import torch from .base.control_base import ControlNodeBase import comfy.model_management import comfy.samplers import random class StreamCFG(ControlNodeBase): """Implements CFG approaches for temporal consistency between workflow runs""" RETURN_TYPES = ("MODEL",) FUNCTION = "update" CATEGORY = "real-time/sampling" @classmethod def INPUT_TYPES(cls): inputs = super().INPUT_TYPES() inputs["required"].update({ "model": ("MODEL",), "cfg_type": (["self", "full", "initialize"], { "default": "self", "tooltip": "Type of CFG to use: full (standard), self (memory efficient), or initialize (memory efficient with initialization)" }), "residual_scale": ("FLOAT", { "default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Scale factor for residual (higher = more temporal consistency)" }), "delta": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1, "tooltip": "Delta parameter for self/initialize CFG types" }), }) return inputs def __init__(self): super().__init__() # Store the last model to detect when we need to reapply the hook self.last_model_hash = None self.post_cfg_function = None def update(self, model, always_execute=True, cfg_type="self", residual_scale=0.4, delta=1.0): print(f"[StreamCFG] Initializing with cfg_type={cfg_type}, residual_scale={residual_scale}, delta={delta}") # Get state with defaults state = self.get_state({ "last_uncond": None, # Store last workflow's unconditioned prediction "initialized": False, "cfg_type": cfg_type, "residual_scale": residual_scale, "delta": delta, "workflow_count": 0, # Track number of workflow runs "current_sigmas": None, # Track sigmas for this workflow "seen_sigmas": set(), # Track which sigmas we've seen this workflow "is_last_step": False, # Track if we're on the last step "alpha_prod_t": None, # Store alpha values for proper scaling "beta_prod_t": None, # Store beta values for proper scaling }) def post_cfg_function(args): # Extract info denoised = args["denoised"] cond = args["cond"] uncond = args["uncond"] cond_denoised = args["cond_denoised"] uncond_denoised = args["uncond_denoised"] cond_scale = args["cond_scale"] # Handle both batched and single sigmas sigma = args["sigma"] if torch.is_tensor(sigma): # For batched sampling, use first sigma in batch # This is safe because we process in order and track seen sigmas sigma = sigma[0].item() if len(sigma.shape) > 0 else sigma.item() # Get step info from model options model_options = args["model_options"] sample_sigmas = model_options["transformer_options"].get("sample_sigmas", None) # Update current sigmas if needed if sample_sigmas is not None and state["current_sigmas"] is None: # Filter out the trailing 0.0 if present sigmas = [s.item() for s in sample_sigmas] if sigmas[-1] == 0.0: sigmas = sigmas[:-1] state["current_sigmas"] = sigmas state["seen_sigmas"] = set() # Calculate alpha and beta values for proper scaling alphas = [1.0 / (1.0 + s**2) for s in sigmas] state["alpha_prod_t"] = torch.tensor(alphas, device=denoised.device, dtype=denoised.dtype) state["beta_prod_t"] = torch.sqrt(1 - state["alpha_prod_t"]) # Track this sigma state["seen_sigmas"].add(sigma) # Check if this is the last step state["is_last_step"] = False if state["current_sigmas"] is not None: # It's the last step if we've seen all sigmas is_last_step = len(state["seen_sigmas"]) >= len(state["current_sigmas"]) # Or if this is the smallest sigma in the sequence if not is_last_step and sigma == min(state["current_sigmas"]): is_last_step = True state["is_last_step"] = is_last_step # First workflow case if state["last_uncond"] is None: if state["is_last_step"]: state["last_uncond"] = uncond_denoised.detach().clone() state["workflow_count"] += 1 state["current_sigmas"] = None # Reset for next workflow if cfg_type == "initialize": state["initialized"] = True self.set_state(state) return denoised # Handle different CFG types for subsequent workflows if cfg_type == "full": result = denoised elif cfg_type == "initialize" and not state["initialized"]: result = denoised if state["is_last_step"]: state["initialized"] = True self.set_state(state) else: # self or initialized initialize # Get current step index current_idx = len(state["seen_sigmas"]) - 1 # Scale