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# coding=utf-8
# 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 gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDM3DPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class StableDiffusionLDM3DPipelineFastTests(unittest.TestCase):
pipeline_class = StableDiffusionLDM3DPipeline
params = TEXT_TO_IMAGE_PARAMS
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
def get_dummy_components(self):
torch.manual_seed(0)
unet = UNet2DConditionModel(
block_out_channels=(32, 64),
layers_per_block=2,
sample_size=32,
in_channels=4,
out_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
cross_attention_dim=32,
)
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=6,
out_channels=6,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
)
torch.manual_seed(0)
text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
text_encoder = CLIPTextModel(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
"image_encoder": None,
}
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)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "np",
}
return inputs
def test_stable_diffusion_ddim(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
image_slice_rgb = rgb[0, -3:, -3:, -1]
image_slice_depth = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
expected_slice_rgb = np.array(
[0.37338176, 0.70247, 0.74203193, 0.51643604, 0.58256793, 0.60932136, 0.4181095, 0.48355877, 0.46535262]
)
expected_slice_depth = np.array([103.46727, 85.812004, 87.849236])
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth).max() < 1e-2
def test_stable_diffusion_prompt_embeds(self):
components = self.get_dummy_components()
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt"] = 3 * [inputs["prompt"]]
# forward
output = ldm3d_pipe(**inputs)
rgb_slice_1, depth_slice_1 = output.rgb, output.depth
rgb_slice_1 = rgb_slice_1[0, -3:, -3:, -1]
depth_slice_1 = depth_slice_1[0, -3:, -1]
inputs = self.get_dummy_inputs(torch_device)
prompt = 3 * [inputs.pop("prompt")]
text_inputs = ldm3d_pipe.tokenizer(
prompt,
padding="max_length",
max_length=ldm3d_pipe.tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_inputs = text_inputs["input_ids"].to(torch_device)
prompt_embeds = ldm3d_pipe.text_encoder(text_inputs)[0]
inputs["prompt_embeds"] = prompt_embeds
# forward
output = ldm3d_pipe(**inputs)
rgb_slice_2, depth_slice_2 = output.rgb, output.depth
rgb_slice_2 = rgb_slice_2[0, -3:, -3:, -1]
depth_slice_2 = depth_slice_2[0, -3:, -1]
assert np.abs(rgb_slice_1.flatten() - rgb_slice_2.flatten()).max() < 1e-4
assert np.abs(depth_slice_1.flatten() - depth_slice_2.flatten()).max() < 1e-4
def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
components["scheduler"] = PNDMScheduler(skip_prk_steps=True)
ldm3d_pipe = StableDiffusionLDM3DPipeline(**components)
ldm3d_pipe = ldm3d_pipe.to(device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
negative_prompt = "french fries"
output = ldm3d_pipe(**inputs, negative_prompt=negative_prompt)
rgb, depth = output.rgb, output.depth
rgb_slice = rgb[0, -3:, -3:, -1]
depth_slice = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
expected_slice_rgb = np.array(
[0.37044, 0.71811503, 0.7223251, 0.48603675, 0.5638391, 0.6364948, 0.42833704, 0.4901315, 0.47926217]
)
expected_slice_depth = np.array([107.84738, 84.62802, 89.962135])
assert np.abs(rgb_slice.flatten() - expected_slice_rgb).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth).max() < 1e-2
@nightly
@require_torch_gpu
class StableDiffusionLDM3DPipelineSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_ldm3d_stable_diffusion(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d")
ldm3d_pipe = ldm3d_pipe.to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
rgb_slice = rgb[0, -3:, -3:, -1].flatten()
depth_slice = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
expected_slice_rgb = np.array(
[0.53805465, 0.56707305, 0.5486515, 0.57012236, 0.5814511, 0.56253487, 0.54843014, 0.55092263, 0.6459706]
)
expected_slice_depth = np.array(
[0.9263781, 0.6678672, 0.5486515, 0.92202145, 0.67831135, 0.56253487, 0.9241694, 0.7551478, 0.6459706]
)
assert np.abs(rgb_slice - expected_slice_rgb).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth).max() < 3e-3
@nightly
@require_torch_gpu
class StableDiffusionPipelineNightlyTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
latents = np.random.RandomState(seed).standard_normal((1, 4, 64, 64))
latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
inputs = {
"prompt": "a photograph of an astronaut riding a horse",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_ldm3d(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d").to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
expected_rgb_mean = 0.495586
expected_rgb_std = 0.33795515
expected_depth_mean = 112.48518
expected_depth_std = 98.489746
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
def test_ldm3d_v2(self):
ldm3d_pipe = StableDiffusionLDM3DPipeline.from_pretrained("Intel/ldm3d-4c").to(torch_device)
ldm3d_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
output = ldm3d_pipe(**inputs)
rgb, depth = output.rgb, output.depth
expected_rgb_mean = 0.4194127
expected_rgb_std = 0.35375586
expected_depth_mean = 0.5638502
expected_depth_std = 0.34686103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean()) < 1e-3
assert np.abs(expected_rgb_std - rgb.std()) < 1e-3
assert np.abs(expected_depth_mean - depth.mean()) < 1e-3
assert np.abs(expected_depth_std - depth.std()) < 1e-3
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