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diffusers/tests/pipelines/controlnet_sd3/test_controlnet_sd3.py
Dhruv Nair 7aa6af1138 [Refactor] Move testing utils out of src (#12238) 
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This reverts commit 65efbcead5.
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# coding=utf-8
# Copyright 2025 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
from typing import Optional
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
SD3Transformer2DModel,
StableDiffusion3ControlNetPipeline,
)
from diffusers.models import SD3ControlNetModel, SD3MultiControlNetModel
from diffusers.utils import load_image
from diffusers.utils.torch_utils import randn_tensor
from ...testing_utils import (
backend_empty_cache,
enable_full_determinism,
numpy_cosine_similarity_distance,
require_big_accelerator,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class StableDiffusion3ControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = StableDiffusion3ControlNetPipeline
params = frozenset(
[
"prompt",
"height",
"width",
"guidance_scale",
"negative_prompt",
"prompt_embeds",
"negative_prompt_embeds",
]
)
batch_params = frozenset(["prompt", "negative_prompt"])
test_layerwise_casting = True
test_group_offloading = True
def get_dummy_components(
self, num_controlnet_layers: int = 3, qk_norm: Optional[str] = "rms_norm", use_dual_attention=False
):
torch.manual_seed(0)
transformer = SD3Transformer2DModel(
sample_size=32,
patch_size=1,
in_channels=8,
num_layers=4,
attention_head_dim=8,
num_attention_heads=4,
joint_attention_dim=32,
caption_projection_dim=32,
pooled_projection_dim=64,
out_channels=8,
qk_norm=qk_norm,
dual_attention_layers=() if not use_dual_attention else (0, 1),
)
torch.manual_seed(0)
controlnet = SD3ControlNetModel(
sample_size=32,
patch_size=1,
in_channels=8,
num_layers=num_controlnet_layers,
attention_head_dim=8,
num_attention_heads=4,
joint_attention_dim=32,
caption_projection_dim=32,
pooled_projection_dim=64,
out_channels=8,
qk_norm=qk_norm,
dual_attention_layers=() if not use_dual_attention else (0,),
)
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)
torch.manual_seed(0)
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=8,
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,
"controlnet": controlnet,
"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)
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": 5.0,
"output_type": "np",
"control_image": control_image,
"controlnet_conditioning_scale": controlnet_conditioning_scale,
}
return inputs
def run_pipe(self, components, use_sd35=False):
sd_pipe = StableDiffusion3ControlNetPipeline(**components)
sd_pipe = sd_pipe.to(torch_device, dtype=torch.float16)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output = sd_pipe(**inputs)
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
if not use_sd35:
expected_slice = np.array([0.5767, 0.7100, 0.5981, 0.5674, 0.5952, 0.4102, 0.5093, 0.5044, 0.6030])
else:
expected_slice = np.array([1.0000, 0.9072, 0.4209, 0.2744, 0.5737, 0.3840, 0.6113, 0.6250, 0.6328])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2, (
f"Expected: {expected_slice}, got: {image_slice.flatten()}"
)
def test_controlnet_sd3(self):
components = self.get_dummy_components()
self.run_pipe(components)
def test_controlnet_sd35(self):
components = self.get_dummy_components(num_controlnet_layers=1, qk_norm="rms_norm", use_dual_attention=True)
self.run_pipe(components, use_sd35=True)
@unittest.skip("xFormersAttnProcessor does not work with SD3 Joint Attention")
def test_xformers_attention_forwardGenerator_pass(self):
pass
@slow
@require_big_accelerator
class StableDiffusion3ControlNetPipelineSlowTests(unittest.TestCase):
pipeline_class = StableDiffusion3ControlNetPipeline
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 test_canny(self):
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload(device=torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text 'InstantX' on image"
n_prompt = "NSFW, nude, naked, porn, ugly"
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
output = pipe(
prompt,
negative_prompt=n_prompt,
control_image=control_image,
controlnet_conditioning_scale=0.5,
guidance_scale=5.0,
num_inference_steps=2,
output_type="np",
generator=generator,
)
image = output.images[0]
assert image.shape == (1024, 1024, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.7314, 0.7075, 0.6611, 0.7539, 0.7563, 0.6650, 0.6123, 0.7275, 0.7222])
assert numpy_cosine_similarity_distance(original_image.flatten(), expected_image) < 1e-2
def test_pose(self):
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Pose", torch_dtype=torch.float16)
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload(device=torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
n_prompt = "NSFW, nude, naked, porn, ugly"
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Pose/resolve/main/pose.jpg")
output = pipe(
prompt,
negative_prompt=n_prompt,
control_image=control_image,
controlnet_conditioning_scale=0.5,
guidance_scale=5.0,
num_inference_steps=2,
output_type="np",
generator=generator,
)
image = output.images[0]
assert image.shape == (1024, 1024, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.9048, 0.8740, 0.8936, 0.8516, 0.8799, 0.9360, 0.8379, 0.8408, 0.8652])
assert numpy_cosine_similarity_distance(original_image.flatten(), expected_image) < 1e-2
def test_tile(self):
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Tile", torch_dtype=torch.float16)
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload(device=torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = 'Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text "InstantX" on image'
n_prompt = "NSFW, nude, naked, porn, ugly"
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Tile/resolve/main/tile.jpg")
output = pipe(
prompt,
negative_prompt=n_prompt,
control_image=control_image,
controlnet_conditioning_scale=0.5,
guidance_scale=5.0,
num_inference_steps=2,
output_type="np",
generator=generator,
)
image = output.images[0]
assert image.shape == (1024, 1024, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.6699, 0.6836, 0.6226, 0.6572, 0.7310, 0.6646, 0.6650, 0.6694, 0.6011])
assert numpy_cosine_similarity_distance(original_image.flatten(), expected_image) < 1e-2
def test_multi_controlnet(self):
controlnet = SD3ControlNetModel.from_pretrained("InstantX/SD3-Controlnet-Canny", torch_dtype=torch.float16)
controlnet = SD3MultiControlNetModel([controlnet, controlnet])
pipe = StableDiffusion3ControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-3-medium-diffusers", controlnet=controlnet, torch_dtype=torch.float16
)
pipe.enable_model_cpu_offload(device=torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "Anime style illustration of a girl wearing a suit. A moon in sky. In the background we see a big rain approaching. text 'InstantX' on image"
n_prompt = "NSFW, nude, naked, porn, ugly"
control_image = load_image("https://huggingface.co/InstantX/SD3-Controlnet-Canny/resolve/main/canny.jpg")
output = pipe(
prompt,
negative_prompt=n_prompt,
control_image=[control_image, control_image],
controlnet_conditioning_scale=[0.25, 0.25],
guidance_scale=5.0,
num_inference_steps=2,
output_type="np",
generator=generator,
)
image = output.images[0]
assert image.shape == (1024, 1024, 3)
original_image = image[-3:, -3:, -1].flatten()
expected_image = np.array([0.7207, 0.7041, 0.6543, 0.7500, 0.7490, 0.6592, 0.6001, 0.7168, 0.7231])
assert numpy_cosine_similarity_distance(original_image.flatten(), expected_image) < 1e-2
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