-nodes to verify code implementation:
+ # https://github.com/black-forest-labs/bfl-comfy-nodes/tree/main
+ start_time = time.time()
+ retries_404 = 0
+ max_retries_404 = 5
+ retry_404_seconds = 2
+ retry_202_seconds = 2
+ retry_pending_seconds = 1
+ request = requests.Request(method=HttpMethod.GET, url=polling_url)
+ # NOTE: should True loop be replaced with checking if workflow has been interrupted?
+ while True:
+ if node_id:
+ time_elapsed = time.time() - start_time
+ PromptServer.instance.send_progress_text(
+ f"Generating ({time_elapsed:.0f}s)", node_id
+ )
+
+ response = requests.Session().send(request.prepare())
+ if response.status_code == 200:
+ result = response.json()
+ if result["status"] == BFLStatus.ready:
+ img_url = result["result"]["sample"]
+ if node_id:
+ PromptServer.instance.send_progress_text(
+ f"Result URL: {img_url}", node_id
+ )
+ img_response = requests.get(img_url)
+ return process_image_response(img_response)
+ elif result["status"] in [
+ BFLStatus.request_moderated,
+ BFLStatus.content_moderated,
+ ]:
+ status = result["status"]
+ raise Exception(
+ f"BFL API did not return an image due to: {status}."
+ )
+ elif result["status"] == BFLStatus.error:
+ raise Exception(f"BFL API encountered an error: {result}.")
+ elif result["status"] == BFLStatus.pending:
+ time.sleep(retry_pending_seconds)
+ continue
+ elif response.status_code == 404:
+ if retries_404 < max_retries_404:
+ retries_404 += 1
+ time.sleep(retry_404_seconds)
+ continue
+ raise Exception(
+ f"BFL API could not find task after {max_retries_404} tries."
+ )
+ elif response.status_code == 202:
+ time.sleep(retry_202_seconds)
+ elif time.time() - start_time > timeout:
+ raise Exception(
+ f"BFL API experienced a timeout; could not return request under {timeout} seconds."
+ )
+ else:
+ raise Exception(f"BFL API encountered an error: {response.json()}")
+
+def convert_image_to_base64(image: torch.Tensor):
+ scaled_image = downscale_image_tensor(image, total_pixels=2048 * 2048)
+ # remove batch dimension if present
+ if len(scaled_image.shape) > 3:
+ scaled_image = scaled_image[0]
+ image_np = (scaled_image.numpy() * 255).astype(np.uint8)
+ img = Image.fromarray(image_np)
+ img_byte_arr = io.BytesIO()
+ img.save(img_byte_arr, format="PNG")
+ return base64.b64encode(img_byte_arr.getvalue()).decode()
+
+
+class FluxProUltraImageNode(ComfyNodeABC):
+ """
+ Generates images using Flux Pro 1.1 Ultra via api based on prompt and resolution.
+ """
+
+ MINIMUM_RATIO = 1 / 4
+ MAXIMUM_RATIO = 4 / 1
+ MINIMUM_RATIO_STR = "1:4"
+ MAXIMUM_RATIO_STR = "4:1"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ "aspect_ratio": (
+ IO.STRING,
+ {
+ "default": "16:9",
+ "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
+ },
+ ),
+ "raw": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "When True, generate less processed, more natural-looking images.",
+ },
+ ),
+ },
+ "optional": {
+ "image_prompt": (IO.IMAGE,),
+ "image_prompt_strength": (
+ IO.FLOAT,
+ {
+ "default": 0.1,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Blend between the prompt and the image prompt.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ @classmethod
+ def VALIDATE_INPUTS(cls, aspect_ratio: str):
+ try:
+ validate_aspect_ratio(
+ aspect_ratio,
+ minimum_ratio=cls.MINIMUM_RATIO,
+ maximum_ratio=cls.MAXIMUM_RATIO,
+ minimum_ratio_str=cls.MINIMUM_RATIO_STR,
+ maximum_ratio_str=cls.MAXIMUM_RATIO_STR,
+ )
+ except Exception as e:
+ return str(e)
+ return True
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ prompt: str,
+ aspect_ratio: str,
+ prompt_upsampling=False,
+ raw=False,
+ seed=0,
+ image_prompt=None,
+ image_prompt_strength=0.1,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ if image_prompt is None:
+ validate_string(prompt, strip_whitespace=False)
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.1-ultra/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxProUltraGenerateRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxProUltraGenerateRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ seed=seed,
+ aspect_ratio=validate_aspect_ratio(
+ aspect_ratio,
+ minimum_ratio=self.MINIMUM_RATIO,
+ maximum_ratio=self.MAXIMUM_RATIO,
+ minimum_ratio_str=self.MINIMUM_RATIO_STR,
+ maximum_ratio_str=self.MAXIMUM_RATIO_STR,
+ ),
+ raw=raw,
+ image_prompt=(
+ image_prompt
+ if image_prompt is None
+ else convert_image_to_base64(image_prompt)
+ ),
+ image_prompt_strength=(
+ None if image_prompt is None else round(image_prompt_strength, 2)
+ ),
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+class FluxKontextProImageNode(ComfyNodeABC):
+ """
+ Edits images using Flux.1 Kontext [pro] via api based on prompt and aspect ratio.
+ """
+
+ MINIMUM_RATIO = 1 / 4
+ MAXIMUM_RATIO = 4 / 1
+ MINIMUM_RATIO_STR = "1:4"
+ MAXIMUM_RATIO_STR = "4:1"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation - specify what and how to edit.",
+ },
+ ),
+ "aspect_ratio": (
+ IO.STRING,
+ {
+ "default": "16:9",
+ "tooltip": "Aspect ratio of image; must be between 1:4 and 4:1.",
+ },
+ ),
+ "guidance": (
+ IO.FLOAT,
+ {
+ "default": 3.0,
+ "min": 0.1,
+ "max": 99.0,
+ "step": 0.1,
+ "tooltip": "Guidance strength for the image generation process"
+ },
+ ),
+ "steps": (
+ IO.INT,
+ {
+ "default": 50,
+ "min": 1,
+ "max": 150,
+ "tooltip": "Number of steps for the image generation process"
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 1234,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ },
+ "optional": {
+ "input_image": (IO.IMAGE,),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ BFL_PATH = "/proxy/bfl/flux-kontext-pro/generate"
+
+ def api_call(
+ self,
+ prompt: str,
+ aspect_ratio: str,
+ guidance: float,
+ steps: int,
+ input_image: Optional[torch.Tensor]=None,
+ seed=0,
+ prompt_upsampling=False,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ aspect_ratio = validate_aspect_ratio(
+ aspect_ratio,
+ minimum_ratio=self.MINIMUM_RATIO,
+ maximum_ratio=self.MAXIMUM_RATIO,
+ minimum_ratio_str=self.MINIMUM_RATIO_STR,
+ maximum_ratio_str=self.MAXIMUM_RATIO_STR,
+ )
+ if input_image is None:
+ validate_string(prompt, strip_whitespace=False)
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=self.BFL_PATH,
+ method=HttpMethod.POST,
+ request_model=BFLFluxKontextProGenerateRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxKontextProGenerateRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ guidance=round(guidance, 1),
+ steps=steps,
+ seed=seed,
+ aspect_ratio=aspect_ratio,
+ input_image=(
+ input_image
+ if input_image is None
+ else convert_image_to_base64(input_image)
+ )
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+class FluxKontextMaxImageNode(FluxKontextProImageNode):
+ """
+ Edits images using Flux.1 Kontext [max] via api based on prompt and aspect ratio.
+ """
+
+ DESCRIPTION = cleandoc(__doc__ or "")
+ BFL_PATH = "/proxy/bfl/flux-kontext-max/generate"
+
+
+class FluxProImageNode(ComfyNodeABC):
+ """
+ Generates images synchronously based on prompt and resolution.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "width": (
+ IO.INT,
+ {
+ "default": 1024,
+ "min": 256,
+ "max": 1440,
+ "step": 32,
+ },
+ ),
+ "height": (
+ IO.INT,
+ {
+ "default": 768,
+ "min": 256,
+ "max": 1440,
+ "step": 32,
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {
+ "image_prompt": (IO.IMAGE,),
+ # "image_prompt_strength": (
+ # IO.FLOAT,
+ # {
+ # "default": 0.1,
+ # "min": 0.0,
+ # "max": 1.0,
+ # "step": 0.01,
+ # "tooltip": "Blend between the prompt and the image prompt.",
+ # },
+ # ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ prompt: str,
+ prompt_upsampling,
+ width: int,
+ height: int,
+ seed=0,
+ image_prompt=None,
+ # image_prompt_strength=0.1,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ image_prompt = (
+ image_prompt
+ if image_prompt is None
+ else convert_image_to_base64(image_prompt)
+ )
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.1/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxProGenerateRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxProGenerateRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ width=width,
+ height=height,
+ seed=seed,
+ image_prompt=image_prompt,
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+class FluxProExpandNode(ComfyNodeABC):
+ """
+ Outpaints image based on prompt.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "top": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2048,
+ "tooltip": "Number of pixels to expand at the top of the image"
+ },
+ ),
+ "bottom": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2048,
+ "tooltip": "Number of pixels to expand at the bottom of the image"
+ },
+ ),
+ "left": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2048,
+ "tooltip": "Number of pixels to expand at the left side of the image"
+ },
+ ),
+ "right": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2048,
+ "tooltip": "Number of pixels to expand at the right side of the image"
+ },
+ ),
+ "guidance": (
+ IO.FLOAT,
+ {
+ "default": 60,
+ "min": 1.5,
+ "max": 100,
+ "tooltip": "Guidance strength for the image generation process"
+ },
+ ),
+ "steps": (
+ IO.INT,
+ {
+ "default": 50,
+ "min": 15,
+ "max": 50,
+ "tooltip": "Number of steps for the image generation process"
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {},
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ prompt: str,
+ prompt_upsampling: bool,
+ top: int,
+ bottom: int,
+ left: int,
+ right: int,
+ steps: int,
+ guidance: float,
+ seed=0,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ image = convert_image_to_base64(image)
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.0-expand/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxExpandImageRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxExpandImageRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ top=top,
+ bottom=bottom,
+ left=left,
+ right=right,
+ steps=steps,
+ guidance=guidance,
+ seed=seed,
+ image=image,
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+
+class FluxProFillNode(ComfyNodeABC):
+ """
+ Inpaints image based on mask and prompt.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "mask": (IO.MASK,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "guidance": (
+ IO.FLOAT,
+ {
+ "default": 60,
+ "min": 1.5,
+ "max": 100,
+ "tooltip": "Guidance strength for the image generation process"
+ },
+ ),
+ "steps": (
+ IO.INT,
+ {
+ "default": 50,
+ "min": 15,
+ "max": 50,
+ "tooltip": "Number of steps for the image generation process"
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {},
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ mask: torch.Tensor,
+ prompt: str,
+ prompt_upsampling: bool,
+ steps: int,
+ guidance: float,
+ seed=0,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ # prepare mask
+ mask = resize_mask_to_image(mask, image)
+ mask = convert_image_to_base64(convert_mask_to_image(mask))
+ # make sure image will have alpha channel removed
+ image = convert_image_to_base64(image[:, :, :, :3])
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.0-fill/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxFillImageRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxFillImageRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ steps=steps,
+ guidance=guidance,
+ seed=seed,
+ image=image,
+ mask=mask,
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+class FluxProCannyNode(ComfyNodeABC):
+ """
+ Generate image using a control image (canny).
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "control_image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "canny_low_threshold": (
+ IO.FLOAT,
+ {
+ "default": 0.1,
+ "min": 0.01,
+ "max": 0.99,
+ "step": 0.01,
+ "tooltip": "Low threshold for Canny edge detection; ignored if skip_processing is True"
+ },
+ ),
+ "canny_high_threshold": (
+ IO.FLOAT,
+ {
+ "default": 0.4,
+ "min": 0.01,
+ "max": 0.99,
+ "step": 0.01,
+ "tooltip": "High threshold for Canny edge detection; ignored if skip_processing is True"
+ },
+ ),
+ "skip_preprocessing": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to skip preprocessing; set to True if control_image already is canny-fied, False if it is a raw image.",
+ },
+ ),
+ "guidance": (
+ IO.FLOAT,
+ {
+ "default": 30,
+ "min": 1,
+ "max": 100,
+ "tooltip": "Guidance strength for the image generation process"
+ },
+ ),
+ "steps": (
+ IO.INT,
+ {
+ "default": 50,
+ "min": 15,
+ "max": 50,
+ "tooltip": "Number of steps for the image generation process"
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {},
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ control_image: torch.Tensor,
+ prompt: str,
+ prompt_upsampling: bool,
+ canny_low_threshold: float,
+ canny_high_threshold: float,
+ skip_preprocessing: bool,
+ steps: int,
+ guidance: float,
+ seed=0,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ control_image = convert_image_to_base64(control_image[:, :, :, :3])
+ preprocessed_image = None
+
+ # scale canny threshold between 0-500, to match BFL's API
+ def scale_value(value: float, min_val=0, max_val=500):
+ return min_val + value * (max_val - min_val)
+ canny_low_threshold = int(round(scale_value(canny_low_threshold)))
+ canny_high_threshold = int(round(scale_value(canny_high_threshold)))
+
+
+ if skip_preprocessing:
+ preprocessed_image = control_image
+ control_image = None
+ canny_low_threshold = None
+ canny_high_threshold = None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.0-canny/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxCannyImageRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxCannyImageRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ steps=steps,
+ guidance=guidance,
+ seed=seed,
+ control_image=control_image,
+ canny_low_threshold=canny_low_threshold,
+ canny_high_threshold=canny_high_threshold,
+ preprocessed_image=preprocessed_image,
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+class FluxProDepthNode(ComfyNodeABC):
+ """
+ Generate image using a control image (depth).
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "control_image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "prompt_upsampling": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to perform upsampling on the prompt. If active, automatically modifies the prompt for more creative generation, but results are nondeterministic (same seed will not produce exactly the same result).",
+ },
+ ),
+ "skip_preprocessing": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to skip preprocessing; set to True if control_image already is depth-ified, False if it is a raw image.",
+ },
+ ),
+ "guidance": (
+ IO.FLOAT,
+ {
+ "default": 15,
+ "min": 1,
+ "max": 100,
+ "tooltip": "Guidance strength for the image generation process"
+ },
+ ),
+ "steps": (
+ IO.INT,
+ {
+ "default": 50,
+ "min": 15,
+ "max": 50,
+ "tooltip": "Number of steps for the image generation process"
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {},
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/BFL"
+
+ def api_call(
+ self,
+ control_image: torch.Tensor,
+ prompt: str,
+ prompt_upsampling: bool,
+ skip_preprocessing: bool,
+ steps: int,
+ guidance: float,
+ seed=0,
+ unique_id: Union[str, None] = None,
+ **kwargs,
+ ):
+ control_image = convert_image_to_base64(control_image[:,:,:,:3])
+ preprocessed_image = None
+
+ if skip_preprocessing:
+ preprocessed_image = control_image
+ control_image = None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/bfl/flux-pro-1.0-depth/generate",
+ method=HttpMethod.POST,
+ request_model=BFLFluxDepthImageRequest,
+ response_model=BFLFluxProGenerateResponse,
+ ),
+ request=BFLFluxDepthImageRequest(
+ prompt=prompt,
+ prompt_upsampling=prompt_upsampling,
+ steps=steps,
+ guidance=guidance,
+ seed=seed,
+ control_image=control_image,
+ preprocessed_image=preprocessed_image,
+ ),
+ auth_kwargs=kwargs,
+ )
+ output_image = handle_bfl_synchronous_operation(operation, node_id=unique_id)
+ return (output_image,)
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "FluxProUltraImageNode": FluxProUltraImageNode,
+ # "FluxProImageNode": FluxProImageNode,
+ "FluxKontextProImageNode": FluxKontextProImageNode,
+ "FluxKontextMaxImageNode": FluxKontextMaxImageNode,
+ "FluxProExpandNode": FluxProExpandNode,
+ "FluxProFillNode": FluxProFillNode,
+ "FluxProCannyNode": FluxProCannyNode,
+ "FluxProDepthNode": FluxProDepthNode,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "FluxProUltraImageNode": "Flux 1.1 [pro] Ultra Image",
+ # "FluxProImageNode": "Flux 1.1 [pro] Image",
+ "FluxKontextProImageNode": "Flux.1 Kontext [pro] Image",
+ "FluxKontextMaxImageNode": "Flux.1 Kontext [max] Image",
+ "FluxProExpandNode": "Flux.1 Expand Image",
+ "FluxProFillNode": "Flux.1 Fill Image",
+ "FluxProCannyNode": "Flux.1 Canny Control Image",
+ "FluxProDepthNode": "Flux.1 Depth Control Image",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_gemini.py b/ComfyUI/comfy_api_nodes/nodes_gemini.py
new file mode 100644
index 0000000000000000000000000000000000000000..5935ab2bb12040073ab720457487314f134c6848
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_gemini.py
@@ -0,0 +1,446 @@
+"""
+API Nodes for Gemini Multimodal LLM Usage via Remote API
+See: https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+"""
+
+import os
+from enum import Enum
+from typing import Optional, Literal
+
+import torch
+
+import folder_paths
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+from comfy_api_nodes.apis import (
+ GeminiContent,
+ GeminiGenerateContentRequest,
+ GeminiGenerateContentResponse,
+ GeminiInlineData,
+ GeminiPart,
+ GeminiMimeType,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+)
+from comfy_api_nodes.apinode_utils import (
+ validate_string,
+ audio_to_base64_string,
+ video_to_base64_string,
+ tensor_to_base64_string,
+)
+
+
+GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
+GEMINI_MAX_INPUT_FILE_SIZE = 20 * 1024 * 1024 # 20 MB
+
+
+class GeminiModel(str, Enum):
+ """
+ Gemini Model Names allowed by comfy-api
+ """
+
+ gemini_2_5_pro_preview_05_06 = "gemini-2.5-pro-preview-05-06"
+ gemini_2_5_flash_preview_04_17 = "gemini-2.5-flash-preview-04-17"
+
+
+def get_gemini_endpoint(
+ model: GeminiModel,
+) -> ApiEndpoint[GeminiGenerateContentRequest, GeminiGenerateContentResponse]:
+ """
+ Get the API endpoint for a given Gemini model.
+
+ Args:
+ model: The Gemini model to use, either as enum or string value.
+
+ Returns:
+ ApiEndpoint configured for the specific Gemini model.
+ """
+ if isinstance(model, str):
+ model = GeminiModel(model)
+ return ApiEndpoint(
+ path=f"{GEMINI_BASE_ENDPOINT}/{model.value}",
+ method=HttpMethod.POST,
+ request_model=GeminiGenerateContentRequest,
+ response_model=GeminiGenerateContentResponse,
+ )
+
+
+class GeminiNode(ComfyNodeABC):
+ """
+ Node to generate text responses from a Gemini model.
+
+ This node allows users to interact with Google's Gemini AI models, providing
+ multimodal inputs (text, images, audio, video, files) to generate coherent
+ text responses. The node works with the latest Gemini models, handling the
+ API communication and response parsing.
+ """
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text inputs to the model, used to generate a response. You can include detailed instructions, questions, or context for the model.",
+ },
+ ),
+ "model": (
+ IO.COMBO,
+ {
+ "tooltip": "The Gemini model to use for generating responses.",
+ "options": [model.value for model in GeminiModel],
+ "default": GeminiModel.gemini_2_5_pro_preview_05_06.value,
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 42,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "When seed is fixed to a specific value, the model makes a best effort to provide the same response for repeated requests. Deterministic output isn't guaranteed. Also, changing the model or parameter settings, such as the temperature, can cause variations in the response even when you use the same seed value. By default, a random seed value is used.",
+ },
+ ),
+ },
+ "optional": {
+ "images": (
+ IO.IMAGE,
+ {
+ "default": None,
+ "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+ },
+ ),
+ "audio": (
+ IO.AUDIO,
+ {
+ "tooltip": "Optional audio to use as context for the model.",
+ "default": None,
+ },
+ ),
+ "video": (
+ IO.VIDEO,
+ {
+ "tooltip": "Optional video to use as context for the model.",
+ "default": None,
+ },
+ ),
+ "files": (
+ "GEMINI_INPUT_FILES",
+ {
+ "default": None,
+ "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the Gemini Generate Content Input Files node.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Generate text responses with Google's Gemini AI model. You can provide multiple types of inputs (text, images, audio, video) as context for generating more relevant and meaningful responses."
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/text/Gemini"
+ API_NODE = True
+
+ def get_parts_from_response(
+ self, response: GeminiGenerateContentResponse
+ ) -> list[GeminiPart]:
+ """
+ Extract all parts from the Gemini API response.
+
+ Args:
+ response: The API response from Gemini.
+
+ Returns:
+ List of response parts from the first candidate.
+ """
+ return response.candidates[0].content.parts
+
+ def get_parts_by_type(
+ self, response: GeminiGenerateContentResponse, part_type: Literal["text"] | str
+ ) -> list[GeminiPart]:
+ """
+ Filter response parts by their type.
+
+ Args:
+ response: The API response from Gemini.
+ part_type: Type of parts to extract ("text" or a MIME type).
+
+ Returns:
+ List of response parts matching the requested type.
+ """
+ parts = []
+ for part in self.get_parts_from_response(response):
+ if part_type == "text" and hasattr(part, "text") and part.text:
+ parts.append(part)
+ elif (
+ hasattr(part, "inlineData")
+ and part.inlineData
+ and part.inlineData.mimeType == part_type
+ ):
+ parts.append(part)
+ # Skip parts that don't match the requested type
+ return parts
+
+ def get_text_from_response(self, response: GeminiGenerateContentResponse) -> str:
+ """
+ Extract and concatenate all text parts from the response.
+
+ Args:
+ response: The API response from Gemini.
+
+ Returns:
+ Combined text from all text parts in the response.
+ """
+ parts = self.get_parts_by_type(response, "text")
+ return "\n".join([part.text for part in parts])
+
+ def create_video_parts(self, video_input: IO.VIDEO, **kwargs) -> list[GeminiPart]:
+ """
+ Convert video input to Gemini API compatible parts.
+
+ Args:
+ video_input: Video tensor from ComfyUI.
+ **kwargs: Additional arguments to pass to the conversion function.
+
+ Returns:
+ List of GeminiPart objects containing the encoded video.
+ """
+ from comfy_api.util import VideoContainer, VideoCodec
+ base_64_string = video_to_base64_string(
+ video_input,
+ container_format=VideoContainer.MP4,
+ codec=VideoCodec.H264
+ )
+ return [
+ GeminiPart(
+ inlineData=GeminiInlineData(
+ mimeType=GeminiMimeType.video_mp4,
+ data=base_64_string,
+ )
+ )
+ ]
+
+ def create_audio_parts(self, audio_input: IO.AUDIO) -> list[GeminiPart]:
+ """
+ Convert audio input to Gemini API compatible parts.
+
+ Args:
+ audio_input: Audio input from ComfyUI, containing waveform tensor and sample rate.
+
+ Returns:
+ List of GeminiPart objects containing the encoded audio.
+ """
+ audio_parts: list[GeminiPart] = []
+ for batch_index in range(audio_input["waveform"].shape[0]):
+ # Recreate an IO.AUDIO object for the given batch dimension index
+ audio_at_index = {
+ "waveform": audio_input["waveform"][batch_index].unsqueeze(0),
+ "sample_rate": audio_input["sample_rate"],
+ }
+ # Convert to MP3 format for compatibility with Gemini API
+ audio_bytes = audio_to_base64_string(
+ audio_at_index,
+ container_format="mp3",
+ codec_name="libmp3lame",
+ )
+ audio_parts.append(
+ GeminiPart(
+ inlineData=GeminiInlineData(
+ mimeType=GeminiMimeType.audio_mp3,
+ data=audio_bytes,
+ )
+ )
+ )
+ return audio_parts
+
+ def create_image_parts(self, image_input: torch.Tensor) -> list[GeminiPart]:
+ """
+ Convert image tensor input to Gemini API compatible parts.
+
+ Args:
+ image_input: Batch of image tensors from ComfyUI.
+
+ Returns:
+ List of GeminiPart objects containing the encoded images.
+ """
+ image_parts: list[GeminiPart] = []
+ for image_index in range(image_input.shape[0]):
+ image_as_b64 = tensor_to_base64_string(
+ image_input[image_index].unsqueeze(0)
+ )
+ image_parts.append(
+ GeminiPart(
+ inlineData=GeminiInlineData(
+ mimeType=GeminiMimeType.image_png,
+ data=image_as_b64,
+ )
+ )
+ )
+ return image_parts
+
+ def create_text_part(self, text: str) -> GeminiPart:
+ """
+ Create a text part for the Gemini API request.
+
+ Args:
+ text: The text content to include in the request.
+
+ Returns:
+ A GeminiPart object with the text content.
+ """
+ return GeminiPart(text=text)
+
+ def api_call(
+ self,
+ prompt: str,
+ model: GeminiModel,
+ images: Optional[IO.IMAGE] = None,
+ audio: Optional[IO.AUDIO] = None,
+ video: Optional[IO.VIDEO] = None,
+ files: Optional[list[GeminiPart]] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[str]:
+ # Validate inputs
+ validate_string(prompt, strip_whitespace=False)
+
+ # Create parts list with text prompt as the first part
+ parts: list[GeminiPart] = [self.create_text_part(prompt)]
+
+ # Add other modal parts
+ if images is not None:
+ image_parts = self.create_image_parts(images)
+ parts.extend(image_parts)
+ if audio is not None:
+ parts.extend(self.create_audio_parts(audio))
+ if video is not None:
+ parts.extend(self.create_video_parts(video))
+ if files is not None:
+ parts.extend(files)
+
+ # Create response
+ response = SynchronousOperation(
+ endpoint=get_gemini_endpoint(model),
+ request=GeminiGenerateContentRequest(
+ contents=[
+ GeminiContent(
+ role="user",
+ parts=parts,
+ )
+ ]
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+
+ # Get result output
+ output_text = self.get_text_from_response(response)
+ if unique_id and output_text:
+ PromptServer.instance.send_progress_text(output_text, node_id=unique_id)
+
+ return (output_text or "Empty response from Gemini model...",)
+
+
+class GeminiInputFiles(ComfyNodeABC):
+ """
+ Loads and formats input files for use with the Gemini API.
+
+ This node allows users to include text (.txt) and PDF (.pdf) files as input
+ context for the Gemini model. Files are converted to the appropriate format
+ required by the API and can be chained together to include multiple files
+ in a single request.
+ """
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ """
+ For details about the supported file input types, see:
+ https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference
+ """
+ input_dir = folder_paths.get_input_directory()
+ input_files = [
+ f
+ for f in os.scandir(input_dir)
+ if f.is_file()
+ and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+ and f.stat().st_size < GEMINI_MAX_INPUT_FILE_SIZE
+ ]
+ input_files = sorted(input_files, key=lambda x: x.name)
+ input_files = [f.name for f in input_files]
+ return {
+ "required": {
+ "file": (
+ IO.COMBO,
+ {
+ "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+ "options": input_files,
+ "default": input_files[0] if input_files else None,
+ },
+ ),
+ },
+ "optional": {
+ "GEMINI_INPUT_FILES": (
+ "GEMINI_INPUT_FILES",
+ {
+ "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+ "default": None,
+ },
+ ),
+ },
+ }
+
+ DESCRIPTION = "Loads and prepares input files to include as inputs for Gemini LLM nodes. The files will be read by the Gemini model when generating a response. The contents of the text file count toward the token limit. 🛈 TIP: Can be chained together with other Gemini Input File nodes."
+ RETURN_TYPES = ("GEMINI_INPUT_FILES",)
+ FUNCTION = "prepare_files"
+ CATEGORY = "api node/text/Gemini"
+
+ def create_file_part(self, file_path: str) -> GeminiPart:
+ mime_type = (
+ GeminiMimeType.application_pdf
+ if file_path.endswith(".pdf")
+ else GeminiMimeType.text_plain
+ )
+ # Use base64 string directly, not the data URI
+ with open(file_path, "rb") as f:
+ file_content = f.read()
+ import base64
+ base64_str = base64.b64encode(file_content).decode("utf-8")
+
+ return GeminiPart(
+ inlineData=GeminiInlineData(
+ mimeType=mime_type,
+ data=base64_str,
+ )
+ )
+
+ def prepare_files(
+ self, file: str, GEMINI_INPUT_FILES: list[GeminiPart] = []
+ ) -> tuple[list[GeminiPart]]:
+ """
+ Loads and formats input files for Gemini API.
+ """
+ file_path = folder_paths.get_annotated_filepath(file)
+ input_file_content = self.create_file_part(file_path)
+ files = [input_file_content] + GEMINI_INPUT_FILES
+ return (files,)
+
+
+NODE_CLASS_MAPPINGS = {
+ "GeminiNode": GeminiNode,
+ "GeminiInputFiles": GeminiInputFiles,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "GeminiNode": "Google Gemini",
+ "GeminiInputFiles": "Gemini Input Files",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_ideogram.py b/ComfyUI/comfy_api_nodes/nodes_ideogram.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8487355fadb6a7d56226df5a6d692bf1b94532a
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_ideogram.py
@@ -0,0 +1,801 @@
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from inspect import cleandoc
+from PIL import Image
+import numpy as np
+import io
+import torch
+from comfy_api_nodes.apis import (
+ IdeogramGenerateRequest,
+ IdeogramGenerateResponse,
+ ImageRequest,
+ IdeogramV3Request,
+ IdeogramV3EditRequest,
+)
+
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+)
+
+from comfy_api_nodes.apinode_utils import (
+ download_url_to_bytesio,
+ bytesio_to_image_tensor,
+ resize_mask_to_image,
+)
+from server import PromptServer
+
+V1_V1_RES_MAP = {
+ "Auto":"AUTO",
+ "512 x 1536":"RESOLUTION_512_1536",
+ "576 x 1408":"RESOLUTION_576_1408",
+ "576 x 1472":"RESOLUTION_576_1472",
+ "576 x 1536":"RESOLUTION_576_1536",
+ "640 x 1024":"RESOLUTION_640_1024",
+ "640 x 1344":"RESOLUTION_640_1344",
+ "640 x 1408":"RESOLUTION_640_1408",
+ "640 x 1472":"RESOLUTION_640_1472",
+ "640 x 1536":"RESOLUTION_640_1536",
+ "704 x 1152":"RESOLUTION_704_1152",
+ "704 x 1216":"RESOLUTION_704_1216",
+ "704 x 1280":"RESOLUTION_704_1280",
+ "704 x 1344":"RESOLUTION_704_1344",
+ "704 x 1408":"RESOLUTION_704_1408",
+ "704 x 1472":"RESOLUTION_704_1472",
+ "720 x 1280":"RESOLUTION_720_1280",
+ "736 x 1312":"RESOLUTION_736_1312",
+ "768 x 1024":"RESOLUTION_768_1024",
+ "768 x 1088":"RESOLUTION_768_1088",
+ "768 x 1152":"RESOLUTION_768_1152",
+ "768 x 1216":"RESOLUTION_768_1216",
+ "768 x 1232":"RESOLUTION_768_1232",
+ "768 x 1280":"RESOLUTION_768_1280",
+ "768 x 1344":"RESOLUTION_768_1344",
+ "832 x 960":"RESOLUTION_832_960",
+ "832 x 1024":"RESOLUTION_832_1024",
+ "832 x 1088":"RESOLUTION_832_1088",
+ "832 x 1152":"RESOLUTION_832_1152",
+ "832 x 1216":"RESOLUTION_832_1216",
+ "832 x 1248":"RESOLUTION_832_1248",
+ "864 x 1152":"RESOLUTION_864_1152",
+ "896 x 960":"RESOLUTION_896_960",
+ "896 x 1024":"RESOLUTION_896_1024",
+ "896 x 1088":"RESOLUTION_896_1088",
+ "896 x 1120":"RESOLUTION_896_1120",
+ "896 x 1152":"RESOLUTION_896_1152",
+ "960 x 832":"RESOLUTION_960_832",
+ "960 x 896":"RESOLUTION_960_896",
+ "960 x 1024":"RESOLUTION_960_1024",
+ "960 x 1088":"RESOLUTION_960_1088",
+ "1024 x 640":"RESOLUTION_1024_640",
+ "1024 x 768":"RESOLUTION_1024_768",
+ "1024 x 832":"RESOLUTION_1024_832",
+ "1024 x 896":"RESOLUTION_1024_896",
+ "1024 x 960":"RESOLUTION_1024_960",
+ "1024 x 1024":"RESOLUTION_1024_1024",
+ "1088 x 768":"RESOLUTION_1088_768",
+ "1088 x 832":"RESOLUTION_1088_832",
+ "1088 x 896":"RESOLUTION_1088_896",
+ "1088 x 960":"RESOLUTION_1088_960",
+ "1120 x 896":"RESOLUTION_1120_896",
+ "1152 x 704":"RESOLUTION_1152_704",
+ "1152 x 768":"RESOLUTION_1152_768",
+ "1152 x 832":"RESOLUTION_1152_832",
+ "1152 x 864":"RESOLUTION_1152_864",
+ "1152 x 896":"RESOLUTION_1152_896",
+ "1216 x 704":"RESOLUTION_1216_704",
+ "1216 x 768":"RESOLUTION_1216_768",
+ "1216 x 832":"RESOLUTION_1216_832",
+ "1232 x 768":"RESOLUTION_1232_768",
+ "1248 x 832":"RESOLUTION_1248_832",
+ "1280 x 704":"RESOLUTION_1280_704",
+ "1280 x 720":"RESOLUTION_1280_720",
+ "1280 x 768":"RESOLUTION_1280_768",
+ "1280 x 800":"RESOLUTION_1280_800",
+ "1312 x 736":"RESOLUTION_1312_736",
+ "1344 x 640":"RESOLUTION_1344_640",
+ "1344 x 704":"RESOLUTION_1344_704",
+ "1344 x 768":"RESOLUTION_1344_768",
+ "1408 x 576":"RESOLUTION_1408_576",
+ "1408 x 640":"RESOLUTION_1408_640",
+ "1408 x 704":"RESOLUTION_1408_704",
+ "1472 x 576":"RESOLUTION_1472_576",
+ "1472 x 640":"RESOLUTION_1472_640",
+ "1472 x 704":"RESOLUTION_1472_704",
+ "1536 x 512":"RESOLUTION_1536_512",
+ "1536 x 576":"RESOLUTION_1536_576",
+ "1536 x 640":"RESOLUTION_1536_640",
+}
+
+V1_V2_RATIO_MAP = {
+ "1:1":"ASPECT_1_1",
+ "4:3":"ASPECT_4_3",
+ "3:4":"ASPECT_3_4",
+ "16:9":"ASPECT_16_9",
+ "9:16":"ASPECT_9_16",
+ "2:1":"ASPECT_2_1",
+ "1:2":"ASPECT_1_2",
+ "3:2":"ASPECT_3_2",
+ "2:3":"ASPECT_2_3",
+ "4:5":"ASPECT_4_5",
+ "5:4":"ASPECT_5_4",
+}
+
+V3_RATIO_MAP = {
+ "1:3":"1x3",
+ "3:1":"3x1",
+ "1:2":"1x2",
+ "2:1":"2x1",
+ "9:16":"9x16",
+ "16:9":"16x9",
+ "10:16":"10x16",
+ "16:10":"16x10",
+ "2:3":"2x3",
+ "3:2":"3x2",
+ "3:4":"3x4",
+ "4:3":"4x3",
+ "4:5":"4x5",
+ "5:4":"5x4",
+ "1:1":"1x1",
+}
+
+V3_RESOLUTIONS= [
+ "Auto",
+ "512x1536",
+ "576x1408",
+ "576x1472",
+ "576x1536",
+ "640x1344",
+ "640x1408",
+ "640x1472",
+ "640x1536",
+ "704x1152",
+ "704x1216",
+ "704x1280",
+ "704x1344",
+ "704x1408",
+ "704x1472",
+ "736x1312",
+ "768x1088",
+ "768x1216",
+ "768x1280",
+ "768x1344",
+ "800x1280",
+ "832x960",
+ "832x1024",
+ "832x1088",
+ "832x1152",
+ "832x1216",
+ "832x1248",
+ "864x1152",
+ "896x960",
+ "896x1024",
+ "896x1088",
+ "896x1120",
+ "896x1152",
+ "960x832",
+ "960x896",
+ "960x1024",
+ "960x1088",
+ "1024x832",
+ "1024x896",
+ "1024x960",
+ "1024x1024",
+ "1088x768",
+ "1088x832",
+ "1088x896",
+ "1088x960",
+ "1120x896",
+ "1152x704",
+ "1152x832",
+ "1152x864",
+ "1152x896",
+ "1216x704",
+ "1216x768",
+ "1216x832",
+ "1248x832",
+ "1280x704",
+ "1280x768",
+ "1280x800",
+ "1312x736",
+ "1344x640",
+ "1344x704",
+ "1344x768",
+ "1408x576",
+ "1408x640",
+ "1408x704",
+ "1472x576",
+ "1472x640",
+ "1472x704",
+ "1536x512",
+ "1536x576",
+ "1536x640"
+]
+
+def download_and_process_images(image_urls):
+ """Helper function to download and process multiple images from URLs"""
+
+ # Initialize list to store image tensors
+ image_tensors = []
+
+ for image_url in image_urls:
+ # Using functions from apinode_utils.py to handle downloading and processing
+ image_bytesio = download_url_to_bytesio(image_url) # Download image content to BytesIO
+ img_tensor = bytesio_to_image_tensor(image_bytesio, mode="RGB") # Convert to torch.Tensor with RGB mode
+ image_tensors.append(img_tensor)
+
+ # Stack tensors to match (N, width, height, channels)
+ if image_tensors:
+ stacked_tensors = torch.cat(image_tensors, dim=0)
+ else:
+ raise Exception("No valid images were processed")
+
+ return stacked_tensors
+
+
+def display_image_urls_on_node(image_urls, node_id):
+ if node_id and image_urls:
+ if len(image_urls) == 1:
+ PromptServer.instance.send_progress_text(
+ f"Generated Image URL:\n{image_urls[0]}", node_id
+ )
+ else:
+ urls_text = "Generated Image URLs:\n" + "\n".join(
+ f"{i+1}. {url}" for i, url in enumerate(image_urls)
+ )
+ PromptServer.instance.send_progress_text(urls_text, node_id)
+
+
+class IdeogramV1(ComfyNodeABC):
+ """
+ Generates images using the Ideogram V1 model.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "turbo": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)",
+ }
+ ),
+ },
+ "optional": {
+ "aspect_ratio": (
+ IO.COMBO,
+ {
+ "options": list(V1_V2_RATIO_MAP.keys()),
+ "default": "1:1",
+ "tooltip": "The aspect ratio for image generation.",
+ },
+ ),
+ "magic_prompt_option": (
+ IO.COMBO,
+ {
+ "options": ["AUTO", "ON", "OFF"],
+ "default": "AUTO",
+ "tooltip": "Determine if MagicPrompt should be used in generation",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "step": 1,
+ "control_after_generate": True,
+ "display": "number",
+ },
+ ),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Description of what to exclude from the image",
+ },
+ ),
+ "num_images": (
+ IO.INT,
+ {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"},
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/Ideogram"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ turbo=False,
+ aspect_ratio="1:1",
+ magic_prompt_option="AUTO",
+ seed=0,
+ negative_prompt="",
+ num_images=1,
+ unique_id=None,
+ **kwargs,
+ ):
+ # Determine the model based on turbo setting
+ aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
+ model = "V_1_TURBO" if turbo else "V_1"
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/ideogram/generate",
+ method=HttpMethod.POST,
+ request_model=IdeogramGenerateRequest,
+ response_model=IdeogramGenerateResponse,
+ ),
+ request=IdeogramGenerateRequest(
+ image_request=ImageRequest(
+ prompt=prompt,
+ model=model,
+ num_images=num_images,
+ seed=seed,
+ aspect_ratio=aspect_ratio if aspect_ratio != "ASPECT_1_1" else None,
+ magic_prompt_option=(
+ magic_prompt_option if magic_prompt_option != "AUTO" else None
+ ),
+ negative_prompt=negative_prompt if negative_prompt else None,
+ )
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ if not response.data or len(response.data) == 0:
+ raise Exception("No images were generated in the response")
+
+ image_urls = [image_data.url for image_data in response.data if image_data.url]
+
+ if not image_urls:
+ raise Exception("No image URLs were generated in the response")
+
+ display_image_urls_on_node(image_urls, unique_id)
+ return (download_and_process_images(image_urls),)
+
+
+class IdeogramV2(ComfyNodeABC):
+ """
+ Generates images using the Ideogram V2 model.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "turbo": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ "tooltip": "Whether to use turbo mode (faster generation, potentially lower quality)",
+ }
+ ),
+ },
+ "optional": {
+ "aspect_ratio": (
+ IO.COMBO,
+ {
+ "options": list(V1_V2_RATIO_MAP.keys()),
+ "default": "1:1",
+ "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to AUTO.",
+ },
+ ),
+ "resolution": (
+ IO.COMBO,
+ {
+ "options": list(V1_V1_RES_MAP.keys()),
+ "default": "Auto",
+ "tooltip": "The resolution for image generation. If not set to AUTO, this overrides the aspect_ratio setting.",
+ },
+ ),
+ "magic_prompt_option": (
+ IO.COMBO,
+ {
+ "options": ["AUTO", "ON", "OFF"],
+ "default": "AUTO",
+ "tooltip": "Determine if MagicPrompt should be used in generation",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "step": 1,
+ "control_after_generate": True,
+ "display": "number",
+ },
+ ),
+ "style_type": (
+ IO.COMBO,
+ {
+ "options": ["AUTO", "GENERAL", "REALISTIC", "DESIGN", "RENDER_3D", "ANIME"],
+ "default": "NONE",
+ "tooltip": "Style type for generation (V2 only)",
+ },
+ ),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Description of what to exclude from the image",
+ },
+ ),
+ "num_images": (
+ IO.INT,
+ {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"},
+ ),
+ #"color_palette": (
+ # IO.STRING,
+ # {
+ # "multiline": False,
+ # "default": "",
+ # "tooltip": "Color palette preset name or hex colors with weights",
+ # },
+ #),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/Ideogram"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ turbo=False,
+ aspect_ratio="1:1",
+ resolution="Auto",
+ magic_prompt_option="AUTO",
+ seed=0,
+ style_type="NONE",
+ negative_prompt="",
+ num_images=1,
+ color_palette="",
+ unique_id=None,
+ **kwargs,
+ ):
+ aspect_ratio = V1_V2_RATIO_MAP.get(aspect_ratio, None)
+ resolution = V1_V1_RES_MAP.get(resolution, None)
+ # Determine the model based on turbo setting
+ model = "V_2_TURBO" if turbo else "V_2"
+
+ # Handle resolution vs aspect_ratio logic
+ # If resolution is not AUTO, it overrides aspect_ratio
+ final_resolution = None
+ final_aspect_ratio = None
+
+ if resolution != "AUTO":
+ final_resolution = resolution
+ else:
+ final_aspect_ratio = aspect_ratio if aspect_ratio != "ASPECT_1_1" else None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/ideogram/generate",
+ method=HttpMethod.POST,
+ request_model=IdeogramGenerateRequest,
+ response_model=IdeogramGenerateResponse,
+ ),
+ request=IdeogramGenerateRequest(
+ image_request=ImageRequest(
+ prompt=prompt,
+ model=model,
+ num_images=num_images,
+ seed=seed,
+ aspect_ratio=final_aspect_ratio,
+ resolution=final_resolution,
+ magic_prompt_option=(
+ magic_prompt_option if magic_prompt_option != "AUTO" else None
+ ),
+ style_type=style_type if style_type != "NONE" else None,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ color_palette=color_palette if color_palette else None,
+ )
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ if not response.data or len(response.data) == 0:
+ raise Exception("No images were generated in the response")
+
+ image_urls = [image_data.url for image_data in response.data if image_data.url]
+
+ if not image_urls:
+ raise Exception("No image URLs were generated in the response")
+
+ display_image_urls_on_node(image_urls, unique_id)
+ return (download_and_process_images(image_urls),)
+
+class IdeogramV3(ComfyNodeABC):
+ """
+ Generates images using the Ideogram V3 model. Supports both regular image generation from text prompts and image editing with mask.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation or editing",
+ },
+ ),
+ },
+ "optional": {
+ "image": (
+ IO.IMAGE,
+ {
+ "default": None,
+ "tooltip": "Optional reference image for image editing.",
+ },
+ ),
+ "mask": (
+ IO.MASK,
+ {
+ "default": None,
+ "tooltip": "Optional mask for inpainting (white areas will be replaced)",
+ },
+ ),
+ "aspect_ratio": (
+ IO.COMBO,
+ {
+ "options": list(V3_RATIO_MAP.keys()),
+ "default": "1:1",
+ "tooltip": "The aspect ratio for image generation. Ignored if resolution is not set to Auto.",
+ },
+ ),
+ "resolution": (
+ IO.COMBO,
+ {
+ "options": V3_RESOLUTIONS,
+ "default": "Auto",
+ "tooltip": "The resolution for image generation. If not set to Auto, this overrides the aspect_ratio setting.",
+ },
+ ),
+ "magic_prompt_option": (
+ IO.COMBO,
+ {
+ "options": ["AUTO", "ON", "OFF"],
+ "default": "AUTO",
+ "tooltip": "Determine if MagicPrompt should be used in generation",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "step": 1,
+ "control_after_generate": True,
+ "display": "number",
+ },
+ ),
+ "num_images": (
+ IO.INT,
+ {"default": 1, "min": 1, "max": 8, "step": 1, "display": "number"},
+ ),
+ "rendering_speed": (
+ IO.COMBO,
+ {
+ "options": ["BALANCED", "TURBO", "QUALITY"],
+ "default": "BALANCED",
+ "tooltip": "Controls the trade-off between generation speed and quality",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/Ideogram"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ image=None,
+ mask=None,
+ resolution="Auto",
+ aspect_ratio="1:1",
+ magic_prompt_option="AUTO",
+ seed=0,
+ num_images=1,
+ rendering_speed="BALANCED",
+ unique_id=None,
+ **kwargs,
+ ):
+ # Check if both image and mask are provided for editing mode
+ if image is not None and mask is not None:
+ # Edit mode
+ path = "/proxy/ideogram/ideogram-v3/edit"
+
+ # Process image and mask
+ input_tensor = image.squeeze().cpu()
+ # Resize mask to match image dimension
+ mask = resize_mask_to_image(mask, image, allow_gradient=False)
+ # Invert mask, as Ideogram API will edit black areas instead of white areas (opposite of convention).
+ mask = 1.0 - mask
+
+ # Validate mask dimensions match image
+ if mask.shape[1:] != image.shape[1:-1]:
+ raise Exception("Mask and Image must be the same size")
+
+ # Process image
+ img_np = (input_tensor.numpy() * 255).astype(np.uint8)
+ img = Image.fromarray(img_np)
+ img_byte_arr = io.BytesIO()
+ img.save(img_byte_arr, format="PNG")
+ img_byte_arr.seek(0)
+ img_binary = img_byte_arr
+ img_binary.name = "image.png"
+
+ # Process mask - white areas will be replaced
+ mask_np = (mask.squeeze().cpu().numpy() * 255).astype(np.uint8)
+ mask_img = Image.fromarray(mask_np)
+ mask_byte_arr = io.BytesIO()
+ mask_img.save(mask_byte_arr, format="PNG")
+ mask_byte_arr.seek(0)
+ mask_binary = mask_byte_arr
+ mask_binary.name = "mask.png"
+
+ # Create edit request
+ edit_request = IdeogramV3EditRequest(
+ prompt=prompt,
+ rendering_speed=rendering_speed,
+ )
+
+ # Add optional parameters
+ if magic_prompt_option != "AUTO":
+ edit_request.magic_prompt = magic_prompt_option
+ if seed != 0:
+ edit_request.seed = seed
+ if num_images > 1:
+ edit_request.num_images = num_images
+
+ # Execute the operation for edit mode
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=IdeogramV3EditRequest,
+ response_model=IdeogramGenerateResponse,
+ ),
+ request=edit_request,
+ files={
+ "image": img_binary,
+ "mask": mask_binary,
+ },
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ elif image is not None or mask is not None:
+ # If only one of image or mask is provided, raise an error
+ raise Exception("Ideogram V3 image editing requires both an image AND a mask")
+ else:
+ # Generation mode
+ path = "/proxy/ideogram/ideogram-v3/generate"
+
+ # Create generation request
+ gen_request = IdeogramV3Request(
+ prompt=prompt,
+ rendering_speed=rendering_speed,
+ )
+
+ # Handle resolution vs aspect ratio
+ if resolution != "Auto":
+ gen_request.resolution = resolution
+ elif aspect_ratio != "1:1":
+ v3_aspect = V3_RATIO_MAP.get(aspect_ratio)
+ if v3_aspect:
+ gen_request.aspect_ratio = v3_aspect
+
+ # Add optional parameters
+ if magic_prompt_option != "AUTO":
+ gen_request.magic_prompt = magic_prompt_option
+ if seed != 0:
+ gen_request.seed = seed
+ if num_images > 1:
+ gen_request.num_images = num_images
+
+ # Execute the operation for generation mode
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=IdeogramV3Request,
+ response_model=IdeogramGenerateResponse,
+ ),
+ request=gen_request,
+ auth_kwargs=kwargs,
+ )
+
+ # Execute the operation and process response
+ response = operation.execute()
+
+ if not response.data or len(response.data) == 0:
+ raise Exception("No images were generated in the response")
+
+ image_urls = [image_data.url for image_data in response.data if image_data.url]
+
+ if not image_urls:
+ raise Exception("No image URLs were generated in the response")
+
+ display_image_urls_on_node(image_urls, unique_id)
+ return (download_and_process_images(image_urls),)
+
+
+NODE_CLASS_MAPPINGS = {
+ "IdeogramV1": IdeogramV1,
+ "IdeogramV2": IdeogramV2,
+ "IdeogramV3": IdeogramV3,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "IdeogramV1": "Ideogram V1",
+ "IdeogramV2": "Ideogram V2",
+ "IdeogramV3": "Ideogram V3",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_kling.py b/ComfyUI/comfy_api_nodes/nodes_kling.py
new file mode 100644
index 0000000000000000000000000000000000000000..69e9e5cf065798ad3a0c8ae77f1442ae7f069b5e
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_kling.py
@@ -0,0 +1,1760 @@
+"""Kling API Nodes
+
+For source of truth on the allowed permutations of request fields, please reference:
+- [Compatibility Table](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+"""
+
+from __future__ import annotations
+from typing import Optional, TypeVar, Any
+from collections.abc import Callable
+import math
+import logging
+
+import torch
+
+from comfy_api_nodes.apis import (
+ KlingTaskStatus,
+ KlingCameraControl,
+ KlingCameraConfig,
+ KlingCameraControlType,
+ KlingVideoGenDuration,
+ KlingVideoGenMode,
+ KlingVideoGenAspectRatio,
+ KlingVideoGenModelName,
+ KlingText2VideoRequest,
+ KlingText2VideoResponse,
+ KlingImage2VideoRequest,
+ KlingImage2VideoResponse,
+ KlingVideoExtendRequest,
+ KlingVideoExtendResponse,
+ KlingLipSyncVoiceLanguage,
+ KlingLipSyncInputObject,
+ KlingLipSyncRequest,
+ KlingLipSyncResponse,
+ KlingVirtualTryOnModelName,
+ KlingVirtualTryOnRequest,
+ KlingVirtualTryOnResponse,
+ KlingVideoResult,
+ KlingImageResult,
+ KlingImageGenerationsRequest,
+ KlingImageGenerationsResponse,
+ KlingImageGenImageReferenceType,
+ KlingImageGenModelName,
+ KlingImageGenAspectRatio,
+ KlingVideoEffectsRequest,
+ KlingVideoEffectsResponse,
+ KlingDualCharacterEffectsScene,
+ KlingSingleImageEffectsScene,
+ KlingDualCharacterEffectInput,
+ KlingSingleImageEffectInput,
+ KlingCharacterEffectModelName,
+ KlingSingleImageEffectModelName,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ tensor_to_base64_string,
+ download_url_to_video_output,
+ upload_video_to_comfyapi,
+ upload_audio_to_comfyapi,
+ download_url_to_image_tensor,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api_nodes.util.validation_utils import (
+ validate_image_dimensions,
+ validate_image_aspect_ratio,
+ validate_video_dimensions,
+ validate_video_duration,
+)
+from comfy_api.input.basic_types import AudioInput
+from comfy_api.input.video_types import VideoInput
+from comfy_api.input_impl import VideoFromFile
+from comfy.comfy_types.node_typing import IO, InputTypeOptions, ComfyNodeABC
+
+KLING_API_VERSION = "v1"
+PATH_TEXT_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/text2video"
+PATH_IMAGE_TO_VIDEO = f"/proxy/kling/{KLING_API_VERSION}/videos/image2video"
+PATH_VIDEO_EXTEND = f"/proxy/kling/{KLING_API_VERSION}/videos/video-extend"
+PATH_LIP_SYNC = f"/proxy/kling/{KLING_API_VERSION}/videos/lip-sync"
+PATH_VIDEO_EFFECTS = f"/proxy/kling/{KLING_API_VERSION}/videos/effects"
+PATH_CHARACTER_IMAGE = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
+PATH_VIRTUAL_TRY_ON = f"/proxy/kling/{KLING_API_VERSION}/images/kolors-virtual-try-on"
+PATH_IMAGE_GENERATIONS = f"/proxy/kling/{KLING_API_VERSION}/images/generations"
+
+MAX_PROMPT_LENGTH_T2V = 2500
+MAX_PROMPT_LENGTH_I2V = 500
+MAX_PROMPT_LENGTH_IMAGE_GEN = 500
+MAX_NEGATIVE_PROMPT_LENGTH_IMAGE_GEN = 200
+MAX_PROMPT_LENGTH_LIP_SYNC = 120
+
+AVERAGE_DURATION_T2V = 319
+AVERAGE_DURATION_I2V = 164
+AVERAGE_DURATION_LIP_SYNC = 455
+AVERAGE_DURATION_VIRTUAL_TRY_ON = 19
+AVERAGE_DURATION_IMAGE_GEN = 32
+AVERAGE_DURATION_VIDEO_EFFECTS = 320
+AVERAGE_DURATION_VIDEO_EXTEND = 320
+
+R = TypeVar("R")
+
+
+class KlingApiError(Exception):
+ """Base exception for Kling API errors."""
