ComfyUI 0.3.57

* convert RunAway API nodes to the V3 schema

* fixed small typo

* fix: add tooltip for "seed" input

comfy/cli_args.py

@@ -143,6 +143,7 @@ class PerformanceFeature(enum.Enum):
     Fp16Accumulation = "fp16_accumulation"
     Fp8MatrixMultiplication = "fp8_matrix_mult"
     CublasOps = "cublas_ops"
+    AutoTune = "autotune"
 
 parser.add_argument("--fast", nargs="*", type=PerformanceFeature, help="Enable some untested and potentially quality deteriorating optimizations. --fast with no arguments enables everything. You can pass a list specific optimizations if you only want to enable specific ones. Current valid optimizations: fp16_accumulation fp8_matrix_mult cublas_ops")
 

comfy/clip_model.py

@@ -61,8 +61,12 @@ class CLIPEncoder(torch.nn.Module):
     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)
 
+        all_intermediate = None
         if intermediate_output is not None:
-            if intermediate_output < 0:
+            if intermediate_output == "all":
+                all_intermediate = []
+                intermediate_output = None
+            elif intermediate_output < 0:
                 intermediate_output = len(self.layers) + intermediate_output
 
         intermediate = None
@@ -70,6 +74,12 @@ class CLIPEncoder(torch.nn.Module):
             x = l(x, mask, optimized_attention)
             if i == intermediate_output:
                 intermediate = x.clone()
+            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)
+
         return x, intermediate
 
 class CLIPEmbeddings(torch.nn.Module):

comfy/clip_vision.py

@@ -50,7 +50,13 @@ class ClipVisionModel():
         self.image_size = config.get("image_size", 224)
         self.image_mean = config.get("image_mean", [0.48145466, 0.4578275, 0.40821073])
         self.image_std = config.get("image_std", [0.26862954, 0.26130258, 0.27577711])
-        model_class = IMAGE_ENCODERS.get(config.get("model_type", "clip_vision_model"))
+        model_type = config.get("model_type", "clip_vision_model")
+        model_class = IMAGE_ENCODERS.get(model_type)
+        if model_type == "siglip_vision_model":
+            self.return_all_hidden_states = True
+        else:
+            self.return_all_hidden_states = False
+
         self.load_device = comfy.model_management.text_encoder_device()
         offload_device = comfy.model_management.text_encoder_offload_device()
         self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
@@ -68,12 +74,18 @@ class ClipVisionModel():
     def encode_image(self, image, crop=True):
         comfy.model_management.load_model_gpu(self.patcher)
         pixel_values = clip_preprocess(image.to(self.load_device), size=self.image_size, mean=self.image_mean, std=self.image_std, crop=crop).float()
-        out = self.model(pixel_values=pixel_values, intermediate_output=-2)
+        out = self.model(pixel_values=pixel_values, intermediate_output='all' if self.return_all_hidden_states else -2)
 
         outputs = Output()
         outputs["last_hidden_state"] = out[0].to(comfy.model_management.intermediate_device())
         outputs["image_embeds"] = out[2].to(comfy.model_management.intermediate_device())
-        outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+        if self.return_all_hidden_states:
+            all_hs = out[1].to(comfy.model_management.intermediate_device())
+            outputs["penultimate_hidden_states"] = all_hs[:, -2]
+            outputs["all_hidden_states"] = all_hs
+        else:
+            outputs["penultimate_hidden_states"] = out[1].to(comfy.model_management.intermediate_device())
+
         outputs["mm_projected"] = out[3]
         return outputs
 

comfy/k_diffusion/sampling.py

@@ -171,6 +171,16 @@ def offset_first_sigma_for_snr(sigmas, model_sampling, percent_offset=1e-4):
     return sigmas
 
 
+def ei_h_phi_1(h: torch.Tensor) -> torch.Tensor:
+    """Compute the result of h*phi_1(h) in exponential integrator methods."""
+    return torch.expm1(h)
+
+
+def ei_h_phi_2(h: torch.Tensor) -> torch.Tensor:
+    """Compute the result of h*phi_2(h) in exponential integrator methods."""
+    return (torch.expm1(h) - h) / h
+
+
 @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)."""
@@ -1550,13 +1560,12 @@ def sample_er_sde(model, x, sigmas, extra_args=None, callback=None, disable=None
 @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
+    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
     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')
@@ -1564,55 +1573,53 @@ def sample_seeds_2(model, x, sigmas, extra_args=None, callback=None, disable=Non
     lambda_fn = partial(sigma_to_half_log_snr, model_sampling=model_sampling)
     sigmas = offset_first_sigma_for_snr(sigmas, model_sampling)
 
+    fac = 1 / (2 * r)
+
     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)
+            continue
 
-            # 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()
+        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 = torch.lerp(lambda_s, lambda_t, r)
+        sigma_s_1 = sigma_fn(lambda_s_1)
 
-            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])
+        alpha_s_1 = sigma_s_1 * lambda_s_1.exp()
+        alpha_t = sigmas[i + 1] * lambda_t.exp()
 
-            # 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 1
+        x_2 = sigma_s_1 / sigmas[i] * (-r * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r * h_eta) * denoised
+        if inject_noise:
+            sde_noise = (-2 * r * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
+            x_2 = x_2 + sde_noise * sigma_s_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
+        # Step 2
+        denoised_d = torch.lerp(denoised, denoised_2, fac)
+        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * ei_h_phi_1(-h_eta) * denoised_d
+        if inject_noise:
+            segment_factor = (r - 1) * h * eta
+            sde_noise = sde_noise * segment_factor.exp()
+            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigmas[i + 1])
+            x = x + sde_noise * sigmas[i + 1] * 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
+    arXiv: https://arxiv.org/abs/2305.14267 (NeurIPS 2023)
     """
     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')
@@ -1624,45 +1631,49 @@ def sample_seeds_3(model, x, sigmas, extra_args=None, callback=None, disable=Non
         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)
+            continue
 
-            # 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()
+        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 = torch.lerp(lambda_s, lambda_t, r_1)
+        lambda_s_2 = torch.lerp(lambda_s, lambda_t, r_2)
+        sigma_s_1, sigma_s_2 = sigma_fn(lambda_s_1), sigma_fn(lambda_s_2)
 
-            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])
+        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()
 
-            # 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 1
+        x_2 = sigma_s_1 / sigmas[i] * (-r_1 * h * eta).exp() * x - alpha_s_1 * ei_h_phi_1(-r_1 * h_eta) * denoised
+        if inject_noise:
+            sde_noise = (-2 * r_1 * h * eta).expm1().neg().sqrt() * noise_sampler(sigmas[i], sigma_s_1)
+            x_2 = x_2 + sde_noise * sigma_s_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 2
+        a3_2 = r_2 / r_1 * ei_h_phi_2(-r_2 * h_eta)
+        a3_1 = ei_h_phi_1(-r_2 * h_eta) - a3_2
+        x_3 = sigma_s_2 / sigmas[i] * (-r_2 * h * eta).exp() * x - alpha_s_2 * (a3_1 * denoised + a3_2 * denoised_2)
+        if inject_noise:
+            segment_factor = (r_1 - r_2) * h * eta
+            sde_noise = sde_noise * segment_factor.exp()
+            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_1, sigma_s_2)
+            x_3 = x_3 + sde_noise * sigma_s_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
+        # Step 3
+        b3 = ei_h_phi_2(-h_eta) / r_2
+        b1 = ei_h_phi_1(-h_eta) - b3
+        x = sigmas[i + 1] / sigmas[i] * (-h * eta).exp() * x - alpha_t * (b1 * denoised + b3 * denoised_3)
+        if inject_noise:
+            segment_factor = (r_2 - 1) * h * eta
+            sde_noise = sde_noise * segment_factor.exp()
+            sde_noise = sde_noise + segment_factor.mul(2).expm1().neg().sqrt() * noise_sampler(sigma_s_2, sigmas[i + 1])
+            x = x + sde_noise * sigmas[i + 1] * s_noise
     return x
 
 

comfy/ldm/ace/attention.py

@@ -133,7 +133,6 @@ class Attention(nn.Module):
         hidden_states: torch.Tensor,
         encoder_hidden_states: Optional[torch.Tensor] = None,
         attention_mask: Optional[torch.Tensor] = None,
-        transformer_options={},
         **cross_attention_kwargs,
     ) -> torch.Tensor:
         return self.processor(
@@ -141,7 +140,6 @@ class Attention(nn.Module):
             hidden_states,
             encoder_hidden_states=encoder_hidden_states,
             attention_mask=attention_mask,
-            transformer_options=transformer_options,
             **cross_attention_kwargs,
         )
 
@@ -368,7 +366,6 @@ class CustomerAttnProcessor2_0:
         encoder_attention_mask: Optional[torch.FloatTensor] = None,
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.Tensor:
@@ -436,7 +433,7 @@ class CustomerAttnProcessor2_0:
 
         # the output of sdp = (batch, num_heads, seq_len, head_dim)
         hidden_states = optimized_attention(
-            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True,
         ).to(query.dtype)
 
         # linear proj
@@ -700,7 +697,6 @@ class LinearTransformerBlock(nn.Module):
         rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
         temb: torch.FloatTensor = None,
-        transformer_options={},
     ):
 
         N = hidden_states.shape[0]
@@ -724,7 +720,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
         else:
             attn_output, _ = self.attn(
@@ -734,7 +729,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=None,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=None,
-                transformer_options=transformer_options,
             )
 
         if self.use_adaln_single:
@@ -749,7 +743,6 @@ class LinearTransformerBlock(nn.Module):
                 encoder_attention_mask=encoder_attention_mask,
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=rotary_freqs_cis_cross,
-                transformer_options=transformer_options,
             )
             hidden_states = attn_output + hidden_states
 

comfy/ldm/ace/model.py

@@ -314,7 +314,6 @@ class ACEStepTransformer2DModel(nn.Module):
         output_length: int = 0,
         block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
         controlnet_scale: Union[float, torch.Tensor] = 1.0,
-        transformer_options={},
     ):
         embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype))
         temb = self.t_block(embedded_timestep)
@@ -340,7 +339,6 @@ class ACEStepTransformer2DModel(nn.Module):
                 rotary_freqs_cis=rotary_freqs_cis,
                 rotary_freqs_cis_cross=encoder_rotary_freqs_cis,
                 temb=temb,
-                transformer_options=transformer_options,
             )
 
         output = self.final_layer(hidden_states, embedded_timestep, output_length)
@@ -395,7 +393,6 @@ class ACEStepTransformer2DModel(nn.Module):
 
         output_length = hidden_states.shape[-1]
 
-        transformer_options = kwargs.get("transformer_options", {})
         output = self.decode(
             hidden_states=hidden_states,
             attention_mask=attention_mask,
@@ -405,7 +402,6 @@ class ACEStepTransformer2DModel(nn.Module):
             output_length=output_length,
             block_controlnet_hidden_states=block_controlnet_hidden_states,
             controlnet_scale=controlnet_scale,
-            transformer_options=transformer_options,
         )
 
         return output

comfy/ldm/audio/dit.py

@@ -298,8 +298,7 @@ class Attention(nn.Module):
         mask = None,
         context_mask = None,
         rotary_pos_emb = None,
-        causal = None,
-        transformer_options={},
+        causal = None
     ):
         h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
 
@@ -364,7 +363,7 @@ class Attention(nn.Module):
             heads_per_kv_head = h // kv_h
             k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
 
-        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
         out = self.to_out(out)
 
         if mask is not None:
@@ -489,8 +488,7 @@ class TransformerBlock(nn.Module):
         global_cond=None,
         mask = None,
         context_mask = None,
-        rotary_pos_emb = None,
-        transformer_options={}
+        rotary_pos_emb = None
     ):
         if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
 
@@ -500,12 +498,12 @@ class TransformerBlock(nn.Module):
             residual = x
             x = self.pre_norm(x)
             x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
             x = x * torch.sigmoid(1 - gate_self)
             x = x + residual
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -519,10 +517,10 @@ class TransformerBlock(nn.Module):
             x = x + residual
 
         else:
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
 
             if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
 
             if self.conformer is not None:
                 x = x + self.conformer(x)
@@ -608,8 +606,7 @@ class ContinuousTransformer(nn.Module):
         return_info = False,
         **kwargs
     ):
-        transformer_options = kwargs.get("transformer_options", {})
-        patches_replace = transformer_options.get("patches_replace", {})
+        patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
         batch, seq, device = *x.shape[:2], x.device
         context = kwargs["context"]
 
@@ -635,7 +632,7 @@ class ContinuousTransformer(nn.Module):
         # Attention layers
 
         if self.rotary_pos_emb is not None:
-            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=torch.float, device=x.device)
         else:
             rotary_pos_emb = None
 
@@ -648,13 +645,13 @@ class ContinuousTransformer(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"])
+                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
             # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
 
             if return_info:

comfy/ldm/aura/mmdit.py

@@ -85,7 +85,7 @@ class SingleAttention(nn.Module):
         )
 
     #@torch.compile()
-    def forward(self, c, transformer_options={}):
+    def forward(self, c):
 
         bsz, seqlen1, _ = c.shape
 
@@ -95,7 +95,7 @@ class SingleAttention(nn.Module):
         v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
         q, k = self.q_norm1(q), self.k_norm1(k)
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
         c = self.w1o(output)
         return c
 
@@ -144,7 +144,7 @@ class DoubleAttention(nn.Module):
 
 
     #@torch.compile()
-    def forward(self, c, x, transformer_options={}):
+    def forward(self, c, x):
 
         bsz, seqlen1, _ = c.shape
         bsz, seqlen2, _ = x.shape
@@ -168,7 +168,7 @@ class DoubleAttention(nn.Module):
             torch.cat([cv, xv], dim=1),
         )
 
-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
 
         c, x = output.split([seqlen1, seqlen2], dim=1)
         c = self.w1o(c)
@@ -207,7 +207,7 @@ class MMDiTBlock(nn.Module):
         self.is_last = is_last
 
     #@torch.compile()
-    def forward(self, c, x, global_cond, transformer_options={}, **kwargs):
+    def forward(self, c, x, global_cond, **kwargs):
 
         cres, xres = c, x
 
@@ -225,7 +225,7 @@ class MMDiTBlock(nn.Module):
         x = modulate(self.normX1(x), xshift_msa, xscale_msa)
 
         # attention
-        c, x = self.attn(c, x, transformer_options=transformer_options)
+        c, x = self.attn(c, x)
 
 
         c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
@@ -255,13 +255,13 @@ class DiTBlock(nn.Module):
         self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)
 
     #@torch.compile()
-    def forward(self, cx, global_cond, transformer_options={}, **kwargs):
+    def forward(self, cx, global_cond, **kwargs):
         cxres = cx
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
             global_cond
         ).chunk(6, dim=1)
         cx = modulate(self.norm1(cx), shift_msa, scale_msa)
-        cx = self.attn(cx, transformer_options=transformer_options)
+        cx = self.attn(cx)
         cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
         mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
         cx = gate_mlp.unsqueeze(1) * mlpout
@@ -473,14 +473,13 @@ class MMDiT(nn.Module):
                         out = {}
                         out["txt"], out["img"] = layer(args["txt"],
                                                        args["img"],
-                                                       args["vec"],
-                                                       transformer_options=args["transformer_options"])
+                                                       args["vec"])
                         return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
                     c = out["txt"]
                     x = out["img"]
                 else:
-                    c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)
+                    c, x = layer(c, x, global_cond, **kwargs)
 
         if len(self.single_layers) > 0:
             c_len = c.size(1)
@@ -489,13 +488,13 @@ class MMDiT(nn.Module):
                 if ("single_block", i) in blocks_replace:
                     def block_wrap(args):
                         out = {}
-                        out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"])
+                        out["img"] = layer(args["img"], args["vec"])
                         return out
 
-                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
                     cx = out["img"]
                 else:
-                    cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs)
+                    cx = layer(cx, global_cond, **kwargs)
 
             x = cx[:, c_len:]
 

comfy/ldm/cascade/common.py

@@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module):
 
         self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
 
-    def forward(self, q, k, v, transformer_options={}):
+    def forward(self, q, k, v):
         q = self.to_q(q)
         k = self.to_k(k)
         v = self.to_v(v)
 
-        out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads)
 
         return self.out_proj(out)
 
@@ -47,13 +47,13 @@ class Attention2D(nn.Module):
         self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
         # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
 
-    def forward(self, x, kv, self_attn=False, transformer_options={}):
+    def forward(self, x, kv, self_attn=False):
         orig_shape = x.shape
         x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
         if self_attn:
             kv = torch.cat([x, kv], dim=1)
         # x = self.attn(x, kv, kv, need_weights=False)[0]
-        x = self.attn(x, kv, kv, transformer_options=transformer_options)
+        x = self.attn(x, kv, kv)
         x = x.permute(0, 2, 1).view(*orig_shape)
         return x
 
@@ -114,9 +114,9 @@ class AttnBlock(nn.Module):
             operations.Linear(c_cond, c, dtype=dtype, device=device)
         )
 
-    def forward(self, x, kv, transformer_options={}):
+    def forward(self, x, kv):
         kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
         return x
 
 

comfy/ldm/cascade/stage_b.py

@@ -173,7 +173,7 @@ class StageB(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -187,7 +187,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -199,7 +199,7 @@ class StageB(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -216,7 +216,7 @@ class StageB(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -228,7 +228,7 @@ class StageB(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
         if pixels is None:
             pixels = x.new_zeros(x.size(0), 3, 8, 8)
 
@@ -245,8 +245,8 @@ class StageB(nn.Module):
             nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
         x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
                                           align_corners=True)
-        level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip)
+        x = self._up_decode(level_outputs, r_embed, clip)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/cascade/stage_c.py

@@ -182,7 +182,7 @@ class StageC(nn.Module):
         clip = self.clip_norm(clip)
         return clip
 
-    def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):
+    def _down_encode(self, x, r_embed, clip, cnet=None):
         level_outputs = []
         block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
         for down_block, downscaler, repmap in block_group:
@@ -201,7 +201,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -213,7 +213,7 @@ class StageC(nn.Module):
             level_outputs.insert(0, x)
         return level_outputs
 
-    def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
         x = level_outputs[0]
         block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
         for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -235,7 +235,7 @@ class StageC(nn.Module):
                     elif isinstance(block, AttnBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   AttnBlock)):
-                        x = block(x, clip, transformer_options=transformer_options)
+                        x = block(x, clip)
                     elif isinstance(block, TimestepBlock) or (
                             hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                   TimestepBlock)):
@@ -247,7 +247,7 @@ class StageC(nn.Module):
             x = upscaler(x)
         return x
 
-    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
         # Process the conditioning embeddings
         r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
         for c in self.t_conds:
@@ -262,8 +262,8 @@ class StageC(nn.Module):
 
         # Model Blocks
         x = self.embedding(x)
-        level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet)
         return self.clf(x)
 
     def update_weights_ema(self, src_model, beta=0.999):

comfy/ldm/chroma/layers.py

@@ -76,7 +76,7 @@ class DoubleStreamBlock(nn.Module):
         )
         self.flipped_img_txt = flipped_img_txt
 
-    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}):
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
         (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec
 
         # prepare image for attention
@@ -95,7 +95,7 @@ class DoubleStreamBlock(nn.Module):
         attn = attention(torch.cat((txt_q, img_q), dim=2),
                          torch.cat((txt_k, img_k), dim=2),
                          torch.cat((txt_v, img_v), dim=2),
-                         pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                         pe=pe, mask=attn_mask)
 
         txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]
 
@@ -148,7 +148,7 @@ class SingleStreamBlock(nn.Module):
 
         self.mlp_act = nn.GELU(approximate="tanh")
 
-    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor:
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
         mod = vec
         x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
         qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
@@ -157,7 +157,7 @@ class SingleStreamBlock(nn.Module):
         q, k = self.norm(q, k, v)
 
         # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
         x.addcmul_(mod.gate, output)

comfy/ldm/chroma/model.py

@@ -193,16 +193,14 @@ class Chroma(nn.Module):
                                                        txt=args["txt"],
                                                        vec=args["vec"],
                                                        pe=args["pe"],
-                                                       attn_mask=args.get("attn_mask"),
-                                                       transformer_options=args.get("transformer_options"))
+                                                       attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("double_block", i)]({"img": img,
                                                                "txt": txt,
                                                                "vec": double_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     txt = out["txt"]
                     img = out["img"]
@@ -211,8 +209,7 @@ class Chroma(nn.Module):
                                      txt=txt,
                                      vec=double_mod,
                                      pe=pe,
-                                     attn_mask=attn_mask,
-                                     transformer_options=transformer_options)
+                                     attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_i = control.get("input")
@@ -232,19 +229,17 @@ class Chroma(nn.Module):
                         out["img"] = block(args["img"],
                                            vec=args["vec"],
                                            pe=args["pe"],
-                                           attn_mask=args.get("attn_mask"),
-                                           transformer_options=args.get("transformer_options"))
+                                           attn_mask=args.get("attn_mask"))
                         return out
 
                     out = blocks_replace[("single_block", i)]({"img": img,
                                                                "vec": single_mod,
                                                                "pe": pe,
-                                                               "attn_mask": attn_mask,
-                                                               "transformer_options": transformer_options},
+                                                               "attn_mask": attn_mask},
                                                               {"original_block": block_wrap})
                     img = out["img"]
                 else:
-                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
 
                 if control is not None: # Controlnet
                     control_o = control.get("output")

comfy/ldm/cosmos/blocks.py

@@ -176,7 +176,6 @@ class Attention(nn.Module):
         context=None,
         mask=None,
         rope_emb=None,
-        transformer_options={},
         **kwargs,
     ):
         """
@@ -185,7 +184,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
-        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
         del q, k, v
         out = rearrange(out, " b n s c -> s b (n c)")
         return self.to_out(out)
@@ -547,7 +546,6 @@ class VideoAttn(nn.Module):
         context: Optional[torch.Tensor] = None,
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for video attention.
@@ -573,7 +571,6 @@ class VideoAttn(nn.Module):
             context_M_B_D,
             crossattn_mask,
             rope_emb=rope_emb_L_1_1_D,
-            transformer_options=transformer_options,
         )
         x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
         return x_T_H_W_B_D
@@ -668,7 +665,6 @@ class DITBuildingBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Forward pass for dynamically configured blocks with adaptive normalization.
@@ -706,7 +702,6 @@ class DITBuildingBlock(nn.Module):
                 adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
                 context=None,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         elif self.block_type in ["cross_attn", "ca"]:
             x = x + gate_1_1_1_B_D * self.block(
@@ -714,7 +709,6 @@ class DITBuildingBlock(nn.Module):
                 context=crossattn_emb,
                 crossattn_mask=crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             )
         else:
             raise ValueError(f"Unknown block type: {self.block_type}")
@@ -790,7 +784,6 @@ class GeneralDITTransformerBlock(nn.Module):
         crossattn_mask: Optional[torch.Tensor] = None,
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_3D: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         for block in self.blocks:
             x = block(
@@ -800,6 +793,5 @@ class GeneralDITTransformerBlock(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
         return x

comfy/ldm/cosmos/model.py

@@ -520,7 +520,6 @@ class GeneralDIT(nn.Module):
                 x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
             ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
 
-        transformer_options = kwargs.get("transformer_options", {})
         for _, block in self.blocks.items():
             assert (
                 self.blocks["block0"].x_format == block.x_format
@@ -535,7 +534,6 @@ class GeneralDIT(nn.Module):
                 crossattn_mask,
                 rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                 adaln_lora_B_3D=adaln_lora_B_3D,
-                transformer_options=transformer_options,
             )
 
         x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")

comfy/ldm/cosmos/predict2.py

@@ -44,7 +44,7 @@ class GPT2FeedForward(nn.Module):
         return x
 
 
-def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
+def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor:
     """Computes multi-head attention using PyTorch's native implementation.
 
