[feat]: add COSMOS 2.5 DiT implementation #897

KyleShao1016 · 2025-11-22T03:14:38Z

Implement NVIDIA's COSMOS 2.5 Diffusion Transformer (DiT) architecture in FastVideo
with verified numerical accuracy against the official cosmos-predict2.5 reference.

Key changes:

Model Architecture (fastvideo/models/dits/cosmos2_5.py):
- Implement Cosmos25Transformer3DModel with 28 transformer blocks
- Add AdaLN-LoRA conditioning with proper modulation parameter computation
- Implement 3D RoPE with NTK-aware extrapolation for spatiotemporal encoding
- Add QK normalization using RMSNorm for improved training stability
- Support optional cross-attention projection for high-dimensional embeddings (Qwen 7B)
- Add learnable positional embeddings as optional feature
- Implement proper patch embedding/unpatchification for video processing
Configuration (fastvideo/configs/models/dits/cosmos2_5.py):
- Add Cosmos25ArchConfig with complete parameter mapping for checkpoint loading
- Map official checkpoint structure (net.blocks.) to FastVideo structure (transformer_blocks.)
- Support AdaLN modulation layers at block level (adaln_modulation_self_attn/cross_attn/mlp)
- Configure cross-attention projection for 100,352-dim Qwen embeddings
- Add LoRA parameter mappings for fine-tuning support
Tests (fastvideo/tests/transformers/test_cosmos2_5.py):
- Add comprehensive parity test against official MinimalV1LVGDiT reference
- Test both single-frame (image) and multi-frame (video) generation
- Verify checkpoint loading and weight consistency
- Achieve 0.173% relative difference in bfloat16 (excellent parity)
- Test video2world conditioning with condition masks
Registry (fastvideo/configs/models/dits/init.py):
- Register Cosmos25VideoConfig for model instantiation

Test Results:

Max difference: 0.046875
Mean difference: 0.001869
Relative difference: 0.173% (excellent parity in bfloat16)

gemini-code-assist · 2025-11-22T03:14:58Z

Summary of Changes

Hello @KyleShao1016, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request integrates NVIDIA's COSMOS 2.5 Diffusion Transformer architecture into the FastVideo framework. The implementation includes key architectural components like AdaLN-LoRA conditioning, 3D Rotary Positional Embeddings, and QK normalization, along with a robust configuration for checkpoint compatibility. Extensive testing confirms high numerical accuracy against the official reference, ensuring reliable video generation capabilities.

Highlights

COSMOS 2.5 DiT Implementation: Added the NVIDIA COSMOS 2.5 Diffusion Transformer (DiT) architecture to FastVideo, enabling advanced video generation capabilities.
Numerical Accuracy Verified: Achieved excellent numerical accuracy, with a 0.173% relative difference in bfloat16, against the official COSMOS 2.5 reference implementation.
Advanced Architectural Features: Implemented key features including 28 transformer blocks, AdaLN-LoRA conditioning, 3D Rotary Positional Embeddings (RoPE) with NTK-aware extrapolation, QK normalization, and optional cross-attention projection for high-dimensional embeddings (e.g., Qwen 7B).
Comprehensive Configuration and Checkpoint Loading: Introduced Cosmos25ArchConfig with detailed parameter mappings to ensure seamless loading of official COSMOS 2.5 checkpoints and support for LoRA fine-tuning.
Robust Testing Suite: Included extensive parity tests covering both single-frame (image) and multi-frame (video) generation, checkpoint loading, and video2world conditioning with condition masks.

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Code Review

This pull request introduces a comprehensive implementation of the NVIDIA COSMOS 2.5 DiT model, including its configuration, architecture, and detailed tests for numerical parity against the reference implementation. The code is well-structured, clearly commented, and the addition of extensive tests is commendable. My review identifies a few areas for improvement related to code style, efficiency, and robustness. Specifically, I've suggested moving an import out of a hot path in the forward method, improving robustness by making zip calls stricter to catch potential configuration errors, and cleaning up unused imports and variables in the test file. Overall, this is a high-quality contribution that significantly extends the model zoo.

gemini-code-assist · 2025-11-22T03:16:45Z

fastvideo/models/dits/cosmos2_5.py

+
+        # 2. Concatenate padding mask if needed
+        if self.concat_padding_mask and padding_mask is not None:
+            from torchvision import transforms


Importing torchvision.transforms inside the forward method is inefficient as it will be executed on every call. This import should be moved to the top of the file with the other imports (e.g., near import torch) to follow standard Python best practices and avoid repeated import overhead.

