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import gc
import unittest
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
FasterCacheConfig,
FlowMatchEulerDiscreteScheduler,
FluxPipeline,
FluxTransformer2DModel,
)
from ...testing_utils import (
backend_empty_cache,
nightly,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow,
torch_device,
)
from ..test_pipelines_common import (
FasterCacheTesterMixin,
FirstBlockCacheTesterMixin,
FluxIPAdapterTesterMixin,
PipelineTesterMixin,
PyramidAttentionBroadcastTesterMixin,
check_qkv_fused_layers_exist,
)
class FluxPipelineFastTests(
PipelineTesterMixin,
FluxIPAdapterTesterMixin,
PyramidAttentionBroadcastTesterMixin,
FasterCacheTesterMixin,
FirstBlockCacheTesterMixin,
unittest.TestCase,
):
pipeline_class = FluxPipeline
params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"])
batch_params = frozenset(["prompt"])
# there is no xformers processor for Flux
test_xformers_attention = False
test_layerwise_casting = True
test_group_offloading = True
faster_cache_config = FasterCacheConfig(
spatial_attention_block_skip_range=2,
spatial_attention_timestep_skip_range=(-1, 901),
unconditional_batch_skip_range=2,
attention_weight_callback=lambda _: 0.5,
is_guidance_distilled=True,
)
def get_dummy_components(self, num_layers: int = 1, num_single_layers: int = 1):
torch.manual_seed(0)
transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=4,
num_layers=num_layers,
num_single_layers=num_single_layers,
attention_head_dim=16,
num_attention_heads=2,
joint_attention_dim=32,
pooled_projection_dim=32,
axes_dims_rope=[4, 4, 8],
)
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 = CLIPTextModel(clip_text_encoder_config)
torch.manual_seed(0)
text_encoder_2 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_2 = 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=1,
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,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"transformer": transformer,
"vae": vae,
"image_encoder": None,
"feature_extractor": None,
}
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,
"height": 8,
"width": 8,
"max_sequence_length": 48,
"output_type": "np",
}
return inputs
def test_flux_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"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
# For some reasons, they don't show large differences
self.assertGreater(max_diff, 1e-6, "Outputs should be different for different prompts.")
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]
# TODO (sayakpaul): will refactor this once `fuse_qkv_projections()` has been added
# to the pipeline level.
pipe.transformer.fuse_qkv_projections()
self.assertTrue(
check_qkv_fused_layers_exist(pipe.transformer, ["to_qkv"]),
("Something wrong with the fused attention layers. Expected all the attention projections to be fused."),
)
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]
self.assertTrue(
np.allclose(original_image_slice, image_slice_fused, atol=1e-3, rtol=1e-3),
("Fusion of QKV projections shouldn't affect the outputs."),
)
self.assertTrue(
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."),
)
self.assertTrue(
np.allclose(original_image_slice, image_slice_disabled, atol=1e-2, rtol=1e-2),
("Original outputs should match when fused QKV projections are disabled."),
)
def test_flux_image_output_shape(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
height_width_pairs = [(32, 32), (72, 57)]
for height, width in height_width_pairs:
expected_height = height - height % (pipe.vae_scale_factor * 2)
expected_width = width - width % (pipe.vae_scale_factor * 2)
inputs.update({"height": height, "width": width})
image = pipe(**inputs).images[0]
output_height, output_width, _ = image.shape
self.assertEqual(
(output_height, output_width),
(expected_height, expected_width),
f"Output shape {image.shape} does not match expected shape {(expected_height, expected_width)}",
)
def test_flux_true_cfg(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
inputs.pop("generator")
no_true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
inputs["negative_prompt"] = "bad quality"
inputs["true_cfg_scale"] = 2.0
true_cfg_out = pipe(**inputs, generator=torch.manual_seed(0)).images[0]
self.assertFalse(
np.allclose(no_true_cfg_out, true_cfg_out), "Outputs should be different when true_cfg_scale is set."
