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Zero
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import random
import unittest
import numpy as np
import torch
# torch_device, # {{ edit_1 }} Removed unused import
from transformers import (
AutoTokenizer,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
T5EncoderModel,
)
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
FluxControlNetInpaintPipeline,
FluxControlNetModel,
FluxTransformer2DModel,
)
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class FluxControlNetInpaintPipelineTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = FluxControlNetInpaintPipeline
params = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"prompt_embeds",
"pooled_prompt_embeds",
"image",
"mask_image",
"control_image",
"strength",
"num_inference_steps",
"controlnet_conditioning_scale",
]
)
batch_params = frozenset(["prompt", "image", "mask_image", "control_image"])
test_xformers_attention = False
def get_dummy_components(self):
torch.manual_seed(0)
transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=8,
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=2,
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(
patch_size=1,
in_channels=8,
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=device).manual_seed(seed)
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
mask_image = torch.ones((1, 1, 32, 32)).to(device)
control_image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"mask_image": mask_image,
"control_image": control_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"height": 32,
"width": 32,
"max_sequence_length": 48,
"strength": 0.8,
"output_type": "np",
}
return inputs
def test_flux_controlnet_inpaint_with_num_images_per_prompt(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)
inputs["num_images_per_prompt"] = 2
output = pipe(**inputs)
images = output.images
assert images.shape == (2, 32, 32, 3)
def test_flux_controlnet_inpaint_with_controlnet_conditioning_scale(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)
output_default = pipe(**inputs)
image_default = output_default.images
inputs["controlnet_conditioning_scale"] = 0.5
output_scaled = pipe(**inputs)
image_scaled = output_scaled.images
# Ensure that changing the controlnet_conditioning_scale produces a different output
assert not np.allclose(image_default, image_scaled, atol=0.01)
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)
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