# coding=utf-8
# Copyright 2024 HuggingFace Inc and The InstantX Team.
#
# 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 pytest
import torch
from huggingface_hub import hf_hub_download
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
FluxControlNetPipeline,
FluxTransformer2DModel,
)
from diffusers.models import FluxControlNetModel
from diffusers.utils import load_image
from diffusers.utils.testing_utils import (
enable_full_determinism,
nightly,
numpy_cosine_similarity_distance,
require_big_gpu_with_torch_cuda,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class FluxControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = FluxControlNetPipeline
params = frozenset(["prompt", "height", "width", "guidance_scale", "prompt_embeds", "pooled_prompt_embeds"])
batch_params = frozenset(["prompt"])
test_layerwise_casting = True
def get_dummy_components(self):
torch.manual_seed(0)
transformer = FluxTransformer2DModel(
patch_size=1,
in_channels=16,
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],
)
torch.manual_seed(0)
controlnet = FluxControlNetModel(
patch_size=1,
in_channels=16,
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 = T5TokenizerFast.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,
"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)
control_image = randn_tensor(
(1, 3, 32, 32),
generator=generator,
device=torch.device(device),
dtype=torch.float16,
)
controlnet_conditioning_scale = 0.5
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 3.5,
"output_type": "np",
"control_image": control_image,
"controlnet_conditioning_scale": controlnet_conditioning_scale,
}
return inputs
def test_controlnet_flux(self):
components = self.get_dummy_components()
flux_pipe = FluxControlNetPipeline(**components)
flux_pipe = flux_pipe.to(torch_device, dtype=torch.float16)
flux_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = flux_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array(
[0.47387695, 0.63134766, 0.5605469, 0.61621094, 0.7207031, 0.7089844, 0.70410156, 0.6113281, 0.64160156]
)
assert (
np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
), f"Expected: {expected_slice}, got: {image_slice.flatten()}"
@unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
def test_xformers_attention_forwardGenerator_pass(self):
pass
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 = pipe(**inputs).images[0]
output_height, output_width, _ = image.shape
assert (output_height, output_width) == (expected_height, expected_width)
@nightly
@require_big_gpu_with_torch_cuda
@pytest.mark.big_gpu_with_torch_cuda
class FluxControlNetPipelineSlowTests(unittest.TestCase):
pipeline_class = FluxControlNetPipeline
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_canny(self):
controlnet = FluxControlNetModel.from_pretrained(
"InstantX/FLUX.1-dev-Controlnet-Canny-alpha", torch_dtype=torch.bfloat16
)
pipe = FluxControlNetPipeline.from_pretrained(
"black-forest-labs/FLUX.1-dev",
text_encoder=None,
text_encoder_2=None,
controlnet=controlnet,
torch_dtype=torch.bfloat16,
).to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
control_image = load_image(
"https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Canny-alpha/resolve/main/canny.jpg"
).resize((512, 512))
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)
output = pipe(
prompt_embeds=prompt_embeds,
pooled_prompt_embeds=pooled_prompt_embeds,
control_image=control_image,
controlnet_conditioning_scale=0.6,
num_inference_steps=2,
guidance_scale=3.5,
max_sequence_length=256,
output_type="np",
height=512,
width=512,
generator=generator,
)
image = output.images[0]
assert image.shape == (512, 512, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.2734, 0.2852, 0.2852, 0.2734, 0.2754, 0.2891, 0.2617, 0.2637, 0.2773])
assert numpy_cosine_similarity_distance(original_image.flatten(), expected_image) < 1e-2