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# coding=utf-8 | |
# Copyright 2023 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, | |
DPMSolverMultistepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
LMSDiscreteScheduler, | |
PNDMScheduler, | |
StableDiffusionPipeline, | |
UNet2DConditionModel, | |
logging, | |
) | |
from diffusers.utils.testing_utils import ( | |
CaptureLogger, | |
enable_full_determinism, | |
load_numpy, | |
nightly, | |
numpy_cosine_similarity_distance, | |
require_torch_gpu, | |
slow, | |
torch_device, | |
) | |
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS | |
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin | |
enable_full_determinism() | |
class StableDiffusion2PipelineFastTests( | |
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase | |
): | |
pipeline_class = StableDiffusionPipeline | |
params = TEXT_TO_IMAGE_PARAMS | |
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS | |
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS | |
image_latents_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, | |
# SD2-specific config below | |
attention_head_dim=(2, 4), | |
use_linear_projection=True, | |
) | |
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=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=512, | |
) | |
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, | |
} | |
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": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_ddim(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
sd_pipe = StableDiffusionPipeline(**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, 64, 64, 3) | |
expected_slice = np.array([0.5753, 0.6113, 0.5005, 0.5036, 0.5464, 0.4725, 0.4982, 0.4865, 0.4861]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_pndm(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = PNDMScheduler(skip_prk_steps=True) | |
sd_pipe = StableDiffusionPipeline(**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, 64, 64, 3) | |
expected_slice = np.array([0.5121, 0.5714, 0.4827, 0.5057, 0.5646, 0.4766, 0.5189, 0.4895, 0.4990]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_k_lms(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config) | |
sd_pipe = StableDiffusionPipeline(**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, 64, 64, 3) | |
expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_k_euler_ancestral(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = EulerAncestralDiscreteScheduler.from_config(components["scheduler"].config) | |
sd_pipe = StableDiffusionPipeline(**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, 64, 64, 3) | |
expected_slice = np.array([0.4864, 0.5440, 0.4842, 0.4994, 0.5543, 0.4846, 0.5196, 0.4942, 0.5063]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_k_euler(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = EulerDiscreteScheduler.from_config(components["scheduler"].config) | |
sd_pipe = StableDiffusionPipeline(**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, 64, 64, 3) | |
expected_slice = np.array([0.4865, 0.5439, 0.4840, 0.4995, 0.5543, 0.4846, 0.5199, 0.4942, 0.5061]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_unflawed(self): | |
device = "cpu" # ensure determinism for the device-dependent torch.Generator | |
components = self.get_dummy_components() | |
components["scheduler"] = DDIMScheduler.from_config( | |
components["scheduler"].config, timestep_spacing="trailing" | |
) | |
sd_pipe = StableDiffusionPipeline(**components) | |
sd_pipe = sd_pipe.to(device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_dummy_inputs(device) | |
inputs["guidance_rescale"] = 0.7 | |
inputs["num_inference_steps"] = 10 | |
image = sd_pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1] | |
assert image.shape == (1, 64, 64, 3) | |
expected_slice = np.array([0.4736, 0.5405, 0.4705, 0.4955, 0.5675, 0.4812, 0.5310, 0.4967, 0.5064]) | |
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 | |
def test_stable_diffusion_long_prompt(self): | |
components = self.get_dummy_components() | |
components["scheduler"] = LMSDiscreteScheduler.from_config(components["scheduler"].config) | |
sd_pipe = StableDiffusionPipeline(**components) | |
sd_pipe = sd_pipe.to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
do_classifier_free_guidance = True | |
negative_prompt = None | |
num_images_per_prompt = 1 | |
logger = logging.get_logger("diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion") | |
logger.setLevel(logging.WARNING) | |
prompt = 25 * "@" | |
with CaptureLogger(logger) as cap_logger_3: | |
text_embeddings_3, negeative_text_embeddings_3 = sd_pipe.encode_prompt( | |
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
) | |
if negeative_text_embeddings_3 is not None: | |
text_embeddings_3 = torch.cat([negeative_text_embeddings_3, text_embeddings_3]) | |
prompt = 100 * "@" | |
with CaptureLogger(logger) as cap_logger: | |
text_embeddings, negative_embeddings = sd_pipe.encode_prompt( | |
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
) | |
if negative_embeddings is not None: | |
text_embeddings = torch.cat([negative_embeddings, text_embeddings]) | |
negative_prompt = "Hello" | |
with CaptureLogger(logger) as cap_logger_2: | |
text_embeddings_2, negative_text_embeddings_2 = sd_pipe.encode_prompt( | |
prompt, torch_device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt | |
) | |
if negative_text_embeddings_2 is not None: | |
text_embeddings_2 = torch.cat([negative_text_embeddings_2, text_embeddings_2]) | |
assert text_embeddings_3.shape == text_embeddings_2.shape == text_embeddings.shape | |
assert text_embeddings.shape[1] == 77 | |
assert cap_logger.out == cap_logger_2.out | |
# 100 - 77 + 1 (BOS token) + 1 (EOS token) = 25 | |
assert cap_logger.out.count("@") == 25 | |
assert cap_logger_3.out == "" | |
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) | |
class StableDiffusion2PipelineSlowTests(unittest.TestCase): | |
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": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_default_ddim(self): | |
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base") | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]) | |
assert np.abs(image_slice - expected_slice).max() < 7e-3 | |
def test_stable_diffusion_pndm(self): | |
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base") | |
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]) | |
