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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from diffusers import CosmosControlNetModel
from diffusers.models.controlnets.controlnet_cosmos import CosmosControlNetOutput
from ...testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class CosmosControlNetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = CosmosControlNetModel
main_input_name = "controls_latents"
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_channels = 16
num_frames = 1
height = 16
width = 16
text_embed_dim = 32
sequence_length = 12
img_context_dim_in = 32
img_context_num_tokens = 4
# Raw latents (not patchified) - the controlnet computes embeddings internally
controls_latents = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
latents = torch.randn((batch_size, num_channels, num_frames, height, width)).to(torch_device)
timestep = torch.tensor([0.5]).to(torch_device) # Diffusion timestep
condition_mask = torch.ones(batch_size, 1, num_frames, height, width).to(torch_device)
padding_mask = torch.zeros(batch_size, 1, height, width).to(torch_device)
# Text embeddings
text_context = torch.randn((batch_size, sequence_length, text_embed_dim)).to(torch_device)
# Image context for Cosmos 2.5
img_context = torch.randn((batch_size, img_context_num_tokens, img_context_dim_in)).to(torch_device)
encoder_hidden_states = (text_context, img_context)
return {
"controls_latents": controls_latents,
"latents": latents,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
"condition_mask": condition_mask,
"conditioning_scale": 1.0,
"padding_mask": padding_mask,
}
@property
def input_shape(self):
return (16, 1, 16, 16)
@property
def output_shape(self):
# Output is tuple of n_controlnet_blocks tensors, each with shape (batch, num_patches, model_channels)
# After stacking by normalize_output: (n_blocks, batch, num_patches, model_channels)
# For test config: n_blocks=2, num_patches=64 (1*8*8), model_channels=32
# output_shape is used as (batch_size,) + output_shape, so: (2, 64, 32)
return (2, 64, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"n_controlnet_blocks": 2,
"in_channels": 16 + 1 + 1, # control_latent_channels + condition_mask + padding_mask
"latent_channels": 16 + 1 + 1, # base_latent_channels (16) + condition_mask (1) + padding_mask (1) = 18
"model_channels": 32,
"num_attention_heads": 2,
"attention_head_dim": 16,
"mlp_ratio": 2,
"text_embed_dim": 32,
"adaln_lora_dim": 4,
"patch_size": (1, 2, 2),
"max_size": (4, 32, 32),
"rope_scale": (2.0, 1.0, 1.0),
"extra_pos_embed_type": None,
"img_context_dim_in": 32,
"img_context_dim_out": 32,
"use_crossattn_projection": False, # Test doesn't need this projection
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output_format(self):
"""Test that the model outputs CosmosControlNetOutput with correct structure."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
self.assertIsInstance(output, CosmosControlNetOutput)
self.assertIsInstance(output.control_block_samples, list)
self.assertEqual(len(output.control_block_samples), init_dict["n_controlnet_blocks"])
for tensor in output.control_block_samples:
self.assertIsInstance(tensor, torch.Tensor)
def test_output_list_format(self):
"""Test that return_dict=False returns a tuple containing a list."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict, return_dict=False)
self.assertIsInstance(output, tuple)
self.assertEqual(len(output), 1)
self.assertIsInstance(output[0], list)
self.assertEqual(len(output[0]), init_dict["n_controlnet_blocks"])
def test_condition_mask_changes_output(self):
"""Test that condition mask affects control outputs."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
inputs_no_mask = dict(inputs_dict)
inputs_no_mask["condition_mask"] = torch.zeros_like(inputs_dict["condition_mask"])
with torch.no_grad():
output_no_mask = model(**inputs_no_mask)
output_with_mask = model(**inputs_dict)
self.assertEqual(len(output_no_mask.control_block_samples), len(output_with_mask.control_block_samples))
for no_mask_tensor, with_mask_tensor in zip(
output_no_mask.control_block_samples, output_with_mask.control_block_samples
):
self.assertFalse(torch.allclose(no_mask_tensor, with_mask_tensor))
def test_conditioning_scale_single(self):
