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import json
from pathlib import Path
from types import SimpleNamespace
import torch
from PIL import Image
from adam.generations import parse_chat_generation_request
from adam.model_plugins import ModelPluginRegistry
from adam.model_plugins_builtin.inrflow.generator import generate
from adam.model_plugins_builtin.inrflow.model import (
INRFlowConfig,
INRFlowModel,
coordinate_grid,
sample_image,
)
from adam.model_plugins_builtin.inrflow.trainer import train
from adam.registry import ToolRegistry
class FakeContext:
def __init__(self, root: Path, tool_id: str, tool_name: str) -> None:
self.root = root
self.job_id = "inrflow-test-job"
self.tool = SimpleNamespace(id=tool_id, name=tool_name)
self.logs: list[str] = []
self.progress_updates: list[tuple[int, str, dict]] = []
self.previews: list[dict] = []
def log(self, message: str) -> None:
self.logs.append(message)
def checkpoint(self) -> None:
pass
def progress(self, percent: int, message: str, **details) -> None:
self.progress_updates.append((percent, message, details))
def preview(self, path, **details) -> None:
self.previews.append({"path": str(path), **details})
def test_inrflow_plugin_is_discovered_with_native_backends() -> None:
plugins = ModelPluginRegistry(Path.cwd())
tools = ToolRegistry(Path.cwd())
plugin = plugins.get("inrflow")
trainer = tools.get("inrflow_trainer")
generator = tools.get("inrflow_generator")
assert plugin.info["architecture"] == "inrflow_ambient_space"
assert plugin.info["method_reference"].endswith("2412.03791")
assert plugin.training_settings["query_points"]["default"] == 1024
assert trainer.backend["module"].endswith("inrflow.trainer")
assert "resume_training" in trainer.capabilities
assert generator.model_trainers == ("inrflow",)
assert "resolution_flexible_generation" in generator.capabilities
assert len(generator.arguments) == len(set(generator.arguments))
def test_inrflow_generation_request_selects_distinct_provider() -> None:
request = parse_chat_generation_request(
'Generate an INRFlow image of tiny cars using model "Coordinate Cars" with 20 steps.'
)
spaced = parse_chat_generation_request("Generate an INR Flow image of landscapes")
assert request is not None
assert request.provider_hint == "inrflow"
assert request.model_query == "Coordinate Cars"
assert request.subject == "tiny cars"
assert request.steps == 20
assert spaced is not None and spaced.provider_hint == "inrflow"
def test_inrflow_predicts_pointwise_velocity_and_seeded_samples() -> None:
config = INRFlowConfig(
resolution=32,
patch_size=8,
hidden_size=64,
depth=1,
num_heads=4,
decoder_layers=1,
fourier_frequencies=2,
)
model = INRFlowModel(config)
coordinates = coordinate_grid(32, 32).unsqueeze(0).expand(2, -1, -1)
values = torch.randn(2, 32 * 32, 3)
prediction = model(
coordinates,
values,
torch.tensor([0.25, 0.75]),
coordinates[:, :37],
values[:, :37],
height=32,
width=32,
)
loss = prediction.square().mean()
loss.backward()
first = sample_image(
model,
resolution=32,
steps=2,
method="Euler",
noise_scale=1.0,
query_chunk_size=256,
generator=torch.Generator().manual_seed(42),
)
second = sample_image(
model,
resolution=32,
steps=2,
method="Euler",
noise_scale=1.0,
query_chunk_size=256,
generator=torch.Generator().manual_seed(42),
)
assert prediction.shape == (2, 37, 3)
assert first.shape == (32, 32, 3)
assert torch.equal(first, second)
def test_inrflow_training_and_generation_round_trip(tmp_path: Path, monkeypatch) -> None:
monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
dataset = tmp_path / "cars"
dataset.mkdir()
for index, color in enumerate(((220, 40, 30), (20, 80, 220))):
image = Image.new("RGB", (40, 28), (225, 225, 225))
for x in range(8, 32):
for y in range(10, 22):
image.putpixel((x, y), color)
image.save(dataset / f"car_{index}.png")
output = tmp_path / "data" / "model_plugin_outputs" / "inrflow" / "Coordinate Cars"
training_context = FakeContext(tmp_path, "inrflow_trainer", "INRFlow Trainer")
result = train(
training_context,
dataset_dir=str(dataset),
model_name="Coordinate Cars",
epochs=1,
output_dir=str(output),
resolution=32,
batch_size=2,
mixed_precision="no",
patch_size=8,
hidden_size=64,
depth=1,
num_heads=4,
decoder_layers=1,
fourier_frequencies=2,
query_points=64,
preview_enabled=False,
)
assert (output / "inrflow_model.pt").is_file()
assert (output / "inrflow_config.json").is_file()
assert (output / "training_metadata.json").is_file()
assert result["assets"][0]["trainer"] == "inrflow"
metadata = json.loads((output / "training_metadata.json").read_text(encoding="utf-8"))
assert metadata["uses_pretrained_compressor"] is False
assert metadata["settings"]["query_points"] == 64
continued_output = (
tmp_path / "data" / "model_plugin_outputs" / "inrflow" / "Coordinate Cars Continued"
)
continuation_context = FakeContext(tmp_path, "inrflow_trainer", "INRFlow Trainer")
train(
continuation_context,
dataset_dir=str(dataset),
model_name="Coordinate Cars Continued",
epochs=1,
output_dir=str(continued_output),
resume_from=str(output),
resolution=64,
batch_size=2,
mixed_precision="no",
query_points=64,
preview_enabled=False,
)
continued_metadata = json.loads(
(continued_output / "training_metadata.json").read_text(encoding="utf-8")
)
assert continued_metadata["completed_epochs"] == 2
assert continued_metadata["settings"]["resolution"] == 32
generation_context = FakeContext(tmp_path, "inrflow_generator", "INRFlow Generator")
generated = generate(
generation_context,
model_name="Coordinate Cars",
model_path=str(output),
prompt="ambient-space comparison",
image_count=1,
steps=2,
seed=123,
sampler="Euler",
aspect_ratio="1:1 (Coordinate Field)",
output_resolution="64",
query_chunk_size=256,
preview_interval=1,
)
generation_folder = Path(generated["output_folder"])
final_images = list(generation_folder.glob("*_64px.png"))
generation_metadata = list(generation_folder.glob("generation_*.json"))
assert len(final_images) == 1
assert len(generation_metadata) == 1
assert generation_context.previews[-1]["current"] == 2
assert generation_context.progress_updates[-1][0] == 100
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