diff --git a/LICENSE b/LICENSE index fccfbeb..b0d79e2 100644 --- a/LICENSE +++ b/LICENSE @@ -90,20 +90,20 @@ to be bound by this Agreement. subject to the terms and provisions of a different license (the "Commercial Use Agreement"), as will be provided by the Licensor. Commercial Entities interested in such a commercial license are - required to contact Licensor. Any commercial use of LTX-2 or - Derivatives of LTX-2 by the Commercial Entities not in accordance - with this Agreement and/or the Commercial Use Agreement is strictly - prohibited and shall be deemed a material breach of this Agreement. - Such material breach will be subject, in addition to any license - fees owed to Licensor for the period such Commercial Entity used - LTX-2 (as will be determined by Licensor), to liquidated damages, - which will be paid to Licensor immediately upon demand, in an - amount equal to double the amount that would otherwise have been - paid by you for the relevant period of time. Such amount reflects - a reasonable estimation of the losses and administrative costs - incurred due to such breach. You agree and understand that this - remedy does not limit the Licensor's right to pursue other remedies - available at law or equity. + required to [contact Licensor](https://ltx.io/model/licensing). + Any commercial use of LTX-2 or Derivatives of LTX-2 by the + Commercial Entities not in accordance with this Agreement and/or + the Commercial Use Agreement is strictly prohibited and shall be + deemed a material breach of this Agreement. Such material breach + will be subject, in addition to any license fees owed to Licensor + for the period such Commercial Entity used LTX-2 (as will be + determined by Licensor), to liquidated damages, which will be paid + to Licensor immediately upon demand, in an amount equal to double + the amount that would otherwise have been paid by you for the + relevant period of time. Such amount reflects a reasonable estimation + of the losses and administrative costs incurred due to such breach. + You agree and understand that this remedy does not limit the Licensor's + right to pursue other remedies available at law or equity. 3. Distribution and Redistribution. You may host for third parties remote access purposes (e.g. software-as-a-service), reproduce diff --git a/packages/ltx-core/README.md b/packages/ltx-core/README.md index 2933522..6464929 100644 --- a/packages/ltx-core/README.md +++ b/packages/ltx-core/README.md @@ -10,6 +10,7 @@ The foundational library for the LTX-2 Audio-Video generation model. This packag - **`loader/`**: Utilities for loading weights from `.safetensors`, fusing LoRAs, and managing memory - **`model/`**: PyTorch implementations of the LTX-2 Transformer, Video VAE, Audio VAE, Vocoder and Upscaler - **`text_encoders/gemma`**: Gemma text encoder implementation with tokenizers, feature extractors, and separate encoders for audio-video and video-only generation +- **`quantization/`**: FP8 quantization backends (FP8-TensorRT-LLM scaled MM, FP8 cast) for reduced memory footprint. ## 🚀 Quick Start @@ -53,6 +54,63 @@ pip install -e packages/ltx-core ### Utilities - **Loader** ([`loader/`](src/ltx_core/loader/)): Model loading from `.safetensors`, LoRA fusion, weight remapping, and memory management +- **Quantization** ([`quantization/`](src/ltx_core/quantization/)): FP8 quantization backends for reduced memory footprint and faster inference + +### Quantization + +The `quantization/` module provides FP8 quantization support for the LTX-2 transformer, significantly reducing memory usage while maintaining quality. Two backends are available: + +#### FP8 Scaled MM (TensorRT-LLM) + +Uses NVIDIA TensorRT-LLM's `cublas_scaled_mm` for efficient FP8 matrix multiplication. Weights are stored in FP8 format with per-tensor scaling, and inputs are quantized dynamically (or statically with calibration data). + +**Requirements**: `uv sync --frozen --extra fp8-trtllm` + +**Usage with QuantizationPolicy:** + +```python +from ltx_core.quantization import QuantizationPolicy + +# Dynamic input quantization (no calibration needed) +policy = QuantizationPolicy.fp8_scaled_mm() + +# Static input quantization with calibration file +policy = QuantizationPolicy.fp8_scaled_mm(calibration_amax_path="/path/to/amax.json") +``` + +The policy provides `sd_ops` and `module_ops` that can be passed to the model builder: + +```python +from ltx_core.loader import SingleGPUModelBuilder + +builder = SingleGPUModelBuilder( + model=model, + device=device, + sd_ops=policy.sd_ops, + module_ops=policy.module_ops, +) +builder.load(checkpoint_path) +``` + +**Calibration File Format** (for static input quantization): + +```json +{ + "amax_values": { + "transformer_blocks.0.attn.to_q.input_quantizer": 12.5, + "transformer_blocks.0.attn.to_k.input_quantizer": 8.3, + ... + } +} +``` + +#### FP8 Cast + +A simpler approach that casts weights to FP8 for storage and upcasts during inference: + +```python +policy = QuantizationPolicy.fp8_cast() +``` For complete, production-ready pipeline implementations that combine these building blocks, see the [`ltx-pipelines`](../ltx-pipelines/) package. diff --git a/packages/ltx-core/pyproject.toml b/packages/ltx-core/pyproject.toml index 94eb744..f8d82df 100644 --- a/packages/ltx-core/pyproject.toml +++ b/packages/ltx-core/pyproject.toml @@ -9,7 +9,7 @@ dependencies = [ "torchaudio", "einops", "numpy", - "transformers~=4.57.0", + "transformers>=4.52", "safetensors", "accelerate", "scipy>=1.14", @@ -17,16 +17,34 @@ dependencies = [ [project.optional-dependencies] xformers = ["xformers"] +fp8-trtllm = [ + "tensorrt-llm==1.0.0", + "onnx>=1.16.0,<1.20.0", + "openmpi", +] +[tool.uv] +conflicts = [ + [ + { extra = "xformers" }, + { extra = "fp8-trtllm" }, + ], +] [tool.uv.sources] xformers = { index = "pytorch" } +tensorrt-llm = { index = "nvidia" } [[tool.uv.index]] name = "pytorch" url = "https://download.pytorch.org/whl/cu129" explicit = true +[[tool.uv.index]] +name = "nvidia" +url = "https://pypi.nvidia.com/" +explicit = true + [build-system] requires = ["uv_build>=0.9.8,<0.10.0"] build-backend = "uv_build" diff --git a/packages/ltx-core/src/ltx_core/loader/fuse_loras.py b/packages/ltx-core/src/ltx_core/loader/fuse_loras.py index 66269dd..2510c7b 100644 --- a/packages/ltx-core/src/ltx_core/loader/fuse_loras.py +++ b/packages/ltx-core/src/ltx_core/loader/fuse_loras.py @@ -1,44 +1,46 @@ import torch -import triton -from ltx_core.loader.kernels import fused_add_round_kernel from ltx_core.loader.primitives import LoraStateDictWithStrength, StateDict - -BLOCK_SIZE = 1024 +from ltx_core.quantization.fp8_cast import calculate_weight_float8 +from ltx_core.quantization.fp8_scaled_mm import quantize_weight_to_fp8_per_tensor -def fused_add_round_launch(target_weight: torch.Tensor, original_weight: torch.Tensor, seed: int) -> torch.Tensor: - if original_weight.dtype == torch.float8_e4m3fn: - exponent_bits, mantissa_bits, exponent_bias = 4, 3, 7 - elif original_weight.dtype == torch.float8_e5m2: - exponent_bits, mantissa_bits, exponent_bias = 5, 2, 15 # noqa: F841 - else: - raise ValueError("Unsupported dtype") +def apply_loras( + model_sd: StateDict, + lora_sd_and_strengths: list[LoraStateDictWithStrength], + dtype: torch.dtype | None = None, + destination_sd: StateDict | None = None, +) -> StateDict: + sd = {} + if destination_sd is not None: + sd = destination_sd.sd + size = 0 + device = torch.device("meta") + inner_dtypes = set() + for key, weight in model_sd.sd.items(): + if weight is None: + continue + # Skip scale keys - they are handled together with their weight keys + if key.endswith(".weight_scale"): + continue + device = weight.device + target_dtype = dtype if dtype is not None else weight.dtype + deltas_dtype = target_dtype if target_dtype not in [torch.float8_e4m3fn, torch.float8_e5m2] else torch.bfloat16 - if target_weight.dtype != torch.bfloat16: - raise ValueError("target_weight dtype must be bfloat16") + scale_key = key.replace(".weight", ".weight_scale") if key.endswith(".weight") else None + is_scaled_fp8 = scale_key is not None and scale_key in model_sd.sd - # Calculate grid and block sizes - n_elements = original_weight.numel() - grid = (triton.cdiv(n_elements, BLOCK_SIZE),) + deltas = _prepare_deltas(lora_sd_and_strengths, key, deltas_dtype, device) + fused = _fuse_deltas(deltas, weight, key, sd, target_dtype, device, is_scaled_fp8, scale_key, model_sd) - # Launch kernel - fused_add_round_kernel[grid]( - original_weight, - target_weight, - seed, - n_elements, - exponent_bias, - mantissa_bits, - BLOCK_SIZE, - ) - return target_weight + sd.update(fused) + for tensor in fused.values(): + inner_dtypes.add(tensor.dtype) + size += tensor.nbytes - -def calculate_weight_float8_(target_weights: torch.Tensor, original_weights: torch.Tensor) -> torch.Tensor: - result = fused_add_round_launch(target_weights, original_weights, seed=0).to(target_weights.dtype) - target_weights.copy_(result, non_blocking=True) - return target_weights + if destination_sd is not None: + return destination_sd + return StateDict(sd, device, size, inner_dtypes) def _prepare_deltas( @@ -60,41 +62,89 @@ def _prepare_deltas( return torch.sum(torch.stack(deltas, dim=0), dim=0) -def apply_loras( +def _fuse_deltas( + deltas: torch.Tensor | None, + weight: torch.Tensor, + key: str, + sd: dict[str, torch.Tensor], + target_dtype: torch.dtype, + device: torch.device, + is_scaled_fp8: bool, + scale_key: str | None, model_sd: StateDict, - lora_sd_and_strengths: list[LoraStateDictWithStrength], - dtype: torch.dtype, - destination_sd: StateDict | None = None, -) -> StateDict: - sd = {} - if destination_sd is not None: - sd = destination_sd.sd - size = 0 - device = torch.device("meta") - inner_dtypes = set() - for