add tests

Author: a-r-r-o-w

tests/lora/test_lora_layers_ltx_video.py

@@ -0,0 +1,185 @@
+# Copyright 2024 HuggingFace Inc.
+#
+# 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 sys
+import unittest
+
+import numpy as np
+import pytest
+import torch
+from transformers import AutoTokenizer, T5EncoderModel
+
+from diffusers import (
+    AutoencoderKLLTXVideo,
+    FlowMatchEulerDiscreteScheduler,
+    LTXPipeline,
+    LTXVideoTransformer3DModel,
+)
+from diffusers.utils.testing_utils import (
+    floats_tensor,
+    is_peft_available,
+    is_torch_version,
+    require_peft_backend,
+    skip_mps,
+    torch_device,
+)
+
+
+if is_peft_available():
+    pass
+
+sys.path.append(".")
+
+from utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set  # noqa: E402
+
+
+@require_peft_backend
+class LTXVideoLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
+    pipeline_class = LTXPipeline
+    scheduler_cls = FlowMatchEulerDiscreteScheduler
+    scheduler_classes = [FlowMatchEulerDiscreteScheduler]
+    scheduler_kwargs = {}
+
+    transformer_kwargs = {
+        "in_channels": 8,
+        "out_channels": 8,
+        "patch_size": 1,
+        "patch_size_t": 1,
+        "num_attention_heads": 4,
+        "attention_head_dim": 8,
+        "cross_attention_dim": 32,
+        "num_layers": 1,
+        "caption_channels": 32,
+    }
+    transformer_cls = LTXVideoTransformer3DModel
+    vae_kwargs = {
+        "latent_channels": 8,
+        "block_out_channels": (8, 8, 8, 8),
+        "spatio_temporal_scaling": (True, True, False, False),
+        "layers_per_block": (1, 1, 1, 1, 1),
+        "patch_size": 1,
+        "patch_size_t": 1,
+        "encoder_causal": True,
+        "decoder_causal": False,
+    }
+    vae_cls = AutoencoderKLLTXVideo
+    tokenizer_cls, tokenizer_id = AutoTokenizer, "hf-internal-testing/tiny-random-t5"
+    text_encoder_cls, text_encoder_id = T5EncoderModel, "hf-internal-testing/tiny-random-t5"
+
+    text_encoder_target_modules = ["q", "k", "v", "o"]
+
+    @property
+    def output_shape(self):
+        return (1, 9, 32, 32, 3)
+
+    def get_dummy_inputs(self, with_generator=True):
+        batch_size = 1
+        sequence_length = 16
+        num_channels = 8
+        num_frames = 9
+        num_latent_frames = 3  # (num_frames - 1) // temporal_compression_ratio + 1
+        latent_height = 8
+        latent_width = 8
+
+        generator = torch.manual_seed(0)
+        noise = floats_tensor((batch_size, num_latent_frames, num_channels, latent_height, latent_width))
+        input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
+
+        pipeline_inputs = {
+            "prompt": "dance monkey",
+            "num_frames": num_frames,
+            "num_inference_steps": 4,
+            "guidance_scale": 6.0,
+            "height": 32,
+            "width": 32,
+            "max_sequence_length": sequence_length,
+            "output_type": "np",
+        }
+        if with_generator:
+            pipeline_inputs.update({"generator": generator})
+
+        return noise, input_ids, pipeline_inputs
+
+    @skip_mps
+    @pytest.mark.xfail(
+        condition=torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
+        reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
+        strict=True,
+    )
+    def test_lora_fuse_nan(self):
+        for scheduler_cls in self.scheduler_classes:
+            components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
+            pipe = self.pipeline_class(**components)
+            pipe = pipe.to(torch_device)
+            pipe.set_progress_bar_config(disable=None)
+            _, _, inputs = self.get_dummy_inputs(with_generator=False)
+
+            pipe.transformer.add_adapter(denoiser_lora_config, "adapter-1")
+
+            self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in denoiser")
+
+            # corrupt one LoRA weight with `inf` values
+            with torch.no_grad():
+                pipe.transformer.transformer_blocks[0].attn1.to_q.lora_A["adapter-1"].weight += float("inf")
+
+            # with `safe_fusing=True` we should see an Error
+            with self.assertRaises(ValueError):
+                pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
+
+            # without we should not see an error, but every image will be black
+            pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
+
+            out = pipe(
+                "test", num_inference_steps=2, max_sequence_length=inputs["max_sequence_length"], output_type="np"
+            )[0]
+
+            self.assertTrue(np.isnan(out).all())
+
+    def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
+        super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
+
+    def test_simple_inference_with_text_denoiser_lora_unfused(self):
+        super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
+
+    @unittest.skip("Not supported in LTXVideo.")
+    def test_simple_inference_with_text_denoiser_block_scale(self):
+        pass
+
+    @unittest.skip("Not supported in LTXVideo.")
+    def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
+        pass
+
+    @unittest.skip("Not supported in LTXVideo.")
+    def test_modify_padding_mode(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
+    def test_simple_inference_with_partial_text_lora(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
+    def test_simple_inference_with_text_lora(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
+    def test_simple_inference_with_text_lora_and_scale(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
+    def test_simple_inference_with_text_lora_fused(self):
+        pass
+
+    @unittest.skip("Text encoder LoRA is not supported in LTXVideo.")
+    def test_simple_inference_with_text_lora_save_load(self):
+        pass