     This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
@@ -71,7 +71,7 @@ def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H
     q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
     k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
     v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)
+    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True)
 
 
 class Attention(nn.Module):
@@ -180,8 +180,8 @@ class Attention(nn.Module):
 
         return q, k, v
 
-    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
-        result = self.attn_op(q, k, v, transformer_options=transformer_options)  # [B, S, H, D]
+    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
+        result = self.attn_op(q, k, v)  # [B, S, H, D]
         return self.output_dropout(self.output_proj(result))
 
     def forward(
@@ -189,7 +189,6 @@ class Attention(nn.Module):
         x: torch.Tensor,
         context: Optional[torch.Tensor] = None,
         rope_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         """
         Args:
@@ -197,7 +196,7 @@ class Attention(nn.Module):
             context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
         """
         q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
-        return self.compute_attention(q, k, v, transformer_options=transformer_options)
+        return self.compute_attention(q, k, v)
 
 
 class Timesteps(nn.Module):
@@ -460,7 +459,6 @@ class Block(nn.Module):
         rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
         adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
         extra_per_block_pos_emb: Optional[torch.Tensor] = None,
-        transformer_options: Optional[dict] = {},
     ) -> torch.Tensor:
         if extra_per_block_pos_emb is not None:
             x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
@@ -514,7 +512,6 @@ class Block(nn.Module):
                 rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                 None,
                 rope_emb=rope_emb_L_1_1_D,
-                transformer_options=transformer_options,
             ),
             "b (t h w) d -> b t h w d",
             t=T,
@@ -528,7 +525,6 @@ class Block(nn.Module):
             layer_norm_cross_attn: Callable,
             _scale_cross_attn_B_T_1_1_D: torch.Tensor,
             _shift_cross_attn_B_T_1_1_D: torch.Tensor,
-            transformer_options: Optional[dict] = {},
         ) -> torch.Tensor:
             _normalized_x_B_T_H_W_D = _fn(
                 _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
@@ -538,7 +534,6 @@ class Block(nn.Module):
                     rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                     crossattn_emb,
                     rope_emb=rope_emb_L_1_1_D,
-                    transformer_options=transformer_options,
                 ),
                 "b (t h w) d -> b t h w d",
                 t=T,
@@ -552,7 +547,6 @@ class Block(nn.Module):
             self.layer_norm_cross_attn,
             scale_cross_attn_B_T_1_1_D,
             shift_cross_attn_B_T_1_1_D,
-            transformer_options=transformer_options,
         )
         x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D
 
@@ -871,7 +865,6 @@ class MiniTrainDIT(nn.Module):
             "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
             "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
             "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
-            "transformer_options": kwargs.get("transformer_options", {}),
         }
         for block in self.blocks:
             x_B_T_H_W_D = block(

comfy/ldm/flux/layers.py

@@ -159,7 +159,7 @@ class DoubleStreamBlock(nn.Module):
         )
         self.flipped_img_txt = flipped_img_txt
 
-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None):
         img_mod1, img_mod2 = self.img_mod(vec)
         txt_mod1, txt_mod2 = self.txt_mod(vec)
 
@@ -182,7 +182,7 @@ class DoubleStreamBlock(nn.Module):
             attn = attention(torch.cat((img_q, txt_q), dim=2),
                              torch.cat((img_k, txt_k), dim=2),
                              torch.cat((img_v, txt_v), dim=2),
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                             pe=pe, mask=attn_mask)
 
             img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
         else:
@@ -190,7 +190,7 @@ class DoubleStreamBlock(nn.Module):
             attn = attention(torch.cat((txt_q, img_q), dim=2),
                              torch.cat((txt_k, img_k), dim=2),
                              torch.cat((txt_v, img_v), dim=2),
-                             pe=pe, mask=attn_mask, transformer_options=transformer_options)
+                             pe=pe, mask=attn_mask)
 
             txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
 
@@ -244,7 +244,7 @@ class SingleStreamBlock(nn.Module):
         self.mlp_act = nn.GELU(approximate="tanh")
         self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)
 
-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor:
         mod, _ = self.modulation(vec)
         qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
@@ -252,7 +252,7 @@ class SingleStreamBlock(nn.Module):
         q, k = self.norm(q, k, v)
 
         # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask)
         # compute activation in mlp stream, cat again and run second linear layer
         output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
         x += apply_mod(output, mod.gate, None, modulation_dims)

comfy/ldm/flux/math.py

@@ -6,7 +6,7 @@ from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
 
-def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
     q_shape = q.shape
     k_shape = k.shape
 
@@ -17,7 +17,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transforme
         k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
 
     heads = q.shape[1]
-    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
+    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
     return x
 
 

comfy/ldm/flux/model.py

@@ -106,6 +106,7 @@ class Flux(nn.Module):
         if y is None:
             y = torch.zeros((img.shape[0], self.params.vec_in_dim), device=img.device, dtype=img.dtype)
 
+        patches = transformer_options.get("patches", {})
         patches_replace = transformer_options.get("patches_replace", {})
         if img.ndim != 3 or txt.ndim != 3:
             raise ValueError("Input img and txt tensors must have 3 dimensions.")
@@ -117,9 +118,17 @@ class Flux(nn.Module):
             if guidance is not None:
                 vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
-        vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
+        vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
         txt = self.txt_in(txt)
 
+        if "post_input" in patches:
+            for p in patches["post_input"]:
+                out = p({"img": img, "txt": txt, "img_ids": img_ids, "txt_ids": txt_ids})
+                img = out["img"]
+                txt = out["txt"]
+                img_ids = out["img_ids"]
+                txt_ids = out["txt_ids"]
+
         if img_ids is not None:
             ids = torch.cat((txt_ids, img_ids), dim=1)
             pe = self.pe_embedder(ids)
@@ -128,7 +137,6 @@ class Flux(nn.Module):
 
         blocks_replace = patches_replace.get("dit", {})
         for i, block in enumerate(self.double_blocks):
-            transformer_options["block"] = ("double_block", i, 2)
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
@@ -136,16 +144,14 @@ class Flux(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args.get("transformer_options"))
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -154,8 +160,7 @@ class Flux(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -170,26 +175,23 @@ class Flux(nn.Module):
         img = torch.cat((txt, img), 1)
 
         for i, block in enumerate(self.single_blocks):
-            transformer_options["block"] = ("single_block", i, 1)
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args.get("transformer_options"))
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")
@@ -240,12 +242,18 @@ class Flux(nn.Module):
             h = 0
             w = 0
             index = 0
-            index_ref_method = kwargs.get("ref_latents_method", "offset") == "index"
+            ref_latents_method = kwargs.get("ref_latents_method", "offset")
             for ref in ref_latents:
-                if index_ref_method:
+                if ref_latents_method == "index":
                     index += 1
                     h_offset = 0
                     w_offset = 0
+                elif ref_latents_method == "uxo":
+                    index = 0
+                    h_offset = h_len * patch_size + h
+                    w_offset = w_len * patch_size + w
+                    h += ref.shape[-2]
+                    w += ref.shape[-1]
                 else:
                     index = 1
                     h_offset = 0

comfy/ldm/genmo/joint_model/asymm_models_joint.py

@@ -109,7 +109,6 @@ class AsymmetricAttention(nn.Module):
         scale_x: torch.Tensor,  # (B, dim_x), modulation for pre-RMSNorm.
         scale_y: torch.Tensor,  # (B, dim_y), modulation for pre-RMSNorm.
         crop_y,
-        transformer_options={},
         **rope_rotation,
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         rope_cos = rope_rotation.get("rope_cos")
@@ -144,7 +143,7 @@ class AsymmetricAttention(nn.Module):
 
         xy = optimized_attention(q,
                                  k,
-                                 v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)
+                                 v, self.num_heads, skip_reshape=True)
 
         x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
         x = self.proj_x(x)
@@ -225,7 +224,6 @@ class AsymmetricJointBlock(nn.Module):
         x: torch.Tensor,
         c: torch.Tensor,
         y: torch.Tensor,
-        transformer_options={},
         **attn_kwargs,
     ):
         """Forward pass of a block.
@@ -258,7 +256,6 @@ class AsymmetricJointBlock(nn.Module):
             y,
             scale_x=scale_msa_x,
             scale_y=scale_msa_y,
-            transformer_options=transformer_options,
             **attn_kwargs,
         )
 
@@ -527,11 +524,10 @@ class AsymmDiTJoint(nn.Module):
                                                     args["txt"],
                                                     rope_cos=args["rope_cos"],
                                                     rope_sin=args["rope_sin"],
-                                                    crop_y=args["num_tokens"],
-                                                    transformer_options=args["transformer_options"]
+                                                    crop_y=args["num_tokens"]
                                                     )
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
                 y_feat = out["txt"]
                 x = out["img"]
             else:
@@ -542,7 +538,6 @@ class AsymmDiTJoint(nn.Module):
                     rope_cos=rope_cos,
                     rope_sin=rope_sin,
                     crop_y=num_tokens,
-                    transformer_options=transformer_options,
                 )  # (B, M, D), (B, L, D)
         del y_feat  # Final layers don't use dense text features.
 

comfy/ldm/hidream/model.py

@@ -72,8 +72,8 @@ class TimestepEmbed(nn.Module):
         return t_emb
 
 
-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}):
-    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options)
+def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor):
+    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2])
 
 
 class HiDreamAttnProcessor_flashattn:
@@ -86,7 +86,6 @@ class HiDreamAttnProcessor_flashattn:
         image_tokens_masks: Optional[torch.FloatTensor] = None,
         text_tokens: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
         *args,
         **kwargs,
     ) -> torch.FloatTensor:
@@ -134,7 +133,7 @@ class HiDreamAttnProcessor_flashattn:
             query = torch.cat([query_1, query_2], dim=-1)
             key = torch.cat([key_1, key_2], dim=-1)
 
-        hidden_states = attention(query, key, value, transformer_options=transformer_options)
+        hidden_states = attention(query, key, value)
 
         if not attn.single:
             hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)
@@ -200,7 +199,6 @@ class HiDreamAttention(nn.Module):
         image_tokens_masks: torch.FloatTensor = None,
         norm_text_tokens: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.Tensor:
         return self.processor(
             self,
@@ -208,7 +206,6 @@ class HiDreamAttention(nn.Module):
             image_tokens_masks = image_tokens_masks,
             text_tokens = norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -409,7 +406,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
+
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \
@@ -422,7 +419,6 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
             norm_image_tokens,
             image_tokens_masks,
             rope = rope,
-            transformer_options=transformer_options,
         )
         image_tokens = gate_msa_i * attn_output_i + image_tokens
 
@@ -487,7 +483,6 @@ class HiDreamImageTransformerBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: Optional[torch.FloatTensor] = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         wtype = image_tokens.dtype
         shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \
@@ -505,7 +500,6 @@ class HiDreamImageTransformerBlock(nn.Module):
             image_tokens_masks,
             norm_text_tokens,
             rope = rope,
-            transformer_options=transformer_options,
         )
 
         image_tokens = gate_msa_i * attn_output_i + image_tokens
@@ -556,7 +550,6 @@ class HiDreamImageBlock(nn.Module):
         text_tokens: Optional[torch.FloatTensor] = None,
         adaln_input: torch.FloatTensor = None,
         rope: torch.FloatTensor = None,
-        transformer_options={},
     ) -> torch.FloatTensor:
         return self.block(
             image_tokens,
@@ -564,7 +557,6 @@ class HiDreamImageBlock(nn.Module):
             text_tokens,
             adaln_input,
             rope,
-            transformer_options=transformer_options,
         )
 
 
@@ -794,7 +786,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens = cur_encoder_hidden_states,
                 adaln_input = adaln_input,
                 rope = rope,
-                transformer_options=transformer_options,
             )
             initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len]
             block_id += 1
@@ -818,7 +809,6 @@ class HiDreamImageTransformer2DModel(nn.Module):
                 text_tokens=None,
                 adaln_input=adaln_input,
                 rope=rope,
-                transformer_options=transformer_options,
             )
             hidden_states = hidden_states[:, :hidden_states_seq_len]
             block_id += 1

comfy/ldm/hunyuan3d/model.py

@@ -99,16 +99,14 @@ class Hunyuan3Dv2(nn.Module):
                                                    txt=args["txt"],
                                                    vec=args["vec"],
                                                    pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"),
-                                                   transformer_options=args["transformer_options"])
+                                                   attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("double_block", i)]({"img": img,
                                                            "txt": txt,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
@@ -117,8 +115,7 @@ class Hunyuan3Dv2(nn.Module):
                                  txt=txt,
                                  vec=vec,
                                  pe=pe,
-                                 attn_mask=attn_mask,
-                                 transformer_options=transformer_options)
+                                 attn_mask=attn_mask)
 
         img = torch.cat((txt, img), 1)
 
@@ -129,19 +126,17 @@ class Hunyuan3Dv2(nn.Module):
                     out["img"] = block(args["img"],
                                        vec=args["vec"],
                                        pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"),
-                                       transformer_options=args["transformer_options"])
+                                       attn_mask=args.get("attn_mask"))
                     return out
 
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask,
-                                                           "transformer_options": transformer_options},
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
 
         img = img[:, txt.shape[1]:, ...]
         img = self.final_layer(img, vec)

comfy/ldm/hunyuan_video/model.py

@@ -78,13 +78,13 @@ class TokenRefinerBlock(nn.Module):
             operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)
 
         norm_x = self.norm1(x)
         qkv = self.self_attn.qkv(norm_x)
         q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
-        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)
+        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True)
 
         x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
         x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
@@ -115,14 +115,14 @@ class IndividualTokenRefiner(nn.Module):
             ]
         )
 
-    def forward(self, x, c, mask, transformer_options={}):
+    def forward(self, x, c, mask):
         m = None
         if mask is not None:
             m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
             m = m + m.transpose(2, 3)
 
         for block in self.blocks:
-            x = block(x, c, m, transformer_options=transformer_options)
+            x = block(x, c, m)
         return x
 
 
@@ -150,7 +150,6 @@ class TokenRefiner(nn.Module):
         x,
         timesteps,
         mask,
-        transformer_options={},
     ):
         t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
         # m = mask.float().unsqueeze(-1)
@@ -159,7 +158,7 @@ class TokenRefiner(nn.Module):
 
         c = t + self.c_embedder(c.to(x.dtype))
         x = self.input_embedder(x)
-        x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
+        x = self.individual_token_refiner(x, c, mask)
         return x
 
 class HunyuanVideo(nn.Module):
@@ -268,7 +267,7 @@ class HunyuanVideo(nn.Module):
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
 
-        txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)
+        txt = self.txt_in(txt, timesteps, txt_mask)
 
         ids = torch.cat((img_ids, txt_ids), dim=1)
         pe = self.pe_embedder(ids)
@@ -286,14 +285,14 @@ class HunyuanVideo(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"])
+                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap})
                 txt = out["txt"]
                 img = out["img"]
             else:
-                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt)
 
             if control is not None: # Controlnet
                 control_i = control.get("input")
@@ -308,13 +307,13 @@ class HunyuanVideo(nn.Module):
             if ("single_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"])
                     return out
 
-                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
                 img = out["img"]
             else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
 
             if control is not None: # Controlnet
                 control_o = control.get("output")

comfy/ldm/lightricks/model.py

@@ -271,7 +271,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, mask=None, pe=None):
         q = self.to_q(x)
         context = x if context is None else context
         k = self.to_k(context)
@@ -285,9 +285,9 @@ class CrossAttention(nn.Module):
             k = apply_rotary_emb(k, pe)
 
         if mask is None:
-            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
@@ -303,12 +303,12 @@ class BasicTransformerBlock(nn.Module):
 
         self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))
 
-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
         shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)
 
-        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
 
-        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)
+        x += self.attn2(x, context=context, mask=attention_mask)
 
         y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
         x += self.ff(y) * gate_mlp
@@ -479,10 +479,10 @@ class LTXVModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
                 x = out["img"]
             else:
                 x = block(
@@ -490,8 +490,7 @@ class LTXVModel(torch.nn.Module):
                     context=context,
                     attention_mask=attention_mask,
                     timestep=timestep,
-                    pe=pe,
-                    transformer_options=transformer_options,
+                    pe=pe
                 )
 
         # 3. Output

comfy/ldm/lumina/model.py

@@ -104,7 +104,6 @@ class JointAttention(nn.Module):
         x: torch.Tensor,
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
-        transformer_options={},
     ) -> torch.Tensor:
         """
 
@@ -141,7 +140,7 @@ class JointAttention(nn.Module):
         if n_rep >= 1:
             xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
             xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
-        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True, transformer_options=transformer_options)
+        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
 
         return self.out(output)
 
@@ -269,7 +268,6 @@ class JointTransformerBlock(nn.Module):
         x_mask: torch.Tensor,
         freqs_cis: torch.Tensor,
         adaln_input: Optional[torch.Tensor]=None,
-        transformer_options={},
     ):
         """
         Perform a forward pass through the TransformerBlock.
@@ -292,7 +290,6 @@ class JointTransformerBlock(nn.Module):
                     modulate(self.attention_norm1(x), scale_msa),
                     x_mask,
                     freqs_cis,
-                    transformer_options=transformer_options,
                 )
             )
             x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
@@ -307,7 +304,6 @@ class JointTransformerBlock(nn.Module):
                     self.attention_norm1(x),
                     x_mask,
                     freqs_cis,
-                    transformer_options=transformer_options,
                 )
             )
             x = x + self.ffn_norm2(
@@ -498,7 +494,7 @@ class NextDiT(nn.Module):
         return imgs
 
     def patchify_and_embed(
-        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
     ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
         bsz = len(x)
         pH = pW = self.patch_size
@@ -558,7 +554,7 @@ class NextDiT(nn.Module):
 
         # refine context
         for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
 
         # refine image
         flat_x = []
@@ -577,7 +573,7 @@ class NextDiT(nn.Module):
         padded_img_embed = self.x_embedder(padded_img_embed)
         padded_img_mask = padded_img_mask.unsqueeze(1)
         for layer in self.noise_refiner:
-            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options)
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
 
         if cap_mask is not None:
             mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
@@ -620,13 +616,12 @@ class NextDiT(nn.Module):
 
         cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
 
-        transformer_options = kwargs.get("transformer_options", {})
         x_is_tensor = isinstance(x, torch.Tensor)
-        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
         freqs_cis = freqs_cis.to(x.device)
 
         for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)
+            x = layer(x, mask, freqs_cis, adaln_input)
 
         x = self.final_layer(x, adaln_input)
         x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]

comfy/ldm/modules/attention.py

@@ -5,9 +5,8 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional, Any, Callable, Union
+from typing import Optional
 import logging
-import functools
 
 from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -18,45 +17,23 @@ if model_management.xformers_enabled():
     import xformers
     import xformers.ops
 
-SAGE_ATTENTION_IS_AVAILABLE = False
-try:
-    from sageattention import sageattn
-    SAGE_ATTENTION_IS_AVAILABLE = True
-except ModuleNotFoundError as e:
-    if model_management.sage_attention_enabled():
+if model_management.sage_attention_enabled():
+    try:
+        from sageattention import sageattn
+    except ModuleNotFoundError as e:
         if e.name == "sageattention":
             logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
         else:
             raise e
         exit(-1)
 
-FLASH_ATTENTION_IS_AVAILABLE = False
-try:
-    from flash_attn import flash_attn_func
-    FLASH_ATTENTION_IS_AVAILABLE = True
-except ModuleNotFoundError:
-    if model_management.flash_attention_enabled():
+if model_management.flash_attention_enabled():
+    try:
+        from flash_attn import flash_attn_func
+    except ModuleNotFoundError:
         logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
         exit(-1)
 