key, weight in model_sd.sd.items(): - if weight is None: - continue - device = weight.device - target_dtype = dtype if dtype is not None else weight.dtype - deltas_dtype = target_dtype if target_dtype not in [torch.float8_e4m3fn, torch.float8_e5m2] else torch.bfloat16 - deltas = _prepare_deltas(lora_sd_and_strengths, key, deltas_dtype, device) - if deltas is None: - if key in sd: - continue - deltas = weight.clone().to(dtype=target_dtype, device=device) - elif weight.dtype == torch.float8_e4m3fn: - if str(device).startswith("cuda"): - deltas = calculate_weight_float8_(deltas, weight) - else: - deltas.add_(weight.to(dtype=deltas.dtype, device=device)) - elif weight.dtype == torch.bfloat16: - deltas.add_(weight) +) -> dict[str, torch.Tensor]: + if deltas is None: + if key in sd: + return {} + fused = _copy_weight_without_lora(weight, key, target_dtype, device, is_scaled_fp8, scale_key, model_sd) + elif weight.dtype == torch.float8_e4m3fn: + if is_scaled_fp8: + fused = _fuse_delta_with_scaled_fp8(deltas, weight, key, scale_key, model_sd) else: - raise ValueError(f"Unsupported dtype: {weight.dtype}") - sd[key] = deltas.to(dtype=target_dtype) - inner_dtypes.add(target_dtype) - size += deltas.nbytes - if destination_sd is not None: - return destination_sd - return StateDict(sd, device, size, inner_dtypes) + fused = _fuse_delta_with_cast_fp8(deltas, weight, key, target_dtype, device) + elif weight.dtype == torch.bfloat16: + fused = _fuse_delta_with_bfloat16(deltas, weight, key, target_dtype) + else: + raise ValueError(f"Unsupported dtype: {weight.dtype}") + + return fused + + +def _copy_weight_without_lora( + weight: torch.Tensor, + key: str, + target_dtype: torch.dtype, + device: torch.device, + is_scaled_fp8: bool, + scale_key: str | None, + model_sd: StateDict, +) -> dict[str, torch.Tensor]: + """Copy original weight (and scale if applicable) when no LoRA affects this key.""" + result = {key: weight.clone().to(dtype=target_dtype, device=device)} + if is_scaled_fp8: + result[scale_key] = model_sd.sd[scale_key].clone() + return result + + +def _fuse_delta_with_scaled_fp8( + deltas: torch.Tensor, + weight: torch.Tensor, + key: str, + scale_key: str, + model_sd: StateDict, +) -> dict[str, torch.Tensor]: + """Dequantize scaled FP8 weight, add LoRA delta, and re-quantize.""" + weight_scale = model_sd.sd[scale_key] + + original_weight = weight.t().to(torch.float32) * weight_scale + + new_weight = original_weight + deltas.to(torch.float32) + + new_fp8_weight, new_weight_scale = quantize_weight_to_fp8_per_tensor(new_weight) + return {key: new_fp8_weight, scale_key: new_weight_scale} + + +def _fuse_delta_with_cast_fp8( + deltas: torch.Tensor, + weight: torch.Tensor, + key: str, + target_dtype: torch.dtype, + device: torch.device, +) -> dict[str, torch.Tensor]: + """Fuse LoRA delta with cast-only FP8 weight (no scale factor).""" + if str(device).startswith("cuda"): + deltas = calculate_weight_float8(deltas, weight) + else: + deltas.add_(weight.to(dtype=deltas.dtype, device=device)) + return {key: deltas.to(dtype=target_dtype)} + + +def _fuse_delta_with_bfloat16( + deltas: torch.Tensor, + weight: torch.Tensor, + key: str, + target_dtype: torch.dtype, +) -> dict[str, torch.Tensor]: + """Fuse LoRA delta with bfloat16 weight.""" + deltas.add_(weight) + return {key: deltas.to(dtype=target_dtype)} diff --git a/packages/ltx-core/src/ltx_core/model/transformer/__init__.py b/packages/ltx-core/src/ltx_core/model/transformer/__init__.py index 6cae4f3..43874b8 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/__init__.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/__init__.py @@ -4,21 +4,15 @@ from ltx_core.model.transformer.modality import Modality from ltx_core.model.transformer.model import LTXModel, X0Model from ltx_core.model.transformer.model_configurator import ( LTXV_MODEL_COMFY_RENAMING_MAP, - LTXV_MODEL_COMFY_RENAMING_WITH_TRANSFORMER_LINEAR_DOWNCAST_MAP, - UPCAST_DURING_INFERENCE, LTXModelConfigurator, LTXVideoOnlyModelConfigurator, - UpcastWithStochasticRounding, ) __all__ = [ "LTXV_MODEL_COMFY_RENAMING_MAP", - "LTXV_MODEL_COMFY_RENAMING_WITH_TRANSFORMER_LINEAR_DOWNCAST_MAP", - "UPCAST_DURING_INFERENCE", "LTXModel", "LTXModelConfigurator", "LTXVideoOnlyModelConfigurator", "Modality", - "UpcastWithStochasticRounding", "X0Model", ] diff --git a/packages/ltx-core/src/ltx_core/model/transformer/attention.py b/packages/ltx-core/src/ltx_core/model/transformer/attention.py index 9420478..3e7464e 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/attention.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/attention.py @@ -150,6 +150,7 @@ class Attention(torch.nn.Module): norm_eps: float = 1e-6, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, attention_function: AttentionCallable | AttentionFunction = AttentionFunction.DEFAULT, + apply_gated_attention: bool = False, ) -> None: super().__init__() self.rope_type = rope_type @@ -168,6 +169,12 @@ class Attention(torch.nn.Module): self.to_k = torch.nn.Linear(context_dim, inner_dim, bias=True) self.to_v = torch.nn.Linear(context_dim, inner_dim, bias=True) + # Optional per-head gating + if apply_gated_attention: + self.to_gate_logits = torch.nn.Linear(query_dim, heads, bias=True) + else: + self.to_gate_logits = None + self.to_out = torch.nn.Sequential(torch.nn.Linear(inner_dim, query_dim, bias=True), torch.nn.Identity()) def forward( @@ -191,5 +198,18 @@ class Attention(torch.nn.Module): k = apply_rotary_emb(k, pe if k_pe is None else k_pe, self.rope_type) # attention_function can be an enum *or* a custom callable - out = self.attention_function(q, k, v, self.heads, mask) + out = self.attention_function(q, k, v, self.heads, mask) # (B, T, H*D) + + # Apply per-head gating if enabled + if self.to_gate_logits is not None: + gate_logits = self.to_gate_logits(x) # (B, T, H) + b, t, _ = out.shape + # Reshape to (B, T, H, D) for per-head gating + out = out.view(b, t, self.heads, self.dim_head) + # Apply gating: 2 * sigmoid(x) so that zero-init gives identity (2 * 0.5 = 1.0) + gates = 2.0 * torch.sigmoid(gate_logits) # (B, T, H) + out = out * gates.unsqueeze(-1) # (B, T, H, D) * (B, T, H, 1) + # Reshape back to (B, T, H*D) + out = out.view(b, t, self.heads * self.dim_head) + return self.to_out(out) diff --git a/packages/ltx-core/src/ltx_core/model/transformer/model.py b/packages/ltx-core/src/ltx_core/model/transformer/model.py index 411e3b4..69e1315 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/model.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/model.py @@ -61,6 +61,7 @@ class LTXModel(torch.nn.Module): av_ca_timestep_scale_multiplier: int = 1, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, double_precision_rope: bool = False, + apply_gated_attention: bool = False, ): super().__init__() self._enable_gradient_checkpointing = False @@ -113,6 +114,7 @@ class LTXModel(torch.nn.Module): audio_cross_attention_dim=audio_cross_attention_dim, norm_eps=norm_eps, attention_type=attention_type, + apply_gated_attention=apply_gated_attention, ) def _init_video( @@ -272,6 +274,7 @@ class LTXModel(torch.nn.Module): audio_cross_attention_dim: int, norm_eps: float, attention_type: AttentionFunction | AttentionCallable, + apply_gated_attention: bool, ) -> None: """Initialize transformer blocks for LTX.""" video_config = ( @@ -280,6 +283,7 @@ class LTXModel(torch.nn.Module): heads=self.num_attention_heads, d_head=attention_head_dim, context_dim=cross_attention_dim, + apply_gated_attention=apply_gated_attention, ) if self.model_type.is_video_enabled() else None @@ -290,6 +294,7 @@ class LTXModel(torch.nn.Module): heads=self.audio_num_attention_heads, d_head=audio_attention_head_dim, context_dim=audio_cross_attention_dim, + apply_gated_attention=apply_gated_attention, ) if self.model_type.is_audio_enabled() else None diff --git a/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py b/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py index 567f436..2f052f0 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/model_configurator.py @@ -1,8 +1,4 @@ -import torch - -from ltx_core.loader.fuse_loras import fused_add_round_launch -from ltx_core.loader.module_ops import ModuleOps -from ltx_core.loader.sd_ops import KeyValueOperationResult, SDOps +from ltx_core.loader.sd_ops import SDOps from ltx_core.model.model_protocol import ModelConfigurator from ltx_core.model.transformer.attention import AttentionFunction from ltx_core.model.transformer.model import LTXModel, LTXModelType @@ -63,6 +59,7 @@ class LTXModelConfigurator(ModelConfigurator[LTXModel]): av_ca_timestep_scale_multiplier=config.get("av_ca_timestep_scale_multiplier", 1), rope_type=LTXRopeType(config.get("rope_type", "interleaved")), double_precision_rope=config.get("frequencies_precision", False) == "float64", + apply_gated_attention=config.get("apply_gated_attention", False), ) @@ -109,129 +106,12 @@ class LTXVideoOnlyModelConfigurator(ModelConfigurator[LTXModel]): use_middle_indices_grid=config.get("use_middle_indices_grid", True), rope_type=LTXRopeType(config.get("rope_type", "interleaved")), double_precision_rope=config.get("frequencies_precision", False) == "float64", + apply_gated_attention=config.get("apply_gated_attention", False), ) -def _naive_weight_or_bias_downcast(key: str, value: torch.Tensor) -> list[KeyValueOperationResult]: - """ - Downcast the weight or bias to the float8_e4m3fn dtype. - """ - return [KeyValueOperationResult(key, value.to(dtype=torch.float8_e4m3fn))] - - -def _upcast_and_round( - weight: torch.Tensor, dtype: torch.dtype, with_stochastic_rounding: bool = False, seed: int = 0 -) -> torch.Tensor: - """ - Upcast the weight to the given dtype and optionally apply stochastic rounding. - Input weight needs to have float8_e4m3fn or float8_e5m2 