chore: allow tests/ directory in gitignore

.gitignore

@@ -8,6 +8,8 @@ logs/
 .idea
 output*
 test*
+!tests/
+!tests/**
 venv
 **/.swp
 **/*.log

tests/test_batch_inference.py

@@ -34,9 +34,11 @@ mock_torch.Generator = MagicMock
 # Create mock diffusers module
 mock_diffusers = MagicMock()
 
+
 # Create mock tqdm that works as a context manager
 class MockTqdm:
     """Mock tqdm that works as a context manager and returns a dummy progress bar."""
+
     def __init__(self, iterable=None, total=None, **kwargs):
         self.iterable = iterable
         self.n = 0
@@ -488,10 +490,7 @@ class TestMultiGPUDistribution:
 
     def test_single_gpu_gets_all_jobs(self):
         """Test that a single GPU processes all jobs."""
-        all_jobs = [
-            BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4")
-            for i in range(10)
-        ]
+        all_jobs = [BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4") for i in range(10)]
 
         # Simulate distribution for single GPU (gpu_id=0, num_gpus=1)
         gpu_id = 0
@@ -502,10 +501,7 @@ class TestMultiGPUDistribution:
 
     def test_two_gpu_distribution(self):
         """Test job distribution across 2 GPUs."""
-        all_jobs = [
-            BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4")
-            for i in range(10)
-        ]
+        all_jobs = [BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4") for i in range(10)]
 
         # GPU 0 gets jobs 0, 2, 4, 6, 8
         gpu0_jobs = [j for i, j in enumerate(all_jobs) if i % 2 == 0]
@@ -519,10 +515,7 @@ class TestMultiGPUDistribution:
 
     def test_four_gpu_distribution(self):
         """Test job distribution across 4 GPUs."""
-        all_jobs = [
-            BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4")
-            for i in range(12)
-        ]
+        all_jobs = [BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4") for i in range(12)]
 
         num_gpus = 4
         for gpu_id in range(num_gpus):
@@ -531,10 +524,7 @@ class TestMultiGPUDistribution:
 
     def test_uneven_distribution(self):
         """Test distribution when jobs don't divide evenly."""
-        all_jobs = [
-            BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4")
-            for i in range(10)
-        ]
+        all_jobs = [BatchJob(prompt=f"Job {i}", output_name=f"video{i}.mp4") for i in range(10)]
 
         num_gpus = 3
         gpu0_jobs = [j for i, j in enumerate(all_jobs) if i % num_gpus == 0]
@@ -842,7 +832,9 @@ class TestRunBatch:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_creates_output_dir(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_creates_output_dir(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that output directory is created if it doesn't exist."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe
@@ -885,7 +877,9 @@ class TestRunBatch:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_handles_errors(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_handles_errors(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that batch continues after individual job failures."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe
@@ -908,7 +902,9 @@ class TestRunBatch:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_logs_errors_to_file(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_logs_errors_to_file(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that errors are logged to errors.log."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe
@@ -930,7 +926,9 @@ class TestRunBatch:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_resume_skips_completed(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_resume_skips_completed(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that resume skips already-completed jobs."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe
@@ -984,10 +982,7 @@ class TestRunBatch:
     def test_run_batch_multi_gpu_distribution(self, mock_generate, mock_load_pipeline, tmp_path):
         """Test multi-GPU job distribution in run_batch."""
         # Create batch file with 6 jobs
-        jobs = [
-            {"prompt": f"Job {i}", "output_name": f"video{i}.mp4"}
-            for i in range(6)
-        ]
+        jobs = [{"prompt": f"Job {i}", "output_name": f"video{i}.mp4"} for i in range(6)]
         batch_file = tmp_path / "batch.jsonl"
         batch_file.write_text("\n".join(json.dumps(j) for j in jobs))
 
@@ -1011,7 +1006,9 @@ class TestRunBatch:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_state_persists_after_each_job(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_state_persists_after_each_job(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that state is saved after each job, not just at the end."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe
@@ -1107,7 +1104,9 @@ class TestEdgeCases:
 
     @patch.object(batch_inference, "load_pipeline")
     @patch.object(batch_inference, "generate_single_video")
-    def test_run_batch_corrupted_state_starts_fresh(self, mock_generate, mock_load_pipeline, batch_file, tmp_path):
+    def test_run_batch_corrupted_state_starts_fresh(
+        self, mock_generate, mock_load_pipeline, batch_file, tmp_path
+    ):
         """Test that corrupted state file doesn't prevent batch from running."""
         mock_pipe = MagicMock()
         mock_load_pipeline.return_value = mock_pipe

tools/batch_inference.py

@@ -107,6 +107,7 @@ RESOLUTION_MAP = {
 @dataclass
 class BatchJob:
     """Represents a single job in the batch."""
+
     prompt: str
     output_name: str
     image_path: Optional[str] = None
@@ -153,6 +154,7 @@ class BatchJob:
 @dataclass
 class BatchState:
     """Tracks batch progress for resume capability."""
+
     batch_file: str
     output_dir: str
     model_path: str
@@ -528,11 +530,9 @@ def run_batch(
                 avg_time = elapsed / (completed + failed)
                 remaining = len(jobs) - (completed + failed)
                 eta_seconds = avg_time * remaining
-                pbar.set_postfix({
-                    "done": completed,
-                    "failed": failed,
-                    "ETA": f"{eta_seconds/60:.1f}m"
-                })
+                pbar.set_postfix(
+                    {"done": completed, "failed": failed, "ETA": f"{eta_seconds/60:.1f}m"}
+                )
 
