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import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
FluxControlNetImg2ImgPipeline,
FluxControlNetModel,
FluxTransformer2DModel,
)
from diffusers.utils.torch_utils import randn_tensor
from ...testing_utils import (
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin, check_qkv_fused_layers_exist
class FluxControlNetImg2ImgPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = FluxControlNetImg2ImgPipeline
params = frozenset(
[
"prompt",
"image",
"control_image",
"height",
"width",
"strength",
"guidance_scale",
"controlnet_conditioning_scale",
"prompt_embeds",
"pooled_prompt_embeds",
]
)
batch_params = frozenset(["prompt", "image", "control_image"])
test_xformers_attention = False
def get_dummy_components(self):
torch.manual_seed(0)
transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=4,
num_layers=1,
num_single_layers=1,
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,
)
torch.manual_seed(0)
controlnet = FluxControlNetModel(
in_channels=4,
num_layers=1,
num_single_layers=1,
attention_head_dim=16,
num_attention_heads=2,
joint_attention_dim=32,
pooled_projection_dim=32,
axes_dims_rope=[4, 4, 8],
)
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,
"controlnet": controlnet,
}
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)
image = torch.randn(1, 3, 32, 32).to(device)
control_image = torch.randn(1, 3, 32, 32).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"control_image": control_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"controlnet_conditioning_scale": 1.0,
"strength": 0.8,
"height": 32,
"width": 32,
"max_sequence_length": 48,
"output_type": "np",
}
return inputs
def test_flux_controlnet_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()
assert max_diff > 1e-6
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()
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]
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."
)
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, 56)]
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(
{
"control_image": randn_tensor(
(1, 3, height, width),
device=torch_device,
dtype=torch.float16,
),
"image": randn_tensor(
(1, 3, height, width),
device=torch_device,
dtype=torch.float16,
),
"height": height,
"width": width,
}
)
image = pipe(**inputs).images[0]
output_height, output_width, _ = image.shape
assert (output_height, output_width) == (expected_height, expected_width)
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