last prediction with proper alpha/beta values noise_pred_uncond = state["last_uncond"] * delta # Apply CFG with scaled prediction result = noise_pred_uncond + cond_scale * (cond_denoised - noise_pred_uncond) # Store last prediction if this is the last step if state["is_last_step"]: # Calculate properly scaled residual scaled_noise = state["beta_prod_t"][current_idx] * state["last_uncond"] delta_x = uncond_denoised - scaled_noise # Scale delta_x with next step's alpha/beta if current_idx < len(state["current_sigmas"]) - 1: alpha_next = state["alpha_prod_t"][current_idx + 1] beta_next = state["beta_prod_t"][current_idx + 1] else: alpha_next = torch.ones_like(state["alpha_prod_t"][0]) beta_next = torch.ones_like(state["beta_prod_t"][0]) delta_x = alpha_next * delta_x / beta_next # Update stored prediction with scaled residual final_update = uncond_denoised + residual_scale * delta_x state["last_uncond"] = final_update state["workflow_count"] += 1 state["current_sigmas"] = None # Reset for next workflow self.set_state(state) return result # Store function reference to prevent garbage collection self.post_cfg_function = post_cfg_function # Only set up post CFG function if model has changed model_hash = hash(str(model)) if model_hash != self.last_model_hash: m = model.clone() m.model_options = m.model_options.copy() m.model_options["sampler_post_cfg_function"] = [self.post_cfg_function] self.last_model_hash = model_hash return (m,) # Make sure our function is still in the list if not any(f is self.post_cfg_function for f in model.model_options.get("sampler_post_cfg_function", [])): m = model.clone() m.model_options = m.model_options.copy() m.model_options["sampler_post_cfg_function"] = [self.post_cfg_function] return (m,) return (model,) class StreamConditioning(ControlNodeBase): """Applies Residual CFG to conditioning for improved temporal consistency with different CFG types""" #NOTE: experimental @classmethod def INPUT_TYPES(s): return { "required": { "positive": ("CONDITIONING",), "negative": ("CONDITIONING",), "cfg_type": (["full", "self", "initialize"], { "default": "full", "tooltip": "Type of CFG to use: full (standard), self (memory efficient), or initialize (memory efficient with initialization)" }), "residual_scale": ("FLOAT", { "default": 0.4, "min": 0.0, "max": 1.0, "step": 0.01, "tooltip": "Scale factor for residual conditioning (higher = more temporal consistency)" }), "delta": ("FLOAT", { "default": 1.0, "min": 0.0, "max": 5.0, "step": 0.1, "tooltip": "Delta parameter for self/initialize CFG types" }), "always_execute": ("BOOLEAN", { "default": False, }), } } RETURN_TYPES = ("CONDITIONING", "CONDITIONING") RETURN_NAMES = ("positive", "negative") FUNCTION = "update" CATEGORY = "real-time/control/utility" def __init__(self): super().__init__() def update(self, positive, negative, cfg_type="full", residual_scale=0.4, delta=1.0, always_execute=False): # Get state with defaults state = self.get_state({ "prev_positive": None, "prev_negative": None, "stock_noise": None, # For self/initialize CFG "initialized": False # For initialize CFG }) # Extract conditioning tensors current_pos_cond = positive[0][0] # Assuming standard ComfyUI conditioning format current_neg_cond = negative[0][0] # First frame case if state["prev_positive"] is None: state["prev_positive"] = current_pos_cond.detach().clone() state["prev_negative"] = current_neg_cond.detach().clone() if cfg_type == "initialize": # For initialize, we use the first negative as our stock noise state["stock_noise"] = current_neg_cond.detach().clone() elif cfg_type == "self": # For self, we start with a scaled version of the negative state["stock_noise"] = current_neg_cond.detach().clone() * delta self.set_state(state) return (positive, negative) # Handle different CFG types if cfg_type == "full": # Standard R-CFG with full negative conditioning pos_residual = current_pos_cond - state["prev_positive"] neg_residual = current_neg_cond - state["prev_negative"] blended_pos = current_pos_cond + residual_scale * pos_residual blended_neg = current_neg_cond + residual_scale * neg_residual # Update state state["prev_positive"] = current_pos_cond.detach().clone() state["prev_negative"] = current_neg_cond.detach().clone() # Reconstruct