Moved the import code to the top section.

gemini-code-assist · 2025-11-22T03:16:45Z

fastvideo/models/dits/cosmos2_5.py

+    ) -> None:
+        super().__init__()
+
+        self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=False)]


Using zip with strict=False can hide potential configuration errors. If max_size and patch_size have different lengths, zip will silently truncate to the shorter iterable, which could lead to unexpected behavior. It's safer to use strict=True to ensure that these tuples have the expected matching length of 3. This will raise a ValueError if their lengths differ, making configuration issues easier to debug.

Suggested change

self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=False)]

self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=True)]

gemini-code-assist · 2025-11-22T03:16:45Z

fastvideo/models/dits/cosmos2_5.py

+    ) -> None:
+        super().__init__()
+
+        self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=False)]


Similar to the Cosmos25RotaryPosEmbed class, using zip with strict=False here can hide configuration errors. If max_size and patch_size have different lengths, it will silently truncate. Using strict=True is recommended for robustness, as it will raise a ValueError if the lengths of the iterables do not match, which helps in catching configuration mistakes early.

Suggested change

self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=False)]

self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=True)]

gemini-code-assist · 2025-11-22T03:16:45Z

fastvideo/tests/transformers/test_cosmos2_5.py

+import numpy as np
+import pytest
+import torch
+
+# Add cosmos-predict2.5 to Python path for loading reference model
+# cosmos-predict2.5 is a sibling directory to FastVideo at video/cosmos-predict2.5
+TEST_DIR = os.path.dirname(os.path.abspath(__file__))
+# From test file: video/FastVideo/fastvideo/tests/transformers/
+# Go up 4 levels to reach: video/
+# Then join with: cosmos-predict2.5
+COSMOS_PREDICT2_5_PATH = os.path.join(TEST_DIR, '..', '..', '..', '..', 'cosmos-predict2.5')
+COSMOS_PREDICT2_5_PATH = os.path.normpath(COSMOS_PREDICT2_5_PATH)
+if os.path.exists(COSMOS_PREDICT2_5_PATH) and COSMOS_PREDICT2_5_PATH not in sys.path:
+    sys.path.insert(0, COSMOS_PREDICT2_5_PATH)
+
+from fastvideo.configs.pipelines import PipelineConfig
+from fastvideo.forward_context import set_forward_context
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.models.loader.component_loader import TransformerLoader


There are several unused imports and variables in this test file that should be removed for code cleanliness:

Unused imports: numpy as np (line 9), PipelineConfig (line 24), FastVideoArgs (line 26), TransformerLoader (line 28).

Unused variable precision_str: defined on line 196 and 479 but never used.

Edenzzzz · 2025-11-23T16:03:51Z

fastvideo/tests/transformers/test_cosmos2_5.py

+        assert max_diff < 1e-1, f"Maximum difference too large: {max_diff.item()}"
+        assert mean_diff < 1e-2, f"Mean difference too large: {mean_diff.item()}"


assert a lower error than this?

Changed the dtype to float32 and reduced the threshold values.

SolitaryThinker · 2025-11-23T21:55:56Z

fastvideo/models/dits/cosmos2_5.py

+        # Attention computation
+        attn_output = torch.nn.functional.scaled_dot_product_attention(


can you replace this with fastvideo's distributedAttention? refer to wan or cosmos2. By default both torch sdpa and fa should be supported

SolitaryThinker · 2025-11-23T21:56:35Z

fastvideo/models/dits/cosmos2_5.py

+        value = value.transpose(1, 2)
+
+        # Attention computation
+        attn_output = torch.nn.functional.scaled_dot_product_attention(


replace this with our LocalAttention layer.