+
+ pass
+
+
+def poll_until_finished(
+ auth_kwargs: dict[str, str],
+ api_endpoint: ApiEndpoint[Any, R],
+ result_url_extractor: Optional[Callable[[R], str]] = None,
+ estimated_duration: Optional[int] = None,
+ node_id: Optional[str] = None,
+) -> R:
+ """Polls the Kling API endpoint until the task reaches a terminal state, then returns the response."""
+ return PollingOperation(
+ poll_endpoint=api_endpoint,
+ completed_statuses=[
+ KlingTaskStatus.succeed.value,
+ ],
+ failed_statuses=[KlingTaskStatus.failed.value],
+ status_extractor=lambda response: (
+ response.data.task_status.value
+ if response.data and response.data.task_status
+ else None
+ ),
+ auth_kwargs=auth_kwargs,
+ result_url_extractor=result_url_extractor,
+ estimated_duration=estimated_duration,
+ node_id=node_id,
+ poll_interval=16.0,
+ max_poll_attempts=256,
+ ).execute()
+
+
+def is_valid_camera_control_configs(configs: list[float]) -> bool:
+ """Verifies that at least one camera control configuration is non-zero."""
+ return any(not math.isclose(value, 0.0) for value in configs)
+
+
+def is_valid_prompt(prompt: str) -> bool:
+ """Verifies that the prompt is not empty."""
+ return bool(prompt)
+
+
+def is_valid_task_creation_response(response: KlingText2VideoResponse) -> bool:
+ """Verifies that the initial response contains a task ID."""
+ return bool(response.data.task_id)
+
+
+def is_valid_video_response(response: KlingText2VideoResponse) -> bool:
+ """Verifies that the response contains a task result with at least one video."""
+ return (
+ response.data is not None
+ and response.data.task_result is not None
+ and response.data.task_result.videos is not None
+ and len(response.data.task_result.videos) > 0
+ )
+
+
+def is_valid_image_response(response: KlingVirtualTryOnResponse) -> bool:
+ """Verifies that the response contains a task result with at least one image."""
+ return (
+ response.data is not None
+ and response.data.task_result is not None
+ and response.data.task_result.images is not None
+ and len(response.data.task_result.images) > 0
+ )
+
+
+def validate_prompts(prompt: str, negative_prompt: str, max_length: int) -> bool:
+ """Verifies that the positive prompt is not empty and that neither promt is too long."""
+ if not prompt:
+ raise ValueError("Positive prompt is empty")
+ if len(prompt) > max_length:
+ raise ValueError(f"Positive prompt is too long: {len(prompt)} characters")
+ if negative_prompt and len(negative_prompt) > max_length:
+ raise ValueError(
+ f"Negative prompt is too long: {len(negative_prompt)} characters"
+ )
+ return True
+
+
+def validate_task_creation_response(response) -> None:
+ """Validates that the Kling task creation request was successful."""
+ if not is_valid_task_creation_response(response):
+ error_msg = f"Kling initial request failed. Code: {response.code}, Message: {response.message}, Data: {response.data}"
+ logging.error(error_msg)
+ raise KlingApiError(error_msg)
+
+
+def validate_video_result_response(response) -> None:
+ """Validates that the Kling task result contains a video."""
+ if not is_valid_video_response(response):
+ error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response."
+ logging.error(f"Error: {error_msg}.\nResponse: {response}")
+ raise KlingApiError(error_msg)
+
+
+def validate_image_result_response(response) -> None:
+ """Validates that the Kling task result contains an image."""
+ if not is_valid_image_response(response):
+ error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response."
+ logging.error(f"Error: {error_msg}.\nResponse: {response}")
+ raise KlingApiError(error_msg)
+
+
+def validate_input_image(image: torch.Tensor) -> None:
+ """
+ Validates the input image adheres to the expectations of the Kling API:
+ - The image resolution should not be less than 300*300px
+ - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
+
+ See: https://app.klingai.com/global/dev/document-api/apiReference/model/imageToVideo
+ """
+ validate_image_dimensions(image, min_width=300, min_height=300)
+ validate_image_aspect_ratio(image, min_aspect_ratio=1 / 2.5, max_aspect_ratio=2.5)
+
+
+def get_camera_control_input_config(
+ tooltip: str, default: float = 0.0
+) -> tuple[IO, InputTypeOptions]:
+ """Returns common InputTypeOptions for Kling camera control configurations."""
+ input_config = {
+ "default": default,
+ "min": -10.0,
+ "max": 10.0,
+ "step": 0.25,
+ "display": "slider",
+ "tooltip": tooltip,
+ }
+ return IO.FLOAT, input_config
+
+
+def get_video_from_response(response) -> KlingVideoResult:
+ """Returns the first video object from the Kling video generation task result.
+ Will raise an error if the response is not valid.
+ """
+ video = response.data.task_result.videos[0]
+ logging.info(
+ "Kling task %s succeeded. Video URL: %s", response.data.task_id, video.url
+ )
+ return video
+
+
+def get_video_url_from_response(response) -> Optional[str]:
+ """Returns the first video url from the Kling video generation task result.
+ Will not raise an error if the response is not valid.
+ """
+ if response and is_valid_video_response(response):
+ return str(get_video_from_response(response).url)
+ else:
+ return None
+
+
+def get_images_from_response(response) -> list[KlingImageResult]:
+ """Returns the list of image objects from the Kling image generation task result.
+ Will raise an error if the response is not valid.
+ """
+ images = response.data.task_result.images
+ logging.info("Kling task %s succeeded. Images: %s", response.data.task_id, images)
+ return images
+
+
+def get_images_urls_from_response(response) -> Optional[str]:
+ """Returns the list of image urls from the Kling image generation task result.
+ Will not raise an error if the response is not valid. If there is only one image, returns the url as a string. If there are multiple images, returns a list of urls.
+ """
+ if response and is_valid_image_response(response):
+ images = get_images_from_response(response)
+ image_urls = [str(image.url) for image in images]
+ return "\n".join(image_urls)
+ else:
+ return None
+
+
+def video_result_to_node_output(
+ video: KlingVideoResult,
+) -> tuple[VideoFromFile, str, str]:
+ """Converts a KlingVideoResult to a tuple of (VideoFromFile, str, str) to be used as a ComfyUI node output."""
+ return (
+ download_url_to_video_output(video.url),
+ str(video.id),
+ str(video.duration),
+ )
+
+
+def image_result_to_node_output(
+ images: list[KlingImageResult],
+) -> torch.Tensor:
+ """
+ Converts a KlingImageResult to a tuple containing a [B, H, W, C] tensor.
+ If multiple images are returned, they will be stacked along the batch dimension.
+ """
+ if len(images) == 1:
+ return download_url_to_image_tensor(images[0].url)
+ else:
+ return torch.cat([download_url_to_image_tensor(image.url) for image in images])
+
+
+class KlingNodeBase(ComfyNodeABC):
+ """Base class for Kling nodes."""
+
+ FUNCTION = "api_call"
+ CATEGORY = "api node/video/Kling"
+ API_NODE = True
+
+
+class KlingCameraControls(KlingNodeBase):
+ """Kling Camera Controls Node"""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "camera_control_type": model_field_to_node_input(
+ IO.COMBO,
+ KlingCameraControl,
+ "type",
+ enum_type=KlingCameraControlType,
+ ),
+ "horizontal_movement": get_camera_control_input_config(
+ "Controls camera's movement along horizontal axis (x-axis). Negative indicates left, positive indicates right"
+ ),
+ "vertical_movement": get_camera_control_input_config(
+ "Controls camera's movement along vertical axis (y-axis). Negative indicates downward, positive indicates upward."
+ ),
+ "pan": get_camera_control_input_config(
+ "Controls camera's rotation in vertical plane (x-axis). Negative indicates downward rotation, positive indicates upward rotation.",
+ default=0.5,
+ ),
+ "tilt": get_camera_control_input_config(
+ "Controls camera's rotation in horizontal plane (y-axis). Negative indicates left rotation, positive indicates right rotation.",
+ ),
+ "roll": get_camera_control_input_config(
+ "Controls camera's rolling amount (z-axis). Negative indicates counterclockwise, positive indicates clockwise.",
+ ),
+ "zoom": get_camera_control_input_config(
+ "Controls change in camera's focal length. Negative indicates narrower field of view, positive indicates wider field of view.",
+ ),
+ }
+ }
+
+ DESCRIPTION = "Allows specifying configuration options for Kling Camera Controls and motion control effects."
+ RETURN_TYPES = ("CAMERA_CONTROL",)
+ RETURN_NAMES = ("camera_control",)
+ FUNCTION = "main"
+ API_NODE = False # This is just a helper node, it doesn't make an API call
+
+ @classmethod
+ def VALIDATE_INPUTS(
+ cls,
+ horizontal_movement: float,
+ vertical_movement: float,
+ pan: float,
+ tilt: float,
+ roll: float,
+ zoom: float,
+ ) -> bool | str:
+ if not is_valid_camera_control_configs(
+ [
+ horizontal_movement,
+ vertical_movement,
+ pan,
+ tilt,
+ roll,
+ zoom,
+ ]
+ ):
+ return "Invalid camera control configs: at least one of the values must be non-zero"
+ return True
+
+ def main(
+ self,
+ camera_control_type: str,
+ horizontal_movement: float,
+ vertical_movement: float,
+ pan: float,
+ tilt: float,
+ roll: float,
+ zoom: float,
+ ) -> tuple[KlingCameraControl]:
+ return (
+ KlingCameraControl(
+ type=KlingCameraControlType(camera_control_type),
+ config=KlingCameraConfig(
+ horizontal=horizontal_movement,
+ vertical=vertical_movement,
+ pan=pan,
+ roll=roll,
+ tilt=tilt,
+ zoom=zoom,
+ ),
+ ),
+ )
+
+
+class KlingTextToVideoNode(KlingNodeBase):
+ """Kling Text to Video Node"""
+
+ @staticmethod
+ def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]:
+ """
+ Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
+ Only includes config combos that support the `image_tail` request field.
+
+ See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+ """
+ return {
+ "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
+ "standard mode / 10s duration / kling-v1": ("std", "10", "kling-v1"),
+ "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
+ "pro mode / 10s duration / kling-v1": ("pro", "10", "kling-v1"),
+ "standard mode / 5s duration / kling-v1-6": ("std", "5", "kling-v1-6"),
+ "standard mode / 10s duration / kling-v1-6": ("std", "10", "kling-v1-6"),
+ "pro mode / 5s duration / kling-v2-master": ("pro", "5", "kling-v2-master"),
+ "pro mode / 10s duration / kling-v2-master": ("pro", "10", "kling-v2-master"),
+ "standard mode / 5s duration / kling-v2-master": ("std", "5", "kling-v2-master"),
+ "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"),
+ }
+
+ @classmethod
+ def INPUT_TYPES(s):
+ modes = list(KlingTextToVideoNode.get_mode_string_mapping().keys())
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING, KlingText2VideoRequest, "negative_prompt", multiline=True
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingText2VideoRequest,
+ "cfg_scale",
+ default=1.0,
+ min=0.0,
+ max=1.0,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingText2VideoRequest,
+ "aspect_ratio",
+ enum_type=KlingVideoGenAspectRatio,
+ ),
+ "mode": (
+ modes,
+ {
+ "default": modes[4],
+ "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO", "STRING", "STRING")
+ RETURN_NAMES = ("VIDEO", "video_id", "duration")
+ DESCRIPTION = "Kling Text to Video Node"
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingText2VideoResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_TEXT_TO_VIDEO}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingText2VideoResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_T2V,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ prompt: str,
+ negative_prompt: str,
+ cfg_scale: float,
+ mode: str,
+ aspect_ratio: str,
+ camera_control: Optional[KlingCameraControl] = None,
+ model_name: Optional[str] = None,
+ duration: Optional[str] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile, str, str]:
+ validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
+ if model_name is None:
+ mode, duration, model_name = self.get_mode_string_mapping()[mode]
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_TEXT_TO_VIDEO,
+ method=HttpMethod.POST,
+ request_model=KlingText2VideoRequest,
+ response_model=KlingText2VideoResponse,
+ ),
+ request=KlingText2VideoRequest(
+ prompt=prompt if prompt else None,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ duration=KlingVideoGenDuration(duration),
+ mode=KlingVideoGenMode(mode),
+ model_name=KlingVideoGenModelName(model_name),
+ cfg_scale=cfg_scale,
+ aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
+ camera_control=camera_control,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+
+ task_id = task_creation_response.data.task_id
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_video_result_response(final_response)
+
+ video = get_video_from_response(final_response)
+ return video_result_to_node_output(video)
+
+
+class KlingCameraControlT2VNode(KlingTextToVideoNode):
+ """
+ Kling Text to Video Camera Control Node. This node is a text to video node, but it supports controlling the camera.
+ Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingText2VideoRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingText2VideoRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingText2VideoRequest,
+ "cfg_scale",
+ default=0.75,
+ min=0.0,
+ max=1.0,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingText2VideoRequest,
+ "aspect_ratio",
+ enum_type=KlingVideoGenAspectRatio,
+ ),
+ "camera_control": (
+ "CAMERA_CONTROL",
+ {
+ "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Transform text into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original text."
+
+ def api_call(
+ self,
+ prompt: str,
+ negative_prompt: str,
+ cfg_scale: float,
+ aspect_ratio: str,
+ camera_control: Optional[KlingCameraControl] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ return super().api_call(
+ model_name=KlingVideoGenModelName.kling_v1,
+ cfg_scale=cfg_scale,
+ mode=KlingVideoGenMode.std,
+ aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
+ duration=KlingVideoGenDuration.field_5,
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ camera_control=camera_control,
+ **kwargs,
+ )
+
+
+class KlingImage2VideoNode(KlingNodeBase):
+ """Kling Image to Video Node"""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "start_frame": model_field_to_node_input(
+ IO.IMAGE,
+ KlingImage2VideoRequest,
+ "image",
+ tooltip="The reference image used to generate the video.",
+ ),
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingImage2VideoRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "model_name": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "model_name",
+ enum_type=KlingVideoGenModelName,
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingImage2VideoRequest,
+ "cfg_scale",
+ default=0.8,
+ min=0.0,
+ max=1.0,
+ ),
+ "mode": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "mode",
+ enum_type=KlingVideoGenMode,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "aspect_ratio",
+ enum_type=KlingVideoGenAspectRatio,
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "duration",
+ enum_type=KlingVideoGenDuration,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO", "STRING", "STRING")
+ RETURN_NAMES = ("VIDEO", "video_id", "duration")
+ DESCRIPTION = "Kling Image to Video Node"
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingImage2VideoResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_IMAGE_TO_VIDEO}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=KlingImage2VideoRequest,
+ response_model=KlingImage2VideoResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_I2V,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ start_frame: torch.Tensor,
+ prompt: str,
+ negative_prompt: str,
+ model_name: str,
+ cfg_scale: float,
+ mode: str,
+ aspect_ratio: str,
+ duration: str,
+ camera_control: Optional[KlingCameraControl] = None,
+ end_frame: Optional[torch.Tensor] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_I2V)
+ validate_input_image(start_frame)
+
+ if camera_control is not None:
+ # Camera control type for image 2 video is always `simple`
+ camera_control.type = KlingCameraControlType.simple
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_IMAGE_TO_VIDEO,
+ method=HttpMethod.POST,
+ request_model=KlingImage2VideoRequest,
+ response_model=KlingImage2VideoResponse,
+ ),
+ request=KlingImage2VideoRequest(
+ model_name=KlingVideoGenModelName(model_name),
+ image=tensor_to_base64_string(start_frame),
+ image_tail=(
+ tensor_to_base64_string(end_frame)
+ if end_frame is not None
+ else None
+ ),
+ prompt=prompt,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ cfg_scale=cfg_scale,
+ mode=KlingVideoGenMode(mode),
+ duration=KlingVideoGenDuration(duration),
+ camera_control=camera_control,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_video_result_response(final_response)
+
+ video = get_video_from_response(final_response)
+ return video_result_to_node_output(video)
+
+
+class KlingCameraControlI2VNode(KlingImage2VideoNode):
+ """
+ Kling Image to Video Camera Control Node. This node is a image to video node, but it supports controlling the camera.
+ Duration, mode, and model_name request fields are hard-coded because camera control is only supported in pro mode with the kling-v1-5 model at 5s duration as of 2025-05-02.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "start_frame": model_field_to_node_input(
+ IO.IMAGE, KlingImage2VideoRequest, "image"
+ ),
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingImage2VideoRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingImage2VideoRequest,
+ "cfg_scale",
+ default=0.75,
+ min=0.0,
+ max=1.0,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "aspect_ratio",
+ enum_type=KlingVideoGenAspectRatio,
+ ),
+ "camera_control": (
+ "CAMERA_CONTROL",
+ {
+ "tooltip": "Can be created using the Kling Camera Controls node. Controls the camera movement and motion during the video generation.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Transform still images into cinematic videos with professional camera movements that simulate real-world cinematography. Control virtual camera actions including zoom, rotation, pan, tilt, and first-person view, while maintaining focus on your original image."
+
+ def api_call(
+ self,
+ start_frame: torch.Tensor,
+ prompt: str,
+ negative_prompt: str,
+ cfg_scale: float,
+ aspect_ratio: str,
+ camera_control: KlingCameraControl,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ return super().api_call(
+ model_name=KlingVideoGenModelName.kling_v1_5,
+ start_frame=start_frame,
+ cfg_scale=cfg_scale,
+ mode=KlingVideoGenMode.pro,
+ aspect_ratio=KlingVideoGenAspectRatio(aspect_ratio),
+ duration=KlingVideoGenDuration.field_5,
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ camera_control=camera_control,
+ unique_id=unique_id,
+ **kwargs,
+ )
+
+
+class KlingStartEndFrameNode(KlingImage2VideoNode):
+ """
+ Kling First Last Frame Node. This node allows creation of a video from a first and last frame. It calls the normal image to video endpoint, but only allows the subset of input options that support the `image_tail` request field.
+ """
+
+ @staticmethod
+ def get_mode_string_mapping() -> dict[str, tuple[str, str, str]]:
+ """
+ Returns a mapping of mode strings to their corresponding (mode, duration, model_name) tuples.
+ Only includes config combos that support the `image_tail` request field.
+
+ See: [Kling API Docs Capability Map](https://app.klingai.com/global/dev/document-api/apiReference/model/skillsMap)
+ """
+ return {
+ "standard mode / 5s duration / kling-v1": ("std", "5", "kling-v1"),
+ "pro mode / 5s duration / kling-v1": ("pro", "5", "kling-v1"),
+ "pro mode / 5s duration / kling-v1-5": ("pro", "5", "kling-v1-5"),
+ "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"),
+ "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"),
+ "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"),
+ }
+
+ @classmethod
+ def INPUT_TYPES(s):
+ modes = list(KlingStartEndFrameNode.get_mode_string_mapping().keys())
+ return {
+ "required": {
+ "start_frame": model_field_to_node_input(
+ IO.IMAGE, KlingImage2VideoRequest, "image"
+ ),
+ "end_frame": model_field_to_node_input(
+ IO.IMAGE, KlingImage2VideoRequest, "image_tail"
+ ),
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingImage2VideoRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingImage2VideoRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingImage2VideoRequest,
+ "cfg_scale",
+ default=0.5,
+ min=0.0,
+ max=1.0,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingImage2VideoRequest,
+ "aspect_ratio",
+ enum_type=KlingVideoGenAspectRatio,
+ ),
+ "mode": (
+ modes,
+ {
+ "default": modes[2],
+ "tooltip": "The configuration to use for the video generation following the format: mode / duration / model_name.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Generate a video sequence that transitions between your provided start and end images. The node creates all frames in between, producing a smooth transformation from the first frame to the last."
+
+ def api_call(
+ self,
+ start_frame: torch.Tensor,
+ end_frame: torch.Tensor,
+ prompt: str,
+ negative_prompt: str,
+ cfg_scale: float,
+ aspect_ratio: str,
+ mode: str,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ mode, duration, model_name = KlingStartEndFrameNode.get_mode_string_mapping()[
+ mode
+ ]
+ return super().api_call(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model_name=model_name,
+ start_frame=start_frame,
+ cfg_scale=cfg_scale,
+ mode=mode,
+ aspect_ratio=aspect_ratio,
+ duration=duration,
+ end_frame=end_frame,
+ unique_id=unique_id,
+ **kwargs,
+ )
+
+
+class KlingVideoExtendNode(KlingNodeBase):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, KlingVideoExtendRequest, "prompt", multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingVideoExtendRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "cfg_scale": model_field_to_node_input(
+ IO.FLOAT,
+ KlingVideoExtendRequest,
+ "cfg_scale",
+ default=0.5,
+ min=0.0,
+ max=1.0,
+ ),
+ "video_id": model_field_to_node_input(
+ IO.STRING, KlingVideoExtendRequest, "video_id", forceInput=True
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO", "STRING", "STRING")
+ RETURN_NAMES = ("VIDEO", "video_id", "duration")
+ DESCRIPTION = "Kling Video Extend Node. Extend videos made by other Kling nodes. The video_id is created by using other Kling Nodes."
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingVideoExtendResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_VIDEO_EXTEND}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingVideoExtendResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_VIDEO_EXTEND,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ prompt: str,
+ negative_prompt: str,
+ cfg_scale: float,
+ video_id: str,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile, str, str]:
+ validate_prompts(prompt, negative_prompt, MAX_PROMPT_LENGTH_T2V)
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_VIDEO_EXTEND,
+ method=HttpMethod.POST,
+ request_model=KlingVideoExtendRequest,
+ response_model=KlingVideoExtendResponse,
+ ),
+ request=KlingVideoExtendRequest(
+ prompt=prompt if prompt else None,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ cfg_scale=cfg_scale,
+ video_id=video_id,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_video_result_response(final_response)
+
+ video = get_video_from_response(final_response)
+ return video_result_to_node_output(video)
+
+
+class KlingVideoEffectsBase(KlingNodeBase):
+ """Kling Video Effects Base"""
+
+ RETURN_TYPES = ("VIDEO", "STRING", "STRING")
+ RETURN_NAMES = ("VIDEO", "video_id", "duration")
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingVideoEffectsResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_VIDEO_EFFECTS}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingVideoEffectsResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_VIDEO_EFFECTS,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ dual_character: bool,
+ effect_scene: KlingDualCharacterEffectsScene | KlingSingleImageEffectsScene,
+ model_name: str,
+ duration: KlingVideoGenDuration,
+ image_1: torch.Tensor,
+ image_2: Optional[torch.Tensor] = None,
+ mode: Optional[KlingVideoGenMode] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ if dual_character:
+ request_input_field = KlingDualCharacterEffectInput(
+ model_name=model_name,
+ mode=mode,
+ images=[
+ tensor_to_base64_string(image_1),
+ tensor_to_base64_string(image_2),
+ ],
+ duration=duration,
+ )
+ else:
+ request_input_field = KlingSingleImageEffectInput(
+ model_name=model_name,
+ image=tensor_to_base64_string(image_1),
+ duration=duration,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_VIDEO_EFFECTS,
+ method=HttpMethod.POST,
+ request_model=KlingVideoEffectsRequest,
+ response_model=KlingVideoEffectsResponse,
+ ),
+ request=KlingVideoEffectsRequest(
+ effect_scene=effect_scene,
+ input=request_input_field,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_video_result_response(final_response)
+
+ video = get_video_from_response(final_response)
+ return video_result_to_node_output(video)
+
+
+class KlingDualCharacterVideoEffectNode(KlingVideoEffectsBase):
+ """Kling Dual Character Video Effect Node"""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image_left": (IO.IMAGE, {"tooltip": "Left side image"}),
+ "image_right": (IO.IMAGE, {"tooltip": "Right side image"}),
+ "effect_scene": model_field_to_node_input(
+ IO.COMBO,
+ KlingVideoEffectsRequest,
+ "effect_scene",
+ enum_type=KlingDualCharacterEffectsScene,
+ ),
+ "model_name": model_field_to_node_input(
+ IO.COMBO,
+ KlingDualCharacterEffectInput,
+ "model_name",
+ enum_type=KlingCharacterEffectModelName,
+ ),
+ "mode": model_field_to_node_input(
+ IO.COMBO,
+ KlingDualCharacterEffectInput,
+ "mode",
+ enum_type=KlingVideoGenMode,
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO,
+ KlingDualCharacterEffectInput,
+ "duration",
+ enum_type=KlingVideoGenDuration,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene. First image will be positioned on left side, second on right side of the composite."
+ RETURN_TYPES = ("VIDEO", "STRING")
+ RETURN_NAMES = ("VIDEO", "duration")
+
+ def api_call(
+ self,
+ image_left: torch.Tensor,
+ image_right: torch.Tensor,
+ effect_scene: KlingDualCharacterEffectsScene,
+ model_name: KlingCharacterEffectModelName,
+ mode: KlingVideoGenMode,
+ duration: KlingVideoGenDuration,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ video, _, duration = super().api_call(
+ dual_character=True,
+ effect_scene=effect_scene,
+ model_name=model_name,
+ mode=mode,
+ duration=duration,
+ image_1=image_left,
+ image_2=image_right,
+ unique_id=unique_id,
+ **kwargs,
+ )
+ return video, duration
+
+
+class KlingSingleImageVideoEffectNode(KlingVideoEffectsBase):
+ """Kling Single Image Video Effect Node"""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (
+ IO.IMAGE,
+ {
+ "tooltip": " Reference Image. URL or Base64 encoded string (without data:image prefix). File size cannot exceed 10MB, resolution not less than 300*300px, aspect ratio between 1:2.5 ~ 2.5:1"
+ },
+ ),
+ "effect_scene": model_field_to_node_input(
+ IO.COMBO,
+ KlingVideoEffectsRequest,
+ "effect_scene",
+ enum_type=KlingSingleImageEffectsScene,
+ ),
+ "model_name": model_field_to_node_input(
+ IO.COMBO,
+ KlingSingleImageEffectInput,
+ "model_name",
+ enum_type=KlingSingleImageEffectModelName,
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO,
+ KlingSingleImageEffectInput,
+ "duration",
+ enum_type=KlingVideoGenDuration,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Achieve different special effects when generating a video based on the effect_scene."
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ effect_scene: KlingSingleImageEffectsScene,
+ model_name: KlingSingleImageEffectModelName,
+ duration: KlingVideoGenDuration,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ return super().api_call(
+ dual_character=False,
+ effect_scene=effect_scene,
+ model_name=model_name,
+ duration=duration,
+ image_1=image,
+ unique_id=unique_id,
+ **kwargs,
+ )
+
+
+class KlingLipSyncBase(KlingNodeBase):
+ """Kling Lip Sync Base"""
+
+ RETURN_TYPES = ("VIDEO", "STRING", "STRING")
+ RETURN_NAMES = ("VIDEO", "video_id", "duration")
+
+ def validate_lip_sync_video(self, video: VideoInput):
+ """
+ Validates the input video adheres to the expectations of the Kling Lip Sync API:
+ - Video length does not exceed 10s and is not shorter than 2s
+ - Length and width dimensions should both be between 720px and 1920px
+
+ See: https://app.klingai.com/global/dev/document-api/apiReference/model/videoTolip
+ """
+ validate_video_dimensions(video, 720, 1920)
+ validate_video_duration(video, 2, 10)
+
+ def validate_text(self, text: str):
+ if not text:
+ raise ValueError("Text is required")
+ if len(text) > MAX_PROMPT_LENGTH_LIP_SYNC:
+ raise ValueError(
+ f"Text is too long. Maximum length is {MAX_PROMPT_LENGTH_LIP_SYNC} characters."
+ )
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingLipSyncResponse:
+ """Polls the Kling API endpoint until the task reaches a terminal state."""
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_LIP_SYNC}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingLipSyncResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_LIP_SYNC,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ video: VideoInput,
+ audio: Optional[AudioInput] = None,
+ voice_language: Optional[str] = None,
+ mode: Optional[str] = None,
+ text: Optional[str] = None,
+ voice_speed: Optional[float] = None,
+ voice_id: Optional[str] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile, str, str]:
+ if text:
+ self.validate_text(text)
+ self.validate_lip_sync_video(video)
+
+ # Upload video to Comfy API and get download URL
+ video_url = upload_video_to_comfyapi(video, auth_kwargs=kwargs)
+ logging.info("Uploaded video to Comfy API. URL: %s", video_url)
+
+ # Upload the audio file to Comfy API and get download URL
+ if audio:
+ audio_url = upload_audio_to_comfyapi(audio, auth_kwargs=kwargs)
+ logging.info("Uploaded audio to Comfy API. URL: %s", audio_url)
+ else:
+ audio_url = None
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_LIP_SYNC,
+ method=HttpMethod.POST,
+ request_model=KlingLipSyncRequest,
+ response_model=KlingLipSyncResponse,
+ ),
+ request=KlingLipSyncRequest(
+ input=KlingLipSyncInputObject(
+ video_url=video_url,
+ mode=mode,
+ text=text,
+ voice_language=voice_language,
+ voice_speed=voice_speed,
+ audio_type="url",
+ audio_url=audio_url,
+ voice_id=voice_id,
+ ),
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_video_result_response(final_response)
+
+ video = get_video_from_response(final_response)
+ return video_result_to_node_output(video)
+
+
+class KlingLipSyncAudioToVideoNode(KlingLipSyncBase):
+ """Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file."""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "video": (IO.VIDEO, {}),
+ "audio": (IO.AUDIO, {}),
+ "voice_language": model_field_to_node_input(
+ IO.COMBO,
+ KlingLipSyncInputObject,
+ "voice_language",
+ enum_type=KlingLipSyncVoiceLanguage,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Kling Lip Sync Audio to Video Node. Syncs mouth movements in a video file to the audio content of an audio file. When using, ensure that the audio contains clearly distinguishable vocals and that the video contains a distinct face. The audio file should not be larger than 5MB. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
+
+ def api_call(
+ self,
+ video: VideoInput,
+ audio: AudioInput,
+ voice_language: str,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ return super().api_call(
+ video=video,
+ audio=audio,
+ voice_language=voice_language,
+ mode="audio2video",
+ unique_id=unique_id,
+ **kwargs,
+ )
+
+
+class KlingLipSyncTextToVideoNode(KlingLipSyncBase):
+ """Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt."""
+
+ @staticmethod
+ def get_voice_config() -> dict[str, tuple[str, str]]:
+ return {
+ # English voices
+ "Melody": ("girlfriend_4_speech02", "en"),
+ "Sunny": ("genshin_vindi2", "en"),
+ "Sage": ("zhinen_xuesheng", "en"),
+ "Ace": ("AOT", "en"),
+ "Blossom": ("ai_shatang", "en"),
+ "Peppy": ("genshin_klee2", "en"),
+ "Dove": ("genshin_kirara", "en"),
+ "Shine": ("ai_kaiya", "en"),
+ "Anchor": ("oversea_male1", "en"),
+ "Lyric": ("ai_chenjiahao_712", "en"),
+ "Tender": ("chat1_female_new-3", "en"),
+ "Siren": ("chat_0407_5-1", "en"),
+ "Zippy": ("cartoon-boy-07", "en"),
+ "Bud": ("uk_boy1", "en"),
+ "Sprite": ("cartoon-girl-01", "en"),
+ "Candy": ("PeppaPig_platform", "en"),
+ "Beacon": ("ai_huangzhong_712", "en"),
+ "Rock": ("ai_huangyaoshi_712", "en"),
+ "Titan": ("ai_laoguowang_712", "en"),
+ "Grace": ("chengshu_jiejie", "en"),
+ "Helen": ("you_pingjing", "en"),
+ "Lore": ("calm_story1", "en"),
+ "Crag": ("uk_man2", "en"),
+ "Prattle": ("laopopo_speech02", "en"),
+ "Hearth": ("heainainai_speech02", "en"),
+ "The Reader": ("reader_en_m-v1", "en"),
+ "Commercial Lady": ("commercial_lady_en_f-v1", "en"),
+ # Chinese voices
+ "阳光少年": ("genshin_vindi2", "zh"),
+ "懂事小弟": ("zhinen_xuesheng", "zh"),
+ "运动少年": ("tiyuxi_xuedi", "zh"),
+ "青春少女": ("ai_shatang", "zh"),
+ "温柔小妹": ("genshin_klee2", "zh"),
+ "元气少女": ("genshin_kirara", "zh"),
+ "阳光男生": ("ai_kaiya", "zh"),
+ "幽默小哥": ("tiexin_nanyou", "zh"),
+ "文艺小哥": ("ai_chenjiahao_712", "zh"),
+ "甜美邻家": ("girlfriend_1_speech02", "zh"),
+ "温柔姐姐": ("chat1_female_new-3", "zh"),
+ "职场女青": ("girlfriend_2_speech02", "zh"),
+ "活泼男童": ("cartoon-boy-07", "zh"),
+ "俏皮女童": ("cartoon-girl-01", "zh"),
+ "稳重老爸": ("ai_huangyaoshi_712", "zh"),
+ "温柔妈妈": ("you_pingjing", "zh"),
+ "严肃上司": ("ai_laoguowang_712", "zh"),
+ "优雅贵妇": ("chengshu_jiejie", "zh"),
+ "慈祥爷爷": ("zhuxi_speech02", "zh"),
+ "唠叨爷爷": ("uk_oldman3", "zh"),
+ "唠叨奶奶": ("laopopo_speech02", "zh"),
+ "和蔼奶奶": ("heainainai_speech02", "zh"),
+ "东北老铁": ("dongbeilaotie_speech02", "zh"),
+ "重庆小伙": ("chongqingxiaohuo_speech02", "zh"),
+ "四川妹子": ("chuanmeizi_speech02", "zh"),
+ "潮汕大叔": ("chaoshandashu_speech02", "zh"),
+ "台湾男生": ("ai_taiwan_man2_speech02", "zh"),
+ "西安掌柜": ("xianzhanggui_speech02", "zh"),
+ "天津姐姐": ("tianjinjiejie_speech02", "zh"),
+ "新闻播报男": ("diyinnansang_DB_CN_M_04-v2", "zh"),
+ "译制片男": ("yizhipiannan-v1", "zh"),
+ "撒娇女友": ("tianmeixuemei-v1", "zh"),
+ "刀片烟嗓": ("daopianyansang-v1", "zh"),
+ "乖巧正太": ("mengwa-v1", "zh"),
+ }
+
+ @classmethod
+ def INPUT_TYPES(s):
+ voice_options = list(s.get_voice_config().keys())
+ return {
+ "required": {
+ "video": (IO.VIDEO, {}),
+ "text": model_field_to_node_input(
+ IO.STRING, KlingLipSyncInputObject, "text", multiline=True
+ ),
+ "voice": (voice_options, {"default": voice_options[0]}),
+ "voice_speed": model_field_to_node_input(
+ IO.FLOAT, KlingLipSyncInputObject, "voice_speed", slider=True
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Kling Lip Sync Text to Video Node. Syncs mouth movements in a video file to a text prompt. The video file should not be larger than 100MB, should have height/width between 720px and 1920px, and should be between 2s and 10s in length."
+
+ def api_call(
+ self,
+ video: VideoInput,
+ text: str,
+ voice: str,
+ voice_speed: float,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ voice_id, voice_language = KlingLipSyncTextToVideoNode.get_voice_config()[voice]
+ return super().api_call(
+ video=video,
+ text=text,
+ voice_language=voice_language,
+ voice_id=voice_id,
+ voice_speed=voice_speed,
+ mode="text2video",
+ unique_id=unique_id,
+ **kwargs,
+ )
+
+
+class KlingImageGenerationBase(KlingNodeBase):
+ """Kling Image Generation Base Node."""
+
+ RETURN_TYPES = ("IMAGE",)
+ CATEGORY = "api node/image/Kling"
+
+ def validate_prompt(self, prompt: str, negative_prompt: Optional[str] = None):
+ if not prompt or len(prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN:
+ raise ValueError(
+ f"Prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters"
+ )
+ if negative_prompt and len(negative_prompt) > MAX_PROMPT_LENGTH_IMAGE_GEN:
+ raise ValueError(
+ f"Negative prompt must be less than {MAX_PROMPT_LENGTH_IMAGE_GEN} characters"
+ )
+
+
+class KlingVirtualTryOnNode(KlingImageGenerationBase):
+ """Kling Virtual Try On Node."""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "human_image": (IO.IMAGE, {}),
+ "cloth_image": (IO.IMAGE, {}),
+ "model_name": model_field_to_node_input(
+ IO.COMBO,
+ KlingVirtualTryOnRequest,
+ "model_name",
+ enum_type=KlingVirtualTryOnModelName,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Kling Virtual Try On Node. Input a human image and a cloth image to try on the cloth on the human. You can merge multiple clothing item pictures into one image with a white background."
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> KlingVirtualTryOnResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_VIRTUAL_TRY_ON}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingVirtualTryOnResponse,
+ ),
+ result_url_extractor=get_images_urls_from_response,
+ estimated_duration=AVERAGE_DURATION_VIRTUAL_TRY_ON,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ human_image: torch.Tensor,
+ cloth_image: torch.Tensor,
+ model_name: KlingVirtualTryOnModelName,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_VIRTUAL_TRY_ON,
+ method=HttpMethod.POST,
+ request_model=KlingVirtualTryOnRequest,
+ response_model=KlingVirtualTryOnResponse,
+ ),
+ request=KlingVirtualTryOnRequest(
+ human_image=tensor_to_base64_string(human_image),
+ cloth_image=tensor_to_base64_string(cloth_image),
+ model_name=model_name,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_image_result_response(final_response)
+
+ images = get_images_from_response(final_response)
+ return (image_result_to_node_output(images),)
+
+
+class KlingImageGenerationNode(KlingImageGenerationBase):
+ """Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingImageGenerationsRequest,
+ "prompt",
+ multiline=True,
+ max_length=MAX_PROMPT_LENGTH_IMAGE_GEN,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ KlingImageGenerationsRequest,
+ "negative_prompt",
+ multiline=True,
+ ),
+ "image_type": model_field_to_node_input(
+ IO.COMBO,
+ KlingImageGenerationsRequest,
+ "image_reference",
+ enum_type=KlingImageGenImageReferenceType,
+ ),
+ "image_fidelity": model_field_to_node_input(
+ IO.FLOAT,
+ KlingImageGenerationsRequest,
+ "image_fidelity",
+ slider=True,
+ step=0.01,
+ ),
+ "human_fidelity": model_field_to_node_input(
+ IO.FLOAT,
+ KlingImageGenerationsRequest,
+ "human_fidelity",
+ slider=True,
+ step=0.01,
+ ),
+ "model_name": model_field_to_node_input(
+ IO.COMBO,
+ KlingImageGenerationsRequest,
+ "model_name",
+ enum_type=KlingImageGenModelName,
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.COMBO,
+ KlingImageGenerationsRequest,
+ "aspect_ratio",
+ enum_type=KlingImageGenAspectRatio,
+ ),
+ "n": model_field_to_node_input(
+ IO.INT,
+ KlingImageGenerationsRequest,
+ "n",
+ ),
+ },
+ "optional": {
+ "image": (IO.IMAGE, {}),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Kling Image Generation Node. Generate an image from a text prompt with an optional reference image."
+
+ def get_response(
+ self,
+ task_id: str,
+ auth_kwargs: Optional[dict[str, str]],
+ node_id: Optional[str] = None,
+ ) -> KlingImageGenerationsResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_IMAGE_GENERATIONS}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=KlingImageGenerationsResponse,
+ ),
+ result_url_extractor=get_images_urls_from_response,
+ estimated_duration=AVERAGE_DURATION_IMAGE_GEN,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ model_name: KlingImageGenModelName,
+ prompt: str,
+ negative_prompt: str,
+ image_type: KlingImageGenImageReferenceType,
+ image_fidelity: float,
+ human_fidelity: float,
+ n: int,
+ aspect_ratio: KlingImageGenAspectRatio,
+ image: Optional[torch.Tensor] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ self.validate_prompt(prompt, negative_prompt)
+
+ if image is not None:
+ image = tensor_to_base64_string(image)
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_IMAGE_GENERATIONS,
+ method=HttpMethod.POST,
+ request_model=KlingImageGenerationsRequest,
+ response_model=KlingImageGenerationsResponse,
+ ),
+ request=KlingImageGenerationsRequest(
+ model_name=model_name,
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ image=image,
+ image_reference=image_type,
+ image_fidelity=image_fidelity,
+ human_fidelity=human_fidelity,
+ n=n,
+ aspect_ratio=aspect_ratio,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.data.task_id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ validate_image_result_response(final_response)
+
+ images = get_images_from_response(final_response)
+ return (image_result_to_node_output(images),)
+
+
+NODE_CLASS_MAPPINGS = {
+ "KlingCameraControls": KlingCameraControls,
+ "KlingTextToVideoNode": KlingTextToVideoNode,
+ "KlingImage2VideoNode": KlingImage2VideoNode,
+ "KlingCameraControlI2VNode": KlingCameraControlI2VNode,
+ "KlingCameraControlT2VNode": KlingCameraControlT2VNode,
+ "KlingStartEndFrameNode": KlingStartEndFrameNode,
+ "KlingVideoExtendNode": KlingVideoExtendNode,
+ "KlingLipSyncAudioToVideoNode": KlingLipSyncAudioToVideoNode,
+ "KlingLipSyncTextToVideoNode": KlingLipSyncTextToVideoNode,
+ "KlingVirtualTryOnNode": KlingVirtualTryOnNode,
+ "KlingImageGenerationNode": KlingImageGenerationNode,
+ "KlingSingleImageVideoEffectNode": KlingSingleImageVideoEffectNode,
+ "KlingDualCharacterVideoEffectNode": KlingDualCharacterVideoEffectNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "KlingCameraControls": "Kling Camera Controls",
+ "KlingTextToVideoNode": "Kling Text to Video",
+ "KlingImage2VideoNode": "Kling Image to Video",
+ "KlingCameraControlI2VNode": "Kling Image to Video (Camera Control)",
+ "KlingCameraControlT2VNode": "Kling Text to Video (Camera Control)",
+ "KlingStartEndFrameNode": "Kling Start-End Frame to Video",
+ "KlingVideoExtendNode": "Kling Video Extend",
+ "KlingLipSyncAudioToVideoNode": "Kling Lip Sync Video with Audio",
+ "KlingLipSyncTextToVideoNode": "Kling Lip Sync Video with Text",
+ "KlingVirtualTryOnNode": "Kling Virtual Try On",
+ "KlingImageGenerationNode": "Kling Image Generation",
+ "KlingSingleImageVideoEffectNode": "Kling Video Effects",
+ "KlingDualCharacterVideoEffectNode": "Kling Dual Character Video Effects",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_luma.py b/ComfyUI/comfy_api_nodes/nodes_luma.py
new file mode 100644
index 0000000000000000000000000000000000000000..525dc38e628b932911ac86177e1970b426a5c8a6
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_luma.py
@@ -0,0 +1,737 @@
+from __future__ import annotations
+from inspect import cleandoc
+from typing import Optional
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+from comfy_api.input_impl.video_types import VideoFromFile
+from comfy_api_nodes.apis.luma_api import (
+ LumaImageModel,
+ LumaVideoModel,
+ LumaVideoOutputResolution,
+ LumaVideoModelOutputDuration,
+ LumaAspectRatio,
+ LumaState,
+ LumaImageGenerationRequest,
+ LumaGenerationRequest,
+ LumaGeneration,
+ LumaCharacterRef,
+ LumaModifyImageRef,
+ LumaImageIdentity,
+ LumaReference,
+ LumaReferenceChain,
+ LumaImageReference,
+ LumaKeyframes,
+ LumaConceptChain,
+ LumaIO,
+ get_luma_concepts,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ upload_images_to_comfyapi,
+ process_image_response,
+ validate_string,
+)
+from server import PromptServer
+
+import requests
+import torch
+from io import BytesIO
+
+LUMA_T2V_AVERAGE_DURATION = 105
+LUMA_I2V_AVERAGE_DURATION = 100
+
+def image_result_url_extractor(response: LumaGeneration):
+ return response.assets.image if hasattr(response, "assets") and hasattr(response.assets, "image") else None
+
+def video_result_url_extractor(response: LumaGeneration):
+ return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None
+
+class LumaReferenceNode(ComfyNodeABC):
+ """
+ Holds an image and weight for use with Luma Generate Image node.
+ """
+
+ RETURN_TYPES = (LumaIO.LUMA_REF,)
+ RETURN_NAMES = ("luma_ref",)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "create_luma_reference"
+ CATEGORY = "api node/image/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (
+ IO.IMAGE,
+ {
+ "tooltip": "Image to use as reference.",
+ },
+ ),
+ "weight": (
+ IO.FLOAT,
+ {
+ "default": 1.0,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Weight of image reference.",
+ },
+ ),
+ },
+ "optional": {"luma_ref": (LumaIO.LUMA_REF,)},
+ }
+
+ def create_luma_reference(
+ self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
+ ):
+ if luma_ref is not None:
+ luma_ref = luma_ref.clone()
+ else:
+ luma_ref = LumaReferenceChain()
+ luma_ref.add(LumaReference(image=image, weight=round(weight, 2)))
+ return (luma_ref,)
+
+
+class LumaConceptsNode(ComfyNodeABC):
+ """
+ Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.
+ """
+
+ RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,)
+ RETURN_NAMES = ("luma_concepts",)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "create_concepts"
+ CATEGORY = "api node/video/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "concept1": (get_luma_concepts(include_none=True),),
+ "concept2": (get_luma_concepts(include_none=True),),
+ "concept3": (get_luma_concepts(include_none=True),),
+ "concept4": (get_luma_concepts(include_none=True),),
+ },
+ "optional": {
+ "luma_concepts": (
+ LumaIO.LUMA_CONCEPTS,
+ {
+ "tooltip": "Optional Camera Concepts to add to the ones chosen here."
+ },
+ ),
+ },
+ }
+
+ def create_concepts(
+ self,
+ concept1: str,
+ concept2: str,
+ concept3: str,
+ concept4: str,
+ luma_concepts: LumaConceptChain = None,
+ ):
+ chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
+ if luma_concepts is not None:
+ chain = luma_concepts.clone_and_merge(chain)
+ return (chain,)
+
+
+class LumaImageGenerationNode(ComfyNodeABC):
+ """
+ Generates images synchronously based on prompt and aspect ratio.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "model": ([model.value for model in LumaImageModel],),
+ "aspect_ratio": (
+ [ratio.value for ratio in LumaAspectRatio],
+ {
+ "default": LumaAspectRatio.ratio_16_9,
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ "style_image_weight": (
+ IO.FLOAT,
+ {
+ "default": 1.0,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Weight of style image. Ignored if no style_image provided.",
+ },
+ ),
+ },
+ "optional": {
+ "image_luma_ref": (
+ LumaIO.LUMA_REF,
+ {
+ "tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered."
+ },
+ ),
+ "style_image": (
+ IO.IMAGE,
+ {"tooltip": "Style reference image; only 1 image will be used."},
+ ),
+ "character_image": (
+ IO.IMAGE,
+ {
+ "tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ seed,
+ style_image_weight: float,
+ image_luma_ref: LumaReferenceChain = None,
+ style_image: torch.Tensor = None,
+ character_image: torch.Tensor = None,
+ unique_id: str = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=True, min_length=3)
+ # handle image_luma_ref
+ api_image_ref = None
+ if image_luma_ref is not None:
+ api_image_ref = self._convert_luma_refs(
+ image_luma_ref, max_refs=4, auth_kwargs=kwargs,
+ )
+ # handle style_luma_ref
+ api_style_ref = None
+ if style_image is not None:
+ api_style_ref = self._convert_style_image(
+ style_image, weight=style_image_weight, auth_kwargs=kwargs,
+ )
+ # handle character_ref images
+ character_ref = None
+ if character_image is not None:
+ download_urls = upload_images_to_comfyapi(
+ character_image, max_images=4, auth_kwargs=kwargs,
+ )
+ character_ref = LumaCharacterRef(
+ identity0=LumaImageIdentity(images=download_urls)
+ )
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/luma/generations/image",
+ method=HttpMethod.POST,
+ request_model=LumaImageGenerationRequest,
+ response_model=LumaGeneration,
+ ),
+ request=LumaImageGenerationRequest(
+ prompt=prompt,
+ model=model,
+ aspect_ratio=aspect_ratio,
+ image_ref=api_image_ref,
+ style_ref=api_style_ref,
+ character_ref=character_ref,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api: LumaGeneration = operation.execute()
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/luma/generations/{response_api.id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=LumaGeneration,
+ ),
+ completed_statuses=[LumaState.completed],
+ failed_statuses=[LumaState.failed],
+ status_extractor=lambda x: x.state,
+ result_url_extractor=image_result_url_extractor,
+ node_id=unique_id,
+ auth_kwargs=kwargs,
+ )
+ response_poll = operation.execute()
+
+ img_response = requests.get(response_poll.assets.image)
+ img = process_image_response(img_response)
+ return (img,)
+
+ def _convert_luma_refs(
+ self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
+ ):
+ luma_urls = []
+ ref_count = 0
+ for ref in luma_ref.refs:
+ download_urls = upload_images_to_comfyapi(
+ ref.image, max_images=1, auth_kwargs=auth_kwargs
+ )
+ luma_urls.append(download_urls[0])
+ ref_count += 1
+ if ref_count >= max_refs:
+ break
+ return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)
+
+ def _convert_style_image(
+ self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
+ ):
+ chain = LumaReferenceChain(
+ first_ref=LumaReference(image=style_image, weight=weight)
+ )
+ return self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
+
+
+class LumaImageModifyNode(ComfyNodeABC):
+ """
+ Modifies images synchronously based on prompt and aspect ratio.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation",
+ },
+ ),
+ "image_weight": (
+ IO.FLOAT,
+ {
+ "default": 0.1,
+ "min": 0.0,
+ "max": 0.98,
+ "step": 0.01,
+ "tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.",
+ },
+ ),
+ "model": ([model.value for model in LumaImageModel],),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {},
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ model: str,
+ image: torch.Tensor,
+ image_weight: float,
+ seed,
+ unique_id: str = None,
+ **kwargs,
+ ):
+ # first, upload image
+ download_urls = upload_images_to_comfyapi(
+ image, max_images=1, auth_kwargs=kwargs,
+ )
+ image_url = download_urls[0]
+ # next, make Luma call with download url provided
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/luma/generations/image",
+ method=HttpMethod.POST,
+ request_model=LumaImageGenerationRequest,
+ response_model=LumaGeneration,
+ ),
+ request=LumaImageGenerationRequest(
+ prompt=prompt,
+ model=model,
+ modify_image_ref=LumaModifyImageRef(
+ url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2)
+ ),
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api: LumaGeneration = operation.execute()
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/luma/generations/{response_api.id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=LumaGeneration,
+ ),
+ completed_statuses=[LumaState.completed],
+ failed_statuses=[LumaState.failed],
+ status_extractor=lambda x: x.state,
+ result_url_extractor=image_result_url_extractor,
+ node_id=unique_id,
+ auth_kwargs=kwargs,
+ )
+ response_poll = operation.execute()
+
+ img_response = requests.get(response_poll.assets.image)
+ img = process_image_response(img_response)
+ return (img,)
+
+
+class LumaTextToVideoGenerationNode(ComfyNodeABC):
+ """
+ Generates videos synchronously based on prompt and output_size.
