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ac2243f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | # Copyright 2025 The HuggingFace Team.
#
# 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 PIL import Image
from transformers import (
AutoTokenizer,
CLIPImageProcessor,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
T5EncoderModel,
)
from diffusers import (
AutoencoderKLWan,
ChronoEditPipeline,
ChronoEditTransformer3DModel,
FlowMatchEulerDiscreteScheduler,
)
from ...testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class ChronoEditPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = ChronoEditPipeline
params = TEXT_TO_IMAGE_PARAMS - {"cross_attention_kwargs", "height", "width"}
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback_on_step_end",
"callback_on_step_end_tensor_inputs",
]
)
test_xformers_attention = False
supports_dduf = False
def get_dummy_components(self):
torch.manual_seed(0)
vae = AutoencoderKLWan(
base_dim=3,
z_dim=16,
dim_mult=[1, 1, 1, 1],
num_res_blocks=1,
temperal_downsample=[False, True, True],
)
torch.manual_seed(0)
# TODO: impl FlowDPMSolverMultistepScheduler
scheduler = FlowMatchEulerDiscreteScheduler(shift=7.0)
text_encoder = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
transformer = ChronoEditTransformer3DModel(
patch_size=(1, 2, 2),
num_attention_heads=2,
attention_head_dim=12,
in_channels=36,
out_channels=16,
text_dim=32,
freq_dim=256,
ffn_dim=32,
num_layers=2,
cross_attn_norm=True,
qk_norm="rms_norm_across_heads",
rope_max_seq_len=32,
image_dim=4,
)
torch.manual_seed(0)
image_encoder_config = CLIPVisionConfig(
hidden_size=4,
projection_dim=4,
num_hidden_layers=2,
num_attention_heads=2,
image_size=32,
intermediate_size=16,
patch_size=1,
)
image_encoder = CLIPVisionModelWithProjection(image_encoder_config)
torch.manual_seed(0)
image_processor = CLIPImageProcessor(crop_size=32, size=32)
components = {
"transformer": transformer,
"vae": vae,
"scheduler": scheduler,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"image_encoder": image_encoder,
"image_processor": image_processor,
}
return components
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device=device).manual_seed(seed)
image_height = 16
image_width = 16
image = Image.new("RGB", (image_width, image_height))
inputs = {
"image": image,
"prompt": "dance monkey",
"negative_prompt": "negative", # TODO
"height": image_height,
"width": image_width,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"num_frames": 5,
"max_sequence_length": 16,
"output_type": "pt",
}
return inputs
def test_inference(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
video = pipe(**inputs).frames
generated_video = video[0]
self.assertEqual(generated_video.shape, (5, 3, 16, 16))
# fmt: off
expected_slice = torch.tensor([0.4525, 0.4520, 0.4485, 0.4534, 0.4523, 0.4522, 0.4529, 0.4528, 0.5022, 0.5064, 0.5011, 0.5061, 0.5028, 0.4979, 0.5117, 0.5192])
# fmt: on
generated_slice = generated_video.flatten()
generated_slice = torch.cat([generated_slice[:8], generated_slice[-8:]])
self.assertTrue(torch.allclose(generated_slice, expected_slice, atol=1e-3))
@unittest.skip("Test not supported")
def test_attention_slicing_forward_pass(self):
pass
@unittest.skip("TODO: revisit failing as it requires a very high threshold to pass")
def test_inference_batch_single_identical(self):
pass
@unittest.skip(
"ChronoEditPipeline has to run in mixed precision. Save/Load the entire pipeline in FP16 will result in errors"
)
def test_save_load_float16(self):
pass
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