-REGISTERED_ATTENTION_FUNCTIONS = {}
-def register_attention_function(name: str, func: Callable):
-    # avoid replacing existing functions
-    if name not in REGISTERED_ATTENTION_FUNCTIONS:
-        REGISTERED_ATTENTION_FUNCTIONS[name] = func
-    else:
-        logging.warning(f"Attention function {name} already registered, skipping registration.")
-
-def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]:
-    if name == "optimized":
-        return optimized_attention
-    elif name not in REGISTERED_ATTENTION_FUNCTIONS:
-        if default is ...:
-            raise KeyError(f"Attention function {name} not found.")
-        else:
-            return default
-    return REGISTERED_ATTENTION_FUNCTIONS[name]
-
 from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -114,27 +91,7 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-
-def wrap_attn(func):
-    @functools.wraps(func)
-    def wrapper(*args, **kwargs):
-        remove_attn_wrapper_key = False
-        try:
-            if "_inside_attn_wrapper" not in kwargs:
-                transformer_options = kwargs.get("transformer_options", None)
-                remove_attn_wrapper_key = True
-                kwargs["_inside_attn_wrapper"] = True
-                if transformer_options is not None:
-                    if "optimized_attention_override" in transformer_options:
-                        return transformer_options["optimized_attention_override"](func, *args, **kwargs)
-            return func(*args, **kwargs)
-        finally:
-            if remove_attn_wrapper_key:
-                del kwargs["_inside_attn_wrapper"]
-    return wrapper
-
-@wrap_attn
-def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -202,8 +159,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
         )
     return out
 
-@wrap_attn
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, query.dtype)
 
     if skip_reshape:
@@ -273,8 +230,7 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
         hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-@wrap_attn
-def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision, q.dtype)
 
     if skip_reshape:
@@ -403,8 +359,7 @@ try:
 except:
     pass
 
-@wrap_attn
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -419,7 +374,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
             disabled_xformers = True
 
     if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
 
     if skip_reshape:
         # b h k d -> b k h d
@@ -472,8 +427,8 @@ else:
     #TODO: other GPUs ?
     SDP_BATCH_LIMIT = 2**31
 
-@wrap_attn
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -515,8 +470,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
             ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
     return out
 
-@wrap_attn
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout = "HND"
@@ -546,7 +501,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
                 lambda t: t.transpose(1, 2),
                 (q, k, v),
             )
-        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
 
     if tensor_layout == "HND":
         if not skip_output_reshape:
@@ -579,8 +534,8 @@ except AttributeError as error:
                     dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
         assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"
 
-@wrap_attn
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
+
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -642,19 +597,6 @@ else:
 
 optimized_attention_masked = optimized_attention
 
-
-# register core-supported attention functions
-if SAGE_ATTENTION_IS_AVAILABLE:
-    register_attention_function("sage", attention_sage)
-if FLASH_ATTENTION_IS_AVAILABLE:
-    register_attention_function("flash", attention_flash)
-if model_management.xformers_enabled():
-    register_attention_function("xformers", attention_xformers)
-register_attention_function("pytorch", attention_pytorch)
-register_attention_function("sub_quad", attention_sub_quad)
-register_attention_function("split", attention_split)
-
-
 def optimized_attention_for_device(device, mask=False, small_input=False):
     if small_input:
         if model_management.pytorch_attention_enabled():
@@ -687,7 +629,7 @@ class CrossAttention(nn.Module):
 
         self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
 
-    def forward(self, x, context=None, value=None, mask=None, transformer_options={}):
+    def forward(self, x, context=None, value=None, mask=None):
         q = self.to_q(x)
         context = default(context, x)
         k = self.to_k(context)
@@ -698,9 +640,9 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
@@ -804,7 +746,7 @@ class BasicTransformerBlock(nn.Module):
             n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
             n = self.attn1.to_out(n)
         else:
-            n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options)
+            n = self.attn1(n, context=context_attn1, value=value_attn1)
 
         if "attn1_output_patch" in transformer_patches:
             patch = transformer_patches["attn1_output_patch"]
@@ -844,7 +786,7 @@ class BasicTransformerBlock(nn.Module):
                 n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
                 n = self.attn2.to_out(n)
             else:
-                n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options)
+                n = self.attn2(n, context=context_attn2, value=value_attn2)
 
         if "attn2_output_patch" in transformer_patches:
             patch = transformer_patches["attn2_output_patch"]
@@ -1075,7 +1017,7 @@ class SpatialVideoTransformer(SpatialTransformer):
 
             B, S, C = x_mix.shape
             x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
-            x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options)
+            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
             x_mix = rearrange(
                 x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
             )

comfy/ldm/modules/diffusionmodules/mmdit.py

@@ -606,7 +606,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs):
         return _block_mixing(*args, **kwargs)
 
 
-def _block_mixing(context, x, context_block, x_block, c, transformer_options={}):
+def _block_mixing(context, x, context_block, x_block, c):
     context_qkv, context_intermediates = context_block.pre_attention(context, c)
 
     if x_block.x_block_self_attn:
@@ -622,7 +622,6 @@ def _block_mixing(context, x, context_block, x_block, c, transformer_options={})
     attn = optimized_attention(
         qkv[0], qkv[1], qkv[2],
         heads=x_block.attn.num_heads,
-        transformer_options=transformer_options,
     )
     context_attn, x_attn = (
         attn[:, : context_qkv[0].shape[1]],
@@ -638,7 +637,6 @@ def _block_mixing(context, x, context_block, x_block, c, transformer_options={})
         attn2 = optimized_attention(
                 x_qkv2[0], x_qkv2[1], x_qkv2[2],
                 heads=x_block.attn2.num_heads,
-                transformer_options=transformer_options,
             )
         x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
     else:
@@ -960,10 +958,10 @@ class MMDiT(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"])
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
                     return out
 
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
                 context = out["txt"]
                 x = out["img"]
             else:
@@ -972,7 +970,6 @@ class MMDiT(nn.Module):
                     x,
                     c=c_mod,
                     use_checkpoint=self.use_checkpoint,
-                    transformer_options=transformer_options,
                 )
             if control is not None:
                 control_o = control.get("output")

comfy/ldm/omnigen/omnigen2.py

@@ -120,7 +120,7 @@ class Attention(nn.Module):
             nn.Dropout(0.0)
         )
 
-    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
         batch_size, sequence_length, _ = hidden_states.shape
 
         query = self.to_q(hidden_states)
@@ -146,7 +146,7 @@ class Attention(nn.Module):
             key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
             value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)
 
-        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
         hidden_states = self.to_out[0](hidden_states)
         return hidden_states
 
@@ -182,16 +182,16 @@ class OmniGen2TransformerBlock(nn.Module):
         self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
         self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
 
-    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
         if self.modulation:
             norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
             hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
             mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
             hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
         else:
             norm_hidden_states = self.norm1(hidden_states)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
             hidden_states = hidden_states + self.norm2(attn_output)
             mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
             hidden_states = hidden_states + self.ffn_norm2(mlp_output)
@@ -390,7 +390,7 @@ class OmniGen2Transformer2DModel(nn.Module):
             ref_img_sizes, img_sizes,
         )
 
-    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}):
+    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
         batch_size = len(hidden_states)
 
         hidden_states = self.x_embedder(hidden_states)
@@ -405,17 +405,17 @@ class OmniGen2Transformer2DModel(nn.Module):
                     shift += ref_img_len
 
         for layer in self.noise_refiner:
-            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options)
+            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
 
         if ref_image_hidden_states is not None:
             for layer in self.ref_image_refiner:
-                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options)
+                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
 
             hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)
 
         return hidden_states
 
-    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
         B, C, H, W = x.shape
         hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
         _, _, H_padded, W_padded = hidden_states.shape
@@ -444,7 +444,7 @@ class OmniGen2Transformer2DModel(nn.Module):
         )
 
         for layer in self.context_refiner:
-            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)
+            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
 
         img_len = hidden_states.shape[1]
         combined_img_hidden_states = self.img_patch_embed_and_refine(
@@ -453,14 +453,13 @@ class OmniGen2Transformer2DModel(nn.Module):
             noise_rotary_emb, ref_img_rotary_emb,
             l_effective_ref_img_len, l_effective_img_len,
             temb,
-            transformer_options=transformer_options,
         )
 
         hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
         attention_mask = None
 
         for layer in self.layers:
-            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options)
+            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
 
         hidden_states = self.norm_out(hidden_states, temb)
 

comfy/ldm/qwen_image/model.py

@@ -132,7 +132,6 @@ class Attention(nn.Module):
         encoder_hidden_states_mask: torch.FloatTensor = None,
         attention_mask: Optional[torch.FloatTensor] = None,
         image_rotary_emb: Optional[torch.Tensor] = None,
-        transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         seq_txt = encoder_hidden_states.shape[1]
 
@@ -160,7 +159,7 @@ class Attention(nn.Module):
         joint_key = joint_key.flatten(start_dim=2)
         joint_value = joint_value.flatten(start_dim=2)
 
-        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options)
+        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask)
 
         txt_attn_output = joint_hidden_states[:, :seq_txt, :]
         img_attn_output = joint_hidden_states[:, seq_txt:, :]
@@ -227,7 +226,6 @@ class QwenImageTransformerBlock(nn.Module):
         encoder_hidden_states_mask: torch.Tensor,
         temb: torch.Tensor,
         image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        transformer_options={},
     ) -> Tuple[torch.Tensor, torch.Tensor]:
         img_mod_params = self.img_mod(temb)
         txt_mod_params = self.txt_mod(temb)
@@ -244,7 +242,6 @@ class QwenImageTransformerBlock(nn.Module):
             encoder_hidden_states=txt_modulated,
             encoder_hidden_states_mask=encoder_hidden_states_mask,
             image_rotary_emb=image_rotary_emb,
-            transformer_options=transformer_options,
         )
 
         hidden_states = hidden_states + img_gate1 * img_attn_output
@@ -437,9 +434,9 @@ class QwenImageTransformer2DModel(nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
+                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"])
                     return out
-                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb}, {"original_block": block_wrap})
                 hidden_states = out["img"]
                 encoder_hidden_states = out["txt"]
             else:
@@ -449,12 +446,11 @@ class QwenImageTransformer2DModel(nn.Module):
                     encoder_hidden_states_mask=encoder_hidden_states_mask,
                     temb=temb,
                     image_rotary_emb=image_rotary_emb,
-                    transformer_options=transformer_options,
                 )
 
             if "double_block" in patches:
                 for p in patches["double_block"]:
-                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options})
+                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i})
                     hidden_states = out["img"]
                     encoder_hidden_states = out["txt"]
 

comfy/ldm/wan/model.py

@@ -52,7 +52,7 @@ class WanSelfAttention(nn.Module):
         self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
         self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, freqs, transformer_options={}):
+    def forward(self, x, freqs):
         r"""
         Args:
             x(Tensor): Shape [B, L, num_heads, C / num_heads]
@@ -75,7 +75,6 @@ class WanSelfAttention(nn.Module):
             k.view(b, s, n * d),
             v,
             heads=self.num_heads,
-            transformer_options=transformer_options,
         )
 
         x = self.o(x)
@@ -84,7 +83,7 @@ class WanSelfAttention(nn.Module):
 
 class WanT2VCrossAttention(WanSelfAttention):
 
-    def forward(self, x, context, transformer_options={}, **kwargs):
+    def forward(self, x, context, **kwargs):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -96,7 +95,7 @@ class WanT2VCrossAttention(WanSelfAttention):
         v = self.v(context)
 
         # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)
+        x = optimized_attention(q, k, v, heads=self.num_heads)
 
         x = self.o(x)
         return x
@@ -117,7 +116,7 @@ class WanI2VCrossAttention(WanSelfAttention):
         # self.alpha = nn.Parameter(torch.zeros((1, )))
         self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
 
-    def forward(self, x, context, context_img_len, transformer_options={}):
+    def forward(self, x, context, context_img_len):
         r"""
         Args:
             x(Tensor): Shape [B, L1, C]
@@ -132,9 +131,9 @@ class WanI2VCrossAttention(WanSelfAttention):
         v = self.v(context)
         k_img = self.norm_k_img(self.k_img(context_img))
         v_img = self.v_img(context_img)
-        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options)
+        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
         # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)
+        x = optimized_attention(q, k, v, heads=self.num_heads)
 
         # output
         x = x + img_x
@@ -207,7 +206,6 @@ class WanAttentionBlock(nn.Module):
         freqs,
         context,
         context_img_len=257,
-        transformer_options={},
     ):
         r"""
         Args:
@@ -226,12 +224,12 @@ class WanAttentionBlock(nn.Module):
         # self-attention
         y = self.self_attn(
             torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
-            freqs, transformer_options=transformer_options)
+            freqs)
 
         x = torch.addcmul(x, y, repeat_e(e[2], x))
 
         # cross-attention & ffn
-        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
         y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
         x = torch.addcmul(x, y, repeat_e(e[5], x))
         return x
@@ -561,12 +559,12 @@ class WanModel(torch.nn.Module):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
 
         # head
         x = self.head(x, e)
@@ -744,17 +742,17 @@ class VaceWanModel(WanModel):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
 
             ii = self.vace_layers_mapping.get(i, None)
             if ii is not None:
                 for iii in range(len(c)):
-                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
                     x += c_skip * vace_strength[iii]
                 del c_skip
         # head
@@ -843,12 +841,12 @@ class CameraWanModel(WanModel):
             if ("double_block", i) in blocks_replace:
                 def block_wrap(args):
                     out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
                     return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
                 x = out["img"]
             else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
 
         # head
         x = self.head(x, e)

comfy/lora.py

@@ -260,6 +260,10 @@ def model_lora_keys_unet(model, key_map={}):
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
                 key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
+        for k in sdk:
+            hidden_size = model.model_config.unet_config.get("hidden_size", 0)
+            if k.endswith(".weight") and ".linear1." in k:
+                key_map["{}".format(k.replace(".linear1.weight", ".linear1_qkv"))] = (k, (0, 0, hidden_size * 3))
 
     if isinstance(model, comfy.model_base.GenmoMochi):
         for k in sdk:

comfy/lora_convert.py

@@ -15,10 +15,29 @@ def convert_lora_bfl_control(sd): #BFL loras for Flux
 def convert_lora_wan_fun(sd): #Wan Fun loras
     return comfy.utils.state_dict_prefix_replace(sd, {"lora_unet__": "lora_unet_"})
 
+def convert_uso_lora(sd):
+    sd_out = {}
+    for k in sd:
+        tensor = sd[k]
+        k_to = "diffusion_model.{}".format(k.replace(".down.weight", ".lora_down.weight")
+                                           .replace(".up.weight", ".lora_up.weight")
+                                           .replace(".qkv_lora2.", ".txt_attn.qkv.")
+                                           .replace(".qkv_lora1.", ".img_attn.qkv.")
+                                           .replace(".proj_lora1.", ".img_attn.proj.")
+                                           .replace(".proj_lora2.", ".txt_attn.proj.")
+                                           .replace(".qkv_lora.", ".linear1_qkv.")
+                                           .replace(".proj_lora.", ".linear2.")
+                                           .replace(".processor.", ".")
+                                           )
+        sd_out[k_to] = tensor
+    return sd_out
+
 
 def convert_lora(sd):
     if "img_in.lora_A.weight" in sd and "single_blocks.0.norm.key_norm.scale" in sd:
         return convert_lora_bfl_control(sd)
     if "lora_unet__blocks_0_cross_attn_k.lora_down.weight" in sd:
         return convert_lora_wan_fun(sd)
+    if "single_blocks.37.processor.qkv_lora.up.weight" in sd and "double_blocks.18.processor.qkv_lora2.up.weight" in sd:
+        return convert_uso_lora(sd)
     return sd

comfy/model_patcher.py

@@ -433,6 +433,9 @@ class ModelPatcher:
     def set_model_double_block_patch(self, patch):
         self.set_model_patch(patch, "double_block")
 
+    def set_model_post_input_patch(self, patch):
+        self.set_model_patch(patch, "post_input")
+
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 

comfy/ops.py

@@ -52,6 +52,9 @@ except (ModuleNotFoundError, TypeError):
 
 cast_to = comfy.model_management.cast_to #TODO: remove once no more references
 
+if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
+    torch.backends.cudnn.benchmark = True
+
 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)
 

comfy_api_nodes/apis/__init__.py

@@ -951,7 +951,11 @@ class MagicPrompt2(str, Enum):
 
 
 class StyleType1(str, Enum):
+    AUTO = 'AUTO'
     GENERAL = 'GENERAL'
+    REALISTIC = 'REALISTIC'
+    DESIGN = 'DESIGN'
+    FICTION = 'FICTION'
 
 
 class ImagenImageGenerationInstance(BaseModel):
@@ -2676,7 +2680,7 @@ class ReleaseNote(BaseModel):
 
 
 class RenderingSpeed(str, Enum):
-    BALANCED = 'BALANCED'
+    DEFAULT = 'DEFAULT'
     TURBO = 'TURBO'
     QUALITY = 'QUALITY'
 
@@ -4918,6 +4922,14 @@ class IdeogramV3EditRequest(BaseModel):
         None,
         description='A set of images to use as style references (maximum total size 10MB across all style references). The images should be in JPEG, PNG or WebP format.',
     )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
 
 
 class IdeogramV3Request(BaseModel):
@@ -4951,6 +4963,14 @@ class IdeogramV3Request(BaseModel):
     style_type: Optional[StyleType1] = Field(
         None, description='The type of style to apply'
     )
+    character_reference_images: Optional[List[str]] = Field(
+        None,
+        description='Generations with character reference are subject to the character reference pricing. A set of images to use as character references (maximum total size 10MB across all character references), currently only supports 1 character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
+    character_reference_images_mask: Optional[List[str]] = Field(
+        None,
+        description='Optional masks for character reference images. When provided, must match the number of character_reference_images. Each mask should be a grayscale image of the same dimensions as the corresponding character reference image. The images should be in JPEG, PNG or WebP format.'
+    )
 
 
 class ImagenGenerateImageResponse(BaseModel):

comfy_api_nodes/nodes_bytedance.py

@@ -0,0 +1,336 @@
+import logging
+from enum import Enum
+from typing import Optional
+from typing_extensions import override
+
+import torch
+from pydantic import BaseModel, Field
+
+from comfy_api.latest import ComfyExtension, io as comfy_io
+from comfy_api_nodes.util.validation_utils import (
+    validate_image_aspect_ratio_range,
+    get_number_of_images,
+)
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+)
+from comfy_api_nodes.apinode_utils import download_url_to_image_tensor, upload_images_to_comfyapi, validate_string
+
+
+BYTEPLUS_ENDPOINT = "/proxy/byteplus/api/v3/images/generations"
+
+
+class Text2ImageModelName(str, Enum):
+    seedream3 = "seedream-3-0-t2i-250415"
+
+
+class Image2ImageModelName(str, Enum):
+    seededit3 = "seededit-3-0-i2i-250628"
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: Text2ImageModelName = Text2ImageModelName.seedream3
+    prompt: str = Field(...)
+    response_format: Optional[str] = Field("url")
+    size: Optional[str] = Field(None)
+    seed: Optional[int] = Field(0, ge=0, le=2147483647)
+    guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0)
+    watermark: Optional[bool] = Field(True)
+
+
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: Image2ImageModelName = Image2ImageModelName.seededit3
+    prompt: str = Field(...)
+    response_format: Optional[str] = Field("url")
+    image: str = Field(..., description="Base64 encoded string or image URL")
+    size: Optional[str] = Field("adaptive")
+    seed: Optional[int] = Field(..., ge=0, le=2147483647)
+    guidance_scale: Optional[float] = Field(..., ge=1.0, le=10.0)
+    watermark: Optional[bool] = Field(True)
+
+
+class ImageTaskCreationResponse(BaseModel):
+    model: str = Field(...)
+    created: int = Field(..., description="Unix timestamp (in seconds) indicating time when the request was created.")
+    data: list = Field([], description="Contains information about the generated image(s).")
+    error: dict = Field({}, description="Contains `code` and `message` fields in case of error.")
+
+
+RECOMMENDED_PRESETS = [
+    ("1024x1024 (1:1)", 1024, 1024),
+    ("864x1152 (3:4)", 864, 1152),
+    ("1152x864 (4:3)", 1152, 864),
+    ("1280x720 (16:9)", 1280, 720),
+    ("720x1280 (9:16)", 720, 1280),
+    ("832x1248 (2:3)", 832, 1248),
+    ("1248x832 (3:2)", 1248, 832),
+    ("1512x648 (21:9)", 1512, 648),
+    ("2048x2048 (1:1)", 2048, 2048),
+    ("Custom", None, None),
+]
+
+
+def get_image_url_from_response(response: ImageTaskCreationResponse) -> str:
+    if response.error:
+        error_msg = f"ByteDance request failed. Code: {response.error['code']}, message: {response.error['message']}"
+        logging.info(error_msg)
+        raise RuntimeError(error_msg)
+    logging.info("ByteDance task succeeded, image URL: %s", response.data[0]["url"])
+    return response.data[0]["url"]
+
+
+class ByteDanceImageNode(comfy_io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="ByteDanceImageNode",
+            display_name="ByteDance Image",
+            category="api node/image/ByteDance",
+            description="Generate images using ByteDance models via api based on prompt",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in Text2ImageModelName],
+                    default=Text2ImageModelName.seedream3.value,
+                    tooltip="Model name",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    tooltip="The text prompt used to generate the image",
+                ),
+                comfy_io.Combo.Input(
+                    "size_preset",
+                    options=[label for label, _, _ in RECOMMENDED_PRESETS],
+                    tooltip="Pick a recommended size. Select Custom to use the width and height below",
+                ),
+                comfy_io.Int.Input(
+                    "width",
+                    default=1024,
+                    min=512,
+                    max=2048,
+                    step=64,
+                    tooltip="Custom width for image. Value is working only if `size_preset` is set to `Custom`",
+                ),
+                comfy_io.Int.Input(
+                    "height",
+                    default=1024,
+                    min=512,
+                    max=2048,
+                    step=64,
+                    tooltip="Custom height for image. Value is working only if `size_preset` is set to `Custom`",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation",
+                    optional=True,
+                ),
+                comfy_io.Float.Input(
+                    "guidance_scale",
+                    default=2.5,
+                    min=1.0,
+                    max=10.0,
+                    step=0.01,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Higher value makes the image follow the prompt more closely",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the image",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        size_preset: str,
+        width: int,
+        height: int,
+        seed: int,
+        guidance_scale: float,
+        watermark: bool,
+    ) -> comfy_io.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        w = h = None
+        for label, tw, th in RECOMMENDED_PRESETS:
+            if label == size_preset:
+                w, h = tw, th
+                break
+
+        if w is None or h is None:
+            w, h = width, height
+            if not (512 <= w <= 2048) or not (512 <= h <= 2048):
+                raise ValueError(
+                    f"Custom size out of range: {w}x{h}. "
+                    "Both width and height must be between 512 and 2048 pixels."
+                )
+
+        payload = Text2ImageTaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            size=f"{w}x{h}",
+            seed=seed,
+            guidance_scale=guidance_scale,
+            watermark=watermark,
+        )
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        response = await SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=BYTEPLUS_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=Text2ImageTaskCreationRequest,
+                response_model=ImageTaskCreationResponse,
+            ),
+            request=payload,
+            auth_kwargs=auth_kwargs,
+        ).execute()
+        return comfy_io.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
+
+
+class ByteDanceImageEditNode(comfy_io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="ByteDanceImageEditNode",
+            display_name="ByteDance Image Edit",
+            category="api node/video/ByteDance",
+            description="Edit images using ByteDance models via api based on prompt",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in Image2ImageModelName],
+                    default=Image2ImageModelName.seededit3.value,
+                    tooltip="Model name",
+                ),
+                comfy_io.Image.Input(
+                    "image",
+                    tooltip="The base image to edit",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Instruction to edit image",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation",
+                    optional=True,
+                ),
+                comfy_io.Float.Input(
+                    "guidance_scale",
+                    default=5.5,
+                    min=1.0,
+                    max=10.0,
+                    step=0.01,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Higher value makes the image follow the prompt more closely",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the image",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: torch.Tensor,
+        prompt: str,
+        seed: int,
+        guidance_scale: float,
+        watermark: bool,
+    ) -> comfy_io.NodeOutput:
+        validate_string(prompt, strip_whitespace=True, min_length=1)
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        validate_image_aspect_ratio_range(image, (1, 3), (3, 1))
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        source_url = (await upload_images_to_comfyapi(
+            image,
+            max_images=1,
+            mime_type="image/png",
+            auth_kwargs=auth_kwargs,
+        ))[0]
+        payload = Image2ImageTaskCreationRequest(
+            model=model,
+            prompt=prompt,
+            image=source_url,
+            seed=seed,
+            guidance_scale=guidance_scale,
+            watermark=watermark,
+        )
+        response = await SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path=BYTEPLUS_ENDPOINT,
+                method=HttpMethod.POST,
+                request_model=Image2ImageTaskCreationRequest,
+                response_model=ImageTaskCreationResponse,
+            ),
+            request=payload,
+            auth_kwargs=auth_kwargs,
+        ).execute()
+        return comfy_io.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
+
+
+class ByteDanceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            ByteDanceImageNode,
+            ByteDanceImageEditNode,
+        ]
+
+async def comfy_entrypoint() -> ByteDanceExtension:
+    return ByteDanceExtension()