     # Final summary
     elapsed_total = time.time() - start_time
@@ -581,21 +581,16 @@ JSONL Format:
   Each line is a JSON object with: prompt (required), output_name (required),
   and optional: image_path, video_path, num_frames, guidance_scale,
   num_inference_steps, seed, width, height
-        """
+        """,
     )
 
     # Required arguments
-    parser.add_argument(
-        "--batch_file",
-        type=str,
-        required=True,
-        help="Path to JSONL batch file"
-    )
+    parser.add_argument("--batch_file", type=str, required=True, help="Path to JSONL batch file")
     parser.add_argument(
         "--model_path",
         type=str,
         default="THUDM/CogVideoX1.5-5B",
-        help="Path to the model (default: THUDM/CogVideoX1.5-5B)"
+        help="Path to the model (default: THUDM/CogVideoX1.5-5B)",
     )
 
     # Output settings
@@ -603,91 +598,59 @@ JSONL Format:
         "--output_dir",
         type=str,
         default="./batch_output",
-        help="Directory for output videos (default: ./batch_output)"
+        help="Directory for output videos (default: ./batch_output)",
     )
     parser.add_argument(
         "--generate_type",
         type=str,
         choices=["t2v", "i2v", "v2v"],
         default="t2v",
-        help="Generation type (default: t2v)"
+        help="Generation type (default: t2v)",
     )
 
     # Model settings
     parser.add_argument(
-        "--lora_path",
-        type=str,
-        default=None,
-        help="Path to LoRA weights (optional)"
+        "--lora_path", type=str, default=None, help="Path to LoRA weights (optional)"
     )
     parser.add_argument(
         "--dtype",
         type=str,
         choices=["float16", "bfloat16"],
         default="bfloat16",
-        help="Data type for computation (default: bfloat16)"
+        help="Data type for computation (default: bfloat16)",
     )
     parser.add_argument(
         "--disable_cpu_offload",
         action="store_true",
-        help="Disable CPU offloading (uses more VRAM but faster)"
+        help="Disable CPU offloading (uses more VRAM but faster)",
     )
 
     # Default generation parameters
     parser.add_argument(
-        "--num_frames",
-        type=int,
-        default=81,
-        help="Default number of frames (default: 81)"
+        "--num_frames", type=int, default=81, help="Default number of frames (default: 81)"
     )
     parser.add_argument(
-        "--guidance_scale",
-        type=float,
-        default=6.0,
-        help="Default guidance scale (default: 6.0)"
+        "--guidance_scale", type=float, default=6.0, help="Default guidance scale (default: 6.0)"
     )
     parser.add_argument(
-        "--num_inference_steps",
-        type=int,
-        default=50,
-        help="Default inference steps (default: 50)"
-    )
-    parser.add_argument(
-        "--seed",
-        type=int,
-        default=42,
-        help="Default random seed (default: 42)"
-    )
-    parser.add_argument(
-        "--fps",
-        type=int,
-        default=16,
-        help="Output video FPS (default: 16)"
+        "--num_inference_steps", type=int, default=50, help="Default inference steps (default: 50)"
     )
+    parser.add_argument("--seed", type=int, default=42, help="Default random seed (default: 42)")
+    parser.add_argument("--fps", type=int, default=16, help="Output video FPS (default: 16)")
 
     # Resume and multi-GPU
     parser.add_argument(
         "--resume",
         action="store_true",
         default=True,
-        help="Resume from previous state (default: True)"
+        help="Resume from previous state (default: True)",
+    )
+    parser.add_argument("--no_resume", action="store_true", help="Don't resume, start fresh")
+    parser.add_argument(
+        "--gpu_id", type=int, default=0, help="GPU ID for multi-GPU distribution (default: 0)"
     )
     parser.add_argument(
-        "--no_resume",
-        action="store_true",
-        help="Don't resume, start fresh"
-    )
-    parser.add_argument(
-        "--gpu_id",
-        type=int,
-        default=0,
-        help="GPU ID for multi-GPU distribution (default: 0)"
-    )
-    parser.add_argument(
-        "--num_gpus",
-        type=int,
-        default=1,
-        help="Total number of GPUs for distribution (default: 1)"
+        "--num_gpus", type=int, default=1, help="Total number of GPUs for distribution (default: 1)"
     )
 
     args = parser.parse_args()