dtype. - """ - if not with_stochastic_rounding: - return weight.to(dtype) - return fused_add_round_launch(torch.zeros_like(weight, dtype=dtype), weight, seed) - - -def replace_fwd_with_upcast(layer: torch.nn.Linear, with_stochastic_rounding: bool = False, seed: int = 0) -> None: - """ - Replace linear.forward and rms_norm.forward with a version that: - - upcasts weight and bias to input's dtype - - returns F.linear or F.rms_norm calculated in that dtype - """ - - layer.original_forward = layer.forward - - def new_linear_forward(*args, **_kwargs) -> torch.Tensor: - # assume first arg is the input tensor - x = args[0] - w_up = _upcast_and_round(layer.weight, x.dtype, with_stochastic_rounding, seed) - b_up = None - - if layer.bias is not None: - b_up = _upcast_and_round(layer.bias, x.dtype, with_stochastic_rounding, seed) - - return torch.nn.functional.linear(x, w_up, b_up) - - layer.forward = new_linear_forward - - -def amend_forward_with_upcast( - model: torch.nn.Module, with_stochastic_rounding: bool = False, seed: int = 0 -) -> torch.nn.Module: - """ - Replace the forward method of the model's Linear and RMSNorm layers to forward - with upcast and optional stochastic rounding. - """ - for m in model.modules(): - if isinstance(m, (torch.nn.Linear)): - replace_fwd_with_upcast(m, with_stochastic_rounding, seed) - return model - - LTXV_MODEL_COMFY_RENAMING_MAP = ( SDOps("LTXV_MODEL_COMFY_PREFIX_MAP") .with_matching(prefix="model.diffusion_model.") .with_replacement("model.diffusion_model.", "") ) - -LTXV_MODEL_COMFY_RENAMING_WITH_TRANSFORMER_LINEAR_DOWNCAST_MAP = ( - SDOps("LTXV_MODEL_COMFY_PREFIX_MAP") - .with_matching(prefix="model.diffusion_model.") - .with_replacement("model.diffusion_model.", "") - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_q.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_q.bias", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_k.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_k.bias", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_v.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_v.bias", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_out.0.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".to_out.0.bias", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".ff.net.0.proj.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".ff.net.0.proj.bias", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".ff.net.2.weight", operation=_naive_weight_or_bias_downcast - ) - .with_kv_operation( - key_prefix="transformer_blocks.", key_suffix=".ff.net.2.bias", operation=_naive_weight_or_bias_downcast - ) -) - -UPCAST_DURING_INFERENCE = ModuleOps( - name="upcast_fp8_during_linear_forward", - matcher=lambda model: isinstance(model, LTXModel), - mutator=lambda model: amend_forward_with_upcast(model, False), -) - - -class UpcastWithStochasticRounding(ModuleOps): - """ - ModuleOps for upcasting the model's float8_e4m3fn weights and biases to the bfloat16 dtype - and applying stochastic rounding during linear forward. - """ - - def __new__(cls, seed: int = 0): - return super().__new__( - cls, - name="upcast_fp8_during_linear_forward_with_stochastic_rounding", - matcher=lambda model: isinstance(model, LTXModel), - mutator=lambda model: amend_forward_with_upcast(model, True, seed), - ) diff --git a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py index 03979b3..bcfa072 100644 --- a/packages/ltx-core/src/ltx_core/model/transformer/transformer.py +++ b/packages/ltx-core/src/ltx_core/model/transformer/transformer.py @@ -16,6 +16,7 @@ class TransformerConfig: heads: int d_head: int context_dim: int + apply_gated_attention: bool = False class BasicAVTransformerBlock(torch.nn.Module): @@ -40,6 +41,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=video.apply_gated_attention, ) self.attn2 = Attention( query_dim=video.dim, @@ -49,6 +51,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=video.apply_gated_attention, ) self.ff = FeedForward(video.dim, dim_out=video.dim) self.scale_shift_table = torch.nn.Parameter(torch.empty(6, video.dim)) @@ -62,6 +65,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=audio.apply_gated_attention, ) self.audio_attn2 = Attention( query_dim=audio.dim, @@ -71,6 +75,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=audio.apply_gated_attention, ) self.audio_ff = FeedForward(audio.dim, dim_out=audio.dim) self.audio_scale_shift_table = torch.nn.Parameter(torch.empty(6, audio.dim)) @@ -85,6 +90,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=video.apply_gated_attention, ) # Q: Audio, K,V: Video @@ -96,6 +102,7 @@ class BasicAVTransformerBlock(torch.nn.Module): rope_type=rope_type, norm_eps=norm_eps, attention_function=attention_function, + apply_gated_attention=audio.apply_gated_attention, ) self.scale_shift_table_a2v_ca_audio = torch.nn.Parameter(torch.empty(5, audio.dim)) diff --git a/packages/ltx-core/src/ltx_core/model/video_vae/tiling.py b/packages/ltx-core/src/ltx_core/model/video_vae/tiling.py index bb79695..1951704 100644 --- a/packages/ltx-core/src/ltx_core/model/video_vae/tiling.py +++ b/packages/ltx-core/src/ltx_core/model/video_vae/tiling.py @@ -44,6 +44,31 @@ def compute_trapezoidal_mask_1d( return mask.clamp_(0, 1) +def compute_rectangular_mask_1d( + length: int, + left_ramp: int, + right_ramp: int, +) -> torch.Tensor: + """ + Generate a 1D rectangular (pulse) mask. + Args: + length: Output length of the mask. + left_ramp: Number of elements at the start of the mask to set to 0. + right_ramp: Number of elements at the end of the mask to set to 0. + Returns: + A 1D tensor of shape `(length,)` with values 0 or 1. + """ + if length <= 0: + raise ValueError("Mask length must be positive.") + + mask = torch.ones(length) + if left_ramp > 0: + mask[:left_ramp] = 0 + if right_ramp > 0: + mask[-right_ramp:] = 0 + return mask + + @dataclass(frozen=True) class SpatialTilingConfig: """Configuration for dividing each frame into spatial tiles with optional overlap. @@ -114,12 +139,16 @@ class TilingConfig: @dataclass(frozen=True) class DimensionIntervals: - """Intervals which a single dimension of the latent space is split into. - Each interval is defined by its start, end, left ramp, and right ramp. - The start and end are the indices of the first and last element (exclusive) in the interval. - Ramps are regions of the interval where the value of the mask tensor is - interpolated between 0 and 1 for blending with neighboring intervals. - The left ramp and right ramp values are the lengths of the left and right ramps. + """Defines how a single dimension is split into overlapping intervals (tiles). + Each list has length N where N is the number of intervals. The i-th element + of each list describes the i-th interval. + Attributes: + starts: Start index of each interval (inclusive). + ends: End index of each interval (exclusive). + left_ramps: Length of the left blend ramp for each interval. + Used to create masks that fade in from 0 to 1. + right_ramps: Length of the right blend ramp for each interval. + Used to create masks that fade out from 1 to 0. """ starts: List[int] @@ -129,9 +158,11 @@ class DimensionIntervals: @dataclass(frozen=True) -class LatentIntervals: - """Intervals which the latent tensor of given shape is split into. - Each dimension of the latent space is split into intervals based on the length along said dimension. +class TensorTilingSpec: + """Specifies how a tensor of a given shape is split into intervals (tiles) along each dimension. + Attributes: + original_shape: Shape of the tensor being tiled. + dimension_intervals: Per-dimension intervals (starts, ends, ramps) for each axis. """ original_shape: torch.Size @@ -209,7 +240,7 @@ class Tile(NamedTuple): def create_tiles_from_intervals_and_mappers( - intervals: LatentIntervals, + intervals: TensorTilingSpec, mappers: List[MappingOperation], ) -> List[Tile]: full_dim_input_slices = [] @@ -241,20 +272,20 @@ def create_tiles_from_intervals_and_mappers( def create_tiles( - latent_shape: torch.Size, + tensor_shape: torch.Size, splitters: List[SplitOperation], mappers: List[MappingOperation], ) -> List[Tile]: - if len(splitters) != len(latent_shape): + if len(splitters) != len(tensor_shape): raise ValueError( - f"Number of splitters must be equal to number of dimensions in latent shape, " - f"got {len(splitters)} and {len(latent_shape)}" + f"Number of splitters must be equal to number of dimensions in tensor shape, " + f"got {len(splitters)} and {len(tensor_shape)}" ) - if len(mappers) != len(latent_shape): + if len(mappers) != len(tensor_shape): raise ValueError( - f"Number of mappers must be equal to number of dimensions in latent shape, " - f"got {len(mappers)} and {len(latent_shape)}" + f"Number of mappers must be equal to number of dimensions in tensor shape, " + f"got {len(mappers)} and {len(tensor_shape)}" ) - intervals = [splitter(length) for splitter, length in zip(splitters, latent_shape, strict=True)] - latent_intervals = LatentIntervals(original_shape=latent_shape, dimension_intervals=tuple(intervals)) - return create_tiles_from_intervals_and_mappers(latent_intervals, mappers) + intervals = [splitter(length) for splitter, length in zip(splitters, tensor_shape, strict=True)] + tiling_spec = TensorTilingSpec(original_shape=tensor_shape, dimension_intervals=tuple(intervals)) + return create_tiles_from_intervals_and_mappers(tiling_spec, mappers) diff --git a/packages/ltx-core/src/ltx_core/model/video_vae/video_vae.py b/packages/ltx-core/src/ltx_core/model/video_vae/video_vae.py index 4638407..77a6780 100644 --- a/packages/ltx-core/src/ltx_core/model/video_vae/video_vae.py +++ b/packages/ltx-core/src/ltx_core/model/video_vae/video_vae.py @@ -1,3 +1,4 @@ +import logging from dataclasses import replace from typing import