comfy_api_nodes/nodes_ideogram.py

@@ -255,6 +255,7 @@ class IdeogramV1(comfy_io.ComfyNode):
             display_name="Ideogram V1",
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V1 model.",
+            is_api_node=True,
             inputs=[
                 comfy_io.String.Input(
                     "prompt",
@@ -383,6 +384,7 @@ class IdeogramV2(comfy_io.ComfyNode):
             display_name="Ideogram V2",
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V2 model.",
+            is_api_node=True,
             inputs=[
                 comfy_io.String.Input(
                     "prompt",
@@ -552,6 +554,7 @@ class IdeogramV3(comfy_io.ComfyNode):
             category="api node/image/Ideogram",
             description="Generates images using the Ideogram V3 model. "
                         "Supports both regular image generation from text prompts and image editing with mask.",
+            is_api_node=True,
             inputs=[
                 comfy_io.String.Input(
                     "prompt",
@@ -612,11 +615,21 @@ class IdeogramV3(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "rendering_speed",
-                    options=["BALANCED", "TURBO", "QUALITY"],
-                    default="BALANCED",
+                    options=["DEFAULT", "TURBO", "QUALITY"],
+                    default="DEFAULT",
                     tooltip="Controls the trade-off between generation speed and quality",
                     optional=True,
                 ),
+                comfy_io.Image.Input(
+                    "character_image",
+                    tooltip="Image to use as character reference.",
+                    optional=True,
+                ),
+                comfy_io.Mask.Input(
+                    "character_mask",
+                    tooltip="Optional mask for character reference image.",
+                    optional=True,
+                ),
             ],
             outputs=[
                 comfy_io.Image.Output(),
@@ -639,12 +652,46 @@ class IdeogramV3(comfy_io.ComfyNode):
         magic_prompt_option="AUTO",
         seed=0,
         num_images=1,
-        rendering_speed="BALANCED",
+        rendering_speed="DEFAULT",
+        character_image=None,
+        character_mask=None,
     ):
         auth = {
             "auth_token": cls.hidden.auth_token_comfy_org,
             "comfy_api_key": cls.hidden.api_key_comfy_org,
         }
+        if rendering_speed == "BALANCED":  # for backward compatibility
+            rendering_speed = "DEFAULT"
+
+        character_img_binary = None
+        character_mask_binary = None
+
+        if character_image is not None:
+            input_tensor = character_image.squeeze().cpu()
+            if character_mask is not None:
+                character_mask = resize_mask_to_image(character_mask, character_image, allow_gradient=False)
+                character_mask = 1.0 - character_mask
+                if character_mask.shape[1:] != character_image.shape[1:-1]:
+                    raise Exception("Character mask and image must be the same size")
+
+                mask_np = (character_mask.squeeze().cpu().numpy() * 255).astype(np.uint8)
+                mask_img = Image.fromarray(mask_np)
+                mask_byte_arr = BytesIO()
+                mask_img.save(mask_byte_arr, format="PNG")
+                mask_byte_arr.seek(0)
+                character_mask_binary = mask_byte_arr
+                character_mask_binary.name = "mask.png"
+
+            img_np = (input_tensor.numpy() * 255).astype(np.uint8)
+            img = Image.fromarray(img_np)
+            img_byte_arr = BytesIO()
+            img.save(img_byte_arr, format="PNG")
+            img_byte_arr.seek(0)
+            character_img_binary = img_byte_arr
+            character_img_binary.name = "image.png"
+        elif character_mask is not None:
+            raise Exception("Character mask requires character image to be present")
+
         # Check if both image and mask are provided for editing mode
         if image is not None and mask is not None:
             # Edit mode
@@ -693,6 +740,15 @@ class IdeogramV3(comfy_io.ComfyNode):
             if num_images > 1:
                 edit_request.num_images = num_images
 
+            files = {
+                "image": img_binary,
+                "mask": mask_binary,
+            }
+            if character_img_binary:
+                files["character_reference_images"] = character_img_binary
+            if character_mask_binary:
+                files["character_mask_binary"] = character_mask_binary
+
             # Execute the operation for edit mode
             operation = SynchronousOperation(
                 endpoint=ApiEndpoint(
@@ -702,10 +758,7 @@ class IdeogramV3(comfy_io.ComfyNode):
                     response_model=IdeogramGenerateResponse,
                 ),
                 request=edit_request,
-                files={
-                    "image": img_binary,
-                    "mask": mask_binary,
-                },
+                files=files,
                 content_type="multipart/form-data",
                 auth_kwargs=auth,
             )
@@ -739,6 +792,14 @@ class IdeogramV3(comfy_io.ComfyNode):
             if num_images > 1:
                 gen_request.num_images = num_images
 
+            files = {}
+            if character_img_binary:
+                files["character_reference_images"] = character_img_binary
+            if character_mask_binary:
+                files["character_mask_binary"] = character_mask_binary
+            if files:
+                gen_request.style_type = "AUTO"
+
             # Execute the operation for generation mode
             operation = SynchronousOperation(
                 endpoint=ApiEndpoint(
@@ -748,6 +809,8 @@ class IdeogramV3(comfy_io.ComfyNode):
                     response_model=IdeogramGenerateResponse,
                 ),
                 request=gen_request,
+                files=files if files else None,
+                content_type="multipart/form-data",
                 auth_kwargs=auth,
             )
 

comfy_api_nodes/nodes_runway.py

@@ -12,6 +12,7 @@ User Guides:
 """
 
 from typing import Union, Optional, Any
+from typing_extensions import override
 from enum import Enum
 
 import torch
@@ -46,9 +47,9 @@ from comfy_api_nodes.apinode_utils import (
     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
+from comfy_api.latest import ComfyExtension, io as comfy_io
+from comfy_api_nodes.util.validation_utils import validate_image_dimensions, validate_image_aspect_ratio
 
 PATH_IMAGE_TO_VIDEO = "/proxy/runway/image_to_video"
 PATH_TEXT_TO_IMAGE = "/proxy/runway/text_to_image"
@@ -85,20 +86,11 @@ class RunwayGen3aAspectRatio(str, Enum):
 
 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:
+    if hasattr(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
-
-
 async def poll_until_finished(
     auth_kwargs: dict[str, str],
     api_endpoint: ApiEndpoint[Any, TaskStatusResponse],
@@ -134,458 +126,438 @@ def extract_progress_from_task_status(
 
 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:
+    if hasattr(response, "output") and len(response.output) > 0:
         return response.output[0]
     return None
 
 
-class RunwayVideoGenNode(ComfyNodeABC):
-    """Runway Video Node Base."""
+async def get_response(
+    task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None, estimated_duration: Optional[int] = None
+) -> TaskStatusResponse:
+    """Poll the task status until it is finished then get the response."""
+    return await 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=estimated_duration,
+        node_id=node_id,
+    )
 
-    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
+async def generate_video(
+    request: RunwayImageToVideoRequest,
+    auth_kwargs: dict[str, str],
+    node_id: Optional[str] = None,
+    estimated_duration: Optional[int] = None,
+) -> 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,
+    )
 
-    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
+    initial_response = await initial_operation.execute()
 
-    async 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 await 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,
+    final_response = await get_response(initial_response.id, auth_kwargs, node_id, estimated_duration)
+    if not final_response.output:
+        raise RunwayApiError("Runway task succeeded but no video data found in response.")
+
+    video_url = get_video_url_from_task_status(final_response)
+    return await download_url_to_video_output(video_url)
+
+
+class RunwayImageToVideoNodeGen3a(comfy_io.ComfyNode):
+
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="RunwayImageToVideoNodeGen3a",
+            display_name="Runway Image to Video (Gen3a Turbo)",
+            category="api node/video/Runway",
+            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.",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
+                ),
+                comfy_io.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[model.value for model in Duration],
+                ),
+                comfy_io.Combo.Input(
+                    "ratio",
+                    options=[model.value for model in RunwayGen3aAspectRatio],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    control_after_generate=True,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Random seed for generation",
+                ),
+            ],
+            outputs=[
+                comfy_io.Video.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
         )
 
-    async 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,
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        start_frame: torch.Tensor,
+        duration: str,
+        ratio: str,
+        seed: int,
+    ) -> comfy_io.NodeOutput:
+        validate_string(prompt, min_length=1)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
+        download_urls = await upload_images_to_comfyapi(
+            start_frame,
+            max_images=1,
+            mime_type="image/png",
             auth_kwargs=auth_kwargs,
         )
 
-        initial_response = await initial_operation.execute()
-        self.validate_task_created(initial_response)
-        task_id = initial_response.id
-
-        final_response = await 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 (await download_url_to_video_output(video_url),)
+        return comfy_io.NodeOutput(
+            await 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=auth_kwargs,
+                node_id=cls.hidden.unique_id,
+            )
+        )
 
 
-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."
+class RunwayImageToVideoNodeGen4(comfy_io.ComfyNode):
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": model_field_to_node_input(
-                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="RunwayImageToVideoNodeGen4",
+            display_name="Runway Image to Video (Gen4 Turbo)",
+            category="api node/video/Runway",
+            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.",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
                 ),
-                "start_frame": (
-                    IO.IMAGE,
-                    {"tooltip": "Start frame to be used for the video"},
+                comfy_io.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
                 ),
-                "duration": model_field_to_node_input(
-                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[model.value for model in Duration],
                 ),
-                "ratio": model_field_to_node_input(
-                    IO.COMBO,
-                    RunwayImageToVideoRequest,
+                comfy_io.Combo.Input(
                     "ratio",
-                    enum_type=RunwayGen3aAspectRatio,
+                    options=[model.value for model in RunwayGen4TurboAspectRatio],
                 ),
-                "seed": model_field_to_node_input(
-                    IO.INT,
-                    RunwayImageToVideoRequest,
+                comfy_io.Int.Input(
                     "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
                     control_after_generate=True,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Random seed for generation",
                 ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
+            ],
+            outputs=[
+                comfy_io.Video.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
-    async def api_call(
-        self,
+    @classmethod
+    async def execute(
+        cls,
         prompt: str,
         start_frame: torch.Tensor,
         duration: str,
         ratio: str,
         seed: int,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ) -> tuple[VideoFromFile]:
-        # Validate inputs
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, min_length=1)
-        validate_input_image(start_frame)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
 
-        # Upload image
         download_urls = await upload_images_to_comfyapi(
             start_frame,
             max_images=1,
             mime_type="image/png",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
         )
-        if len(download_urls) != 1:
-            raise RunwayApiError("Failed to upload one or more images to comfy api.")
 
-        return await 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"
-                        )
-                    ]
+        return comfy_io.NodeOutput(
+            await 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,
+                auth_kwargs=auth_kwargs,
+                node_id=cls.hidden.unique_id,
+                estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            )
         )
 
 
-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."
+class RunwayFirstLastFrameNode(comfy_io.ComfyNode):
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": model_field_to_node_input(
-                    IO.STRING, RunwayImageToVideoRequest, "promptText", multiline=True
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="RunwayFirstLastFrameNode",
+            display_name="Runway First-Last-Frame to Video",
+            category="api node/video/Runway",
+            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.",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
                 ),
-                "start_frame": (
-                    IO.IMAGE,
-                    {"tooltip": "Start frame to be used for the video"},
+                comfy_io.Image.Input(
+                    "start_frame",
+                    tooltip="Start frame to be used for the video",
                 ),
-                "duration": model_field_to_node_input(
-                    IO.COMBO, RunwayImageToVideoRequest, "duration", enum_type=Duration
+                comfy_io.Image.Input(
+                    "end_frame",
+                    tooltip="End frame to be used for the video. Supported for gen3a_turbo only.",
                 ),
-                "ratio": model_field_to_node_input(
-                    IO.COMBO,
-                    RunwayImageToVideoRequest,
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[model.value for model in Duration],
+                ),
+                comfy_io.Combo.Input(
                     "ratio",
-                    enum_type=RunwayGen4TurboAspectRatio,
+                    options=[model.value for model in RunwayGen3aAspectRatio],
                 ),
-                "seed": model_field_to_node_input(
-                    IO.INT,
-                    RunwayImageToVideoRequest,
+                comfy_io.Int.Input(
                     "seed",
+                    default=0,
+                    min=0,
+                    max=4294967295,
+                    step=1,
                     control_after_generate=True,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Random seed for generation",
                 ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async 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 = await 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 await 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."
-
-    async def get_response(
-        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
-    ) -> RunwayImageToVideoResponse:
-        return await 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,
+            ],
+            outputs=[
+                comfy_io.Video.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
         )
 
     @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",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
         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
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, min_length=1)
-        validate_input_image(start_frame)
-        validate_input_image(end_frame)
+        validate_image_dimensions(start_frame, max_width=7999, max_height=7999)
+        validate_image_dimensions(end_frame, max_width=7999, max_height=7999)
+        validate_image_aspect_ratio(start_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+        validate_image_aspect_ratio(end_frame, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
+
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
 
-        # Upload images
         stacked_input_images = image_tensor_pair_to_batch(start_frame, end_frame)
         download_urls = await upload_images_to_comfyapi(
             stacked_input_images,
             max_images=2,
             mime_type="image/png",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
         )
         if len(download_urls) != 2:
             raise RunwayApiError("Failed to upload one or more images to comfy api.")
 
-        return await 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"
-                        ),
-                    ]
+        return comfy_io.NodeOutput(
+            await 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,
+                auth_kwargs=auth_kwargs,
+                node_id=cls.hidden.unique_id,
+                estimated_duration=AVERAGE_DURATION_FLF_SECONDS,
+            )
         )
 
 
-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."
+class RunwayTextToImageNode(comfy_io.ComfyNode):
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": model_field_to_node_input(
-                    IO.STRING, RunwayTextToImageRequest, "promptText", multiline=True
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="RunwayTextToImageNode",
+            display_name="Runway Text to Image",
+            category="api node/image/Runway",
+            description="Generate an image from a text prompt using Runway's Gen 4 model. "
+                        "You can also include reference image to guide the generation.",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt for the generation",
                 ),
-                "ratio": model_field_to_node_input(
-                    IO.COMBO,
-                    RunwayTextToImageRequest,
+                comfy_io.Combo.Input(
                     "ratio",
-                    enum_type=RunwayTextToImageAspectRatioEnum,
+                    options=[model.value for model in 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
-
-    async 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 await 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,
+                comfy_io.Image.Input(
+                    "reference_image",
+                    tooltip="Optional reference image to guide the generation",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
         )
 
-    async def api_call(
-        self,
+    @classmethod
+    async def execute(
+        cls,
         prompt: str,
         ratio: str,
         reference_image: Optional[torch.Tensor] = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ) -> tuple[torch.Tensor]:
-        # Validate inputs
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, min_length=1)
 
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         # Prepare reference images if provided
         reference_images = None
         if reference_image is not None:
-            validate_input_image(reference_image)
+            validate_image_dimensions(reference_image, max_width=7999, max_height=7999)
+            validate_image_aspect_ratio(reference_image, min_aspect_ratio=0.5, max_aspect_ratio=2.0)
             download_urls = await upload_images_to_comfyapi(
                 reference_image,
                 max_images=1,
                 mime_type="image/png",
-                auth_kwargs=kwargs,
+                auth_kwargs=auth_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,
@@ -593,7 +565,6 @@ class RunwayTextToImageNode(ComfyNodeABC):
             referenceImages=reference_images,
         )
 
-        # Execute initial request
         initial_operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path=PATH_TEXT_TO_IMAGE,
@@ -602,34 +573,33 @@ class RunwayTextToImageNode(ComfyNodeABC):
                 response_model=RunwayTextToImageResponse,
             ),
             request=request,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
         )
 
         initial_response = await initial_operation.execute()
-        self.validate_task_created(initial_response)
-        task_id = initial_response.id
 
         # Poll for completion
-        final_response = await self.get_response(
-            task_id, auth_kwargs=kwargs, node_id=unique_id
+        final_response = await get_response(
+            initial_response.id,
+            auth_kwargs=auth_kwargs,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=AVERAGE_DURATION_T2I_SECONDS,
         )
-        self.validate_response(final_response)
+        if not final_response.output:
+            raise RunwayApiError("Runway task succeeded but no image data found in response.")
 