+ """
+
+ RETURN_TYPES = (IO.VIDEO,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/video/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the video generation",
+ },
+ ),
+ "model": ([model.value for model in LumaVideoModel],),
+ "aspect_ratio": (
+ [ratio.value for ratio in LumaAspectRatio],
+ {
+ "default": LumaAspectRatio.ratio_16_9,
+ },
+ ),
+ "resolution": (
+ [resolution.value for resolution in LumaVideoOutputResolution],
+ {
+ "default": LumaVideoOutputResolution.res_540p,
+ },
+ ),
+ "duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
+ "loop": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "luma_concepts": (
+ LumaIO.LUMA_CONCEPTS,
+ {
+ "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ model: str,
+ aspect_ratio: str,
+ resolution: str,
+ duration: str,
+ loop: bool,
+ seed,
+ luma_concepts: LumaConceptChain = None,
+ unique_id: str = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False, min_length=3)
+ duration = duration if model != LumaVideoModel.ray_1_6 else None
+ resolution = resolution if model != LumaVideoModel.ray_1_6 else None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/luma/generations",
+ method=HttpMethod.POST,
+ request_model=LumaGenerationRequest,
+ response_model=LumaGeneration,
+ ),
+ request=LumaGenerationRequest(
+ prompt=prompt,
+ model=model,
+ resolution=resolution,
+ aspect_ratio=aspect_ratio,
+ duration=duration,
+ loop=loop,
+ concepts=luma_concepts.create_api_model() if luma_concepts else None,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api: LumaGeneration = operation.execute()
+
+ if unique_id:
+ PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/luma/generations/{response_api.id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=LumaGeneration,
+ ),
+ completed_statuses=[LumaState.completed],
+ failed_statuses=[LumaState.failed],
+ status_extractor=lambda x: x.state,
+ result_url_extractor=video_result_url_extractor,
+ node_id=unique_id,
+ estimated_duration=LUMA_T2V_AVERAGE_DURATION,
+ auth_kwargs=kwargs,
+ )
+ response_poll = operation.execute()
+
+ vid_response = requests.get(response_poll.assets.video)
+ return (VideoFromFile(BytesIO(vid_response.content)),)
+
+
+class LumaImageToVideoGenerationNode(ComfyNodeABC):
+ """
+ Generates videos synchronously based on prompt, input images, and output_size.
+ """
+
+ RETURN_TYPES = (IO.VIDEO,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/video/Luma"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the video generation",
+ },
+ ),
+ "model": ([model.value for model in LumaVideoModel],),
+ # "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
+ # "default": LumaAspectRatio.ratio_16_9,
+ # }),
+ "resolution": (
+ [resolution.value for resolution in LumaVideoOutputResolution],
+ {
+ "default": LumaVideoOutputResolution.res_540p,
+ },
+ ),
+ "duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
+ "loop": (
+ IO.BOOLEAN,
+ {
+ "default": False,
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "first_image": (
+ IO.IMAGE,
+ {"tooltip": "First frame of generated video."},
+ ),
+ "last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}),
+ "luma_concepts": (
+ LumaIO.LUMA_CONCEPTS,
+ {
+ "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ model: str,
+ resolution: str,
+ duration: str,
+ loop: bool,
+ seed,
+ first_image: torch.Tensor = None,
+ last_image: torch.Tensor = None,
+ luma_concepts: LumaConceptChain = None,
+ unique_id: str = None,
+ **kwargs,
+ ):
+ if first_image is None and last_image is None:
+ raise Exception(
+ "At least one of first_image and last_image requires an input."
+ )
+ keyframes = self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs)
+ duration = duration if model != LumaVideoModel.ray_1_6 else None
+ resolution = resolution if model != LumaVideoModel.ray_1_6 else None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/luma/generations",
+ method=HttpMethod.POST,
+ request_model=LumaGenerationRequest,
+ response_model=LumaGeneration,
+ ),
+ request=LumaGenerationRequest(
+ prompt=prompt,
+ model=model,
+ aspect_ratio=LumaAspectRatio.ratio_16_9, # ignored, but still needed by the API for some reason
+ resolution=resolution,
+ duration=duration,
+ loop=loop,
+ keyframes=keyframes,
+ concepts=luma_concepts.create_api_model() if luma_concepts else None,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api: LumaGeneration = operation.execute()
+
+ if unique_id:
+ PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/luma/generations/{response_api.id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=LumaGeneration,
+ ),
+ completed_statuses=[LumaState.completed],
+ failed_statuses=[LumaState.failed],
+ status_extractor=lambda x: x.state,
+ result_url_extractor=video_result_url_extractor,
+ node_id=unique_id,
+ estimated_duration=LUMA_I2V_AVERAGE_DURATION,
+ auth_kwargs=kwargs,
+ )
+ response_poll = operation.execute()
+
+ vid_response = requests.get(response_poll.assets.video)
+ return (VideoFromFile(BytesIO(vid_response.content)),)
+
+ def _convert_to_keyframes(
+ self,
+ first_image: torch.Tensor = None,
+ last_image: torch.Tensor = None,
+ auth_kwargs: Optional[dict[str,str]] = None,
+ ):
+ if first_image is None and last_image is None:
+ return None
+ frame0 = None
+ frame1 = None
+ if first_image is not None:
+ download_urls = upload_images_to_comfyapi(
+ first_image, max_images=1, auth_kwargs=auth_kwargs,
+ )
+ frame0 = LumaImageReference(type="image", url=download_urls[0])
+ if last_image is not None:
+ download_urls = upload_images_to_comfyapi(
+ last_image, max_images=1, auth_kwargs=auth_kwargs,
+ )
+ frame1 = LumaImageReference(type="image", url=download_urls[0])
+ return LumaKeyframes(frame0=frame0, frame1=frame1)
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "LumaImageNode": LumaImageGenerationNode,
+ "LumaImageModifyNode": LumaImageModifyNode,
+ "LumaVideoNode": LumaTextToVideoGenerationNode,
+ "LumaImageToVideoNode": LumaImageToVideoGenerationNode,
+ "LumaReferenceNode": LumaReferenceNode,
+ "LumaConceptsNode": LumaConceptsNode,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "LumaImageNode": "Luma Text to Image",
+ "LumaImageModifyNode": "Luma Image to Image",
+ "LumaVideoNode": "Luma Text to Video",
+ "LumaImageToVideoNode": "Luma Image to Video",
+ "LumaReferenceNode": "Luma Reference",
+ "LumaConceptsNode": "Luma Concepts",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_minimax.py b/ComfyUI/comfy_api_nodes/nodes_minimax.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b46636dbbd1d96fc2505e7f73cfbacb43b1f5f5
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_minimax.py
@@ -0,0 +1,332 @@
+from typing import Union
+import logging
+import torch
+
+from comfy.comfy_types.node_typing import IO
+from comfy_api.input_impl.video_types import VideoFromFile
+from comfy_api_nodes.apis import (
+ MinimaxVideoGenerationRequest,
+ MinimaxVideoGenerationResponse,
+ MinimaxFileRetrieveResponse,
+ MinimaxTaskResultResponse,
+ SubjectReferenceItem,
+ Model
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ download_url_to_bytesio,
+ upload_images_to_comfyapi,
+ validate_string,
+)
+from server import PromptServer
+
+
+I2V_AVERAGE_DURATION = 114
+T2V_AVERAGE_DURATION = 234
+
+class MinimaxTextToVideoNode:
+ """
+ Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API.
+ """
+
+ AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt_text": (
+ "STRING",
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt to guide the video generation",
+ },
+ ),
+ "model": (
+ [
+ "T2V-01",
+ "T2V-01-Director",
+ ],
+ {
+ "default": "T2V-01",
+ "tooltip": "Model to use for video generation",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO",)
+ DESCRIPTION = "Generates videos from prompts using MiniMax's API"
+ FUNCTION = "generate_video"
+ CATEGORY = "api node/video/MiniMax"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+ def generate_video(
+ self,
+ prompt_text,
+ seed=0,
+ model="T2V-01",
+ image: torch.Tensor=None, # used for ImageToVideo
+ subject: torch.Tensor=None, # used for SubjectToVideo
+ unique_id: Union[str, None]=None,
+ **kwargs,
+ ):
+ '''
+ Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments.
+ '''
+ if image is None:
+ validate_string(prompt_text, field_name="prompt_text")
+ # upload image, if passed in
+ image_url = None
+ if image is not None:
+ image_url = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)[0]
+
+ # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
+ subject_reference = None
+ if subject is not None:
+ subject_url = upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs)[0]
+ subject_reference = [SubjectReferenceItem(image=subject_url)]
+
+
+ video_generate_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/minimax/video_generation",
+ method=HttpMethod.POST,
+ request_model=MinimaxVideoGenerationRequest,
+ response_model=MinimaxVideoGenerationResponse,
+ ),
+ request=MinimaxVideoGenerationRequest(
+ model=Model(model),
+ prompt=prompt_text,
+ callback_url=None,
+ first_frame_image=image_url,
+ subject_reference=subject_reference,
+ prompt_optimizer=None,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response = video_generate_operation.execute()
+
+ task_id = response.task_id
+ if not task_id:
+ raise Exception(f"MiniMax generation failed: {response.base_resp}")
+
+ video_generate_operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path="/proxy/minimax/query/video_generation",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=MinimaxTaskResultResponse,
+ query_params={"task_id": task_id},
+ ),
+ completed_statuses=["Success"],
+ failed_statuses=["Fail"],
+ status_extractor=lambda x: x.status.value,
+ estimated_duration=self.AVERAGE_DURATION,
+ node_id=unique_id,
+ auth_kwargs=kwargs,
+ )
+ task_result = video_generate_operation.execute()
+
+ file_id = task_result.file_id
+ if file_id is None:
+ raise Exception("Request was not successful. Missing file ID.")
+ file_retrieve_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/minimax/files/retrieve",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=MinimaxFileRetrieveResponse,
+ query_params={"file_id": int(file_id)},
+ ),
+ request=EmptyRequest(),
+ auth_kwargs=kwargs,
+ )
+ file_result = file_retrieve_operation.execute()
+
+ file_url = file_result.file.download_url
+ if file_url is None:
+ raise Exception(
+ f"No video was found in the response. Full response: {file_result.model_dump()}"
+ )
+ logging.info(f"Generated video URL: {file_url}")
+ if unique_id:
+ if hasattr(file_result.file, "backup_download_url"):
+ message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
+ else:
+ message = f"Result URL: {file_url}"
+ PromptServer.instance.send_progress_text(message, unique_id)
+
+ video_io = download_url_to_bytesio(file_url)
+ if video_io is None:
+ error_msg = f"Failed to download video from {file_url}"
+ logging.error(error_msg)
+ raise Exception(error_msg)
+ return (VideoFromFile(video_io),)
+
+
+class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
+ """
+ Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
+ """
+
+ AVERAGE_DURATION = I2V_AVERAGE_DURATION
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (
+ IO.IMAGE,
+ {
+ "tooltip": "Image to use as first frame of video generation"
+ },
+ ),
+ "prompt_text": (
+ "STRING",
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt to guide the video generation",
+ },
+ ),
+ "model": (
+ [
+ "I2V-01-Director",
+ "I2V-01",
+ "I2V-01-live",
+ ],
+ {
+ "default": "I2V-01",
+ "tooltip": "Model to use for video generation",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO",)
+ DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API"
+ FUNCTION = "generate_video"
+ CATEGORY = "api node/video/MiniMax"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+
+class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
+ """
+ Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
+ """
+
+ AVERAGE_DURATION = T2V_AVERAGE_DURATION
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "subject": (
+ IO.IMAGE,
+ {
+ "tooltip": "Image of subject to reference video generation"
+ },
+ ),
+ "prompt_text": (
+ "STRING",
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt to guide the video generation",
+ },
+ ),
+ "model": (
+ [
+ "S2V-01",
+ ],
+ {
+ "default": "S2V-01",
+ "tooltip": "Model to use for video generation",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("VIDEO",)
+ DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API"
+ FUNCTION = "generate_video"
+ CATEGORY = "api node/video/MiniMax"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "MinimaxTextToVideoNode": MinimaxTextToVideoNode,
+ "MinimaxImageToVideoNode": MinimaxImageToVideoNode,
+ # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "MinimaxTextToVideoNode": "MiniMax Text to Video",
+ "MinimaxImageToVideoNode": "MiniMax Image to Video",
+ "MinimaxSubjectToVideoNode": "MiniMax Subject to Video",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_moonvalley.py b/ComfyUI/comfy_api_nodes/nodes_moonvalley.py
new file mode 100644
index 0000000000000000000000000000000000000000..789fcef0286475447f0fe8a7ff1db2e2a9dc9dde
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_moonvalley.py
@@ -0,0 +1,743 @@
+import logging
+from typing import Any, Callable, Optional, TypeVar
+import random
+import torch
+from comfy_api_nodes.util.validation_utils import (
+ get_image_dimensions,
+ validate_image_dimensions,
+)
+
+
+from comfy_api_nodes.apis import (
+ MoonvalleyTextToVideoRequest,
+ MoonvalleyTextToVideoInferenceParams,
+ MoonvalleyVideoToVideoInferenceParams,
+ MoonvalleyVideoToVideoRequest,
+ MoonvalleyPromptResponse,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ download_url_to_video_output,
+ upload_images_to_comfyapi,
+ upload_video_to_comfyapi,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+from comfy_api.input.video_types import VideoInput
+from comfy.comfy_types.node_typing import IO
+from comfy_api.input_impl import VideoFromFile
+import av
+import io
+
+API_UPLOADS_ENDPOINT = "/proxy/moonvalley/uploads"
+API_PROMPTS_ENDPOINT = "/proxy/moonvalley/prompts"
+API_VIDEO2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/video-to-video"
+API_TXT2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/text-to-video"
+API_IMG2VIDEO_ENDPOINT = "/proxy/moonvalley/prompts/image-to-video"
+
+MIN_WIDTH = 300
+MIN_HEIGHT = 300
+
+MAX_WIDTH = 10000
+MAX_HEIGHT = 10000
+
+MIN_VID_WIDTH = 300
+MIN_VID_HEIGHT = 300
+
+MAX_VID_WIDTH = 10000
+MAX_VID_HEIGHT = 10000
+
+MAX_VIDEO_SIZE = 1024 * 1024 * 1024 # 1 GB max for in-memory video processing
+
+MOONVALLEY_MAREY_MAX_PROMPT_LENGTH = 5000
+R = TypeVar("R")
+
+
+class MoonvalleyApiError(Exception):
+ """Base exception for Moonvalley API errors."""
+
+ pass
+
+
+def is_valid_task_creation_response(response: MoonvalleyPromptResponse) -> bool:
+ """Verifies that the initial response contains a task ID."""
+ return bool(response.id)
+
+
+def validate_task_creation_response(response) -> None:
+ if not is_valid_task_creation_response(response):
+ error_msg = f"Moonvalley Marey API: Initial request failed. Code: {response.code}, Message: {response.message}, Data: {response}"
+ logging.error(error_msg)
+ raise MoonvalleyApiError(error_msg)
+
+
+def get_video_from_response(response):
+ video = response.output_url
+ logging.info(
+ "Moonvalley Marey API: Task %s succeeded. Video URL: %s", response.id, video
+ )
+ return video
+
+
+def get_video_url_from_response(response) -> Optional[str]:
+ """Returns the first video url from the Moonvalley video generation task result.
+ Will not raise an error if the response is not valid.
+ """
+ if response:
+ return str(get_video_from_response(response))
+ else:
+ return None
+
+
+def poll_until_finished(
+ auth_kwargs: dict[str, str],
+ api_endpoint: ApiEndpoint[Any, R],
+ result_url_extractor: Optional[Callable[[R], str]] = None,
+ node_id: Optional[str] = None,
+) -> R:
+ """Polls the Moonvalley API endpoint until the task reaches a terminal state, then returns the response."""
+ return PollingOperation(
+ poll_endpoint=api_endpoint,
+ completed_statuses=[
+ "completed",
+ ],
+ max_poll_attempts=240, # 64 minutes with 16s interval
+ poll_interval=16.0,
+ failed_statuses=["error"],
+ status_extractor=lambda response: (
+ response.status if response and response.status else None
+ ),
+ auth_kwargs=auth_kwargs,
+ result_url_extractor=result_url_extractor,
+ node_id=node_id,
+ ).execute()
+
+
+def validate_prompts(
+ prompt: str, negative_prompt: str, max_length=MOONVALLEY_MAREY_MAX_PROMPT_LENGTH
+):
+ """Verifies that the prompt isn't empty and that neither prompt is too long."""
+ if not prompt:
+ raise ValueError("Positive prompt is empty")
+ if len(prompt) > max_length:
+ raise ValueError(f"Positive prompt is too long: {len(prompt)} characters")
+ if negative_prompt and len(negative_prompt) > max_length:
+ raise ValueError(
+ f"Negative prompt is too long: {len(negative_prompt)} characters"
+ )
+ return True
+
+
+def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None):
+ # inference validation
+ # T = num_frames
+ # in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition...
+ # with image conditioning: H*W must be divisible by 8192
+ # without image conditioning: T divisible by 32
+ if num_frames_in and not num_frames_in % 16 == 0:
+ return False, ("The input video total frame count must be divisible by 16!")
+
+ if height % 8 != 0 or width % 8 != 0:
+ return False, (
+ f"Height ({height}) and width ({width}) must be " "divisible by 8"
+ )
+
+ if with_frame_conditioning:
+ if (height * width) % 8192 != 0:
+ return False, (
+ f"Height * width ({height * width}) must be "
+ "divisible by 8192 for frame conditioning"
+ )
+ else:
+ if num_frames_in and not num_frames_in % 32 == 0:
+ return False, ("The input video total frame count must be divisible by 32!")
+
+
+def validate_input_image(
+ image: torch.Tensor, with_frame_conditioning: bool = False
+) -> None:
+ """
+ Validates the input image adheres to the expectations of the API:
+ - The image resolution should not be less than 300*300px
+ - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
+
+ """
+ height, width = get_image_dimensions(image)
+ validate_input_media(width, height, with_frame_conditioning)
+ validate_image_dimensions(
+ image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH
+ )
+
+
+def validate_video_to_video_input(video: VideoInput) -> VideoInput:
+ """
+ Validates and processes video input for Moonvalley Video-to-Video generation.
+
+ Args:
+ video: Input video to validate
+
+ Returns:
+ Validated and potentially trimmed video
+
+ Raises:
+ ValueError: If video doesn't meet requirements
+ MoonvalleyApiError: If video duration is too short
+ """
+ width, height = _get_video_dimensions(video)
+ _validate_video_dimensions(width, height)
+ _validate_container_format(video)
+
+ return _validate_and_trim_duration(video)
+
+
+def _get_video_dimensions(video: VideoInput) -> tuple[int, int]:
+ """Extracts video dimensions with error handling."""
+ try:
+ return video.get_dimensions()
+ except Exception as e:
+ logging.error("Error getting dimensions of video: %s", e)
+ raise ValueError(f"Cannot get video dimensions: {e}") from e
+
+
+def _validate_video_dimensions(width: int, height: int) -> None:
+ """Validates video dimensions meet Moonvalley V2V requirements."""
+ supported_resolutions = {
+ (1920, 1080), (1080, 1920), (1152, 1152),
+ (1536, 1152), (1152, 1536)
+ }
+
+ if (width, height) not in supported_resolutions:
+ supported_list = ', '.join([f'{w}x{h}' for w, h in sorted(supported_resolutions)])
+ raise ValueError(f"Resolution {width}x{height} not supported. Supported: {supported_list}")
+
+
+def _validate_container_format(video: VideoInput) -> None:
+ """Validates video container format is MP4."""
+ container_format = video.get_container_format()
+ if container_format not in ['mp4', 'mov,mp4,m4a,3gp,3g2,mj2']:
+ raise ValueError(f"Only MP4 container format supported. Got: {container_format}")
+
+
+def _validate_and_trim_duration(video: VideoInput) -> VideoInput:
+ """Validates video duration and trims to 5 seconds if needed."""
+ duration = video.get_duration()
+ _validate_minimum_duration(duration)
+ return _trim_if_too_long(video, duration)
+
+
+def _validate_minimum_duration(duration: float) -> None:
+ """Ensures video is at least 5 seconds long."""
+ if duration < 5:
+ raise MoonvalleyApiError("Input video must be at least 5 seconds long.")
+
+
+def _trim_if_too_long(video: VideoInput, duration: float) -> VideoInput:
+ """Trims video to 5 seconds if longer."""
+ if duration > 5:
+ return trim_video(video, 5)
+ return video
+
+
+
+def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
+ """
+ Returns a new VideoInput object trimmed from the beginning to the specified duration,
+ using av to avoid loading entire video into memory.
+
+ Args:
+ video: Input video to trim
+ duration_sec: Duration in seconds to keep from the beginning
+
+ Returns:
+ VideoFromFile object that owns the output buffer
+ """
+ output_buffer = io.BytesIO()
+
+ input_container = None
+ output_container = None
+
+ try:
+ # Get the stream source - this avoids loading entire video into memory
+ # when the source is already a file path
+ input_source = video.get_stream_source()
+
+ # Open containers
+ input_container = av.open(input_source, mode="r")
+ output_container = av.open(output_buffer, mode="w", format="mp4")
+
+ # Set up output streams for re-encoding
+ video_stream = None
+ audio_stream = None
+
+ for stream in input_container.streams:
+ logging.info(f"Found stream: type={stream.type}, class={type(stream)}")
+ if isinstance(stream, av.VideoStream):
+ # Create output video stream with same parameters
+ video_stream = output_container.add_stream(
+ "h264", rate=stream.average_rate
+ )
+ video_stream.width = stream.width
+ video_stream.height = stream.height
+ video_stream.pix_fmt = "yuv420p"
+ logging.info(
+ f"Added video stream: {stream.width}x{stream.height} @ {stream.average_rate}fps"
+ )
+ elif isinstance(stream, av.AudioStream):
+ # Create output audio stream with same parameters
+ audio_stream = output_container.add_stream(
+ "aac", rate=stream.sample_rate
+ )
+ audio_stream.sample_rate = stream.sample_rate
+ audio_stream.layout = stream.layout
+ logging.info(
+ f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels"
+ )
+
+ # Calculate target frame count that's divisible by 16
+ fps = input_container.streams.video[0].average_rate
+ estimated_frames = int(duration_sec * fps)
+ target_frames = (estimated_frames // 16) * 16 # Round down to nearest multiple of 16
+
+ if target_frames == 0:
+ raise ValueError("Video too short: need at least 16 frames for Moonvalley")
+
+ frame_count = 0
+ audio_frame_count = 0
+
+ # Decode and re-encode video frames
+ if video_stream:
+ for frame in input_container.decode(video=0):
+ if frame_count >= target_frames:
+ break
+
+ # Re-encode frame
+ for packet in video_stream.encode(frame):
+ output_container.mux(packet)
+ frame_count += 1
+
+ # Flush encoder
+ for packet in video_stream.encode():
+ output_container.mux(packet)
+
+ logging.info(
+ f"Encoded {frame_count} video frames (target: {target_frames})"
+ )
+
+ # Decode and re-encode audio frames
+ if audio_stream:
+ input_container.seek(0) # Reset to beginning for audio
+ for frame in input_container.decode(audio=0):
+ if frame.time >= duration_sec:
+ break
+
+ # Re-encode frame
+ for packet in audio_stream.encode(frame):
+ output_container.mux(packet)
+ audio_frame_count += 1
+
+ # Flush encoder
+ for packet in audio_stream.encode():
+ output_container.mux(packet)
+
+ logging.info(f"Encoded {audio_frame_count} audio frames")
+
+ # Close containers
+ output_container.close()
+ input_container.close()
+
+ # Return as VideoFromFile using the buffer
+ output_buffer.seek(0)
+ return VideoFromFile(output_buffer)
+
+ except Exception as e:
+ # Clean up on error
+ if input_container is not None:
+ input_container.close()
+ if output_container is not None:
+ output_container.close()
+ raise RuntimeError(f"Failed to trim video: {str(e)}") from e
+
+
+# --- BaseMoonvalleyVideoNode ---
+class BaseMoonvalleyVideoNode:
+ def parseWidthHeightFromRes(self, resolution: str):
+ # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict
+ res_map = {
+ "16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
+ "9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
+ "1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
+ "4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
+ "3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
+ "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
+ }
+ if resolution in res_map:
+ return res_map[resolution]
+ else:
+ # Default to 1920x1080 if unknown
+ return {"width": 1920, "height": 1080}
+
+ def parseControlParameter(self, value):
+ control_map = {
+ "Motion Transfer": "motion_control",
+ "Canny": "canny_control",
+ "Pose Transfer": "pose_control",
+ "Depth": "depth_control",
+ }
+ if value in control_map:
+ return control_map[value]
+ else:
+ return control_map["Motion Transfer"]
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> MoonvalleyPromptResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{API_PROMPTS_ENDPOINT}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=MoonvalleyPromptResponse,
+ ),
+ result_url_extractor=get_video_url_from_response,
+ node_id=node_id,
+ )
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING,
+ MoonvalleyTextToVideoRequest,
+ "prompt_text",
+ multiline=True,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ MoonvalleyTextToVideoInferenceParams,
+ "negative_prompt",
+ multiline=True,
+ default="low-poly, flat shader, bad rigging, stiff animation, uncanny eyes, low-quality textures, looping glitch, cheap effect, overbloom, bloom spam, default lighting, game asset, stiff face, ugly specular, AI artifacts",
+ ),
+ "resolution": (
+ IO.COMBO,
+ {
+ "options": [
+ "16:9 (1920 x 1080)",
+ "9:16 (1080 x 1920)",
+ "1:1 (1152 x 1152)",
+ "4:3 (1440 x 1080)",
+ "3:4 (1080 x 1440)",
+ "21:9 (2560 x 1080)",
+ ],
+ "default": "16:9 (1920 x 1080)",
+ "tooltip": "Resolution of the output video",
+ },
+ ),
+ # "length": (IO.COMBO,{"options":['5s','10s'], "default": '5s'}),
+ "prompt_adherence": model_field_to_node_input(
+ IO.FLOAT,
+ MoonvalleyTextToVideoInferenceParams,
+ "guidance_scale",
+ default=7.0,
+ step=1,
+ min=1,
+ max=20,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ MoonvalleyTextToVideoInferenceParams,
+ "seed",
+ default=random.randint(0, 2**32 - 1),
+ min=0,
+ max=4294967295,
+ step=1,
+ display="number",
+ tooltip="Random seed value",
+ control_after_generate=True,
+ ),
+ "steps": model_field_to_node_input(
+ IO.INT,
+ MoonvalleyTextToVideoInferenceParams,
+ "steps",
+ default=100,
+ min=1,
+ max=100,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ "optional": {
+ "image": model_field_to_node_input(
+ IO.IMAGE,
+ MoonvalleyTextToVideoRequest,
+ "image_url",
+ tooltip="The reference image used to generate the video",
+ ),
+ },
+ }
+
+ RETURN_TYPES = ("STRING",)
+ FUNCTION = "generate"
+ CATEGORY = "api node/video/Moonvalley Marey"
+ API_NODE = True
+
+ def generate(self, **kwargs):
+ return None
+
+
+# --- MoonvalleyImg2VideoNode ---
+class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return super().INPUT_TYPES()
+
+ RETURN_TYPES = ("VIDEO",)
+ RETURN_NAMES = ("video",)
+ DESCRIPTION = "Moonvalley Marey Image to Video Node"
+
+ def generate(
+ self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+ ):
+ image = kwargs.get("image", None)
+ if image is None:
+ raise MoonvalleyApiError("image is required")
+
+ validate_input_image(image, True)
+ validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
+ width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+
+ inference_params = MoonvalleyTextToVideoInferenceParams(
+ negative_prompt=negative_prompt,
+ steps=kwargs.get("steps"),
+ seed=kwargs.get("seed"),
+ guidance_scale=kwargs.get("prompt_adherence"),
+ num_frames=128,
+ width=width_height.get("width"),
+ height=width_height.get("height"),
+ use_negative_prompts=True,
+ )
+ """Upload image to comfy backend to have a URL available for further processing"""
+ # Get MIME type from tensor - assuming PNG format for image tensors
+ mime_type = "image/png"
+
+ image_url = upload_images_to_comfyapi(
+ image, max_images=1, auth_kwargs=kwargs, mime_type=mime_type
+ )[0]
+
+ request = MoonvalleyTextToVideoRequest(
+ image_url=image_url, prompt_text=prompt, inference_params=inference_params
+ )
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=API_IMG2VIDEO_ENDPOINT,
+ method=HttpMethod.POST,
+ request_model=MoonvalleyTextToVideoRequest,
+ response_model=MoonvalleyPromptResponse,
+ ),
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ video = download_url_to_video_output(final_response.output_url)
+ return (video,)
+
+
+# --- MoonvalleyVid2VidNode ---
+class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
+ def __init__(self):
+ super().__init__()
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, MoonvalleyVideoToVideoRequest, "prompt_text",
+ multiline=True
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ MoonvalleyVideoToVideoInferenceParams,
+ "negative_prompt",
+ multiline=True,
+ default="low-poly, flat shader, bad rigging, stiff animation, uncanny eyes, low-quality textures, looping glitch, cheap effect, overbloom, bloom spam, default lighting, game asset, stiff face, ugly specular, AI artifacts"
+ ),
+ "seed": model_field_to_node_input(IO.INT,MoonvalleyVideoToVideoInferenceParams, "seed", default=random.randint(0, 2**32 - 1), min=0, max=4294967295, step=1, display="number", tooltip="Random seed value", control_after_generate=True),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ "optional": {
+ "video": (IO.VIDEO, {"default": "", "multiline": False, "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported."}),
+ "control_type": (
+ ["Motion Transfer", "Pose Transfer"],
+ {"default": "Motion Transfer"},
+ ),
+ "motion_intensity": (
+ "INT",
+ {
+ "default": 100,
+ "step": 1,
+ "min": 0,
+ "max": 100,
+ "tooltip": "Only used if control_type is 'Motion Transfer'",
+ },
+ )
+ }
+ }
+
+ RETURN_TYPES = ("VIDEO",)
+ RETURN_NAMES = ("video",)
+
+ def generate(
+ self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+ ):
+ video = kwargs.get("video")
+
+ if not video:
+ raise MoonvalleyApiError("video is required")
+
+ video_url = ""
+ if video:
+ validated_video = validate_video_to_video_input(video)
+ video_url = upload_video_to_comfyapi(validated_video, auth_kwargs=kwargs)
+
+ control_type = kwargs.get("control_type")
+ motion_intensity = kwargs.get("motion_intensity")
+
+ """Validate prompts and inference input"""
+ validate_prompts(prompt, negative_prompt)
+
+ # Only include motion_intensity for Motion Transfer
+ control_params = {}
+ if control_type == "Motion Transfer" and motion_intensity is not None:
+ control_params['motion_intensity'] = motion_intensity
+
+ inference_params=MoonvalleyVideoToVideoInferenceParams(
+ negative_prompt=negative_prompt,
+ seed=kwargs.get("seed"),
+ control_params=control_params
+ )
+
+ control = self.parseControlParameter(control_type)
+
+ request = MoonvalleyVideoToVideoRequest(
+ control_type=control,
+ video_url=video_url,
+ prompt_text=prompt,
+ inference_params=inference_params,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=API_VIDEO2VIDEO_ENDPOINT,
+ method=HttpMethod.POST,
+ request_model=MoonvalleyVideoToVideoRequest,
+ response_model=MoonvalleyPromptResponse,
+ ),
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+
+ video = download_url_to_video_output(final_response.output_url)
+
+ return (video,)
+
+
+# --- MoonvalleyTxt2VideoNode ---
+class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
+ def __init__(self):
+ super().__init__()
+
+ RETURN_TYPES = ("VIDEO",)
+ RETURN_NAMES = ("video",)
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ input_types = super().INPUT_TYPES()
+ # Remove image-specific parameters
+ for param in ["image"]:
+ if param in input_types["optional"]:
+ del input_types["optional"][param]
+ return input_types
+
+ def generate(
+ self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
+ ):
+ validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
+ width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+
+ inference_params=MoonvalleyTextToVideoInferenceParams(
+ negative_prompt=negative_prompt,
+ steps=kwargs.get("steps"),
+ seed=kwargs.get("seed"),
+ guidance_scale=kwargs.get("prompt_adherence"),
+ num_frames=128,
+ width=width_height.get("width"),
+ height=width_height.get("height"),
+ )
+ request = MoonvalleyTextToVideoRequest(
+ prompt_text=prompt, inference_params=inference_params
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=API_TXT2VIDEO_ENDPOINT,
+ method=HttpMethod.POST,
+ request_model=MoonvalleyTextToVideoRequest,
+ response_model=MoonvalleyPromptResponse,
+ ),
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ task_creation_response = initial_operation.execute()
+ validate_task_creation_response(task_creation_response)
+ task_id = task_creation_response.id
+
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+
+ video = download_url_to_video_output(final_response.output_url)
+ return (video,)
+
+
+NODE_CLASS_MAPPINGS = {
+ "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode,
+ "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode,
+ "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video",
+ "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video",
+ "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_openai.py b/ComfyUI/comfy_api_nodes/nodes_openai.py
new file mode 100644
index 0000000000000000000000000000000000000000..be1d2de4aeb8ae64ba1bb29cbb492efb1b53dddf
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_openai.py
@@ -0,0 +1,1008 @@
+import io
+from typing import TypedDict, Optional
+import json
+import os
+import time
+import re
+import uuid
+from enum import Enum
+from inspect import cleandoc
+import numpy as np
+import torch
+from PIL import Image
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeDict
+from server import PromptServer
+import folder_paths
+
+
+from comfy_api_nodes.apis import (
+ OpenAIImageGenerationRequest,
+ OpenAIImageEditRequest,
+ OpenAIImageGenerationResponse,
+ OpenAICreateResponse,
+ OpenAIResponse,
+ CreateModelResponseProperties,
+ Item,
+ Includable,
+ OutputContent,
+ InputImageContent,
+ Detail,
+ InputTextContent,
+ InputMessage,
+ InputMessageContentList,
+ InputContent,
+ InputFileContent,
+)
+
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+
+from comfy_api_nodes.apinode_utils import (
+ downscale_image_tensor,
+ validate_and_cast_response,
+ validate_string,
+ tensor_to_base64_string,
+ text_filepath_to_data_uri,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+
+RESPONSES_ENDPOINT = "/proxy/openai/v1/responses"
+STARTING_POINT_ID_PATTERN = r""
+
+
+class HistoryEntry(TypedDict):
+ """Type definition for a single history entry in the chat."""
+
+ prompt: str
+ response: str
+ response_id: str
+ timestamp: float
+
+
+class ChatHistory(TypedDict):
+ """Type definition for the chat history dictionary."""
+
+ __annotations__: dict[str, list[HistoryEntry]]
+
+
+class SupportedOpenAIModel(str, Enum):
+ o4_mini = "o4-mini"
+ o1 = "o1"
+ o3 = "o3"
+ o1_pro = "o1-pro"
+ gpt_4o = "gpt-4o"
+ gpt_4_1 = "gpt-4.1"
+ gpt_4_1_mini = "gpt-4.1-mini"
+ gpt_4_1_nano = "gpt-4.1-nano"
+
+
+class OpenAIDalle2(ComfyNodeABC):
+ """
+ Generates images synchronously via OpenAI's DALL·E 2 endpoint.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt for DALL·E",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2**31 - 1,
+ "step": 1,
+ "display": "number",
+ "control_after_generate": True,
+ "tooltip": "not implemented yet in backend",
+ },
+ ),
+ "size": (
+ IO.COMBO,
+ {
+ "options": ["256x256", "512x512", "1024x1024"],
+ "default": "1024x1024",
+ "tooltip": "Image size",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 8,
+ "step": 1,
+ "display": "number",
+ "tooltip": "How many images to generate",
+ },
+ ),
+ "image": (
+ IO.IMAGE,
+ {
+ "default": None,
+ "tooltip": "Optional reference image for image editing.",
+ },
+ ),
+ "mask": (
+ IO.MASK,
+ {
+ "default": None,
+ "tooltip": "Optional mask for inpainting (white areas will be replaced)",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/OpenAI"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ seed=0,
+ image=None,
+ mask=None,
+ n=1,
+ size="1024x1024",
+ unique_id=None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ model = "dall-e-2"
+ path = "/proxy/openai/images/generations"
+ content_type = "application/json"
+ request_class = OpenAIImageGenerationRequest
+ img_binary = None
+
+ if image is not None and mask is not None:
+ path = "/proxy/openai/images/edits"
+ content_type = "multipart/form-data"
+ request_class = OpenAIImageEditRequest
+
+ input_tensor = image.squeeze().cpu()
+ height, width, channels = input_tensor.shape
+ rgba_tensor = torch.ones(height, width, 4, device="cpu")
+ rgba_tensor[:, :, :channels] = input_tensor
+
+ if mask.shape[1:] != image.shape[1:-1]:
+ raise Exception("Mask and Image must be the same size")
+ rgba_tensor[:, :, 3] = 1 - mask.squeeze().cpu()
+
+ rgba_tensor = downscale_image_tensor(rgba_tensor.unsqueeze(0)).squeeze()
+
+ image_np = (rgba_tensor.numpy() * 255).astype(np.uint8)
+ img = Image.fromarray(image_np)
+ img_byte_arr = io.BytesIO()
+ img.save(img_byte_arr, format="PNG")
+ img_byte_arr.seek(0)
+ img_binary = img_byte_arr # .getvalue()
+ img_binary.name = "image.png"
+ elif image is not None or mask is not None:
+ raise Exception("Dall-E 2 image editing requires an image AND a mask")
+
+ # Build the operation
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=request_class,
+ response_model=OpenAIImageGenerationResponse,
+ ),
+ request=request_class(
+ model=model,
+ prompt=prompt,
+ n=n,
+ size=size,
+ seed=seed,
+ ),
+ files=(
+ {
+ "image": img_binary,
+ }
+ if img_binary
+ else None
+ ),
+ content_type=content_type,
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ img_tensor = validate_and_cast_response(response, node_id=unique_id)
+ return (img_tensor,)
+
+
+class OpenAIDalle3(ComfyNodeABC):
+ """
+ Generates images synchronously via OpenAI's DALL·E 3 endpoint.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt for DALL·E",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2**31 - 1,
+ "step": 1,
+ "display": "number",
+ "control_after_generate": True,
+ "tooltip": "not implemented yet in backend",
+ },
+ ),
+ "quality": (
+ IO.COMBO,
+ {
+ "options": ["standard", "hd"],
+ "default": "standard",
+ "tooltip": "Image quality",
+ },
+ ),
+ "style": (
+ IO.COMBO,
+ {
+ "options": ["natural", "vivid"],
+ "default": "natural",
+ "tooltip": "Vivid causes the model to lean towards generating hyper-real and dramatic images. Natural causes the model to produce more natural, less hyper-real looking images.",
+ },
+ ),
+ "size": (
+ IO.COMBO,
+ {
+ "options": ["1024x1024", "1024x1792", "1792x1024"],
+ "default": "1024x1024",
+ "tooltip": "Image size",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/OpenAI"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ seed=0,
+ style="natural",
+ quality="standard",
+ size="1024x1024",
+ unique_id=None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ model = "dall-e-3"
+
+ # build the operation
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/openai/images/generations",
+ method=HttpMethod.POST,
+ request_model=OpenAIImageGenerationRequest,
+ response_model=OpenAIImageGenerationResponse,
+ ),
+ request=OpenAIImageGenerationRequest(
+ model=model,
+ prompt=prompt,
+ quality=quality,
+ size=size,
+ style=style,
+ seed=seed,
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ img_tensor = validate_and_cast_response(response, node_id=unique_id)
+ return (img_tensor,)
+
+
+class OpenAIGPTImage1(ComfyNodeABC):
+ """
+ Generates images synchronously via OpenAI's GPT Image 1 endpoint.
+ """
+
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text prompt for GPT Image 1",
+ },
+ ),
+ },
+ "optional": {
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2**31 - 1,
+ "step": 1,
+ "display": "number",
+ "control_after_generate": True,
+ "tooltip": "not implemented yet in backend",
+ },
+ ),
+ "quality": (
+ IO.COMBO,
+ {
+ "options": ["low", "medium", "high"],
+ "default": "low",
+ "tooltip": "Image quality, affects cost and generation time.",
+ },
+ ),
+ "background": (
+ IO.COMBO,
+ {
+ "options": ["opaque", "transparent"],
+ "default": "opaque",
+ "tooltip": "Return image with or without background",
+ },
+ ),
+ "size": (
+ IO.COMBO,
+ {
+ "options": ["auto", "1024x1024", "1024x1536", "1536x1024"],
+ "default": "auto",
+ "tooltip": "Image size",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 8,
+ "step": 1,
+ "display": "number",
+ "tooltip": "How many images to generate",
+ },
+ ),
+ "image": (
+ IO.IMAGE,
+ {
+ "default": None,
+ "tooltip": "Optional reference image for image editing.",
+ },
+ ),
+ "mask": (
+ IO.MASK,
+ {
+ "default": None,
+ "tooltip": "Optional mask for inpainting (white areas will be replaced)",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.IMAGE,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/OpenAI"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ API_NODE = True
+
+ def api_call(
+ self,
+ prompt,
+ seed=0,
+ quality="low",
+ background="opaque",
+ image=None,
+ mask=None,
+ n=1,
+ size="1024x1024",
+ unique_id=None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ model = "gpt-image-1"
+ path = "/proxy/openai/images/generations"
+ content_type = "application/json"
+ request_class = OpenAIImageGenerationRequest
+ img_binaries = []
+ mask_binary = None
+ files = []
+
+ if image is not None:
+ path = "/proxy/openai/images/edits"
+ request_class = OpenAIImageEditRequest
+ content_type = "multipart/form-data"
+
+ batch_size = image.shape[0]
+
+ for i in range(batch_size):
+ single_image = image[i : i + 1]
+ scaled_image = downscale_image_tensor(single_image).squeeze()
+
+ image_np = (scaled_image.numpy() * 255).astype(np.uint8)
+ img = Image.fromarray(image_np)
+ img_byte_arr = io.BytesIO()
+ img.save(img_byte_arr, format="PNG")
+ img_byte_arr.seek(0)
+ img_binary = img_byte_arr
+ img_binary.name = f"image_{i}.png"
+
+ img_binaries.append(img_binary)
+ if batch_size == 1:
+ files.append(("image", img_binary))
+ else:
+ files.append(("image[]", img_binary))
+
+ if mask is not None:
+ if image is None:
+ raise Exception("Cannot use a mask without an input image")
+ if image.shape[0] != 1:
+ raise Exception("Cannot use a mask with multiple image")
+ if mask.shape[1:] != image.shape[1:-1]:
+ raise Exception("Mask and Image must be the same size")
+ batch, height, width = mask.shape
+ rgba_mask = torch.zeros(height, width, 4, device="cpu")
+ rgba_mask[:, :, 3] = 1 - mask.squeeze().cpu()
+
+ scaled_mask = downscale_image_tensor(rgba_mask.unsqueeze(0)).squeeze()
+
+ mask_np = (scaled_mask.numpy() * 255).astype(np.uint8)
+ mask_img = Image.fromarray(mask_np)
+ mask_img_byte_arr = io.BytesIO()
+ mask_img.save(mask_img_byte_arr, format="PNG")
+ mask_img_byte_arr.seek(0)
+ mask_binary = mask_img_byte_arr
+ mask_binary.name = "mask.png"
+ files.append(("mask", mask_binary))
+
+ # Build the operation
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=request_class,
+ response_model=OpenAIImageGenerationResponse,
+ ),
+ request=request_class(
+ model=model,
+ prompt=prompt,
+ quality=quality,
+ background=background,
+ n=n,
+ seed=seed,
+ size=size,
+ ),
+ files=files if files else None,
+ content_type=content_type,
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ img_tensor = validate_and_cast_response(response, node_id=unique_id)
+ return (img_tensor,)
+
+
+class OpenAITextNode(ComfyNodeABC):
+ """
+ Base class for OpenAI text generation nodes.
+ """
+
+ RETURN_TYPES = (IO.STRING,)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/text/OpenAI"
+ API_NODE = True
+
+
+class OpenAIChatNode(OpenAITextNode):
+ """
+ Node to generate text responses from an OpenAI model.
+ """
+
+ def __init__(self) -> None:
+ """Initialize the chat node with a new session ID and empty history."""
+ self.current_session_id: str = str(uuid.uuid4())
+ self.history: dict[str, list[HistoryEntry]] = {}
+ self.previous_response_id: Optional[str] = None
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text inputs to the model, used to generate a response.",
+ },
+ ),
+ "persist_context": (
+ IO.BOOLEAN,
+ {
+ "default": True,
+ "tooltip": "Persist chat context between calls (multi-turn conversation)",
+ },
+ ),
+ "model": model_field_to_node_input(
+ IO.COMBO,
+ OpenAICreateResponse,
+ "model",
+ enum_type=SupportedOpenAIModel,
+ ),
+ },
+ "optional": {
+ "images": (
+ IO.IMAGE,
+ {
+ "default": None,
+ "tooltip": "Optional image(s) to use as context for the model. To include multiple images, you can use the Batch Images node.",
+ },
+ ),
+ "files": (
+ "OPENAI_INPUT_FILES",
+ {
+ "default": None,
+ "tooltip": "Optional file(s) to use as context for the model. Accepts inputs from the OpenAI Chat Input Files node.",
+ },
+ ),
+ "advanced_options": (
+ "OPENAI_CHAT_CONFIG",
+ {
+ "default": None,
+ "tooltip": "Optional configuration for the model. Accepts inputs from the OpenAI Chat Advanced Options node.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Generate text responses from an OpenAI model."
+
+ def get_result_response(
+ self,
+ response_id: str,
+ include: Optional[list[Includable]] = None,
+ auth_kwargs: Optional[dict[str, str]] = None,
+ ) -> OpenAIResponse:
+ """
+ Retrieve a model response with the given ID from the OpenAI API.
+
+ Args:
+ response_id (str): The ID of the response to retrieve.
+ include (Optional[List[Includable]]): Additional fields to include
+ in the response. See the `include` parameter for Response
+ creation above for more information.
+
+ """
+ return PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"{RESPONSES_ENDPOINT}/{response_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=OpenAIResponse,
+ query_params={"include": include},
+ ),
+ completed_statuses=["completed"],
+ failed_statuses=["failed"],
+ status_extractor=lambda response: response.status,
+ auth_kwargs=auth_kwargs,
+ ).execute()
+
+ def get_message_content_from_response(
+ self, response: OpenAIResponse
+ ) -> list[OutputContent]:
+ """Extract message content from the API response."""
+ for output in response.output:
+ if output.root.type == "message":
+ return output.root.content
+ raise TypeError("No output message found in response")
+
+ def get_text_from_message_content(
+ self, message_content: list[OutputContent]
+ ) -> str:
+ """Extract text content from message content."""
+ for content_item in message_content:
+ if content_item.root.type == "output_text":
+ return str(content_item.root.text)
+ return "No text output found in response"
+
+ def get_history_text(self, session_id: str) -> str:
+ """Convert the entire history for a given session to JSON string."""
+ return json.dumps(self.history[session_id])
+
+ def display_history_on_node(self, session_id: str, node_id: str) -> None:
+ """Display formatted chat history on the node UI."""
+ render_spec = {
+ "node_id": node_id,
+ "component": "ChatHistoryWidget",
+ "props": {
+ "history": self.get_history_text(session_id),
+ },
+ }
+ PromptServer.instance.send_sync(
+ "display_component",
+ render_spec,
+ )
+
+ def add_to_history(
+ self, session_id: str, prompt: str, output_text: str, response_id: str
+ ) -> None:
+ """Add a new entry to the chat history."""
+ if session_id not in self.history:
+ self.history[session_id] = []
+ self.history[session_id].append(
+ {
+ "prompt": prompt,
+ "response": output_text,
+ "response_id": response_id,
+ "timestamp": time.time(),
+ }
+ )
+
+ def parse_output_text_from_response(self, response: OpenAIResponse) -> str:
+ """Extract text output from the API response."""
+ message_contents = self.get_message_content_from_response(response)
+ return self.get_text_from_message_content(message_contents)
+
+ def generate_new_session_id(self) -> str:
+ """Generate a new unique session ID."""
+ return str(uuid.uuid4())
+
+ def get_session_id(self, persist_context: bool) -> str:
+ """Get the current or generate a new session ID based on context persistence."""
+ return (
+ self.current_session_id
+ if persist_context
+ else self.generate_new_session_id()
+ )
+
+ def tensor_to_input_image_content(
+ self, image: torch.Tensor, detail_level: Detail = "auto"
+ ) -> InputImageContent:
+ """Convert a tensor to an input image content object."""
+ return InputImageContent(
+ detail=detail_level,
+ image_url=f"data:image/png;base64,{tensor_to_base64_string(image)}",
+ type="input_image",
+ )
+
+ def create_input_message_contents(
+ self,
+ prompt: str,
+ image: Optional[torch.Tensor] = None,
+ files: Optional[list[InputFileContent]] = None,
+ ) -> InputMessageContentList:
+ """Create a list of input message contents from prompt and optional image."""
+ content_list: list[InputContent] = [
+ InputTextContent(text=prompt, type="input_text"),
+ ]
+ if image is not None:
+ for i in range(image.shape[0]):
+ content_list.append(
+ self.tensor_to_input_image_content(image[i].unsqueeze(0))
+ )
+ if files is not None:
+ content_list.extend(files)
+
+ return InputMessageContentList(
+ root=content_list,
+ )
+
+ def parse_response_id_from_prompt(self, prompt: str) -> Optional[str]:
+ """Extract response ID from prompt if it exists."""
+ parsed_id = re.search(STARTING_POINT_ID_PATTERN, prompt)
+ return parsed_id.group(1) if parsed_id else None
+
+ def strip_response_tag_from_prompt(self, prompt: str) -> str:
+ """Remove the response ID tag from the prompt."""
+ return re.sub(STARTING_POINT_ID_PATTERN, "", prompt.strip())
+
+ def delete_history_after_response_id(
+ self, new_start_id: str, session_id: str
+ ) -> None:
+ """Delete history entries after a specific response ID."""
+ if session_id not in self.history:
+ return
+
+ new_history = []
+ i = 0
+ while (
+ i < len(self.history[session_id])
+ and self.history[session_id][i]["response_id"] != new_start_id
+ ):
+ new_history.append(self.history[session_id][i])
+ i += 1
+
+ # Since it's the new starting point (not the response being edited), we include it as well
+ if i < len(self.history[session_id]):
+ new_history.append(self.history[session_id][i])
+
+ self.history[session_id] = new_history
+
+ def api_call(
+ self,
+ prompt: str,
+ persist_context: bool,
+ model: SupportedOpenAIModel,
+ unique_id: Optional[str] = None,
+ images: Optional[torch.Tensor] = None,
+ files: Optional[list[InputFileContent]] = None,
+ advanced_options: Optional[CreateModelResponseProperties] = None,
+ **kwargs,
+ ) -> tuple[str]:
+ # Validate inputs
+ validate_string(prompt, strip_whitespace=False)
+
+ session_id = self.get_session_id(persist_context)
+ response_id_override = self.parse_response_id_from_prompt(prompt)
+ if response_id_override:
+ is_starting_from_beginning = response_id_override == "start"
+ if is_starting_from_beginning:
+ self.history[session_id] = []
+ previous_response_id = None
+ else:
+ previous_response_id = response_id_override
+ self.delete_history_after_response_id(response_id_override, session_id)
+ prompt = self.strip_response_tag_from_prompt(prompt)
+ elif persist_context:
+ previous_response_id = self.previous_response_id
+ else:
+ previous_response_id = None
+
+ # Create response
+ create_response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=RESPONSES_ENDPOINT,
+ method=HttpMethod.POST,
+ request_model=OpenAICreateResponse,
+ response_model=OpenAIResponse,
+ ),
+ request=OpenAICreateResponse(
+ input=[
+ Item(
+ root=InputMessage(
+ content=self.create_input_message_contents(
+ prompt, images, files
+ ),
+ role="user",
+ )
+ ),
+ ],
+ store=True,
+ stream=False,
+ model=model,
+ previous_response_id=previous_response_id,
+ **(
+ advanced_options.model_dump(exclude_none=True)
+ if advanced_options
+ else {}
+ ),
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ response_id = create_response.id
+
+ # Get result output
+ result_response = self.get_result_response(response_id, auth_kwargs=kwargs)
+ output_text = self.parse_output_text_from_response(result_response)
+
+ # Update history
+ self.add_to_history(session_id, prompt, output_text, response_id)
+ self.display_history_on_node(session_id, unique_id)
+ self.previous_response_id = response_id
+
+ return (output_text,)
+
+
+class OpenAIInputFiles(ComfyNodeABC):
+ """
+ Loads and formats input files for OpenAI API.