SolitaryThinker · 2025-11-23T21:57:38Z

also please run pre-commit and fix lint

Implement NVIDIA's COSMOS 2.5 Diffusion Transformer with AdaLN-LoRA conditioning, 3D RoPE, and QK normalization. Achieves 0.173% relative error vs official reference in bfloat16. - Add Cosmos25Transformer3DModel with 28 transformer blocks - Add configuration, checkpoint mappings, and parity tests - Support optional cross-attention projection for high-dim embeddings

- Integrated DistributedAttention and LocalAttention for flexible backend support in Cosmos25SelfAttention and Cosmos25CrossAttention classes. - Updated attention computation to handle both distributed and local scenarios. - Refactored attention backend initialization to check for distributed environment. - Cleaned up unused comments and improved code readability in the test suite for Cosmos 2.5. This update improves the model's adaptability to different hardware configurations while maintaining performance.

…onality - Added torchvision transforms import to the Cosmos 2.5 model for enhanced functionality. - Updated max_size calculation in positional embedding classes to enforce strict behavior. - Changed precision from bfloat16 to float32 in test cases to improve numerical stability. - Tightened numerical difference assertions in tests to ensure higher accuracy in model outputs. These changes enhance the model's robustness and ensure better alignment with reference implementations.

- Changed precision from float32 to bfloat16 to support FlashAttention backend * FlashAttention only supports bfloat16 and float16 dtypes * This aligns with the official Cosmos2.5 inference pipeline which uses bfloat16 - Adjusted numerical difference thresholds to account for bfloat16 precision * Relaxed max_diff and mean_diff assertions to accommodate lower precision * New thresholds are empirically determined to pass tests while maintaining correctness verification These changes improve test compatibility with optimized attention backends and better reflect the model's actual inference configuration.

SolitaryThinker · 2025-12-08T02:04:31Z

fastvideo/models/dits/cosmos2_5.py

+        try:
+            from fastvideo.distributed.parallel_state import model_parallel_is_initialized
+            use_distributed = torch.distributed.is_initialized() and model_parallel_is_initialized()
+        except:
+            use_distributed = False
+
+        if use_distributed:


we shouldn't need to check this, just always using distributedAttention is fine

Updated!
I used DistributedAttention for self attention and LocalAttention for cross attention.

…butedAttention for self-attention and LocalAttention for cross-attention. This change simplifies the code by removing the distributed environment checks and ensures consistent behavior across different configurations.

gemini-code-assist bot reviewed Nov 22, 2025

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Edenzzzz added the go Trigger Buildkite CI label Nov 23, 2025

Edenzzzz reviewed Nov 23, 2025

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SolitaryThinker reviewed Nov 23, 2025

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SolitaryThinker mentioned this pull request Nov 24, 2025

[Feature] Development Roadmap 2025 Q4/2026 Q1 #899

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KyleShao1016 force-pushed the cosmos2.5 branch from 9a1f5c3 to d3997f6 Compare December 7, 2025 00:39

KyleS1016 and others added 3 commits December 7, 2025 04:32

KyleShao1016 force-pushed the cosmos2.5 branch from d3997f6 to b96adb1 Compare December 7, 2025 04:37

SolitaryThinker reviewed Dec 8, 2025

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SolitaryThinker approved these changes Dec 8, 2025

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SolitaryThinker changed the title ~~feat: add COSMOS 2.5 DiT implementation~~ [feat]: add COSMOS 2.5 DiT implementation Dec 8, 2025

SolitaryThinker merged commit e04a192 into hao-ai-lab:main Dec 8, 2025
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	self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=False)]
	self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=True)]

		assert max_diff < 1e-1, f"Maximum difference too large: {max_diff.item()}"
		assert mean_diff < 1e-2, f"Mean difference too large: {mean_diff.item()}"

		# Attention computation
		attn_output = torch.nn.functional.scaled_dot_product_attention(

[feat]: add COSMOS 2.5 DiT implementation #897

[feat]: add COSMOS 2.5 DiT implementation #897

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KyleShao1016 commented Nov 22, 2025

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SolitaryThinker commented Nov 23, 2025

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