-        # Download and return image
-        image_url = get_image_url_from_task_status(final_response)
-        return (await download_url_to_image_tensor(image_url),)
+        return comfy_io.NodeOutput(await download_url_to_image_tensor(get_image_url_from_task_status(final_response)))
 
 
-NODE_CLASS_MAPPINGS = {
-    "RunwayFirstLastFrameNode": RunwayFirstLastFrameNode,
-    "RunwayImageToVideoNodeGen3a": RunwayImageToVideoNodeGen3a,
-    "RunwayImageToVideoNodeGen4": RunwayImageToVideoNodeGen4,
-    "RunwayTextToImageNode": RunwayTextToImageNode,
-}
+class RunwayExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            RunwayFirstLastFrameNode,
+            RunwayImageToVideoNodeGen3a,
+            RunwayImageToVideoNodeGen4,
+            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",
-}
+async def comfy_entrypoint() -> RunwayExtension:
+    return RunwayExtension()

comfy_api_nodes/nodes_stability.py

@@ -1,5 +1,8 @@
 from inspect import cleandoc
-from comfy.comfy_types.node_typing import IO
+from typing import Optional
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io as comfy_io
 from comfy_api_nodes.apis.stability_api import (
     StabilityUpscaleConservativeRequest,
     StabilityUpscaleCreativeRequest,
@@ -46,87 +49,94 @@ def get_async_dummy_status(x: StabilityResultsGetResponse):
     return StabilityPollStatus.in_progress
 
 
-class StabilityStableImageUltraNode:
+class StabilityStableImageUltraNode(comfy_io.ComfyNode):
     """
     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" +
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="StabilityStableImageUltraNode",
+            display_name="Stability AI Stable Image Ultra",
+            category="api node/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    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. " +
                                     "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."
-                    },
+                                    "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.",
-                    },
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[x.value for x in StabilityAspectRatio],
+                    default=StabilityAspectRatio.ratio_1_1.value,
+                    tooltip="Aspect ratio of generated image.",
                 ),
-                "style_preset": (get_stability_style_presets(),
-                    {
-                        "tooltip": "Optional desired style of generated image.",
-                    },
+                comfy_io.Combo.Input(
+                    "style_preset",
+                    options=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.",
-                    },
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    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."
-                    },
+                comfy_io.Image.Input(
+                    "image",
+                    optional=True,
                 ),
-                "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.",
-                    },
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="A blurb of text describing what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
                 ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
+                comfy_io.Float.Input(
+                    "image_denoise",
+                    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.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
-    async 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):
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        aspect_ratio: str,
+        style_preset: str,
+        seed: int,
+        image: Optional[torch.Tensor] = None,
+        negative_prompt: str = "",
+        image_denoise: Optional[float] = 0.5,
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         # prepare image binary if image present
         image_binary = None
@@ -144,6 +154,11 @@ class StabilityStableImageUltraNode:
             "image": image_binary
         }
 
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path="/proxy/stability/v2beta/stable-image/generate/ultra",
@@ -161,7 +176,7 @@ class StabilityStableImageUltraNode:
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
         )
         response_api = await operation.execute()
 
@@ -171,95 +186,106 @@ class StabilityStableImageUltraNode:
         image_data = base64.b64decode(response_api.image)
         returned_image = bytesio_to_image_tensor(BytesIO(image_data))
 
-        return (returned_image,)
+        return comfy_io.NodeOutput(returned_image)
 
 
-class StabilityStableImageSD_3_5Node:
+class StabilityStableImageSD_3_5Node(comfy_io.ComfyNode):
     """
     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 define_schema(cls):
+        return comfy_io.Schema(
+            node_id="StabilityStableImageSD_3_5Node",
+            display_name="Stability AI Stable Diffusion 3.5 Image",
+            category="api node/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    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.",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[x.value for x in Stability_SD3_5_Model],
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[x.value for x in StabilityAspectRatio],
+                    default=StabilityAspectRatio.ratio_1_1.value,
+                    tooltip="Aspect ratio of generated image.",
+                ),
+                comfy_io.Combo.Input(
+                    "style_preset",
+                    options=get_stability_style_presets(),
+                    tooltip="Optional desired style of generated image.",
+                ),
+                comfy_io.Float.Input(
+                    "cfg_scale",
+                    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)",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                comfy_io.Image.Input(
+                    "image",
+                    optional=True,
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                ),
+                comfy_io.Float.Input(
+                    "image_denoise",
+                    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.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @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",
-            },
-        }
-
-    async 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):
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        aspect_ratio: str,
+        style_preset: str,
+        seed: int,
+        cfg_scale: float,
+        image: Optional[torch.Tensor] = None,
+        negative_prompt: str = "",
+        image_denoise: Optional[float] = 0.5,
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         # prepare image binary if image present
         image_binary = None
@@ -280,6 +306,11 @@ class StabilityStableImageSD_3_5Node:
             "image": image_binary
         }
 
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path="/proxy/stability/v2beta/stable-image/generate/sd3",
@@ -300,7 +331,7 @@ class StabilityStableImageSD_3_5Node:
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
         )
         response_api = await operation.execute()
 
@@ -310,72 +341,75 @@ class StabilityStableImageSD_3_5Node:
         image_data = base64.b64decode(response_api.image)
         returned_image = bytesio_to_image_tensor(BytesIO(image_data))
 
-        return (returned_image,)
+        return comfy_io.NodeOutput(returned_image)
 
 
-class StabilityUpscaleConservativeNode:
+class StabilityUpscaleConservativeNode(comfy_io.ComfyNode):
     """
     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 define_schema(cls):
+        return comfy_io.Schema(
+            node_id="StabilityUpscaleConservativeNode",
+            display_name="Stability AI Upscale Conservative",
+            category="api node/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("image"),
+                comfy_io.String.Input(
+                    "prompt",
+                    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.",
+                ),
+                comfy_io.Float.Input(
+                    "creativity",
+                    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.",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @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",
-            },
-        }
-
-    async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, seed: int, negative_prompt: str=None,
-                 **kwargs):
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        creativity: float,
+        seed: int,
+        negative_prompt: str = "",
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
 
@@ -386,6 +420,11 @@ class StabilityUpscaleConservativeNode:
             "image": image_binary
         }
 
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path="/proxy/stability/v2beta/stable-image/upscale/conservative",
@@ -401,7 +440,7 @@ class StabilityUpscaleConservativeNode:
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
         )
         response_api = await operation.execute()
 
@@ -411,77 +450,81 @@ class StabilityUpscaleConservativeNode:
         image_data = base64.b64decode(response_api.image)
         returned_image = bytesio_to_image_tensor(BytesIO(image_data))
 
-        return (returned_image,)
+        return comfy_io.NodeOutput(returned_image)
 
 
-class StabilityUpscaleCreativeNode:
+class StabilityUpscaleCreativeNode(comfy_io.ComfyNode):
     """
     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 define_schema(cls):
+        return comfy_io.Schema(
+            node_id="StabilityUpscaleCreativeNode",
+            display_name="Stability AI Upscale Creative",
+            category="api node/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("image"),
+                comfy_io.String.Input(
+                    "prompt",
+                    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.",
+                ),
+                comfy_io.Float.Input(
+                    "creativity",
+                    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.",
+                ),
+                comfy_io.Combo.Input(
+                    "style_preset",
+                    options=get_stability_style_presets(),
+                    tooltip="Optional desired style of generated image.",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=4294967294,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    tooltip="Keywords of what you do not wish to see in the output image. This is an advanced feature.",
+                    force_input=True,
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @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",
-            },
-        }
-
-    async def api_call(self, image: torch.Tensor, prompt: str, creativity: float, style_preset: str, seed: int, negative_prompt: str=None,
-                 **kwargs):
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        creativity: float,
+        style_preset: str,
+        seed: int,
+        negative_prompt: str = "",
+    ) -> comfy_io.NodeOutput:
         validate_string(prompt, strip_whitespace=False)
         image_binary = tensor_to_bytesio(image, total_pixels=1024*1024).read()
 
@@ -494,6 +537,11 @@ class StabilityUpscaleCreativeNode:
             "image": image_binary
         }
 
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path="/proxy/stability/v2beta/stable-image/upscale/creative",
@@ -510,7 +558,7 @@ class StabilityUpscaleCreativeNode:
             ),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
         )
         response_api = await operation.execute()
 
@@ -525,7 +573,8 @@ class StabilityUpscaleCreativeNode:
             completed_statuses=[StabilityPollStatus.finished],
             failed_statuses=[StabilityPollStatus.failed],
             status_extractor=lambda x: get_async_dummy_status(x),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
+            node_id=cls.hidden.unique_id,
         )
         response_poll: StabilityResultsGetResponse = await operation.execute()
 
@@ -535,41 +584,48 @@ class StabilityUpscaleCreativeNode:
         image_data = base64.b64decode(response_poll.result)
         returned_image = bytesio_to_image_tensor(BytesIO(image_data))
 
-        return (returned_image,)
+        return comfy_io.NodeOutput(returned_image)
 
 
-class StabilityUpscaleFastNode:
+class StabilityUpscaleFastNode(comfy_io.ComfyNode):
     """
     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 define_schema(cls):
+        return comfy_io.Schema(
+            node_id="StabilityUpscaleFastNode",
+            display_name="Stability AI Upscale Fast",
+            category="api node/image/Stability AI",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("image"),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @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",
-            },
-        }
-
-    async def api_call(self, image: torch.Tensor, **kwargs):
+    async def execute(cls, image: torch.Tensor) -> comfy_io.NodeOutput:
         image_binary = tensor_to_bytesio(image, total_pixels=4096*4096).read()
 
         files = {
             "image": image_binary
         }
 
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+
         operation = SynchronousOperation(
             endpoint=ApiEndpoint(
                 path="/proxy/stability/v2beta/stable-image/upscale/fast",
@@ -580,7 +636,7 @@ class StabilityUpscaleFastNode:
             request=EmptyRequest(),
             files=files,
             content_type="multipart/form-data",
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
         )
         response_api = await operation.execute()
 
@@ -590,24 +646,20 @@ class StabilityUpscaleFastNode:
         image_data = base64.b64decode(response_api.image)
         returned_image = bytesio_to_image_tensor(BytesIO(image_data))
 
-        return (returned_image,)
+        return comfy_io.NodeOutput(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,
-}
+class StabilityExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            StabilityStableImageUltraNode,
+            StabilityStableImageSD_3_5Node,
+            StabilityUpscaleConservativeNode,
+            StabilityUpscaleCreativeNode,
+            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",
-}
+
+async def comfy_entrypoint() -> StabilityExtension:
+    return StabilityExtension()

comfy_extras/nodes_align_your_steps.py

@@ -1,6 +1,10 @@
 #from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
 import numpy as np
 import torch
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
 
 def loglinear_interp(t_steps, num_steps):
     """
@@ -19,25 +23,30 @@ NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.694615152
                 "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:
+class AlignYourStepsScheduler(io.ComfyNode):
     @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 define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AlignYourStepsScheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Combo.Input("model_type", options=["SD1", "SDXL", "SVD"]),
+                io.Int.Input("steps", default=10, min=1, max=10000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[io.Sigmas.Output()],
+        )
 
     def get_sigmas(self, model_type, steps, denoise):
+        # Deprecated: use the V3 schema's `execute` method instead of this.
+        return AlignYourStepsScheduler().execute(model_type, steps, denoise).result
+
+    @classmethod
+    def execute(cls, model_type, steps, denoise) -> io.NodeOutput:
         total_steps = steps
         if denoise < 1.0:
             if denoise <= 0.0:
-                return (torch.FloatTensor([]),)
+                return io.NodeOutput(torch.FloatTensor([]))
             total_steps = round(steps * denoise)
 
         sigmas = NOISE_LEVELS[model_type][:]
@@ -46,8 +55,15 @@ class AlignYourStepsScheduler:
 
         sigmas = sigmas[-(total_steps + 1):]
         sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas), )
+        return io.NodeOutput(torch.FloatTensor(sigmas))
 
-NODE_CLASS_MAPPINGS = {
-    "AlignYourStepsScheduler": AlignYourStepsScheduler,
-}
+
+class AlignYourStepsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            AlignYourStepsScheduler,
+        ]
+
+async def comfy_entrypoint() -> AlignYourStepsExtension:
+    return AlignYourStepsExtension()

comfy_extras/nodes_easysortblock.py

@@ -1,503 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, Union
-
-from comfy_api.latest import io, ComfyExtension
-import comfy.patcher_extension
-import logging
-import torch
-import math
-import comfy.model_patcher
-if TYPE_CHECKING:
-    from uuid import UUID
-
-
-
-def easysortblock_predict_noise_wrapper(executor, *args, **kwargs):
-    # get values from args
-    x: torch.Tensor = args[0]
-    timestep: float = args[1]
-    model_options: dict[str] = args[2]
-    easycache: EasySortblockHolder = model_options["transformer_options"]["easycache"]
-
-    # initialize predict_ratios
-    if easycache.initial_step:
-        sample_sigmas = model_options["transformer_options"]["sample_sigmas"]
-        relevant_sigmas = []
-        for i,sigma in enumerate(sample_sigmas):
-            if easycache.check_if_within_timesteps(sigma):
-                relevant_sigmas.append((i, sigma))
-        start_index = relevant_sigmas[0][0]
-        end_index = relevant_sigmas[-1][0]
-        easycache.predict_ratios = torch.linspace(easycache.start_predict_ratio, easycache.end_predict_ratio, end_index - start_index + 1)
-        easycache.predict_start_index = start_index
-
-    easycache.skip_current_step = False
-    if easycache.is_past_end_timestep(timestep):
-        return executor(*args, **kwargs)
-    # prepare next x_prev
-    next_x_prev = x
-    input_change = None
-    do_easycache = easycache.should_do_easycache(timestep)
-    if do_easycache:
-        easycache.check_metadata(x)
-        if easycache.has_x_prev_subsampled():
-            if easycache.has_x_prev_subsampled():
-                input_change = (easycache.subsample(x, clone=False) - easycache.x_prev_subsampled).flatten().abs().mean()
-            if easycache.has_output_prev_norm() and easycache.has_relative_transformation_rate():
-                approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
-                easycache.cumulative_change_rate += approx_output_change_rate
-                if easycache.cumulative_change_rate < easycache.reuse_threshold:
-                    if easycache.verbose:
-                        logging.info(f"EasySortblock [verbose] - skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
-                    # other conds should also skip this step
-                    easycache.skip_current_step = True
-                    easycache.steps_skipped.append(easycache.step_count)
-                else:
-                    if easycache.verbose:
-                        logging.info(f"EasySortblock [verbose] - NOT skipping step; cumulative_change_rate: {easycache.cumulative_change_rate}, reuse_threshold: {easycache.reuse_threshold}")
-                    easycache.cumulative_change_rate = 0.0
-    output: torch.Tensor = executor(*args, **kwargs)
-    if easycache.has_output_prev_norm():
-        output_change = (easycache.subsample(output, clone=False) - easycache.output_prev_subsampled).flatten().abs().mean()
-        if easycache.verbose:
-            output_change_rate = output_change / easycache.output_prev_norm
-            easycache.output_change_rates.append(output_change_rate.item())
-        if easycache.has_relative_transformation_rate():
-            approx_output_change_rate = (easycache.relative_transformation_rate * input_change) / easycache.output_prev_norm
-            easycache.approx_output_change_rates.append(approx_output_change_rate.item())
-            if easycache.verbose:
-                logging.info(f"EasySortblock [verbose] - approx_output_change_rate: {approx_output_change_rate}")
-        if input_change is not None:
-            easycache.relative_transformation_rate = output_change / input_change
-        if easycache.verbose:
-            logging.info(f"EasySortblock [verbose] - output_change_rate: {output_change_rate}")
-    easycache.x_prev_subsampled = easycache.subsample(next_x_prev)
-    easycache.output_prev_subsampled = easycache.subsample(output)
-    easycache.output_prev_norm = output.flatten().abs().mean()
-    if easycache.verbose:
-        logging.info(f"EasySortblock [verbose] - x_prev_subsampled: {easycache.x_prev_subsampled.shape}")
-
-    # increment step count
-    easycache.step_count += 1
-    easycache.initial_step = False
-
-    return output
-
-
-def easysortblock_outer_sample_wrapper(executor, *args, **kwargs):
-    """
-    This OUTER_SAMPLE wrapper makes sure EasySortblock is prepped for current run, and all memory usage is cleared at the end.
-    """
-    try:
-        guider = executor.class_obj
-        orig_model_options = guider.model_options
-        guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
-        # clone and prepare timesteps
-        guider.model_options["transformer_options"]["easycache"] = guider.model_options["transformer_options"]["easycache"].clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
-        easycache: EasySortblockHolder = guider.model_options['transformer_options']['easycache']
-        logging.info(f"{easycache.name} enabled - threshold: {easycache.reuse_threshold}, start_percent: {easycache.start_percent}, end_percent: {easycache.end_percent}")
-        return executor(*args, **kwargs)
-    finally:
-        easycache = guider.model_options['transformer_options']['easycache']
-        output_change_rates = easycache.output_change_rates
-        approx_output_change_rates = easycache.approx_output_change_rates
-        if easycache.verbose:
-            logging.info(f"{easycache.name} [verbose] - output_change_rates {len(output_change_rates)}: {output_change_rates}")
-            logging.info(f"{easycache.name} [verbose] - approx_output_change_rates {len(approx_output_change_rates)}: {approx_output_change_rates}")
-        total_steps = len(args[3])-1
-        logging.info(f"{easycache.name} - skipped {len(easycache.steps_skipped)}/{total_steps} steps")# ({total_steps/(total_steps-easycache.total_steps_skipped):.2f}x speedup).")
-        logging.info(f"{easycache.name} - skipped steps: {easycache.steps_skipped}")
-        easycache.reset()
-        guider.model_options = orig_model_options
-
-
-def model_forward_wrapper(executor, *args, **kwargs):
-    # TODO: make work with batches of conds
-    transformer_options: dict[str] = args[-1]
-    if not isinstance(transformer_options, dict):
-        transformer_options = kwargs.get("transformer_options")
-        if not transformer_options:
-            transformer_options = args[-2]
-    sigmas = transformer_options["sigmas"]
-    sb_holder: EasySortblockHolder = transformer_options["easycache"]
-
-    # if initial step, prepare everything for Sortblock
-    if sb_holder.initial_step:
-        logging.info(f"EasySortblock: inside model {executor.class_obj.__class__.__name__}")
-        # TODO: generalize for other models
-        # these won't stick around past this step; should store on sb_holder instead
-        logging.info(f"EasySortblock: preparing {len(executor.class_obj.double_blocks)} double blocks and {len(executor.class_obj.single_blocks)} single blocks")
-        if hasattr(executor.class_obj, "double_blocks"):
-            for block in executor.class_obj.double_blocks:
-                prepare_block(block, sb_holder)
-        if hasattr(executor.class_obj, "single_blocks"):
-            for block in executor.class_obj.single_blocks:
-                prepare_block(block, sb_holder)
-        if hasattr(executor.class_obj, "blocks"):
-            for block in executor.class_obj.block:
-                prepare_block(block, sb_holder)
-
-    if sb_holder.skip_current_step:
-        predict_index = max(0, sb_holder.step_count - sb_holder.predict_start_index)
-        predict_ratio = sb_holder.predict_ratios[predict_index]
-        logging.info(f"EasySortblock: skipping step {sb_holder.step_count}, predict_ratio: {predict_ratio}")
-        # reuse_ratio = 1.0 - predict_ratio
-        for block_type, blocks in sb_holder.blocks_per_type.items():
-            for block in blocks:
-                cache: BlockCache = block.__block_cache
-                cache.allowed_to_skip = False
-            sorted_blocks = sorted(blocks, key=lambda x: (x.__block_cache.consecutive_skipped_steps, x.__block_cache.prev_change_rate))
-            # for block in sorted_blocks:
-            #     pass
-            threshold_index = int(len(sorted_blocks) * predict_ratio)
-            # blocks with lower similarity are marked for recomputation
-            for block in sorted_blocks[:threshold_index]:
-                cache: BlockCache = block.__block_cache
-                cache.allowed_to_skip = True
-                logging.info(f"EasySortblock: skip block {block.__class__.__name__} - consecutive_skipped_steps: {block.__block_cache.consecutive_skipped_steps}, prev_change_rate: {block.__block_cache.prev_change_rate}, index: {block.__block_cache.block_index}")
-            not_skipped  = [block for block in blocks if not block.__block_cache.allowed_to_skip]
-            for block in not_skipped:
-                logging.info(f"EasySortblock: reco block {block.__class__.__name__} - consecutive_skipped_steps: {block.__block_cache.consecutive_skipped_steps}, prev_change_rate: {block.__block_cache.prev_change_rate}, index: {block.__block_cache.block_index}")
-            logging.info(f"EasySortblock: for {block_type}, selected {len(sorted_blocks[:threshold_index])} blocks for prediction and {len(sorted_blocks[threshold_index:])} blocks for recomputation")
-        # return executor(*args, **kwargs)
-    to_return = executor(*args, **kwargs)
-
-    return to_return
-
-
-
-
-
-def block_forward_factory(func, block):
-    def block_forward_wrapper(*args, **kwargs):
-        transformer_options: dict[str] = kwargs.get("transformer_options")
-        sigmas = transformer_options["sigmas"]
-        sb_holder: EasySortblockHolder = transformer_options["easycache"]
-        cache: BlockCache = block.__block_cache
-        # make sure stream count is properly set for this block
-        if sb_holder.initial_step:
-            sb_holder.add_to_blocks_per_type(block, transformer_options['block'][0])
-            cache.block_index = transformer_options['block'][1]
-            cache.stream_count = transformer_options['block'][2]
-
-        if sb_holder.is_past_end_timestep(sigmas):
-            return func(*args, **kwargs)
-        # do sortblock stuff
-        x = cache.get_next_x_prev(args, kwargs)
-        # prepare next_x_prev
-        next_x_prev = cache.get_next_x_prev(args, kwargs, clone=True)
-        input_change = None
-        do_sortblock = sb_holder.should_do_easycache(sigmas)
-        if do_sortblock:
-            # TODO: checkmetadata
-            if cache.has_x_prev_subsampled():
-                input_change = (cache.subsample(x, clone=False) - cache.x_prev_subsampled).flatten().abs().mean()
-            if cache.has_output_prev_norm() and cache.has_relative_transformation_rate():
-                approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm
-                cache.cumulative_change_rate += approx_output_change_rate
-                if cache.allowed_to_skip:
-                # if cache.cumulative_change_rate < sb_holder.reuse_threshold:
-                    # accumulate error + skip block
-                    # cache.want_to_skip = True
-                    # if cache.allowed_to_skip:
-                    cache.consecutive_skipped_steps += 1
-                    cache.prev_change_rate = approx_output_change_rate
-                    return cache.apply_cache_diff(x, sb_holder)
-                else:
-                    # reset error; NOT skipping block and recalculating
-                    cache.cumulative_change_rate = 0.0
-                    cache.prev_change_rate = approx_output_change_rate
-                    cache.want_to_skip = False
-                    cache.consecutive_skipped_steps = 0
-        # output_raw is expected to have cache.stream_count elements if count is greaater than 1 (double block, etc.)
-        output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]] = func(*args, **kwargs)
-        # if more than one stream from block, only use first one
-        if isinstance(output_raw, tuple):
-            output = output_raw[0]
-        else:
-            output = output_raw
-        if cache.has_output_prev_norm():
-            output_change = (cache.subsample(output, clone=False) - cache.output_prev_subsampled).flatten().abs().mean()
-            # if verbose in future
-            output_change_rate = output_change / cache.output_prev_norm
-            cache.output_change_rates.append(output_change_rate.item())
-            if cache.has_relative_transformation_rate():
-                approx_output_change_rate = (cache.relative_transformation_rate * input_change) / cache.output_prev_norm
-                cache.approx_output_change_rates.append(approx_output_change_rate.item())
-            if input_change is not None:
-                cache.relative_transformation_rate = output_change / input_change
-        # TODO: allow cache_diff to be offloaded
-        cache.update_cache_diff(output_raw, next_x_prev)
-        cache.x_prev_subsampled = cache.subsample(next_x_prev)
-        cache.output_prev_subsampled = cache.subsample(output)
-        cache.output_prev_norm = output.flatten().abs().mean()
-        return output_raw
-    return block_forward_wrapper
-
-def prepare_block(block, sb_holder: EasySortblockHolder, stream_count: int=1):
-    sb_holder.add_to_all_blocks(block)
-    block.__original_forward = block.forward
-    block.forward = block_forward_factory(block.__original_forward, block)
-    block.__block_cache = BlockCache(subsample_factor=sb_holder.subsample_factor, verbose=sb_holder.verbose)
-
-def clean_block(block):
-    block.forward = block.__original_forward
-    del block.__original_forward
-    del block.__block_cache
-
-class BlockCache:
-    def __init__(self, subsample_factor: int=8, verbose: bool=False):
-        self.subsample_factor = subsample_factor
-        self.verbose = verbose
-        self.stream_count = 1
-        self.block_index = 0
-        # control values
-        self.relative_transformation_rate: float = None
-        self.cumulative_change_rate = 0.0
-        self.prev_change_rate = 0.0
-        # cached values
-        self.x_prev_subsampled: torch.Tensor = None
-        self.output_prev_subsampled: torch.Tensor = None
-        self.output_prev_norm: torch.Tensor = None
-        self.cache_diff: list[torch.Tensor] = []
-        self.output_change_rates = []
-        self.approx_output_change_rates = []
-        self.steps_skipped: list[int] = []
-        self.consecutive_skipped_steps = 0
-        # self.state_metadata = None
-        self.want_to_skip = False
-        self.allowed_to_skip = False
-
-    def has_cache_diff(self) -> bool:
-        return self.cache_diff[0] is not None
-
-    def has_x_prev_subsampled(self) -> bool:
-        return self.x_prev_subsampled is not None
-
-    def has_output_prev_subsampled(self) -> bool:
-        return self.output_prev_subsampled is not None
-
-    def has_output_prev_norm(self) -> bool:
-        return self.output_prev_norm is not None
-
-    def has_relative_transformation_rate(self) -> bool:
-        return self.relative_transformation_rate is not None
-
-    def get_next_x_prev(self, d_args: tuple[torch.Tensor, ...], d_kwargs: dict[str, torch.Tensor], clone: bool=False) -> tuple[torch.Tensor, ...]:
-        if self.stream_count == 1:
-            if clone:
-                return d_args[0].clone()
-            return d_args[0]
-        keys = list(d_kwargs.keys())[:self.stream_count]
-        orig_inputs = []
-        for key in keys:
-            if clone:
-                orig_inputs.append(d_kwargs[key].clone())
-            else:
-                orig_inputs.append(d_kwargs[key])
-        return tuple(orig_inputs)
-
-    def subsample(self, x: Union[torch.Tensor, tuple[torch.Tensor, ...]], clone: bool = True) -> torch.Tensor:
-        # subsample only the first compoenent
-        if isinstance(x, tuple):
-            return self.subsample(x[0], clone)
-        if self.subsample_factor > 1:
-            to_return = x[..., ::self.subsample_factor, ::self.subsample_factor]
-            if clone:
-                return to_return.clone()
-            return to_return
-        if clone:
-            return x.clone()
-        return x
-
-    def apply_cache_diff(self, x: Union[torch.Tensor, tuple[torch.Tensor, ...]], sb_holder: EasySortblockHolder):
-        self.steps_skipped.append(sb_holder.step_count)
-        if not isinstance(x, tuple):
-            x = (x, )
-        to_return = tuple([x[i] + self.cache_diff[i] for i in range(self.stream_count)])
-        if len(to_return) == 1:
-            return to_return[0]
-        return to_return
-
-    def update_cache_diff(self, output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]], x: Union[torch.Tensor, tuple[torch.Tensor, ...]]):
-        if not isinstance(output_raw, tuple):
-            output_raw = (output_raw, )
-        if not isinstance(x, tuple):
-            x = (x, )
-        self.cache_diff = tuple([output_raw[i] - x[i] for i in range(self.stream_count)])
-
-    def reset(self):
-        self.relative_transformation_rate = 0.0
-        self.cumulative_change_rate = 0.0
-        self.prev_change_rate = 0.0
-        self.x_prev_subsampled = None
-        self.output_prev_subsampled = None
-        self.output_prev_norm = None
-        self.cache_diff = []
-        self.output_change_rates = []
-        self.approx_output_change_rates = []
-        self.steps_skipped = []
-        self.consecutive_skipped_steps = 0
-        self.want_to_skip = False
-        self.allowed_to_skip = False
-        return self
-
-
-class EasySortblockHolder:
-    def __init__(self, reuse_threshold: float, start_predict_ratio: float, end_predict_ratio: float, max_skipped_steps: int,
-                       start_percent: float, end_percent: float, subsample_factor: int, verbose: bool=False):
-        self.name = "EasySortblock"
-        self.reuse_threshold = reuse_threshold
-        self.start_predict_ratio = start_predict_ratio
-        self.end_predict_ratio = end_predict_ratio
-        self.max_skipped_steps = max_skipped_steps
-        self.start_percent = start_percent
-        self.end_percent = end_percent
-        self.subsample_factor = subsample_factor
-        self.verbose = verbose
-        # timestep values
-        self.start_t = 0.0
-        self.end_t = 0.0
-        # control values
-        self.relative_transformation_rate: float = None
-        self.cumulative_change_rate = 0.0
-        self.initial_step = True
-        self.step_count = 0
-        self.predict_ratios = []
-        self.skip_current_step = False
-        self.predict_start_index = 0
-        # cache values
-        self.x_prev_subsampled: torch.Tensor = None
-        self.output_prev_subsampled: torch.Tensor = None
-        self.output_prev_norm: torch.Tensor = None
-        self.steps_skipped: list[int] = []
-        self.output_change_rates = []
-        self.approx_output_change_rates = []
-        self.state_metadata = None
-        self.all_blocks = []
-        self.blocks_per_type = {}
-
-    def add_to_all_blocks(self, block):
-        self.all_blocks.append(block)
-
-    def add_to_blocks_per_type(self, block, block_type: str):
-        self.blocks_per_type.setdefault(block_type, []).append(block)
-
-    def is_past_end_timestep(self, timestep: float) -> bool:
-        return not (timestep[0] > self.end_t).item()
-
-    def should_do_easycache(self, timestep: float) -> bool:
-        return (timestep[0] <= self.start_t).item()
-
-    def check_if_within_timesteps(self, timestep: Union[float, torch.Tensor]) -> bool:
-        return (timestep <= self.start_t).item() and (timestep > self.end_t).item()
-
-    def has_x_prev_subsampled(self) -> bool:
-        return self.x_prev_subsampled is not None
-
-    def has_output_prev_subsampled(self) -> bool:
-        return self.output_prev_subsampled is not None
-
-    def has_output_prev_norm(self) -> bool:
-        return self.output_prev_norm is not None
-
-    def has_relative_transformation_rate(self) -> bool:
-        return self.relative_transformation_rate is not None
-
-    def prepare_timesteps(self, model_sampling):
-        self.start_t = model_sampling.percent_to_sigma(self.start_percent)
-        self.end_t = model_sampling.percent_to_sigma(self.end_percent)
-        return self
-
-    def subsample(self, x: torch.Tensor, clone: bool = True) -> torch.Tensor:
-        if self.subsample_factor > 1:
-            to_return = x[..., ::self.subsample_factor, ::self.subsample_factor]
-            if clone:
-                return to_return.clone()
-            return to_return
-        if clone:
-            return x.clone()
-        return x
-
-    def check_metadata(self, x: torch.Tensor) -> bool:
-        metadata = (x.device, x.dtype, x.shape)
-        if self.state_metadata is None:
-            self.state_metadata = metadata
-            return True
-        if metadata == self.state_metadata:
-            return True
-        logging.warning(f"{self.name} - Tensor shape, dtype or device changed, resetting state")
-        self.reset()
-        return False
-
-    def reset(self):
-        logging.info(f"EasySortblock: resetting {len(self.all_blocks)} blocks")
-        for block in self.all_blocks:
-            clean_block(block)
-        self.relative_transformation_rate = 0.0
-        self.cumulative_change_rate = 0.0
-        self.initial_step = True
-        self.step_count = 0
-        self.predict_ratios = []
-        self.skip_current_step = False
-        self.predict_start_index = 0
-        self.x_prev_subsampled = None
-        self.output_prev_subsampled = None
-        self.output_prev_norm = None
-        self.steps_skipped = []
-        self.output_change_rates = []
-        self.approx_output_change_rates = []
-        self.state_metadata = None
-        self.all_blocks = []
-        self.blocks_per_type = {}
-        return self
-
-    def clone(self):
-        return EasySortblockHolder(self.reuse_threshold, self.start_predict_ratio, self.end_predict_ratio, self.max_skipped_steps,
-                                   self.start_percent, self.end_percent, self.subsample_factor, self.verbose)
-
-class EasySortblockScaledNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="EasySortblockScaled",
-            display_name="EasySortblockScaled",
-            description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
-            category="advanced/debug/model",
-            is_experimental=True,
-            inputs=[
-                io.Model.Input("model", tooltip="The model to add Sortblock to."),
-                io.Float.Input("reuse_threshold", min=0.0, default=0.2, max=3.0, step=0.01, tooltip="The threshold for reusing cached steps."),
-                io.Float.Input("start_predict_ratio", min=0.0, default=0.2, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
-                io.Float.Input("end_predict_ratio", min=0.0, default=0.9, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
-                io.Int.Input("policy_refresh_interval", min=3, default=5, max=100, step=1, tooltip="The interval at which to refresh the policy."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of Sortblock."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of Sortblock."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
-            ],
-            outputs=[
-                io.Model.Output(tooltip="The model with Sortblock."),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, model: io.Model.Type, reuse_threshold: float, start_predict_ratio: float, end_predict_ratio: float, policy_refresh_interval: int, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
-        # TODO: check for specific flavors of supported models
-        model = model.clone()
-        model.model_options["transformer_options"]["easycache"] = EasySortblockHolder(reuse_threshold, start_predict_ratio, end_predict_ratio, policy_refresh_interval, start_percent, end_percent, subsample_factor=8, verbose=verbose)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "sortblock", easysortblock_predict_noise_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", easysortblock_outer_sample_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper)
-        return io.NodeOutput(model)
-
-
-class EasySortblockExtension(ComfyExtension):
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
-            # EasySortblockNode,
-            EasySortblockScaledNode,
-        ]
-
-def comfy_entrypoint():
-    return EasySortblockExtension()
-