+ """
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ """
+ For details about the supported file input types, see:
+ https://platform.openai.com/docs/guides/pdf-files?api-mode=responses
+ """
+ input_dir = folder_paths.get_input_directory()
+ input_files = [
+ f
+ for f in os.scandir(input_dir)
+ if f.is_file()
+ and (f.name.endswith(".txt") or f.name.endswith(".pdf"))
+ and f.stat().st_size < 32 * 1024 * 1024
+ ]
+ input_files = sorted(input_files, key=lambda x: x.name)
+ input_files = [f.name for f in input_files]
+ return {
+ "required": {
+ "file": (
+ IO.COMBO,
+ {
+ "tooltip": "Input files to include as context for the model. Only accepts text (.txt) and PDF (.pdf) files for now.",
+ "options": input_files,
+ "default": input_files[0] if input_files else None,
+ },
+ ),
+ },
+ "optional": {
+ "OPENAI_INPUT_FILES": (
+ "OPENAI_INPUT_FILES",
+ {
+ "tooltip": "An optional additional file(s) to batch together with the file loaded from this node. Allows chaining of input files so that a single message can include multiple input files.",
+ "default": None,
+ },
+ ),
+ },
+ }
+
+ DESCRIPTION = "Loads and prepares input files (text, pdf, etc.) to include as inputs for the OpenAI Chat Node. The files will be read by the OpenAI model when generating a response. 🛈 TIP: Can be chained together with other OpenAI Input File nodes."
+ RETURN_TYPES = ("OPENAI_INPUT_FILES",)
+ FUNCTION = "prepare_files"
+ CATEGORY = "api node/text/OpenAI"
+
+ def create_input_file_content(self, file_path: str) -> InputFileContent:
+ return InputFileContent(
+ file_data=text_filepath_to_data_uri(file_path),
+ filename=os.path.basename(file_path),
+ type="input_file",
+ )
+
+ def prepare_files(
+ self, file: str, OPENAI_INPUT_FILES: list[InputFileContent] = []
+ ) -> tuple[list[InputFileContent]]:
+ """
+ Loads and formats input files for OpenAI API.
+ """
+ file_path = folder_paths.get_annotated_filepath(file)
+ input_file_content = self.create_input_file_content(file_path)
+ files = [input_file_content] + OPENAI_INPUT_FILES
+ return (files,)
+
+
+class OpenAIChatConfig(ComfyNodeABC):
+ """Allows setting additional configuration for the OpenAI Chat Node."""
+
+ RETURN_TYPES = ("OPENAI_CHAT_CONFIG",)
+ FUNCTION = "configure"
+ DESCRIPTION = (
+ "Allows specifying advanced configuration options for the OpenAI Chat Nodes."
+ )
+ CATEGORY = "api node/text/OpenAI"
+
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "truncation": (
+ IO.COMBO,
+ {
+ "options": ["auto", "disabled"],
+ "default": "auto",
+ "tooltip": "The truncation strategy to use for the model response. auto: If the context of this response and previous ones exceeds the model's context window size, the model will truncate the response to fit the context window by dropping input items in the middle of the conversation.disabled: If a model response will exceed the context window size for a model, the request will fail with a 400 error",
+ },
+ ),
+ },
+ "optional": {
+ "max_output_tokens": model_field_to_node_input(
+ IO.INT,
+ OpenAICreateResponse,
+ "max_output_tokens",
+ min=16,
+ default=4096,
+ max=16384,
+ tooltip="An upper bound for the number of tokens that can be generated for a response, including visible output tokens",
+ ),
+ "instructions": model_field_to_node_input(
+ IO.STRING, OpenAICreateResponse, "instructions", multiline=True
+ ),
+ },
+ }
+
+ def configure(
+ self,
+ truncation: bool,
+ instructions: Optional[str] = None,
+ max_output_tokens: Optional[int] = None,
+ ) -> tuple[CreateModelResponseProperties]:
+ """
+ Configure advanced options for the OpenAI Chat Node.
+
+ Note:
+ While `top_p` and `temperature` are listed as properties in the
+ spec, they are not supported for all models (e.g., o4-mini).
+ They are not exposed as inputs at all to avoid having to manually
+ remove depending on model choice.
+ """
+ return (
+ CreateModelResponseProperties(
+ instructions=instructions,
+ truncation=truncation,
+ max_output_tokens=max_output_tokens,
+ ),
+ )
+
+
+NODE_CLASS_MAPPINGS = {
+ "OpenAIDalle2": OpenAIDalle2,
+ "OpenAIDalle3": OpenAIDalle3,
+ "OpenAIGPTImage1": OpenAIGPTImage1,
+ "OpenAIChatNode": OpenAIChatNode,
+ "OpenAIInputFiles": OpenAIInputFiles,
+ "OpenAIChatConfig": OpenAIChatConfig,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "OpenAIDalle2": "OpenAI DALL·E 2",
+ "OpenAIDalle3": "OpenAI DALL·E 3",
+ "OpenAIGPTImage1": "OpenAI GPT Image 1",
+ "OpenAIChatNode": "OpenAI Chat",
+ "OpenAIInputFiles": "OpenAI Chat Input Files",
+ "OpenAIChatConfig": "OpenAI Chat Advanced Options",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_pika.py b/ComfyUI/comfy_api_nodes/nodes_pika.py
new file mode 100644
index 0000000000000000000000000000000000000000..1cc7085645ebfbdd8e0415736a03c5b8b26f4412
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_pika.py
@@ -0,0 +1,782 @@
+"""
+Pika x ComfyUI API Nodes
+
+Pika API docs: https://pika-827374fb.mintlify.app/api-reference
+"""
+from __future__ import annotations
+
+import io
+import logging
+from typing import Optional, TypeVar
+
+import numpy as np
+import torch
+
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC, InputTypeOptions
+from comfy_api.input_impl import VideoFromFile
+from comfy_api.input_impl.video_types import VideoCodec, VideoContainer, VideoInput
+from comfy_api_nodes.apinode_utils import (
+ download_url_to_video_output,
+ tensor_to_bytesio,
+)
+from comfy_api_nodes.apis import (
+ IngredientsMode,
+ PikaBodyGenerate22C2vGenerate22PikascenesPost,
+ PikaBodyGenerate22I2vGenerate22I2vPost,
+ PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
+ PikaBodyGenerate22T2vGenerate22T2vPost,
+ PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+ PikaDurationEnum,
+ Pikaffect,
+ PikaGenerateResponse,
+ PikaResolutionEnum,
+ PikaVideoResponse,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ EmptyRequest,
+ HttpMethod,
+ PollingOperation,
+ SynchronousOperation,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+
+R = TypeVar("R")
+
+PATH_PIKADDITIONS = "/proxy/pika/generate/pikadditions"
+PATH_PIKASWAPS = "/proxy/pika/generate/pikaswaps"
+PATH_PIKAFFECTS = "/proxy/pika/generate/pikaffects"
+
+PIKA_API_VERSION = "2.2"
+PATH_TEXT_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/t2v"
+PATH_IMAGE_TO_VIDEO = f"/proxy/pika/generate/{PIKA_API_VERSION}/i2v"
+PATH_PIKAFRAMES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikaframes"
+PATH_PIKASCENES = f"/proxy/pika/generate/{PIKA_API_VERSION}/pikascenes"
+
+PATH_VIDEO_GET = "/proxy/pika/videos"
+
+
+class PikaApiError(Exception):
+ """Exception for Pika API errors."""
+
+ pass
+
+
+def is_valid_video_response(response: PikaVideoResponse) -> bool:
+ """Check if the video response is valid."""
+ return hasattr(response, "url") and response.url is not None
+
+
+def is_valid_initial_response(response: PikaGenerateResponse) -> bool:
+ """Check if the initial response is valid."""
+ return hasattr(response, "video_id") and response.video_id is not None
+
+
+class PikaNodeBase(ComfyNodeABC):
+ """Base class for Pika nodes."""
+
+ @classmethod
+ def get_base_inputs_types(
+ cls, request_model
+ ) -> dict[str, tuple[IO, InputTypeOptions]]:
+ """Get the base required inputs types common to all Pika nodes."""
+ return {
+ "prompt_text": model_field_to_node_input(
+ IO.STRING,
+ request_model,
+ "promptText",
+ multiline=True,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ request_model,
+ "negativePrompt",
+ multiline=True,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ request_model,
+ "seed",
+ min=0,
+ max=0xFFFFFFFF,
+ control_after_generate=True,
+ ),
+ "resolution": model_field_to_node_input(
+ IO.COMBO,
+ request_model,
+ "resolution",
+ enum_type=PikaResolutionEnum,
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO,
+ request_model,
+ "duration",
+ enum_type=PikaDurationEnum,
+ ),
+ }
+
+ CATEGORY = "api node/video/Pika"
+ API_NODE = True
+ FUNCTION = "api_call"
+ RETURN_TYPES = ("VIDEO",)
+
+ def poll_for_task_status(
+ self,
+ task_id: str,
+ auth_kwargs: Optional[dict[str, str]] = None,
+ node_id: Optional[str] = None,
+ ) -> PikaGenerateResponse:
+ polling_operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"{PATH_VIDEO_GET}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=PikaVideoResponse,
+ ),
+ completed_statuses=[
+ "finished",
+ ],
+ failed_statuses=["failed", "cancelled"],
+ status_extractor=lambda response: (
+ response.status.value if response.status else None
+ ),
+ progress_extractor=lambda response: (
+ response.progress if hasattr(response, "progress") else None
+ ),
+ auth_kwargs=auth_kwargs,
+ result_url_extractor=lambda response: (
+ response.url if hasattr(response, "url") else None
+ ),
+ node_id=node_id,
+ estimated_duration=60
+ )
+ return polling_operation.execute()
+
+ def execute_task(
+ self,
+ initial_operation: SynchronousOperation[R, PikaGenerateResponse],
+ auth_kwargs: Optional[dict[str, str]] = None,
+ node_id: Optional[str] = None,
+ ) -> tuple[VideoFromFile]:
+ """Executes the initial operation then polls for the task status until it is completed.
+
+ Args:
+ initial_operation: The initial operation to execute.
+ auth_kwargs: The authentication token(s) to use for the API call.
+
+ Returns:
+ A tuple containing the video file as a VIDEO output.
+ """
+ initial_response = initial_operation.execute()
+ if not is_valid_initial_response(initial_response):
+ error_msg = f"Pika initial request failed. Code: {initial_response.code}, Message: {initial_response.message}, Data: {initial_response.data}"
+ logging.error(error_msg)
+ raise PikaApiError(error_msg)
+
+ task_id = initial_response.video_id
+ final_response = self.poll_for_task_status(task_id, auth_kwargs)
+ if not is_valid_video_response(final_response):
+ error_msg = (
+ f"Pika task {task_id} succeeded but no video data found in response."
+ )
+ logging.error(error_msg)
+ raise PikaApiError(error_msg)
+
+ video_url = str(final_response.url)
+ logging.info("Pika task %s succeeded. Video URL: %s", task_id, video_url)
+
+ return (download_url_to_video_output(video_url),)
+
+
+class PikaImageToVideoV2_2(PikaNodeBase):
+ """Pika 2.2 Image to Video Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": (
+ IO.IMAGE,
+ {"tooltip": "The image to convert to video"},
+ ),
+ **cls.get_base_inputs_types(PikaBodyGenerate22I2vGenerate22I2vPost),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Sends an image and prompt to the Pika API v2.2 to generate a video."
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ resolution: str,
+ duration: int,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Convert image to BytesIO
+ image_bytes_io = tensor_to_bytesio(image)
+ image_bytes_io.seek(0)
+
+ pika_files = {"image": ("image.png", image_bytes_io, "image/png")}
+
+ # Prepare non-file data
+ pika_request_data = PikaBodyGenerate22I2vGenerate22I2vPost(
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ resolution=resolution,
+ duration=duration,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_IMAGE_TO_VIDEO,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGenerate22I2vGenerate22I2vPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=pika_request_data,
+ files=pika_files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikaTextToVideoNodeV2_2(PikaNodeBase):
+ """Pika Text2Video v2.2 Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ **cls.get_base_inputs_types(PikaBodyGenerate22T2vGenerate22T2vPost),
+ "aspect_ratio": model_field_to_node_input(
+ IO.FLOAT,
+ PikaBodyGenerate22T2vGenerate22T2vPost,
+ "aspectRatio",
+ step=0.001,
+ min=0.4,
+ max=2.5,
+ default=1.7777777777777777,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Sends a text prompt to the Pika API v2.2 to generate a video."
+
+ def api_call(
+ self,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ resolution: str,
+ duration: int,
+ aspect_ratio: float,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_TEXT_TO_VIDEO,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGenerate22T2vGenerate22T2vPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=PikaBodyGenerate22T2vGenerate22T2vPost(
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ resolution=resolution,
+ duration=duration,
+ aspectRatio=aspect_ratio,
+ ),
+ auth_kwargs=kwargs,
+ content_type="application/x-www-form-urlencoded",
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikaScenesV2_2(PikaNodeBase):
+ """PikaScenes v2.2 Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ image_ingredient_input = (
+ IO.IMAGE,
+ {"tooltip": "Image that will be used as ingredient to create a video."},
+ )
+ return {
+ "required": {
+ **cls.get_base_inputs_types(
+ PikaBodyGenerate22C2vGenerate22PikascenesPost,
+ ),
+ "ingredients_mode": model_field_to_node_input(
+ IO.COMBO,
+ PikaBodyGenerate22C2vGenerate22PikascenesPost,
+ "ingredientsMode",
+ enum_type=IngredientsMode,
+ default="creative",
+ ),
+ "aspect_ratio": model_field_to_node_input(
+ IO.FLOAT,
+ PikaBodyGenerate22C2vGenerate22PikascenesPost,
+ "aspectRatio",
+ step=0.001,
+ min=0.4,
+ max=2.5,
+ default=1.7777777777777777,
+ ),
+ },
+ "optional": {
+ "image_ingredient_1": image_ingredient_input,
+ "image_ingredient_2": image_ingredient_input,
+ "image_ingredient_3": image_ingredient_input,
+ "image_ingredient_4": image_ingredient_input,
+ "image_ingredient_5": image_ingredient_input,
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Combine your images to create a video with the objects in them. Upload multiple images as ingredients and generate a high-quality video that incorporates all of them."
+
+ def api_call(
+ self,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ resolution: str,
+ duration: int,
+ ingredients_mode: str,
+ aspect_ratio: float,
+ unique_id: str,
+ image_ingredient_1: Optional[torch.Tensor] = None,
+ image_ingredient_2: Optional[torch.Tensor] = None,
+ image_ingredient_3: Optional[torch.Tensor] = None,
+ image_ingredient_4: Optional[torch.Tensor] = None,
+ image_ingredient_5: Optional[torch.Tensor] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Convert all passed images to BytesIO
+ all_image_bytes_io = []
+ for image in [
+ image_ingredient_1,
+ image_ingredient_2,
+ image_ingredient_3,
+ image_ingredient_4,
+ image_ingredient_5,
+ ]:
+ if image is not None:
+ image_bytes_io = tensor_to_bytesio(image)
+ image_bytes_io.seek(0)
+ all_image_bytes_io.append(image_bytes_io)
+
+ pika_files = [
+ ("images", (f"image_{i}.png", image_bytes_io, "image/png"))
+ for i, image_bytes_io in enumerate(all_image_bytes_io)
+ ]
+
+ pika_request_data = PikaBodyGenerate22C2vGenerate22PikascenesPost(
+ ingredientsMode=ingredients_mode,
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ resolution=resolution,
+ duration=duration,
+ aspectRatio=aspect_ratio,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_PIKASCENES,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGenerate22C2vGenerate22PikascenesPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=pika_request_data,
+ files=pika_files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikAdditionsNode(PikaNodeBase):
+ """Pika Pikadditions Node. Add an image into a video."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "video": (IO.VIDEO, {"tooltip": "The video to add an image to."}),
+ "image": (IO.IMAGE, {"tooltip": "The image to add to the video."}),
+ "prompt_text": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ "promptText",
+ multiline=True,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ "negativePrompt",
+ multiline=True,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ "seed",
+ min=0,
+ max=0xFFFFFFFF,
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Add any object or image into your video. Upload a video and specify what you'd like to add to create a seamlessly integrated result."
+
+ def api_call(
+ self,
+ video: VideoInput,
+ image: torch.Tensor,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Convert video to BytesIO
+ video_bytes_io = io.BytesIO()
+ video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
+ video_bytes_io.seek(0)
+
+ # Convert image to BytesIO
+ image_bytes_io = tensor_to_bytesio(image)
+ image_bytes_io.seek(0)
+
+ pika_files = [
+ ("video", ("video.mp4", video_bytes_io, "video/mp4")),
+ ("image", ("image.png", image_bytes_io, "image/png")),
+ ]
+
+ # Prepare non-file data
+ pika_request_data = PikaBodyGeneratePikadditionsGeneratePikadditionsPost(
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_PIKADDITIONS,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=pika_request_data,
+ files=pika_files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikaSwapsNode(PikaNodeBase):
+ """Pika Pikaswaps Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "video": (IO.VIDEO, {"tooltip": "The video to swap an object in."}),
+ "image": (
+ IO.IMAGE,
+ {
+ "tooltip": "The image used to replace the masked object in the video."
+ },
+ ),
+ "mask": (
+ IO.MASK,
+ {"tooltip": "Use the mask to define areas in the video to replace"},
+ ),
+ "prompt_text": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+ "promptText",
+ multiline=True,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+ "negativePrompt",
+ multiline=True,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ PikaBodyGeneratePikaswapsGeneratePikaswapsPost,
+ "seed",
+ min=0,
+ max=0xFFFFFFFF,
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Swap out any object or region of your video with a new image or object. Define areas to replace either with a mask or coordinates."
+ RETURN_TYPES = ("VIDEO",)
+
+ def api_call(
+ self,
+ video: VideoInput,
+ image: torch.Tensor,
+ mask: torch.Tensor,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Convert video to BytesIO
+ video_bytes_io = io.BytesIO()
+ video.save_to(video_bytes_io, format=VideoContainer.MP4, codec=VideoCodec.H264)
+ video_bytes_io.seek(0)
+
+ # Convert mask to binary mask with three channels
+ mask = torch.round(mask)
+ mask = mask.repeat(1, 3, 1, 1)
+
+ # Convert 3-channel binary mask to BytesIO
+ mask_bytes_io = io.BytesIO()
+ mask_bytes_io.write(mask.numpy().astype(np.uint8))
+ mask_bytes_io.seek(0)
+
+ # Convert image to BytesIO
+ image_bytes_io = tensor_to_bytesio(image)
+ image_bytes_io.seek(0)
+
+ pika_files = [
+ ("video", ("video.mp4", video_bytes_io, "video/mp4")),
+ ("image", ("image.png", image_bytes_io, "image/png")),
+ ("modifyRegionMask", ("mask.png", mask_bytes_io, "image/png")),
+ ]
+
+ # Prepare non-file data
+ pika_request_data = PikaBodyGeneratePikaswapsGeneratePikaswapsPost(
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ )
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_PIKADDITIONS,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGeneratePikadditionsGeneratePikadditionsPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=pika_request_data,
+ files=pika_files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikaffectsNode(PikaNodeBase):
+ """Pika Pikaffects Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image": (
+ IO.IMAGE,
+ {"tooltip": "The reference image to apply the Pikaffect to."},
+ ),
+ "pikaffect": model_field_to_node_input(
+ IO.COMBO,
+ PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ "pikaffect",
+ enum_type=Pikaffect,
+ default="Cake-ify",
+ ),
+ "prompt_text": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ "promptText",
+ multiline=True,
+ ),
+ "negative_prompt": model_field_to_node_input(
+ IO.STRING,
+ PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ "negativePrompt",
+ multiline=True,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ "seed",
+ min=0,
+ max=0xFFFFFFFF,
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Generate a video with a specific Pikaffect. Supported Pikaffects: Cake-ify, Crumble, Crush, Decapitate, Deflate, Dissolve, Explode, Eye-pop, Inflate, Levitate, Melt, Peel, Poke, Squish, Ta-da, Tear"
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ pikaffect: str,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_PIKAFFECTS,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGeneratePikaffectsGeneratePikaffectsPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=PikaBodyGeneratePikaffectsGeneratePikaffectsPost(
+ pikaffect=pikaffect,
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ ),
+ files={"image": ("image.png", tensor_to_bytesio(image), "image/png")},
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+class PikaStartEndFrameNode2_2(PikaNodeBase):
+ """PikaFrames v2.2 Node."""
+
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "image_start": (IO.IMAGE, {"tooltip": "The first image to combine."}),
+ "image_end": (IO.IMAGE, {"tooltip": "The last image to combine."}),
+ **cls.get_base_inputs_types(
+ PikaBodyGenerate22KeyframeGenerate22PikaframesPost
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Generate a video by combining your first and last frame. Upload two images to define the start and end points, and let the AI create a smooth transition between them."
+
+ def api_call(
+ self,
+ image_start: torch.Tensor,
+ image_end: torch.Tensor,
+ prompt_text: str,
+ negative_prompt: str,
+ seed: int,
+ resolution: str,
+ duration: int,
+ unique_id: str,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+
+ pika_files = [
+ (
+ "keyFrames",
+ ("image_start.png", tensor_to_bytesio(image_start), "image/png"),
+ ),
+ ("keyFrames", ("image_end.png", tensor_to_bytesio(image_end), "image/png")),
+ ]
+
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_PIKAFRAMES,
+ method=HttpMethod.POST,
+ request_model=PikaBodyGenerate22KeyframeGenerate22PikaframesPost,
+ response_model=PikaGenerateResponse,
+ ),
+ request=PikaBodyGenerate22KeyframeGenerate22PikaframesPost(
+ promptText=prompt_text,
+ negativePrompt=negative_prompt,
+ seed=seed,
+ resolution=resolution,
+ duration=duration,
+ ),
+ files=pika_files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ return self.execute_task(initial_operation, auth_kwargs=kwargs, node_id=unique_id)
+
+
+NODE_CLASS_MAPPINGS = {
+ "PikaImageToVideoNode2_2": PikaImageToVideoV2_2,
+ "PikaTextToVideoNode2_2": PikaTextToVideoNodeV2_2,
+ "PikaScenesV2_2": PikaScenesV2_2,
+ "Pikadditions": PikAdditionsNode,
+ "Pikaswaps": PikaSwapsNode,
+ "Pikaffects": PikaffectsNode,
+ "PikaStartEndFrameNode2_2": PikaStartEndFrameNode2_2,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "PikaImageToVideoNode2_2": "Pika Image to Video",
+ "PikaTextToVideoNode2_2": "Pika Text to Video",
+ "PikaScenesV2_2": "Pika Scenes (Video Image Composition)",
+ "Pikadditions": "Pikadditions (Video Object Insertion)",
+ "Pikaswaps": "Pika Swaps (Video Object Replacement)",
+ "Pikaffects": "Pikaffects (Video Effects)",
+ "PikaStartEndFrameNode2_2": "Pika Start and End Frame to Video",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_pixverse.py b/ComfyUI/comfy_api_nodes/nodes_pixverse.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef4a9a8023f29475d3608ecaebc239e53532e1a1
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_pixverse.py
@@ -0,0 +1,525 @@
+from inspect import cleandoc
+from typing import Optional
+from comfy_api_nodes.apis.pixverse_api import (
+ PixverseTextVideoRequest,
+ PixverseImageVideoRequest,
+ PixverseTransitionVideoRequest,
+ PixverseImageUploadResponse,
+ PixverseVideoResponse,
+ PixverseGenerationStatusResponse,
+ PixverseAspectRatio,
+ PixverseQuality,
+ PixverseDuration,
+ PixverseMotionMode,
+ PixverseStatus,
+ PixverseIO,
+ pixverse_templates,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ tensor_to_bytesio,
+ validate_string,
+)
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+from comfy_api.input_impl import VideoFromFile
+
+import torch
+import requests
+from io import BytesIO
+
+
+AVERAGE_DURATION_T2V = 32
+AVERAGE_DURATION_I2V = 30
+AVERAGE_DURATION_T2T = 52
+
+
+def get_video_url_from_response(
+ response: PixverseGenerationStatusResponse,
+) -> Optional[str]:
+ if response.Resp is None or response.Resp.url is None:
+ return None
+ return str(response.Resp.url)
+
+
+def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
+ # first, upload image to Pixverse and get image id to use in actual generation call
+ files = {"image": tensor_to_bytesio(image)}
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/pixverse/image/upload",
+ method=HttpMethod.POST,
+ request_model=EmptyRequest,
+ response_model=PixverseImageUploadResponse,
+ ),
+ request=EmptyRequest(),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=auth_kwargs,
+ )
+ response_upload: PixverseImageUploadResponse = operation.execute()
+
+ if response_upload.Resp is None:
+ raise Exception(
+ f"PixVerse image upload request failed: '{response_upload.ErrMsg}'"
+ )
+
+ return response_upload.Resp.img_id
+
+
+class PixverseTemplateNode:
+ """
+ Select template for PixVerse Video generation.
+ """
+
+ RETURN_TYPES = (PixverseIO.TEMPLATE,)
+ RETURN_NAMES = ("pixverse_template",)
+ FUNCTION = "create_template"
+ CATEGORY = "api node/video/PixVerse"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "template": (list(pixverse_templates.keys()),),
+ }
+ }
+
+ def create_template(self, template: str):
+ template_id = pixverse_templates.get(template, None)
+ if template_id is None:
+ raise Exception(f"Template '{template}' is not recognized.")
+ # just return the integer
+ return (template_id,)
+
+
+class PixverseTextToVideoNode(ComfyNodeABC):
+ """
+ Generates videos based on prompt and output_size.
+ """
+
+ RETURN_TYPES = (IO.VIDEO,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/video/PixVerse"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the video generation",
+ },
+ ),
+ "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],),
+ "quality": (
+ [resolution.value for resolution in PixverseQuality],
+ {
+ "default": PixverseQuality.res_540p,
+ },
+ ),
+ "duration_seconds": ([dur.value for dur in PixverseDuration],),
+ "motion_mode": ([mode.value for mode in PixverseMotionMode],),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "control_after_generate": True,
+ "tooltip": "Seed for video generation.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ "pixverse_template": (
+ PixverseIO.TEMPLATE,
+ {
+ "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ aspect_ratio: str,
+ quality: str,
+ duration_seconds: int,
+ motion_mode: str,
+ seed,
+ negative_prompt: str = None,
+ pixverse_template: int = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ # 1080p is limited to 5 seconds duration
+ # only normal motion_mode supported for 1080p or for non-5 second duration
+ if quality == PixverseQuality.res_1080p:
+ motion_mode = PixverseMotionMode.normal
+ duration_seconds = PixverseDuration.dur_5
+ elif duration_seconds != PixverseDuration.dur_5:
+ motion_mode = PixverseMotionMode.normal
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/pixverse/video/text/generate",
+ method=HttpMethod.POST,
+ request_model=PixverseTextVideoRequest,
+ response_model=PixverseVideoResponse,
+ ),
+ request=PixverseTextVideoRequest(
+ prompt=prompt,
+ aspect_ratio=aspect_ratio,
+ quality=quality,
+ duration=duration_seconds,
+ motion_mode=motion_mode,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ template_id=pixverse_template,
+ seed=seed,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.Resp is None:
+ raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=PixverseGenerationStatusResponse,
+ ),
+ completed_statuses=[PixverseStatus.successful],
+ failed_statuses=[
+ PixverseStatus.contents_moderation,
+ PixverseStatus.failed,
+ PixverseStatus.deleted,
+ ],
+ status_extractor=lambda x: x.Resp.status,
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_T2V,
+ )
+ response_poll = operation.execute()
+
+ vid_response = requests.get(response_poll.Resp.url)
+
+ return (VideoFromFile(BytesIO(vid_response.content)),)
+
+
+class PixverseImageToVideoNode(ComfyNodeABC):
+ """
+ Generates videos based on prompt and output_size.
+ """
+
+ RETURN_TYPES = (IO.VIDEO,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/video/PixVerse"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the video generation",
+ },
+ ),
+ "quality": (
+ [resolution.value for resolution in PixverseQuality],
+ {
+ "default": PixverseQuality.res_540p,
+ },
+ ),
+ "duration_seconds": ([dur.value for dur in PixverseDuration],),
+ "motion_mode": ([mode.value for mode in PixverseMotionMode],),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "control_after_generate": True,
+ "tooltip": "Seed for video generation.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ "pixverse_template": (
+ PixverseIO.TEMPLATE,
+ {
+ "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ prompt: str,
+ quality: str,
+ duration_seconds: int,
+ motion_mode: str,
+ seed,
+ negative_prompt: str = None,
+ pixverse_template: int = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ img_id = upload_image_to_pixverse(image, auth_kwargs=kwargs)
+
+ # 1080p is limited to 5 seconds duration
+ # only normal motion_mode supported for 1080p or for non-5 second duration
+ if quality == PixverseQuality.res_1080p:
+ motion_mode = PixverseMotionMode.normal
+ duration_seconds = PixverseDuration.dur_5
+ elif duration_seconds != PixverseDuration.dur_5:
+ motion_mode = PixverseMotionMode.normal
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/pixverse/video/img/generate",
+ method=HttpMethod.POST,
+ request_model=PixverseImageVideoRequest,
+ response_model=PixverseVideoResponse,
+ ),
+ request=PixverseImageVideoRequest(
+ img_id=img_id,
+ prompt=prompt,
+ quality=quality,
+ duration=duration_seconds,
+ motion_mode=motion_mode,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ template_id=pixverse_template,
+ seed=seed,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.Resp is None:
+ raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=PixverseGenerationStatusResponse,
+ ),
+ completed_statuses=[PixverseStatus.successful],
+ failed_statuses=[
+ PixverseStatus.contents_moderation,
+ PixverseStatus.failed,
+ PixverseStatus.deleted,
+ ],
+ status_extractor=lambda x: x.Resp.status,
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_I2V,
+ )
+ response_poll = operation.execute()
+
+ vid_response = requests.get(response_poll.Resp.url)
+ return (VideoFromFile(BytesIO(vid_response.content)),)
+
+
+class PixverseTransitionVideoNode(ComfyNodeABC):
+ """
+ Generates videos based on prompt and output_size.
+ """
+
+ RETURN_TYPES = (IO.VIDEO,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/video/PixVerse"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "first_frame": (IO.IMAGE,),
+ "last_frame": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the video generation",
+ },
+ ),
+ "quality": (
+ [resolution.value for resolution in PixverseQuality],
+ {
+ "default": PixverseQuality.res_540p,
+ },
+ ),
+ "duration_seconds": ([dur.value for dur in PixverseDuration],),
+ "motion_mode": ([mode.value for mode in PixverseMotionMode],),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 2147483647,
+ "control_after_generate": True,
+ "tooltip": "Seed for video generation.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ first_frame: torch.Tensor,
+ last_frame: torch.Tensor,
+ prompt: str,
+ quality: str,
+ duration_seconds: int,
+ motion_mode: str,
+ seed,
+ negative_prompt: str = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False)
+ first_frame_id = upload_image_to_pixverse(first_frame, auth_kwargs=kwargs)
+ last_frame_id = upload_image_to_pixverse(last_frame, auth_kwargs=kwargs)
+
+ # 1080p is limited to 5 seconds duration
+ # only normal motion_mode supported for 1080p or for non-5 second duration
+ if quality == PixverseQuality.res_1080p:
+ motion_mode = PixverseMotionMode.normal
+ duration_seconds = PixverseDuration.dur_5
+ elif duration_seconds != PixverseDuration.dur_5:
+ motion_mode = PixverseMotionMode.normal
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/pixverse/video/transition/generate",
+ method=HttpMethod.POST,
+ request_model=PixverseTransitionVideoRequest,
+ response_model=PixverseVideoResponse,
+ ),
+ request=PixverseTransitionVideoRequest(
+ first_frame_img=first_frame_id,
+ last_frame_img=last_frame_id,
+ prompt=prompt,
+ quality=quality,
+ duration=duration_seconds,
+ motion_mode=motion_mode,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ seed=seed,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.Resp is None:
+ raise Exception(f"PixVerse request failed: '{response_api.ErrMsg}'")
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/pixverse/video/result/{response_api.Resp.video_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=PixverseGenerationStatusResponse,
+ ),
+ completed_statuses=[PixverseStatus.successful],
+ failed_statuses=[
+ PixverseStatus.contents_moderation,
+ PixverseStatus.failed,
+ PixverseStatus.deleted,
+ ],
+ status_extractor=lambda x: x.Resp.status,
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ result_url_extractor=get_video_url_from_response,
+ estimated_duration=AVERAGE_DURATION_T2V,
+ )
+ response_poll = operation.execute()
+
+ vid_response = requests.get(response_poll.Resp.url)
+ return (VideoFromFile(BytesIO(vid_response.content)),)
+
+
+NODE_CLASS_MAPPINGS = {
+ "PixverseTextToVideoNode": PixverseTextToVideoNode,
+ "PixverseImageToVideoNode": PixverseImageToVideoNode,
+ "PixverseTransitionVideoNode": PixverseTransitionVideoNode,
+ "PixverseTemplateNode": PixverseTemplateNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "PixverseTextToVideoNode": "PixVerse Text to Video",
+ "PixverseImageToVideoNode": "PixVerse Image to Video",
+ "PixverseTransitionVideoNode": "PixVerse Transition Video",
+ "PixverseTemplateNode": "PixVerse Template",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_recraft.py b/ComfyUI/comfy_api_nodes/nodes_recraft.py
new file mode 100644
index 0000000000000000000000000000000000000000..e369c4b7e2830ff780b5a9f60d439951ff21b615
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_recraft.py
@@ -0,0 +1,1138 @@
+from __future__ import annotations
+from inspect import cleandoc
+from typing import Optional
+from comfy.utils import ProgressBar
+from comfy_extras.nodes_images import SVG # Added
+from comfy.comfy_types.node_typing import IO
+from comfy_api_nodes.apis.recraft_api import (
+ RecraftImageGenerationRequest,
+ RecraftImageGenerationResponse,
+ RecraftImageSize,
+ RecraftModel,
+ RecraftStyle,
+ RecraftStyleV3,
+ RecraftColor,
+ RecraftColorChain,
+ RecraftControls,
+ RecraftIO,
+ get_v3_substyles,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ bytesio_to_image_tensor,
+ download_url_to_bytesio,
+ tensor_to_bytesio,
+ resize_mask_to_image,
+ validate_string,
+)
+from server import PromptServer
+
+import torch
+from io import BytesIO
+from PIL import UnidentifiedImageError
+
+
+def handle_recraft_file_request(
+ image: torch.Tensor,
+ path: str,
+ mask: torch.Tensor=None,
+ total_pixels=4096*4096,
+ timeout=1024,
+ request=None,
+ auth_kwargs: dict[str,str] = None,
+ ) -> list[BytesIO]:
+ """
+ Handle sending common Recraft file-only request to get back file bytes.
+ """
+ if request is None:
+ request = EmptyRequest()
+
+ files = {
+ 'image': tensor_to_bytesio(image, total_pixels=total_pixels).read()
+ }
+ if mask is not None:
+ files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=type(request),
+ response_model=RecraftImageGenerationResponse,
+ ),
+ request=request,
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=auth_kwargs,
+ multipart_parser=recraft_multipart_parser,
+ )
+ response: RecraftImageGenerationResponse = operation.execute()
+ all_bytesio = []
+ if response.image is not None:
+ all_bytesio.append(download_url_to_bytesio(response.image.url, timeout=timeout))
+ else:
+ for data in response.data:
+ all_bytesio.append(download_url_to_bytesio(data.url, timeout=timeout))
+
+ return all_bytesio
+
+
+def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict:
+ """
+ Formats data such that multipart/form-data will work with requests library
+ when both files and data are present.
+
+ The OpenAI client that Recraft uses has a bizarre way of serializing lists:
+
+ It does NOT keep track of indeces of each list, so for background_color, that must be serialized as:
+ 'background_color[rgb][]' = [0, 0, 255]
+ where the array is assigned to a key that has '[]' at the end, to signal it's an array.
+
+ This has the consequence of nested lists having the exact same key, forcing arrays to merge; all colors inputs fall under the same key:
+ if 1 color -> 'controls[colors][][rgb][]' = [0, 0, 255]
+ if 2 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0]
+ if 3 colors -> 'controls[colors][][rgb][]' = [0, 0, 255, 255, 0, 0, 0, 255, 0]
+ etc.
+ Whoever made this serialization up at OpenAI added the constraint that lists must be of uniform length on objects of same 'type'.
+ """
+ # Modification of a function that handled a different type of multipart parsing, big ups:
+ # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b
+
+ def handle_converted_lists(data, parent_key, lists_to_check=tuple[list]):
+ # if list already exists exists, just extend list with data
+ for check_list in lists_to_check:
+ for conv_tuple in check_list:
+ if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list:
+ conv_tuple[1].append(formatter(data))
+ return True
+ return False
+
+ if converted_to_check is None:
+ converted_to_check = []
+
+
+ if formatter is None:
+ formatter = lambda v: v # Multipart representation of value
+
+ if type(data) is not dict:
+ # if list already exists exists, just extend list with data
+ added = handle_converted_lists(data, parent_key, converted_to_check)
+ if added:
+ return {}
+ # otherwise if is_list, create new list with data
+ if is_list:
+ return {parent_key: [formatter(data)]}
+ # return new key with data
+ return {parent_key: formatter(data)}
+
+ converted = []
+ next_check = [converted]
+ next_check.extend(converted_to_check)
+
+ for key, value in data.items():
+ current_key = key if parent_key is None else f"{parent_key}[{key}]"
+ if type(value) is dict:
+ converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items())
+ elif type(value) is list:
+ for ind, list_value in enumerate(value):
+ iter_key = f"{current_key}[]"
+ converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items())
+ else:
+ converted.append((current_key, formatter(value)))
+
+ return dict(converted)
+
+
+class handle_recraft_image_output:
+ """
+ Catch an exception related to receiving SVG data instead of image, when Infinite Style Library style_id is in use.
+ """
+ def __init__(self):
+ pass
+
+ def __enter__(self):
+ pass
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ if exc_type is not None and exc_type is UnidentifiedImageError:
+ raise Exception("Received output data was not an image; likely an SVG. If you used style_id, make sure it is not a Vector art style.")
+
+
+class RecraftColorRGBNode:
+ """
+ Create Recraft Color by choosing specific RGB values.
+ """
+
+ RETURN_TYPES = (RecraftIO.COLOR,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ RETURN_NAMES = ("recraft_color",)
+ FUNCTION = "create_color"
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "r": (IO.INT, {
+ "default": 0,
+ "min": 0,
+ "max": 255,
+ "tooltip": "Red value of color."
+ }),
+ "g": (IO.INT, {
+ "default": 0,
+ "min": 0,
+ "max": 255,
+ "tooltip": "Green value of color."
+ }),
+ "b": (IO.INT, {
+ "default": 0,
+ "min": 0,
+ "max": 255,
+ "tooltip": "Blue value of color."
+ }),
+ },
+ "optional": {
+ "recraft_color": (RecraftIO.COLOR,),
+ }
+ }
+
+ def create_color(self, r: int, g: int, b: int, recraft_color: RecraftColorChain=None):
+ recraft_color = recraft_color.clone() if recraft_color else RecraftColorChain()
+ recraft_color.add(RecraftColor(r, g, b))
+ return (recraft_color, )
+
+
+class RecraftControlsNode:
+ """
+ Create Recraft Controls for customizing Recraft generation.
+ """
+
+ RETURN_TYPES = (RecraftIO.CONTROLS,)
+ RETURN_NAMES = ("recraft_controls",)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "create_controls"
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ },
+ "optional": {
+ "colors": (RecraftIO.COLOR,),
+ "background_color": (RecraftIO.COLOR,),
+ }
+ }
+
+ def create_controls(self, colors: RecraftColorChain=None, background_color: RecraftColorChain=None):
+ return (RecraftControls(colors=colors, background_color=background_color), )
+
+
+class RecraftStyleV3RealisticImageNode:
+ """
+ Select realistic_image style and optional substyle.
+ """
+
+ RETURN_TYPES = (RecraftIO.STYLEV3,)
+ RETURN_NAMES = ("recraft_style",)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "create_style"
+ CATEGORY = "api node/image/Recraft"
+
+ RECRAFT_STYLE = RecraftStyleV3.realistic_image
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "substyle": (get_v3_substyles(s.RECRAFT_STYLE),),
+ }
+ }
+
+ def create_style(self, substyle: str):
+ if substyle == "None":
+ substyle = None
+ return (RecraftStyle(self.RECRAFT_STYLE, substyle),)
+
+
+class RecraftStyleV3DigitalIllustrationNode(RecraftStyleV3RealisticImageNode):
+ """
+ Select digital_illustration style and optional substyle.
+ """
+
+ RECRAFT_STYLE = RecraftStyleV3.digital_illustration
+
+
+class RecraftStyleV3VectorIllustrationNode(RecraftStyleV3RealisticImageNode):
+ """
+ Select vector_illustration style and optional substyle.
+ """
+
+ RECRAFT_STYLE = RecraftStyleV3.vector_illustration
+
+
+class RecraftStyleV3LogoRasterNode(RecraftStyleV3RealisticImageNode):
+ """
+ Select vector_illustration style and optional substyle.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "substyle": (get_v3_substyles(s.RECRAFT_STYLE, include_none=False),),
+ }
+ }
+
+ RECRAFT_STYLE = RecraftStyleV3.logo_raster
+
+
+class RecraftStyleInfiniteStyleLibrary:
+ """
+ Select style based on preexisting UUID from Recraft's Infinite Style Library.
+ """
+
+ RETURN_TYPES = (RecraftIO.STYLEV3,)
+ RETURN_NAMES = ("recraft_style",)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "create_style"
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "style_id": (IO.STRING, {
+ "default": "",
+ "tooltip": "UUID of style from Infinite Style Library.",
+ })
+ }
+ }
+
+ def create_style(self, style_id: str):
+ if not style_id:
+ raise Exception("The style_id input cannot be empty.")
+ return (RecraftStyle(style_id=style_id),)
+
+
+class RecraftTextToImageNode:
+ """
+ Generates images synchronously based on prompt and resolution.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation.",
+ },
+ ),
+ "size": (
+ [res.value for res in RecraftImageSize],
+ {
+ "default": RecraftImageSize.res_1024x1024,
+ "tooltip": "The size of the generated image.",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 6,
+ "tooltip": "The number of images to generate.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "recraft_style": (RecraftIO.STYLEV3,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ "recraft_controls": (
+ RecraftIO.CONTROLS,
+ {
+ "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ size: str,
+ n: int,
+ seed,
+ recraft_style: RecraftStyle = None,
+ negative_prompt: str = None,
+ recraft_controls: RecraftControls = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False, max_length=1000)
+ default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+ if recraft_style is None:
+ recraft_style = default_style
+
+ controls_api = None
+ if recraft_controls:
+ controls_api = recraft_controls.create_api_model()
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/recraft/image_generation",
+ method=HttpMethod.POST,
+ request_model=RecraftImageGenerationRequest,
+ response_model=RecraftImageGenerationResponse,
+ ),
+ request=RecraftImageGenerationRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model=RecraftModel.recraftv3,
+ size=size,
+ n=n,
+ style=recraft_style.style,
+ substyle=recraft_style.substyle,
+ style_id=recraft_style.style_id,
+ controls=controls_api,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response: RecraftImageGenerationResponse = operation.execute()
+ images = []
+ urls = []
+ for data in response.data:
+ with handle_recraft_image_output():
+ if unique_id and data.url:
+ urls.append(data.url)
+ urls_string = '\n'.join(urls)
+ PromptServer.instance.send_progress_text(
+ f"Result URL: {urls_string}", unique_id
+ )
+ image = bytesio_to_image_tensor(
+ download_url_to_bytesio(data.url, timeout=1024)
+ )
+ if len(image.shape) < 4:
+ image = image.unsqueeze(0)
+ images.append(image)
+ output_image = torch.cat(images, dim=0)
+
+ return (output_image,)
+
+
+class RecraftImageToImageNode:
+ """
+ Modify image based on prompt and strength.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation.",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 6,
+ "tooltip": "The number of images to generate.",
+ },
+ ),
+ "strength": (
+ IO.FLOAT,
+ {
+ "default": 0.5,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Defines the difference with the original image, should lie in [0, 1], where 0 means almost identical, and 1 means miserable similarity."
+ }
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "recraft_style": (RecraftIO.STYLEV3,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ "recraft_controls": (
+ RecraftIO.CONTROLS,
+ {
+ "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ prompt: str,
+ n: int,
+ strength: float,
+ seed,
+ recraft_style: RecraftStyle = None,
+ negative_prompt: str = None,
+ recraft_controls: RecraftControls = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False, max_length=1000)
+ default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+ if recraft_style is None:
+ recraft_style = default_style
+
+ controls_api = None
+ if recraft_controls:
+ controls_api = recraft_controls.create_api_model()
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ request = RecraftImageGenerationRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model=RecraftModel.recraftv3,
+ n=n,
+ strength=round(strength, 2),
+ style=recraft_style.style,
+ substyle=recraft_style.substyle,
+ style_id=recraft_style.style_id,
+ controls=controls_api,
+ )
+
+ images = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ path="/proxy/recraft/images/imageToImage",
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ with handle_recraft_image_output():
+ images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+ pbar.update(1)
+
+ images_tensor = torch.cat(images, dim=0)
+ return (images_tensor, )
+
+
+class RecraftImageInpaintingNode:
+ """
+ Modify image based on prompt and mask.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ "mask": (IO.MASK, ),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation.",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 6,
+ "tooltip": "The number of images to generate.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "recraft_style": (RecraftIO.STYLEV3,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ mask: torch.Tensor,
+ prompt: str,
+ n: int,
+ seed,
+ recraft_style: RecraftStyle = None,
+ negative_prompt: str = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False, max_length=1000)
+ default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+ if recraft_style is None:
+ recraft_style = default_style
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ request = RecraftImageGenerationRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model=RecraftModel.recraftv3,
+ n=n,
+ style=recraft_style.style,
+ substyle=recraft_style.substyle,
+ style_id=recraft_style.style_id,
+ )
+
+ # prepare mask tensor
+ mask = resize_mask_to_image(mask, image, allow_gradient=False, add_channel_dim=True)
+
+ images = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ mask=mask[i:i+1],
+ path="/proxy/recraft/images/inpaint",
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ with handle_recraft_image_output():
+ images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+ pbar.update(1)
+
+ images_tensor = torch.cat(images, dim=0)
+ return (images_tensor, )
+
+
+class RecraftTextToVectorNode:
+ """
+ Generates SVG synchronously based on prompt and resolution.
+ """
+
+ RETURN_TYPES = ("SVG",) # Changed
+ DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation.",
+ },
+ ),
+ "substyle": (get_v3_substyles(RecraftStyleV3.vector_illustration),),
+ "size": (
+ [res.value for res in RecraftImageSize],
+ {
+ "default": RecraftImageSize.res_1024x1024,
+ "tooltip": "The size of the generated image.",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 6,
+ "tooltip": "The number of images to generate.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ "recraft_controls": (
+ RecraftIO.CONTROLS,
+ {
+ "tooltip": "Optional additional controls over the generation via the Recraft Controls node."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ substyle: str,
+ size: str,
+ n: int,
+ seed,
+ negative_prompt: str = None,
+ recraft_controls: RecraftControls = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ validate_string(prompt, strip_whitespace=False, max_length=1000)
+ # create RecraftStyle so strings will be formatted properly (i.e. "None" will become None)
+ recraft_style = RecraftStyle(RecraftStyleV3.vector_illustration, substyle=substyle)
+
+ controls_api = None
+ if recraft_controls:
+ controls_api = recraft_controls.create_api_model()
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/recraft/image_generation",
+ method=HttpMethod.POST,
+ request_model=RecraftImageGenerationRequest,
+ response_model=RecraftImageGenerationResponse,
+ ),
+ request=RecraftImageGenerationRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model=RecraftModel.recraftv3,
+ size=size,
+ n=n,
+ style=recraft_style.style,
+ substyle=recraft_style.substyle,
+ controls=controls_api,
+ ),
+ auth_kwargs=kwargs,
+ )
+ response: RecraftImageGenerationResponse = operation.execute()
+ svg_data = []
+ urls = []
+ for data in response.data:
+ if unique_id and data.url:
+ urls.append(data.url)
+ # Print result on each iteration in case of error
+ PromptServer.instance.send_progress_text(
+ f"Result URL: {' '.join(urls)}", unique_id
+ )
+ svg_data.append(download_url_to_bytesio(data.url, timeout=1024))
+
+ return (SVG(svg_data),)
+
+
+class RecraftVectorizeImageNode:
+ """
+ Generates SVG synchronously from an input image.
+ """
+
+ RETURN_TYPES = ("SVG",) # Changed
+ DESCRIPTION = cleandoc(__doc__ or "") if 'cleandoc' in globals() else __doc__ # Keep cleandoc if other nodes use it
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ },
+ "optional": {
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ **kwargs,
+ ):
+ svgs = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ path="/proxy/recraft/images/vectorize",
+ auth_kwargs=kwargs,
+ )
+ svgs.append(SVG(sub_bytes))
+ pbar.update(1)
+
+ return (SVG.combine_all(svgs), )
+
+
+class RecraftReplaceBackgroundNode:
+ """
+ Replace background on image, based on provided prompt.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Prompt for the image generation.",
+ },
+ ),
+ "n": (
+ IO.INT,
+ {
+ "default": 1,
+ "min": 1,
+ "max": 6,
+ "tooltip": "The number of images to generate.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFFFFFFFFFF,
+ "control_after_generate": True,
+ "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+ },
+ ),
+ },
+ "optional": {
+ "recraft_style": (RecraftIO.STYLEV3,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "An optional text description of undesired elements on an image.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ prompt: str,
+ n: int,
+ seed,
+ recraft_style: RecraftStyle = None,
+ negative_prompt: str = None,
+ **kwargs,
+ ):
+ default_style = RecraftStyle(RecraftStyleV3.realistic_image)
+ if recraft_style is None:
+ recraft_style = default_style
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ request = RecraftImageGenerationRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ model=RecraftModel.recraftv3,
+ n=n,
+ style=recraft_style.style,
+ substyle=recraft_style.substyle,
+ style_id=recraft_style.style_id,
+ )
+
+ images = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ path="/proxy/recraft/images/replaceBackground",
+ request=request,
+ auth_kwargs=kwargs,
+ )
+ images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+ pbar.update(1)
+
+ images_tensor = torch.cat(images, dim=0)
+ return (images_tensor, )
+
+
+class RecraftRemoveBackgroundNode:
+ """
+ Remove background from image, and return processed image and mask.
+ """
+
+ RETURN_TYPES = (IO.IMAGE, IO.MASK)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ },
+ "optional": {
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ **kwargs,
+ ):
+ images = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ path="/proxy/recraft/images/removeBackground",
+ auth_kwargs=kwargs,
+ )
+ images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+ pbar.update(1)
+
+ images_tensor = torch.cat(images, dim=0)
+ # use alpha channel as masks, in B,H,W format
+ masks_tensor = images_tensor[:,:,:,-1:].squeeze(-1)
+ return (images_tensor, masks_tensor)
+
+
+class RecraftCrispUpscaleNode:
+ """
+ Upscale image synchronously.
+ Enhances a given raster image using ‘crisp upscale’ tool, increasing image resolution, making the image sharper and cleaner.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ RECRAFT_PATH = "/proxy/recraft/images/crispUpscale"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE, ),
+ },
+ "optional": {
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ image: torch.Tensor,
+ **kwargs,
+ ):
+ images = []
+ total = image.shape[0]
+ pbar = ProgressBar(total)
+ for i in range(total):
+ sub_bytes = handle_recraft_file_request(
+ image=image[i],
+ path=self.RECRAFT_PATH,
+ auth_kwargs=kwargs,
+ )
+ images.append(torch.cat([bytesio_to_image_tensor(x) for x in sub_bytes], dim=0))
+ pbar.update(1)
+
+ images_tensor = torch.cat(images, dim=0)
+ return (images_tensor,)
+
+
+class RecraftCreativeUpscaleNode(RecraftCrispUpscaleNode):
+ """
+ Upscale image synchronously.