Any, Callable, Iterator, List, Tuple @@ -20,10 +21,13 @@ from ltx_core.model.video_vae.tiling import ( SplitOperation, Tile, TilingConfig, + compute_rectangular_mask_1d, compute_trapezoidal_mask_1d, create_tiles, ) -from ltx_core.types import SpatioTemporalScaleFactors, VideoLatentShape +from ltx_core.types import VIDEO_SCALE_FACTORS, SpatioTemporalScaleFactors, VideoLatentShape + +logger: logging.Logger = logging.getLogger(__name__) def _make_encoder_block( @@ -248,18 +252,22 @@ class VideoEncoder(nn.Module): r""" Encode video frames into normalized latent representation. Args: - sample: Input video (B, C, F, H, W). F must be 1 + 8*k (e.g., 1, 9, 17, 25, 33...). + sample: Input video (B, C, F, H, W). F should be 1 + 8*k (e.g., 1, 9, 17, 25, 33...). + If not, the encoder crops the last frames to the nearest valid length. Returns: Normalized latent means (B, 128, F', H', W') where F' = 1+(F-1)/8, H' = H/32, W' = W/32. Example: (B, 3, 33, 512, 512) -> (B, 128, 5, 16, 16). """ - # Validate frame count + # Validate frame count (crop to nearest valid length if needed) frames_count = sample.shape[2] if ((frames_count - 1) % 8) != 0: - raise ValueError( - "Invalid number of frames: Encode input must have 1 + 8 * x frames " - "(e.g., 1, 9, 17, ...). Please check your input." + frames_to_crop = (frames_count - 1) % 8 + logger.warning( + "Invalid number of frames %s for encode; cropping last %s frames to satisfy 1 + 8*k.", + frames_count, + frames_to_crop, ) + sample = sample[:, :, :-frames_to_crop, ...] # Initial spatial compression: trade spatial resolution for channel depth # This reduces H,W by patch_size and increases channels, making convolutions more efficient @@ -311,6 +319,152 @@ class VideoEncoder(nn.Module): means, _ = torch.chunk(sample, 2, dim=1) return self.per_channel_statistics.normalize(means) + def tiled_encode( + self, + video: torch.Tensor, + tiling_config: TilingConfig | None = None, + ) -> torch.Tensor: + """Encode video to latent using tiled processing of the given video tensor. + Device Handling: + - Input video can be on CPU or GPU + - Accumulation buffers are created on model's device + - Each tile is automatically moved to model's device before encoding + - Output latent is returned on model's device + Args: + video: Input video tensor (B, 3, F, H, W) in range [-1, 1] + tiling_config: Tiling configuration for the video tensor + Returns: + Latent tensor (B, 128, F', H', W') on model's device + where F' = 1 + (F-1)/8, H' = H/32, W' = W/32 + """ + # Detect model device and dtype + model_device = next(self.parameters()).device + model_dtype = next(self.parameters()).dtype + + # Extract shape components + batch, _, frames, height, width = video.shape + + # Check frame count and crop if needed + if (frames - 1) % VIDEO_SCALE_FACTORS.time != 0: + frames_to_crop = (frames - 1) % VIDEO_SCALE_FACTORS.time + logger.warning( + f"Number of frames {frames} of input video is not ({VIDEO_SCALE_FACTORS.time} * k + 1), " + f"last {frames_to_crop} frames will be cropped" + ) + video = video[:, :, :-frames_to_crop, ...] + # Update frames after cropping + frames = video.shape[2] + + # Calculate output latent shape (inverse of upscale) + latent_shape = VideoLatentShape( + batch=batch, + channels=self.latent_channels, # 128 for standard VAE + frames=(frames - 1) // VIDEO_SCALE_FACTORS.time + 1, + height=height // VIDEO_SCALE_FACTORS.height, + width=width // VIDEO_SCALE_FACTORS.width, + ) + + # Prepare tiles (operates on VIDEO dimensions) + tiles = prepare_tiles_for_encoding(video, tiling_config) + + # Initialize accumulation buffers on model device + latent_buffer = torch.zeros( + latent_shape.to_torch_shape(), + device=model_device, + dtype=model_dtype, + ) + weights_buffer = torch.zeros_like(latent_buffer) + + # Process each tile + for tile in tiles: + # Extract video tile from input (may be on CPU) + video_tile = video[tile.in_coords] + + # Move tile to model device if needed + if video_tile.device != model_device or video_tile.dtype != model_dtype: + video_tile = video_tile.to(device=model_device, dtype=model_dtype) + + # Encode tile to latent (output on model device) + latent_tile = self.forward(video_tile) + + # Move blend mask to model device + mask = tile.blend_mask.to( + device=model_device, + dtype=model_dtype, + ) + + # Weighted accumulation in latent space + latent_buffer[tile.out_coords] += latent_tile * mask + weights_buffer[tile.out_coords] += mask + + del latent_tile, mask, video_tile + + # Normalize by accumulated weights + weights_buffer = weights_buffer.clamp(min=1e-8) + return latent_buffer / weights_buffer + + +def prepare_tiles_for_encoding( + video: torch.Tensor, + tiling_config: TilingConfig | None = None, +) -> List[Tile]: + """Prepare tiles for VAE encoding. + Args: + video: Input video tensor (B, 3, F, H, W) in range [-1, 1] + tiling_config: Tiling configuration for the video tensor + Returns: + List of tiles for the video tensor + """ + + splitters = [DEFAULT_SPLIT_OPERATION] * len(video.shape) + mappers = [DEFAULT_MAPPING_OPERATION] * len(video.shape) + minimum_spatial_overlap_px = 64 + minimum_temporal_overlap_frames = 16 + + if tiling_config is not None and tiling_config.spatial_config is not None: + cfg = tiling_config.spatial_config + + tile_size_px = cfg.tile_size_in_pixels + overlap_px = cfg.tile_overlap_in_pixels + + # Set minimum spatial overlap to 64 pixels in order to allow cutting padding from + # the front and back of the tiles and concatenate tiles without artifacts. + # The encoder uses symmetric padding (pad=1) in H and W at each conv layer. At tile + # boundaries, convs see padding (zeros/reflect) instead of real neighbor pixels, causing + # incorrect context near edges. + # For each overlap we discard 1 latent per edge (32px at scale 32) and concatenate tiles at a + # shared region with the next tile. + if overlap_px < minimum_spatial_overlap_px: + logger.warning( + f"Overlap pixels {overlap_px} in spatial tiling is less than \ + {minimum_spatial_overlap_px}, setting to minimum required {minimum_spatial_overlap_px}" + ) + overlap_px = minimum_spatial_overlap_px + + # Define split and map operations for the spatial dimensions + + # Height axis (H) + splitters[3] = split_with_symmetric_overlaps(tile_size_px, overlap_px) + mappers[3] = make_mapping_operation(map_spatial_interval_to_latent, scale=VIDEO_SCALE_FACTORS.height) + + # Width axis (W) + splitters[4] = split_with_symmetric_overlaps(tile_size_px, overlap_px) + mappers[4] = make_mapping_operation(map_spatial_interval_to_latent, scale=VIDEO_SCALE_FACTORS.width) + + if tiling_config is not None and tiling_config.temporal_config is not None: + cfg = tiling_config.temporal_config + tile_size_frames = cfg.tile_size_in_frames + overlap_frames = cfg.tile_overlap_in_frames + + if overlap_frames < minimum_temporal_overlap_frames: + logger.warning(f"Overlap frames {overlap_frames} is less than 16, setting to minimum required 16") + overlap_frames = minimum_temporal_overlap_frames + + splitters[2] = split_temporal_frames(tile_size_frames, overlap_frames) + mappers[2] = make_mapping_operation(map_temporal_interval_to_latent, scale=VIDEO_SCALE_FACTORS.time) + + return create_tiles(video.shape, splitters, mappers) + def _make_decoder_block( block_name: str, @@ -631,8 +785,8 @@ class VideoDecoder(nn.Module): axis_length = latent.shape[axis_idx] lower_threshold = max(2, overlap + 1) tile_size = max(lower_threshold, round(size * axis_length / long_side)) - splitters[axis_idx] = split_in_spatial(tile_size, overlap) - mappers[axis_idx] = to_mapping_operation(map_spatial_slice, factor) + splitters[axis_idx] = split_with_symmetric_overlaps(tile_size, overlap) + mappers[axis_idx] = make_mapping_operation(map_spatial_interval_to_pixel, scale=factor) enable_on_axis(3, self.video_downscale_factors.height) enable_on_axis(4, self.video_downscale_factors.width) @@ -641,8 +795,8 @@ class VideoDecoder(nn.Module): cfg = tiling_config.temporal_config tile_size = cfg.tile_size_in_frames // self.video_downscale_factors.time overlap = cfg.tile_overlap_in_frames // self.video_downscale_factors.time - splitters[2] = split_in_temporal(tile_size, overlap) - mappers[2] = to_mapping_operation(map_temporal_slice, self.video_downscale_factors.time) + splitters[2] = split_temporal_latents(tile_size, overlap) + mappers[2] = make_mapping_operation(map_temporal_interval_to_frame, scale=self.video_downscale_factors.time) return create_tiles(latent.shape, splitters, mappers) @@ -856,7 +1010,7 @@ def get_video_chunks_number(num_frames: int, tiling_config: TilingConfig | None return (num_frames - 1 + frame_stride - 1) // frame_stride -def split_in_spatial(size: int, overlap: int) -> SplitOperation: +def split_with_symmetric_overlaps(size: int, overlap: int) -> SplitOperation: def split(dimension_size: int) -> DimensionIntervals: if dimension_size <= size: return DEFAULT_SPLIT_OPERATION(dimension_size) @@ -871,26 +1025,81 @@ def split_in_spatial(size: int, overlap: int) -> SplitOperation: return split -def split_in_temporal(size: int, overlap: int) -> SplitOperation: - non_causal_split = split_in_spatial(size, overlap) +def split_temporal_latents(size: int, overlap: int) -> SplitOperation: + """Split a temporal axis into overlapping tiles with causal handling. + Example with size=24, overlap=8 (units are whatever axis you split): + Non-causal split would produce: + Tile 0: [0, 24), left_ramp=0, right_ramp=8 + Tile 1: [16, 40), left_ramp=8, right_ramp=8 + Tile 2: [32, 56), left_ramp=8, right_ramp=0 + Causal split produces: + Tile 0: [0, 24), left_ramp=0, right_ramp=8 (unchanged - starts at anchor) + Tile 1: [15, 40), left_ramp=9, right_ramp=8 (shifted back 1, ramp +1) + Tile 2: [31, 56), left_ramp=9, right_ramp=0 (shifted back 1, ramp +1) + This