mirror of
https://github.com/huggingface/diffusers.git
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f9d19895e6
* refactor remaining s series pipelines * remove tests for pipelines that are deprecated * fix stable audio tests * fix skyreels stuff.
340 lines
12 KiB
Python
340 lines
12 KiB
Python
# coding=utf-8
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# Copyright 2026 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
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import random
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import numpy as np
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import pytest
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import torch
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from PIL import Image
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from transformers import (
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CLIPTextConfig,
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CLIPTextModel,
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CLIPTokenizer,
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DPTConfig,
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DPTForDepthEstimation,
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DPTImageProcessor,
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)
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from diffusers import (
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AutoencoderKL,
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PNDMScheduler,
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StableDiffusionDepth2ImgPipeline,
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UNet2DConditionModel,
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)
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from ...testing_utils import (
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assert_tensors_close,
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backend_empty_cache,
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enable_full_determinism,
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floats_tensor,
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load_image,
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load_numpy,
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nightly,
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require_torch_accelerator,
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skip_mps,
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slow,
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torch_device,
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)
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from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
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from ..testing_utils import (
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BasePipelineTesterConfig,
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MemoryTesterMixin,
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PipelineTesterMixin,
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)
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enable_full_determinism()
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class StableDiffusionDepth2ImgPipelineTesterConfig(BasePipelineTesterConfig):
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pipeline_class = StableDiffusionDepth2ImgPipeline
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# `TEXT_GUIDED_IMAGE_VARIATION_PARAMS` without `height` / `width`: the output resolution follows the image.
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required_input_params_in_call_signature = frozenset(
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["prompt", "image", "guidance_scale", "negative_prompt", "prompt_embeds", "negative_prompt_embeds"]
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)
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batch_input_params = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
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callback_cfg_params = frozenset(["prompt_embeds", "depth_mask"])
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# The pipeline derives its latents from the input image, so it takes no `latents` argument.
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optional_input_params = frozenset(
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["num_inference_steps", "num_images_per_prompt", "generator", "output_type", "return_dict"]
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)
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output_shape = (3, 32, 32)
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=5,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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attention_head_dim=(2, 4),
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use_linear_projection=True,
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)
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scheduler = PNDMScheduler(skip_prk_steps=True)
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torch.manual_seed(0)
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vae = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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torch.manual_seed(0)
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text_encoder_config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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text_encoder = CLIPTextModel(text_encoder_config)
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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backbone_config = {
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"global_padding": "same",
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"layer_type": "bottleneck",
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"depths": [3, 4, 9],
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"out_features": ["stage1", "stage2", "stage3"],
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"embedding_dynamic_padding": True,
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"hidden_sizes": [96, 192, 384, 768],
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"num_groups": 2,
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}
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depth_estimator_config = DPTConfig(
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image_size=32,
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patch_size=16,
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num_channels=3,
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hidden_size=32,
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num_hidden_layers=4,
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backbone_out_indices=(0, 1, 2, 3),
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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is_decoder=False,
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initializer_range=0.02,
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is_hybrid=True,
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backbone_config=backbone_config,
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backbone_featmap_shape=[1, 384, 24, 24],
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)
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depth_estimator = DPTForDepthEstimation(depth_estimator_config).eval()
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feature_extractor = DPTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-DPTForDepthEstimation")
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return {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"depth_estimator": depth_estimator,
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"feature_extractor": feature_extractor,
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}
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def get_dummy_inputs(self):
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image = floats_tensor((1, 3, 32, 32), rng=random.Random(0))
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image = image.permute(0, 2, 3, 1)[0]
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image = Image.fromarray(np.uint8(image)).convert("RGB").resize((32, 32))
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return {
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"prompt": "A painting of a squirrel eating a burger",
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"image": image,
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"generator": self.get_generator(0),
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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# Request torch outputs so tests compare torch tensors directly (see `BasePipelineTesterConfig`).
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# Note `"pt"` images are `(batch, channels, height, width)`, unlike `"np"` (`(batch, h, w, c)`).
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"output_type": "pt",
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}
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@skip_mps
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class TestStableDiffusionDepth2ImgPipeline(StableDiffusionDepth2ImgPipelineTesterConfig, PipelineTesterMixin):
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def test_stable_diffusion_depth2img_default_case(self):
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# Run on CPU: the expected slice below is CPU-specific.
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pipe = self.get_pipeline()
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -1, -3:, -3:]
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assert image.shape == (1, *self.output_shape)
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expected_slice = torch.tensor([0.4082, 0.5014, 0.4256, 0.3948, 0.4573, 0.4061, 0.3515, 0.4656, 0.4529])
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assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-3)
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def test_stable_diffusion_depth2img_negative_prompt(self):
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# Run on CPU: the expected slice below is CPU-specific.