+ Enhances a given raster image using ‘creative upscale’ tool, boosting resolution with a focus on refining small details and faces.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Recraft"
+
+ RECRAFT_PATH = "/proxy/recraft/images/creativeUpscale"
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "RecraftTextToImageNode": RecraftTextToImageNode,
+ "RecraftImageToImageNode": RecraftImageToImageNode,
+ "RecraftImageInpaintingNode": RecraftImageInpaintingNode,
+ "RecraftTextToVectorNode": RecraftTextToVectorNode,
+ "RecraftVectorizeImageNode": RecraftVectorizeImageNode,
+ "RecraftRemoveBackgroundNode": RecraftRemoveBackgroundNode,
+ "RecraftReplaceBackgroundNode": RecraftReplaceBackgroundNode,
+ "RecraftCrispUpscaleNode": RecraftCrispUpscaleNode,
+ "RecraftCreativeUpscaleNode": RecraftCreativeUpscaleNode,
+ "RecraftStyleV3RealisticImage": RecraftStyleV3RealisticImageNode,
+ "RecraftStyleV3DigitalIllustration": RecraftStyleV3DigitalIllustrationNode,
+ "RecraftStyleV3LogoRaster": RecraftStyleV3LogoRasterNode,
+ "RecraftStyleV3InfiniteStyleLibrary": RecraftStyleInfiniteStyleLibrary,
+ "RecraftColorRGB": RecraftColorRGBNode,
+ "RecraftControls": RecraftControlsNode,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "RecraftTextToImageNode": "Recraft Text to Image",
+ "RecraftImageToImageNode": "Recraft Image to Image",
+ "RecraftImageInpaintingNode": "Recraft Image Inpainting",
+ "RecraftTextToVectorNode": "Recraft Text to Vector",
+ "RecraftVectorizeImageNode": "Recraft Vectorize Image",
+ "RecraftRemoveBackgroundNode": "Recraft Remove Background",
+ "RecraftReplaceBackgroundNode": "Recraft Replace Background",
+ "RecraftCrispUpscaleNode": "Recraft Crisp Upscale Image",
+ "RecraftCreativeUpscaleNode": "Recraft Creative Upscale Image",
+ "RecraftStyleV3RealisticImage": "Recraft Style - Realistic Image",
+ "RecraftStyleV3DigitalIllustration": "Recraft Style - Digital Illustration",
+ "RecraftStyleV3LogoRaster": "Recraft Style - Logo Raster",
+ "RecraftStyleV3InfiniteStyleLibrary": "Recraft Style - Infinite Style Library",
+ "RecraftColorRGB": "Recraft Color RGB",
+ "RecraftControls": "Recraft Controls",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_rodin.py b/ComfyUI/comfy_api_nodes/nodes_rodin.py
new file mode 100644
index 0000000000000000000000000000000000000000..67f90478c1bc6ee6f035db8eb4338fec4fcb623e
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_rodin.py
@@ -0,0 +1,462 @@
+"""
+ComfyUI X Rodin3D(Deemos) API Nodes
+
+Rodin API docs: https://developer.hyper3d.ai/
+
+"""
+
+from __future__ import annotations
+from inspect import cleandoc
+from comfy.comfy_types.node_typing import IO
+import folder_paths as comfy_paths
+import requests
+import os
+import datetime
+import shutil
+import time
+import io
+import logging
+import math
+from PIL import Image
+from comfy_api_nodes.apis.rodin_api import (
+ Rodin3DGenerateRequest,
+ Rodin3DGenerateResponse,
+ Rodin3DCheckStatusRequest,
+ Rodin3DCheckStatusResponse,
+ Rodin3DDownloadRequest,
+ Rodin3DDownloadResponse,
+ JobStatus,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+)
+
+
+COMMON_PARAMETERS = {
+ "Seed": (
+ IO.INT,
+ {
+ "default":0,
+ "min":0,
+ "max":65535,
+ "display":"number"
+ }
+ ),
+ "Material_Type": (
+ IO.COMBO,
+ {
+ "options": ["PBR", "Shaded"],
+ "default": "PBR"
+ }
+ ),
+ "Polygon_count": (
+ IO.COMBO,
+ {
+ "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
+ "default": "18K-Quad"
+ }
+ )
+}
+
+def create_task_error(response: Rodin3DGenerateResponse):
+ """Check if the response has error"""
+ return hasattr(response, "error")
+
+
+
+class Rodin3DAPI:
+ """
+ Generate 3D Assets using Rodin API
+ """
+ RETURN_TYPES = (IO.STRING,)
+ RETURN_NAMES = ("3D Model Path",)
+ CATEGORY = "api node/3d/Rodin"
+ DESCRIPTION = cleandoc(__doc__ or "")
+ FUNCTION = "api_call"
+ API_NODE = True
+
+ def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048):
+ """
+ Converts a PyTorch tensor to a file-like object.
+
+ Args:
+ - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
+ where C is the number of channels (3 for RGB), H is height, and W is width.
+
+ Returns:
+ - io.BytesIO: A file-like object containing the image data.
+ """
+ array = tensor.cpu().numpy()
+ array = (array * 255).astype('uint8')
+ image = Image.fromarray(array, 'RGB')
+
+ original_width, original_height = image.size
+ original_pixels = original_width * original_height
+ if original_pixels > max_pixels:
+ scale = math.sqrt(max_pixels / original_pixels)
+ new_width = int(original_width * scale)
+ new_height = int(original_height * scale)
+ else:
+ new_width, new_height = original_width, original_height
+
+ if new_width != original_width or new_height != original_height:
+ image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+ img_byte_arr = io.BytesIO()
+ image.save(img_byte_arr, format='PNG') # PNG is used for lossless compression
+ img_byte_arr.seek(0)
+ return img_byte_arr
+
+ def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str:
+ has_failed = any(job.status == JobStatus.Failed for job in response.jobs)
+ all_done = all(job.status == JobStatus.Done for job in response.jobs)
+ status_list = [str(job.status) for job in response.jobs]
+ logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
+ if has_failed:
+ logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
+ raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
+ elif all_done:
+ return "DONE"
+ else:
+ return "Generating"
+
+ def CreateGenerateTask(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs):
+ if images == None:
+ raise Exception("Rodin 3D generate requires at least 1 image.")
+ if len(images) >= 5:
+ raise Exception("Rodin 3D generate requires up to 5 image.")
+
+ path = "/proxy/rodin/api/v2/rodin"
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=Rodin3DGenerateRequest,
+ response_model=Rodin3DGenerateResponse,
+ ),
+ request=Rodin3DGenerateRequest(
+ seed=seed,
+ tier=tier,
+ material=material,
+ quality=quality,
+ mesh_mode=mesh_mode
+ ),
+ files=[
+ (
+ "images",
+ open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image)
+ )
+ for image in images if image is not None
+ ],
+ content_type = "multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+
+ response = operation.execute()
+
+ if create_task_error(response):
+ error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
+ logging.error(error_message)
+ raise Exception(error_message)
+
+ logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
+ subscription_key = response.jobs.subscription_key
+ task_uuid = response.uuid
+ logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
+ return task_uuid, subscription_key
+
+ def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse:
+
+ path = "/proxy/rodin/api/v2/status"
+
+ poll_operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path = path,
+ method=HttpMethod.POST,
+ request_model=Rodin3DCheckStatusRequest,
+ response_model=Rodin3DCheckStatusResponse,
+ ),
+ request=Rodin3DCheckStatusRequest(
+ subscription_key = subscription_key
+ ),
+ completed_statuses=["DONE"],
+ failed_statuses=["FAILED"],
+ status_extractor=self.check_rodin_status,
+ poll_interval=3.0,
+ auth_kwargs=kwargs,
+ )
+
+ logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
+
+ return poll_operation.execute()
+
+
+
+ def GetRodinDownloadList(self, uuid, **kwargs) -> Rodin3DDownloadResponse:
+ logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+
+ path = "/proxy/rodin/api/v2/download"
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=path,
+ method=HttpMethod.POST,
+ request_model=Rodin3DDownloadRequest,
+ response_model=Rodin3DDownloadResponse,
+ ),
+ request=Rodin3DDownloadRequest(
+ task_uuid=uuid
+ ),
+ auth_kwargs=kwargs
+ )
+
+ return operation.execute()
+
+ def GetQualityAndMode(self, PolyCount):
+ if PolyCount == "200K-Triangle":
+ mesh_mode = "Raw"
+ quality = "medium"
+ else:
+ mesh_mode = "Quad"
+ if PolyCount == "4K-Quad":
+ quality = "extra-low"
+ elif PolyCount == "8K-Quad":
+ quality = "low"
+ elif PolyCount == "18K-Quad":
+ quality = "medium"
+ elif PolyCount == "50K-Quad":
+ quality = "high"
+ else:
+ quality = "medium"
+
+ return mesh_mode, quality
+
+ def DownLoadFiles(self, Url_List):
+ Save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
+ os.makedirs(Save_path, exist_ok=True)
+ model_file_path = None
+ for Item in Url_List.list:
+ url = Item.url
+ file_name = Item.name
+ file_path = os.path.join(Save_path, file_name)
+ if file_path.endswith(".glb"):
+ model_file_path = file_path
+ logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
+ max_retries = 5
+ for attempt in range(max_retries):
+ try:
+ with requests.get(url, stream=True) as r:
+ r.raise_for_status()
+ with open(file_path, "wb") as f:
+ shutil.copyfileobj(r.raw, f)
+ break
+ except Exception as e:
+ logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
+ if attempt < max_retries - 1:
+ logging.info("Retrying...")
+ time.sleep(2)
+ else:
+ logging.info(f"[ Rodin3D API - download_files ] Failed to download {file_path} after {max_retries} attempts.")
+
+ return model_file_path
+
+
+class Rodin3D_Regular(Rodin3DAPI):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "Images":
+ (
+ IO.IMAGE,
+ {
+ "forceInput":True,
+ }
+ )
+ },
+ "optional": {
+ **COMMON_PARAMETERS
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ Images,
+ Seed,
+ Material_Type,
+ Polygon_count,
+ **kwargs
+ ):
+ tier = "Regular"
+ num_images = Images.shape[0]
+ m_images = []
+ for i in range(num_images):
+ m_images.append(Images[i])
+ mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+ task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+ self.poll_for_task_status(subscription_key, **kwargs)
+ Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+ model = self.DownLoadFiles(Download_List)
+
+ return (model,)
+
+class Rodin3D_Detail(Rodin3DAPI):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "Images":
+ (
+ IO.IMAGE,
+ {
+ "forceInput":True,
+ }
+ )
+ },
+ "optional": {
+ **COMMON_PARAMETERS
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ Images,
+ Seed,
+ Material_Type,
+ Polygon_count,
+ **kwargs
+ ):
+ tier = "Detail"
+ num_images = Images.shape[0]
+ m_images = []
+ for i in range(num_images):
+ m_images.append(Images[i])
+ mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+ task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+ self.poll_for_task_status(subscription_key, **kwargs)
+ Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+ model = self.DownLoadFiles(Download_List)
+
+ return (model,)
+
+class Rodin3D_Smooth(Rodin3DAPI):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "Images":
+ (
+ IO.IMAGE,
+ {
+ "forceInput":True,
+ }
+ )
+ },
+ "optional": {
+ **COMMON_PARAMETERS
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ Images,
+ Seed,
+ Material_Type,
+ Polygon_count,
+ **kwargs
+ ):
+ tier = "Smooth"
+ num_images = Images.shape[0]
+ m_images = []
+ for i in range(num_images):
+ m_images.append(Images[i])
+ mesh_mode, quality = self.GetQualityAndMode(Polygon_count)
+ task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=Material_Type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+ self.poll_for_task_status(subscription_key, **kwargs)
+ Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+ model = self.DownLoadFiles(Download_List)
+
+ return (model,)
+
+class Rodin3D_Sketch(Rodin3DAPI):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "Images":
+ (
+ IO.IMAGE,
+ {
+ "forceInput":True,
+ }
+ )
+ },
+ "optional": {
+ "Seed":
+ (
+ IO.INT,
+ {
+ "default":0,
+ "min":0,
+ "max":65535,
+ "display":"number"
+ }
+ )
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ Images,
+ Seed,
+ **kwargs
+ ):
+ tier = "Sketch"
+ num_images = Images.shape[0]
+ m_images = []
+ for i in range(num_images):
+ m_images.append(Images[i])
+ material_type = "PBR"
+ quality = "medium"
+ mesh_mode = "Quad"
+ task_uuid, subscription_key = self.CreateGenerateTask(images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs)
+ self.poll_for_task_status(subscription_key, **kwargs)
+ Download_List = self.GetRodinDownloadList(task_uuid, **kwargs)
+ model = self.DownLoadFiles(Download_List)
+
+ return (model,)
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "Rodin3D_Regular": Rodin3D_Regular,
+ "Rodin3D_Detail": Rodin3D_Detail,
+ "Rodin3D_Smooth": Rodin3D_Smooth,
+ "Rodin3D_Sketch": Rodin3D_Sketch,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate",
+ "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
+ "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
+ "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_runway.py b/ComfyUI/comfy_api_nodes/nodes_runway.py
new file mode 100644
index 0000000000000000000000000000000000000000..af4b321f96d08cb2a4bb8f9c041f81d35e407b32
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_runway.py
@@ -0,0 +1,635 @@
+"""Runway API Nodes
+
+API Docs:
+ - https://docs.dev.runwayml.com/api/#tag/Task-management/paths/~1v1~1tasks~1%7Bid%7D/delete
+
+User Guides:
+ - https://help.runwayml.com/hc/en-us/sections/30265301423635-Gen-3-Alpha
+ - https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video
+ - https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo
+ - https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3
+
+"""
+
+from typing import Union, Optional, Any
+from enum import Enum
+
+import torch
+
+from comfy_api_nodes.apis import (
+ RunwayImageToVideoRequest,
+ RunwayImageToVideoResponse,
+ RunwayTaskStatusResponse as TaskStatusResponse,
+ RunwayTaskStatusEnum as TaskStatus,
+ RunwayModelEnum as Model,
+ RunwayDurationEnum as Duration,
+ RunwayAspectRatioEnum as AspectRatio,
+ RunwayPromptImageObject,
+ RunwayPromptImageDetailedObject,
+ RunwayTextToImageRequest,
+ RunwayTextToImageResponse,
+ Model4,
+ ReferenceImage,
+ RunwayTextToImageAspectRatioEnum,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ upload_images_to_comfyapi,
+ download_url_to_video_output,
+ image_tensor_pair_to_batch,
+ validate_string,
+ download_url_to_image_tensor,
+)
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy_api.input_impl import VideoFromFile
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+
+PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
+PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
+PATH_GET_TASK_STATUS = "/proxy/runway/tasks"
+
+AVERAGE_DURATION_I2V_SECONDS = 64
+AVERAGE_DURATION_FLF_SECONDS = 256
+AVERAGE_DURATION_T2I_SECONDS = 41
+
+
+class RunwayApiError(Exception):
+ """Base exception for Runway API errors."""
+
+ pass
+
+
+class RunwayGen4TurboAspectRatio(str, Enum):
+ """Aspect ratios supported for Image to Video API when using gen4_turbo model."""
+
+ field_1280_720 = "1280:720"
+ field_720_1280 = "720:1280"
+ field_1104_832 = "1104:832"
+ field_832_1104 = "832:1104"
+ field_960_960 = "960:960"
+ field_1584_672 = "1584:672"
+
+
+class RunwayGen3aAspectRatio(str, Enum):
+ """Aspect ratios supported for Image to Video API when using gen3a_turbo model."""
+
+ field_768_1280 = "768:1280"
+ field_1280_768 = "1280:768"
+
+
+def get_video_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+ """Returns the video URL from the task status response if it exists."""
+ if response.output and len(response.output) > 0:
+ return response.output[0]
+ return None
+
+
+# TODO: replace with updated image validation utils (upstream)
+def validate_input_image(image: torch.Tensor) -> bool:
+ """
+ Validate the input image is within the size limits for the Runway API.
+ See: https://docs.dev.runwayml.com/assets/inputs/#common-error-reasons
+ """
+ return image.shape[2] < 8000 and image.shape[1] < 8000
+
+
+def poll_until_finished(
+ auth_kwargs: dict[str, str],
+ api_endpoint: ApiEndpoint[Any, TaskStatusResponse],
+ estimated_duration: Optional[int] = None,
+ node_id: Optional[str] = None,
+) -> TaskStatusResponse:
+ """Polls the Runway API endpoint until the task reaches a terminal state, then returns the response."""
+ return PollingOperation(
+ poll_endpoint=api_endpoint,
+ completed_statuses=[
+ TaskStatus.SUCCEEDED.value,
+ ],
+ failed_statuses=[
+ TaskStatus.FAILED.value,
+ TaskStatus.CANCELLED.value,
+ ],
+ status_extractor=lambda response: (response.status.value),
+ auth_kwargs=auth_kwargs,
+ result_url_extractor=get_video_url_from_task_status,
+ estimated_duration=estimated_duration,
+ node_id=node_id,
+ progress_extractor=extract_progress_from_task_status,
+ ).execute()
+
+
+def extract_progress_from_task_status(
+ response: TaskStatusResponse,
+) -> Union[float, None]:
+ if hasattr(response, "progress") and response.progress is not None:
+ return response.progress * 100
+ return None
+
+
+def get_image_url_from_task_status(response: TaskStatusResponse) -> Union[str, None]:
+ """Returns the image URL from the task status response if it exists."""
+ if response.output and len(response.output) > 0:
+ return response.output[0]
+ return None
+
+
+class RunwayVideoGenNode(ComfyNodeABC):
+ """Runway Video Node Base."""
+
+ RETURN_TYPES = ("VIDEO",)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/video/Runway"
+ API_NODE = True
+
+ def validate_task_created(self, response: RunwayImageToVideoResponse) -> bool:
+ """
+ Validate the task creation response from the Runway API matches
+ expected format.
+ """
+ if not bool(response.id):
+ raise RunwayApiError("Invalid initial response from Runway API.")
+ return True
+
+ def validate_response(self, response: RunwayImageToVideoResponse) -> bool:
+ """
+ Validate the successful task status response from the Runway API
+ matches expected format.
+ """
+ if not response.output or len(response.output) == 0:
+ raise RunwayApiError(
+ "Runway task succeeded but no video data found in response."
+ )
+ return True
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> RunwayImageToVideoResponse:
+ """Poll the task status until it is finished then get the response."""
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=TaskStatusResponse,
+ ),
+ estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+ node_id=node_id,
+ )
+
+ def generate_video(
+ self,
+ request: RunwayImageToVideoRequest,
+ auth_kwargs: dict[str, str],
+ node_id: Optional[str] = None,
+ ) -> tuple[VideoFromFile]:
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_IMAGE_TO_VIDEO,
+ method=HttpMethod.POST,
+ request_model=RunwayImageToVideoRequest,
+ response_model=RunwayImageToVideoResponse,
+ ),
+ request=request,
+ auth_kwargs=auth_kwargs,
+ )
+
+ initial_response = initial_operation.execute()
+ self.validate_task_created(initial_response)
+ task_id = initial_response.id
+
+ final_response = self.get_response(task_id, auth_kwargs, node_id)
+ self.validate_response(final_response)
+
+ video_url = get_video_url_from_task_status(final_response)
+ return (download_url_to_video_output(video_url),)
+
+
+class RunwayImageToVideoNodeGen3a(RunwayVideoGenNode):
+ """Runway Image to Video Node using Gen3a Turbo model."""
+
+ DESCRIPTION = "Generate a video from a single starting frame using Gen3a Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/33927968552339-Creating-with-Act-One-on-Gen-3-Alpha-and-Turbo."
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+ ),
+ "start_frame": (
+ IO.IMAGE,
+ {"tooltip": "Start frame to be used for the video"},
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+ ),
+ "ratio": model_field_to_node_input(
+ IO.COMBO,
+ RunwayImageToVideoRequest,
+ "ratio",
+ enum_type=RunwayGen3aAspectRatio,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ RunwayImageToVideoRequest,
+ "seed",
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ start_frame: torch.Tensor,
+ duration: str,
+ ratio: str,
+ seed: int,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Validate inputs
+ validate_string(prompt, min_length=1)
+ validate_input_image(start_frame)
+
+ # Upload image
+ download_urls = upload_images_to_comfyapi(
+ start_frame,
+ max_images=1,
+ mime_type="image/png",
+ auth_kwargs=kwargs,
+ )
+ if len(download_urls) != 1:
+ raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+ return self.generate_video(
+ RunwayImageToVideoRequest(
+ promptText=prompt,
+ seed=seed,
+ model=Model("gen3a_turbo"),
+ duration=Duration(duration),
+ ratio=AspectRatio(ratio),
+ promptImage=RunwayPromptImageObject(
+ root=[
+ RunwayPromptImageDetailedObject(
+ uri=str(download_urls[0]), position="first"
+ )
+ ]
+ ),
+ ),
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ )
+
+
+class RunwayImageToVideoNodeGen4(RunwayVideoGenNode):
+ """Runway Image to Video Node using Gen4 Turbo model."""
+
+ DESCRIPTION = "Generate a video from a single starting frame using Gen4 Turbo model. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/37327109429011-Creating-with-Gen-4-Video."
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+ ),
+ "start_frame": (
+ IO.IMAGE,
+ {"tooltip": "Start frame to be used for the video"},
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+ ),
+ "ratio": model_field_to_node_input(
+ IO.COMBO,
+ RunwayImageToVideoRequest,
+ "ratio",
+ enum_type=RunwayGen4TurboAspectRatio,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ RunwayImageToVideoRequest,
+ "seed",
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ start_frame: torch.Tensor,
+ duration: str,
+ ratio: str,
+ seed: int,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Validate inputs
+ validate_string(prompt, min_length=1)
+ validate_input_image(start_frame)
+
+ # Upload image
+ download_urls = upload_images_to_comfyapi(
+ start_frame,
+ max_images=1,
+ mime_type="image/png",
+ auth_kwargs=kwargs,
+ )
+ if len(download_urls) != 1:
+ raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+ return self.generate_video(
+ RunwayImageToVideoRequest(
+ promptText=prompt,
+ seed=seed,
+ model=Model("gen4_turbo"),
+ duration=Duration(duration),
+ ratio=AspectRatio(ratio),
+ promptImage=RunwayPromptImageObject(
+ root=[
+ RunwayPromptImageDetailedObject(
+ uri=str(download_urls[0]), position="first"
+ )
+ ]
+ ),
+ ),
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ )
+
+
+class RunwayFirstLastFrameNode(RunwayVideoGenNode):
+ """Runway First-Last Frame Node."""
+
+ DESCRIPTION = "Upload first and last keyframes, draft a prompt, and generate a video. More complex transitions, such as cases where the Last frame is completely different from the First frame, may benefit from the longer 10s duration. This would give the generation more time to smoothly transition between the two inputs. Before diving in, review these best practices to ensure that your input selections will set your generation up for success: https://help.runwayml.com/hc/en-us/articles/34170748696595-Creating-with-Keyframes-on-Gen-3."
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> RunwayImageToVideoResponse:
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=TaskStatusResponse,
+ ),
+ estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+ node_id=node_id,
+ )
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+ ),
+ "start_frame": (
+ IO.IMAGE,
+ {"tooltip": "Start frame to be used for the video"},
+ ),
+ "end_frame": (
+ IO.IMAGE,
+ {
+ "tooltip": "End frame to be used for the video. Supported for gen3a_turbo only."
+ },
+ ),
+ "duration": model_field_to_node_input(
+ IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+ ),
+ "ratio": model_field_to_node_input(
+ IO.COMBO,
+ RunwayImageToVideoRequest,
+ "ratio",
+ enum_type=RunwayGen3aAspectRatio,
+ ),
+ "seed": model_field_to_node_input(
+ IO.INT,
+ RunwayImageToVideoRequest,
+ "seed",
+ control_after_generate=True,
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(
+ self,
+ prompt: str,
+ start_frame: torch.Tensor,
+ end_frame: torch.Tensor,
+ duration: str,
+ ratio: str,
+ seed: int,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[VideoFromFile]:
+ # Validate inputs
+ validate_string(prompt, min_length=1)
+ validate_input_image(start_frame)
+ validate_input_image(end_frame)
+
+ # Upload images
+ stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
+ download_urls = upload_images_to_comfyapi(
+ stacked_input_images,
+ max_images=2,
+ mime_type="image/png",
+ auth_kwargs=kwargs,
+ )
+ if len(download_urls) != 2:
+ raise RunwayApiError("Failed to upload one or more images to comfy api.")
+
+ return self.generate_video(
+ RunwayImageToVideoRequest(
+ promptText=prompt,
+ seed=seed,
+ model=Model("gen3a_turbo"),
+ duration=Duration(duration),
+ ratio=AspectRatio(ratio),
+ promptImage=RunwayPromptImageObject(
+ root=[
+ RunwayPromptImageDetailedObject(
+ uri=str(download_urls[0]), position="first"
+ ),
+ RunwayPromptImageDetailedObject(
+ uri=str(download_urls[1]), position="last"
+ ),
+ ]
+ ),
+ ),
+ auth_kwargs=kwargs,
+ node_id=unique_id,
+ )
+
+
+class RunwayTextToImageNode(ComfyNodeABC):
+ """Runway Text to Image Node."""
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "api_call"
+ CATEGORY = "api node/image/Runway"
+ API_NODE = True
+ DESCRIPTION = "Generate an image from a text prompt using Runway's Gen 4 model. You can also include reference images to guide the generation."
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": model_field_to_node_input(
+ IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True
+ ),
+ "ratio": model_field_to_node_input(
+ IO.COMBO,
+ RunwayTextToImageRequest,
+ "ratio",
+ enum_type=RunwayTextToImageAspectRatioEnum,
+ ),
+ },
+ "optional": {
+ "reference_image": (
+ IO.IMAGE,
+ {"tooltip": "Optional reference image to guide the generation"},
+ )
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ def validate_task_created(self, response: RunwayTextToImageResponse) -> bool:
+ """
+ Validate the task creation response from the Runway API matches
+ expected format.
+ """
+ if not bool(response.id):
+ raise RunwayApiError("Invalid initial response from Runway API.")
+ return True
+
+ def validate_response(self, response: TaskStatusResponse) -> bool:
+ """
+ Validate the successful task status response from the Runway API
+ matches expected format.
+ """
+ if not response.output or len(response.output) == 0:
+ raise RunwayApiError(
+ "Runway task succeeded but no image data found in response."
+ )
+ return True
+
+ def get_response(
+ self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+ ) -> TaskStatusResponse:
+ """Poll the task status until it is finished then get the response."""
+ return poll_until_finished(
+ auth_kwargs,
+ ApiEndpoint(
+ path=f"{PATH_GET_TASK_STATUS}/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=TaskStatusResponse,
+ ),
+ estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
+ node_id=node_id,
+ )
+
+ def api_call(
+ self,
+ prompt: str,
+ ratio: str,
+ reference_image: Optional[torch.Tensor] = None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ) -> tuple[torch.Tensor]:
+ # Validate inputs
+ validate_string(prompt, min_length=1)
+
+ # Prepare reference images if provided
+ reference_images = None
+ if reference_image is not None:
+ validate_input_image(reference_image)
+ download_urls = upload_images_to_comfyapi(
+ reference_image,
+ max_images=1,
+ mime_type="image/png",
+ auth_kwargs=kwargs,
+ )
+ if len(download_urls) != 1:
+ raise RunwayApiError("Failed to upload reference image to comfy api.")
+
+ reference_images = [ReferenceImage(uri=str(download_urls[0]))]
+
+ # Create request
+ request = RunwayTextToImageRequest(
+ promptText=prompt,
+ model=Model4.gen4_image,
+ ratio=ratio,
+ referenceImages=reference_images,
+ )
+
+ # Execute initial request
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path=PATH_TEXT_TO_IMAGE,
+ method=HttpMethod.POST,
+ request_model=RunwayTextToImageRequest,
+ response_model=RunwayTextToImageResponse,
+ ),
+ request=request,
+ auth_kwargs=kwargs,
+ )
+
+ initial_response = initial_operation.execute()
+ self.validate_task_created(initial_response)
+ task_id = initial_response.id
+
+ # Poll for completion
+ final_response = self.get_response(
+ task_id, auth_kwargs=kwargs, node_id=unique_id
+ )
+ self.validate_response(final_response)
+
+ # Download and return image
+ image_url = get_image_url_from_task_status(final_response)
+ return (download_url_to_image_tensor(image_url),)
+
+
+NODE_CLASS_MAPPINGS = {
+ "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode,
+ "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a,
+ "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4,
+ "RunwayTextToImageNode": RunwayTextToImageNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "RunwayFirstLastFrameNode": "Runway First-Last-Frame to Video",
+ "RunwayImageToVideoNodeGen3a": "Runway Image to Video (Gen3a Turbo)",
+ "RunwayImageToVideoNodeGen4": "Runway Image to Video (Gen4 Turbo)",
+ "RunwayTextToImageNode": "Runway Text to Image",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_stability.py b/ComfyUI/comfy_api_nodes/nodes_stability.py
new file mode 100644
index 0000000000000000000000000000000000000000..02e4216780b937e7a01c75c775b5f1a18b8cabc3
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_stability.py
@@ -0,0 +1,614 @@
+from inspect import cleandoc
+from comfy.comfy_types.node_typing import IO
+from comfy_api_nodes.apis.stability_api import (
+ StabilityUpscaleConservativeRequest,
+ StabilityUpscaleCreativeRequest,
+ StabilityAsyncResponse,
+ StabilityResultsGetResponse,
+ StabilityStable3_5Request,
+ StabilityStableUltraRequest,
+ StabilityStableUltraResponse,
+ StabilityAspectRatio,
+ Stability_SD3_5_Model,
+ Stability_SD3_5_GenerationMode,
+ get_stability_style_presets,
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ bytesio_to_image_tensor,
+ tensor_to_bytesio,
+ validate_string,
+)
+
+import torch
+import base64
+from io import BytesIO
+from enum import Enum
+
+
+class StabilityPollStatus(str, Enum):
+ finished = "finished"
+ in_progress = "in_progress"
+ failed = "failed"
+
+
+def get_async_dummy_status(x: StabilityResultsGetResponse):
+ if x.name is not None or x.errors is not None:
+ return StabilityPollStatus.failed
+ elif x.finish_reason is not None:
+ return StabilityPollStatus.finished
+ return StabilityPollStatus.in_progress
+
+
+class StabilityStableImageUltraNode:
+ """
+ Generates images synchronously based on prompt and resolution.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Stability AI"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" +
+ "What you wish to see in the output image. A strong, descriptive prompt that clearly defines" +
+ "elements, colors, and subjects will lead to better results. " +
+ "To control the weight of a given word use the format `(word:weight)`," +
+ "where `word` is the word you'd like to control the weight of and `weight`" +
+ "is a value between 0 and 1. For example: `The sky was a crisp (blue:0.3) and (green:0.8)`" +
+ "would convey a sky that was blue and green, but more green than blue."
+ },
+ ),
+ "aspect_ratio": ([x.value for x in StabilityAspectRatio],
+ {
+ "default": StabilityAspectRatio.ratio_1_1,
+ "tooltip": "Aspect ratio of generated image.",
+ },
+ ),
+ "style_preset": (get_stability_style_presets(),
+ {
+ "tooltip": "Optional desired style of generated image.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 4294967294,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {
+ "image": (IO.IMAGE,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "A blurb of text describing what you do not wish to see in the output image. This is an advanced feature."
+ },
+ ),
+ "image_denoise": (
+ IO.FLOAT,
+ {
+ "default": 0.5,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(self, prompt: str, aspect_ratio: str, style_preset: str, seed: int,
+ negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
+ **kwargs):
+ validate_string(prompt, strip_whitespace=False)
+ # prepare image binary if image present
+ image_binary = None
+ if image is not None:
+ image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
+ else:
+ image_denoise = None
+
+ if not negative_prompt:
+ negative_prompt = None
+ if style_preset == "None":
+ style_preset = None
+
+ files = {
+ "image": image_binary
+ }
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/stability/v2beta/stable-image/generate/ultra",
+ method=HttpMethod.POST,
+ request_model=StabilityStableUltraRequest,
+ response_model=StabilityStableUltraResponse,
+ ),
+ request=StabilityStableUltraRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ aspect_ratio=aspect_ratio,
+ seed=seed,
+ strength=image_denoise,
+ style_preset=style_preset,
+ ),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.finish_reason != "SUCCESS":
+ raise Exception(f"Stable Image Ultra generation failed: {response_api.finish_reason}.")
+
+ image_data = base64.b64decode(response_api.image)
+ returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+ return (returned_image,)
+
+
+class StabilityStableImageSD_3_5Node:
+ """
+ Generates images synchronously based on prompt and resolution.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Stability AI"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
+ },
+ ),
+ "model": ([x.value for x in Stability_SD3_5_Model],),
+ "aspect_ratio": ([x.value for x in StabilityAspectRatio],
+ {
+ "default": StabilityAspectRatio.ratio_1_1,
+ "tooltip": "Aspect ratio of generated image.",
+ },
+ ),
+ "style_preset": (get_stability_style_presets(),
+ {
+ "tooltip": "Optional desired style of generated image.",
+ },
+ ),
+ "cfg_scale": (
+ IO.FLOAT,
+ {
+ "default": 4.0,
+ "min": 1.0,
+ "max": 10.0,
+ "step": 0.1,
+ "tooltip": "How strictly the diffusion process adheres to the prompt text (higher values keep your image closer to your prompt)",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 4294967294,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {
+ "image": (IO.IMAGE,),
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature."
+ },
+ ),
+ "image_denoise": (
+ IO.FLOAT,
+ {
+ "default": 0.5,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01,
+ "tooltip": "Denoise of input image; 0.0 yields image identical to input, 1.0 is as if no image was provided at all.",
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(self, model: str, prompt: str, aspect_ratio: str, style_preset: str, seed: int, cfg_scale: float,
+ negative_prompt: str=None, image: torch.Tensor = None, image_denoise: float=None,
+ **kwargs):
+ validate_string(prompt, strip_whitespace=False)
+ # prepare image binary if image present
+ image_binary = None
+ mode = Stability_SD3_5_GenerationMode.text_to_image
+ if image is not None:
+ image_binary = tensor_to_bytesio(image, total_pixels=1504*1504).read()
+ mode = Stability_SD3_5_GenerationMode.image_to_image
+ aspect_ratio = None
+ else:
+ image_denoise = None
+
+ if not negative_prompt:
+ negative_prompt = None
+ if style_preset == "None":
+ style_preset = None
+
+ files = {
+ "image": image_binary
+ }
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/stability/v2beta/stable-image/generate/sd3",
+ method=HttpMethod.POST,
+ request_model=StabilityStable3_5Request,
+ response_model=StabilityStableUltraResponse,
+ ),
+ request=StabilityStable3_5Request(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ aspect_ratio=aspect_ratio,
+ seed=seed,
+ strength=image_denoise,
+ style_preset=style_preset,
+ cfg_scale=cfg_scale,
+ model=model,
+ mode=mode,
+ ),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.finish_reason != "SUCCESS":
+ raise Exception(f"Stable Diffusion 3.5 Image generation failed: {response_api.finish_reason}.")
+
+ image_data = base64.b64decode(response_api.image)
+ returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+ return (returned_image,)
+
+
+class StabilityUpscaleConservativeNode:
+ """
+ Upscale image with minimal alterations to 4K resolution.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Stability AI"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
+ },
+ ),
+ "creativity": (
+ IO.FLOAT,
+ {
+ "default": 0.35,
+ "min": 0.2,
+ "max": 0.5,
+ "step": 0.01,
+ "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 4294967294,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
+ **kwargs):
+ validate_string(prompt, strip_whitespace=False)
+ image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
+
+ if not negative_prompt:
+ negative_prompt = None
+
+ files = {
+ "image": image_binary
+ }
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/stability/v2beta/stable-image/upscale/conservative",
+ method=HttpMethod.POST,
+ request_model=StabilityUpscaleConservativeRequest,
+ response_model=StabilityStableUltraResponse,
+ ),
+ request=StabilityUpscaleConservativeRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ creativity=round(creativity,2),
+ seed=seed,
+ ),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.finish_reason != "SUCCESS":
+ raise Exception(f"Stability Upscale Conservative generation failed: {response_api.finish_reason}.")
+
+ image_data = base64.b64decode(response_api.image)
+ returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+ return (returned_image,)
+
+
+class StabilityUpscaleCreativeNode:
+ """
+ Upscale image with minimal alterations to 4K resolution.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Stability AI"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "What you wish to see in the output image. A strong, descriptive prompt that clearly defines elements, colors, and subjects will lead to better results."
+ },
+ ),
+ "creativity": (
+ IO.FLOAT,
+ {
+ "default": 0.3,
+ "min": 0.1,
+ "max": 0.5,
+ "step": 0.01,
+ "tooltip": "Controls the likelihood of creating additional details not heavily conditioned by the init image.",
+ },
+ ),
+ "style_preset": (get_stability_style_presets(),
+ {
+ "tooltip": "Optional desired style of generated image.",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 4294967294,
+ "control_after_generate": True,
+ "tooltip": "The random seed used for creating the noise.",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "default": "",
+ "forceInput": True,
+ "tooltip": "Keywords of what you do not wish to see in the output image. This is an advanced feature."
+ },
+ ),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
+ **kwargs):
+ validate_string(prompt, strip_whitespace=False)
+ image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
+
+ if not negative_prompt:
+ negative_prompt = None
+ if style_preset == "None":
+ style_preset = None
+
+ files = {
+ "image": image_binary
+ }
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/stability/v2beta/stable-image/upscale/creative",
+ method=HttpMethod.POST,
+ request_model=StabilityUpscaleCreativeRequest,
+ response_model=StabilityAsyncResponse,
+ ),
+ request=StabilityUpscaleCreativeRequest(
+ prompt=prompt,
+ negative_prompt=negative_prompt,
+ creativity=round(creativity,2),
+ style_preset=style_preset,
+ seed=seed,
+ ),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/stability/v2beta/results/{response_api.id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=StabilityResultsGetResponse,
+ ),
+ poll_interval=3,
+ completed_statuses=[StabilityPollStatus.finished],
+ failed_statuses=[StabilityPollStatus.failed],
+ status_extractor=lambda x: get_async_dummy_status(x),
+ auth_kwargs=kwargs,
+ )
+ response_poll: StabilityResultsGetResponse = operation.execute()
+
+ if response_poll.finish_reason != "SUCCESS":
+ raise Exception(f"Stability Upscale Creative generation failed: {response_poll.finish_reason}.")
+
+ image_data = base64.b64decode(response_poll.result)
+ returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+ return (returned_image,)
+
+
+class StabilityUpscaleFastNode:
+ """
+ Quickly upscales an image via Stability API call to 4x its original size; intended for upscaling low-quality/compressed images.
+ """
+
+ RETURN_TYPES = (IO.IMAGE,)
+ DESCRIPTION = cleandoc(__doc__ or "") # Handle potential None value
+ FUNCTION = "api_call"
+ API_NODE = True
+ CATEGORY = "api node/image/Stability AI"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": (IO.IMAGE,),
+ },
+ "optional": {
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ },
+ }
+
+ def api_call(self, image: torch.Tensor,
+ **kwargs):
+ image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
+
+ files = {
+ "image": image_binary
+ }
+
+ operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/stability/v2beta/stable-image/upscale/fast",
+ method=HttpMethod.POST,
+ request_model=EmptyRequest,
+ response_model=StabilityStableUltraResponse,
+ ),
+ request=EmptyRequest(),
+ files=files,
+ content_type="multipart/form-data",
+ auth_kwargs=kwargs,
+ )
+ response_api = operation.execute()
+
+ if response_api.finish_reason != "SUCCESS":
+ raise Exception(f"Stability Upscale Fast failed: {response_api.finish_reason}.")
+
+ image_data = base64.b64decode(response_api.image)
+ returned_image = bytesio_to_image_tensor(BytesIO(image_data))
+
+ return (returned_image,)
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+ "StabilityStableImageUltraNode": StabilityStableImageUltraNode,
+ "StabilityStableImageSD_3_5Node": StabilityStableImageSD_3_5Node,
+ "StabilityUpscaleConservativeNode": StabilityUpscaleConservativeNode,
+ "StabilityUpscaleCreativeNode": StabilityUpscaleCreativeNode,
+ "StabilityUpscaleFastNode": StabilityUpscaleFastNode,
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "StabilityStableImageUltraNode": "Stability AI Stable Image Ultra",
+ "StabilityStableImageSD_3_5Node": "Stability AI Stable Diffusion 3.5 Image",
+ "StabilityUpscaleConservativeNode": "Stability AI Upscale Conservative",
+ "StabilityUpscaleCreativeNode": "Stability AI Upscale Creative",
+ "StabilityUpscaleFastNode": "Stability AI Upscale Fast",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_tripo.py b/ComfyUI/comfy_api_nodes/nodes_tripo.py
new file mode 100644
index 0000000000000000000000000000000000000000..65f3b21f5cc1e7b11edb90f7b72cf5bbbc29bd29
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_tripo.py
@@ -0,0 +1,574 @@
+import os
+from folder_paths import get_output_directory
+from comfy_api_nodes.mapper_utils import model_field_to_node_input
+from comfy.comfy_types.node_typing import IO
+from comfy_api_nodes.apis import (
+ TripoOrientation,
+ TripoModelVersion,
+)
+from comfy_api_nodes.apis.tripo_api import (
+ TripoTaskType,
+ TripoStyle,
+ TripoFileReference,
+ TripoFileEmptyReference,
+ TripoUrlReference,
+ TripoTaskResponse,
+ TripoTaskStatus,
+ TripoTextToModelRequest,
+ TripoImageToModelRequest,
+ TripoMultiviewToModelRequest,
+ TripoTextureModelRequest,
+ TripoRefineModelRequest,
+ TripoAnimateRigRequest,
+ TripoAnimateRetargetRequest,
+ TripoConvertModelRequest,
+)
+
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+ EmptyRequest,
+)
+from comfy_api_nodes.apinode_utils import (
+ upload_images_to_comfyapi,
+ download_url_to_bytesio,
+)
+
+
+def upload_image_to_tripo(image, **kwargs):
+ urls = upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs)
+ return TripoFileReference(TripoUrlReference(url=urls[0], type="jpeg"))
+
+def get_model_url_from_response(response: TripoTaskResponse) -> str:
+ if response.data is not None:
+ for key in ["pbr_model", "model", "base_model"]:
+ if getattr(response.data.output, key, None) is not None:
+ return getattr(response.data.output, key)
+ raise RuntimeError(f"Failed to get model url from response: {response}")
+
+
+def poll_until_finished(
+ kwargs: dict[str, str],
+ response: TripoTaskResponse,
+) -> tuple[str, str]:
+ """Polls the Tripo API endpoint until the task reaches a terminal state, then returns the response."""
+ if response.code != 0:
+ raise RuntimeError(f"Failed to generate mesh: {response.error}")
+ task_id = response.data.task_id
+ response_poll = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path=f"/proxy/tripo/v2/openapi/task/{task_id}",
+ method=HttpMethod.GET,
+ request_model=EmptyRequest,
+ response_model=TripoTaskResponse,
+ ),
+ completed_statuses=[TripoTaskStatus.SUCCESS],
+ failed_statuses=[
+ TripoTaskStatus.FAILED,
+ TripoTaskStatus.CANCELLED,
+ TripoTaskStatus.UNKNOWN,
+ TripoTaskStatus.BANNED,
+ TripoTaskStatus.EXPIRED,
+ ],
+ status_extractor=lambda x: x.data.status,
+ auth_kwargs=kwargs,
+ node_id=kwargs["unique_id"],
+ result_url_extractor=get_model_url_from_response,
+ progress_extractor=lambda x: x.data.progress,
+ ).execute()
+ if response_poll.data.status == TripoTaskStatus.SUCCESS:
+ url = get_model_url_from_response(response_poll)
+ bytesio = download_url_to_bytesio(url)
+ # Save the downloaded model file
+ model_file = f"tripo_model_{task_id}.glb"
+ with open(os.path.join(get_output_directory(), model_file), "wb") as f:
+ f.write(bytesio.getvalue())
+ return model_file, task_id
+ raise RuntimeError(f"Failed to generate mesh: {response_poll}")
+
+class TripoTextToModelNode:
+ """
+ Generates 3D models synchronously based on a text prompt using Tripo's API.
+ """
+ AVERAGE_DURATION = 80
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": ("STRING", {"multiline": True}),
+ },
+ "optional": {
+ "negative_prompt": ("STRING", {"multiline": True}),
+ "model_version": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "model_version", enum_type=TripoModelVersion),
+ "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+ "texture": ("BOOLEAN", {"default": True}),
+ "pbr": ("BOOLEAN", {"default": True}),
+ "image_seed": ("INT", {"default": 42}),
+ "model_seed": ("INT", {"default": 42}),
+ "texture_seed": ("INT", {"default": 42}),
+ "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+ "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+ "quad": ("BOOLEAN", {"default": False})
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+ RETURN_NAMES = ("model_file", "model task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+ def generate_mesh(self, prompt, negative_prompt=None, model_version=None, style=None, texture=None, pbr=None, image_seed=None, model_seed=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+ style_enum = None if style == "None" else style
+ if not prompt:
+ raise RuntimeError("Prompt is required")
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoTextToModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoTextToModelRequest(
+ type=TripoTaskType.TEXT_TO_MODEL,
+ prompt=prompt,
+ negative_prompt=negative_prompt if negative_prompt else None,
+ model_version=model_version,
+ style=style_enum,
+ texture=texture,
+ pbr=pbr,
+ image_seed=image_seed,
+ model_seed=model_seed,
+ texture_seed=texture_seed,
+ texture_quality=texture_quality,
+ face_limit=face_limit,
+ auto_size=True,
+ quad=quad
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+class TripoImageToModelNode:
+ """
+ Generates 3D models synchronously based on a single image using Tripo's API.
+ """
+ AVERAGE_DURATION = 80
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ },
+ "optional": {
+ "model_version": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "model_version", enum_type=TripoModelVersion),
+ "style": model_field_to_node_input(IO.COMBO, TripoTextToModelRequest, "style", enum_type=TripoStyle, default="None"),
+ "texture": ("BOOLEAN", {"default": True}),
+ "pbr": ("BOOLEAN", {"default": True}),
+ "model_seed": ("INT", {"default": 42}),
+ "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+ "texture_seed": ("INT", {"default": 42}),
+ "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+ "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+ "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+ "quad": ("BOOLEAN", {"default": False})
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+ RETURN_NAMES = ("model_file", "model task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+ def generate_mesh(self, image, model_version=None, style=None, texture=None, pbr=None, model_seed=None, orientation=None, texture_alignment=None, texture_seed=None, texture_quality=None, face_limit=None, quad=None, **kwargs):
+ style_enum = None if style == "None" else style
+ if image is None:
+ raise RuntimeError("Image is required")
+ tripo_file = upload_image_to_tripo(image, **kwargs)
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoImageToModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoImageToModelRequest(
+ type=TripoTaskType.IMAGE_TO_MODEL,
+ file=tripo_file,
+ model_version=model_version,
+ style=style_enum,
+ texture=texture,
+ pbr=pbr,
+ model_seed=model_seed,
+ orientation=orientation,
+ texture_alignment=texture_alignment,
+ texture_seed=texture_seed,
+ texture_quality=texture_quality,
+ face_limit=face_limit,
+ auto_size=True,
+ quad=quad
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+class TripoMultiviewToModelNode:
+ """
+ Generates 3D models synchronously based on up to four images (front, left, back, right) using Tripo's API.
+ """
+ AVERAGE_DURATION = 80
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ },
+ "optional": {
+ "image_left": ("IMAGE",),
+ "image_back": ("IMAGE",),
+ "image_right": ("IMAGE",),
+ "model_version": model_field_to_node_input(IO.COMBO, TripoMultiviewToModelRequest, "model_version", enum_type=TripoModelVersion),
+ "orientation": model_field_to_node_input(IO.COMBO, TripoImageToModelRequest, "orientation", enum_type=TripoOrientation),
+ "texture": ("BOOLEAN", {"default": True}),
+ "pbr": ("BOOLEAN", {"default": True}),
+ "model_seed": ("INT", {"default": 42}),
+ "texture_seed": ("INT", {"default": 42}),
+ "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+ "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+ "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+ "quad": ("BOOLEAN", {"default": False})
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+ RETURN_NAMES = ("model_file", "model task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+
+ def generate_mesh(self, image, image_left=None, image_back=None, image_right=None, model_version=None, orientation=None, texture=None, pbr=None, model_seed=None, texture_seed=None, texture_quality=None, texture_alignment=None, face_limit=None, quad=None, **kwargs):
+ if image is None:
+ raise RuntimeError("front image for multiview is required")
+ images = []
+ image_dict = {
+ "image": image,
+ "image_left": image_left,
+ "image_back": image_back,
+ "image_right": image_right
+ }
+ if image_left is None and image_back is None and image_right is None:
+ raise RuntimeError("At least one of left, back, or right image must be provided for multiview")
+ for image_name in ["image", "image_left", "image_back", "image_right"]:
+ image_ = image_dict[image_name]
+ if image_ is not None:
+ tripo_file = upload_image_to_tripo(image_, **kwargs)
+ images.append(tripo_file)
+ else:
+ images.append(TripoFileEmptyReference())
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoMultiviewToModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoMultiviewToModelRequest(
+ type=TripoTaskType.MULTIVIEW_TO_MODEL,
+ files=images,
+ model_version=model_version,
+ orientation=orientation,
+ texture=texture,
+ pbr=pbr,
+ model_seed=model_seed,
+ texture_seed=texture_seed,
+ texture_quality=texture_quality,
+ texture_alignment=texture_alignment,
+ face_limit=face_limit,
+ quad=quad,
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+class TripoTextureNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model_task_id": ("MODEL_TASK_ID",),
+ },
+ "optional": {
+ "texture": ("BOOLEAN", {"default": True}),
+ "pbr": ("BOOLEAN", {"default": True}),
+ "texture_seed": ("INT", {"default": 42}),
+ "texture_quality": (["standard", "detailed"], {"default": "standard"}),
+ "texture_alignment": (["original_image", "geometry"], {"default": "original_image"}),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+ RETURN_NAMES = ("model_file", "model task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+ AVERAGE_DURATION = 80
+
+ def generate_mesh(self, model_task_id, texture=None, pbr=None, texture_seed=None, texture_quality=None, texture_alignment=None, **kwargs):
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoTextureModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoTextureModelRequest(
+ original_model_task_id=model_task_id,
+ texture=texture,
+ pbr=pbr,
+ texture_seed=texture_seed,
+ texture_quality=texture_quality,
+ texture_alignment=texture_alignment
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+
+class TripoRefineNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model_task_id": ("MODEL_TASK_ID", {
+ "tooltip": "Must be a v1.4 Tripo model"
+ }),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ DESCRIPTION = "Refine a draft model created by v1.4 Tripo models only."
+
+ RETURN_TYPES = ("STRING", "MODEL_TASK_ID",)
+ RETURN_NAMES = ("model_file", "model task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+ AVERAGE_DURATION = 240
+
+ def generate_mesh(self, model_task_id, **kwargs):
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoRefineModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoRefineModelRequest(
+ draft_model_task_id=model_task_id
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+
+class TripoRigNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "original_model_task_id": ("MODEL_TASK_ID",),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "RIG_TASK_ID")
+ RETURN_NAMES = ("model_file", "rig task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+ AVERAGE_DURATION = 180
+
+ def generate_mesh(self, original_model_task_id, **kwargs):
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoAnimateRigRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoAnimateRigRequest(
+ original_model_task_id=original_model_task_id,
+ out_format="glb",
+ spec="tripo"
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+class TripoRetargetNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "original_model_task_id": ("RIG_TASK_ID",),
+ "animation": ([
+ "preset:idle",
+ "preset:walk",
+ "preset:climb",
+ "preset:jump",
+ "preset:slash",
+ "preset:shoot",
+ "preset:hurt",
+ "preset:fall",
+ "preset:turn",
+ ],),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = ("STRING", "RETARGET_TASK_ID")
+ RETURN_NAMES = ("model_file", "retarget task_id")
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+ AVERAGE_DURATION = 30
+
+ def generate_mesh(self, animation, original_model_task_id, **kwargs):
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoAnimateRetargetRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoAnimateRetargetRequest(
+ original_model_task_id=original_model_task_id,
+ animation=animation,
+ out_format="glb",
+ bake_animation=True
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+class TripoConversionNode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "original_model_task_id": ("MODEL_TASK_ID,RIG_TASK_ID,RETARGET_TASK_ID",),
+ "format": (["GLTF", "USDZ", "FBX", "OBJ", "STL", "3MF"],),
+ },
+ "optional": {
+ "quad": ("BOOLEAN", {"default": False}),
+ "face_limit": ("INT", {"min": -1, "max": 500000, "default": -1}),
+ "texture_size": ("INT", {"min": 128, "max": 4096, "default": 4096}),
+ "texture_format": (["BMP", "DPX", "HDR", "JPEG", "OPEN_EXR", "PNG", "TARGA", "TIFF", "WEBP"], {"default": "JPEG"})
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ @classmethod
+ def VALIDATE_INPUTS(cls, input_types):
+ # The min and max of input1 and input2 are still validated because
+ # we didn't take `input1` or `input2` as arguments
+ if input_types["original_model_task_id"] not in ("MODEL_TASK_ID", "RIG_TASK_ID", "RETARGET_TASK_ID"):
+ return "original_model_task_id must be MODEL_TASK_ID, RIG_TASK_ID or RETARGET_TASK_ID type"
+ return True
+
+ RETURN_TYPES = ()
+ FUNCTION = "generate_mesh"
+ CATEGORY = "api node/3d/Tripo"
+ API_NODE = True
+ OUTPUT_NODE = True
+ AVERAGE_DURATION = 30
+
+ def generate_mesh(self, original_model_task_id, format, quad, face_limit, texture_size, texture_format, **kwargs):
+ if not original_model_task_id:
+ raise RuntimeError("original_model_task_id is required")
+ response = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/tripo/v2/openapi/task",
+ method=HttpMethod.POST,
+ request_model=TripoConvertModelRequest,
+ response_model=TripoTaskResponse,
+ ),
+ request=TripoConvertModelRequest(
+ original_model_task_id=original_model_task_id,
+ format=format,
+ quad=quad if quad else None,
+ face_limit=face_limit if face_limit != -1 else None,
+ texture_size=texture_size if texture_size != 4096 else None,
+ texture_format=texture_format if texture_format != "JPEG" else None
+ ),
+ auth_kwargs=kwargs,
+ ).execute()
+ return poll_until_finished(kwargs, response)
+
+NODE_CLASS_MAPPINGS = {
+ "TripoTextToModelNode": TripoTextToModelNode,
+ "TripoImageToModelNode": TripoImageToModelNode,
+ "TripoMultiviewToModelNode": TripoMultiviewToModelNode,
+ "TripoTextureNode": TripoTextureNode,
+ "TripoRefineNode": TripoRefineNode,
+ "TripoRigNode": TripoRigNode,
+ "TripoRetargetNode": TripoRetargetNode,
+ "TripoConversionNode": TripoConversionNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "TripoTextToModelNode": "Tripo: Text to Model",
+ "TripoImageToModelNode": "Tripo: Image to Model",
+ "TripoMultiviewToModelNode": "Tripo: Multiview to Model",
+ "TripoTextureNode": "Tripo: Texture model",
+ "TripoRefineNode": "Tripo: Refine Draft model",
+ "TripoRigNode": "Tripo: Rig model",
+ "TripoRetargetNode": "Tripo: Retarget rigged model",
+ "TripoConversionNode": "Tripo: Convert model",
+}
diff --git a/ComfyUI/comfy_api_nodes/nodes_veo2.py b/ComfyUI/comfy_api_nodes/nodes_veo2.py
new file mode 100644
index 0000000000000000000000000000000000000000..df846d5dd1d105f78659b0bd9b74d3c1e3803cd6
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/nodes_veo2.py
@@ -0,0 +1,308 @@
+import io
+import logging
+import base64
+import requests
+import torch
+from typing import Optional
+
+from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+from comfy_api.input_impl.video_types import VideoFromFile
+from comfy_api_nodes.apis import (
+ Veo2GenVidRequest,
+ Veo2GenVidResponse,
+ Veo2GenVidPollRequest,
+ Veo2GenVidPollResponse
+)
+from comfy_api_nodes.apis.client import (
+ ApiEndpoint,
+ HttpMethod,
+ SynchronousOperation,
+ PollingOperation,
+)
+
+from comfy_api_nodes.apinode_utils import (
+ downscale_image_tensor,
+ tensor_to_base64_string
+)
+
+AVERAGE_DURATION_VIDEO_GEN = 32
+
+def convert_image_to_base64(image: torch.Tensor):
+ if image is None:
+ return None
+
+ scaled_image = downscale_image_tensor(image, total_pixels=2048*2048)
+ return tensor_to_base64_string(scaled_image)
+
+
+def get_video_url_from_response(poll_response: Veo2GenVidPollResponse) -> Optional[str]:
+ if (
+ poll_response.response
+ and hasattr(poll_response.response, "videos")
+ and poll_response.response.videos
+ and len(poll_response.response.videos) > 0
+ ):
+ video = poll_response.response.videos[0]
+ else:
+ return None
+ if hasattr(video, "gcsUri") and video.gcsUri:
+ return str(video.gcsUri)
+ return None
+
+
+class VeoVideoGenerationNode(ComfyNodeABC):
+ """
+ Generates videos from text prompts using Google's Veo API.