ensures each tile can causally depend on frames from previous tiles while maintaining + proper temporal continuity through the blend ramps. + Args: + size: Tile size in *axis units* (latent steps for LTX time tiling) + overlap: Overlap between tiles in the same units + Returns: + Split operation that divides temporal dimension with causal handling + """ + non_causal_split = split_with_symmetric_overlaps(size, overlap) def split(dimension_size: int) -> DimensionIntervals: if dimension_size <= size: return DEFAULT_SPLIT_OPERATION(dimension_size) intervals = non_causal_split(dimension_size) + starts = intervals.starts starts[1:] = [s - 1 for s in starts[1:]] + + # Extend blend ramps by 1 for non-first tiles to blend over the extra frame left_ramps = intervals.left_ramps left_ramps[1:] = [r + 1 for r in left_ramps[1:]] + return replace(intervals, starts=starts, left_ramps=left_ramps) return split -def to_mapping_operation( - map_func: Callable[[int, int, int, int, int], Tuple[slice, torch.Tensor]], +def split_temporal_frames(tile_size_frames: int, overlap_frames: int) -> SplitOperation: + """Split a temporal axis in video frame space into overlapping tiles. + Args: + tile_size_frames: Tile length in frames. + overlap_frames: Overlap between consecutive tiles in frames. + Returns: + Split operation that takes frame count and returns DimensionIntervals in frame indices. + """ + non_causal_split = split_with_symmetric_overlaps(tile_size_frames, overlap_frames) + + def split(dimension_size: int) -> DimensionIntervals: + if dimension_size <= tile_size_frames: + return DEFAULT_SPLIT_OPERATION(dimension_size) + intervals = non_causal_split(dimension_size) + ends = intervals.ends + ends[:-1] = [e + 1 for e in ends[:-1]] + right_ramps = [0] * len(intervals.right_ramps) + return replace(intervals, ends=ends, right_ramps=right_ramps) + + return split + + +def make_mapping_operation( + map_func: Callable[[int, int, int, int, int], Tuple[slice, torch.Tensor | None]], scale: int, ) -> MappingOperation: + """Create a mapping operation over a set of tiling intervals. + The given mapping function is applied to each interval in the input dimension. The result function is used for + creating tiles in the output dimension. + Args: + map_func: Mapping function to create the mapping operation from + scale: Scale factor for the transformation, used as an argument for the mapping function + Returns: + Mapping operation that takes a set of tiling intervals and returns a set of slices and masks in the output + dimension. + """ + def map_op(intervals: DimensionIntervals) -> tuple[list[slice], list[torch.Tensor | None]]: output_slices: list[slice] = [] masks_1d: list[torch.Tensor | None] = [] @@ -908,19 +1117,104 @@ def to_mapping_operation( return map_op -def map_temporal_slice(begin: int, end: int, left_ramp: int, right_ramp: int, scale: int) -> Tuple[slice, torch.Tensor]: +def map_temporal_interval_to_frame( + begin: int, + end: int, + left_ramp: int, + right_ramp: int, + scale: int, +) -> Tuple[slice, torch.Tensor]: + """Map temporal interval in latent space to video frame space. + Args: + begin: Start position in latent space + end: End position in latent space + left_ramp: Left ramp size in latent space + right_ramp: Right ramp size in latent space + scale: Scale factor for transformation + Returns: + Tuple of (output_slice, blend_mask) + """ start = begin * scale stop = 1 + (end - 1) * scale - left_ramp = 1 + (left_ramp - 1) * scale - right_ramp = right_ramp * scale - return slice(start, stop), compute_trapezoidal_mask_1d(stop - start, left_ramp, right_ramp, True) + left_ramp_frames = 0 if left_ramp == 0 else 1 + (left_ramp - 1) * scale + right_ramp_frames = right_ramp * scale + + mask_1d = compute_trapezoidal_mask_1d(stop - start, left_ramp_frames, right_ramp_frames, True) + return slice(start, stop), mask_1d -def map_spatial_slice(begin: int, end: int, left_ramp: int, right_ramp: int, scale: int) -> Tuple[slice, torch.Tensor]: +def map_temporal_interval_to_latent( + begin: int, end: int, left_ramp: int, right_ramp: int | None = None, scale: int = 1 +) -> Tuple[slice, torch.Tensor]: + """ + Map temporal interval in video frame space to latent space. + Args: + begin: Start position in video frame space + end: End position in video frame space + left_ramp: Left ramp size in video frame space + right_ramp: Right ramp size in video frame space + scale: Scale factor for transformation + Returns: + Tuple of (output_slice, blend_mask) + """ + start = begin // scale + stop = (end - 1) // scale + 1 + + left_ramp_latents = 0 if left_ramp == 0 else 1 + (left_ramp - 1) // scale + right_ramp_latents = right_ramp // scale + + if right_ramp_latents != 0: + raise ValueError("For tiled encoding, temporal tiles are expected to have a right ramp equal to 0") + + mask_1d = compute_rectangular_mask_1d(stop - start, left_ramp_latents, right_ramp_latents) + + return slice(start, stop), mask_1d + + +def map_spatial_interval_to_pixel( + begin: int, + end: int, + left_ramp: int, + right_ramp: int, + scale: int, +) -> Tuple[slice, torch.Tensor]: + """Map spatial interval in latent space to pixel space. + Args: + begin: Start position in latent space + end: End position in latent space + left_ramp: Left ramp size in latent space + right_ramp: Right ramp size in latent space + scale: Scale factor for transformation + """ start = begin * scale stop = end * scale - left_ramp = left_ramp * scale - right_ramp = right_ramp * scale + mask_1d = compute_trapezoidal_mask_1d(stop - start, left_ramp * scale, right_ramp * scale, False) + return slice(start, stop), mask_1d - return slice(start, stop), compute_trapezoidal_mask_1d(stop - start, left_ramp, right_ramp, False) + +def map_spatial_interval_to_latent( + begin: int, + end: int, + left_ramp: int, + right_ramp: int, + scale: int, +) -> Tuple[slice, torch.Tensor]: + """Map spatial interval in pixel space to latent space. + Args: + begin: Start position in pixel space + end: End position in pixel space + left_ramp: Left ramp size in pixel space + right_ramp: Right ramp size in pixel space + scale: Scale factor for transformation + Returns: + Tuple of (output_slice, blend_mask) + """ + start = begin // scale + stop = end // scale + left_ramp = max(0, left_ramp // scale - 1) + + right_ramp = 0 if right_ramp == 0 else 1 + + mask_1d = compute_rectangular_mask_1d(stop - start, left_ramp, right_ramp) + return slice(start, stop), mask_1d diff --git a/packages/ltx-core/src/ltx_core/quantization/__init__.py b/packages/ltx-core/src/ltx_core/quantization/__init__.py new file mode 100644 index 0000000..8910327 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/quantization/__init__.py @@ -0,0 +1,16 @@ +from ltx_core.quantization.fp8_cast import ( + TRANSFORMER_LINEAR_DOWNCAST_MAP, + UPCAST_DURING_INFERENCE, + UpcastWithStochasticRounding, +) +from ltx_core.quantization.fp8_scaled_mm import FP8_PREPARE_MODULE_OPS, FP8_TRANSPOSE_SD_OPS +from ltx_core.quantization.policy import QuantizationPolicy + +__all__ = [ + "FP8_PREPARE_MODULE_OPS", + "FP8_TRANSPOSE_SD_OPS", + "TRANSFORMER_LINEAR_DOWNCAST_MAP", + "UPCAST_DURING_INFERENCE", + "QuantizationPolicy", + "UpcastWithStochasticRounding", +] diff --git a/packages/ltx-core/src/ltx_core/quantization/fp8_cast.py b/packages/ltx-core/src/ltx_core/quantization/fp8_cast.py new file mode 100644 index 0000000..2ba8c34 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/quantization/fp8_cast.py @@ -0,0 +1,163 @@ +import torch + +from ltx_core.loader.module_ops import ModuleOps +from ltx_core.loader.sd_ops import KeyValueOperationResult, SDOps +from ltx_core.model.transformer.model import LTXModel + +BLOCK_SIZE = 1024 + + +def calculate_weight_float8(target_weights: torch.Tensor, original_weights: torch.Tensor) -> torch.Tensor: + result = _fused_add_round_launch(target_weights, original_weights, seed=0).to(target_weights.dtype) + target_weights.copy_(result, non_blocking=True) + return target_weights + + +def _fused_add_round_launch(target_weight: torch.Tensor, original_weight: torch.Tensor, seed: int) -> torch.Tensor: + # Lazy import triton - only available on CUDA platforms + import triton # noqa: PLC0415 + + from ltx_core.loader.kernels import fused_add_round_kernel # noqa: PLC0415 + + if original_weight.dtype == torch.float8_e4m3fn: + exponent_bits, mantissa_bits, exponent_bias = 4, 3, 7 + elif original_weight.dtype == torch.float8_e5m2: + exponent_bits, mantissa_bits, exponent_bias = 5, 2, 15 # noqa: F841 + else: + raise ValueError("Unsupported dtype") + + if target_weight.dtype != torch.bfloat16: + raise ValueError("target_weight dtype must be bfloat16") + + # Calculate grid and block sizes + n_elements = original_weight.numel() + grid = (triton.cdiv(n_elements, BLOCK_SIZE),) + + # Launch kernel + fused_add_round_kernel[grid]( + original_weight, + target_weight, + seed, + n_elements, + exponent_bias, + mantissa_bits, + BLOCK_SIZE, + ) + return target_weight + + +def _naive_weight_or_bias_downcast(key: str, value: torch.Tensor) -> list[KeyValueOperationResult]: + """ + Downcast the weight or bias to the float8_e4m3fn dtype. + """ + return [KeyValueOperationResult(key, value.to(dtype=torch.float8_e4m3fn))] + + +def _upcast_and_round( + weight: torch.Tensor, dtype: torch.dtype, with_stochastic_rounding: bool = False, seed: int = 0 +) -> torch.Tensor: + """ + Upcast the weight to the given dtype and optionally apply stochastic rounding. + Input weight needs to have float8_e4m3fn or float8_e5m2 dtype. + """ + if not with_stochastic_rounding: + return weight.to(dtype) + return _fused_add_round_launch(torch.zeros_like(weight, dtype=dtype), weight, seed) + + +def _replace_fwd_with_upcast(layer: torch.nn.Linear, with_stochastic_rounding: bool = False, seed: int = 0) -> None: + """ + Replace linear.forward and rms_norm.forward with a version that: + - upcasts weight and bias to input's dtype + - returns F.linear or F.rms_norm calculated in that dtype + """ + + layer.original_forward = layer.forward + + def new_linear_forward(*args, **_kwargs) -> torch.Tensor: + # assume first arg is the input tensor + x = args[0] + w_up = _upcast_and_round(layer.weight, x.dtype, with_stochastic_rounding, seed) + b_up = None + + if layer.bias is not None: + b_up = _upcast_and_round(layer.bias, x.dtype, with_stochastic_rounding, seed) + + return torch.nn.functional.linear(x, w_up, b_up) + + layer.forward = new_linear_forward + + +def _amend_forward_with_upcast( + model: torch.nn.Module, with_stochastic_rounding: bool = False, seed: int = 0 +) -> torch.nn.Module: + """ + Replace the forward method of the model's Linear and RMSNorm layers to forward + with upcast and optional stochastic rounding. + """ + for m in model.modules(): + if isinstance(m, (torch.nn.Linear)): + _replace_fwd_with_upcast(m, with_stochastic_rounding, seed) + return model + + +TRANSFORMER_LINEAR_DOWNCAST_MAP = ( + SDOps("TRANSFORMER_LINEAR_DOWNCAST_MAP") + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_q.