comfy_extras/nodes_flux.py

@@ -105,7 +105,7 @@ class FluxKontextMultiReferenceLatentMethod:
     def INPUT_TYPES(s):
         return {"required": {
             "conditioning": ("CONDITIONING", ),
-            "reference_latents_method": (("offset", "index"), ),
+            "reference_latents_method": (("offset", "index", "uxo/uno"), ),
             }}
 
     RETURN_TYPES = ("CONDITIONING",)
@@ -115,6 +115,8 @@ class FluxKontextMultiReferenceLatentMethod:
     CATEGORY = "advanced/conditioning/flux"
 
     def append(self, conditioning, reference_latents_method):
+        if "uxo" in reference_latents_method or "uso" in reference_latents_method:
+            reference_latents_method = "uxo"
         c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method})
         return (c, )
 

comfy_extras/nodes_images.py

@@ -625,6 +625,37 @@ class ImageFlip:
 
         return (image,)
 
+class ImageScaleToMaxDimension:
+    upscale_methods = ["area", "lanczos", "bilinear", "nearest-exact", "bilinear", "bicubic"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image": ("IMAGE",),
+                             "upscale_method": (s.upscale_methods,),
+                             "largest_size": ("INT", {"default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1})}}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "upscale"
+
+    CATEGORY = "image/upscaling"
+
+    def upscale(self, image, upscale_method, largest_size):
+        height = image.shape[1]
+        width = image.shape[2]
+
+        if height > width:
+            width = round((width / height) * largest_size)
+            height = largest_size
+        elif width > height:
+            height = round((height / width) * largest_size)
+            width = largest_size
+        else:
+            height = largest_size
+            width = largest_size
+
+        samples = image.movedim(-1, 1)
+        s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
+        s = s.movedim(1, -1)
+        return (s,)
 
 NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
@@ -639,4 +670,5 @@ NODE_CLASS_MAPPINGS = {
     "GetImageSize": GetImageSize,
     "ImageRotate": ImageRotate,
     "ImageFlip": ImageFlip,
+    "ImageScaleToMaxDimension": ImageScaleToMaxDimension,
 }

comfy_extras/nodes_model_patch.py

@@ -1,4 +1,5 @@
 import torch
+from torch import nn
 import folder_paths
 import comfy.utils
 import comfy.ops
@@ -58,6 +59,136 @@ class QwenImageBlockWiseControlNet(torch.nn.Module):
         return self.controlnet_blocks[block_id](img, controlnet_conditioning)
 
 
+class SigLIPMultiFeatProjModel(torch.nn.Module):
+    """
+    SigLIP Multi-Feature Projection Model for processing style features from different layers
+    and projecting them into a unified hidden space.
+
+    Args:
+        siglip_token_nums (int): Number of SigLIP tokens, default 257
+        style_token_nums (int): Number of style tokens, default 256
+        siglip_token_dims (int): Dimension of SigLIP tokens, default 1536
+        hidden_size (int): Hidden layer size, default 3072
+        context_layer_norm (bool): Whether to use context layer normalization, default False
+    """
+
+    def __init__(
+        self,
+        siglip_token_nums: int = 729,
+        style_token_nums: int = 64,
+        siglip_token_dims: int = 1152,
+        hidden_size: int = 3072,
+        context_layer_norm: bool = True,
+        device=None, dtype=None, operations=None
+    ):
+        super().__init__()
+
+        # High-level feature processing (layer -2)
+        self.high_embedding_linear = nn.Sequential(
+            operations.Linear(siglip_token_nums, style_token_nums),
+            nn.SiLU()
+        )
+        self.high_layer_norm = (
+            operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
+        )
+        self.high_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True)
+
+        # Mid-level feature processing (layer -11)
+        self.mid_embedding_linear = nn.Sequential(
+            operations.Linear(siglip_token_nums, style_token_nums),
+            nn.SiLU()
+        )
+        self.mid_layer_norm = (
+            operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
+        )
+        self.mid_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True)
+
+        # Low-level feature processing (layer -20)
+        self.low_embedding_linear = nn.Sequential(
+            operations.Linear(siglip_token_nums, style_token_nums),
+            nn.SiLU()
+        )
+        self.low_layer_norm = (
+            operations.LayerNorm(siglip_token_dims) if context_layer_norm else nn.Identity()
+        )
+        self.low_projection = operations.Linear(siglip_token_dims, hidden_size, bias=True)
+
+    def forward(self, siglip_outputs):
+        """
+        Forward pass function
+
+        Args:
+            siglip_outputs: Output from SigLIP model, containing hidden_states
+
+        Returns:
+            torch.Tensor: Concatenated multi-layer features with shape [bs, 3*style_token_nums, hidden_size]
+        """
+        dtype = next(self.high_embedding_linear.parameters()).dtype
+
+        # Process high-level features (layer -2)
+        high_embedding = self._process_layer_features(
+            siglip_outputs[2],
+            self.high_embedding_linear,
+            self.high_layer_norm,
+            self.high_projection,
+            dtype
+        )
+
+        # Process mid-level features (layer -11)
+        mid_embedding = self._process_layer_features(
+            siglip_outputs[1],
+            self.mid_embedding_linear,
+            self.mid_layer_norm,
+            self.mid_projection,
+            dtype
+        )
+
+        # Process low-level features (layer -20)
+        low_embedding = self._process_layer_features(
+            siglip_outputs[0],
+            self.low_embedding_linear,
+            self.low_layer_norm,
+            self.low_projection,
+            dtype
+        )
+
+        # Concatenate features from all layersmodel_patch
+        return torch.cat((high_embedding, mid_embedding, low_embedding), dim=1)
+
+    def _process_layer_features(
+        self,
+        hidden_states: torch.Tensor,
+        embedding_linear: nn.Module,
+        layer_norm: nn.Module,
+        projection: nn.Module,
+        dtype: torch.dtype
+    ) -> torch.Tensor:
+        """
+        Helper function to process features from a single layer
+
+        Args:
+            hidden_states: Input hidden states [bs, seq_len, dim]
+            embedding_linear: Embedding linear layer
+            layer_norm: Layer normalization
+            projection: Projection layer
+            dtype: Target data type
+
+        Returns:
+            torch.Tensor: Processed features [bs, style_token_nums, hidden_size]
+        """
+        # Transform dimensions: [bs, seq_len, dim] -> [bs, dim, seq_len] -> [bs, dim, style_token_nums] -> [bs, style_token_nums, dim]
+        embedding = embedding_linear(
+            hidden_states.to(dtype).transpose(1, 2)
+        ).transpose(1, 2)
+
+        # Apply layer normalization
+        embedding = layer_norm(embedding)
+
+        # Project to target hidden space
+        embedding = projection(embedding)
+
+        return embedding
+
 class ModelPatchLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -73,9 +204,14 @@ class ModelPatchLoader:
         model_patch_path = folder_paths.get_full_path_or_raise("model_patches", name)
         sd = comfy.utils.load_torch_file(model_patch_path, safe_load=True)
         dtype = comfy.utils.weight_dtype(sd)
-        # TODO: this node will work with more types of model patches
-        additional_in_dim = sd["img_in.weight"].shape[1] - 64
-        model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+
+        if 'controlnet_blocks.0.y_rms.weight' in sd:
+            additional_in_dim = sd["img_in.weight"].shape[1] - 64
+            model = QwenImageBlockWiseControlNet(additional_in_dim=additional_in_dim, device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+        elif 'feature_embedder.mid_layer_norm.bias' in sd:
+            sd = comfy.utils.state_dict_prefix_replace(sd, {"feature_embedder.": ""}, filter_keys=True)
+            model = SigLIPMultiFeatProjModel(device=comfy.model_management.unet_offload_device(), dtype=dtype, operations=comfy.ops.manual_cast)
+
         model.load_state_dict(sd)
         model = comfy.model_patcher.ModelPatcher(model, load_device=comfy.model_management.get_torch_device(), offload_device=comfy.model_management.unet_offload_device())
         return (model,)
@@ -157,7 +293,51 @@ class QwenImageDiffsynthControlnet:
         return (model_patched,)
 
 
+class UsoStyleProjectorPatch:
+    def __init__(self, model_patch, encoded_image):
+        self.model_patch = model_patch
+        self.encoded_image = encoded_image
+
+    def __call__(self, kwargs):
+        txt_ids = kwargs.get("txt_ids")
+        txt = kwargs.get("txt")
+        siglip_embedding = self.model_patch.model(self.encoded_image.to(txt.dtype)).to(txt.dtype)
+        txt = torch.cat([siglip_embedding, txt], dim=1)
+        kwargs['txt'] = txt
+        kwargs['txt_ids'] = torch.cat([torch.zeros(siglip_embedding.shape[0], siglip_embedding.shape[1], 3, dtype=txt_ids.dtype, device=txt_ids.device), txt_ids], dim=1)
+        return kwargs
+
+    def to(self, device_or_dtype):
+        if isinstance(device_or_dtype, torch.device):
+            self.encoded_image = self.encoded_image.to(device_or_dtype)
+        return self
+
+    def models(self):
+        return [self.model_patch]
+
+
+class USOStyleReference:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL",),
+                             "model_patch": ("MODEL_PATCH",),
+                             "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply_patch"
+    EXPERIMENTAL = True
+
+    CATEGORY = "advanced/model_patches/flux"
+
+    def apply_patch(self, model, model_patch, clip_vision_output):
+        encoded_image = torch.stack((clip_vision_output.all_hidden_states[:, -20], clip_vision_output.all_hidden_states[:, -11], clip_vision_output.penultimate_hidden_states))
+        model_patched = model.clone()
+        model_patched.set_model_post_input_patch(UsoStyleProjectorPatch(model_patch, encoded_image))
+        return (model_patched,)
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelPatchLoader": ModelPatchLoader,
     "QwenImageDiffsynthControlnet": QwenImageDiffsynthControlnet,
+    "USOStyleReference": USOStyleReference,
 }

comfy_extras/nodes_primitive.py

@@ -1,98 +1,109 @@
-# Primitive nodes that are evaluated at backend.
-from __future__ import annotations
-
 import sys
+from typing_extensions import override
 
-from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, IO
+from comfy_api.latest import ComfyExtension, io
 
 
-class String(ComfyNodeABC):
+class String(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {"value": (IO.STRING, {})},
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PrimitiveString",
+            display_name="String",
+            category="utils/primitive",
+            inputs=[
+                io.String.Input("value"),
+            ],
+            outputs=[io.String.Output()],
+        )
 
-    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,)
+    def execute(cls, value: str) -> io.NodeOutput:
+        return io.NodeOutput(value)
 
 
-class Int(ComfyNodeABC):
+class StringMultiline(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {"value": (IO.INT, {"min": -sys.maxsize, "max": sys.maxsize, "control_after_generate": True})},
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PrimitiveStringMultiline",
+            display_name="String (Multiline)",
+            category="utils/primitive",
+            inputs=[
+                io.String.Input("value", multiline=True),
+            ],
+            outputs=[io.String.Output()],
+        )
 
-    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,)
+    def execute(cls, value: str) -> io.NodeOutput:
+        return io.NodeOutput(value)
 