+
+ This node can create videos from text descriptions and optional image inputs,
+ with control over parameters like aspect ratio, duration, and more.
+ """
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Text description of the video",
+ },
+ ),
+ "aspect_ratio": (
+ IO.COMBO,
+ {
+ "options": ["16:9", "9:16"],
+ "default": "16:9",
+ "tooltip": "Aspect ratio of the output video",
+ },
+ ),
+ },
+ "optional": {
+ "negative_prompt": (
+ IO.STRING,
+ {
+ "multiline": True,
+ "default": "",
+ "tooltip": "Negative text prompt to guide what to avoid in the video",
+ },
+ ),
+ "duration_seconds": (
+ IO.INT,
+ {
+ "default": 5,
+ "min": 5,
+ "max": 8,
+ "step": 1,
+ "display": "number",
+ "tooltip": "Duration of the output video in seconds",
+ },
+ ),
+ "enhance_prompt": (
+ IO.BOOLEAN,
+ {
+ "default": True,
+ "tooltip": "Whether to enhance the prompt with AI assistance",
+ }
+ ),
+ "person_generation": (
+ IO.COMBO,
+ {
+ "options": ["ALLOW", "BLOCK"],
+ "default": "ALLOW",
+ "tooltip": "Whether to allow generating people in the video",
+ },
+ ),
+ "seed": (
+ IO.INT,
+ {
+ "default": 0,
+ "min": 0,
+ "max": 0xFFFFFFFF,
+ "step": 1,
+ "display": "number",
+ "control_after_generate": True,
+ "tooltip": "Seed for video generation (0 for random)",
+ },
+ ),
+ "image": (IO.IMAGE, {
+ "default": None,
+ "tooltip": "Optional reference image to guide video generation",
+ }),
+ },
+ "hidden": {
+ "auth_token": "AUTH_TOKEN_COMFY_ORG",
+ "comfy_api_key": "API_KEY_COMFY_ORG",
+ "unique_id": "UNIQUE_ID",
+ },
+ }
+
+ RETURN_TYPES = (IO.VIDEO,)
+ FUNCTION = "generate_video"
+ CATEGORY = "api node/video/Veo"
+ DESCRIPTION = "Generates videos from text prompts using Google's Veo API"
+ API_NODE = True
+
+ def generate_video(
+ self,
+ prompt,
+ aspect_ratio="16:9",
+ negative_prompt="",
+ duration_seconds=5,
+ enhance_prompt=True,
+ person_generation="ALLOW",
+ seed=0,
+ image=None,
+ unique_id: Optional[str] = None,
+ **kwargs,
+ ):
+ # Prepare the instances for the request
+ instances = []
+
+ instance = {
+ "prompt": prompt
+ }
+
+ # Add image if provided
+ if image is not None:
+ image_base64 = convert_image_to_base64(image)
+ if image_base64:
+ instance["image"] = {
+ "bytesBase64Encoded": image_base64,
+ "mimeType": "image/png"
+ }
+
+ instances.append(instance)
+
+ # Create parameters dictionary
+ parameters = {
+ "aspectRatio": aspect_ratio,
+ "personGeneration": person_generation,
+ "durationSeconds": duration_seconds,
+ "enhancePrompt": enhance_prompt,
+ }
+
+ # Add optional parameters if provided
+ if negative_prompt:
+ parameters["negativePrompt"] = negative_prompt
+ if seed > 0:
+ parameters["seed"] = seed
+
+ # Initial request to start video generation
+ initial_operation = SynchronousOperation(
+ endpoint=ApiEndpoint(
+ path="/proxy/veo/generate",
+ method=HttpMethod.POST,
+ request_model=Veo2GenVidRequest,
+ response_model=Veo2GenVidResponse
+ ),
+ request=Veo2GenVidRequest(
+ instances=instances,
+ parameters=parameters
+ ),
+ auth_kwargs=kwargs,
+ )
+
+ initial_response = initial_operation.execute()
+ operation_name = initial_response.name
+
+ logging.info(f"Veo generation started with operation name: {operation_name}")
+
+ # Define status extractor function
+ def status_extractor(response):
+ # Only return "completed" if the operation is done, regardless of success or failure
+ # We'll check for errors after polling completes
+ return "completed" if response.done else "pending"
+
+ # Define progress extractor function
+ def progress_extractor(response):
+ # Could be enhanced if the API provides progress information
+ return None
+
+ # Define the polling operation
+ poll_operation = PollingOperation(
+ poll_endpoint=ApiEndpoint(
+ path="/proxy/veo/poll",
+ method=HttpMethod.POST,
+ request_model=Veo2GenVidPollRequest,
+ response_model=Veo2GenVidPollResponse
+ ),
+ completed_statuses=["completed"],
+ failed_statuses=[], # No failed statuses, we'll handle errors after polling
+ status_extractor=status_extractor,
+ progress_extractor=progress_extractor,
+ request=Veo2GenVidPollRequest(
+ operationName=operation_name
+ ),
+ auth_kwargs=kwargs,
+ poll_interval=5.0,
+ result_url_extractor=get_video_url_from_response,
+ node_id=unique_id,
+ estimated_duration=AVERAGE_DURATION_VIDEO_GEN,
+ )
+
+ # Execute the polling operation
+ poll_response = poll_operation.execute()
+
+ # Now check for errors in the final response
+ # Check for error in poll response
+ if hasattr(poll_response, 'error') and poll_response.error:
+ error_message = f"Veo API error: {poll_response.error.message} (code: {poll_response.error.code})"
+ logging.error(error_message)
+ raise Exception(error_message)
+
+ # Check for RAI filtered content
+ if (hasattr(poll_response.response, 'raiMediaFilteredCount') and
+ poll_response.response.raiMediaFilteredCount > 0):
+
+ # Extract reason message if available
+ if (hasattr(poll_response.response, 'raiMediaFilteredReasons') and
+ poll_response.response.raiMediaFilteredReasons):
+ reason = poll_response.response.raiMediaFilteredReasons[0]
+ error_message = f"Content filtered by Google's Responsible AI practices: {reason} ({poll_response.response.raiMediaFilteredCount} videos filtered.)"
+ else:
+ error_message = f"Content filtered by Google's Responsible AI practices ({poll_response.response.raiMediaFilteredCount} videos filtered.)"
+
+ logging.error(error_message)
+ raise Exception(error_message)
+
+ # Extract video data
+ video_data = None
+ if poll_response.response and hasattr(poll_response.response, 'videos') and poll_response.response.videos and len(poll_response.response.videos) > 0:
+ video = poll_response.response.videos[0]
+
+ # Check if video is provided as base64 or URL
+ if hasattr(video, 'bytesBase64Encoded') and video.bytesBase64Encoded:
+ # Decode base64 string to bytes
+ video_data = base64.b64decode(video.bytesBase64Encoded)
+ elif hasattr(video, 'gcsUri') and video.gcsUri:
+ # Download from URL
+ video_url = video.gcsUri
+ video_response = requests.get(video_url)
+ video_data = video_response.content
+ else:
+ raise Exception("Video returned but no data or URL was provided")
+ else:
+ raise Exception("Video generation completed but no video was returned")
+
+ if not video_data:
+ raise Exception("No video data was returned")
+
+ logging.info("Video generation completed successfully")
+
+ # Convert video data to BytesIO object
+ video_io = io.BytesIO(video_data)
+
+ # Return VideoFromFile object
+ return (VideoFromFile(video_io),)
+
+
+# Register the node
+NODE_CLASS_MAPPINGS = {
+ "VeoVideoGenerationNode": VeoVideoGenerationNode,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "VeoVideoGenerationNode": "Google Veo2 Video Generation",
+}
diff --git a/ComfyUI/comfy_api_nodes/redocly-dev.yaml b/ComfyUI/comfy_api_nodes/redocly-dev.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d9e3cab70ff18a924faf3f793a71710846beeae5
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/redocly-dev.yaml
@@ -0,0 +1,10 @@
+# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes.
+# This is used for development purposes to generate stubs for unreleased API endpoints.
+apis:
+ filter:
+ root: openapi.yaml
+ decorators:
+ filter-in:
+ property: tags
+ value: ['API Nodes']
+ matchStrategy: all
diff --git a/ComfyUI/comfy_api_nodes/redocly.yaml b/ComfyUI/comfy_api_nodes/redocly.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d102345b1ec932577e310f2e07c4e32017c189d5
--- /dev/null
+++ b/ComfyUI/comfy_api_nodes/redocly.yaml
@@ -0,0 +1,10 @@
+# This file is used to filter the Comfy Org OpenAPI spec for schemas related to API Nodes.
+
+apis:
+ filter:
+ root: openapi.yaml
+ decorators:
+ filter-in:
+ property: tags
+ value: ['API Nodes', 'Released']
+ matchStrategy: all
diff --git a/ComfyUI/comfy_config/config_parser.py b/ComfyUI/comfy_config/config_parser.py
new file mode 100644
index 0000000000000000000000000000000000000000..8da7bd901f421f3aaa5b620c9595313c30585ae1
--- /dev/null
+++ b/ComfyUI/comfy_config/config_parser.py
@@ -0,0 +1,152 @@
+import os
+from pathlib import Path
+from typing import Optional
+
+from pydantic_settings import PydanticBaseSettingsSource, TomlConfigSettingsSource
+
+from comfy_config.types import (
+ ComfyConfig,
+ ProjectConfig,
+ PyProjectConfig,
+ PyProjectSettings
+)
+
+def validate_and_extract_os_classifiers(classifiers: list) -> list:
+ os_classifiers = [c for c in classifiers if c.startswith("Operating System :: ")]
+ if not os_classifiers:
+ return []
+
+ os_values = [c[len("Operating System :: ") :] for c in os_classifiers]
+ valid_os_prefixes = {"Microsoft", "POSIX", "MacOS", "OS Independent"}
+
+ for os_value in os_values:
+ if not any(os_value.startswith(prefix) for prefix in valid_os_prefixes):
+ return []
+
+ return os_values
+
+
+def validate_and_extract_accelerator_classifiers(classifiers: list) -> list:
+ accelerator_classifiers = [c for c in classifiers if c.startswith("Environment ::")]
+ if not accelerator_classifiers:
+ return []
+
+ accelerator_values = [c[len("Environment :: ") :] for c in accelerator_classifiers]
+
+ valid_accelerators = {
+ "GPU :: NVIDIA CUDA",
+ "GPU :: AMD ROCm",
+ "GPU :: Intel Arc",
+ "NPU :: Huawei Ascend",
+ "GPU :: Apple Metal",
+ }
+
+ for accelerator_value in accelerator_values:
+ if accelerator_value not in valid_accelerators:
+ return []
+
+ return accelerator_values
+
+
+"""
+Extract configuration from a custom node directory's pyproject.toml file or a Python file.
+
+This function reads and parses the pyproject.toml file in the specified directory
+to extract project and ComfyUI-specific configuration information. If no
+pyproject.toml file is found, it creates a minimal configuration using the
+folder name as the project name. If a Python file is provided, it uses the
+file name (without extension) as the project name.
+
+Args:
+ path (str): Path to the directory containing the pyproject.toml file, or
+ path to a .py file. If pyproject.toml doesn't exist in a directory,
+ the folder name will be used as the default project name. If a .py
+ file is provided, the filename (without .py extension) will be used
+ as the project name.
+
+Returns:
+ Optional[PyProjectConfig]: A PyProjectConfig object containing:
+ - project: Basic project information (name, version, dependencies, etc.)
+ - tool_comfy: ComfyUI-specific configuration (publisher_id, models, etc.)
+ Returns None if configuration extraction fails or if the provided file
+ is not a Python file.
+
+Notes:
+ - If pyproject.toml is missing in a directory, creates a default config with folder name
+ - If a .py file is provided, creates a default config with filename (without extension)
+ - Returns None for non-Python files
+
+Example:
+ >>> from comfy_config import config_parser
+ >>> # For directory
+ >>> custom_node_dir = os.path.dirname(os.path.realpath(__file__))
+ >>> project_config = config_parser.extract_node_configuration(custom_node_dir)
+ >>> print(project_config.project.name) # "my_custom_node" or name from pyproject.toml
+ >>>
+ >>> # For single-file Python node file
+ >>> py_file_path = os.path.realpath(__file__) # "/path/to/my_node.py"
+ >>> project_config = config_parser.extract_node_configuration(py_file_path)
+ >>> print(project_config.project.name) # "my_node"
+"""
+def extract_node_configuration(path) -> Optional[PyProjectConfig]:
+ if os.path.isfile(path):
+ file_path = Path(path)
+
+ if file_path.suffix.lower() != '.py':
+ return None
+
+ project_name = file_path.stem
+ project = ProjectConfig(name=project_name)
+ comfy = ComfyConfig()
+ return PyProjectConfig(project=project, tool_comfy=comfy)
+
+ folder_name = os.path.basename(path)
+ toml_path = Path(path) / "pyproject.toml"
+
+ if not toml_path.exists():
+ project = ProjectConfig(name=folder_name)
+ comfy = ComfyConfig()
+ return PyProjectConfig(project=project, tool_comfy=comfy)
+
+ raw_settings = load_pyproject_settings(toml_path)
+
+ project_data = raw_settings.project
+
+ tool_data = raw_settings.tool
+ comfy_data = tool_data.get("comfy", {}) if tool_data else {}
+
+ dependencies = project_data.get("dependencies", [])
+ supported_comfyui_frontend_version = ""
+ for dep in dependencies:
+ if isinstance(dep, str) and dep.startswith("comfyui-frontend-package"):
+ supported_comfyui_frontend_version = dep.removeprefix("comfyui-frontend-package")
+ break
+
+ supported_comfyui_version = comfy_data.get("requires-comfyui", "")
+
+ classifiers = project_data.get('classifiers', [])
+ supported_os = validate_and_extract_os_classifiers(classifiers)
+ supported_accelerators = validate_and_extract_accelerator_classifiers(classifiers)
+
+ project_data['supported_os'] = supported_os
+ project_data['supported_accelerators'] = supported_accelerators
+ project_data['supported_comfyui_frontend_version'] = supported_comfyui_frontend_version
+ project_data['supported_comfyui_version'] = supported_comfyui_version
+
+ return PyProjectConfig(project=project_data, tool_comfy=comfy_data)
+
+
+def load_pyproject_settings(toml_path: Path) -> PyProjectSettings:
+ class PyProjectLoader(PyProjectSettings):
+ @classmethod
+ def settings_customise_sources(
+ cls,
+ settings_cls,
+ init_settings: PydanticBaseSettingsSource,
+ env_settings: PydanticBaseSettingsSource,
+ dotenv_settings: PydanticBaseSettingsSource,
+ file_secret_settings: PydanticBaseSettingsSource,
+ ):
+ return (TomlConfigSettingsSource(settings_cls, toml_path),)
+
+ return PyProjectLoader()
diff --git a/ComfyUI/comfy_config/types.py b/ComfyUI/comfy_config/types.py
new file mode 100644
index 0000000000000000000000000000000000000000..59448466b2cb6725c622fea1151f51e521d850fc
--- /dev/null
+++ b/ComfyUI/comfy_config/types.py
@@ -0,0 +1,97 @@
+from pydantic import BaseModel, Field, field_validator
+from pydantic_settings import BaseSettings, SettingsConfigDict
+from typing import List, Optional
+
+# IMPORTANT: The type definitions specified in pyproject.toml for custom nodes
+# must remain synchronized with the corresponding files in the https://github.com/Comfy-Org/comfy-cli/blob/main/comfy_cli/registry/types.py.
+# Any changes to one must be reflected in the other to maintain consistency.
+
+class NodeVersion(BaseModel):
+ changelog: str
+ dependencies: List[str]
+ deprecated: bool
+ id: str
+ version: str
+ download_url: str
+
+
+class Node(BaseModel):
+ id: str
+ name: str
+ description: str
+ author: Optional[str] = None
+ license: Optional[str] = None
+ icon: Optional[str] = None
+ repository: Optional[str] = None
+ tags: List[str] = Field(default_factory=list)
+ latest_version: Optional[NodeVersion] = None
+
+
+class PublishNodeVersionResponse(BaseModel):
+ node_version: NodeVersion
+ signedUrl: str
+
+
+class URLs(BaseModel):
+ homepage: str = Field(default="", alias="Homepage")
+ documentation: str = Field(default="", alias="Documentation")
+ repository: str = Field(default="", alias="Repository")
+ issues: str = Field(default="", alias="Issues")
+
+
+class Model(BaseModel):
+ location: str
+ model_url: str
+
+
+class ComfyConfig(BaseModel):
+ publisher_id: str = Field(default="", alias="PublisherId")
+ display_name: str = Field(default="", alias="DisplayName")
+ icon: str = Field(default="", alias="Icon")
+ models: List[Model] = Field(default_factory=list, alias="Models")
+ includes: List[str] = Field(default_factory=list)
+ web: Optional[str] = None
+ banner_url: str = ""
+
+class License(BaseModel):
+ file: str = ""
+ text: str = ""
+
+
+class ProjectConfig(BaseModel):
+ name: str = ""
+ description: str = ""
+ version: str = "1.0.0"
+ requires_python: str = Field(default=">= 3.9", alias="requires-python")
+ dependencies: List[str] = Field(default_factory=list)
+ license: License = Field(default_factory=License)
+ urls: URLs = Field(default_factory=URLs)
+ supported_os: List[str] = Field(default_factory=list)
+ supported_accelerators: List[str] = Field(default_factory=list)
+ supported_comfyui_version: str = ""
+ supported_comfyui_frontend_version: str = ""
+
+ @field_validator('license', mode='before')
+ @classmethod
+ def validate_license(cls, v):
+ if isinstance(v, str):
+ return License(text=v)
+ elif isinstance(v, dict):
+ return License(**v)
+ elif isinstance(v, License):
+ return v
+ else:
+ return License()
+
+
+class PyProjectConfig(BaseModel):
+ project: ProjectConfig = Field(default_factory=ProjectConfig)
+ tool_comfy: ComfyConfig = Field(default_factory=ComfyConfig)
+
+
+class PyProjectSettings(BaseSettings):
+ project: dict = Field(default_factory=dict)
+
+ tool: dict = Field(default_factory=dict)
+
+ model_config = SettingsConfigDict(extra='allow')
diff --git a/ComfyUI/comfy_execution/caching.py b/ComfyUI/comfy_execution/caching.py
new file mode 100644
index 0000000000000000000000000000000000000000..41224ce3b82e0432ed89523101c2b3c8ef5f8f1a
--- /dev/null
+++ b/ComfyUI/comfy_execution/caching.py
@@ -0,0 +1,472 @@
+import itertools
+from typing import Sequence, Mapping, Dict
+from comfy_execution.graph import DynamicPrompt
+from abc import ABC, abstractmethod
+
+import nodes
+
+from comfy_execution.graph_utils import is_link
+
+NODE_CLASS_CONTAINS_UNIQUE_ID: Dict[str, bool] = {}
+
+
+def include_unique_id_in_input(class_type: str) -> bool:
+ if class_type in NODE_CLASS_CONTAINS_UNIQUE_ID:
+ return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+ class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+ NODE_CLASS_CONTAINS_UNIQUE_ID[class_type] = "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values()
+ return NODE_CLASS_CONTAINS_UNIQUE_ID[class_type]
+
+class CacheKeySet(ABC):
+ def __init__(self, dynprompt, node_ids, is_changed_cache):
+ self.keys = {}
+ self.subcache_keys = {}
+
+ @abstractmethod
+ async def add_keys(self, node_ids):
+ raise NotImplementedError()
+
+ def all_node_ids(self):
+ return set(self.keys.keys())
+
+ def get_used_keys(self):
+ return self.keys.values()
+
+ def get_used_subcache_keys(self):
+ return self.subcache_keys.values()
+
+ def get_data_key(self, node_id):
+ return self.keys.get(node_id, None)
+
+ def get_subcache_key(self, node_id):
+ return self.subcache_keys.get(node_id, None)
+
+class Unhashable:
+ def __init__(self):
+ self.value = float("NaN")
+
+def to_hashable(obj):
+ # So that we don't infinitely recurse since frozenset and tuples
+ # are Sequences.
+ if isinstance(obj, (int, float, str, bool, type(None))):
+ return obj
+ elif isinstance(obj, Mapping):
+ return frozenset([(to_hashable(k), to_hashable(v)) for k, v in sorted(obj.items())])
+ elif isinstance(obj, Sequence):
+ return frozenset(zip(itertools.count(), [to_hashable(i) for i in obj]))
+ else:
+ # TODO - Support other objects like tensors?
+ return Unhashable()
+
+class CacheKeySetID(CacheKeySet):
+ def __init__(self, dynprompt, node_ids, is_changed_cache):
+ super().__init__(dynprompt, node_ids, is_changed_cache)
+ self.dynprompt = dynprompt
+
+ async def add_keys(self, node_ids):
+ for node_id in node_ids:
+ if node_id in self.keys:
+ continue
+ if not self.dynprompt.has_node(node_id):
+ continue
+ node = self.dynprompt.get_node(node_id)
+ self.keys[node_id] = (node_id, node["class_type"])
+ self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+class CacheKeySetInputSignature(CacheKeySet):
+ def __init__(self, dynprompt, node_ids, is_changed_cache):
+ super().__init__(dynprompt, node_ids, is_changed_cache)
+ self.dynprompt = dynprompt
+ self.is_changed_cache = is_changed_cache
+
+ def include_node_id_in_input(self) -> bool:
+ return False
+
+ async def add_keys(self, node_ids):
+ for node_id in node_ids:
+ if node_id in self.keys:
+ continue
+ if not self.dynprompt.has_node(node_id):
+ continue
+ node = self.dynprompt.get_node(node_id)
+ self.keys[node_id] = await self.get_node_signature(self.dynprompt, node_id)
+ self.subcache_keys[node_id] = (node_id, node["class_type"])
+
+ async def get_node_signature(self, dynprompt, node_id):
+ signature = []
+ ancestors, order_mapping = self.get_ordered_ancestry(dynprompt, node_id)
+ signature.append(await self.get_immediate_node_signature(dynprompt, node_id, order_mapping))
+ for ancestor_id in ancestors:
+ signature.append(await self.get_immediate_node_signature(dynprompt, ancestor_id, order_mapping))
+ return to_hashable(signature)
+
+ async def get_immediate_node_signature(self, dynprompt, node_id, ancestor_order_mapping):
+ if not dynprompt.has_node(node_id):
+ # This node doesn't exist -- we can't cache it.
+ return [float("NaN")]
+ node = dynprompt.get_node(node_id)
+ class_type = node["class_type"]
+ class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+ signature = [class_type, await self.is_changed_cache.get(node_id)]
+ if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or include_unique_id_in_input(class_type):
+ signature.append(node_id)
+ inputs = node["inputs"]
+ for key in sorted(inputs.keys()):
+ if is_link(inputs[key]):
+ (ancestor_id, ancestor_socket) = inputs[key]
+ ancestor_index = ancestor_order_mapping[ancestor_id]
+ signature.append((key,("ANCESTOR", ancestor_index, ancestor_socket)))
+ else:
+ signature.append((key, inputs[key]))
+ return signature
+
+ # This function returns a list of all ancestors of the given node. The order of the list is
+ # deterministic based on which specific inputs the ancestor is connected by.
+ def get_ordered_ancestry(self, dynprompt, node_id):
+ ancestors = []
+ order_mapping = {}
+ self.get_ordered_ancestry_internal(dynprompt, node_id, ancestors, order_mapping)
+ return ancestors, order_mapping
+
+ def get_ordered_ancestry_internal(self, dynprompt, node_id, ancestors, order_mapping):
+ if not dynprompt.has_node(node_id):
+ return
+ inputs = dynprompt.get_node(node_id)["inputs"]
+ input_keys = sorted(inputs.keys())
+ for key in input_keys:
+ if is_link(inputs[key]):
+ ancestor_id = inputs[key][0]
+ if ancestor_id not in order_mapping:
+ ancestors.append(ancestor_id)
+ order_mapping[ancestor_id] = len(ancestors) - 1
+ self.get_ordered_ancestry_internal(dynprompt, ancestor_id, ancestors, order_mapping)
+
+class BasicCache:
+ def __init__(self, key_class):
+ self.key_class = key_class
+ self.initialized = False
+ self.dynprompt: DynamicPrompt
+ self.cache_key_set: CacheKeySet
+ self.cache = {}
+ self.subcaches = {}
+
+ async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+ self.dynprompt = dynprompt
+ self.cache_key_set = self.key_class(dynprompt, node_ids, is_changed_cache)
+ await self.cache_key_set.add_keys(node_ids)
+ self.is_changed_cache = is_changed_cache
+ self.initialized = True
+
+ def all_node_ids(self):
+ assert self.initialized
+ node_ids = self.cache_key_set.all_node_ids()
+ for subcache in self.subcaches.values():
+ node_ids = node_ids.union(subcache.all_node_ids())
+ return node_ids
+
+ def _clean_cache(self):
+ preserve_keys = set(self.cache_key_set.get_used_keys())
+ to_remove = []
+ for key in self.cache:
+ if key not in preserve_keys:
+ to_remove.append(key)
+ for key in to_remove:
+ del self.cache[key]
+
+ def _clean_subcaches(self):
+ preserve_subcaches = set(self.cache_key_set.get_used_subcache_keys())
+
+ to_remove = []
+ for key in self.subcaches:
+ if key not in preserve_subcaches:
+ to_remove.append(key)
+ for key in to_remove:
+ del self.subcaches[key]
+
+ def clean_unused(self):
+ assert self.initialized
+ self._clean_cache()
+ self._clean_subcaches()
+
+ def _set_immediate(self, node_id, value):
+ assert self.initialized
+ cache_key = self.cache_key_set.get_data_key(node_id)
+ self.cache[cache_key] = value
+
+ def _get_immediate(self, node_id):
+ if not self.initialized:
+ return None
+ cache_key = self.cache_key_set.get_data_key(node_id)
+ if cache_key in self.cache:
+ return self.cache[cache_key]
+ else:
+ return None
+
+ async def _ensure_subcache(self, node_id, children_ids):
+ subcache_key = self.cache_key_set.get_subcache_key(node_id)
+ subcache = self.subcaches.get(subcache_key, None)
+ if subcache is None:
+ subcache = BasicCache(self.key_class)
+ self.subcaches[subcache_key] = subcache
+ await subcache.set_prompt(self.dynprompt, children_ids, self.is_changed_cache)
+ return subcache
+
+ def _get_subcache(self, node_id):
+ assert self.initialized
+ subcache_key = self.cache_key_set.get_subcache_key(node_id)
+ if subcache_key in self.subcaches:
+ return self.subcaches[subcache_key]
+ else:
+ return None
+
+ def recursive_debug_dump(self):
+ result = []
+ for key in self.cache:
+ result.append({"key": key, "value": self.cache[key]})
+ for key in self.subcaches:
+ result.append({"subcache_key": key, "subcache": self.subcaches[key].recursive_debug_dump()})
+ return result
+
+class HierarchicalCache(BasicCache):
+ def __init__(self, key_class):
+ super().__init__(key_class)
+
+ def _get_cache_for(self, node_id):
+ assert self.dynprompt is not None
+ parent_id = self.dynprompt.get_parent_node_id(node_id)
+ if parent_id is None:
+ return self
+
+ hierarchy = []
+ while parent_id is not None:
+ hierarchy.append(parent_id)
+ parent_id = self.dynprompt.get_parent_node_id(parent_id)
+
+ cache = self
+ for parent_id in reversed(hierarchy):
+ cache = cache._get_subcache(parent_id)
+ if cache is None:
+ return None
+ return cache
+
+ def get(self, node_id):
+ cache = self._get_cache_for(node_id)
+ if cache is None:
+ return None
+ return cache._get_immediate(node_id)
+
+ def set(self, node_id, value):
+ cache = self._get_cache_for(node_id)
+ assert cache is not None
+ cache._set_immediate(node_id, value)
+
+ async def ensure_subcache_for(self, node_id, children_ids):
+ cache = self._get_cache_for(node_id)
+ assert cache is not None
+ return await cache._ensure_subcache(node_id, children_ids)
+
+class LRUCache(BasicCache):
+ def __init__(self, key_class, max_size=100):
+ super().__init__(key_class)
+ self.max_size = max_size
+ self.min_generation = 0
+ self.generation = 0
+ self.used_generation = {}
+ self.children = {}
+
+ async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+ await super().set_prompt(dynprompt, node_ids, is_changed_cache)
+ self.generation += 1
+ for node_id in node_ids:
+ self._mark_used(node_id)
+
+ def clean_unused(self):
+ while len(self.cache) > self.max_size and self.min_generation < self.generation:
+ self.min_generation += 1
+ to_remove = [key for key in self.cache if self.used_generation[key] < self.min_generation]
+ for key in to_remove:
+ del self.cache[key]
+ del self.used_generation[key]
+ if key in self.children:
+ del self.children[key]
+ self._clean_subcaches()
+
+ def get(self, node_id):
+ self._mark_used(node_id)
+ return self._get_immediate(node_id)
+
+ def _mark_used(self, node_id):
+ cache_key = self.cache_key_set.get_data_key(node_id)
+ if cache_key is not None:
+ self.used_generation[cache_key] = self.generation
+
+ def set(self, node_id, value):
+ self._mark_used(node_id)
+ return self._set_immediate(node_id, value)
+
+ async def ensure_subcache_for(self, node_id, children_ids):
+ # Just uses subcaches for tracking 'live' nodes
+ await super()._ensure_subcache(node_id, children_ids)
+
+ await self.cache_key_set.add_keys(children_ids)
+ self._mark_used(node_id)
+ cache_key = self.cache_key_set.get_data_key(node_id)
+ self.children[cache_key] = []
+ for child_id in children_ids:
+ self._mark_used(child_id)
+ self.children[cache_key].append(self.cache_key_set.get_data_key(child_id))
+ return self
+
+
+class DependencyAwareCache(BasicCache):
+ """
+ A cache implementation that tracks dependencies between nodes and manages
+ their execution and caching accordingly. It extends the BasicCache class.
+ Nodes are removed from this cache once all of their descendants have been
+ executed.
+ """
+
+ def __init__(self, key_class):
+ """
+ Initialize the DependencyAwareCache.
+
+ Args:
+ key_class: The class used for generating cache keys.
+ """
+ super().__init__(key_class)
+ self.descendants = {} # Maps node_id -> set of descendant node_ids
+ self.ancestors = {} # Maps node_id -> set of ancestor node_ids
+ self.executed_nodes = set() # Tracks nodes that have been executed
+
+ async def set_prompt(self, dynprompt, node_ids, is_changed_cache):
+ """
+ Clear the entire cache and rebuild the dependency graph.
+
+ Args:
+ dynprompt: The dynamic prompt object containing node information.
+ node_ids: List of node IDs to initialize the cache for.
+ is_changed_cache: Flag indicating if the cache has changed.
+ """
+ # Clear all existing cache data
+ self.cache.clear()
+ self.subcaches.clear()
+ self.descendants.clear()
+ self.ancestors.clear()
+ self.executed_nodes.clear()
+
+ # Call the parent method to initialize the cache with the new prompt
+ await super().set_prompt(dynprompt, node_ids, is_changed_cache)
+
+ # Rebuild the dependency graph
+ self._build_dependency_graph(dynprompt, node_ids)
+
+ def _build_dependency_graph(self, dynprompt, node_ids):
+ """
+ Build the dependency graph for all nodes.
+
+ Args:
+ dynprompt: The dynamic prompt object containing node information.
+ node_ids: List of node IDs to build the graph for.
+ """
+ self.descendants.clear()
+ self.ancestors.clear()
+ for node_id in node_ids:
+ self.descendants[node_id] = set()
+ self.ancestors[node_id] = set()
+
+ for node_id in node_ids:
+ inputs = dynprompt.get_node(node_id)["inputs"]
+ for input_data in inputs.values():
+ if is_link(input_data): # Check if the input is a link to another node
+ ancestor_id = input_data[0]
+ self.descendants[ancestor_id].add(node_id)
+ self.ancestors[node_id].add(ancestor_id)
+
+ def set(self, node_id, value):
+ """
+ Mark a node as executed and store its value in the cache.
+
+ Args:
+ node_id: The ID of the node to store.
+ value: The value to store for the node.
+ """
+ self._set_immediate(node_id, value)
+ self.executed_nodes.add(node_id)
+ self._cleanup_ancestors(node_id)
+
+ def get(self, node_id):
+ """
+ Retrieve the cached value for a node.
+
+ Args:
+ node_id: The ID of the node to retrieve.
+
+ Returns:
+ The cached value for the node.
+ """
+ return self._get_immediate(node_id)
+
+ async def ensure_subcache_for(self, node_id, children_ids):
+ """
+ Ensure a subcache exists for a node and update dependencies.
+
+ Args:
+ node_id: The ID of the parent node.
+ children_ids: List of child node IDs to associate with the parent node.
+
+ Returns:
+ The subcache object for the node.
+ """
+ subcache = await super()._ensure_subcache(node_id, children_ids)
+ for child_id in children_ids:
+ self.descendants[node_id].add(child_id)
+ self.ancestors[child_id].add(node_id)
+ return subcache
+
+ def _cleanup_ancestors(self, node_id):
+ """
+ Check if ancestors of a node can be removed from the cache.
+
+ Args:
+ node_id: The ID of the node whose ancestors are to be checked.
+ """
+ for ancestor_id in self.ancestors.get(node_id, []):
+ if ancestor_id in self.executed_nodes:
+ # Remove ancestor if all its descendants have been executed
+ if all(descendant in self.executed_nodes for descendant in self.descendants[ancestor_id]):
+ self._remove_node(ancestor_id)
+
+ def _remove_node(self, node_id):
+ """
+ Remove a node from the cache.
+
+ Args:
+ node_id: The ID of the node to remove.
+ """
+ cache_key = self.cache_key_set.get_data_key(node_id)
+ if cache_key in self.cache:
+ del self.cache[cache_key]
+ subcache_key = self.cache_key_set.get_subcache_key(node_id)
+ if subcache_key in self.subcaches:
+ del self.subcaches[subcache_key]
+
+ def clean_unused(self):
+ """
+ Clean up unused nodes. This is a no-op for this cache implementation.
+ """
+ pass
+
+ def recursive_debug_dump(self):
+ """
+ Dump the cache and dependency graph for debugging.
+
+ Returns:
+ A list containing the cache state and dependency graph.
+ """
+ result = super().recursive_debug_dump()
+ result.append({
+ "descendants": self.descendants,
+ "ancestors": self.ancestors,
+ "executed_nodes": list(self.executed_nodes),
+ })
+ return result
diff --git a/ComfyUI/comfy_execution/graph.py b/ComfyUI/comfy_execution/graph.py
new file mode 100644
index 0000000000000000000000000000000000000000..60e2ab91ef26194801774180a3b34e462a1774e5
--- /dev/null
+++ b/ComfyUI/comfy_execution/graph.py
@@ -0,0 +1,314 @@
+from __future__ import annotations
+from typing import Type, Literal
+
+import nodes
+import asyncio
+import inspect
+from comfy_execution.graph_utils import is_link
+from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
+
+class DependencyCycleError(Exception):
+ pass
+
+class NodeInputError(Exception):
+ pass
+
+class NodeNotFoundError(Exception):
+ pass
+
+class DynamicPrompt:
+ def __init__(self, original_prompt):
+ # The original prompt provided by the user
+ self.original_prompt = original_prompt
+ # Any extra pieces of the graph created during execution
+ self.ephemeral_prompt = {}
+ self.ephemeral_parents = {}
+ self.ephemeral_display = {}
+
+ def get_node(self, node_id):
+ if node_id in self.ephemeral_prompt:
+ return self.ephemeral_prompt[node_id]
+ if node_id in self.original_prompt:
+ return self.original_prompt[node_id]
+ raise NodeNotFoundError(f"Node {node_id} not found")
+
+ def has_node(self, node_id):
+ return node_id in self.original_prompt or node_id in self.ephemeral_prompt
+
+ def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
+ self.ephemeral_prompt[node_id] = node_info
+ self.ephemeral_parents[node_id] = parent_id
+ self.ephemeral_display[node_id] = display_id
+
+ def get_real_node_id(self, node_id):
+ while node_id in self.ephemeral_parents:
+ node_id = self.ephemeral_parents[node_id]
+ return node_id
+
+ def get_parent_node_id(self, node_id):
+ return self.ephemeral_parents.get(node_id, None)
+
+ def get_display_node_id(self, node_id):
+ while node_id in self.ephemeral_display:
+ node_id = self.ephemeral_display[node_id]
+ return node_id
+
+ def all_node_ids(self):
+ return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
+
+ def get_original_prompt(self):
+ return self.original_prompt
+
+def get_input_info(
+ class_def: Type[ComfyNodeABC],
+ input_name: str,
+ valid_inputs: InputTypeDict | None = None
+) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]:
+ """Get the input type, category, and extra info for a given input name.
+
+ Arguments:
+ class_def: The class definition of the node.
+ input_name: The name of the input to get info for.
+ valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES().
+
+ Returns:
+ tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name.
+ """
+
+ valid_inputs = valid_inputs or class_def.INPUT_TYPES()
+ input_info = None
+ input_category = None
+ if "required" in valid_inputs and input_name in valid_inputs["required"]:
+ input_category = "required"
+ input_info = valid_inputs["required"][input_name]
+ elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
+ input_category = "optional"
+ input_info = valid_inputs["optional"][input_name]
+ elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
+ input_category = "hidden"
+ input_info = valid_inputs["hidden"][input_name]
+ if input_info is None:
+ return None, None, None
+ input_type = input_info[0]
+ if len(input_info) > 1:
+ extra_info = input_info[1]
+ else:
+ extra_info = {}
+ return input_type, input_category, extra_info
+
+class TopologicalSort:
+ def __init__(self, dynprompt):
+ self.dynprompt = dynprompt
+ self.pendingNodes = {}
+ self.blockCount = {} # Number of nodes this node is directly blocked by
+ self.blocking = {} # Which nodes are blocked by this node
+ self.externalBlocks = 0
+ self.unblockedEvent = asyncio.Event()
+
+ def get_input_info(self, unique_id, input_name):
+ class_type = self.dynprompt.get_node(unique_id)["class_type"]
+ class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+ return get_input_info(class_def, input_name)
+
+ def make_input_strong_link(self, to_node_id, to_input):
+ inputs = self.dynprompt.get_node(to_node_id)["inputs"]
+ if to_input not in inputs:
+ raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
+ value = inputs[to_input]
+ if not is_link(value):
+ raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
+ from_node_id, from_socket = value
+ self.add_strong_link(from_node_id, from_socket, to_node_id)
+
+ def add_strong_link(self, from_node_id, from_socket, to_node_id):
+ if not self.is_cached(from_node_id):
+ self.add_node(from_node_id)
+ if to_node_id not in self.blocking[from_node_id]:
+ self.blocking[from_node_id][to_node_id] = {}
+ self.blockCount[to_node_id] += 1
+ self.blocking[from_node_id][to_node_id][from_socket] = True
+
+ def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None):
+ node_ids = [node_unique_id]
+ links = []
+
+ while len(node_ids) > 0:
+ unique_id = node_ids.pop()
+ if unique_id in self.pendingNodes:
+ continue
+
+ self.pendingNodes[unique_id] = True
+ self.blockCount[unique_id] = 0
+ self.blocking[unique_id] = {}
+
+ inputs = self.dynprompt.get_node(unique_id)["inputs"]
+ for input_name in inputs:
+ value = inputs[input_name]
+ if is_link(value):
+ from_node_id, from_socket = value
+ if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
+ continue
+ _, _, input_info = self.get_input_info(unique_id, input_name)
+ is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
+ if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
+ node_ids.append(from_node_id)
+ links.append((from_node_id, from_socket, unique_id))
+
+ for link in links:
+ self.add_strong_link(*link)
+
+ def add_external_block(self, node_id):
+ assert node_id in self.blockCount, "Can't add external block to a node that isn't pending"
+ self.externalBlocks += 1
+ self.blockCount[node_id] += 1
+ def unblock():
+ self.externalBlocks -= 1
+ self.blockCount[node_id] -= 1
+ self.unblockedEvent.set()
+ return unblock
+
+ def is_cached(self, node_id):
+ return False
+
+ def get_ready_nodes(self):
+ return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
+
+ def pop_node(self, unique_id):
+ del self.pendingNodes[unique_id]
+ for blocked_node_id in self.blocking[unique_id]:
+ self.blockCount[blocked_node_id] -= 1
+ del self.blocking[unique_id]
+
+ def is_empty(self):
+ return len(self.pendingNodes) == 0
+
+class ExecutionList(TopologicalSort):
+ """
+ ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
+ it can still be returned to the graph after having further dependencies added.
+ """
+ def __init__(self, dynprompt, output_cache):
+ super().__init__(dynprompt)
+ self.output_cache = output_cache
+ self.staged_node_id = None
+
+ def is_cached(self, node_id):
+ return self.output_cache.get(node_id) is not None
+
+ async def stage_node_execution(self):
+ assert self.staged_node_id is None
+ if self.is_empty():
+ return None, None, None
+ available = self.get_ready_nodes()
+ while len(available) == 0 and self.externalBlocks > 0:
+ # Wait for an external block to be released
+ await self.unblockedEvent.wait()
+ self.unblockedEvent.clear()
+ available = self.get_ready_nodes()
+ if len(available) == 0:
+ cycled_nodes = self.get_nodes_in_cycle()
+ # Because cycles composed entirely of static nodes are caught during initial validation,
+ # we will 'blame' the first node in the cycle that is not a static node.
+ blamed_node = cycled_nodes[0]
+ for node_id in cycled_nodes:
+ display_node_id = self.dynprompt.get_display_node_id(node_id)
+ if display_node_id != node_id:
+ blamed_node = display_node_id
+ break
+ ex = DependencyCycleError("Dependency cycle detected")
+ error_details = {
+ "node_id": blamed_node,
+ "exception_message": str(ex),
+ "exception_type": "graph.DependencyCycleError",
+ "traceback": [],
+ "current_inputs": []
+ }
+ return None, error_details, ex
+
+ self.staged_node_id = self.ux_friendly_pick_node(available)
+ return self.staged_node_id, None, None
+
+ def ux_friendly_pick_node(self, node_list):
+ # If an output node is available, do that first.
+ # Technically this has no effect on the overall length of execution, but it feels better as a user
+ # for a PreviewImage to display a result as soon as it can
+ # Some other heuristics could probably be used here to improve the UX further.
+ def is_output(node_id):
+ class_type = self.dynprompt.get_node(node_id)["class_type"]
+ class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+ if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
+ return True
+ return False
+
+ # If an available node is async, do that first.
+ # This will execute the asynchronous function earlier, reducing the overall time.
+ def is_async(node_id):
+ class_type = self.dynprompt.get_node(node_id)["class_type"]
+ class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+ return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
+
+ for node_id in node_list:
+ if is_output(node_id) or is_async(node_id):
+ return node_id
+
+ #This should handle the VAEDecode -> preview case
+ for node_id in node_list:
+ for blocked_node_id in self.blocking[node_id]:
+ if is_output(blocked_node_id):
+ return node_id
+
+ #This should handle the VAELoader -> VAEDecode -> preview case
+ for node_id in node_list:
+ for blocked_node_id in self.blocking[node_id]:
+ for blocked_node_id1 in self.blocking[blocked_node_id]:
+ if is_output(blocked_node_id1):
+ return node_id
+
+ #TODO: this function should be improved
+ return node_list[0]
+
+ def unstage_node_execution(self):
+ assert self.staged_node_id is not None
+ self.staged_node_id = None
+
+ def complete_node_execution(self):
+ node_id = self.staged_node_id
+ self.pop_node(node_id)
+ self.staged_node_id = None
+
+ def get_nodes_in_cycle(self):
+ # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
+ # We're skipping some of the performance optimizations from the original TopologicalSort to keep
+ # the code simple (and because having a cycle in the first place is a catastrophic error)
+ blocked_by = { node_id: {} for node_id in self.pendingNodes }
+ for from_node_id in self.blocking:
+ for to_node_id in self.blocking[from_node_id]:
+ if True in self.blocking[from_node_id][to_node_id].values():
+ blocked_by[to_node_id][from_node_id] = True
+ to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+ while len(to_remove) > 0:
+ for node_id in to_remove:
+ for to_node_id in blocked_by:
+ if node_id in blocked_by[to_node_id]:
+ del blocked_by[to_node_id][node_id]
+ del blocked_by[node_id]
+ to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
+ return list(blocked_by.keys())
+
+class ExecutionBlocker:
+ """
+ Return this from a node and any users will be blocked with the given error message.
+ If the message is None, execution will be blocked silently instead.
+ Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
+ possible, a lazy input will be more efficient and have a better user experience.
+ This functionality is useful in two cases:
+ 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
+ like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
+ lazy evaluation to let it conditionally disable itself.)
+ 2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
+ (I would recommend not making nodes like this in the future -- instead, make multiple nodes with
+ different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
+ """
+ def __init__(self, message):
+ self.message = message
+
diff --git a/ComfyUI/comfy_execution/graph_utils.py b/ComfyUI/comfy_execution/graph_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..8595e942d32160152c3c0163c85ad9bcd2e68d45
--- /dev/null
+++ b/ComfyUI/comfy_execution/graph_utils.py
@@ -0,0 +1,139 @@
+def is_link(obj):
+ if not isinstance(obj, list):
+ return False
+ if len(obj) != 2:
+ return False
+ if not isinstance(obj[0], str):
+ return False
+ if not isinstance(obj[1], int) and not isinstance(obj[1], float):
+ return False
+ return True
+
+# The GraphBuilder is just a utility class that outputs graphs in the form expected by the ComfyUI back-end
+class GraphBuilder:
+ _default_prefix_root = ""
+ _default_prefix_call_index = 0
+ _default_prefix_graph_index = 0
+
+ def __init__(self, prefix = None):
+ if prefix is None:
+ self.prefix = GraphBuilder.alloc_prefix()
+ else:
+ self.prefix = prefix
+ self.nodes = {}
+ self.id_gen = 1
+
+ @classmethod
+ def set_default_prefix(cls, prefix_root, call_index, graph_index = 0):
+ cls._default_prefix_root = prefix_root
+ cls._default_prefix_call_index = call_index
+ cls._default_prefix_graph_index = graph_index
+
+ @classmethod
+ def alloc_prefix(cls, root=None, call_index=None, graph_index=None):
+ if root is None:
+ root = GraphBuilder._default_prefix_root
+ if call_index is None:
+ call_index = GraphBuilder._default_prefix_call_index
+ if graph_index is None:
+ graph_index = GraphBuilder._default_prefix_graph_index
+ result = f"{root}.{call_index}.{graph_index}."
+ GraphBuilder._default_prefix_graph_index += 1
+ return result
+
+ def node(self, class_type, id=None, **kwargs):
+ if id is None:
+ id = str(self.id_gen)
+ self.id_gen += 1
+ id = self.prefix + id
+ if id in self.nodes:
+ return self.nodes[id]
+
+ node = Node(id, class_type, kwargs)
+ self.nodes[id] = node
+ return node
+
+ def lookup_node(self, id):
+ id = self.prefix + id
+ return self.nodes.get(id)
+
+ def finalize(self):
+ output = {}
+ for node_id, node in self.nodes.items():
+ output[node_id] = node.serialize()
+ return output
+
+ def replace_node_output(self, node_id, index, new_value):
+ node_id = self.prefix + node_id
+ to_remove = []
+ for node in self.nodes.values():
+ for key, value in node.inputs.items():
+ if is_link(value) and value[0] == node_id and value[1] == index:
+ if new_value is None:
+ to_remove.append((node, key))
+ else:
+ node.inputs[key] = new_value
+ for node, key in to_remove:
+ del node.inputs[key]
+
+ def remove_node(self, id):
+ id = self.prefix + id
+ del self.nodes[id]
+
+class Node:
+ def __init__(self, id, class_type, inputs):
+ self.id = id
+ self.class_type = class_type
+ self.inputs = inputs
+ self.override_display_id = None
+
+ def out(self, index):
+ return [self.id, index]
+
+ def set_input(self, key, value):
+ if value is None:
+ if key in self.inputs:
+ del self.inputs[key]
+ else:
+ self.inputs[key] = value
+
+ def get_input(self, key):
+ return self.inputs.get(key)
+
+ def set_override_display_id(self, override_display_id):
+ self.override_display_id = override_display_id
+
+ def serialize(self):
+ serialized = {
+ "class_type": self.class_type,
+ "inputs": self.inputs
+ }
+ if self.override_display_id is not None:
+ serialized["override_display_id"] = self.override_display_id
+ return serialized
+
+def add_graph_prefix(graph, outputs, prefix):
+ # Change the node IDs and any internal links
+ new_graph = {}
+ for node_id, node_info in graph.items():
+ # Make sure the added nodes have unique IDs
+ new_node_id = prefix + node_id
+ new_node = { "class_type": node_info["class_type"], "inputs": {} }
+ for input_name, input_value in node_info.get("inputs", {}).items():
+ if is_link(input_value):
+ new_node["inputs"][input_name] = [prefix + input_value[0], input_value[1]]
+ else:
+ new_node["inputs"][input_name] = input_value
+ new_graph[new_node_id] = new_node
+
+ # Change the node IDs in the outputs
+ new_outputs = []
+ for n in range(len(outputs)):
+ output = outputs[n]
+ if is_link(output):
+ new_outputs.append([prefix + output[0], output[1]])
+ else:
+ new_outputs.append(output)
+
+ return new_graph, tuple(new_outputs)
+
diff --git a/ComfyUI/comfy_execution/progress.py b/ComfyUI/comfy_execution/progress.py
new file mode 100644
index 0000000000000000000000000000000000000000..73dba3f75e0874b64499015bf87b5ae23544711d
--- /dev/null
+++ b/ComfyUI/comfy_execution/progress.py
@@ -0,0 +1,347 @@
+from typing import TypedDict, Dict, Optional
+from typing_extensions import override
+from PIL import Image
+from enum import Enum
+from abc import ABC
+from tqdm import tqdm
+from typing import TYPE_CHECKING
+if TYPE_CHECKING:
+ from comfy_execution.graph import DynamicPrompt
+from protocol import BinaryEventTypes
+from comfy_api import feature_flags
+
+
+class NodeState(Enum):
+ Pending = "pending"
+ Running = "running"
+ Finished = "finished"
+ Error = "error"
+
+
+class NodeProgressState(TypedDict):
+ """
+ A class to represent the state of a node's progress.
