PinPoint / BrushNet /tests /pipelines /stable_cascade /test_stable_cascade_decoder.py
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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 gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import DDPMWuerstchenScheduler, StableCascadeDecoderPipeline
from diffusers.image_processor import VaeImageProcessor
from diffusers.models import StableCascadeUNet
from diffusers.pipelines.wuerstchen import PaellaVQModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_image,
load_pt,
require_torch_gpu,
skip_mps,
slow,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class StableCascadeDecoderPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
pipeline_class = StableCascadeDecoderPipeline
params = ["prompt"]
batch_params = ["image_embeddings", "prompt", "negative_prompt"]
required_optional_params = [
"num_images_per_prompt",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
test_xformers_attention = False
callback_cfg_params = ["image_embeddings", "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,
projection_dim=self.text_embedder_hidden_size,
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 CLIPTextModelWithProjection(config).eval()
@property
def dummy_vqgan(self):
torch.manual_seed(0)
model_kwargs = {
"bottleneck_blocks": 1,
"num_vq_embeddings": 2,
}
model = PaellaVQModel(**model_kwargs)
return model.eval()
@property
def dummy_decoder(self):
torch.manual_seed(0)
model_kwargs = {
"in_channels": 4,
"out_channels": 4,
"conditioning_dim": 128,
"block_out_channels": [16, 32, 64, 128],
"num_attention_heads": [-1, -1, 1, 2],
"down_num_layers_per_block": [1, 1, 1, 1],
"up_num_layers_per_block": [1, 1, 1, 1],
"down_blocks_repeat_mappers": [1, 1, 1, 1],
"up_blocks_repeat_mappers": [3, 3, 2, 2],
"block_types_per_layer": [
["SDCascadeResBlock", "SDCascadeTimestepBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
["SDCascadeResBlock", "SDCascadeTimestepBlock", "SDCascadeAttnBlock"],
],
"switch_level": None,
"clip_text_pooled_in_channels": 32,
"dropout": [0.1, 0.1, 0.1, 0.1],
}
model = StableCascadeUNet(**model_kwargs)
return model.eval()
def get_dummy_components(self):
decoder = self.dummy_decoder
text_encoder = self.dummy_text_encoder
tokenizer = self.dummy_tokenizer
vqgan = self.dummy_vqgan
scheduler = DDPMWuerstchenScheduler()
components = {
"decoder": decoder,
"vqgan": vqgan,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"scheduler": scheduler,
"latent_dim_scale": 4.0,
}
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 = {
"image_embeddings": torch.ones((1, 4, 4, 4), device=device),
"prompt": "horse",
"generator": generator,
"guidance_scale": 2.0,
"num_inference_steps": 2,
"output_type": "np",
}
return inputs
def test_wuerstchen_decoder(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.images
image_from_tuple = pipe(**self.get_dummy_inputs(device), return_dict=False)
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def test_inference_batch_single_identical(self):
self._test_inference_batch_single_identical(expected_max_diff=1e-2)
@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="fp16 not supported")
def test_float16_inference(self):
super().test_float16_inference()
@slow
@require_torch_gpu
class StableCascadeDecoderPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_stable_cascade_decoder(self):
pipe = StableCascadeDecoderPipeline.from_pretrained(
"diffusers/StableCascade-decoder", torch_dtype=torch.bfloat16
)
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
prompt = "A photograph of the inside of a subway train. There are raccoons sitting on the seats. One of them is reading a newspaper. The window shows the city in the background."
generator = torch.Generator(device="cpu").manual_seed(0)
image_embedding = load_pt(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/image_embedding.pt"
)
image = pipe(
prompt=prompt, image_embeddings=image_embedding, num_inference_steps=10, generator=generator
).images[0]
assert image.size == (1024, 1024)
expected_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_cascade/t2i.png"
)
image_processor = VaeImageProcessor()
image_np = image_processor.pil_to_numpy(image)
expected_image_np = image_processor.pil_to_numpy(expected_image)
self.assertTrue(np.allclose(image_np, expected_image_np, atol=53e-2))