"""Test that a single conditioning scale is broadcast to all blocks."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
inputs_dict["conditioning_scale"] = 0.5
with torch.no_grad():
output = model(**inputs_dict)
self.assertEqual(len(output.control_block_samples), init_dict["n_controlnet_blocks"])
def test_conditioning_scale_list(self):
"""Test that a list of conditioning scales is applied per block."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# Provide a scale for each block
inputs_dict["conditioning_scale"] = [0.5, 1.0]
with torch.no_grad():
output = model(**inputs_dict)
self.assertEqual(len(output.control_block_samples), init_dict["n_controlnet_blocks"])
def test_forward_with_none_img_context(self):
"""Test forward pass when img_context is None."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# Set encoder_hidden_states to (text_context, None)
text_context = inputs_dict["encoder_hidden_states"][0]
inputs_dict["encoder_hidden_states"] = (text_context, None)
with torch.no_grad():
output = model(**inputs_dict)
self.assertIsInstance(output, CosmosControlNetOutput)
self.assertEqual(len(output.control_block_samples), init_dict["n_controlnet_blocks"])
def test_forward_without_img_context_proj(self):
"""Test forward pass when img_context_proj is not configured."""
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
# Disable img_context_proj
init_dict["img_context_dim_in"] = None
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# When img_context is disabled, pass only text context (not a tuple)
text_context = inputs_dict["encoder_hidden_states"][0]
inputs_dict["encoder_hidden_states"] = text_context
with torch.no_grad():
output = model(**inputs_dict)
self.assertIsInstance(output, CosmosControlNetOutput)
self.assertEqual(len(output.control_block_samples), init_dict["n_controlnet_blocks"])
def test_gradient_checkpointing_is_applied(self):
expected_set = {"CosmosControlNetModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
# Note: test_set_attn_processor_for_determinism already handles uses_custom_attn_processor=True
# so no explicit skip needed for it
# Note: test_forward_signature and test_set_default_attn_processor don't exist in base class
# Skip tests that don't apply to this architecture
@unittest.skip("CosmosControlNetModel doesn't use norm groups.")
def test_forward_with_norm_groups(self):
pass
# Skip tests that expect .sample attribute - ControlNets don't have this
@unittest.skip("ControlNet output doesn't have .sample attribute")
def test_effective_gradient_checkpointing(self):
pass
# Skip tests that compute MSE loss against single tensor output
@unittest.skip("ControlNet outputs list of control blocks, not single tensor for MSE loss")
def test_ema_training(self):
pass
@unittest.skip("ControlNet outputs list of control blocks, not single tensor for MSE loss")
def test_training(self):
pass
# Skip tests where output shape comparison doesn't apply to ControlNets
@unittest.skip("ControlNet output shape doesn't match input shape by design")
def test_output(self):
pass
# Skip outputs_equivalence - dict/list comparison logic not compatible (recursive_check expects dict.values())
@unittest.skip("ControlNet output structure not compatible with recursive dict check")
def test_outputs_equivalence(self):
pass
# Skip model parallelism - base test uses torch.allclose(base_output[0], new_output[0]) which fails
# because output[0] is the list of control_block_samples, not a tensor
@unittest.skip("test_model_parallelism uses torch.allclose on output[0] which is a list, not a tensor")
def test_model_parallelism(self):
pass
# Skip layerwise casting tests - these have two issues:
# 1. _inference and _memory: dtype compatibility issues with learnable_pos_embed and float8/bfloat16
# 2. _training: same as test_training - mse_loss expects tensor, not list
@unittest.skip("Layerwise casting has dtype issues with learnable_pos_embed")
def test_layerwise_casting_inference(self):
pass
@unittest.skip("Layerwise casting has dtype issues with learnable_pos_embed")
def test_layerwise_casting_memory(self):
pass
@unittest.skip("test_layerwise_casting_training computes mse_loss on list output")
def test_layerwise_casting_training(self):
pass
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