 
-class Boolean(ComfyNodeABC):
+class Int(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
-        return {
-            "required": {"value": (IO.BOOLEAN, {})},
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PrimitiveInt",
+            display_name="Int",
+            category="utils/primitive",
+            inputs=[
+                io.Int.Input("value", min=-sys.maxsize, max=sys.maxsize, control_after_generate=True),
+            ],
+            outputs=[io.Int.Output()],
+        )
 
-    RETURN_TYPES = (IO.BOOLEAN,)
-    FUNCTION = "execute"
-    CATEGORY = "utils/primitive"
-
-    def execute(self, value: bool) -> tuple[bool]:
-        return (value,)
+    @classmethod
+    def execute(cls, value: int) -> io.NodeOutput:
+        return io.NodeOutput(value)
 
 
-NODE_CLASS_MAPPINGS = {
-    "PrimitiveString": String,
-    "PrimitiveStringMultiline": StringMultiline,
-    "PrimitiveInt": Int,
-    "PrimitiveFloat": Float,
-    "PrimitiveBoolean": Boolean,
-}
+class Float(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PrimitiveFloat",
+            display_name="Float",
+            category="utils/primitive",
+            inputs=[
+                io.Float.Input("value", min=-sys.maxsize, max=sys.maxsize),
+            ],
+            outputs=[io.Float.Output()],
+        )
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PrimitiveString": "String",
-    "PrimitiveStringMultiline": "String (Multiline)",
-    "PrimitiveInt": "Int",
-    "PrimitiveFloat": "Float",
-    "PrimitiveBoolean": "Boolean",
-}
+    @classmethod
+    def execute(cls, value: float) -> io.NodeOutput:
+        return io.NodeOutput(value)
+
+
+class Boolean(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PrimitiveBoolean",
+            display_name="Boolean",
+            category="utils/primitive",
+            inputs=[
+                io.Boolean.Input("value"),
+            ],
+            outputs=[io.Boolean.Output()],
+        )
+
+    @classmethod
+    def execute(cls, value: bool) -> io.NodeOutput:
+        return io.NodeOutput(value)
+
+
+class PrimitivesExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            String,
+            StringMultiline,
+            Int,
+            Float,
+            Boolean,
+        ]
+
+async def comfy_entrypoint() -> PrimitivesExtension:
+    return PrimitivesExtension()

comfy_extras/nodes_sortblock.py

@@ -1,462 +0,0 @@
-from __future__ import annotations
-from typing import TYPE_CHECKING, Union
-
-from comfy_api.latest import io, ComfyExtension
-import comfy.patcher_extension
-import logging
-import torch
-import math
-import comfy.model_patcher
-if TYPE_CHECKING:
-    from uuid import UUID
-
-
-def prepare_noise_wrapper(executor, *args, **kwargs):
-    try:
-        transformer_options: dict[str] = args[2]["transformer_options"]
-        sb_holder: SortblockHolder = transformer_options["sortblock"]
-        if sb_holder.initial_step:
-            sample_sigmas = transformer_options["sample_sigmas"]
-            relevant_sigmas = []
-            # find start and end steps, then use to interpolate between start and end predict ratios
-            for i,sigma in enumerate(sample_sigmas):
-                if sb_holder.check_if_within_timesteps(sigma):
-                    relevant_sigmas.append((i, sigma))
-            start_index = relevant_sigmas[0][0]
-            end_index = relevant_sigmas[-1][0]
-            sb_holder.predict_ratios = torch.linspace(sb_holder.start_predict_ratio, sb_holder.end_predict_ratio, end_index - start_index + 1)
-            sb_holder.predict_start_index = start_index
-
-        return executor(*args, **kwargs)
-    finally:
-        transformer_options: dict[str] = args[2]["transformer_options"]
-        sb_holder: SortblockHolder = transformer_options["sortblock"]
-        sb_holder.step_count += 1
-        if sb_holder.should_do_sortblock():
-            sb_holder.active_steps += 1
-
-
-def outer_sample_wrapper(executor, *args, **kwargs):
-    try:
-        logging.info("Sortblock: inside outer_sample!")
-        guider = executor.class_obj
-        orig_model_options = guider.model_options
-        guider.model_options = comfy.model_patcher.create_model_options_clone(orig_model_options)
-        # clone and prepare timesteps
-        sb_holder = guider.model_options["transformer_options"]["sortblock"]
-        guider.model_options["transformer_options"]["sortblock"] = sb_holder.clone().prepare_timesteps(guider.model_patcher.model.model_sampling)
-        sb_holder: SortblockHolder = guider.model_options["transformer_options"]["sortblock"]
-        logging.info(f"Sortblock: enabled - threshold: {sb_holder.start_predict_ratio}, start_percent: {sb_holder.start_percent}, end_percent: {sb_holder.end_percent}")
-        return executor(*args, **kwargs)
-    finally:
-        sb_holder = guider.model_options["transformer_options"]["sortblock"]
-        logging.info(f"Sortblock: final step count: {sb_holder.step_count}")
-        sb_holder.reset()
-        guider.model_options = orig_model_options
-
-
-def model_forward_wrapper(executor, *args, **kwargs):
-    # TODO: make work with batches of conds
-    transformer_options: dict[str] = args[-1]
-    if not isinstance(transformer_options, dict):
-        transformer_options = kwargs.get("transformer_options")
-        if not transformer_options:
-            transformer_options = args[-2]
-    sigmas = transformer_options["sigmas"]
-    sb_holder: SortblockHolder = transformer_options["sortblock"]
-    sb_holder.update_should_do_sortblock(sigmas)
-
-    # if initial step, prepare everything for Sortblock
-    if sb_holder.initial_step:
-        logging.info(f"Sortblock: inside model {executor.class_obj.__class__.__name__}")
-        # TODO: generalize for other models
-        # these won't stick around past this step; should store on sb_holder instead
-        logging.info(f"Sortblock: preparing {len(executor.class_obj.double_blocks)} double blocks and {len(executor.class_obj.single_blocks)} single blocks")
-        if hasattr(executor.class_obj, "double_blocks"):
-            for block in executor.class_obj.double_blocks:
-                prepare_block(block, sb_holder)
-        if hasattr(executor.class_obj, "single_blocks"):
-            for block in executor.class_obj.single_blocks:
-                prepare_block(block, sb_holder)
-        if hasattr(executor.class_obj, "blocks"):
-            for block in executor.class_obj.block:
-                prepare_block(block, sb_holder)
-
-    # when 0: Initialization(1)
-    if sb_holder.step_modulus == 0:
-        logging.info(f"Sortblock: for step {sb_holder.step_count}, all blocks are marked for recomputation")
-        # all features are computed, input-outputs changes for all DiT blocks are stored for relative step 'k'
-        sb_holder.activated_steps.append(sb_holder.step_count)
-        for block in sb_holder.all_blocks:
-            cache: BlockCache = block.__block_cache
-            cache.mark_recompute()
-
-    # all block operations are performed in forward pass of model
-    to_return = executor(*args, **kwargs)
-
-    # when 1: Select DiT blocks(4)
-    if sb_holder.step_modulus == 1:
-        predict_index = max(0, sb_holder.step_count - sb_holder.predict_start_index)
-        predict_ratio = sb_holder.predict_ratios[predict_index]
-        logging.info(f"Sortblock: for step {sb_holder.step_count}, selecting blocks for recomputation and prediction, predict_ratio: {predict_ratio}")
-        reuse_ratio = 1.0 - predict_ratio
-        for block_type, blocks in sb_holder.blocks_per_type.items():
-            sorted_blocks = sorted(blocks, key=lambda x: x.__block_cache.cosine_similarity)
-            threshold_index = int(len(sorted_blocks) * reuse_ratio)
-            # blocks with lower similarity are marked for recomputation
-            for block in sorted_blocks[:threshold_index]:
-                cache: BlockCache = block.__block_cache
-                cache.mark_recompute()
-            # blocks with higher similarity are marked for prediction
-            for block in sorted_blocks[threshold_index:]:
-                cache: BlockCache = block.__block_cache
-                cache.mark_predict()
-            logging.info(f"Sortblock: for {block_type}, selected {len(sorted_blocks[:threshold_index])} blocks for recomputation and {len(sorted_blocks[threshold_index:])} blocks for prediction")
-
-    if sb_holder.initial_step:
-        sb_holder.initial_step = False
-    return to_return
-
-def block_forward_factory(func, block):
-    def block_forward_wrapper(*args, **kwargs):
-        transformer_options: dict[str] = kwargs.get("transformer_options")
-        sb_holder: SortblockHolder = transformer_options["sortblock"]
-        cache: BlockCache = block.__block_cache
-        # make sure stream count is properly set for this block
-        if sb_holder.initial_step:
-            sb_holder.add_to_blocks_per_type(block, transformer_options['block'][0])
-            cache.block_index = transformer_options['block'][1]
-            cache.stream_count = transformer_options['block'][2]
-        # do sortblock stuff
-        if cache.recompute and sb_holder.step_modulus != 1:
-            # clone relevant inputs
-            orig_inputs = cache.get_orig_inputs(args, kwargs, clone=True)
-            # get block outputs
-            # NOTE: output_raw is expected to have cache.stream_count elements if count is greaater than 1 (double block, etc.)
-            if cache.stream_count == 1:
-                zzz = 10
-            output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]] = func(*args, **kwargs)
-            # perform derivative approximation;
-            cache.derivative_approximation(sb_holder, output_raw, orig_inputs)
-            # if step_modulus is 0, input-output changes for DiT block are stored
-            if sb_holder.step_modulus == 0:
-                cache.cache_previous_residual(output_raw, orig_inputs)
-        else:
-            # if not to recompute, predict features for current timestep
-            orig_inputs = cache.get_orig_inputs(args, kwargs, clone=False)
-            # when 1: Linear Prediction(2)
-            # if step_modulus is 1, store block residuals as 'current' after applying taylor_formula
-            if sb_holder.step_modulus == 1:
-                cache.cache_current_residual(sb_holder)
-            # based on features computed in last timestep, all features for current timestep are predicted using Eq. 4,
-            # input-output changes for all DiT blocks are stored for relative step 'k+1'
-            output_raw = cache.apply_linear_prediction(sb_holder, orig_inputs)
-
-        # when 1: Identify Changes(3)
-        if sb_holder.step_modulus == 1:
-            # based on features computed in last timestep, all features for current timestep are predicted using Eq. 4,
-            # input-output changes for all DiT blocks are stored for relative step 'k+1'
-            cache.calculate_cosine_similarity()
-
-        # return output_raw
-        return output_raw
-    return block_forward_wrapper
-
-
-def perform_sortblock(blocks: list):
-    ...
-
-def prepare_block(block, sb_holder: SortblockHolder, stream_count: int=1):
-    sb_holder.add_to_all_blocks(block)
-    block.__original_forward = block.forward
-    block.forward = block_forward_factory(block.__original_forward, block)
-    block.__block_cache = BlockCache(subsample_factor=sb_holder.subsample_factor, verbose=sb_holder.verbose)
-
-def clean_block(block):
-    block.forward = block.__original_forward
-    del block.__original_forward
-    del block.__block_cache
-
-def subsample(x: torch.Tensor, factor: int, clone: bool=True) -> torch.Tensor:
-    if factor > 1:
-        to_return = x[..., ::factor, ::factor]
-        if clone:
-            return to_return.clone()
-        return to_return
-    if clone:
-        return x.clone()
-    return x
-
-class BlockCache:
-    def __init__(self, subsample_factor: int=8, verbose: bool=False):
-        self.subsample_factor = subsample_factor
-        self.verbose = verbose
-        self.stream_count = 1
-        self.recompute = False
-        self.block_index = 0
-        # cached values
-        self.previous_residual_subsampled: torch.Tensor = None
-        self.current_residual_subsampled: torch.Tensor = None
-        self.cosine_similarity: float = None
-        self.previous_taylor_factors: dict[int, torch.Tensor] = {}
-        self.current_taylor_factors: dict[int, torch.Tensor] = {}
-
-    def mark_recompute(self):
-        self.recompute = True
-
-    def mark_predict(self):
-        self.recompute = False
-
-    def cache_previous_residual(self, output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]], orig_inputs: Union[torch.Tensor, tuple[torch.Tensor, ...]]):
-        if isinstance(output_raw, tuple):
-            output_raw = output_raw[0]
-        if isinstance(orig_inputs, tuple):
-            orig_inputs = orig_inputs[0]
-        del self.previous_residual_subsampled
-        self.previous_residual_subsampled = subsample(output_raw - orig_inputs, self.subsample_factor, clone=True)
-
-    def cache_current_residual(self, sb_holder: SortblockHolder):
-        del self.current_residual_subsampled
-        self.current_residual_subsampled = subsample(self.use_taylor_formula(sb_holder)[0], self.subsample_factor, clone=True)
-
-    def get_orig_inputs(self, d_args: tuple, d_kwargs: dict, clone: bool=True) -> tuple[torch.Tensor, ...]:
-        if self.stream_count == 1:
-            if clone:
-                return d_args[0].clone()
-            return d_args[0]
-        keys = list(d_kwargs.keys())[:self.stream_count]
-        orig_inputs = []
-        for key in keys:
-            if clone:
-                orig_inputs.append(d_kwargs[key].clone())
-            else:
-                orig_inputs.append(d_kwargs[key])
-        return tuple(orig_inputs)
-
-    def apply_linear_prediction(self, sb_holder: SortblockHolder, orig_inputs: Union[torch.Tensor, tuple[torch.Tensor, ...]]) -> None:
-        drop_tuple = False
-        if not isinstance(orig_inputs, tuple):
-            orig_inputs = (orig_inputs,)
-            drop_tuple = True
-        taylor_results = self.use_taylor_formula(sb_holder)
-        for output, taylor_result in zip(orig_inputs, taylor_results):
-            if output.shape != taylor_result.shape:
-                zzz = 10
-            output += taylor_result
-        if drop_tuple:
-            orig_inputs = orig_inputs[0]
-        return orig_inputs
-
-    def calculate_cosine_similarity(self) -> None:
-        self.cosine_similarity = torch.nn.functional.cosine_similarity(self.previous_residual_subsampled, self.current_residual_subsampled, dim=-1).mean().item()
-
-    def derivative_approximation(self, sb_holder: SortblockHolder, output_raw: Union[torch.Tensor, tuple[torch.Tensor, ...]], orig_inputs: Union[torch.Tensor, tuple[torch.Tensor, ...]]):
-        activation_distance = sb_holder.activated_steps[-1] - sb_holder.activated_steps[-2]
-        # make tuple if not already tuple, so that works with both single and double blocks
-        if not isinstance(output_raw, tuple):
-            output_raw = (output_raw,)
-        if not isinstance(orig_inputs, tuple):
-            orig_inputs = (orig_inputs,)
-
-        for i, (output, x) in enumerate(zip(output_raw, orig_inputs)):
-            feature = output.clone() - x
-            has_previous_taylor_factor = self.previous_taylor_factors.get(i, None) is not None
-            # NOTE: not sure why - 2, but that's what's in the original implementation. Maybe consider changing values?
-            if has_previous_taylor_factor and sb_holder.step_count > (sb_holder.first_enhance - 2):
-                self.current_taylor_factors[i] = (
-                    feature - self.previous_taylor_factors[i]
-                ) / activation_distance
-
-            self.previous_taylor_factors[i] = feature
-
-    def use_taylor_formula(self, sb_holder: SortblockHolder) -> tuple[torch.Tensor, ...]:
-        step_distance = sb_holder.step_count - sb_holder.activated_steps[-1]
-
-        output_predicted = []
-
-        for key in self.previous_taylor_factors.keys():
-            previous_tf = self.previous_taylor_factors[key]
-            current_tf = self.current_taylor_factors[key]
-            predicted = taylor_formula(previous_tf, 0, step_distance)
-            predicted += taylor_formula(current_tf, 1, step_distance)
-            output_predicted.append(predicted)
-
-        return tuple(output_predicted)
-
-    def reset(self):
-        self.recompute = False
-        self.current_residual_subsampled = None
-        self.previous_residual_subsampled = None
-        self.cosine_similarity = None
-        self.previous_taylor_factors = {}
-        self.current_taylor_factors = {}
-
-def taylor_formula(taylor_factor: torch.Tensor, i: int, step_distance: int):
-    return (
-        (1 / math.factorial(i))
-        * taylor_factor
-        * (step_distance ** i)
-    )
-
-class SortblockHolder:
-    def __init__(self, start_predict_ratio: float, end_predict_ratio: float, policy_refresh_interval: int,
-                 start_percent: float, end_percent: float, subsample_factor: int=8, verbose: bool=False):
-        self.start_predict_ratio = start_predict_ratio
-        self.end_predict_ratio = end_predict_ratio
-        self.start_percent = start_percent
-        self.end_percent = end_percent
-        self.subsample_factor = subsample_factor
-        self.verbose = verbose
-
-        # NOTE: number represents steps
-        self.policy_refresh_interval = policy_refresh_interval
-        self.active_policy_refresh_interval = 1
-        self.first_enhance = 3  # NOTE: this value is 2 higher than the one actually used in code (subtracted by 2 in derivative_approximation)
-        # timestep values
-        self.start_t = 0.0
-        self.end_t = 0.0
-        self.curr_t = 0.0
-        # control values
-        self.initial_step = True
-        self.step_count = 0
-        self.activated_steps: list[int] = [0]
-        self.step_modulus = 0
-        self.do_sortblock = False
-        self.active_steps = 0
-        self.predict_ratios = []
-        self.predict_start_index = 0
-
-        # cache values
-        self.all_blocks = []
-        self.blocks_per_type = {}
-
-    def add_to_all_blocks(self, block):
-        self.all_blocks.append(block)
-
-    def add_to_blocks_per_type(self, block, block_type: str):
-        self.blocks_per_type.setdefault(block_type, []).append(block)
-
-    def prepare_timesteps(self, model_sampling):
-        self.start_t = model_sampling.percent_to_sigma(self.start_percent)
-        self.end_t = model_sampling.percent_to_sigma(self.end_percent)
-        return self
-
-    def check_if_within_timesteps(self, timestep: Union[float, torch.Tensor]) -> bool:
-        return (timestep <= self.start_t).item() and (timestep > self.end_t).item()
-
-    def update_should_do_sortblock(self, timestep: float) -> bool:
-        self.do_sortblock = (timestep[0] <= self.start_t).item() and (timestep[0] > self.end_t).item()
-        self.curr_t = timestep
-        if self.do_sortblock:
-            self.active_policy_refresh_interval = self.policy_refresh_interval
-        else:
-            self.active_policy_refresh_interval = 1
-        self.update_step_modulus()
-        return self.do_sortblock
-
-    def update_step_modulus(self):
-        self.step_modulus = int(self.step_count % self.active_policy_refresh_interval)
-
-    def should_do_sortblock(self) -> bool:
-        return self.do_sortblock
-
-    def reset(self):
-        self.initial_step = True
-        self.curr_t = 0.0
-        logging.info(f"Sortblock: resetting {len(self.all_blocks)} blocks")
-        for block in self.all_blocks:
-            clean_block(block)
-        self.all_blocks = []
-        self.blocks_per_type = {}
-        self.step_count = 0
-        self.activated_steps = [0]
-        self.step_modulus = 0
-        self.active_steps = 0
-        self.predict_ratios = []
-        self.do_sortblock = False
-        self.predict_start_index = 0
-        return self
-
-    def clone(self):
-        return SortblockHolder(start_predict_ratio=self.start_predict_ratio, end_predict_ratio=self.end_predict_ratio, policy_refresh_interval=self.policy_refresh_interval,
-            start_percent=self.start_percent, end_percent=self.end_percent, subsample_factor=self.subsample_factor,
-            verbose=self.verbose)
-
-
-class SortblockNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="Sortblock",
-            display_name="Sortblock",
-            description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
-            category="advanced/debug/model",
-            is_experimental=True,
-            inputs=[
-                io.Model.Input("model", tooltip="The model to add Sortblock to."),
-                io.Float.Input("predict_ratio", min=0.0, default=0.8, max=3.0, step=0.01, tooltip="The ratio of blocks to predict."),
-                io.Int.Input("policy_refresh_interval", min=3, default=5, max=100, step=1, tooltip="The interval at which to refresh the policy."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of Sortblock."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of Sortblock."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
-            ],
-            outputs=[
-                io.Model.Output(tooltip="The model with Sortblock."),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, model: io.Model.Type, predict_ratio: float, policy_refresh_interval: int, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
-        # TODO: check for specific flavors of supported models
-        model = model.clone()
-        model.model_options["transformer_options"]["sortblock"] = SortblockHolder(start_predict_ratio=predict_ratio, end_predict_ratio=predict_ratio, policy_refresh_interval=policy_refresh_interval,
-                                                                                  start_percent=start_percent, end_percent=end_percent, subsample_factor=8, verbose=verbose)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "sortblock", prepare_noise_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", outer_sample_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper)
-        return io.NodeOutput(model)
-
-
-class SortblockScaledNode(io.ComfyNode):
-    @classmethod
-    def define_schema(cls) -> io.Schema:
-        return io.Schema(
-            node_id="SortblockScaled",
-            display_name="SortblockScaled",
-            description="A homebrew version of EasyCache - even 'easier' version of EasyCache to implement. Overall works worse than EasyCache, but better in some rare cases AND universal compatibility with everything in ComfyUI.",
-            category="advanced/debug/model",
-            is_experimental=True,
-            inputs=[
-                io.Model.Input("model", tooltip="The model to add Sortblock to."),
-                io.Float.Input("start_predict_ratio", min=0.0, default=0.2, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
-                io.Float.Input("end_predict_ratio", min=0.0, default=0.9, max=1.0, step=0.01, tooltip="The ratio of blocks to predict."),
-                io.Int.Input("policy_refresh_interval", min=3, default=5, max=100, step=1, tooltip="The interval at which to refresh the policy."),
-                io.Float.Input("start_percent", min=0.0, default=0.15, max=1.0, step=0.01, tooltip="The relative sampling step to begin use of Sortblock."),
-                io.Float.Input("end_percent", min=0.0, default=0.95, max=1.0, step=0.01, tooltip="The relative sampling step to end use of Sortblock."),
-                io.Boolean.Input("verbose", default=False, tooltip="Whether to log verbose information."),
-            ],
-            outputs=[
-                io.Model.Output(tooltip="The model with Sortblock."),
-            ],
-        )
-
-    @classmethod
-    def execute(cls, model: io.Model.Type, start_predict_ratio: float, end_predict_ratio: float, policy_refresh_interval: int, start_percent: float, end_percent: float, verbose: bool) -> io.NodeOutput:
-        # TODO: check for specific flavors of supported models
-        model = model.clone()
-        model.model_options["transformer_options"]["sortblock"] = SortblockHolder(start_predict_ratio, end_predict_ratio, policy_refresh_interval, start_percent, end_percent, subsample_factor=8, verbose=verbose)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.PREDICT_NOISE, "sortblock", prepare_noise_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.OUTER_SAMPLE, "sortblock", outer_sample_wrapper)
-        model.add_wrapper_with_key(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, "sortblock", model_forward_wrapper)
-        return io.NodeOutput(model)
-
-
-class SortblockExtension(ComfyExtension):
-    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-        return [
-            SortblockNode,
-            SortblockScaledNode,
-        ]
-
-def comfy_entrypoint():
-    return SortblockExtension()

comfy_extras/nodes_stable_cascade.py

@@ -17,55 +17,61 @@
 """
 
 import torch
-import nodes
+from typing_extensions import override
+
 import comfy.utils
+import nodes
+from comfy_api.latest import ComfyExtension, io
 
