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import gc
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler, SD3Transformer2DModel, StableDiffusion3Pipeline
from diffusers.utils.testing_utils import (
numpy_cosine_similarity_distance,
require_torch_gpu,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
class StableDiffusion3PipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = StableDiffusion3Pipeline
params = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
batch_params = frozenset(["prompt", "negative_prompt"])
def get_dummy_components(self):
torch.manual_seed(0)
transformer = SD3Transformer2DModel(
sample_size=32,
patch_size=1,
in_channels=4,
num_layers=1,
attention_head_dim=8,
num_attention_heads=4,
caption_projection_dim=32,
joint_attention_dim=32,
pooled_projection_dim=64,
out_channels=4,
)
clip_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,
hidden_act="gelu",
projection_dim=32,
)
torch.manual_seed(0)
text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)
torch.manual_seed(0)
text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
vae = AutoencoderKL(
sample_size=32,
in_channels=3,
out_channels=3,
block_out_channels=(4,),
layers_per_block=1,
latent_channels=4,
norm_num_groups=1,
use_quant_conv=False,
use_post_quant_conv=False,
shift_factor=0.0609,
scaling_factor=1.5035,
)
scheduler = FlowMatchEulerDiscreteScheduler()
return {
"scheduler": scheduler,
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
"text_encoder_3": text_encoder_3,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"tokenizer_3": tokenizer_3,
"transformer": transformer,
"vae": vae,
}
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
}
return inputs
def test_stable_diffusion_3_different_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "a different prompt"
inputs["prompt_3"] = "another different prompt"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
def test_stable_diffusion_3_different_negative_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt_2"] = "deformed"
inputs["negative_prompt_3"] = "blurry"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
def test_stable_diffusion_3_prompt_embeds(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_with_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
prompt = inputs.pop("prompt")
do_classifier_free_guidance = inputs["guidance_scale"] > 1
(
prompt_embeds,
negative_prompt_embeds,
pooled_prompt_embeds,
negative_pooled_prompt_embeds,
) = pipe.encode_prompt(
prompt,
prompt_2=None,
prompt_3=None,
do_classifier_free_guidance=do_classifier_free_guidance,
device=torch_device,
)
output_with_embeds = pipe(
prompt_embeds=prompt_embeds,
negative_prompt_embeds=negative_prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
**inputs,
).images[0]
max_diff = np.abs(output_with_prompt - output_with_embeds).max()
assert max_diff < 1e-4
def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
original_image_slice = image[0, -3:, -3:, -1]
pipe.transformer.fuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_fused = image[0, -3:, -3:, -1]
pipe.transformer.unfuse_qkv_projections()
inputs = self.get_dummy_inputs(device)
image = pipe(**inputs).images
image_slice_disabled = image[0, -3:, -3:, -1]
assert np.allclose(
original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3
), "Fusion of QKV projections shouldn't affect the outputs."
assert np.allclose(
image_slice_fused, image_slice_disabled, atol=1e-3, rtol=1e-3
), "Outputs, with QKV projection fusion enabled, shouldn't change when fused QKV projections are disabled."
assert np.allclose(
original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2
), "Original outputs should match when fused QKV projections are disabled."
@slow
@require_torch_gpu
class StableDiffusion3PipelineSlowTests(unittest.TestCase):
pipeline_class = StableDiffusion3Pipeline
repo_id = "stabilityai/stable-diffusion-3-medium-diffusers"
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, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
return {
"prompt": "A photo of a cat",
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
"generator": generator,
}
def test_sd3_inference(self):
pipe = self.pipeline_class.from_pretrained(self.repo_id, torch_dtype=torch.float16)
pipe.enable_model_cpu_offload()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
expected_slice = np.array(
[
[0.36132812, 0.30004883, 0.25830078],
[0.36669922, 0.31103516, 0.23754883],
[0.34814453, 0.29248047, 0.23583984],
[0.35791016, 0.30981445, 0.23999023],
[0.36328125, 0.31274414, 0.2607422],
[0.37304688, 0.32177734, 0.26171875],
[0.3671875, 0.31933594, 0.25756836],
[0.36035156, 0.31103516, 0.2578125],
[0.3857422, 0.33789062, 0.27563477],
[0.3701172, 0.31982422, 0.265625],
],
dtype=np.float32,
)
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
assert max_diff < 1e-4
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