conditioning format positive_out = [[blended_pos, positive[0][1]]] negative_out = [[blended_neg, negative[0][1]]] else: # self or initialize # Calculate residual for positive conditioning pos_residual = current_pos_cond - state["prev_positive"] blended_pos = current_pos_cond + residual_scale * pos_residual # Update stock noise based on current prediction if cfg_type == "initialize" and not state["initialized"]: # First prediction for initialize type state["stock_noise"] = current_neg_cond.detach().clone() state["initialized"] = True else: # Update stock noise with temporal consistency stock_residual = current_neg_cond - state["stock_noise"] state["stock_noise"] = current_neg_cond + residual_scale * stock_residual # Scale stock noise by delta scaled_stock = state["stock_noise"] * delta # Update state state["prev_positive"] = current_pos_cond.detach().clone() state["prev_negative"] = scaled_stock.detach().clone() # Reconstruct conditioning format positive_out = [[blended_pos, positive[0][1]]] negative_out = [[scaled_stock, negative[0][1]]] self.set_state(state) return (positive_out, negative_out) class StreamBatchSampler(ControlNodeBase): """Implements batched denoising for faster inference by processing multiple steps in parallel""" RETURN_TYPES = ("SAMPLER",) FUNCTION = "update" CATEGORY = "real-time/sampling" @classmethod def INPUT_TYPES(cls): inputs = super().INPUT_TYPES() inputs["required"].update({ "batch_size": ("INT", { "default": 2, "min": 1, "max": 10, "step": 1, "tooltip": "Number of steps to batch together. Higher values use more memory but are faster." }), }) return inputs def __init__(self): super().__init__() self.batch_size = None def sample(self, model, noise, sigmas, extra_args=None, callback=None, disable=None): """Sample with batched denoising steps""" extra_args = {} if extra_args is None else extra_args print(f"[StreamBatchSampler] Starting sampling with {len(sigmas)-1} steps, batch_size={self.batch_size}") print(f"[StreamBatchSampler] Input noise shape: {noise.shape}, device: {noise.device}") print(f"[StreamBatchSampler] Sigmas: {sigmas.tolist()}") # Prepare batched sampling num_sigmas = len(sigmas) - 1 num_batches = (num_sigmas + self.batch_size - 1) // self.batch_size x = noise for batch_idx in range(num_batches): # Get sigmas for this batch start_idx = batch_idx * self.batch_size end_idx = min(start_idx + self.batch_size, num_sigmas) batch_sigmas = sigmas[start_idx:end_idx+1] print(f"\n[StreamBatchSampler] Batch {batch_idx+1}/{num_batches}") print(f"[StreamBatchSampler] Processing steps {start_idx}-{end_idx}") print(f"[StreamBatchSampler] Batch sigmas: {batch_sigmas.tolist()}") # Create batch of identical latents batch_size = end_idx - start_idx x_batch = x.repeat(batch_size, 1, 1, 1) # Create batch of sigmas sigma_batch = batch_sigmas[:-1] # All but last sigma # Run model on entire batch at once with torch.no_grad(): # Process all steps in parallel denoised_batch = model(x_batch, sigma_batch, **extra_args) print(f"[StreamBatchSampler] Denoised batch shape: {denoised_batch.shape}") # Process results one at a time to maintain callback for i in range(batch_size): sigma = sigma_batch[i] sigma_next = batch_sigmas[i + 1] denoised = denoised_batch[i:i+1] # Calculate step size (now always positive as we go from high to low sigma) dt = sigma - sigma_next # Update x using Euler method # The (denoised - x) term gives us the direction to move # dt/sigma scales how far we move based on current noise level x = x + (denoised - x) * (dt / sigma) print(f"[StreamBatchSampler] Step {start_idx+i}: sigma={sigma:.4f}, next_sigma={sigma_next:.4f}, dt={dt:.4f}") # Call callback if provided if callback is not None: callback({'x': x, 'i': start_idx + i, 'sigma': sigma, 'sigma_hat': sigma, 'denoised': denoised}) print(f"\n[StreamBatchSampler] Sampling complete. Final x shape: {x.shape}") return x def update(self, batch_size=2, always_execute=True): """Create sampler with specified settings""" self.batch_size = batch_size sampler = comfy.samplers.KSAMPLER(self.sample) return (sampler,) class StreamScheduler(ControlNodeBase): """Implements StreamDiffusion's