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# coding=utf-8
# Copyright 2024 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 unittest
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import DDPMWuerstchenScheduler, WuerstchenPriorPipeline
from diffusers.loaders import AttnProcsLayers
from diffusers.models.attention_processor import (
LoRAAttnProcessor,
LoRAAttnProcessor2_0,
)
from diffusers.pipelines.wuerstchen import WuerstchenPrior
from diffusers.utils.import_utils import is_peft_available
from diffusers.utils.testing_utils import enable_full_determinism, require_peft_backend, skip_mps, torch_device
if is_peft_available():
from peft import LoraConfig
from peft.tuners.tuners_utils import BaseTunerLayer
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
def create_prior_lora_layers(unet: nn.Module):
lora_attn_procs = {}
for name in unet.attn_processors.keys():
lora_attn_processor_class = (
LoRAAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else LoRAAttnProcessor
)
lora_attn_procs[name] = lora_attn_processor_class(
hidden_size=unet.config.c,
)
unet_lora_layers = AttnProcsLayers(lora_attn_procs)
return lora_attn_procs, unet_lora_layers
class WuerstchenPriorPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = WuerstchenPriorPipeline
params = ["prompt"]
batch_params = ["prompt", "negative_prompt"]
required_optional_params = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
test_xformers_attention = False
callback_cfg_params = ["text_encoder_hidden_states"]
@property
def text_embedder_hidden_size(self):
return 32
@property
def time_input_dim(self):
return 32
@property
def block_out_channels_0(self):
return self.time_input_dim
@property
def time_embed_dim(self):
return self.time_input_dim * 4
@property
def dummy_tokenizer(self):
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
return tokenizer
@property
def dummy_text_encoder(self):
torch.manual_seed(0)
config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=self.text_embedder_hidden_size,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
)
return CLIPTextModel(config).eval()
@property
def dummy_prior(self):
torch.manual_seed(0)
model_kwargs = {
"c_in": 2,
"c": 8,
"depth": 2,
"c_cond": 32,
"c_r": 8,
"nhead": 2,
}
model = WuerstchenPrior(**model_kwargs)
return model.eval()
def get_dummy_components(self):
prior = self.dummy_prior
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
scheduler = DDPMWuerstchenScheduler()
components = {
"prior": prior,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
}
return components
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)
inputs = {
"prompt": "horse",
"generator": generator,
"guidance_scale": 4.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_wuerstchen_prior(self):
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output = pipe(**self.get_dummy_inputs(device))
image = output.image_embeddings
image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)[0]
image_slice = image[0, 0, 0, -10:]
image_from_tuple_slice = image_from_tuple[0, 0, 0, -10:]
assert image.shape == (1, 2, 24, 24)
expected_slice = np.array(
[
-7172.837,
-3438.855,
-1093.312,
388.8835,
-7471.467,
-7998.1206,
-5328.259,
218.00089,
-2731.5745,
-8056.734,
]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 5e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5e-2
@skip_mps
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(
expected_max_diff=2e-1,
)
@skip_mps
def test_attention_slicing_forward_pass(self):
test_max_difference = torch_device == "cpu"
test_mean_pixel_difference = False
self._test_attention_slicing_forward_pass(
test_max_difference=test_max_difference,
test_mean_pixel_difference=test_mean_pixel_difference,
)
@unittest.skip(reason="flaky for now")
def test_float16_inference(self):
super().test_float16_inference()
# override because we need to make sure latent_mean and latent_std to be 0
def test_callback_inputs(self):
components = self.get_dummy_components()
components["latent_mean"] = 0
components["latent_std"] = 0
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
self.assertTrue(
hasattr(pipe, "_callback_tensor_inputs"),
f" {self.pipeline_class} should have `_callback_tensor_inputs` that defines a list of tensor variables its callback function can use as inputs",
)
def callback_inputs_test(pipe, i, t, callback_kwargs):
missing_callback_inputs = set()
for v in pipe._callback_tensor_inputs:
if v not in callback_kwargs:
missing_callback_inputs.add(v)
self.assertTrue(
len(missing_callback_inputs) == 0, f"Missing callback tensor inputs: {missing_callback_inputs}"
)
last_i = pipe.num_timesteps - 1
if i == last_i:
callback_kwargs["latents"] = torch.zeros_like(callback_kwargs["latents"])
return callback_kwargs
inputs = self.get_dummy_inputs(torch_device)
inputs["callback_on_step_end"] = callback_inputs_test
inputs["callback_on_step_end_tensor_inputs"] = pipe._callback_tensor_inputs
inputs["output_type"] = "latent"
output = pipe(**inputs)[0]
assert output.abs().sum() == 0
def check_if_lora_correctly_set(self, model) -> bool:
"""
Checks if the LoRA layers are correctly set with peft
"""
for module in model.modules():
if isinstance(module, BaseTunerLayer):
return True
return False
def get_lora_components(self):
prior = self.dummy_prior
prior_lora_config = LoraConfig(
r=4, lora_alpha=4, target_modules=["to_q", "to_k", "to_v", "to_out.0"], init_lora_weights=False
)
prior_lora_attn_procs, prior_lora_layers = create_prior_lora_layers(prior)
lora_components = {
"prior_lora_layers": prior_lora_layers,
"prior_lora_attn_procs": prior_lora_attn_procs,
}
return prior, prior_lora_config, lora_components
@require_peft_backend
def test_inference_with_prior_lora(self):
_, prior_lora_config, _ = self.get_lora_components()
device = "cpu"
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(device)
pipe.set_progress_bar_config(disable=None)
output_no_lora = pipe(**self.get_dummy_inputs(device))
image_embed = output_no_lora.image_embeddings
self.assertTrue(image_embed.shape == (1, 2, 24, 24))
pipe.prior.add_adapter(prior_lora_config)
self.assertTrue(self.check_if_lora_correctly_set(pipe.prior), "Lora not correctly set in prior")
output_lora = pipe(**self.get_dummy_inputs(device))
lora_image_embed = output_lora.image_embeddings
self.assertTrue(image_embed.shape == lora_image_embed.shape)
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