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Add SDXL refiner only tests (#5041)

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* add refiner only tests

* make style
This commit is contained in:
Dhruv Nair
2023-09-15 12:58:03 +05:30
committed by GitHub
parent e70cb1243f
commit 5fd42e5d61
1 changed files with 250 additions and 28 deletions
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278
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_img2img.py
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@@ -26,7 +26,12 @@ from diffusers import (
StableDiffusionXLImg2ImgPipeline,
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
require_torch_gpu,
torch_device,
)
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
@@ -159,24 +164,6 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_stable_diffusion_xl_refiner(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components(skip_first_text_encoder=True)
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4578, 0.4981, 0.4301, 0.6454, 0.5588, 0.4442, 0.5678, 0.5940, 0.5176])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
@@ -195,7 +182,8 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
@@ -204,7 +192,8 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
@@ -248,7 +237,8 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
for pipe in pipes:
pipe.unet.set_default_attn_processor()
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
@@ -261,13 +251,15 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
sd_pipe = self.pipeline_class(**components).to(torch_device)
# forward with single prompt
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = inputs["prompt"]
output = sd_pipe(**inputs)
@@ -277,7 +269,8 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different prompt
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["prompt_2"] = "different prompt"
output = sd_pipe(**inputs)
@@ -287,14 +280,16 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
# manually set a negative_prompt
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with same negative_prompt duplicated
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = inputs["negative_prompt"]
@@ -305,7 +300,8 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
# forward with different negative_prompt
inputs = self.get_dummy_inputs(torch_device)
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
inputs["num_inference_steps"] = 5
inputs["negative_prompt"] = "negative prompt"
inputs["negative_prompt_2"] = "different negative prompt"
@@ -342,3 +338,229 @@ class StableDiffusionXLImg2ImgPipelineFastTests(PipelineLatentTesterMixin, Pipel
np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
> 1e-4
)
class StableDiffusionXLImg2ImgRefinerOnlyPipelineFastTests(
PipelineLatentTesterMixin, PipelineTesterMixin, unittest.TestCase
):
pipeline_class = StableDiffusionXLImg2ImgPipeline
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
required_optional_params = PipelineTesterMixin.required_optional_params - {"latents"}
batch_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
image_latents_params = IMAGE_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"),
# SD2-specific config below
attention_head_dim=(2, 4),
use_linear_projection=True,
addition_embed_type="text_time",
addition_time_embed_dim=8,
transformer_layers_per_block=(1, 2),
projection_class_embeddings_input_dim=72, # 5 * 8 + 32
cross_attention_dim=32,
)
scheduler = EulerDiscreteScheduler(
beta_start=0.00085,
beta_end=0.012,
steps_offset=1,
beta_schedule="scaled_linear",
timestep_spacing="leading",
)
torch.manual_seed(0)
vae = AutoencoderKL(
block_out_channels=[32, 64],
in_channels=3,
out_channels=3,
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
latent_channels=4,
sample_size=128,
)
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,
# SD2-specific config below
hidden_act="gelu",
projection_dim=32,
)
text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
components = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"tokenizer": None,
"text_encoder": None,
"text_encoder_2": text_encoder_2,
"tokenizer_2": tokenizer_2,
"requires_aesthetics_score": True,
}
return components
def test_components_function(self):
init_components = self.get_dummy_components()
init_components.pop("requires_aesthetics_score")
pipe = self.pipeline_class(**init_components)
self.assertTrue(hasattr(pipe, "components"))
self.assertTrue(set(pipe.components.keys()) == set(init_components.keys()))
def get_dummy_inputs(self, device, seed=0):
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
image = image / 2 + 0.5
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",
"image": image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"strength": 0.8,
}
return inputs
def test_stable_diffusion_xl_img2img_euler(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4745, 0.4924, 0.4338, 0.6468, 0.5547, 0.4419, 0.5646, 0.5897, 0.5146])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@require_torch_gpu
def test_stable_diffusion_xl_offloads(self):
pipes = []
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_model_cpu_offload()
pipes.append(sd_pipe)
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe.enable_sequential_cpu_offload()
pipes.append(sd_pipe)
image_slices = []
for pipe in pipes:
pipe.unet.set_default_attn_processor()
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
image = pipe(**inputs).images
image_slices.append(image[0, -3:, -3:, -1].flatten())
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
def test_stable_diffusion_xl_img2img_negative_conditions(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
sd_pipe = self.pipeline_class(**components)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = sd_pipe(**inputs).images
image_slice_with_no_neg_conditions = image[0, -3:, -3:, -1]
image = sd_pipe(
**inputs,
negative_original_size=(512, 512),
negative_crops_coords_top_left=(
0,
0,
),
negative_target_size=(1024, 1024),
).images
image_slice_with_neg_conditions = image[0, -3:, -3:, -1]
assert (
np.abs(image_slice_with_no_neg_conditions.flatten() - image_slice_with_neg_conditions.flatten()).max()
> 1e-4
)
def test_stable_diffusion_xl_img2img_negative_prompt_embeds(self):
components = self.get_dummy_components()
sd_pipe = StableDiffusionXLImg2ImgPipeline(**components)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
# forward without prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
inputs["negative_prompt"] = negative_prompt
inputs["prompt"] = 3 * [inputs["prompt"]]
output = sd_pipe(**inputs)
image_slice_1 = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
generator_device = "cpu"
inputs = self.get_dummy_inputs(generator_device)
negative_prompt = 3 * ["this is a negative prompt"]
prompt = 3 * [inputs.pop("prompt")]
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = sd_pipe.encode_prompt(prompt, negative_prompt=negative_prompt)
output = sd_pipe(
**inputs,
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
)
image_slice_2 = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
def test_attention_slicing_forward_pass(self):
super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
def test_inference_batch_single_identical(self):
super().test_inference_batch_single_identical(expected_max_diff=3e-3)