efficient timestep selection""" RETURN_TYPES = ("SIGMAS",) FUNCTION = "update" CATEGORY = "real-time/sampling" @classmethod def INPUT_TYPES(cls): inputs = super().INPUT_TYPES() inputs["required"].update({ "model": ("MODEL",), "t_index_list": ("STRING", { "default": "32,45", "tooltip": "Comma-separated list of timesteps to actually use for denoising. Examples: '32,45' for img2img or '0,16,32,45' for txt2img" }), "num_inference_steps": ("INT", { "default": 50, "min": 1, "max": 1000, "step": 1, "tooltip": "Total number of timesteps in schedule. StreamDiffusion uses 50 by default. Only timesteps specified in t_index_list are actually used." }), }) return inputs def update(self, model, t_index_list="32,45", num_inference_steps=50, always_execute=True): # Get model's sampling parameters model_sampling = model.get_model_object("model_sampling") print(f"[StreamScheduler] Model sampling max sigma: {model_sampling.sigma_max}, min sigma: {model_sampling.sigma_min}") # Parse timestep list try: t_index_list = [int(t.strip()) for t in t_index_list.split(",")] except ValueError as e: print(f"Error parsing timesteps: {e}. Using default [32,45]") t_index_list = [32, 45] print(f"[StreamScheduler] Using timesteps: {t_index_list}") # Create full schedule using normal scheduler full_sigmas = comfy.samplers.normal_scheduler(model_sampling, num_inference_steps) print(f"[StreamScheduler] Full sigma schedule: {full_sigmas.tolist()}") # Select only the sigmas at our desired indices, but in reverse order # This ensures we go from high noise to low noise selected_sigmas = [] for t in sorted(t_index_list, reverse=True): # Sort in reverse to go from high noise to low if t < 0 or t >= num_inference_steps: print(f"Warning: timestep {t} out of range [0,{num_inference_steps}), skipping") continue selected_sigmas.append(float(full_sigmas[t])) print(f"[StreamScheduler] Selected sigmas: {selected_sigmas}") # Add final sigma selected_sigmas.append(0.0) print(f"[StreamScheduler] Final sigma schedule: {selected_sigmas}") # Convert to tensor and move to appropriate device selected_sigmas = torch.FloatTensor(selected_sigmas).to(comfy.model_management.get_torch_device()) return (selected_sigmas,) class StreamCrossAttention(ControlNodeBase): """Implements optimized cross attention with KV-cache for real-time generation""" RETURN_TYPES = ("MODEL",) FUNCTION = "update" CATEGORY = "real-time/sampling" @classmethod def INPUT_TYPES(cls): inputs = super().INPUT_TYPES() inputs["required"].update({ "model": ("MODEL",), "qk_norm": ("BOOLEAN", { "default": True, "tooltip": "Whether to apply layer normalization to query and key tensors" }), "use_rope": ("BOOLEAN", { "default": True, "tooltip": "Whether to use rotary position embeddings for better temporal consistency" }), "context_size": ("INT", { "default": 4, "min": 1, "max": 32, "step": 1, "tooltip": "Maximum number of past frames to keep in context. Higher values use more memory but may improve temporal consistency." }), "use_kv_cache": ("BOOLEAN", { "default": True, "tooltip": "Whether to cache key-value pairs for static prompts to avoid recomputation" }), }) return inputs def __init__(self): super().__init__() self.last_model_hash = None self.cross_attention_hook = None def update(self, model, always_execute=True, qk_norm=True, use_rope=True, context_size=4, use_kv_cache=True): print(f"[StreamCrossAttention] Initializing with qk_norm={qk_norm}, use_rope={use_rope}, context_size={context_size}, use_kv_cache={use_kv_cache}") # Get state with defaults state = self.get_state({ "qk_norm": qk_norm, "use_rope": use_rope, "context_size": context_size, "use_kv_cache": use_kv_cache, "workflow_count": 0, "context_queue": [], # Store past context tensors "kv_cache": {}, # Store cached key-value pairs for each prompt "last_prompt_embeds": None, # Store last prompt embeddings for cache comparison }) def cross_attention_forward(module, x, context=None, mask=None, value=None): q = module.to_q(x) context = x if context is None else context # Check if we can use cached KV pairs cache_hit = False if state["use_kv_cache"] and state["last_prompt_embeds"] is not None: # Compare current context with cached prompt embeddings if