)
@nightly
@require_big_accelerator
class FluxPipelineSlowTests(unittest.TestCase):
pipeline_class = FluxPipeline
repo_id = "black-forest-labs/FLUX.1-schnell"
def setUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, seed=0):
generator = torch.Generator(device="cpu").manual_seed(seed)
prompt_embeds = torch.load(
hf_hub_download(repo_id="diffusers/test-slices", repo_type="dataset", filename="flux/prompt_embeds.pt")
).to(torch_device)
pooled_prompt_embeds = torch.load(
hf_hub_download(
repo_id="diffusers/test-slices", repo_type="dataset", filename="flux/pooled_prompt_embeds.pt"
)
).to(torch_device)
return {
"prompt_embeds": prompt_embeds,
"pooled_prompt_embeds": pooled_prompt_embeds,
"num_inference_steps": 2,
"guidance_scale": 0.0,
"max_sequence_length": 256,
"output_type": "np",
"generator": generator,
}
def test_flux_inference(self):
pipe = self.pipeline_class.from_pretrained(
self.repo_id, torch_dtype=torch.bfloat16, text_encoder=None, text_encoder_2=None
).to(torch_device)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
# fmt: off
expected_slice = np.array(
[0.3242, 0.3203, 0.3164, 0.3164, 0.3125, 0.3125, 0.3281, 0.3242, 0.3203, 0.3301, 0.3262, 0.3242, 0.3281, 0.3242, 0.3203, 0.3262, 0.3262, 0.3164, 0.3262, 0.3281, 0.3184, 0.3281, 0.3281, 0.3203, 0.3281, 0.3281, 0.3164, 0.3320, 0.3320, 0.3203],
dtype=np.float32,
)
# fmt: on
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
self.assertLess(
max_diff, 1e-4, f"Image slice is different from expected slice: {image_slice} != {expected_slice}"
)
@slow
@require_big_accelerator
class FluxIPAdapterPipelineSlowTests(unittest.TestCase):
pipeline_class = FluxPipeline
repo_id = "black-forest-labs/FLUX.1-dev"
image_encoder_pretrained_model_name_or_path = "openai/clip-vit-large-patch14"
weight_name = "ip_adapter.safetensors"
ip_adapter_repo_id = "XLabs-AI/flux-ip-adapter"
def setUp(self):
super().setUp()
gc.collect()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
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)
prompt_embeds = torch.load(
hf_hub_download(repo_id="diffusers/test-slices", repo_type="dataset", filename="flux/prompt_embeds.pt")
)
pooled_prompt_embeds = torch.load(
hf_hub_download(
repo_id="diffusers/test-slices", repo_type="dataset", filename="flux/pooled_prompt_embeds.pt"
)
)
negative_prompt_embeds = torch.zeros_like(prompt_embeds)
negative_pooled_prompt_embeds = torch.zeros_like(pooled_prompt_embeds)
ip_adapter_image = np.zeros((1024, 1024, 3), dtype=np.uint8)
return {
"prompt_embeds": prompt_embeds,
"pooled_prompt_embeds": pooled_prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"negative_pooled_prompt_embeds": negative_pooled_prompt_embeds,
"ip_adapter_image": ip_adapter_image,
"num_inference_steps": 2,
"guidance_scale": 3.5,
"true_cfg_scale": 4.0,
"max_sequence_length": 256,
"output_type": "np",
"generator": generator,
}
def test_flux_ip_adapter_inference(self):
pipe = self.pipeline_class.from_pretrained(
self.repo_id, torch_dtype=torch.bfloat16, text_encoder=None, text_encoder_2=None
)
pipe.load_ip_adapter(
self.ip_adapter_repo_id,
weight_name=self.weight_name,
image_encoder_pretrained_model_name_or_path=self.image_encoder_pretrained_model_name_or_path,
)
pipe.set_ip_adapter_scale(1.0)
pipe.enable_model_cpu_offload()
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
# fmt: off
expected_slice = np.array(
[0.1855, 0.1680, 0.1406, 0.1953, 0.1699, 0.1465, 0.2012, 0.1738, 0.1484, 0.2051, 0.1797, 0.1523, 0.2012, 0.1719, 0.1445, 0.2070, 0.1777, 0.1465, 0.2090, 0.1836, 0.1484, 0.2129, 0.1875, 0.1523, 0.2090, 0.1816, 0.1484, 0.2110, 0.1836, 0.1543],
dtype=np.float32,
)
# fmt: on
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
self.assertLess(
max_diff, 1e-4, f"Image slice is different from expected slice: {image_slice} != {expected_slice}"
)
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