+ """
+
+ state: NodeState
+ value: float
+ max: float
+
+
+class ProgressHandler(ABC):
+ """
+ Abstract base class for progress handlers.
+ Progress handlers receive progress updates and display them in various ways.
+ """
+
+ def __init__(self, name: str):
+ self.name = name
+ self.enabled = True
+
+ def set_registry(self, registry: "ProgressRegistry"):
+ pass
+
+ def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ """Called when a node starts processing"""
+ pass
+
+ def update_handler(
+ self,
+ node_id: str,
+ value: float,
+ max_value: float,
+ state: NodeProgressState,
+ prompt_id: str,
+ image: Optional[Image.Image] = None,
+ ):
+ """Called when a node's progress is updated"""
+ pass
+
+ def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ """Called when a node finishes processing"""
+ pass
+
+ def reset(self):
+ """Called when the progress registry is reset"""
+ pass
+
+ def enable(self):
+ """Enable this handler"""
+ self.enabled = True
+
+ def disable(self):
+ """Disable this handler"""
+ self.enabled = False
+
+
+class CLIProgressHandler(ProgressHandler):
+ """
+ Handler that displays progress using tqdm progress bars in the CLI.
+ """
+
+ def __init__(self):
+ super().__init__("cli")
+ self.progress_bars: Dict[str, tqdm] = {}
+
+ @override
+ def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ # Create a new tqdm progress bar
+ if node_id not in self.progress_bars:
+ self.progress_bars[node_id] = tqdm(
+ total=state["max"],
+ desc=f"Node {node_id}",
+ unit="steps",
+ leave=True,
+ position=len(self.progress_bars),
+ )
+
+ @override
+ def update_handler(
+ self,
+ node_id: str,
+ value: float,
+ max_value: float,
+ state: NodeProgressState,
+ prompt_id: str,
+ image: Optional[Image.Image] = None,
+ ):
+ # Handle case where start_handler wasn't called
+ if node_id not in self.progress_bars:
+ self.progress_bars[node_id] = tqdm(
+ total=max_value,
+ desc=f"Node {node_id}",
+ unit="steps",
+ leave=True,
+ position=len(self.progress_bars),
+ )
+ self.progress_bars[node_id].update(value)
+ else:
+ # Update existing progress bar
+ if max_value != self.progress_bars[node_id].total:
+ self.progress_bars[node_id].total = max_value
+ # Calculate the update amount (difference from current position)
+ current_position = self.progress_bars[node_id].n
+ update_amount = value - current_position
+ if update_amount > 0:
+ self.progress_bars[node_id].update(update_amount)
+
+ @override
+ def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ # Complete and close the progress bar if it exists
+ if node_id in self.progress_bars:
+ # Ensure the bar shows 100% completion
+ remaining = state["max"] - self.progress_bars[node_id].n
+ if remaining > 0:
+ self.progress_bars[node_id].update(remaining)
+ self.progress_bars[node_id].close()
+ del self.progress_bars[node_id]
+
+ @override
+ def reset(self):
+ # Close all progress bars
+ for bar in self.progress_bars.values():
+ bar.close()
+ self.progress_bars.clear()
+
+
+class WebUIProgressHandler(ProgressHandler):
+ """
+ Handler that sends progress updates to the WebUI via WebSockets.
+ """
+
+ def __init__(self, server_instance):
+ super().__init__("webui")
+ self.server_instance = server_instance
+
+ def set_registry(self, registry: "ProgressRegistry"):
+ self.registry = registry
+
+ def _send_progress_state(self, prompt_id: str, nodes: Dict[str, NodeProgressState]):
+ """Send the current progress state to the client"""
+ if self.server_instance is None:
+ return
+
+ # Only send info for non-pending nodes
+ active_nodes = {
+ node_id: {
+ "value": state["value"],
+ "max": state["max"],
+ "state": state["state"].value,
+ "node_id": node_id,
+ "prompt_id": prompt_id,
+ "display_node_id": self.registry.dynprompt.get_display_node_id(node_id),
+ "parent_node_id": self.registry.dynprompt.get_parent_node_id(node_id),
+ "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+ }
+ for node_id, state in nodes.items()
+ if state["state"] != NodeState.Pending
+ }
+
+ # Send a combined progress_state message with all node states
+ self.server_instance.send_sync(
+ "progress_state", {"prompt_id": prompt_id, "nodes": active_nodes}
+ )
+
+ @override
+ def start_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ # Send progress state of all nodes
+ if self.registry:
+ self._send_progress_state(prompt_id, self.registry.nodes)
+
+ @override
+ def update_handler(
+ self,
+ node_id: str,
+ value: float,
+ max_value: float,
+ state: NodeProgressState,
+ prompt_id: str,
+ image: Optional[Image.Image] = None,
+ ):
+ # Send progress state of all nodes
+ if self.registry:
+ self._send_progress_state(prompt_id, self.registry.nodes)
+ if image:
+ # Only send new format if client supports it
+ if feature_flags.supports_feature(
+ self.server_instance.sockets_metadata,
+ self.server_instance.client_id,
+ "supports_preview_metadata",
+ ):
+ metadata = {
+ "node_id": node_id,
+ "prompt_id": prompt_id,
+ "display_node_id": self.registry.dynprompt.get_display_node_id(
+ node_id
+ ),
+ "parent_node_id": self.registry.dynprompt.get_parent_node_id(
+ node_id
+ ),
+ "real_node_id": self.registry.dynprompt.get_real_node_id(node_id),
+ }
+ self.server_instance.send_sync(
+ BinaryEventTypes.PREVIEW_IMAGE_WITH_METADATA,
+ (image, metadata),
+ self.server_instance.client_id,
+ )
+
+ @override
+ def finish_handler(self, node_id: str, state: NodeProgressState, prompt_id: str):
+ # Send progress state of all nodes
+ if self.registry:
+ self._send_progress_state(prompt_id, self.registry.nodes)
+
+
+class ProgressRegistry:
+ """
+ Registry that maintains node progress state and notifies registered handlers.
+ """
+
+ def __init__(self, prompt_id: str, dynprompt: "DynamicPrompt"):
+ self.prompt_id = prompt_id
+ self.dynprompt = dynprompt
+ self.nodes: Dict[str, NodeProgressState] = {}
+ self.handlers: Dict[str, ProgressHandler] = {}
+
+ def register_handler(self, handler: ProgressHandler) -> None:
+ """Register a progress handler"""
+ self.handlers[handler.name] = handler
+
+ def unregister_handler(self, handler_name: str) -> None:
+ """Unregister a progress handler"""
+ if handler_name in self.handlers:
+ # Allow handler to clean up resources
+ self.handlers[handler_name].reset()
+ del self.handlers[handler_name]
+
+ def enable_handler(self, handler_name: str) -> None:
+ """Enable a progress handler"""
+ if handler_name in self.handlers:
+ self.handlers[handler_name].enable()
+
+ def disable_handler(self, handler_name: str) -> None:
+ """Disable a progress handler"""
+ if handler_name in self.handlers:
+ self.handlers[handler_name].disable()
+
+ def ensure_entry(self, node_id: str) -> NodeProgressState:
+ """Ensure a node entry exists"""
+ if node_id not in self.nodes:
+ self.nodes[node_id] = NodeProgressState(
+ state=NodeState.Pending, value=0, max=1
+ )
+ return self.nodes[node_id]
+
+ def start_progress(self, node_id: str) -> None:
+ """Start progress tracking for a node"""
+ entry = self.ensure_entry(node_id)
+ entry["state"] = NodeState.Running
+ entry["value"] = 0.0
+ entry["max"] = 1.0
+
+ # Notify all enabled handlers
+ for handler in self.handlers.values():
+ if handler.enabled:
+ handler.start_handler(node_id, entry, self.prompt_id)
+
+ def update_progress(
+ self, node_id: str, value: float, max_value: float, image: Optional[Image.Image]
+ ) -> None:
+ """Update progress for a node"""
+ entry = self.ensure_entry(node_id)
+ entry["state"] = NodeState.Running
+ entry["value"] = value
+ entry["max"] = max_value
+
+ # Notify all enabled handlers
+ for handler in self.handlers.values():
+ if handler.enabled:
+ handler.update_handler(
+ node_id, value, max_value, entry, self.prompt_id, image
+ )
+
+ def finish_progress(self, node_id: str) -> None:
+ """Finish progress tracking for a node"""
+ entry = self.ensure_entry(node_id)
+ entry["state"] = NodeState.Finished
+ entry["value"] = entry["max"]
+
+ # Notify all enabled handlers
+ for handler in self.handlers.values():
+ if handler.enabled:
+ handler.finish_handler(node_id, entry, self.prompt_id)
+
+ def reset_handlers(self) -> None:
+ """Reset all handlers"""
+ for handler in self.handlers.values():
+ handler.reset()
+
+# Global registry instance
+global_progress_registry: ProgressRegistry = None
+
+def reset_progress_state(prompt_id: str, dynprompt: "DynamicPrompt") -> None:
+ global global_progress_registry
+
+ # Reset existing handlers if registry exists
+ if global_progress_registry is not None:
+ global_progress_registry.reset_handlers()
+
+ # Create new registry
+ global_progress_registry = ProgressRegistry(prompt_id, dynprompt)
+
+
+def add_progress_handler(handler: ProgressHandler) -> None:
+ registry = get_progress_state()
+ handler.set_registry(registry)
+ registry.register_handler(handler)
+
+
+def get_progress_state() -> ProgressRegistry:
+ global global_progress_registry
+ if global_progress_registry is None:
+ from comfy_execution.graph import DynamicPrompt
+
+ global_progress_registry = ProgressRegistry(
+ prompt_id="", dynprompt=DynamicPrompt({})
+ )
+ return global_progress_registry
diff --git a/ComfyUI/comfy_execution/utils.py b/ComfyUI/comfy_execution/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..62d32f1013df633889b59d2207154d1cbbbfe347
--- /dev/null
+++ b/ComfyUI/comfy_execution/utils.py
@@ -0,0 +1,46 @@
+import contextvars
+from typing import Optional, NamedTuple
+
+class ExecutionContext(NamedTuple):
+ """
+ Context information about the currently executing node.
+
+ Attributes:
+ node_id: The ID of the currently executing node
+ list_index: The index in a list being processed (for operations on batches/lists)
+ """
+ prompt_id: str
+ node_id: str
+ list_index: Optional[int]
+
+current_executing_context: contextvars.ContextVar[Optional[ExecutionContext]] = contextvars.ContextVar("current_executing_context", default=None)
+
+def get_executing_context() -> Optional[ExecutionContext]:
+ return current_executing_context.get(None)
+
+class CurrentNodeContext:
+ """
+ Context manager for setting the current executing node context.
+
+ Sets the current_executing_context on enter and resets it on exit.
+
+ Example:
+ with CurrentNodeContext(node_id="123", list_index=0):
+ # Code that should run with the current node context set
+ process_image()
+ """
+ def __init__(self, prompt_id: str, node_id: str, list_index: Optional[int] = None):
+ self.context = ExecutionContext(
+ prompt_id= prompt_id,
+ node_id= node_id,
+ list_index= list_index
+ )
+ self.token = None
+
+ def __enter__(self):
+ self.token = current_executing_context.set(self.context)
+ return self
+
+ def __exit__(self, exc_type, exc_val, exc_tb):
+ if self.token is not None:
+ current_executing_context.reset(self.token)
diff --git a/ComfyUI/comfy_execution/validation.py b/ComfyUI/comfy_execution/validation.py
new file mode 100644
index 0000000000000000000000000000000000000000..cec105fc9f1bfbecdc17c37cfec9ecf60f188814
--- /dev/null
+++ b/ComfyUI/comfy_execution/validation.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+
+def validate_node_input(
+ received_type: str, input_type: str, strict: bool = False
+) -> bool:
+ """
+ received_type and input_type are both strings of the form "T1,T2,...".
+
+ If strict is True, the input_type must contain the received_type.
+ For example, if received_type is "STRING" and input_type is "STRING,INT",
+ this will return True. But if received_type is "STRING,INT" and input_type is
+ "INT", this will return False.
+
+ If strict is False, the input_type must have overlap with the received_type.
+ For example, if received_type is "STRING,BOOLEAN" and input_type is "STRING,INT",
+ this will return True.
+
+ Supports pre-union type extension behaviour of ``__ne__`` overrides.
+ """
+ # If the types are exactly the same, we can return immediately
+ # Use pre-union behaviour: inverse of `__ne__`
+ if not received_type != input_type:
+ return True
+
+ # Not equal, and not strings
+ if not isinstance(received_type, str) or not isinstance(input_type, str):
+ return False
+
+ # Split the type strings into sets for comparison
+ received_types = set(t.strip() for t in received_type.split(","))
+ input_types = set(t.strip() for t in input_type.split(","))
+
+ if strict:
+ # In strict mode, all received types must be in the input types
+ return received_types.issubset(input_types)
+ else:
+ # In non-strict mode, there must be at least one type in common
+ return len(received_types.intersection(input_types)) > 0
diff --git a/ComfyUI/comfy_extras/nodes_ace.py b/ComfyUI/comfy_extras/nodes_ace.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbfec15a2198e542171508704e931021d6df569c
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_ace.py
@@ -0,0 +1,49 @@
+import torch
+import comfy.model_management
+import node_helpers
+
+class TextEncodeAceStepAudio:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "clip": ("CLIP", ),
+ "tags": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+ "lyrics": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+ "lyrics_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "encode"
+
+ CATEGORY = "conditioning"
+
+ def encode(self, clip, tags, lyrics, lyrics_strength):
+ tokens = clip.tokenize(tags, lyrics=lyrics)
+ conditioning = clip.encode_from_tokens_scheduled(tokens)
+ conditioning = node_helpers.conditioning_set_values(conditioning, {"lyrics_strength": lyrics_strength})
+ return (conditioning, )
+
+
+class EmptyAceStepLatentAudio:
+ def __init__(self):
+ self.device = comfy.model_management.intermediate_device()
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"seconds": ("FLOAT", {"default": 120.0, "min": 1.0, "max": 1000.0, "step": 0.1}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
+ }}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "generate"
+
+ CATEGORY = "latent/audio"
+
+ def generate(self, seconds, batch_size):
+ length = int(seconds * 44100 / 512 / 8)
+ latent = torch.zeros([batch_size, 8, 16, length], device=self.device)
+ return ({"samples": latent, "type": "audio"}, )
+
+
+NODE_CLASS_MAPPINGS = {
+ "TextEncodeAceStepAudio": TextEncodeAceStepAudio,
+ "EmptyAceStepLatentAudio": EmptyAceStepLatentAudio,
+}
diff --git a/ComfyUI/comfy_extras/nodes_advanced_samplers.py b/ComfyUI/comfy_extras/nodes_advanced_samplers.py
new file mode 100644
index 0000000000000000000000000000000000000000..5fbb096fbf808e0c6d96159bee024873fb1f4c3e
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_advanced_samplers.py
@@ -0,0 +1,111 @@
+import comfy.samplers
+import comfy.utils
+import torch
+import numpy as np
+from tqdm.auto import trange
+
+
+@torch.no_grad()
+def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
+ extra_args = {} if extra_args is None else extra_args
+
+ if upscale_steps is None:
+ upscale_steps = max(len(sigmas) // 2 + 1, 2)
+ else:
+ upscale_steps += 1
+ upscale_steps = min(upscale_steps, len(sigmas) + 1)
+
+ upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
+
+ orig_shape = x.size()
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ denoised = model(x, sigmas[i] * s_in, **extra_args)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+ x = denoised
+ if i < len(upscales):
+ x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
+
+ if sigmas[i + 1] > 0:
+ x += sigmas[i + 1] * torch.randn_like(x)
+ return x
+
+
+class SamplerLCMUpscale:
+ upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
+ "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
+ "upscale_method": (s.upscale_methods,),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, scale_ratio, scale_steps, upscale_method):
+ if scale_steps < 0:
+ scale_steps = None
+ sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
+ return (sampler, )
+
+from comfy.k_diffusion.sampling import to_d
+import comfy.model_patcher
+
+@torch.no_grad()
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+ extra_args = {} if extra_args is None else extra_args
+
+ temp = [0]
+ def post_cfg_function(args):
+ temp[0] = args["uncond_denoised"]
+ return args["denoised"]
+
+ model_options = extra_args.get("model_options", {}).copy()
+ extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+ s_in = x.new_ones([x.shape[0]])
+ for i in trange(len(sigmas) - 1, disable=disable):
+ sigma_hat = sigmas[i]
+ denoised = model(x, sigma_hat * s_in, **extra_args)
+ d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+ if callback is not None:
+ callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+ dt = sigmas[i + 1] - sigma_hat
+ x = x + d * dt
+ return x
+
+
+class SamplerEulerCFGpp:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"version": (["regular", "alternative"],),}
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ # CATEGORY = "sampling/custom_sampling/samplers"
+ CATEGORY = "_for_testing"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, version):
+ if version == "alternative":
+ sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
+ else:
+ sampler = comfy.samplers.ksampler("euler_cfg_pp")
+ return (sampler, )
+
+NODE_CLASS_MAPPINGS = {
+ "SamplerLCMUpscale": SamplerLCMUpscale,
+ "SamplerEulerCFGpp": SamplerEulerCFGpp,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "SamplerEulerCFGpp": "SamplerEulerCFG++",
+}
diff --git a/ComfyUI/comfy_extras/nodes_align_your_steps.py b/ComfyUI/comfy_extras/nodes_align_your_steps.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d856d0e8592414df823af27d53d421af7753f27
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_align_your_steps.py
@@ -0,0 +1,53 @@
+#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+import numpy as np
+import torch
+
+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
+
+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]}
+
+class AlignYourStepsScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model_type": (["SD1", "SDXL", "SVD"], ),
+ "steps": ("INT", {"default": 10, "min": 1, "max": 10000}),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ 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 = 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), )
+
+NODE_CLASS_MAPPINGS = {
+ "AlignYourStepsScheduler": AlignYourStepsScheduler,
+}
diff --git a/ComfyUI/comfy_extras/nodes_apg.py b/ComfyUI/comfy_extras/nodes_apg.py
new file mode 100644
index 0000000000000000000000000000000000000000..25b21b1b8b2b6ecc61911e7c594be59deeab60dc
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_apg.py
@@ -0,0 +1,76 @@
+import torch
+
+def project(v0, v1):
+ v1 = torch.nn.functional.normalize(v1, dim=[-1, -2, -3])
+ v0_parallel = (v0 * v1).sum(dim=[-1, -2, -3], keepdim=True) * v1
+ v0_orthogonal = v0 - v0_parallel
+ return v0_parallel, v0_orthogonal
+
+class APG:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "model": ("MODEL",),
+ "eta": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01, "tooltip": "Controls the scale of the parallel guidance vector. Default CFG behavior at a setting of 1."}),
+ "norm_threshold": ("FLOAT", {"default": 5.0, "min": 0.0, "max": 50.0, "step": 0.1, "tooltip": "Normalize guidance vector to this value, normalization disable at a setting of 0."}),
+ "momentum": ("FLOAT", {"default": 0.0, "min": -5.0, "max": 1.0, "step": 0.01, "tooltip":"Controls a running average of guidance during diffusion, disabled at a setting of 0."}),
+ }
+ }
+ RETURN_TYPES = ("MODEL",)
+ FUNCTION = "patch"
+ CATEGORY = "sampling/custom_sampling"
+
+ def patch(self, model, eta, norm_threshold, momentum):
+ running_avg = 0
+ prev_sigma = None
+
+ def pre_cfg_function(args):
+ nonlocal running_avg, prev_sigma
+
+ if len(args["conds_out"]) == 1: return args["conds_out"]
+
+ cond = args["conds_out"][0]
+ uncond = args["conds_out"][1]
+ sigma = args["sigma"][0]
+ cond_scale = args["cond_scale"]
+
+ if prev_sigma is not None and sigma > prev_sigma:
+ running_avg = 0
+ prev_sigma = sigma
+
+ guidance = cond - uncond
+
+ if momentum != 0:
+ if not torch.is_tensor(running_avg):
+ running_avg = guidance
+ else:
+ running_avg = momentum * running_avg + guidance
+ guidance = running_avg
+
+ if norm_threshold > 0:
+ guidance_norm = guidance.norm(p=2, dim=[-1, -2, -3], keepdim=True)
+ scale = torch.minimum(
+ torch.ones_like(guidance_norm),
+ norm_threshold / guidance_norm
+ )
+ guidance = guidance * scale
+
+ guidance_parallel, guidance_orthogonal = project(guidance, cond)
+ modified_guidance = guidance_orthogonal + eta * guidance_parallel
+
+ modified_cond = (uncond + modified_guidance) + (cond - uncond) / cond_scale
+
+ return [modified_cond, uncond] + args["conds_out"][2:]
+
+ m = model.clone()
+ m.set_model_sampler_pre_cfg_function(pre_cfg_function)
+ return (m,)
+
+NODE_CLASS_MAPPINGS = {
+ "APG": APG,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "APG": "Adaptive Projected Guidance",
+}
diff --git a/ComfyUI/comfy_extras/nodes_audio.py b/ComfyUI/comfy_extras/nodes_audio.py
new file mode 100644
index 0000000000000000000000000000000000000000..a90b317795aecc3372e155477d5b990a40156377
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_audio.py
@@ -0,0 +1,370 @@
+from __future__ import annotations
+
+import av
+import torchaudio
+import torch
+import comfy.model_management
+import folder_paths
+import os
+import io
+import json
+import random
+import hashlib
+import node_helpers
+from comfy.cli_args import args
+from comfy.comfy_types import FileLocator
+
+class EmptyLatentAudio:
+ def __init__(self):
+ self.device = comfy.model_management.intermediate_device()
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096, "tooltip": "The number of latent images in the batch."}),
+ }}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "generate"
+
+ CATEGORY = "latent/audio"
+
+ def generate(self, seconds, batch_size):
+ length = round((seconds * 44100 / 2048) / 2) * 2
+ latent = torch.zeros([batch_size, 64, length], device=self.device)
+ return ({"samples":latent, "type": "audio"}, )
+
+class ConditioningStableAudio:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "seconds_start": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
+ "seconds_total": ("FLOAT", {"default": 47.0, "min": 0.0, "max": 1000.0, "step": 0.1}),
+ }}
+
+ RETURN_TYPES = ("CONDITIONING","CONDITIONING")
+ RETURN_NAMES = ("positive", "negative")
+
+ FUNCTION = "append"
+
+ CATEGORY = "conditioning"
+
+ def append(self, positive, negative, seconds_start, seconds_total):
+ positive = node_helpers.conditioning_set_values(positive, {"seconds_start": seconds_start, "seconds_total": seconds_total})
+ negative = node_helpers.conditioning_set_values(negative, {"seconds_start": seconds_start, "seconds_total": seconds_total})
+ return (positive, negative)
+
+class VAEEncodeAudio:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "encode"
+
+ CATEGORY = "latent/audio"
+
+ def encode(self, vae, audio):
+ sample_rate = audio["sample_rate"]
+ if 44100 != sample_rate:
+ waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
+ else:
+ waveform = audio["waveform"]
+
+ t = vae.encode(waveform.movedim(1, -1))
+ return ({"samples":t}, )
+
+class VAEDecodeAudio:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
+ RETURN_TYPES = ("AUDIO",)
+ FUNCTION = "decode"
+
+ CATEGORY = "latent/audio"
+
+ def decode(self, vae, samples):
+ audio = vae.decode(samples["samples"]).movedim(-1, 1)
+ std = torch.std(audio, dim=[1,2], keepdim=True) * 5.0
+ std[std < 1.0] = 1.0
+ audio /= std
+ return ({"waveform": audio, "sample_rate": 44100}, )
+
+
+def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None, quality="128k"):
+
+ filename_prefix += self.prefix_append
+ full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+ results: list[FileLocator] = []
+
+ # Prepare metadata dictionary
+ metadata = {}
+ if not args.disable_metadata:
+ if prompt is not None:
+ metadata["prompt"] = json.dumps(prompt)
+ if extra_pnginfo is not None:
+ for x in extra_pnginfo:
+ metadata[x] = json.dumps(extra_pnginfo[x])
+
+ # Opus supported sample rates
+ OPUS_RATES = [8000, 12000, 16000, 24000, 48000]
+
+ for (batch_number, waveform) in enumerate(audio["waveform"].cpu()):
+ filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+ file = f"{filename_with_batch_num}_{counter:05}_.{format}"
+ output_path = os.path.join(full_output_folder, file)
+
+ # Use original sample rate initially
+ sample_rate = audio["sample_rate"]
+
+ # Handle Opus sample rate requirements
+ if format == "opus":
+ if sample_rate > 48000:
+ sample_rate = 48000
+ elif sample_rate not in OPUS_RATES:
+ # Find the next highest supported rate
+ for rate in sorted(OPUS_RATES):
+ if rate > sample_rate:
+ sample_rate = rate
+ break
+ if sample_rate not in OPUS_RATES: # Fallback if still not supported
+ sample_rate = 48000
+
+ # Resample if necessary
+ if sample_rate != audio["sample_rate"]:
+ waveform = torchaudio.functional.resample(waveform, audio["sample_rate"], sample_rate)
+
+ # Create output with specified format
+ output_buffer = io.BytesIO()
+ output_container = av.open(output_buffer, mode='w', format=format)
+
+ # Set metadata on the container
+ for key, value in metadata.items():
+ output_container.metadata[key] = value
+
+ # Set up the output stream with appropriate properties
+ if format == "opus":
+ out_stream = output_container.add_stream("libopus", rate=sample_rate)
+ if quality == "64k":
+ out_stream.bit_rate = 64000
+ elif quality == "96k":
+ out_stream.bit_rate = 96000
+ elif quality == "128k":
+ out_stream.bit_rate = 128000
+ elif quality == "192k":
+ out_stream.bit_rate = 192000
+ elif quality == "320k":
+ out_stream.bit_rate = 320000
+ elif format == "mp3":
+ out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+ if quality == "V0":
+ #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
+ out_stream.codec_context.qscale = 1
+ elif quality == "128k":
+ out_stream.bit_rate = 128000
+ elif quality == "320k":
+ out_stream.bit_rate = 320000
+ else: #format == "flac":
+ out_stream = output_container.add_stream("flac", rate=sample_rate)
+
+ frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[0] == 1 else 'stereo')
+ frame.sample_rate = sample_rate
+ frame.pts = 0
+ output_container.mux(out_stream.encode(frame))
+
+ # Flush encoder
+ output_container.mux(out_stream.encode(None))
+
+ # Close containers
+ output_container.close()
+
+ # Write the output to file
+ output_buffer.seek(0)
+ with open(output_path, 'wb') as f:
+ f.write(output_buffer.getbuffer())
+
+ results.append({
+ "filename": file,
+ "subfolder": subfolder,
+ "type": self.type
+ })
+ counter += 1
+
+ return { "ui": { "audio": results } }
+
+class SaveAudio:
+ def __init__(self):
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+ self.prefix_append = ""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "audio": ("AUDIO", ),
+ "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+ },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "save_flac"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "audio"
+
+ def save_flac(self, audio, filename_prefix="ComfyUI", format="flac", prompt=None, extra_pnginfo=None):
+ return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo)
+
+class SaveAudioMP3:
+ def __init__(self):
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+ self.prefix_append = ""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "audio": ("AUDIO", ),
+ "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+ "quality": (["V0", "128k", "320k"], {"default": "V0"}),
+ },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "save_mp3"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "audio"
+
+ def save_mp3(self, audio, filename_prefix="ComfyUI", format="mp3", prompt=None, extra_pnginfo=None, quality="128k"):
+ return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
+
+class SaveAudioOpus:
+ def __init__(self):
+ self.output_dir = folder_paths.get_output_directory()
+ self.type = "output"
+ self.prefix_append = ""
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "audio": ("AUDIO", ),
+ "filename_prefix": ("STRING", {"default": "audio/ComfyUI"}),
+ "quality": (["64k", "96k", "128k", "192k", "320k"], {"default": "128k"}),
+ },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+ RETURN_TYPES = ()
+ FUNCTION = "save_opus"
+
+ OUTPUT_NODE = True
+
+ CATEGORY = "audio"
+
+ def save_opus(self, audio, filename_prefix="ComfyUI", format="opus", prompt=None, extra_pnginfo=None, quality="V3"):
+ return save_audio(self, audio, filename_prefix, format, prompt, extra_pnginfo, quality)
+
+class PreviewAudio(SaveAudio):
+ def __init__(self):
+ self.output_dir = folder_paths.get_temp_directory()
+ self.type = "temp"
+ self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"audio": ("AUDIO", ), },
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+ }
+
+def f32_pcm(wav: torch.Tensor) -> torch.Tensor:
+ """Convert audio to float 32 bits PCM format."""
+ if wav.dtype.is_floating_point:
+ return wav
+ elif wav.dtype == torch.int16:
+ return wav.float() / (2 ** 15)
+ elif wav.dtype == torch.int32:
+ return wav.float() / (2 ** 31)
+ raise ValueError(f"Unsupported wav dtype: {wav.dtype}")
+
+def load(filepath: str) -> tuple[torch.Tensor, int]:
+ with av.open(filepath) as af:
+ if not af.streams.audio:
+ raise ValueError("No audio stream found in the file.")
+
+ stream = af.streams.audio[0]
+ sr = stream.codec_context.sample_rate
+ n_channels = stream.channels
+
+ frames = []
+ length = 0
+ for frame in af.decode(streams=stream.index):
+ buf = torch.from_numpy(frame.to_ndarray())
+ if buf.shape[0] != n_channels:
+ buf = buf.view(-1, n_channels).t()
+
+ frames.append(buf)
+ length += buf.shape[1]
+
+ if not frames:
+ raise ValueError("No audio frames decoded.")
+
+ wav = torch.cat(frames, dim=1)
+ wav = f32_pcm(wav)
+ return wav, sr
+
+class LoadAudio:
+ @classmethod
+ def INPUT_TYPES(s):
+ input_dir = folder_paths.get_input_directory()
+ files = folder_paths.filter_files_content_types(os.listdir(input_dir), ["audio", "video"])
+ return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
+
+ CATEGORY = "audio"
+
+ RETURN_TYPES = ("AUDIO", )
+ FUNCTION = "load"
+
+ def load(self, audio):
+ audio_path = folder_paths.get_annotated_filepath(audio)
+ waveform, sample_rate = load(audio_path)
+ audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+ return (audio, )
+
+ @classmethod
+ def IS_CHANGED(s, audio):
+ image_path = folder_paths.get_annotated_filepath(audio)
+ m = hashlib.sha256()
+ with open(image_path, 'rb') as f:
+ m.update(f.read())
+ return m.digest().hex()
+
+ @classmethod
+ def VALIDATE_INPUTS(s, audio):
+ if not folder_paths.exists_annotated_filepath(audio):
+ return "Invalid audio file: {}".format(audio)
+ return True
+
+NODE_CLASS_MAPPINGS = {
+ "EmptyLatentAudio": EmptyLatentAudio,
+ "VAEEncodeAudio": VAEEncodeAudio,
+ "VAEDecodeAudio": VAEDecodeAudio,
+ "SaveAudio": SaveAudio,
+ "SaveAudioMP3": SaveAudioMP3,
+ "SaveAudioOpus": SaveAudioOpus,
+ "LoadAudio": LoadAudio,
+ "PreviewAudio": PreviewAudio,
+ "ConditioningStableAudio": ConditioningStableAudio,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "EmptyLatentAudio": "Empty Latent Audio",
+ "VAEEncodeAudio": "VAE Encode Audio",
+ "VAEDecodeAudio": "VAE Decode Audio",
+ "PreviewAudio": "Preview Audio",
+ "LoadAudio": "Load Audio",
+ "SaveAudio": "Save Audio (FLAC)",
+ "SaveAudioMP3": "Save Audio (MP3)",
+ "SaveAudioOpus": "Save Audio (Opus)",
+}
diff --git a/ComfyUI/comfy_extras/nodes_camera_trajectory.py b/ComfyUI/comfy_extras/nodes_camera_trajectory.py
new file mode 100644
index 0000000000000000000000000000000000000000..5e0e39f914d8ad23eca3b04a433c35decf527546
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_camera_trajectory.py
@@ -0,0 +1,218 @@
+import nodes
+import torch
+import numpy as np
+from einops import rearrange
+import comfy.model_management
+
+
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+CAMERA_DICT = {
+ "base_T_norm": 1.5,
+ "base_angle": np.pi/3,
+ "Static": { "angle":[0., 0., 0.], "T":[0., 0., 0.]},
+ "Pan Up": { "angle":[0., 0., 0.], "T":[0., -1., 0.]},
+ "Pan Down": { "angle":[0., 0., 0.], "T":[0.,1.,0.]},
+ "Pan Left": { "angle":[0., 0., 0.], "T":[-1.,0.,0.]},
+ "Pan Right": { "angle":[0., 0., 0.], "T": [1.,0.,0.]},
+ "Zoom In": { "angle":[0., 0., 0.], "T": [0.,0.,2.]},
+ "Zoom Out": { "angle":[0., 0., 0.], "T": [0.,0.,-2.]},
+ "Anti Clockwise (ACW)": { "angle": [0., 0., -1.], "T":[0., 0., 0.]},
+ "ClockWise (CW)": { "angle": [0., 0., 1.], "T":[0., 0., 0.]},
+}
+
+
+def process_pose_params(cam_params, width=672, height=384, original_pose_width=1280, original_pose_height=720, device='cpu'):
+
+ def get_relative_pose(cam_params):
+ """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+ """
+ abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
+ abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
+ cam_to_origin = 0
+ target_cam_c2w = np.array([
+ [1, 0, 0, 0],
+ [0, 1, 0, -cam_to_origin],
+ [0, 0, 1, 0],
+ [0, 0, 0, 1]
+ ])
+ abs2rel = target_cam_c2w @ abs_w2cs[0]
+ ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
+ ret_poses = np.array(ret_poses, dtype=np.float32)
+ return ret_poses
+
+ """Modified from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+ """
+ cam_params = [Camera(cam_param) for cam_param in cam_params]
+
+ sample_wh_ratio = width / height
+ pose_wh_ratio = original_pose_width / original_pose_height # Assuming placeholder ratios, change as needed
+
+ if pose_wh_ratio > sample_wh_ratio:
+ resized_ori_w = height * pose_wh_ratio
+ for cam_param in cam_params:
+ cam_param.fx = resized_ori_w * cam_param.fx / width
+ else:
+ resized_ori_h = width / pose_wh_ratio
+ for cam_param in cam_params:
+ cam_param.fy = resized_ori_h * cam_param.fy / height
+
+ intrinsic = np.asarray([[cam_param.fx * width,
+ cam_param.fy * height,
+ cam_param.cx * width,
+ cam_param.cy * height]
+ for cam_param in cam_params], dtype=np.float32)
+
+ K = torch.as_tensor(intrinsic)[None] # [1, 1, 4]
+ c2ws = get_relative_pose(cam_params) # Assuming this function is defined elsewhere
+ c2ws = torch.as_tensor(c2ws)[None] # [1, n_frame, 4, 4]
+ plucker_embedding = ray_condition(K, c2ws, height, width, device=device)[0].permute(0, 3, 1, 2).contiguous() # V, 6, H, W
+ plucker_embedding = plucker_embedding[None]
+ plucker_embedding = rearrange(plucker_embedding, "b f c h w -> b f h w c")[0]
+ return plucker_embedding
+
+class Camera(object):
+ """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+ """
+ def __init__(self, entry):
+ fx, fy, cx, cy = entry[1:5]
+ self.fx = fx
+ self.fy = fy
+ self.cx = cx
+ self.cy = cy
+ c2w_mat = np.array(entry[7:]).reshape(4, 4)
+ self.c2w_mat = c2w_mat
+ self.w2c_mat = np.linalg.inv(c2w_mat)
+
+def ray_condition(K, c2w, H, W, device):
+ """Copied from https://github.com/hehao13/CameraCtrl/blob/main/inference.py
+ """
+ # c2w: B, V, 4, 4
+ # K: B, V, 4
+
+ B = K.shape[0]
+
+ j, i = torch.meshgrid(
+ torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
+ torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
+ indexing='ij'
+ )
+ i = i.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
+ j = j.reshape([1, 1, H * W]).expand([B, 1, H * W]) + 0.5 # [B, HxW]
+
+ fx, fy, cx, cy = K.chunk(4, dim=-1) # B,V, 1
+
+ zs = torch.ones_like(i) # [B, HxW]
+ xs = (i - cx) / fx * zs
+ ys = (j - cy) / fy * zs
+ zs = zs.expand_as(ys)
+
+ directions = torch.stack((xs, ys, zs), dim=-1) # B, V, HW, 3
+ directions = directions / directions.norm(dim=-1, keepdim=True) # B, V, HW, 3
+
+ rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2) # B, V, 3, HW
+ rays_o = c2w[..., :3, 3] # B, V, 3
+ rays_o = rays_o[:, :, None].expand_as(rays_d) # B, V, 3, HW
+ # c2w @ dirctions
+ rays_dxo = torch.cross(rays_o, rays_d)
+ plucker = torch.cat([rays_dxo, rays_d], dim=-1)
+ plucker = plucker.reshape(B, c2w.shape[1], H, W, 6) # B, V, H, W, 6
+ # plucker = plucker.permute(0, 1, 4, 2, 3)
+ return plucker
+
+def get_camera_motion(angle, T, speed, n=81):
+ def compute_R_form_rad_angle(angles):
+ theta_x, theta_y, theta_z = angles
+ Rx = np.array([[1, 0, 0],
+ [0, np.cos(theta_x), -np.sin(theta_x)],
+ [0, np.sin(theta_x), np.cos(theta_x)]])
+
+ Ry = np.array([[np.cos(theta_y), 0, np.sin(theta_y)],
+ [0, 1, 0],
+ [-np.sin(theta_y), 0, np.cos(theta_y)]])
+
+ Rz = np.array([[np.cos(theta_z), -np.sin(theta_z), 0],
+ [np.sin(theta_z), np.cos(theta_z), 0],
+ [0, 0, 1]])
+
+ R = np.dot(Rz, np.dot(Ry, Rx))
+ return R
+ RT = []
+ for i in range(n):
+ _angle = (i/n)*speed*(CAMERA_DICT["base_angle"])*angle
+ R = compute_R_form_rad_angle(_angle)
+ _T=(i/n)*speed*(CAMERA_DICT["base_T_norm"])*(T.reshape(3,1))
+ _RT = np.concatenate([R,_T], axis=1)
+ RT.append(_RT)
+ RT = np.stack(RT)
+ return RT
+
+class WanCameraEmbedding:
+ @classmethod
+ def INPUT_TYPES(cls):
+ return {
+ "required": {
+ "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}),
+ "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+ "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
+ "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}),
+ },
+ "optional":{
+ "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}),
+ "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+ "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
+ "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+ "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
+ }
+
+ }
+
+ RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT")
+ RETURN_NAMES = ("camera_embedding","width","height","length")
+ FUNCTION = "run"
+ CATEGORY = "camera"
+
+ def run(self, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5):
+ """
+ Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
+ Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
+ """
+ motion_list = [camera_pose]
+ speed = speed
+ angle = np.array(CAMERA_DICT[motion_list[0]]["angle"])
+ T = np.array(CAMERA_DICT[motion_list[0]]["T"])
+ RT = get_camera_motion(angle, T, speed, length)
+
+ trajs=[]
+ for cp in RT.tolist():
+ traj=[fx,fy,cx,cy,0,0]
+ traj.extend(cp[0])
+ traj.extend(cp[1])
+ traj.extend(cp[2])
+ traj.extend([0,0,0,1])
+ trajs.append(traj)
+
+ cam_params = np.array([[float(x) for x in pose] for pose in trajs])
+ cam_params = np.concatenate([np.zeros_like(cam_params[:, :1]), cam_params], 1)
+ control_camera_video = process_pose_params(cam_params, width=width, height=height)
+ control_camera_video = control_camera_video.permute([3, 0, 1, 2]).unsqueeze(0).to(device=comfy.model_management.intermediate_device())
+
+ control_camera_video = torch.concat(
+ [
+ torch.repeat_interleave(control_camera_video[:, :, 0:1], repeats=4, dim=2),
+ control_camera_video[:, :, 1:]
+ ], dim=2
+ ).transpose(1, 2)
+
+ # Reshape, transpose, and view into desired shape
+ b, f, c, h, w = control_camera_video.shape
+ control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
+ control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)
+
+ return (control_camera_video, width, height, length)
+
+
+NODE_CLASS_MAPPINGS = {
+ "WanCameraEmbedding": WanCameraEmbedding,
+}
diff --git a/ComfyUI/comfy_extras/nodes_canny.py b/ComfyUI/comfy_extras/nodes_canny.py
new file mode 100644
index 0000000000000000000000000000000000000000..d85e6b85691dcdcc55e31705039934846d466fc1
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_canny.py
@@ -0,0 +1,25 @@
+from kornia.filters import canny
+import comfy.model_management
+
+
+class Canny:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"image": ("IMAGE",),
+ "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
+ "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
+ }}
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "detect_edge"
+
+ CATEGORY = "image/preprocessors"
+
+ def detect_edge(self, image, low_threshold, high_threshold):
+ output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
+ img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
+ return (img_out,)
+
+NODE_CLASS_MAPPINGS = {
+ "Canny": Canny,
+}
diff --git a/ComfyUI/comfy_extras/nodes_cfg.py b/ComfyUI/comfy_extras/nodes_cfg.py
new file mode 100644
index 0000000000000000000000000000000000000000..5abdc115ab4f9e82ca43151ec8826d234a16b172
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_cfg.py
@@ -0,0 +1,72 @@
+import torch
+
+# https://github.com/WeichenFan/CFG-Zero-star
+def optimized_scale(positive, negative):
+ positive_flat = positive.reshape(positive.shape[0], -1)
+ negative_flat = negative.reshape(negative.shape[0], -1)
+
+ # Calculate dot production
+ dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
+
+ # Squared norm of uncondition
+ squared_norm = torch.sum(negative_flat ** 2, dim=1, keepdim=True) + 1e-8
+
+ # st_star = v_cond^T * v_uncond / ||v_uncond||^2
+ st_star = dot_product / squared_norm
+
+ return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))
+
+class CFGZeroStar:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"model": ("MODEL",),
+ }}
+ RETURN_TYPES = ("MODEL",)
+ RETURN_NAMES = ("patched_model",)
+ FUNCTION = "patch"
+ CATEGORY = "advanced/guidance"
+
+ def patch(self, model):
+ m = model.clone()
+ def cfg_zero_star(args):
+ guidance_scale = args['cond_scale']
+ x = args['input']
+ cond_p = args['cond_denoised']
+ uncond_p = args['uncond_denoised']
+ out = args["denoised"]
+ alpha = optimized_scale(x - cond_p, x - uncond_p)
+
+ return out + uncond_p * (alpha - 1.0) + guidance_scale * uncond_p * (1.0 - alpha)
+ m.set_model_sampler_post_cfg_function(cfg_zero_star)
+ return (m, )
+
+class CFGNorm:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"model": ("MODEL",),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("MODEL",)
+ RETURN_NAMES = ("patched_model",)
+ FUNCTION = "patch"
+ CATEGORY = "advanced/guidance"
+ EXPERIMENTAL = True
+
+ def patch(self, model, strength):
+ m = model.clone()
+ def cfg_norm(args):
+ cond_p = args['cond_denoised']
+ pred_text_ = args["denoised"]
+
+ norm_full_cond = torch.norm(cond_p, dim=1, keepdim=True)
+ norm_pred_text = torch.norm(pred_text_, dim=1, keepdim=True)
+ scale = (norm_full_cond / (norm_pred_text + 1e-8)).clamp(min=0.0, max=1.0)
+ return pred_text_ * scale * strength
+
+ m.set_model_sampler_post_cfg_function(cfg_norm)
+ return (m, )
+
+NODE_CLASS_MAPPINGS = {
+ "CFGZeroStar": CFGZeroStar,
+ "CFGNorm": CFGNorm,
+}
diff --git a/ComfyUI/comfy_extras/nodes_compositing.py b/ComfyUI/comfy_extras/nodes_compositing.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f994fa11d370d31dc5412da90d0582fe6bd6159
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_compositing.py
@@ -0,0 +1,214 @@
+import torch
+import comfy.utils
+from enum import Enum
+
+def resize_mask(mask, shape):
+ return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
+
+class PorterDuffMode(Enum):
+ ADD = 0
+ CLEAR = 1
+ DARKEN = 2
+ DST = 3
+ DST_ATOP = 4
+ DST_IN = 5
+ DST_OUT = 6
+ DST_OVER = 7
+ LIGHTEN = 8
+ MULTIPLY = 9
+ OVERLAY = 10
+ SCREEN = 11
+ SRC = 12
+ SRC_ATOP = 13
+ SRC_IN = 14
+ SRC_OUT = 15
+ SRC_OVER = 16
+ XOR = 17
+
+
+def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
+ # convert mask to alpha
+ src_alpha = 1 - src_alpha
+ dst_alpha = 1 - dst_alpha
+ # premultiply alpha
+ src_image = src_image * src_alpha
+ dst_image = dst_image * dst_alpha
+
+ # composite ops below assume alpha-premultiplied images
+ if mode == PorterDuffMode.ADD:
+ out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
+ out_image = torch.clamp(src_image + dst_image, 0, 1)
+ elif mode == PorterDuffMode.CLEAR:
+ out_alpha = torch.zeros_like(dst_alpha)
+ out_image = torch.zeros_like(dst_image)
+ elif mode == PorterDuffMode.DARKEN:
+ out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+ out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
+ elif mode == PorterDuffMode.DST:
+ out_alpha = dst_alpha
+ out_image = dst_image
+ elif mode == PorterDuffMode.DST_ATOP:
+ out_alpha = src_alpha
+ out_image = src_alpha * dst_image + (1 - dst_alpha) * src_image
+ elif mode == PorterDuffMode.DST_IN:
+ out_alpha = src_alpha * dst_alpha
+ out_image = dst_image * src_alpha
+ elif mode == PorterDuffMode.DST_OUT:
+ out_alpha = (1 - src_alpha) * dst_alpha
+ out_image = (1 - src_alpha) * dst_image
+ elif mode == PorterDuffMode.DST_OVER:
+ out_alpha = dst_alpha + (1 - dst_alpha) * src_alpha
+ out_image = dst_image + (1 - dst_alpha) * src_image
+ elif mode == PorterDuffMode.LIGHTEN:
+ out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+ out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.max(src_image, dst_image)
+ elif mode == PorterDuffMode.MULTIPLY:
+ out_alpha = src_alpha * dst_alpha
+ out_image = src_image * dst_image
+ elif mode == PorterDuffMode.OVERLAY:
+ out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+ out_image = torch.where(2 * dst_image < dst_alpha, 2 * src_image * dst_image,
+ src_alpha * dst_alpha - 2 * (dst_alpha - src_image) * (src_alpha - dst_image))
+ elif mode == PorterDuffMode.SCREEN:
+ out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
+ out_image = src_image + dst_image - src_image * dst_image
+ elif mode == PorterDuffMode.SRC:
+ out_alpha = src_alpha
+ out_image = src_image
+ elif mode == PorterDuffMode.SRC_ATOP:
+ out_alpha = dst_alpha
+ out_image = dst_alpha * src_image + (1 - src_alpha) * dst_image
+ elif mode == PorterDuffMode.SRC_IN:
+ out_alpha = src_alpha * dst_alpha
+ out_image = src_image * dst_alpha
+ elif mode == PorterDuffMode.SRC_OUT:
+ out_alpha = (1 - dst_alpha) * src_alpha
+ out_image = (1 - dst_alpha) * src_image
+ elif mode == PorterDuffMode.SRC_OVER:
+ out_alpha = src_alpha + (1 - src_alpha) * dst_alpha
+ out_image = src_image + (1 - src_alpha) * dst_image
+ elif mode == PorterDuffMode.XOR:
+ out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
+ out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
+ else:
+ return None, None
+
+ # back to non-premultiplied alpha
+ out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
+ out_image = torch.clamp(out_image, 0, 1)
+ # convert alpha to mask
+ out_alpha = 1 - out_alpha
+ return out_image, out_alpha
+
+
+class PorterDuffImageComposite:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "source": ("IMAGE",),
+ "source_alpha": ("MASK",),
+ "destination": ("IMAGE",),
+ "destination_alpha": ("MASK",),
+ "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE", "MASK")
+ FUNCTION = "composite"
+ CATEGORY = "mask/compositing"
+
+ def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
+ batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
+ out_images = []
+ out_alphas = []
+
+ for i in range(batch_size):
+ src_image = source[i]
+ dst_image = destination[i]
+
+ assert src_image.shape[2] == dst_image.shape[2] # inputs need to have same number of channels
+
+ src_alpha = source_alpha[i].unsqueeze(2)
+ dst_alpha = destination_alpha[i].unsqueeze(2)
+
+ if dst_alpha.shape[:2] != dst_image.shape[:2]:
+ upscale_input = dst_alpha.unsqueeze(0).permute(0, 3, 1, 2)
+ upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+ dst_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+ if src_image.shape != dst_image.shape:
+ upscale_input = src_image.unsqueeze(0).permute(0, 3, 1, 2)
+ upscale_output = comfy.utils.common_upscale(upscale_input, dst_image.shape[1], dst_image.shape[0], upscale_method='bicubic', crop='center')
+ src_image = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+ if src_alpha.shape != dst_alpha.shape:
+ upscale_input = src_alpha.unsqueeze(0).permute(0, 3, 1, 2)
+ upscale_output = comfy.utils.common_upscale(upscale_input, dst_alpha.shape[1], dst_alpha.shape[0], upscale_method='bicubic', crop='center')
+ src_alpha = upscale_output.permute(0, 2, 3, 1).squeeze(0)
+
+ out_image, out_alpha = porter_duff_composite(src_image, src_alpha, dst_image, dst_alpha, PorterDuffMode[mode])
+
+ out_images.append(out_image)
+ out_alphas.append(out_alpha.squeeze(2))
+
+ result = (torch.stack(out_images), torch.stack(out_alphas))
+ return result
+
+
+class SplitImageWithAlpha:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ }
+ }
+
+ CATEGORY = "mask/compositing"
+ RETURN_TYPES = ("IMAGE", "MASK")
+ FUNCTION = "split_image_with_alpha"
+
+ def split_image_with_alpha(self, image: torch.Tensor):
+ out_images = [i[:,:,:3] for i in image]
+ out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
+ result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))
+ return result
+
+
+class JoinImageWithAlpha:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "alpha": ("MASK",),
+ }
+ }
+
+ CATEGORY = "mask/compositing"
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "join_image_with_alpha"
+
+ def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
+ batch_size = min(len(image), len(alpha))
+ out_images = []
+
+ alpha = 1.0 - resize_mask(alpha, image.shape[1:])
+ for i in range(batch_size):
+ out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
+
+ result = (torch.stack(out_images),)
+ return result
+
+
+NODE_CLASS_MAPPINGS = {
+ "PorterDuffImageComposite": PorterDuffImageComposite,
+ "SplitImageWithAlpha": SplitImageWithAlpha,
+ "JoinImageWithAlpha": JoinImageWithAlpha,
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "PorterDuffImageComposite": "Porter-Duff Image Composite",
+ "SplitImageWithAlpha": "Split Image with Alpha",
+ "JoinImageWithAlpha": "Join Image with Alpha",
+}
diff --git a/ComfyUI/comfy_extras/nodes_cond.py b/ComfyUI/comfy_extras/nodes_cond.py
new file mode 100644
index 0000000000000000000000000000000000000000..58c16f621cd1c5f5abd66dba639ebf5740862789
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_cond.py
@@ -0,0 +1,49 @@
+
+
+class CLIPTextEncodeControlnet:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "encode"
+
+ CATEGORY = "_for_testing/conditioning"
+
+ def encode(self, clip, conditioning, text):
+ tokens = clip.tokenize(text)
+ cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+ c = []
+ for t in conditioning:
+ n = [t[0], t[1].copy()]
+ n[1]['cross_attn_controlnet'] = cond
+ n[1]['pooled_output_controlnet'] = pooled
+ c.append(n)
+ return (c, )
+
+class T5TokenizerOptions:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "clip": ("CLIP", ),
+ "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+ "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
+ }
+ }
+
+ CATEGORY = "_for_testing/conditioning"
+ RETURN_TYPES = ("CLIP",)
+ FUNCTION = "set_options"
+
+ def set_options(self, clip, min_padding, min_length):
+ clip = clip.clone()
+ for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
+ clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
+ clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)
+
+ return (clip, )
+
+NODE_CLASS_MAPPINGS = {
+ "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet,
+ "T5TokenizerOptions": T5TokenizerOptions,
+}
diff --git a/ComfyUI/comfy_extras/nodes_cosmos.py b/ComfyUI/comfy_extras/nodes_cosmos.py
new file mode 100644
index 0000000000000000000000000000000000000000..4f49605510e4986f5d030d7bbde14fbf2f7a9c2d
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_cosmos.py
@@ -0,0 +1,128 @@
+import nodes
+import torch
+import comfy.model_management
+import comfy.utils
+import comfy.latent_formats
+
+
+class EmptyCosmosLatentVideo:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "generate"
+
+ CATEGORY = "latent/video"
+
+ def generate(self, width, height, length, batch_size=1):
+ latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+ return ({"samples": latent}, )
+
+
+def vae_encode_with_padding(vae, image, width, height, length, padding=0):
+ pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+ pixel_len = min(pixels.shape[0], length)
+ padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7)
+ padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
+ padded_pixels[:pixel_len] = pixels[:pixel_len]
+ latent_len = ((pixel_len - 1) // 8) + 1
+ latent_temp = vae.encode(padded_pixels)
+ return latent_temp[:, :, :latent_len]
+
+
+class CosmosImageToVideoLatent:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"vae": ("VAE", ),
+ "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+ },
+ "optional": {"start_image": ("IMAGE", ),
+ "end_image": ("IMAGE", ),
+ }}
+
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "encode"
+
+ CATEGORY = "conditioning/inpaint"
+