 
-class StableCascade_EmptyLatentImage:
-    def __init__(self, device="cpu"):
-        self.device = device
+class StableCascade_EmptyLatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableCascade_EmptyLatentImage",
+            category="latent/stable_cascade",
+            inputs=[
+                io.Int.Input("width", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("height", default=1024, min=256, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="stage_c"),
+                io.Latent.Output(display_name="stage_b"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
-            "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
-            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
-            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
-        }}
-    RETURN_TYPES = ("LATENT", "LATENT")
-    RETURN_NAMES = ("stage_c", "stage_b")
-    FUNCTION = "generate"
-
-    CATEGORY = "latent/stable_cascade"
-
-    def generate(self, width, height, compression, batch_size=1):
+    def execute(cls, width, height, compression, batch_size=1):
         c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
         b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
-        return ({
+        return io.NodeOutput({
             "samples": c_latent,
         }, {
             "samples": b_latent,
         })
 
-class StableCascade_StageC_VAEEncode:
-    def __init__(self, device="cpu"):
-        self.device = device
+
+class StableCascade_StageC_VAEEncode(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableCascade_StageC_VAEEncode",
+            category="latent/stable_cascade",
+            inputs=[
+                io.Image.Input("image"),
+                io.Vae.Input("vae"),
+                io.Int.Input("compression", default=42, min=4, max=128, step=1),
+            ],
+            outputs=[
+                io.Latent.Output(display_name="stage_c"),
+                io.Latent.Output(display_name="stage_b"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "image": ("IMAGE",),
-            "vae": ("VAE", ),
-            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
-        }}
-    RETURN_TYPES = ("LATENT", "LATENT")
-    RETURN_NAMES = ("stage_c", "stage_b")
-    FUNCTION = "generate"
-
-    CATEGORY = "latent/stable_cascade"
-
-    def generate(self, image, vae, compression):
+    def execute(cls, image, vae, compression):
         width = image.shape[-2]
         height = image.shape[-3]
         out_width = (width // compression) * vae.downscale_ratio
@@ -75,51 +81,59 @@ class StableCascade_StageC_VAEEncode:
 
         c_latent = vae.encode(s[:,:,:,:3])
         b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
-        return ({
+        return io.NodeOutput({
             "samples": c_latent,
         }, {
             "samples": b_latent,
         })
 
-class StableCascade_StageB_Conditioning:
+
+class StableCascade_StageB_Conditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "conditioning": ("CONDITIONING",),
-                              "stage_c": ("LATENT",),
-                             }}
-    RETURN_TYPES = ("CONDITIONING",)
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableCascade_StageB_Conditioning",
+            category="conditioning/stable_cascade",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Latent.Input("stage_c"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    FUNCTION = "set_prior"
-
-    CATEGORY = "conditioning/stable_cascade"
-
-    def set_prior(self, conditioning, stage_c):
+    @classmethod
+    def execute(cls, conditioning, stage_c):
         c = []
         for t in conditioning:
             d = t[1].copy()
-            d['stable_cascade_prior'] = stage_c['samples']
+            d["stable_cascade_prior"] = stage_c["samples"]
             n = [t[0], d]
             c.append(n)
-        return (c, )
+        return io.NodeOutput(c)
 
-class StableCascade_SuperResolutionControlnet:
-    def __init__(self, device="cpu"):
-        self.device = device
+
+class StableCascade_SuperResolutionControlnet(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="StableCascade_SuperResolutionControlnet",
+            category="_for_testing/stable_cascade",
+            is_experimental=True,
+            inputs=[
+                io.Image.Input("image"),
+                io.Vae.Input("vae"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="controlnet_input"),
+                io.Latent.Output(display_name="stage_c"),
+                io.Latent.Output(display_name="stage_b"),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "image": ("IMAGE",),
-            "vae": ("VAE", ),
-        }}
-    RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
-    RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
-    FUNCTION = "generate"
-
-    EXPERIMENTAL = True
-    CATEGORY = "_for_testing/stable_cascade"
-
-    def generate(self, image, vae):
+    def execute(cls, image, vae):
         width = image.shape[-2]
         height = image.shape[-3]
         batch_size = image.shape[0]
@@ -127,15 +141,22 @@ class StableCascade_SuperResolutionControlnet:
 
         c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
         b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
-        return (controlnet_input, {
+        return io.NodeOutput(controlnet_input, {
             "samples": c_latent,
         }, {
             "samples": b_latent,
         })
 
-NODE_CLASS_MAPPINGS = {
-    "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage,
-    "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning,
-    "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode,
-    "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet,
-}
+
+class StableCascadeExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            StableCascade_EmptyLatentImage,
+            StableCascade_StageB_Conditioning,
+            StableCascade_StageC_VAEEncode,
+            StableCascade_SuperResolutionControlnet,
+        ]
+
+async def comfy_entrypoint() -> StableCascadeExtension:
+    return StableCascadeExtension()

comfy_extras/nodes_video.py

@@ -5,52 +5,49 @@ import av
 import torch
 import folder_paths
 import json
-from typing import Optional, Literal
+from typing import Optional
+from typing_extensions import override
 from fractions import Fraction
-from comfy.comfy_types import IO, FileLocator, ComfyNodeABC
-from comfy_api.latest import Input, InputImpl, Types
+from comfy_api.input import AudioInput, ImageInput, VideoInput
+from comfy_api.input_impl import VideoFromComponents, VideoFromFile
+from comfy_api.util import VideoCodec, VideoComponents, VideoContainer
+from comfy_api.latest import ComfyExtension, io, ui
 from comfy.cli_args import args
 
-class SaveWEBM:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type = "output"
-        self.prefix_append = ""
+class SaveWEBM(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveWEBM",
+            category="image/video",
+            is_experimental=True,
+            inputs=[
+                io.Image.Input("images"),
+                io.String.Input("filename_prefix", default="ComfyUI"),
+                io.Combo.Input("codec", options=["vp9", "av1"]),
+                io.Float.Input("fps", default=24.0, min=0.01, max=1000.0, step=0.01),
+                io.Float.Input("crf", default=32.0, min=0, max=63.0, step=1, tooltip="Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."),
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"images": ("IMAGE", ),
-                     "filename_prefix": ("STRING", {"default": "ComfyUI"}),
-                     "codec": (["vp9", "av1"],),
-                     "fps": ("FLOAT", {"default": 24.0, "min": 0.01, "max": 1000.0, "step": 0.01}),
-                     "crf": ("FLOAT", {"default": 32.0, "min": 0, "max": 63.0, "step": 1, "tooltip": "Higher crf means lower quality with a smaller file size, lower crf means higher quality higher filesize."}),
-                     },
-                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
-                }
-
-    RETURN_TYPES = ()
-    FUNCTION = "save_images"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "image/video"
-
-    EXPERIMENTAL = True
-
-    def save_images(self, images, codec, fps, filename_prefix, crf, prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
+    def execute(cls, images, codec, fps, filename_prefix, crf) -> io.NodeOutput:
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
+            filename_prefix, folder_paths.get_output_directory(), images[0].shape[1], images[0].shape[0]
+        )
 
         file = f"{filename}_{counter:05}_.webm"
         container = av.open(os.path.join(full_output_folder, file), mode="w")
 
-        if prompt is not None:
-            container.metadata["prompt"] = json.dumps(prompt)
+        if cls.hidden.prompt is not None:
+            container.metadata["prompt"] = json.dumps(cls.hidden.prompt)
 
-        if extra_pnginfo is not None:
-            for x in extra_pnginfo:
-                container.metadata[x] = json.dumps(extra_pnginfo[x])
+        if cls.hidden.extra_pnginfo is not None:
+            for x in cls.hidden.extra_pnginfo:
+                container.metadata[x] = json.dumps(cls.hidden.extra_pnginfo[x])
 
         codec_map = {"vp9": "libvpx-vp9", "av1": "libsvtav1"}
         stream = container.add_stream(codec_map[codec], rate=Fraction(round(fps * 1000), 1000))
@@ -69,63 +66,46 @@ class SaveWEBM:
         container.mux(stream.encode())
         container.close()
 
-        results: list[FileLocator] = [{
-            "filename": file,
-            "subfolder": subfolder,
-            "type": self.type
-        }]
+        return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)]))
 
-        return {"ui": {"images": results, "animated": (True,)}}  # TODO: frontend side
-
-class SaveVideo(ComfyNodeABC):
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
-        self.type: Literal["output"] = "output"
-        self.prefix_append = ""
+class SaveVideo(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SaveVideo",
+            display_name="Save Video",
+            category="image/video",
+            description="Saves the input images to your ComfyUI output directory.",
+            inputs=[
+                io.Video.Input("video", tooltip="The video to save."),
+                io.String.Input("filename_prefix", default="video/ComfyUI", tooltip="The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."),
+                io.Combo.Input("format", options=VideoContainer.as_input(), default="auto", tooltip="The format to save the video as."),
+                io.Combo.Input("codec", options=VideoCodec.as_input(), default="auto", tooltip="The codec to use for the video."),
+            ],
+            outputs=[],
+            hidden=[io.Hidden.prompt, io.Hidden.extra_pnginfo],
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "video": (IO.VIDEO, {"tooltip": "The video to save."}),
-                "filename_prefix": ("STRING", {"default": "video/ComfyUI", "tooltip": "The prefix for the file to save. This may include formatting information such as %date:yyyy-MM-dd% or %Empty Latent Image.width% to include values from nodes."}),
-                "format": (Types.VideoContainer.as_input(), {"default": "auto", "tooltip": "The format to save the video as."}),
-                "codec": (Types.VideoCodec.as_input(), {"default": "auto", "tooltip": "The codec to use for the video."}),
-            },
-            "hidden": {
-                "prompt": "PROMPT",
-                "extra_pnginfo": "EXTRA_PNGINFO"
-            },
-        }
-
-    RETURN_TYPES = ()
-    FUNCTION = "save_video"
-
-    OUTPUT_NODE = True
-
-    CATEGORY = "image/video"
-    DESCRIPTION = "Saves the input images to your ComfyUI output directory."
-
-    def save_video(self, video: Input.Video, filename_prefix, format, codec, prompt=None, extra_pnginfo=None):
-        filename_prefix += self.prefix_append
+    def execute(cls, video: VideoInput, filename_prefix, format, codec) -> io.NodeOutput:
         width, height = video.get_dimensions()
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(
             filename_prefix,
-            self.output_dir,
+            folder_paths.get_output_directory(),
             width,
             height
         )
-        results: list[FileLocator] = list()
         saved_metadata = None
         if not args.disable_metadata:
             metadata = {}
-            if extra_pnginfo is not None:
-                metadata.update(extra_pnginfo)
-            if prompt is not None:
-                metadata["prompt"] = prompt
+            if cls.hidden.extra_pnginfo is not None:
+                metadata.update(cls.hidden.extra_pnginfo)
+            if cls.hidden.prompt is not None:
+                metadata["prompt"] = cls.hidden.prompt
             if len(metadata) > 0:
                 saved_metadata = metadata
-        file = f"{filename}_{counter:05}_.{Types.VideoContainer.get_extension(format)}"
+        file = f"{filename}_{counter:05}_.{VideoContainer.get_extension(format)}"
         video.save_to(
             os.path.join(full_output_folder, file),
             format=format,
@@ -133,83 +113,82 @@ class SaveVideo(ComfyNodeABC):
             metadata=saved_metadata
         )
 
-        results.append({
-            "filename": file,
-            "subfolder": subfolder,
-            "type": self.type
-        })
-        counter += 1
+        return io.NodeOutput(ui=ui.PreviewVideo([ui.SavedResult(file, subfolder, io.FolderType.output)]))
 
-        return { "ui": { "images": results, "animated": (True,) } }
 
-class CreateVideo(ComfyNodeABC):
+class CreateVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "images": (IO.IMAGE, {"tooltip": "The images to create a video from."}),
-                "fps": ("FLOAT", {"default": 30.0, "min": 1.0, "max": 120.0, "step": 1.0}),
-            },
-            "optional": {
-                "audio": (IO.AUDIO, {"tooltip": "The audio to add to the video."}),
-            }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CreateVideo",
+            display_name="Create Video",
+            category="image/video",
+            description="Create a video from images.",
+            inputs=[
+                io.Image.Input("images", tooltip="The images to create a video from."),
+                io.Float.Input("fps", default=30.0, min=1.0, max=120.0, step=1.0),
+                io.Audio.Input("audio", optional=True, tooltip="The audio to add to the video."),
+            ],
+            outputs=[
+                io.Video.Output(),
+            ],
+        )
 
-    RETURN_TYPES = (IO.VIDEO,)
-    FUNCTION = "create_video"
-
-    CATEGORY = "image/video"
-    DESCRIPTION = "Create a video from images."
-
-    def create_video(self, images: Input.Image, fps: float, audio: Optional[Input.Audio] = None):
-        return (InputImpl.VideoFromComponents(
-            Types.VideoComponents(
-            images=images,
-            audio=audio,
-            frame_rate=Fraction(fps),
-            )
-        ),)
-
-class GetVideoComponents(ComfyNodeABC):
     @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "video": (IO.VIDEO, {"tooltip": "The video to extract components from."}),
-            }
-        }
-    RETURN_TYPES = (IO.IMAGE, IO.AUDIO, IO.FLOAT)
-    RETURN_NAMES = ("images", "audio", "fps")
-    FUNCTION = "get_components"
+    def execute(cls, images: ImageInput, fps: float, audio: Optional[AudioInput] = None) -> io.NodeOutput:
+        return io.NodeOutput(
+            VideoFromComponents(VideoComponents(images=images, audio=audio, frame_rate=Fraction(fps)))
+        )
 
-    CATEGORY = "image/video"
-    DESCRIPTION = "Extracts all components from a video: frames, audio, and framerate."
+class GetVideoComponents(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GetVideoComponents",
+            display_name="Get Video Components",
+            category="image/video",
+            description="Extracts all components from a video: frames, audio, and framerate.",
+            inputs=[
+                io.Video.Input("video", tooltip="The video to extract components from."),
+            ],
+            outputs=[
+                io.Image.Output(display_name="images"),
+                io.Audio.Output(display_name="audio"),
+                io.Float.Output(display_name="fps"),
+            ],
+        )
 
-    def get_components(self, video: Input.Video):
+    @classmethod
+    def execute(cls, video: VideoInput) -> io.NodeOutput:
         components = video.get_components()
 
-        return (components.images, components.audio, float(components.frame_rate))
+        return io.NodeOutput(components.images, components.audio, float(components.frame_rate))
 
-class LoadVideo(ComfyNodeABC):
+class LoadVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls):
+    def define_schema(cls):
         input_dir = folder_paths.get_input_directory()
         files = [f for f in os.listdir(input_dir) if os.path.isfile(os.path.join(input_dir, f))]
         files = folder_paths.filter_files_content_types(files, ["video"])
-        return {"required":
-                    {"file": (sorted(files), {"video_upload": True})},
-                }
-
-    CATEGORY = "image/video"
-
-    RETURN_TYPES = (IO.VIDEO,)
-    FUNCTION = "load_video"
-    def load_video(self, file):
-        video_path = folder_paths.get_annotated_filepath(file)
-        return (InputImpl.VideoFromFile(video_path),)
+        return io.Schema(
+            node_id="LoadVideo",
+            display_name="Load Video",
+            category="image/video",
+            inputs=[
+                io.Combo.Input("file", options=sorted(files), upload=io.UploadType.video),
+            ],
+            outputs=[
+                io.Video.Output(),
+            ],
+        )
 
     @classmethod
-    def IS_CHANGED(cls, file):
+    def execute(cls, file) -> io.NodeOutput:
+        video_path = folder_paths.get_annotated_filepath(file)
+        return io.NodeOutput(VideoFromFile(video_path))
+
+    @classmethod
+    def fingerprint_inputs(s, file):
         video_path = folder_paths.get_annotated_filepath(file)
         mod_time = os.path.getmtime(video_path)
         # Instead of hashing the file, we can just use the modification time to avoid
@@ -217,24 +196,23 @@ class LoadVideo(ComfyNodeABC):
         return mod_time
 
     @classmethod
-    def VALIDATE_INPUTS(cls, file):
+    def validate_inputs(s, file):
         if not folder_paths.exists_annotated_filepath(file):
             return "Invalid video file: {}".format(file)
 
         return True
 
-NODE_CLASS_MAPPINGS = {
-    "SaveWEBM": SaveWEBM,
-    "SaveVideo": SaveVideo,
-    "CreateVideo": CreateVideo,
-    "GetVideoComponents": GetVideoComponents,
-    "LoadVideo": LoadVideo,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "SaveVideo": "Save Video",
-    "CreateVideo": "Create Video",
-    "GetVideoComponents": "Get Video Components",
-    "LoadVideo": "Load Video",
-}
+class VideoExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SaveWEBM,
+            SaveVideo,
+            CreateVideo,
+            GetVideoComponents,
+            LoadVideo,
+        ]
 
+async def comfy_entrypoint() -> VideoExtension:
+    return VideoExtension()

comfyui_version.py

@@ -1,3 +1,3 @@
 # This file is automatically generated by the build process when version is
 # updated in pyproject.toml.
-__version__ = "0.3.56"
+__version__ = "0.3.57"

main.py

@@ -113,7 +113,6 @@ import gc
 
 if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
-    logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
 if __name__ == "__main__":
     if args.default_device is not None:

nodes.py

@@ -2325,8 +2325,6 @@ async def init_builtin_extra_nodes():
         "nodes_model_patch.py",
         "nodes_easycache.py",
         "nodes_audio_encoder.py",
-        "nodes_sortblock.py",
-        "nodes_easysortblock.py",
     ]
 
     import_failed = []
@@ -2346,6 +2344,7 @@ async def init_builtin_api_nodes():
         "nodes_veo2.py",
         "nodes_kling.py",
         "nodes_bfl.py",
+        "nodes_bytedance.py",
         "nodes_luma.py",
         "nodes_recraft.py",
         "nodes_pixverse.py",

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "ComfyUI"
-version = "0.3.56"
+version = "0.3.57"
 readme = "README.md"
 license = { file = "LICENSE" }
 requires-python = ">=3.9"

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.25.11
-comfyui-workflow-templates==0.1.70
+comfyui-workflow-templates==0.1.75
 comfyui-embedded-docs==0.2.6
 torch
 torchsde

script_examples/basic_api_example.py

@@ -3,11 +3,7 @@ from urllib import request
 
 #This is the ComfyUI api prompt format.
 
-#If you want it for a specific workflow you can "enable dev mode options"
-#in the settings of the UI (gear beside the "Queue Size: ") this will enable
-#a button on the UI to save workflows in api format.
-
-#keep in mind ComfyUI is pre alpha software so this format will change a bit.
+#If you want it for a specific workflow you can "File -> Export (API)" in the interface.
 
 #this is the one for the default workflow
 prompt_text = """

server.py

@@ -729,7 +729,34 @@ class PromptServer():
 
         @routes.post("/interrupt")
         async def post_interrupt(request):
-            nodes.interrupt_processing()
+            try:
+                json_data = await request.json()
+            except json.JSONDecodeError:
+                json_data = {}
+
+            # Check if a specific prompt_id was provided for targeted interruption
+            prompt_id = json_data.get('prompt_id')
+            if prompt_id:
+                currently_running, _ = self.prompt_queue.get_current_queue()
+
+                # Check if the prompt_id matches any currently running prompt
+                should_interrupt = False
+                for item in currently_running:
+                    # item structure: (number, prompt_id, prompt, extra_data, outputs_to_execute)
+                    if item[1] == prompt_id:
+                        logging.info(f"Interrupting prompt {prompt_id}")
+                        should_interrupt = True
+                        break
+
+                if should_interrupt:
+                    nodes.interrupt_processing()
+                else:
+                    logging.info(f"Prompt {prompt_id} is not currently running, skipping interrupt")
+            else:
+                # No prompt_id provided, do a global interrupt
+                logging.info("Global interrupt (no prompt_id specified)")
+                nodes.interrupt_processing()
+
             return web.Response(status=200)
 
         @routes.post("/free")