assert np.abs(image_slice - expected_slice).max() < 7e-3 | |
def test_stable_diffusion_k_lms(self): | |
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base") | |
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = pipe(**inputs).images | |
image_slice = image[0, -3:, -3:, -1].flatten() | |
assert image.shape == (1, 512, 512, 3) | |
expected_slice = np.array([0.10440, 0.13115, 0.11100, 0.10141, 0.11440, 0.07215, 0.11332, 0.09693, 0.10006]) | |
assert np.abs(image_slice - expected_slice).max() < 3e-3 | |
def test_stable_diffusion_attention_slicing(self): | |
torch.cuda.reset_peak_memory_stats() | |
pipe = StableDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16 | |
) | |
pipe.unet.set_default_attn_processor() | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
# enable attention slicing | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
image_sliced = pipe(**inputs).images | |
mem_bytes = torch.cuda.max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
# make sure that less than 3.3 GB is allocated | |
assert mem_bytes < 3.3 * 10**9 | |
# disable slicing | |
pipe.disable_attention_slicing() | |
pipe.unet.set_default_attn_processor() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
image = pipe(**inputs).images | |
# make sure that more than 3.3 GB is allocated | |
mem_bytes = torch.cuda.max_memory_allocated() | |
assert mem_bytes > 3.3 * 10**9 | |
assert np.abs(image_sliced - image).max() < 1e-3 | |
def test_stable_diffusion_text2img_intermediate_state(self): | |
number_of_steps = 0 | |
def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None: | |
callback_fn.has_been_called = True | |
nonlocal number_of_steps | |
number_of_steps += 1 | |
if step == 1: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array( | |
[-0.3862, -0.4507, -1.1729, 0.0686, -1.1045, 0.7124, -1.8301, 0.1903, 1.2773] | |
) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
elif step == 2: | |
latents = latents.detach().cpu().numpy() | |
assert latents.shape == (1, 4, 64, 64) | |
latents_slice = latents[0, -3:, -3:, -1] | |
expected_slice = np.array( | |
[0.2720, -0.1863, -0.7383, -0.5029, -0.7534, 0.3970, -0.7646, 0.4468, 1.2686] | |
) | |
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2 | |
callback_fn.has_been_called = False | |
pipe = StableDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16 | |
) | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
pipe(**inputs, callback=callback_fn, callback_steps=1) | |
assert callback_fn.has_been_called | |
assert number_of_steps == inputs["num_inference_steps"] | |
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
pipe = StableDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16 | |
) | |
pipe = pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
pipe.enable_attention_slicing(1) | |
pipe.enable_sequential_cpu_offload() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
_ = pipe(**inputs) | |
mem_bytes = torch.cuda.max_memory_allocated() | |
# make sure that less than 2.8 GB is allocated | |
assert mem_bytes < 2.8 * 10**9 | |
def test_stable_diffusion_pipeline_with_model_offloading(self): | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
# Normal inference | |
pipe = StableDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-base", | |
torch_dtype=torch.float16, | |
) | |
pipe.unet.set_default_attn_processor() | |
pipe.to(torch_device) | |
pipe.set_progress_bar_config(disable=None) | |
outputs = pipe(**inputs) | |
mem_bytes = torch.cuda.max_memory_allocated() | |
# With model offloading | |
# Reload but don't move to cuda | |
pipe = StableDiffusionPipeline.from_pretrained( | |
"stabilityai/stable-diffusion-2-base", | |
torch_dtype=torch.float16, | |
) | |
pipe.unet.set_default_attn_processor() | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
pipe.enable_model_cpu_offload() | |
pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device, dtype=torch.float16) | |
outputs_offloaded = pipe(**inputs) | |
mem_bytes_offloaded = torch.cuda.max_memory_allocated() | |
images = outputs.images | |
images_offloaded = outputs_offloaded.images | |
max_diff = numpy_cosine_similarity_distance(images.flatten(), images_offloaded.flatten()) | |
assert max_diff < 1e-3 | |
assert mem_bytes_offloaded < mem_bytes | |
assert mem_bytes_offloaded < 3 * 10**9 | |
for module in pipe.text_encoder, pipe.unet, pipe.vae: | |
assert module.device == torch.device("cpu") | |
# With attention slicing | |
torch.cuda.empty_cache() | |
torch.cuda.reset_max_memory_allocated() | |
torch.cuda.reset_peak_memory_stats() | |
pipe.enable_attention_slicing() | |
_ = pipe(**inputs) | |
mem_bytes_slicing = torch.cuda.max_memory_allocated() | |
assert mem_bytes_slicing < mem_bytes_offloaded | |
class StableDiffusion2PipelineNightlyTests(unittest.TestCase): | |
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": "numpy", | |
} | |
return inputs | |
def test_stable_diffusion_2_0_default_ddim(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base").to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_0_base_ddim.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_stable_diffusion_2_1_default_pndm(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_pndm.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_stable_diffusion_ddim(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) | |
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_ddim.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_stable_diffusion_lms(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) | |
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_lms.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_stable_diffusion_euler(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) | |
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_euler.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |
def test_stable_diffusion_dpm(self): | |
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) | |
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) | |
sd_pipe.set_progress_bar_config(disable=None) | |
inputs = self.get_inputs(torch_device) | |
inputs["num_inference_steps"] = 25 | |
image = sd_pipe(**inputs).images[0] | |
expected_image = load_numpy( | |
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" | |
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_dpm_multi.npy" | |
) | |
max_diff = np.abs(expected_image - image).max() | |
assert max_diff < 1e-3 | |