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_q.bias", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_k.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_k.bias", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_v.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_v.bias", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_out.0.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".to_out.0.bias", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".ff.net.0.proj.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".ff.net.0.proj.bias", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".ff.net.2.weight", operation=_naive_weight_or_bias_downcast + ) + .with_kv_operation( + key_prefix="transformer_blocks.", key_suffix=".ff.net.2.bias", operation=_naive_weight_or_bias_downcast + ) +) + +UPCAST_DURING_INFERENCE = ModuleOps( + name="upcast_fp8_during_linear_forward", + matcher=lambda model: isinstance(model, LTXModel), + mutator=lambda model: _amend_forward_with_upcast(model, False), +) + + +class UpcastWithStochasticRounding(ModuleOps): + """ + ModuleOps for upcasting the model's float8_e4m3fn weights and biases to the bfloat16 dtype + and applying stochastic rounding during linear forward. + """ + + def __new__(cls, seed: int = 0): + return super().__new__( + cls, + name="upcast_fp8_during_linear_forward_with_stochastic_rounding", + matcher=lambda model: isinstance(model, LTXModel), + mutator=lambda model: _amend_forward_with_upcast(model, True, seed), + ) diff --git a/packages/ltx-core/src/ltx_core/quantization/fp8_scaled_mm.py b/packages/ltx-core/src/ltx_core/quantization/fp8_scaled_mm.py new file mode 100644 index 0000000..5f53ac4 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/quantization/fp8_scaled_mm.py @@ -0,0 +1,207 @@ +from typing import Callable + +import torch +from torch import nn + +from ltx_core.loader.module_ops import ModuleOps +from ltx_core.loader.sd_ops import KeyValueOperationResult, SDOps +from ltx_core.model.transformer import LTXModel + + +class FP8Linear(nn.Module): + """Linear layer with FP8 weight storage for scaled matrix multiplication.""" + + in_features: int + out_features: int + + def __init__( + self, + in_features: int, + out_features: int, + bias: bool = True, + device: torch.device | str | None = None, + ): + super().__init__() + self.in_features = in_features + self.out_features = out_features + + fp8_shape = (in_features, out_features) + self.weight = nn.Parameter(torch.empty(fp8_shape, dtype=torch.float8_e4m3fn, device=device)) + # Weight scale for FP8 dequantization (shape matches checkpoint format) + self.weight_scale = nn.Parameter(torch.empty((), dtype=torch.float32, device=device)) + # Input scale for static quantization (pre-quantized checkpoints) + self.input_scale = nn.Parameter(torch.empty((), dtype=torch.float32, device=device)) + + if bias: + self.bias = nn.Parameter(torch.empty(out_features, device=device)) + else: + self.register_parameter("bias", None) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + origin_shape = x.shape + + # Static quantization: use pre-computed scale + qinput, cur_input_scale = torch.ops.tensorrt_llm.static_quantize_e4m3_per_tensor(x, self.input_scale) + + # Flatten to 2D for matmul + if qinput.dim() == 3: + qinput = qinput.reshape(-1, qinput.shape[-1]) + + # FP8 scaled matmul + output = torch.ops.trtllm.cublas_scaled_mm( + qinput, + self.weight, + scale_a=cur_input_scale, + scale_b=self.weight_scale, + bias=None, + out_dtype=x.dtype, + ) + + # Add bias + if self.bias is not None: + bias = self.bias + if bias.dtype != output.dtype: + bias = bias.to(output.dtype) + output = output + bias + + # Restore original shape + if output.dim() != len(origin_shape): + output_shape = list(origin_shape) + output_shape[-1] = output.shape[-1] + output = output.reshape(output_shape) + + return output + + +def quantize_weight_to_fp8_per_tensor(weight: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]: + """ + Quantize a weight tensor to FP8 (float8_e4m3fn) using per-tensor scaling. + Args: + weight: The weight tensor to quantize (any dtype, will be cast to float32) + Returns: + Tuple of (quantized_weight, weight_scale): + - quantized_weight: FP8 tensor, transposed for cublas_scaled_mm + - weight_scale: Per-tensor scale factor (reciprocal of quantization scale) + """ + weight_fp32 = weight.to(torch.float32) + + fp8_min = torch.finfo(torch.float8_e4m3fn).min + fp8_max = torch.finfo(torch.float8_e4m3fn).max + + max_abs = torch.amax(torch.abs(weight_fp32)) + scale = fp8_max / max_abs + + @torch.compiler.disable + def _quantize( + weight_fp32: torch.Tensor, scale: torch.Tensor, fp8_min: torch.Tensor, fp8_max: torch.Tensor + ) -> tuple[torch.Tensor, torch.Tensor]: + quantized_weight = torch.clamp(weight_fp32 * scale, min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn) + quantized_weight = quantized_weight.t() + weight_scale = scale.reciprocal() + return quantized_weight, weight_scale + + quantized_weight, weight_scale = _quantize(weight_fp32, scale, fp8_min, fp8_max) + return quantized_weight, weight_scale + + +def _should_skip_layer(layer_name: str, excluded_layer_substrings: tuple[str, ...]) -> bool: + return any(substring in layer_name for substring in excluded_layer_substrings) + + +EXCLUDED_LAYER_SUBSTRINGS = ( + "patchify_proj", + "adaln_single", + "av_ca_video_scale_shift_adaln_single", + "av_ca_a2v_gate_adaln_single", + "caption_projection", + "proj_out", + "audio_patchify_proj", + "audio_adaln_single", + "av_ca_audio_scale_shift_adaln_single", + "av_ca_v2a_gate_adaln_single", + "audio_caption_projection", + "audio_proj_out", + "transformer_blocks.0.", + *[f"transformer_blocks.{i}." for i in range(43, 48)], +) + + +def _linear_to_fp8linear(layer: nn.Linear) -> FP8Linear: + """ + Create an FP8Linear layer from an nn.Linear layer. + Args: + layer: The nn.Linear layer to convert (typically on meta device) + Returns: + A new FP8Linear with the same configuration + """ + return FP8Linear( + in_features=layer.in_features, + out_features=layer.out_features, + bias=layer.bias is not None, + device=layer.weight.device, + ) + + +def _apply_fp8_prepare_to_model(model: nn.Module, excluded_layer_substrings: tuple[str, ...]) -> nn.Module: + """Replace nn.Linear layers with FP8Linear in the module tree.""" + replacements: list[tuple[nn.Module, str, nn.Linear]] = [] + + for name, module in model.named_modules(): + if not isinstance(module, nn.Linear) or isinstance(module, FP8Linear): + continue + + if _should_skip_layer(name, excluded_layer_substrings): + continue + + if "." in name: + parent_name, attr_name = name.rsplit(".", 1) + parent = model.get_submodule(parent_name) + else: + parent = model + attr_name = name + + replacements.append((parent, attr_name, module)) + + for parent, attr_name, linear in replacements: + setattr(parent, attr_name, _linear_to_fp8linear(linear)) + + return model + + +def _create_transpose_kv_operation( + excluded_layer_substrings: tuple[str, ...], +) -> Callable[[str, torch.Tensor], list[KeyValueOperationResult]]: + def transpose_if_matches(key: str, value: torch.Tensor) -> list[KeyValueOperationResult]: + # Only process .weight keys + if not key.endswith(".weight"): + return [KeyValueOperationResult(key, value)] + + # Only transpose 2D FP8 tensors (Linear weights) + if value.dim() != 2 or value.dtype != torch.float8_e4m3fn: + return [KeyValueOperationResult(key, value)] + + # Check if the layer is excluded + layer_name = key.rsplit(".weight", 1)[0] + if _should_skip_layer(layer_name, excluded_layer_substrings): + return [KeyValueOperationResult(key, value)] + + # Transpose to cuBLAS layout (in, out) + transposed_weight = value.t() + + return [KeyValueOperationResult(key, transposed_weight)] + + return transpose_if_matches + + +FP8_TRANSPOSE_SD_OPS = SDOps("fp8_transpose_weights").with_kv_operation( + _create_transpose_kv_operation(EXCLUDED_LAYER_SUBSTRINGS), + key_prefix="transformer_blocks.", + key_suffix=".weight", +) + + +FP8_PREPARE_MODULE_OPS = ModuleOps( + name="fp8_prepare_for_loading", + matcher=lambda model: isinstance(model, LTXModel), + mutator=lambda model: _apply_fp8_prepare_to_model(model, EXCLUDED_LAYER_SUBSTRINGS), +) diff --git a/packages/ltx-core/src/ltx_core/quantization/policy.py b/packages/ltx-core/src/ltx_core/quantization/policy.py new file mode 100644 index 0000000..56dad51 --- /dev/null +++ b/packages/ltx-core/src/ltx_core/quantization/policy.py @@ -0,0 +1,39 @@ +from dataclasses import dataclass + +from ltx_core.loader.module_ops import