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c5cfae9 | 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 | #!/usr/bin/env python3
"""Smoke-test all IntrinsicWeather pipelines on CUDA with bfloat16."""
from __future__ import annotations
import gc
import sys
from pathlib import Path
import torch
REPO = Path(__file__).resolve().parent
sys.path.insert(0, str(REPO))
DTYPE = torch.bfloat16
DEVICE = "cuda"
IMAGE_SIZE = 512
STEPS = 2
def _clear():
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
def test_inverse() -> None:
from imaa.imaa import IMAA
from pipeline_intrinsic_weather_inverse import IntrinsicWeatherInversePipeline
from safetensors.torch import load_file
print("[inverse] loading pipeline ...")
pipe = IntrinsicWeatherInversePipeline.from_pretrained(
REPO,
transformer_subfolder="inverse-512",
device=DEVICE,
local_files_only=True,
torch_dtype=DTYPE,
)
assert next(pipe.transformer.parameters()).dtype == DTYPE
assert next(pipe.transformer.parameters()).device.type == "cuda"
print(f"[inverse] transformer in_channels={pipe.transformer.config.in_channels}")
imaa = IMAA(dino_model=None, processor=None, num_maps=5, map_embedding_dim=256, common_dim=128).to(DEVICE)
imaa.load_state_dict(load_file((REPO / "imaa" / "model.safetensors").as_posix()))
imaa.eval()
image = torch.rand(1, 3, IMAGE_SIZE, IMAGE_SIZE, device=DEVICE, dtype=DTYPE)
prompt_embeds, _, pooled_prompt_embeds, _ = pipe.encode_prompt(
prompt="Albedo (diffuse basecolor)",
prompt_2=None,
prompt_3=None,
do_classifier_free_guidance=False,
)
print("[inverse] running 2-step inference ...")
out = pipe(
image=image,
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
guidance_scale=0.0,
image_guidance_scale=0.0,
num_inference_steps=STEPS,
output_type="pt",
aov=["albedo"],
map_aware_mask=None,
)
print(f"[inverse] output shape={tuple(out.images[0].shape)} dtype={out.images[0].dtype}")
del pipe, imaa, out
_clear()
def test_forward() -> None:
from pipeline_intrinsic_weather_forward import IntrinsicWeatherForwardPipeline
print("[forward] loading pipeline ...")
pipe = IntrinsicWeatherForwardPipeline.from_pretrained(
REPO,
transformer_subfolder="forward",
device=DEVICE,
local_files_only=True,
torch_dtype=DTYPE,
load_lora=True,
)
assert next(pipe.transformer.parameters()).dtype == DTYPE
assert next(pipe.transformer.parameters()).device.type == "cuda"
print(f"[forward] transformer in_channels={pipe.transformer.config.in_channels}")
aov = torch.randn(1, 3, IMAGE_SIZE, IMAGE_SIZE, device=DEVICE, dtype=DTYPE)
print("[forward] running 2-step inference ...")
out = pipe(
albedo=aov,
normal=aov,
roughness=aov,
metallic=aov,
irradiance=aov,
prompt=["A rainy day."],
guidance_scale=6.0,
image_guidance_scale=1.5,
num_inference_steps=STEPS,
required_aovs=["albedo", "normal", "roughness", "metallic", "irradiance"],
generator=torch.Generator(device=DEVICE).manual_seed(0),
)
print(f"[forward] output size={out.images[0].size}")
del pipe, out
_clear()
def test_unified() -> None:
from pipeline_intrinsic_weather import IntrinsicWeatherPipeline
print("[unified] loading pipeline ...")
pipe = IntrinsicWeatherPipeline.from_pretrained(
REPO,
inverse_transformer_subfolder="inverse-512",
forward_transformer_subfolder="forward",
device=DEVICE,
local_files_only=True,
torch_dtype=DTYPE,
load_lora=True,
load_imaa=True,
)
assert next(pipe.inverse_transformer.parameters()).dtype == DTYPE
assert next(pipe.forward_transformer.parameters()).dtype == DTYPE
print("[unified] loaded inverse + forward transformers and IMAA")
del pipe
_clear()
def main() -> None:
if not torch.cuda.is_available():
raise SystemExit("CUDA is required for this test.")
print(f"device={torch.cuda.get_device_name(0)} dtype={DTYPE}")
test_inverse()
test_forward()
test_unified()
print("All pipeline tests passed.")
if __name__ == "__main__":
main()
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