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pipe = self.get_pipeline()
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inputs = self.get_dummy_inputs()
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negative_prompt = "french fries"
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output = pipe(**inputs, negative_prompt=negative_prompt)
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image = output.images
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image_slice = image[0, -1, -3:, -3:]
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assert image.shape == (1, *self.output_shape)
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expected_slice = torch.tensor([0.4168, 0.4955, 0.4142, 0.4035, 0.4646, 0.4082, 0.3500, 0.4635, 0.4475])
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assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-3)
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def test_stable_diffusion_depth2img_multiple_init_images(self):
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# Run on CPU: the expected slice below is CPU-specific.
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pipe = self.get_pipeline()
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inputs = self.get_dummy_inputs()
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inputs["prompt"] = [inputs["prompt"]] * 2
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inputs["image"] = 2 * [inputs["image"]]
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image = pipe(**inputs).images
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image_slice = image[-1, -1, -3:, -3:]
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assert image.shape == (2, *self.output_shape)
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expected_slice = torch.tensor([0.6261, 0.5452, 0.5330, 0.5160, 0.4768, 0.5216, 0.4413, 0.4797, 0.4777])
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assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-3)
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def test_stable_diffusion_depth2img_pil(self):
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# Run on CPU: the expected slice below is CPU-specific.
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pipe = self.get_pipeline()
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inputs = self.get_dummy_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, -1, -3:, -3:]
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expected_slice = torch.tensor([0.4082, 0.5014, 0.4256, 0.3948, 0.4573, 0.4061, 0.3515, 0.4656, 0.4529])
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assert_tensors_close(image_slice.flatten(), expected_slice, atol=1e-3)
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def test_inference_batch_single_identical(self, batch_size=3, expected_max_diff=7e-3):
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super().test_inference_batch_single_identical(batch_size=batch_size, expected_max_diff=expected_max_diff)
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def test_save_load_float16(self, tmp_path, expected_max_diff=2e-2):
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super().test_save_load_float16(tmp_path, expected_max_diff=expected_max_diff)
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@pytest.mark.skip("`depth_estimator` and `feature_extractor` are required to build the depth mask.")
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def test_save_load_optional_components(self):
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pass
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def test_encode_prompt_works_in_isolation(self):
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extra_required_param_value_dict = {
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"device": torch.device(torch_device).type,
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"do_classifier_free_guidance": self.get_dummy_inputs().get("guidance_scale", 1.0) > 1.0,
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}
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return super().test_encode_prompt_works_in_isolation(extra_required_param_value_dict)
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@skip_mps
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class TestStableDiffusionDepth2ImgPipelineMemory(StableDiffusionDepth2ImgPipelineTesterConfig, MemoryTesterMixin):
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"""Memory optimization tests (CPU offload, group offload, layerwise casting) for the SD2 depth2img pipeline."""
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@slow
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@require_torch_accelerator
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class TestStableDiffusionDepth2ImgPipelineSlow:
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@pytest.fixture(autouse=True)
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def cleanup(self):
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gc.collect()
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backend_empty_cache(torch_device)
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yield
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gc.collect()
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backend_empty_cache(torch_device)
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def get_inputs(self, device="cpu", dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
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)
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inputs = {
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"prompt": "two tigers",
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"image": init_image,
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"generator": generator,
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"num_inference_steps": 3,
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"strength": 0.75,
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"guidance_scale": 7.5,
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"output_type": "np",
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}
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return inputs
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def test_stable_diffusion_depth2img_pipeline_default(self):
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pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
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"stabilityai/stable-diffusion-2-depth", safety_checker=None
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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inputs = self.get_inputs()
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image = pipe(**inputs).images
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image_slice = image[0, 253:256, 253:256, -1].flatten()
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assert image.shape == (1, 480, 640, 3)
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expected_slice = np.array([0.5435, 0.4992, 0.3783, 0.4411, 0.5842, 0.4654, 0.3786, 0.5077, 0.4655])
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assert np.abs(expected_slice - image_slice).max() < 6e-1
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@nightly
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@require_torch_accelerator
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class TestStableDiffusionDepth2ImgPipelineNightly:
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@pytest.fixture(autouse=True)
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def cleanup(self):
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gc.collect()
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backend_empty_cache(torch_device)
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yield
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gc.collect()
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backend_empty_cache(torch_device)
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def get_inputs(self, device="cpu", dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).manual_seed(seed)
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/depth2img/two_cats.png"
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)
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inputs = {
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"prompt": "two tigers",
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"image": init_image,
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"generator": generator,
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"num_inference_steps": 2,
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"strength": 0.75,
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"guidance_scale": 7.5,
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"output_type": "np",
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}
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return inputs
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def test_depth2img(self):
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pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs()
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image = pipe(**inputs).images[0]
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expected_image = load_numpy(
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"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
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"/stable_diffusion_depth2img/stable_diffusion_2_0_pndm.npy"
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)
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max_diff = np.abs(expected_image - image).max()
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assert max_diff < 1e-3
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