mirror of
https://github.com/huggingface/diffusers.git
synced 2026-01-29 07:22:12 +03:00
7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
432 lines
16 KiB
Python
432 lines
16 KiB
Python
# coding=utf-8
|
|
# Copyright 2025 HuggingFace Inc.
|
|
#
|
|
# 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 random
|
|
import unittest
|
|
|
|
import numpy as np
|
|
import torch
|
|
from PIL import Image
|
|
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
|
|
|
|
from diffusers import (
|
|
AutoencoderKL,
|
|
DDIMScheduler,
|
|
EulerAncestralDiscreteScheduler,
|
|
LMSDiscreteScheduler,
|
|
PNDMScheduler,
|
|
StableDiffusionInstructPix2PixPipeline,
|
|
UNet2DConditionModel,
|
|
)
|
|
from diffusers.image_processor import VaeImageProcessor
|
|
|
|
from ...testing_utils import (
|
|
backend_empty_cache,
|
|
backend_max_memory_allocated,
|
|
backend_reset_max_memory_allocated,
|
|
backend_reset_peak_memory_stats,
|
|
enable_full_determinism,
|
|
floats_tensor,
|
|
load_image,
|
|
require_torch_accelerator,
|
|
slow,
|
|
torch_device,
|
|
)
|
|
from ..pipeline_params import (
|
|
IMAGE_TO_IMAGE_IMAGE_PARAMS,
|
|
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
|
|
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
|
|
TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
|
|
)
|
|
from ..test_pipelines_common import (
|
|
PipelineKarrasSchedulerTesterMixin,
|
|
PipelineLatentTesterMixin,
|
|
PipelineTesterMixin,
|
|
)
|
|
|
|
|
|
enable_full_determinism()
|
|
|
|
|
|
class StableDiffusionInstructPix2PixPipelineFastTests(
|
|
PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
|
|
):
|
|
pipeline_class = StableDiffusionInstructPix2PixPipeline
|
|
params = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width", "cross_attention_kwargs"}
|
|
batch_params = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
|
|
image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
|
|
image_latents_params = IMAGE_TO_IMAGE_IMAGE_PARAMS
|
|
callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"image_latents"}) - {"negative_prompt_embeds"}
|
|
|
|
def get_dummy_components(self):
|
|
torch.manual_seed(0)
|
|
unet = UNet2DConditionModel(
|
|
block_out_channels=(32, 64),
|
|
layers_per_block=2,
|
|
sample_size=32,
|
|
in_channels=8,
|
|
out_channels=4,
|
|
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
|
|
up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
|
|
cross_attention_dim=32,
|
|
)
|
|
scheduler = PNDMScheduler(skip_prk_steps=True)
|
|
torch.manual_seed(0)
|
|
vae = AutoencoderKL(
|
|
block_out_channels=[32, 64],
|
|
in_channels=3,
|
|
out_channels=3,
|
|
down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
|
|
up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
|
|
latent_channels=4,
|
|
)
|
|
torch.manual_seed(0)
|
|
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,
|
|
)
|
|
text_encoder = CLIPTextModel(text_encoder_config)
|
|
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
|
|
|
|
components = {
|
|
"unet": unet,
|
|
"scheduler": scheduler,
|
|
"vae": vae,
|
|
"text_encoder": text_encoder,
|
|
"tokenizer": tokenizer,
|
|
"safety_checker": None,
|
|
"feature_extractor": None,
|
|
"image_encoder": None,
|
|
}
|
|
return components
|
|
|
|
def get_dummy_inputs(self, device, seed=0):
|
|
image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
|
|
image = image.cpu().permute(0, 2, 3, 1)[0]
|
|
image = Image.fromarray(np.uint8(image)).convert("RGB")
|
|
if str(device).startswith("mps"):
|
|
generator = torch.manual_seed(seed)
|
|
else:
|
|
generator = torch.Generator(device=device).manual_seed(seed)
|
|
inputs = {
|
|
"prompt": "A painting of a squirrel eating a burger",
|
|
"image": image,
|
|
"generator": generator,
|
|
"num_inference_steps": 2,
|
|
"guidance_scale": 6.0,
|
|
"image_guidance_scale": 1,
|
|
"output_type": "np",
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_pix2pix_default_case(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_negative_prompt(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
negative_prompt = "french fries"
|
|
output = sd_pipe(**inputs, negative_prompt=negative_prompt)
|
|
image = output.images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_multiple_init_images(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
inputs["prompt"] = [inputs["prompt"]] * 2
|
|
|
|
image = np.array(inputs["image"]).astype(np.float32) / 255.0
|
|
image = torch.from_numpy(image).unsqueeze(0).to(device)
|
|
image = image / 2 + 0.5
|
|
image = image.permute(0, 3, 1, 2)
|
|
inputs["image"] = image.repeat(2, 1, 1, 1)
|
|
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[-1, -3:, -3:, -1]
|
|
|
|
assert image.shape == (2, 32, 32, 3)
|
|
expected_slice = np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_euler(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
components["scheduler"] = EulerAncestralDiscreteScheduler(
|
|
beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
|
|
)
|
|
sd_pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1]
|
|
|
|
assert image.shape == (1, 32, 32, 3)
|
|
expected_slice = np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
|
|
|
def test_inference_batch_single_identical(self):
|
|
super().test_inference_batch_single_identical(expected_max_diff=3e-3)
|
|
|
|
# Overwrite the default test_latents_inputs because pix2pix encode the image differently
|
|
def test_latents_input(self):
|
|
components = self.get_dummy_components()
|
|
pipe = StableDiffusionInstructPix2PixPipeline(**components)
|
|
pipe.image_processor = VaeImageProcessor(do_resize=False, do_normalize=False)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