ModuleOps +from ltx_core.loader.sd_ops import SDOps +from ltx_core.quantization.fp8_cast import TRANSFORMER_LINEAR_DOWNCAST_MAP, UPCAST_DURING_INFERENCE +from ltx_core.quantization.fp8_scaled_mm import FP8_PREPARE_MODULE_OPS, FP8_TRANSPOSE_SD_OPS + + +@dataclass(frozen=True) +class QuantizationPolicy: + """Configuration for model quantization during loading. + Attributes: + sd_ops: State dict operations for weight transformation. + module_ops: Post-load module transformations. + """ + + sd_ops: SDOps | None = None + module_ops: tuple[ModuleOps, ...] = () + + @classmethod + def fp8_cast(cls) -> "QuantizationPolicy": + """Create policy using FP8 casting with upcasting during inference.""" + return cls( + sd_ops=TRANSFORMER_LINEAR_DOWNCAST_MAP, + module_ops=(UPCAST_DURING_INFERENCE,), + ) + + @classmethod + def fp8_scaled_mm(cls) -> "QuantizationPolicy": + """Create policy using FP8 scaled matrix multiplication.""" + try: + import tensorrt_llm # noqa: F401, PLC0415 + except ImportError as e: + raise ImportError("tensorrt_llm is not installed, skipping FP8 scaled MM quantization") from e + + return cls( + sd_ops=FP8_TRANSPOSE_SD_OPS, + module_ops=(FP8_PREPARE_MODULE_OPS,), + ) diff --git a/packages/ltx-core/src/ltx_core/text_encoders/gemma/embeddings_connector.py b/packages/ltx-core/src/ltx_core/text_encoders/gemma/embeddings_connector.py index a5cdd0a..5e4998d 100644 --- a/packages/ltx-core/src/ltx_core/text_encoders/gemma/embeddings_connector.py +++ b/packages/ltx-core/src/ltx_core/text_encoders/gemma/embeddings_connector.py @@ -19,6 +19,7 @@ class _BasicTransformerBlock1D(torch.nn.Module): heads: int, dim_head: int, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, + apply_gated_attention: bool = False, ): super().__init__() @@ -27,6 +28,7 @@ class _BasicTransformerBlock1D(torch.nn.Module): heads=heads, dim_head=dim_head, rope_type=rope_type, + apply_gated_attention=apply_gated_attention, ) self.ff = FeedForward( @@ -99,6 +101,7 @@ class Embeddings1DConnector(torch.nn.Module): num_learnable_registers: int | None = 128, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, double_precision_rope: bool = False, + apply_gated_attention: bool = False, ): super().__init__() self.num_attention_heads = num_attention_heads @@ -117,6 +120,7 @@ class Embeddings1DConnector(torch.nn.Module): heads=num_attention_heads, dim_head=attention_head_dim, rope_type=rope_type, + apply_gated_attention=apply_gated_attention, ) for _ in range(num_layers) ] @@ -206,5 +210,6 @@ class Embeddings1DConnectorConfigurator(ModelConfigurator[Embeddings1DConnector] positional_embedding_max_pos=pe_max_pos, rope_type=rope_type, double_precision_rope=double_precision_rope, + apply_gated_attention=config.get("connector_apply_gated_attention", False), ) return connector diff --git a/packages/ltx-pipelines/src/ltx_pipelines/distilled.py b/packages/ltx-pipelines/src/ltx_pipelines/distilled.py index cd7a6f2..eb68104 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/distilled.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/distilled.py @@ -11,6 +11,7 @@ from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.upsampler import upsample_video from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number from ltx_core.model.video_vae import decode_video as vae_decode_video +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import encode_text from ltx_core.types import LatentState, VideoPixelShape from ltx_pipelines.utils import ModelLedger @@ -50,7 +51,7 @@ class DistilledPipeline: spatial_upsampler_path: str, loras: list[LoraPathStrengthAndSDOps], device: torch.device = device, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.device = device self.dtype = torch.bfloat16 @@ -62,7 +63,7 @@ class DistilledPipeline: spatial_upsampler_path=spatial_upsampler_path, gemma_root_path=gemma_root, loras=loras, - fp8transformer=fp8transformer, + quantization=quantization, ) self.pipeline_components = PipelineComponents( @@ -204,7 +205,7 @@ def main() -> None: spatial_upsampler_path=args.spatial_upsampler_path, gemma_root=args.gemma_root, loras=args.lora, - fp8transformer=args.enable_fp8, + quantization=args.quantization, ) tiling_config = TilingConfig.default() video_chunks_number = get_video_chunks_number(args.num_frames, tiling_config) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py index 339e0ea..3ba95c5 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ic_lora.py @@ -13,6 +13,7 @@ from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.upsampler import upsample_video from ltx_core.model.video_vae import TilingConfig, VideoEncoder, get_video_chunks_number from ltx_core.model.video_vae import decode_video as vae_decode_video +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import encode_text from ltx_core.types import LatentState, VideoPixelShape from ltx_pipelines.utils import ModelLedger @@ -55,7 +56,7 @@ class ICLoraPipeline: gemma_root: str, loras: list[LoraPathStrengthAndSDOps], device: torch.device = device, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.dtype = torch.bfloat16 self.stage_1_model_ledger = ModelLedger( @@ -65,7 +66,7 @@ class ICLoraPipeline: spatial_upsampler_path=spatial_upsampler_path, gemma_root_path=gemma_root, loras=loras, - fp8transformer=fp8transformer, + quantization=quantization, ) self.stage_2_model_ledger = ModelLedger( dtype=self.dtype, @@ -74,7 +75,7 @@ class ICLoraPipeline: spatial_upsampler_path=spatial_upsampler_path, gemma_root_path=gemma_root, loras=[], - fp8transformer=fp8transformer, + quantization=quantization, ) self.pipeline_components = PipelineComponents( dtype=self.dtype, @@ -314,7 +315,7 @@ def main() -> None: spatial_upsampler_path=args.spatial_upsampler_path, gemma_root=args.gemma_root, loras=args.lora, - fp8transformer=args.enable_fp8, + quantization=args.quantization, ) tiling_config = TilingConfig.default() video_chunks_number = get_video_chunks_number(args.num_frames, tiling_config) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py b/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py index cc522b0..f2d0056 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/keyframe_interpolation.py @@ -13,6 +13,7 @@ from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.upsampler import upsample_video from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number from ltx_core.model.video_vae import decode_video as vae_decode_video +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import encode_text from ltx_core.types import LatentState, VideoPixelShape from ltx_pipelines.utils import ModelLedger @@ -54,7 +55,7 @@ class KeyframeInterpolationPipeline: gemma_root: str, loras: list[LoraPathStrengthAndSDOps], device: torch.device = device, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.device = device self.dtype = torch.bfloat16 @@ -65,7 +66,7 @@ class KeyframeInterpolationPipeline: spatial_upsampler_path=spatial_upsampler_path, gemma_root_path=gemma_root, loras=loras, - fp8transformer=fp8transformer, + quantization=quantization, ) self.stage_2_model_ledger = self.stage_1_model_ledger.with_loras( loras=distilled_lora, @@ -248,7 +249,7 @@ def main() -> None: spatial_upsampler_path=args.spatial_upsampler_path, gemma_root=args.gemma_root, loras=args.lora, - fp8transformer=args.enable_fp8, + quantization=args.quantization, ) tiling_config = TilingConfig.default() video_chunks_number = get_video_chunks_number(args.num_frames, tiling_config) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py index 36defea..145178a 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_one_stage.py @@ -11,6 +11,7 @@ from ltx_core.components.schedulers import LTX2Scheduler from ltx_core.loader import LoraPathStrengthAndSDOps from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.video_vae import decode_video as vae_decode_video +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import encode_text from ltx_core.types import LatentState, VideoPixelShape from ltx_pipelines.utils import ModelLedger @@ -46,7 +47,7 @@ class TI2VidOneStagePipeline: gemma_root: str, loras: list[LoraPathStrengthAndSDOps], device: torch.device = device, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.dtype = torch.bfloat16 self.device = device @@ -56,7 +57,7 @@ class TI2VidOneStagePipeline: checkpoint_path=checkpoint_path, gemma_root_path=gemma_root, loras=loras, - fp8transformer=fp8transformer, + quantization=quantization, ) self.pipeline_components = PipelineComponents( dtype=self.dtype, @@ -169,7 +170,7 @@ def main() -> None: checkpoint_path=args.checkpoint_path, gemma_root=args.gemma_root, loras=args.lora, - fp8transformer=args.enable_fp8, + quantization=args.quantization, ) video, audio = pipeline( prompt=args.prompt, diff --git a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py index 6119de1..1369454 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/ti2vid_two_stages.py @@ -13,6 +13,7 @@ from ltx_core.model.audio_vae import decode_audio as vae_decode_audio from ltx_core.model.upsampler import upsample_video from ltx_core.model.video_vae import TilingConfig, get_video_chunks_number from ltx_core.model.video_vae import decode_video as vae_decode_video +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import encode_text from ltx_core.types import LatentState, VideoPixelShape from ltx_pipelines.utils import ModelLedger @@ -54,7 +55,7 @@ class TI2VidTwoStagesPipeline: gemma_root: str, loras: list[LoraPathStrengthAndSDOps], device: str = device, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.device = device self.dtype = torch.bfloat16 @@ -65,7 +66,7 @@ class TI2VidTwoStagesPipeline: gemma_root_path=gemma_root, spatial_upsampler_path=spatial_upsampler_path, loras=loras, - fp8transformer=fp8transformer, + quantization=quantization, ) self.stage_2_model_ledger = self.stage_1_model_ledger.with_loras( @@ -251,7 +252,7 @@ def main() -> None: spatial_upsampler_path=args.spatial_upsampler_path, gemma_root=args.gemma_root, loras=args.lora, - fp8transformer=args.enable_fp8, + quantization=args.quantization, ) tiling_config = TilingConfig.default() video_chunks_number = get_video_chunks_number(args.num_frames, tiling_config) diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py index 2fa0163..15b3d49 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/args.py @@ -2,6 +2,7 @@ import argparse from pathlib import Path from ltx_core.loader import LTXV_LORA_COMFY_RENAMING_MAP, LoraPathStrengthAndSDOps +from ltx_core.quantization import QuantizationPolicy from ltx_pipelines.utils.constants import ( DEFAULT_1_STAGE_HEIGHT, DEFAULT_1_STAGE_WIDTH, @@ -78,6 +79,40 @@ def resolve_path(path: str) -> str: return str(Path(path).expanduser().resolve().as_posix()) +QUANTIZATION_POLICIES = ("fp8-cast", "fp8-scaled-mm") + + +class QuantizationAction(argparse.Action): + def __call__( + self, + parser: argparse.ArgumentParser, # noqa: ARG002 + namespace: argparse.Namespace, + values: list[str], + option_string: str | None = None, + ) -> None: + if len(values) > 2: + msg = ( + f"{option_string} accepts at most 2 arguments (POLICY and optional AMAX_PATH), got {len(values)} values" + ) + raise argparse.ArgumentError(self, msg) + + policy_name = values[0] + if policy_name not in QUANTIZATION_POLICIES: + msg = f"Unknown quantization policy '{policy_name}'. Choose from: {', '.join(QUANTIZATION_POLICIES)}" + raise argparse.ArgumentError(self, msg) + + if policy_name == "fp8-cast": + if len(values) > 1: + msg = f"{option_string} fp8-cast does not accept additional arguments" + raise argparse.ArgumentError(self, msg) + policy = QuantizationPolicy.fp8_cast() + elif policy_name == "fp8-scaled-mm": + amax_path = resolve_path(values[1]) if len(values) > 1 else None + policy = QuantizationPolicy.fp8_scaled_mm(amax_path) + + setattr(namespace, self.dest, policy) + + def basic_arg_parser() -> argparse.ArgumentParser: parser = argparse.ArgumentParser() parser.add_argument( @@ -174,13 +209,22 @@ def basic_arg_parser() -> argparse.ArgumentParser: "Example: --lora path/to/lora1.safetensors 0.8 --lora path/to/lora2.safetensors" ), ) - parser.add_argument( - "--enable-fp8", - action="store_true", - help="Enable FP8 mode to reduce memory footprint by keeping model in lower precision. " - "Note that calculations are still performed in bfloat16 precision.", - ) + parser.add_argument("--enhance-prompt", action="store_true") + parser.add_argument( + "--quantization", + dest="quantization", + action=QuantizationAction, + nargs="+", + metavar=("POLICY", "AMAX_PATH"), + default=None, + help=( + f"Quantization policy: {', '.join(QUANTIZATION_POLICIES)}. " + "fp8-cast uses FP8 casting with upcasting during inference. " + "fp8-scaled-mm uses FP8 scaled matrix multiplication (optionally provide amax calibration file path). " + "Example: --quantization fp8-cast or --quantization fp8-scaled-mm /path/to/amax.json" + ), + ) return parser diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py index e1081b7..349499b 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/media_io.py @@ -1,3 +1,4 @@ +import logging import math from collections.abc import Generator, Iterator from fractions import Fraction @@ -13,6 +14,8 @@ from tqdm import tqdm from ltx_pipelines.utils.constants import DEFAULT_IMAGE_CRF +logger = logging.getLogger(__name__) + def resize_aspect_ratio_preserving(image: torch.Tensor, long_side: int) -> torch.Tensor: """ @@ -227,6 +230,7 @@ def encode_video( _write_audio(container, audio_stream, audio, audio_sample_rate) container.close() + logger.info(f"Video saved to {output_path}") def decode_audio_from_file(path: str, device: torch.device) -> torch.Tensor | None: diff --git a/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py b/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py index 1ae36f3..d5e18c7 100644 --- a/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py +++ b/packages/ltx-pipelines/src/ltx_pipelines/utils/model_ledger.py @@ -2,6 +2,7 @@ from dataclasses import replace import torch +from ltx_core.loader import SDOps from ltx_core.loader.primitives import LoraPathStrengthAndSDOps from ltx_core.loader.registry import DummyRegistry, Registry from ltx_core.loader.single_gpu_model_builder import SingleGPUModelBuilder as Builder @@ -15,8 +16,6 @@ from ltx_core.model.audio_vae import ( ) from ltx_core.model.transformer import ( LTXV_MODEL_COMFY_RENAMING_MAP, - LTXV_MODEL_COMFY_RENAMING_WITH_TRANSFORMER_LINEAR_DOWNCAST_MAP, - UPCAST_DURING_INFERENCE, LTXModelConfigurator, X0Model, ) @@ -29,6 +28,7 @@ from ltx_core.model.video_vae import ( VideoEncoder, VideoEncoderConfigurator, ) +from ltx_core.quantization import QuantizationPolicy from ltx_core.text_encoders.gemma import ( AV_GEMMA_TEXT_ENCODER_KEY_OPS, AVGemmaTextEncoderModel, @@ -78,8 +78,9 @@ class ModelLedger: registry: Optional :class:`Registry` instance for weight caching across builders. Defaults to :class:`DummyRegistry` which performs no cross-builder caching. - fp8transformer: - If ``True``, builds the transformer with FP8 quantization and upcasting during inference. + quantization: + Optional :class:`QuantizationPolicy` controlling how transformer weights + are stored and how matmul is executed. Defaults to None, which means no quantization. ### Creating Variants Use :meth:`with_loras` to create a new ``ModelLedger`` instance that includes additional LoRA configurations while sharing the same registry for weight caching. @@ -94,7 +95,7 @@ class ModelLedger: spatial_upsampler_path: str | None = None, loras: LoraPathStrengthAndSDOps | None = None, registry: Registry | None = None, - fp8transformer: bool = False, + quantization: QuantizationPolicy | None = None, ): self.dtype = dtype self.device = device @@ -103,7 +104,7 @@ class ModelLedger: self.spatial_upsampler_path = spatial_upsampler_path self.loras = loras or () self.registry = registry or DummyRegistry() - self.fp8transformer = fp8transformer + self.quantization = quantization self.build_model_builders() def build_model_builders(self) -> None: @@ -179,7 +180,7 @@ class ModelLedger: spatial_upsampler_path=self.spatial_upsampler_path, loras=(*self.loras, *loras), registry=self.registry, - fp8transformer=self.fp8transformer, + quantization=self.quantization, ) def transformer(self) -> X0Model: @@ -187,19 +188,26 @@ class ModelLedger: raise ValueError( "Transformer not initialized. Please provide a checkpoint path to the ModelLedger constructor." ) - if self.fp8transformer: - fp8_builder = replace( - self.transformer_builder, - module_ops=(UPCAST_DURING_INFERENCE,), - model_sd_ops=LTXV_MODEL_COMFY_RENAMING_WITH_TRANSFORMER_LINEAR_DOWNCAST_MAP, - ) - return X0Model(fp8_builder.build(device=self._target_device())).to(self.device).eval() - else: + + if self.quantization is None: return ( X0Model(self.transformer_builder.build(device=self._target_device(), dtype=self.dtype)) .to(self.device) .eval() ) + else: + sd_ops = self.transformer_builder.model_sd_ops + if self.quantization.sd_ops is not None: + sd_ops = SDOps( + name=f"sd_ops_chain_{sd_ops.name}+{self.quantization.sd_ops.name}", + mapping=(*sd_ops.mapping, *self.quantization.sd_ops.mapping), + ) + builder = replace( + self.transformer_builder, + module_ops=(*self.transformer_builder.module_ops, *self.quantization.module_ops), + model_sd_ops=sd_ops, + ) + return X0Model(builder.build(device=self._target_device())).to(self.device).eval() def video_decoder(self) -> VideoDecoder: if not hasattr(self, "vae_decoder_builder"): diff --git a/packages/ltx-trainer/pyproject.toml b/packages/ltx-trainer/pyproject.toml index 19735d3..ac725c5 100644 --- a/packages/ltx-trainer/pyproject.toml +++ b/packages/ltx-trainer/pyproject.toml @@ -26,12 +26,12 @@ dependencies = [ "scenedetect>=0.6.5.2", "sentencepiece>=0.2.0", "torch>=2.6.0", - "torchaudio>=2.9.0", + "torchaudio>=2.7.0", "torchcodec>=0.8.1", "torchvision>=0.21.0", "typer>=0.15.1", "wandb>=0.19.11", - "setuptools>=80.9.0", + "setuptools>=79.0.0", ] [dependency-groups] diff --git a/uv.lock b/uv.lock index 63afc81..432f4e9 100644 --- a/uv.lock +++ b/uv.lock @@ -2,13 +2,39 @@ version = 1 revision = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'linux'", - "python_full_version >= '3.12' and sys_platform != 'linux'", - "python_full_version == 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and sys_platform != 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra == 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.13' and sys_platform == 'darwin' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.12.*' and sys_platform == 'darwin' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and sys_platform == 'darwin' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and sys_platform == 'darwin' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.12' and sys_platform == 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version >= '3.12' and sys_platform != 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and sys_platform == 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version == '3.11.*' and sys_platform != 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and sys_platform == 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", + "python_full_version < '3.11' and sys_platform != 'linux' and extra != 'extra-8-ltx-core-fp8-trtllm' and extra != 'extra-8-ltx-core-xformers'", ] +conflicts = [[ + { package = "ltx-core", extra = "fp8-trtllm" }, + { package = "ltx-core", extra = "xformers" }, +]] [manifest] members = [ @@ -30,19 +56,172 @@ version = "1.12.0" source = { registry = 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