torch.allclose(context, state["last_prompt_embeds"], rtol=1e-5, atol=1e-5): cache_hit = True k, v = state["kv_cache"].get(module, (None, None)) if k is not None and v is not None: print("[StreamCrossAttention] Using cached KV pairs") if not cache_hit: # Update context queue for temporal attention if len(state["context_queue"]) >= state["context_size"]: state["context_queue"].pop(0) state["context_queue"].append(context.detach().clone()) # Concatenate current context with past contexts full_context = torch.cat(state["context_queue"], dim=1) # Generate k/v for full context k = module.to_k(full_context) v = value if value is not None else module.to_v(full_context) # Cache KV pairs if this is a prompt context if state["use_kv_cache"]: state["last_prompt_embeds"] = context.detach().clone() state["kv_cache"][module] = (k.detach().clone(), v.detach().clone()) # Apply QK normalization if enabled if state["qk_norm"]: q_norm = torch.nn.LayerNorm(q.shape[-1], device=q.device, dtype=q.dtype) k_norm = torch.nn.LayerNorm(k.shape[-1], device=k.device, dtype=k.dtype) q = q_norm(q) k = k_norm(k) # Apply rotary embeddings if enabled if state["use_rope"]: # Calculate position embeddings batch_size = q.shape[0] seq_len = q.shape[1] full_seq_len = k.shape[1] # Use full context length for k/v dim = q.shape[2] # Create position indices for q and k separately q_position = torch.arange(seq_len, device=q.device).unsqueeze(0).unsqueeze(-1) k_position = torch.arange(full_seq_len, device=k.device).unsqueeze(0).unsqueeze(-1) q_position = q_position.repeat(batch_size, 1, dim//2) k_position = k_position.repeat(batch_size, 1, dim//2) # Calculate frequencies freq = 10000.0 ** (-torch.arange(0, dim//2, 2, device=q.device) / dim) freq = freq.repeat((dim + 1) // 2)[:dim//2] # Calculate rotation angles q_theta = q_position * freq k_theta = k_position * freq # Apply rotations to q q_cos = torch.cos(q_theta) q_sin = torch.sin(q_theta) q_reshaped = q.view(*q.shape[:-1], -1, 2) q_out = torch.cat([ q_reshaped[..., 0] * q_cos - q_reshaped[..., 1] * q_sin, q_reshaped[..., 0] * q_sin + q_reshaped[..., 1] * q_cos ], dim=-1) # Apply rotations to k k_cos = torch.cos(k_theta) k_sin = torch.sin(k_theta) k_reshaped = k.view(*k.shape[:-1], -1, 2) k_out = torch.cat([ k_reshaped[..., 0] * k_cos - k_reshaped[..., 1] * k_sin, k_reshaped[..., 0] * k_sin + k_reshaped[..., 1] * k_cos ], dim=-1) q = q_out k = k_out # Compute attention with optimized memory access pattern batch_size = q.shape[0] q_seq_len = q.shape[1] k_seq_len = k.shape[1] head_dim = q.shape[-1] // module.heads # Reshape for multi-head attention q = q.view(batch_size, q_seq_len, module.heads, head_dim) k = k.view(batch_size, k_seq_len, module.heads, head_dim) v = v.view(batch_size, k_seq_len, module.heads, head_dim) # Transpose for attention computation q = q.transpose(1, 2) k = k.transpose(1, 2) v = v.transpose(1, 2) # Compute attention scores scale = head_dim ** -0.5 scores = torch.matmul(q, k.transpose(-2, -1)) * scale if mask is not None: scores = scores + mask # Apply attention attn = torch.softmax(scores, dim=-1) out = torch.matmul(attn, v) # Reshape back out = out.transpose(1, 2).contiguous() out = out.view(batch_size, q_seq_len, -1) # Project back to original dimension out = module.to_out[0](out) return out def hook_cross_attention(module, input, output): if isinstance(module, torch.nn.Module) and hasattr(module, "to_q"): # Store original forward if not hasattr(module, "_original_forward"): module._original_forward = module.forward # Replace with our optimized version module.forward = lambda *args, **kwargs: cross_attention_forward(module, *args, **kwargs) return output # Only set up hooks if model has changed model_hash = hash(str(model)) if model_hash != self.last_model_hash: m = model.clone() # Remove old hooks if they exist if self.cross_attention_hook is not None: self.cross_attention_hook.remove() # Register hook for cross attention modules def register_hooks(module): if isinstance(module, torch.nn.Module) and hasattr(module, "to_q"): self.cross_attention_hook = module.register_forward_hook(hook_cross_attention) m.model.apply(register_hooks) self.last_model_hash = model_hash return (m,) return (model,)