out = pipe(**self.get_dummy_inputs_by_type(torch_device, input_image_type="pt"))[0]
|
|
|
|
vae = components["vae"]
|
|
inputs = self.get_dummy_inputs_by_type(torch_device, input_image_type="pt")
|
|
|
|
for image_param in self.image_latents_params:
|
|
if image_param in inputs.keys():
|
|
inputs[image_param] = vae.encode(inputs[image_param]).latent_dist.mode()
|
|
|
|
out_latents_inputs = pipe(**inputs)[0]
|
|
|
|
max_diff = np.abs(out - out_latents_inputs).max()
|
|
self.assertLess(max_diff, 1e-4, "passing latents as image input generate different result from passing image")
|
|
|
|
# Override the default test_callback_cfg because pix2pix create inputs for cfg differently
|
|
def test_callback_cfg(self):
|
|
components = self.get_dummy_components()
|
|
pipe = self.pipeline_class(**components)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
|
|
def callback_no_cfg(pipe, i, t, callback_kwargs):
|
|
if i == 1:
|
|
for k, w in callback_kwargs.items():
|
|
if k in self.callback_cfg_params:
|
|
callback_kwargs[k] = callback_kwargs[k].chunk(3)[0]
|
|
pipe._guidance_scale = 1.0
|
|
|
|
return callback_kwargs
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["guidance_scale"] = 1.0
|
|
inputs["num_inference_steps"] = 2
|
|
out_no_cfg = pipe(**inputs)[0]
|
|
|
|
inputs["guidance_scale"] = 7.5
|
|
inputs["callback_on_step_end"] = callback_no_cfg
|
|
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
|
|
out_callback_no_cfg = pipe(**inputs)[0]
|
|
|
|
assert out_no_cfg.shape == out_callback_no_cfg.shape
|
|
|
|
|
|
@slow
|
|
@require_torch_accelerator
|
|
class StableDiffusionInstructPix2PixPipelineSlowTests(unittest.TestCase):
|
|
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, seed=0):
|
|
generator = torch.manual_seed(seed)
|
|
image = load_image(
|
|
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"
|
|
)
|
|
inputs = {
|
|
"prompt": "turn him into a cyborg",
|
|
"image": image,
|
|
"generator": generator,
|
|
"num_inference_steps": 3,
|
|
"guidance_scale": 7.5,
|
|
"image_guidance_scale": 1.0,
|
|
"output_type": "np",
|
|
}
|
|
return inputs
|
|
|
|
def test_stable_diffusion_pix2pix_default(self):
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
"timbrooks/instruct-pix2pix", safety_checker=None
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555])
|
|
|
|
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_k_lms(self):
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
"timbrooks/instruct-pix2pix", safety_checker=None
|
|
)
|
|
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301])
|
|
|
|
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_ddim(self):
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
"timbrooks/instruct-pix2pix", safety_checker=None
|
|
)
|
|
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs()
|
|
image = pipe(**inputs).images
|
|
image_slice = image[0, -3:, -3:, -1].flatten()
|
|
|
|
assert image.shape == (1, 512, 512, 3)
|
|
expected_slice = np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753])
|
|
|
|
assert np.abs(expected_slice - image_slice).max() < 1e-3
|
|
|
|
def test_stable_diffusion_pix2pix_intermediate_state(self):
|
|
number_of_steps = 0
|
|
|
|
def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:
|
|
callback_fn.has_been_called = True
|
|
nonlocal number_of_steps
|
|
number_of_steps += 1
|
|
if step == 1:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983])
|
|
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
|
|
elif step == 2:
|
|
latents = latents.detach().cpu().numpy()
|
|
assert latents.shape == (1, 4, 64, 64)
|
|
latents_slice = latents[0, -3:, -3:, -1]
|
|
expected_slice = np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115])
|
|
|
|
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
|
|
|
|
callback_fn.has_been_called = False
|
|
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
|
|
)
|
|
pipe = pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
inputs = self.get_inputs()
|
|
pipe(**inputs, callback=callback_fn, callback_steps=1)
|
|
assert callback_fn.has_been_called
|
|
assert number_of_steps == 3
|
|
|
|
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
|
|
backend_empty_cache(torch_device)
|
|
backend_reset_max_memory_allocated(torch_device)
|
|
backend_reset_peak_memory_stats(torch_device)
|
|
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
"timbrooks/instruct-pix2pix", safety_checker=None, torch_dtype=torch.float16
|
|
)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing(1)
|
|
pipe.enable_sequential_cpu_offload(device=torch_device)
|
|
|
|
inputs = self.get_inputs()
|
|
_ = pipe(**inputs)
|
|
|
|
mem_bytes = backend_max_memory_allocated(torch_device)
|
|
# make sure that less than 2.2 GB is allocated
|
|
assert mem_bytes < 2.2 * 10**9
|
|
|
|
def test_stable_diffusion_pix2pix_pipeline_multiple_of_8(self):
|
|
inputs = self.get_inputs()
|
|
# resize to resolution that is divisible by 8 but not 16 or 32
|
|
inputs["image"] = inputs["image"].resize((504, 504))
|
|
|
|
model_id = "timbrooks/instruct-pix2pix"
|
|
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
|
|
model_id,
|
|
safety_checker=None,
|
|
)
|
|
pipe.to(torch_device)
|
|
pipe.set_progress_bar_config(disable=None)
|
|
pipe.enable_attention_slicing()
|
|
|
|
output = pipe(**inputs)
|
|
image = output.images[0]
|
|
|
|
image_slice = image[255:258, 383:386, -1]
|
|
|
|
assert image.shape == (504, 504, 3)
|
|
expected_slice = np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590])
|
|
|
|
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-3
|