+ def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+ latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+ if start_image is None and end_image is None:
+ out_latent = {}
+ out_latent["samples"] = latent
+ return (out_latent,)
+
+ mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+ if start_image is not None:
+ latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+ latent[:, :, :latent_temp.shape[-3]] = latent_temp
+ mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+ if end_image is not None:
+ latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+ latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+ mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+ out_latent = {}
+ out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+ out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+ return (out_latent,)
+
+class CosmosPredict2ImageToVideoLatent:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"vae": ("VAE", ),
+ "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+ "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
+ "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+ },
+ "optional": {"start_image": ("IMAGE", ),
+ "end_image": ("IMAGE", ),
+ }}
+
+
+ RETURN_TYPES = ("LATENT",)
+ FUNCTION = "encode"
+
+ CATEGORY = "conditioning/inpaint"
+
+ def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+ latent = torch.zeros([1, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+ if start_image is None and end_image is None:
+ out_latent = {}
+ out_latent["samples"] = latent
+ return (out_latent,)
+
+ mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+ if start_image is not None:
+ latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+ latent[:, :, :latent_temp.shape[-3]] = latent_temp
+ mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+ if end_image is not None:
+ latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+ latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+ mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+ out_latent = {}
+ latent_format = comfy.latent_formats.Wan21()
+ latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
+ out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+ out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+ return (out_latent,)
+
+NODE_CLASS_MAPPINGS = {
+ "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
+ "CosmosImageToVideoLatent": CosmosImageToVideoLatent,
+ "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent,
+}
diff --git a/ComfyUI/comfy_extras/nodes_custom_sampler.py b/ComfyUI/comfy_extras/nodes_custom_sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..d011f433b5db84d665dc20349b9b9aa75a703df9
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_custom_sampler.py
@@ -0,0 +1,932 @@
+import math
+import comfy.samplers
+import comfy.sample
+from comfy.k_diffusion import sampling as k_diffusion_sampling
+from comfy.k_diffusion import sa_solver
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+import latent_preview
+import torch
+import comfy.utils
+import node_helpers
+
+
+class BasicScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "scheduler": (comfy.samplers.SCHEDULER_NAMES, ),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, model, scheduler, steps, denoise):
+ total_steps = steps
+ if denoise < 1.0:
+ if denoise <= 0.0:
+ return (torch.FloatTensor([]),)
+ total_steps = int(steps/denoise)
+
+ sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
+ sigmas = sigmas[-(steps + 1):]
+ return (sigmas, )
+
+
+class KarrasScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, steps, sigma_max, sigma_min, rho):
+ sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
+ return (sigmas, )
+
+class ExponentialScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, steps, sigma_max, sigma_min):
+ sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max)
+ return (sigmas, )
+
+class PolyexponentialScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, steps, sigma_max, sigma_min, rho):
+ sigmas = k_diffusion_sampling.get_sigmas_polyexponential(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, rho=rho)
+ return (sigmas, )
+
+class LaplaceScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "mu": ("FLOAT", {"default": 0.0, "min": -10.0, "max": 10.0, "step":0.1, "round": False}),
+ "beta": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step":0.1, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, steps, sigma_max, sigma_min, mu, beta):
+ sigmas = k_diffusion_sampling.get_sigmas_laplace(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, mu=mu, beta=beta)
+ return (sigmas, )
+
+
+class SDTurboScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "steps": ("INT", {"default": 1, "min": 1, "max": 10}),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, model, steps, denoise):
+ start_step = 10 - int(10 * denoise)
+ timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
+ sigmas = model.get_model_object("model_sampling").sigma(timesteps)
+ sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
+ return (sigmas, )
+
+class BetaSamplingScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "alpha": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+ "beta": ("FLOAT", {"default": 0.6, "min": 0.0, "max": 50.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, model, steps, alpha, beta):
+ sigmas = comfy.samplers.beta_scheduler(model.get_model_object("model_sampling"), steps, alpha=alpha, beta=beta)
+ return (sigmas, )
+
+class VPScheduler:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
+ "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values
+ "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+ "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/schedulers"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, steps, beta_d, beta_min, eps_s):
+ sigmas = k_diffusion_sampling.get_sigmas_vp(n=steps, beta_d=beta_d, beta_min=beta_min, eps_s=eps_s)
+ return (sigmas, )
+
+class SplitSigmas:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sigmas": ("SIGMAS", ),
+ "step": ("INT", {"default": 0, "min": 0, "max": 10000}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS","SIGMAS")
+ RETURN_NAMES = ("high_sigmas", "low_sigmas")
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, sigmas, step):
+ sigmas1 = sigmas[:step + 1]
+ sigmas2 = sigmas[step:]
+ return (sigmas1, sigmas2)
+
+class SplitSigmasDenoise:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sigmas": ("SIGMAS", ),
+ "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS","SIGMAS")
+ RETURN_NAMES = ("high_sigmas", "low_sigmas")
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, sigmas, denoise):
+ steps = max(sigmas.shape[-1] - 1, 0)
+ total_steps = round(steps * denoise)
+ sigmas1 = sigmas[:-(total_steps)]
+ sigmas2 = sigmas[-(total_steps + 1):]
+ return (sigmas1, sigmas2)
+
+class FlipSigmas:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sigmas": ("SIGMAS", ),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "get_sigmas"
+
+ def get_sigmas(self, sigmas):
+ if len(sigmas) == 0:
+ return (sigmas,)
+
+ sigmas = sigmas.flip(0)
+ if sigmas[0] == 0:
+ sigmas[0] = 0.0001
+ return (sigmas,)
+
+class SetFirstSigma:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sigmas": ("SIGMAS", ),
+ "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "set_first_sigma"
+
+ def set_first_sigma(self, sigmas, sigma):
+ sigmas = sigmas.clone()
+ sigmas[0] = sigma
+ return (sigmas, )
+
+class ExtendIntermediateSigmas:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sigmas": ("SIGMAS", ),
+ "steps": ("INT", {"default": 2, "min": 1, "max": 100}),
+ "start_at_sigma": ("FLOAT", {"default": -1.0, "min": -1.0, "max": 20000.0, "step": 0.01, "round": False}),
+ "end_at_sigma": ("FLOAT", {"default": 12.0, "min": 0.0, "max": 20000.0, "step": 0.01, "round": False}),
+ "spacing": (['linear', 'cosine', 'sine'],),
+ }
+ }
+ RETURN_TYPES = ("SIGMAS",)
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "extend"
+
+ def extend(self, sigmas: torch.Tensor, steps: int, start_at_sigma: float, end_at_sigma: float, spacing: str):
+ if start_at_sigma < 0:
+ start_at_sigma = float("inf")
+
+ interpolator = {
+ 'linear': lambda x: x,
+ 'cosine': lambda x: torch.sin(x*math.pi/2),
+ 'sine': lambda x: 1 - torch.cos(x*math.pi/2)
+ }[spacing]
+
+ # linear space for our interpolation function
+ x = torch.linspace(0, 1, steps + 1, device=sigmas.device)[1:-1]
+ computed_spacing = interpolator(x)
+
+ extended_sigmas = []
+ for i in range(len(sigmas) - 1):
+ sigma_current = sigmas[i]
+ sigma_next = sigmas[i+1]
+
+ extended_sigmas.append(sigma_current)
+
+ if end_at_sigma <= sigma_current <= start_at_sigma:
+ interpolated_steps = computed_spacing * (sigma_next - sigma_current) + sigma_current
+ extended_sigmas.extend(interpolated_steps.tolist())
+
+ # Add the last sigma value
+ if len(sigmas) > 0:
+ extended_sigmas.append(sigmas[-1])
+
+ extended_sigmas = torch.FloatTensor(extended_sigmas)
+
+ return (extended_sigmas,)
+
+
+class SamplingPercentToSigma:
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "model": (IO.MODEL, {}),
+ "sampling_percent": (IO.FLOAT, {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.0001}),
+ "return_actual_sigma": (IO.BOOLEAN, {"default": False, "tooltip": "Return the actual sigma value instead of the value used for interval checks.\nThis only affects results at 0.0 and 1.0."}),
+ }
+ }
+
+ RETURN_TYPES = (IO.FLOAT,)
+ RETURN_NAMES = ("sigma_value",)
+ CATEGORY = "sampling/custom_sampling/sigmas"
+
+ FUNCTION = "get_sigma"
+
+ def get_sigma(self, model, sampling_percent, return_actual_sigma):
+ model_sampling = model.get_model_object("model_sampling")
+ sigma_val = model_sampling.percent_to_sigma(sampling_percent)
+ if return_actual_sigma:
+ if sampling_percent == 0.0:
+ sigma_val = model_sampling.sigma_max.item()
+ elif sampling_percent == 1.0:
+ sigma_val = model_sampling.sigma_min.item()
+ return (sigma_val,)
+
+
+class KSamplerSelect:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"sampler_name": (comfy.samplers.SAMPLER_NAMES, ),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, sampler_name):
+ sampler = comfy.samplers.sampler_object(sampler_name)
+ return (sampler, )
+
+class SamplerDPMPP_3M_SDE:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "noise_device": (['gpu', 'cpu'], ),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, eta, s_noise, noise_device):
+ if noise_device == 'cpu':
+ sampler_name = "dpmpp_3m_sde"
+ else:
+ sampler_name = "dpmpp_3m_sde_gpu"
+ sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
+ return (sampler, )
+
+class SamplerDPMPP_2M_SDE:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"solver_type": (['midpoint', 'heun'], ),
+ "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "noise_device": (['gpu', 'cpu'], ),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, solver_type, eta, s_noise, noise_device):
+ if noise_device == 'cpu':
+ sampler_name = "dpmpp_2m_sde"
+ else:
+ sampler_name = "dpmpp_2m_sde_gpu"
+ sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "solver_type": solver_type})
+ return (sampler, )
+
+
+class SamplerDPMPP_SDE:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "r": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "noise_device": (['gpu', 'cpu'], ),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, eta, s_noise, r, noise_device):
+ if noise_device == 'cpu':
+ sampler_name = "dpmpp_sde"
+ else:
+ sampler_name = "dpmpp_sde_gpu"
+ sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
+ return (sampler, )
+
+class SamplerDPMPP_2S_Ancestral:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, eta, s_noise):
+ sampler = comfy.samplers.ksampler("dpmpp_2s_ancestral", {"eta": eta, "s_noise": s_noise})
+ return (sampler, )
+
+class SamplerEulerAncestral:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, eta, s_noise):
+ sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
+ return (sampler, )
+
+class SamplerEulerAncestralCFGPP:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}),
+ }}
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, eta, s_noise):
+ sampler = comfy.samplers.ksampler(
+ "euler_ancestral_cfg_pp",
+ {"eta": eta, "s_noise": s_noise})
+ return (sampler, )
+
+class SamplerLMS:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"order": ("INT", {"default": 4, "min": 1, "max": 100}),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, order):
+ sampler = comfy.samplers.ksampler("lms", {"order": order})
+ return (sampler, )
+
+class SamplerDPMAdaptative:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"order": ("INT", {"default": 3, "min": 2, "max": 3}),
+ "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+ }
+ }
+ RETURN_TYPES = ("SAMPLER",)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise):
+ sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
+ "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
+ "s_noise":s_noise })
+ return (sampler, )
+
+
+class SamplerER_SDE(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "solver_type": (IO.COMBO, {"options": ["ER-SDE", "Reverse-time SDE", "ODE"]}),
+ "max_stage": (IO.INT, {"default": 3, "min": 1, "max": 3}),
+ "eta": (
+ IO.FLOAT,
+ {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False, "tooltip": "Stochastic strength of reverse-time SDE.\nWhen eta=0, it reduces to deterministic ODE. This setting doesn't apply to ER-SDE solver type."},
+ ),
+ "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False}),
+ }
+ }
+
+ RETURN_TYPES = (IO.SAMPLER,)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, solver_type, max_stage, eta, s_noise):
+ if solver_type == "ODE" or (solver_type == "Reverse-time SDE" and eta == 0):
+ eta = 0
+ s_noise = 0
+
+ def reverse_time_sde_noise_scaler(x):
+ return x ** (eta + 1)
+
+ if solver_type == "ER-SDE":
+ # Use the default one in sample_er_sde()
+ noise_scaler = None
+ else:
+ noise_scaler = reverse_time_sde_noise_scaler
+
+ sampler_name = "er_sde"
+ sampler = comfy.samplers.ksampler(sampler_name, {"s_noise": s_noise, "noise_scaler": noise_scaler, "max_stage": max_stage})
+ return (sampler,)
+
+
+class SamplerSASolver(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {
+ "model": (IO.MODEL, {}),
+ "eta": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01, "round": False},),
+ "sde_start_percent": (IO.FLOAT, {"default": 0.2, "min": 0.0, "max": 1.0, "step": 0.001},),
+ "sde_end_percent": (IO.FLOAT, {"default": 0.8, "min": 0.0, "max": 1.0, "step": 0.001},),
+ "s_noise": (IO.FLOAT, {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": False},),
+ "predictor_order": (IO.INT, {"default": 3, "min": 1, "max": 6}),
+ "corrector_order": (IO.INT, {"default": 4, "min": 0, "max": 6}),
+ "use_pece": (IO.BOOLEAN, {}),
+ "simple_order_2": (IO.BOOLEAN, {}),
+ }
+ }
+
+ RETURN_TYPES = (IO.SAMPLER,)
+ CATEGORY = "sampling/custom_sampling/samplers"
+
+ FUNCTION = "get_sampler"
+
+ def get_sampler(self, model, eta, sde_start_percent, sde_end_percent, s_noise, predictor_order, corrector_order, use_pece, simple_order_2):
+ model_sampling = model.get_model_object("model_sampling")
+ start_sigma = model_sampling.percent_to_sigma(sde_start_percent)
+ end_sigma = model_sampling.percent_to_sigma(sde_end_percent)
+ tau_func = sa_solver.get_tau_interval_func(start_sigma, end_sigma, eta=eta)
+
+ sampler_name = "sa_solver"
+ sampler = comfy.samplers.ksampler(
+ sampler_name,
+ {
+ "tau_func": tau_func,
+ "s_noise": s_noise,
+ "predictor_order": predictor_order,
+ "corrector_order": corrector_order,
+ "use_pece": use_pece,
+ "simple_order_2": simple_order_2,
+ },
+ )
+ return (sampler,)
+
+
+class Noise_EmptyNoise:
+ def __init__(self):
+ self.seed = 0
+
+ def generate_noise(self, input_latent):
+ latent_image = input_latent["samples"]
+ return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+
+
+class Noise_RandomNoise:
+ def __init__(self, seed):
+ self.seed = seed
+
+ def generate_noise(self, input_latent):
+ latent_image = input_latent["samples"]
+ batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
+ return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
+
+class SamplerCustom:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "add_noise": ("BOOLEAN", {"default": True}),
+ "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "control_after_generate": True}),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "sampler": ("SAMPLER", ),
+ "sigmas": ("SIGMAS", ),
+ "latent_image": ("LATENT", ),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT","LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+
+ FUNCTION = "sample"
+
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
+ latent = latent_image
+ latent_image = latent["samples"]
+ latent = latent.copy()
+ latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+ latent["samples"] = latent_image
+
+ if not add_noise:
+ noise = Noise_EmptyNoise().generate_noise(latent)
+ else:
+ noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
+
+ noise_mask = None
+ if "noise_mask" in latent:
+ noise_mask = latent["noise_mask"]
+
+ x0_output = {}
+ callback = latent_preview.prepare_callback(model, sigmas.shape[-1] - 1, x0_output)
+
+ disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+ samples = comfy.sample.sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise_seed)
+
+ out = latent.copy()
+ out["samples"] = samples
+ if "x0" in x0_output:
+ out_denoised = latent.copy()
+ out_denoised["samples"] = model.model.process_latent_out(x0_output["x0"].cpu())
+ else:
+ out_denoised = out
+ return (out, out_denoised)
+
+class Guider_Basic(comfy.samplers.CFGGuider):
+ def set_conds(self, positive):
+ self.inner_set_conds({"positive": positive})
+
+class BasicGuider:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "conditioning": ("CONDITIONING", ),
+ }
+ }
+
+ RETURN_TYPES = ("GUIDER",)
+
+ FUNCTION = "get_guider"
+ CATEGORY = "sampling/custom_sampling/guiders"
+
+ def get_guider(self, model, conditioning):
+ guider = Guider_Basic(model)
+ guider.set_conds(conditioning)
+ return (guider,)
+
+class CFGGuider:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "positive": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ }
+ }
+
+ RETURN_TYPES = ("GUIDER",)
+
+ FUNCTION = "get_guider"
+ CATEGORY = "sampling/custom_sampling/guiders"
+
+ def get_guider(self, model, positive, negative, cfg):
+ guider = comfy.samplers.CFGGuider(model)
+ guider.set_conds(positive, negative)
+ guider.set_cfg(cfg)
+ return (guider,)
+
+class Guider_DualCFG(comfy.samplers.CFGGuider):
+ def set_cfg(self, cfg1, cfg2, nested=False):
+ self.cfg1 = cfg1
+ self.cfg2 = cfg2
+ self.nested = nested
+
+ def set_conds(self, positive, middle, negative):
+ middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
+ self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative})
+
+ def predict_noise(self, x, timestep, model_options={}, seed=None):
+ negative_cond = self.conds.get("negative", None)
+ middle_cond = self.conds.get("middle", None)
+ positive_cond = self.conds.get("positive", None)
+
+ if self.nested:
+ out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+ pred_text = comfy.samplers.cfg_function(self.inner_model, out[2], out[1], self.cfg1, x, timestep, model_options=model_options, cond=positive_cond, uncond=middle_cond)
+ return out[0] + self.cfg2 * (pred_text - out[0])
+ else:
+ if model_options.get("disable_cfg1_optimization", False) == False:
+ if math.isclose(self.cfg2, 1.0):
+ negative_cond = None
+ if math.isclose(self.cfg1, 1.0):
+ middle_cond = None
+
+ out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, positive_cond], x, timestep, model_options)
+ return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+
+class DualCFGGuider:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "cond1": ("CONDITIONING", ),
+ "cond2": ("CONDITIONING", ),
+ "negative": ("CONDITIONING", ),
+ "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+ "style": (["regular", "nested"],),
+ }
+ }
+
+ RETURN_TYPES = ("GUIDER",)
+
+ FUNCTION = "get_guider"
+ CATEGORY = "sampling/custom_sampling/guiders"
+
+ def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative, style):
+ guider = Guider_DualCFG(model)
+ guider.set_conds(cond1, cond2, negative)
+ guider.set_cfg(cfg_conds, cfg_cond2_negative, nested=(style == "nested"))
+ return (guider,)
+
+class DisableNoise:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":{
+ }
+ }
+
+ RETURN_TYPES = ("NOISE",)
+ FUNCTION = "get_noise"
+ CATEGORY = "sampling/custom_sampling/noise"
+
+ def get_noise(self):
+ return (Noise_EmptyNoise(),)
+
+
+class RandomNoise(DisableNoise):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "noise_seed": ("INT", {
+ "default": 0,
+ "min": 0,
+ "max": 0xffffffffffffffff,
+ "control_after_generate": True,
+ }),
+ }
+ }
+
+ def get_noise(self, noise_seed):
+ return (Noise_RandomNoise(noise_seed),)
+
+
+class SamplerCustomAdvanced:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"noise": ("NOISE", ),
+ "guider": ("GUIDER", ),
+ "sampler": ("SAMPLER", ),
+ "sigmas": ("SIGMAS", ),
+ "latent_image": ("LATENT", ),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT","LATENT")
+ RETURN_NAMES = ("output", "denoised_output")
+
+ FUNCTION = "sample"
+
+ CATEGORY = "sampling/custom_sampling"
+
+ def sample(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)
+
+class AddNoise:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required":
+ {"model": ("MODEL",),
+ "noise": ("NOISE", ),
+ "sigmas": ("SIGMAS", ),
+ "latent_image": ("LATENT", ),
+ }
+ }
+
+ RETURN_TYPES = ("LATENT",)
+
+ FUNCTION = "add_noise"
+
+ CATEGORY = "_for_testing/custom_sampling/noise"
+
+ def add_noise(self, model, noise, sigmas, latent_image):
+ if len(sigmas) == 0:
+ return latent_image
+
+ latent = latent_image
+ latent_image = latent["samples"]
+
+ noisy = noise.generate_noise(latent)
+
+ model_sampling = model.get_model_object("model_sampling")
+ process_latent_out = model.get_model_object("process_latent_out")
+ process_latent_in = model.get_model_object("process_latent_in")
+
+ if len(sigmas) > 1:
+ scale = torch.abs(sigmas[0] - sigmas[-1])
+ else:
+ scale = sigmas[0]
+
+ if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+ latent_image = process_latent_in(latent_image)
+ noisy = model_sampling.noise_scaling(scale, noisy, latent_image)
+ noisy = process_latent_out(noisy)
+ noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
+
+ out = latent.copy()
+ out["samples"] = noisy
+ return (out,)
+
+
+NODE_CLASS_MAPPINGS = {
+ "SamplerCustom": SamplerCustom,
+ "BasicScheduler": BasicScheduler,
+ "KarrasScheduler": KarrasScheduler,
+ "ExponentialScheduler": ExponentialScheduler,
+ "PolyexponentialScheduler": PolyexponentialScheduler,
+ "LaplaceScheduler": LaplaceScheduler,
+ "VPScheduler": VPScheduler,
+ "BetaSamplingScheduler": BetaSamplingScheduler,
+ "SDTurboScheduler": SDTurboScheduler,
+ "KSamplerSelect": KSamplerSelect,
+ "SamplerEulerAncestral": SamplerEulerAncestral,
+ "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP,
+ "SamplerLMS": SamplerLMS,
+ "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
+ "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
+ "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+ "SamplerDPMPP_2S_Ancestral": SamplerDPMPP_2S_Ancestral,
+ "SamplerDPMAdaptative": SamplerDPMAdaptative,
+ "SamplerER_SDE": SamplerER_SDE,
+ "SamplerSASolver": SamplerSASolver,
+ "SplitSigmas": SplitSigmas,
+ "SplitSigmasDenoise": SplitSigmasDenoise,
+ "FlipSigmas": FlipSigmas,
+ "SetFirstSigma": SetFirstSigma,
+ "ExtendIntermediateSigmas": ExtendIntermediateSigmas,
+ "SamplingPercentToSigma": SamplingPercentToSigma,
+
+ "CFGGuider": CFGGuider,
+ "DualCFGGuider": DualCFGGuider,
+ "BasicGuider": BasicGuider,
+ "RandomNoise": RandomNoise,
+ "DisableNoise": DisableNoise,
+ "AddNoise": AddNoise,
+ "SamplerCustomAdvanced": SamplerCustomAdvanced,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
+}
diff --git a/ComfyUI/comfy_extras/nodes_edit_model.py b/ComfyUI/comfy_extras/nodes_edit_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b69f7971591b383774d322b022e3b3b39ec0d704
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_edit_model.py
@@ -0,0 +1,26 @@
+import node_helpers
+
+
+class ReferenceLatent:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"conditioning": ("CONDITIONING", ),
+ },
+ "optional": {"latent": ("LATENT", ),}
+ }
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "append"
+
+ CATEGORY = "advanced/conditioning/edit_models"
+ DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images."
+
+ def append(self, conditioning, latent=None):
+ if latent is not None:
+ conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True)
+ return (conditioning, )
+
+
+NODE_CLASS_MAPPINGS = {
+ "ReferenceLatent": ReferenceLatent,
+}
diff --git a/ComfyUI/comfy_extras/nodes_flux.py b/ComfyUI/comfy_extras/nodes_flux.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a8a1769801c046c8001d1f3f7ad913794997147
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_flux.py
@@ -0,0 +1,108 @@
+import node_helpers
+import comfy.utils
+
+class CLIPTextEncodeFlux:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "clip": ("CLIP", ),
+ "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+ "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+ "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "encode"
+
+ CATEGORY = "advanced/conditioning/flux"
+
+ def encode(self, clip, clip_l, t5xxl, guidance):
+ tokens = clip.tokenize(clip_l)
+ tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+
+ return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), )
+
+class FluxGuidance:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "conditioning": ("CONDITIONING", ),
+ "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
+ }}
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "append"
+
+ CATEGORY = "advanced/conditioning/flux"
+
+ def append(self, conditioning, guidance):
+ c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
+ return (c, )
+
+
+class FluxDisableGuidance:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "conditioning": ("CONDITIONING", ),
+ }}
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "append"
+
+ CATEGORY = "advanced/conditioning/flux"
+ DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
+
+ def append(self, conditioning):
+ c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+ return (c, )
+
+
+PREFERED_KONTEXT_RESOLUTIONS = [
+ (672, 1568),
+ (688, 1504),
+ (720, 1456),
+ (752, 1392),
+ (800, 1328),
+ (832, 1248),
+ (880, 1184),
+ (944, 1104),
+ (1024, 1024),
+ (1104, 944),
+ (1184, 880),
+ (1248, 832),
+ (1328, 800),
+ (1392, 752),
+ (1456, 720),
+ (1504, 688),
+ (1568, 672),
+]
+
+
+class FluxKontextImageScale:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"image": ("IMAGE", ),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "scale"
+
+ CATEGORY = "advanced/conditioning/flux"
+ DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext."
+
+ def scale(self, image):
+ width = image.shape[2]
+ height = image.shape[1]
+ aspect_ratio = width / height
+ _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
+ image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
+ return (image, )
+
+
+NODE_CLASS_MAPPINGS = {
+ "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
+ "FluxGuidance": FluxGuidance,
+ "FluxDisableGuidance": FluxDisableGuidance,
+ "FluxKontextImageScale": FluxKontextImageScale,
+}
diff --git a/ComfyUI/comfy_extras/nodes_photomaker.py b/ComfyUI/comfy_extras/nodes_photomaker.py
new file mode 100644
index 0000000000000000000000000000000000000000..d358ed6d5b75a37f9195e1f0c663a188eb8aedc3
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_photomaker.py
@@ -0,0 +1,188 @@
+import torch
+import torch.nn as nn
+import folder_paths
+import comfy.clip_model
+import comfy.clip_vision
+import comfy.ops
+
+# code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
+VISION_CONFIG_DICT = {
+ "hidden_size": 1024,
+ "image_size": 224,
+ "intermediate_size": 4096,
+ "num_attention_heads": 16,
+ "num_channels": 3,
+ "num_hidden_layers": 24,
+ "patch_size": 14,
+ "projection_dim": 768,
+ "hidden_act": "quick_gelu",
+ "model_type": "clip_vision_model",
+}
+
+class MLP(nn.Module):
+ def __init__(self, in_dim, out_dim, hidden_dim, use_residual=True, operations=comfy.ops):
+ super().__init__()
+ if use_residual:
+ assert in_dim == out_dim
+ self.layernorm = operations.LayerNorm(in_dim)
+ self.fc1 = operations.Linear(in_dim, hidden_dim)
+ self.fc2 = operations.Linear(hidden_dim, out_dim)
+ self.use_residual = use_residual
+ self.act_fn = nn.GELU()
+
+ def forward(self, x):
+ residual = x
+ x = self.layernorm(x)
+ x = self.fc1(x)
+ x = self.act_fn(x)
+ x = self.fc2(x)
+ if self.use_residual:
+ x = x + residual
+ return x
+
+
+class FuseModule(nn.Module):
+ def __init__(self, embed_dim, operations):
+ super().__init__()
+ self.mlp1 = MLP(embed_dim * 2, embed_dim, embed_dim, use_residual=False, operations=operations)
+ self.mlp2 = MLP(embed_dim, embed_dim, embed_dim, use_residual=True, operations=operations)
+ self.layer_norm = operations.LayerNorm(embed_dim)
+
+ def fuse_fn(self, prompt_embeds, id_embeds):
+ stacked_id_embeds = torch.cat([prompt_embeds, id_embeds], dim=-1)
+ stacked_id_embeds = self.mlp1(stacked_id_embeds) + prompt_embeds
+ stacked_id_embeds = self.mlp2(stacked_id_embeds)
+ stacked_id_embeds = self.layer_norm(stacked_id_embeds)
+ return stacked_id_embeds
+
+ def forward(
+ self,
+ prompt_embeds,
+ id_embeds,
+ class_tokens_mask,
+ ) -> torch.Tensor:
+ # id_embeds shape: [b, max_num_inputs, 1, 2048]
+ id_embeds = id_embeds.to(prompt_embeds.dtype)
+ num_inputs = class_tokens_mask.sum().unsqueeze(0) # TODO: check for training case
+ batch_size, max_num_inputs = id_embeds.shape[:2]
+ # seq_length: 77
+ seq_length = prompt_embeds.shape[1]
+ # flat_id_embeds shape: [b*max_num_inputs, 1, 2048]
+ flat_id_embeds = id_embeds.view(
+ -1, id_embeds.shape[-2], id_embeds.shape[-1]
+ )
+ # valid_id_mask [b*max_num_inputs]
+ valid_id_mask = (
+ torch.arange(max_num_inputs, device=flat_id_embeds.device)[None, :]
+ < num_inputs[:, None]
+ )
+ valid_id_embeds = flat_id_embeds[valid_id_mask.flatten()]
+
+ prompt_embeds = prompt_embeds.view(-1, prompt_embeds.shape[-1])
+ class_tokens_mask = class_tokens_mask.view(-1)
+ valid_id_embeds = valid_id_embeds.view(-1, valid_id_embeds.shape[-1])
+ # slice out the image token embeddings
+ image_token_embeds = prompt_embeds[class_tokens_mask]
+ stacked_id_embeds = self.fuse_fn(image_token_embeds, valid_id_embeds)
+ assert class_tokens_mask.sum() == stacked_id_embeds.shape[0], f"{class_tokens_mask.sum()} != {stacked_id_embeds.shape[0]}"
+ prompt_embeds.masked_scatter_(class_tokens_mask[:, None], stacked_id_embeds.to(prompt_embeds.dtype))
+ updated_prompt_embeds = prompt_embeds.view(batch_size, seq_length, -1)
+ return updated_prompt_embeds
+
+class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection):
+ def __init__(self):
+ self.load_device = comfy.model_management.text_encoder_device()
+ offload_device = comfy.model_management.text_encoder_offload_device()
+ dtype = comfy.model_management.text_encoder_dtype(self.load_device)
+
+ super().__init__(VISION_CONFIG_DICT, dtype, offload_device, comfy.ops.manual_cast)
+ self.visual_projection_2 = comfy.ops.manual_cast.Linear(1024, 1280, bias=False)
+ self.fuse_module = FuseModule(2048, comfy.ops.manual_cast)
+
+ def forward(self, id_pixel_values, prompt_embeds, class_tokens_mask):
+ b, num_inputs, c, h, w = id_pixel_values.shape
+ id_pixel_values = id_pixel_values.view(b * num_inputs, c, h, w)
+
+ shared_id_embeds = self.vision_model(id_pixel_values)[2]
+ id_embeds = self.visual_projection(shared_id_embeds)
+ id_embeds_2 = self.visual_projection_2(shared_id_embeds)
+
+ id_embeds = id_embeds.view(b, num_inputs, 1, -1)
+ id_embeds_2 = id_embeds_2.view(b, num_inputs, 1, -1)
+
+ id_embeds = torch.cat((id_embeds, id_embeds_2), dim=-1)
+ updated_prompt_embeds = self.fuse_module(prompt_embeds, id_embeds, class_tokens_mask)
+
+ return updated_prompt_embeds
+
+
+class PhotoMakerLoader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}}
+
+ RETURN_TYPES = ("PHOTOMAKER",)
+ FUNCTION = "load_photomaker_model"
+
+ CATEGORY = "_for_testing/photomaker"
+
+ def load_photomaker_model(self, photomaker_model_name):
+ photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name)
+ photomaker_model = PhotoMakerIDEncoder()
+ data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
+ if "id_encoder" in data:
+ data = data["id_encoder"]
+ photomaker_model.load_state_dict(data)
+ return (photomaker_model,)
+
+
+class PhotoMakerEncode:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "photomaker": ("PHOTOMAKER",),
+ "image": ("IMAGE",),
+ "clip": ("CLIP", ),
+ "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}),
+ }}
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "apply_photomaker"
+
+ CATEGORY = "_for_testing/photomaker"
+
+ def apply_photomaker(self, photomaker, image, clip, text):
+ special_token = "photomaker"
+ pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
+ try:
+ index = text.split(" ").index(special_token) + 1
+ except ValueError:
+ index = -1
+ tokens = clip.tokenize(text, return_word_ids=True)
+ out_tokens = {}
+ for k in tokens:
+ out_tokens[k] = []
+ for t in tokens[k]:
+ f = list(filter(lambda x: x[2] != index, t))
+ while len(f) < len(t):
+ f.append(t[-1])
+ out_tokens[k].append(f)
+
+ cond, pooled = clip.encode_from_tokens(out_tokens, return_pooled=True)
+
+ if index > 0:
+ token_index = index - 1
+ num_id_images = 1
+ class_tokens_mask = [True if token_index <= i < token_index+num_id_images else False for i in range(77)]
+ out = photomaker(id_pixel_values=pixel_values.unsqueeze(0), prompt_embeds=cond.to(photomaker.load_device),
+ class_tokens_mask=torch.tensor(class_tokens_mask, dtype=torch.bool, device=photomaker.load_device).unsqueeze(0))
+ else:
+ out = cond
+
+ return ([[out, {"pooled_output": pooled}]], )
+
+
+NODE_CLASS_MAPPINGS = {
+ "PhotoMakerLoader": PhotoMakerLoader,
+ "PhotoMakerEncode": PhotoMakerEncode,
+}
+
diff --git a/ComfyUI/comfy_extras/nodes_pixart.py b/ComfyUI/comfy_extras/nodes_pixart.py
new file mode 100644
index 0000000000000000000000000000000000000000..8d9276afe4b12c51c818a879cce1cd5453895552
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_pixart.py
@@ -0,0 +1,24 @@
+from nodes import MAX_RESOLUTION
+
+class CLIPTextEncodePixArtAlpha:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {
+ "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+ "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+ # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
+ }}
+
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "encode"
+ CATEGORY = "advanced/conditioning"
+ DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma."
+
+ def encode(self, clip, width, height, text):
+ tokens = clip.tokenize(text)
+ return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),)
+
+NODE_CLASS_MAPPINGS = {
+ "CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha,
+}
diff --git a/ComfyUI/comfy_extras/nodes_post_processing.py b/ComfyUI/comfy_extras/nodes_post_processing.py
new file mode 100644
index 0000000000000000000000000000000000000000..cb1a0d88303eef19fff34ce2b19611cfcb162e91
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_post_processing.py
@@ -0,0 +1,281 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+from PIL import Image
+import math
+
+import comfy.utils
+import comfy.model_management
+import node_helpers
+
+class Blend:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image1": ("IMAGE",),
+ "image2": ("IMAGE",),
+ "blend_factor": ("FLOAT", {
+ "default": 0.5,
+ "min": 0.0,
+ "max": 1.0,
+ "step": 0.01
+ }),
+ "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "blend_images"
+
+ CATEGORY = "image/postprocessing"
+
+ def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
+ image1, image2 = node_helpers.image_alpha_fix(image1, image2)
+ image2 = image2.to(image1.device)
+ if image1.shape != image2.shape:
+ image2 = image2.permute(0, 3, 1, 2)
+ image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
+ image2 = image2.permute(0, 2, 3, 1)
+
+ blended_image = self.blend_mode(image1, image2, blend_mode)
+ blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
+ blended_image = torch.clamp(blended_image, 0, 1)
+ return (blended_image,)
+
+ def blend_mode(self, img1, img2, mode):
+ if mode == "normal":
+ return img2
+ elif mode == "multiply":
+ return img1 * img2
+ elif mode == "screen":
+ return 1 - (1 - img1) * (1 - img2)
+ elif mode == "overlay":
+ return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
+ elif mode == "soft_light":
+ return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+ elif mode == "difference":
+ return img1 - img2
+ else:
+ raise ValueError(f"Unsupported blend mode: {mode}")
+
+ def g(self, x):
+ return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
+
+def gaussian_kernel(kernel_size: int, sigma: float, device=None):
+ x, y = torch.meshgrid(torch.linspace(-1, 1, kernel_size, device=device), torch.linspace(-1, 1, kernel_size, device=device), indexing="ij")
+ d = torch.sqrt(x * x + y * y)
+ g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
+ return g / g.sum()
+
+class Blur:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "blur_radius": ("INT", {
+ "default": 1,
+ "min": 1,
+ "max": 31,
+ "step": 1
+ }),
+ "sigma": ("FLOAT", {
+ "default": 1.0,
+ "min": 0.1,
+ "max": 10.0,
+ "step": 0.1
+ }),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "blur"
+
+ CATEGORY = "image/postprocessing"
+
+ def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
+ if blur_radius == 0:
+ return (image,)
+
+ image = image.to(comfy.model_management.get_torch_device())
+ batch_size, height, width, channels = image.shape
+
+ kernel_size = blur_radius * 2 + 1
+ kernel = gaussian_kernel(kernel_size, sigma, device=image.device).repeat(channels, 1, 1).unsqueeze(1)
+
+ image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
+ padded_image = F.pad(image, (blur_radius,blur_radius,blur_radius,blur_radius), 'reflect')
+ blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
+ blurred = blurred.permute(0, 2, 3, 1)
+
+ return (blurred.to(comfy.model_management.intermediate_device()),)
+
+class Quantize:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "colors": ("INT", {
+ "default": 256,
+ "min": 1,
+ "max": 256,
+ "step": 1
+ }),
+ "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "quantize"
+
+ CATEGORY = "image/postprocessing"
+
+ @staticmethod
+ def bayer(im, pal_im, order):
+ def normalized_bayer_matrix(n):
+ if n == 0:
+ return np.zeros((1,1), "float32")
+ else:
+ q = 4 ** n
+ m = q * normalized_bayer_matrix(n - 1)
+ return np.bmat(((m-1.5, m+0.5), (m+1.5, m-0.5))) / q
+
+ num_colors = len(pal_im.getpalette()) // 3
+ spread = 2 * 256 / num_colors
+ bayer_n = int(math.log2(order))
+ bayer_matrix = torch.from_numpy(spread * normalized_bayer_matrix(bayer_n) + 0.5)
+
+ result = torch.from_numpy(np.array(im).astype(np.float32))
+ tw = math.ceil(result.shape[0] / bayer_matrix.shape[0])
+ th = math.ceil(result.shape[1] / bayer_matrix.shape[1])
+ tiled_matrix = bayer_matrix.tile(tw, th).unsqueeze(-1)
+ result.add_(tiled_matrix[:result.shape[0],:result.shape[1]]).clamp_(0, 255)
+ result = result.to(dtype=torch.uint8)
+
+ im = Image.fromarray(result.cpu().numpy())
+ im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
+ return im
+
+ def quantize(self, image: torch.Tensor, colors: int, dither: str):
+ batch_size, height, width, _ = image.shape
+ result = torch.zeros_like(image)
+
+ for b in range(batch_size):
+ im = Image.fromarray((image[b] * 255).to(torch.uint8).numpy(), mode='RGB')
+
+ pal_im = im.quantize(colors=colors) # Required as described in https://github.com/python-pillow/Pillow/issues/5836
+
+ if dither == "none":
+ quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
+ elif dither == "floyd-steinberg":
+ quantized_image = im.quantize(palette=pal_im, dither=Image.Dither.FLOYDSTEINBERG)
+ elif dither.startswith("bayer"):
+ order = int(dither.split('-')[-1])
+ quantized_image = Quantize.bayer(im, pal_im, order)
+
+ quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
+ result[b] = quantized_array
+
+ return (result,)
+
+class Sharpen:
+ def __init__(self):
+ pass
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("IMAGE",),
+ "sharpen_radius": ("INT", {
+ "default": 1,
+ "min": 1,
+ "max": 31,
+ "step": 1
+ }),
+ "sigma": ("FLOAT", {
+ "default": 1.0,
+ "min": 0.1,
+ "max": 10.0,
+ "step": 0.01
+ }),
+ "alpha": ("FLOAT", {
+ "default": 1.0,
+ "min": 0.0,
+ "max": 5.0,
+ "step": 0.01
+ }),
+ },
+ }
+
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "sharpen"
+
+ CATEGORY = "image/postprocessing"
+
+ def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
+ if sharpen_radius == 0:
+ return (image,)
+
+ batch_size, height, width, channels = image.shape
+ image = image.to(comfy.model_management.get_torch_device())
+
+ kernel_size = sharpen_radius * 2 + 1
+ kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
+ center = kernel_size // 2
+ kernel[center, center] = kernel[center, center] - kernel.sum() + 1.0
+ kernel = kernel.repeat(channels, 1, 1).unsqueeze(1)
+
+ tensor_image = image.permute(0, 3, 1, 2) # Torch wants (B, C, H, W) we use (B, H, W, C)
+ tensor_image = F.pad(tensor_image, (sharpen_radius,sharpen_radius,sharpen_radius,sharpen_radius), 'reflect')
+ sharpened = F.conv2d(tensor_image, kernel, padding=center, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
+ sharpened = sharpened.permute(0, 2, 3, 1)
+
+ result = torch.clamp(sharpened, 0, 1)
+
+ return (result.to(comfy.model_management.intermediate_device()),)
+
+class ImageScaleToTotalPixels:
+ upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
+ crop_methods = ["disabled", "center"]
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
+ "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "upscale"
+
+ CATEGORY = "image/upscaling"
+
+ def upscale(self, image, upscale_method, megapixels):
+ samples = image.movedim(-1,1)
+ total = int(megapixels * 1024 * 1024)
+
+ scale_by = math.sqrt(total / (samples.shape[3] * samples.shape[2]))
+ width = round(samples.shape[3] * scale_by)
+ height = round(samples.shape[2] * scale_by)
+
+ s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+ s = s.movedim(1,-1)
+ return (s,)
+
+NODE_CLASS_MAPPINGS = {
+ "ImageBlend": Blend,
+ "ImageBlur": Blur,
+ "ImageQuantize": Quantize,
+ "ImageSharpen": Sharpen,
+ "ImageScaleToTotalPixels": ImageScaleToTotalPixels,
+}
diff --git a/ComfyUI/comfy_extras/nodes_primitive.py b/ComfyUI/comfy_extras/nodes_primitive.py
new file mode 100644
index 0000000000000000000000000000000000000000..1f93f87a79531b3650981a651d355466db518ede
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_primitive.py
@@ -0,0 +1,98 @@
+# Primitive nodes that are evaluated at backend.
+from __future__ import annotations
+
+import sys
+
+from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO
+
+
+class String(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {"value": (IO.STRING, {})},
+ }
+
+ RETURN_TYPES = (IO.STRING,)
+ FUNCTION = "execute"
+ CATEGORY = "utils/primitive"
+
+ def execute(self, value: str) -> tuple[str]:
+ return (value,)
+
+
+class StringMultiline(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {"value": (IO.STRING, {"multiline": True,},)},
+ }
+
+ RETURN_TYPES = (IO.STRING,)
+ FUNCTION = "execute"
+ CATEGORY = "utils/primitive"
+
+ def execute(self, value: str) -> tuple[str]:
+ return (value,)
+
+
+class Int(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {"value": (IO.INT, {"min": -sys.maxsize, "max": sys.maxsize, "control_after_generate": True})},
+ }
+
+ RETURN_TYPES = (IO.INT,)
+ FUNCTION = "execute"
+ CATEGORY = "utils/primitive"
+
+ def execute(self, value: int) -> tuple[int]:
+ return (value,)
+
+
+class Float(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {"value": (IO.FLOAT, {"min": -sys.maxsize, "max": sys.maxsize})},
+ }
+
+ RETURN_TYPES = (IO.FLOAT,)
+ FUNCTION = "execute"
+ CATEGORY = "utils/primitive"
+
+ def execute(self, value: float) -> tuple[float]:
+ return (value,)
+
+
+class Boolean(ComfyNodeABC):
+ @classmethod
+ def INPUT_TYPES(cls) -> InputTypeDict:
+ return {
+ "required": {"value": (IO.BOOLEAN, {})},
+ }
+
+ RETURN_TYPES = (IO.BOOLEAN,)
+ FUNCTION = "execute"
+ CATEGORY = "utils/primitive"
+
+ def execute(self, value: bool) -> tuple[bool]:
+ return (value,)
+
+
+NODE_CLASS_MAPPINGS = {
+ "PrimitiveString": String,
+ "PrimitiveStringMultiline": StringMultiline,
+ "PrimitiveInt": Int,
+ "PrimitiveFloat": Float,
+ "PrimitiveBoolean": Boolean,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "PrimitiveString": "String",
+ "PrimitiveStringMultiline": "String (Multiline)",
+ "PrimitiveInt": "Int",
+ "PrimitiveFloat": "Float",
+ "PrimitiveBoolean": "Boolean",
+}
diff --git a/ComfyUI/comfy_extras/nodes_video_model.py b/ComfyUI/comfy_extras/nodes_video_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f760aa26627f7dcc9e0d4483e05dae1326a4c66
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_video_model.py
@@ -0,0 +1,161 @@
+import nodes
+import torch
+import comfy.utils
+import comfy.sd
+import folder_paths
+import comfy_extras.nodes_model_merging
+import node_helpers
+
+
+class ImageOnlyCheckpointLoader:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
+ }}
+ RETURN_TYPES = ("MODEL", "CLIP_VISION", "VAE")
+ FUNCTION = "load_checkpoint"
+
+ CATEGORY = "loaders/video_models"
+
+ def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+ ckpt_path = folder_paths.get_full_path_or_raise("checkpoints", ckpt_name)
+ out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=False, output_clipvision=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
+ return (out[0], out[3], out[2])
+
+
+class SVD_img2vid_Conditioning:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "clip_vision": ("CLIP_VISION",),
+ "init_image": ("IMAGE",),
+ "vae": ("VAE",),
+ "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+ "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+ "video_frames": ("INT", {"default": 14, "min": 1, "max": 4096}),
+ "motion_bucket_id": ("INT", {"default": 127, "min": 1, "max": 1023}),
+ "fps": ("INT", {"default": 6, "min": 1, "max": 1024}),
+ "augmentation_level": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.01})
+ }}
+ RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+ RETURN_NAMES = ("positive", "negative", "latent")
+
+ FUNCTION = "encode"
+
+ CATEGORY = "conditioning/video_models"
+
+ def encode(self, clip_vision, init_image, vae, width, height, video_frames, motion_bucket_id, fps, augmentation_level):
+ output = clip_vision.encode_image(init_image)
+ pooled = output.image_embeds.unsqueeze(0)
+ pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+ encode_pixels = pixels[:,:,:,:3]
+ if augmentation_level > 0:
+ encode_pixels += torch.randn_like(pixels) * augmentation_level
+ t = vae.encode(encode_pixels)
+ positive = [[pooled, {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": t}]]
+ negative = [[torch.zeros_like(pooled), {"motion_bucket_id": motion_bucket_id, "fps": fps, "augmentation_level": augmentation_level, "concat_latent_image": torch.zeros_like(t)}]]
+ latent = torch.zeros([video_frames, 4, height // 8, width // 8])
+ return (positive, negative, {"samples":latent})
+
+class VideoLinearCFGGuidance:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "model": ("MODEL",),
+ "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+ }}
+ RETURN_TYPES = ("MODEL",)
+ FUNCTION = "patch"
+
+ CATEGORY = "sampling/video_models"
+
+ def patch(self, model, min_cfg):
+ def linear_cfg(args):
+ cond = args["cond"]
+ uncond = args["uncond"]
+ cond_scale = args["cond_scale"]
+
+ scale = torch.linspace(min_cfg, cond_scale, cond.shape[0], device=cond.device).reshape((cond.shape[0], 1, 1, 1))
+ return uncond + scale * (cond - uncond)
+
+ m = model.clone()
+ m.set_model_sampler_cfg_function(linear_cfg)
+ return (m, )
+
+class VideoTriangleCFGGuidance:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "model": ("MODEL",),
+ "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+ }}
+ RETURN_TYPES = ("MODEL",)
+ FUNCTION = "patch"
+
+ CATEGORY = "sampling/video_models"
+
+ def patch(self, model, min_cfg):
+ def linear_cfg(args):
+ cond = args["cond"]
+ uncond = args["uncond"]
+ cond_scale = args["cond_scale"]
+ period = 1.0
+ values = torch.linspace(0, 1, cond.shape[0], device=cond.device)
+ values = 2 * (values / period - torch.floor(values / period + 0.5)).abs()
+ scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1))
+
+ return uncond + scale * (cond - uncond)
+
+ m = model.clone()
+ m.set_model_sampler_cfg_function(linear_cfg)
+ return (m, )
+
+class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
+ CATEGORY = "advanced/model_merging"
+
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": { "model": ("MODEL",),
+ "clip_vision": ("CLIP_VISION",),
+ "vae": ("VAE",),
+ "filename_prefix": ("STRING", {"default": "checkpoints/ComfyUI"}),},
+ "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},}
+
+ def save(self, model, clip_vision, vae, filename_prefix, prompt=None, extra_pnginfo=None):
+ comfy_extras.nodes_model_merging.save_checkpoint(model, clip_vision=clip_vision, vae=vae, filename_prefix=filename_prefix, output_dir=self.output_dir, prompt=prompt, extra_pnginfo=extra_pnginfo)
+ return {}
+
+
+class ConditioningSetAreaPercentageVideo:
+ @classmethod
+ def INPUT_TYPES(s):
+ return {"required": {"conditioning": ("CONDITIONING", ),
+ "width": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+ "height": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+ "temporal": ("FLOAT", {"default": 1.0, "min": 0, "max": 1.0, "step": 0.01}),
+ "x": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+ "y": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+ "z": ("FLOAT", {"default": 0, "min": 0, "max": 1.0, "step": 0.01}),
+ "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+ }}
+ RETURN_TYPES = ("CONDITIONING",)
+ FUNCTION = "append"
+
+ CATEGORY = "conditioning"
+
+ def append(self, conditioning, width, height, temporal, x, y, z, strength):
+ c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", temporal, height, width, z, y, x),
+ "strength": strength,
+ "set_area_to_bounds": False})
+ return (c, )
+
+
+NODE_CLASS_MAPPINGS = {
+ "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
+ "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
+ "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
+ "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
+ "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
+ "ConditioningSetAreaPercentageVideo": ConditioningSetAreaPercentageVideo,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "ImageOnlyCheckpointLoader": "Image Only Checkpoint Loader (img2vid model)",
+}
diff --git a/ComfyUI/comfy_extras/nodes_webcam.py b/ComfyUI/comfy_extras/nodes_webcam.py
new file mode 100644
index 0000000000000000000000000000000000000000..5bf80b4c6e055f70ed277ec0a9bbbeb180151a3d
--- /dev/null
+++ b/ComfyUI/comfy_extras/nodes_webcam.py
@@ -0,0 +1,37 @@
+import nodes
+import folder_paths
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+
+class WebcamCapture(nodes.LoadImage):
+ @classmethod
+ def INPUT_TYPES(s):
+ return {
+ "required": {
+ "image": ("WEBCAM", {}),
+ "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+ "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+ "capture_on_queue": ("BOOLEAN", {"default": True}),
+ }
+ }
+ RETURN_TYPES = ("IMAGE",)
+ FUNCTION = "load_capture"
+
+ CATEGORY = "image"
+
+ def load_capture(self, image, **kwargs):
+ return super().load_image(folder_paths.get_annotated_filepath(image))
+
+ @classmethod
+ def IS_CHANGED(cls, image, width, height, capture_on_queue):
+ return super().IS_CHANGED(image)
+
+
+NODE_CLASS_MAPPINGS = {
+ "WebcamCapture": WebcamCapture,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+ "WebcamCapture": "Webcam Capture",
+}