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7aa6af1138
* update
* update
* update
* update
* update
* merge main
* Revert "merge main"
This reverts commit 65efbcead5.
219 lines
7.2 KiB
Python
219 lines
7.2 KiB
Python
# coding=utf-8
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# Copyright 2025 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 unittest
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import numpy as np
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import torch
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
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from ...testing_utils import (
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backend_empty_cache,
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enable_full_determinism,
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load_numpy,
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nightly,
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require_torch_accelerator,
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torch_device,
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)
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from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
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from ..test_pipelines_common import PipelineTesterMixin
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enable_full_determinism()
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class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = LDMTextToImagePipeline
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params = TEXT_TO_IMAGE_PARAMS - {
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"negative_prompt",
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"negative_prompt_embeds",
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"cross_attention_kwargs",
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"prompt_embeds",
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}
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required_optional_params = PipelineTesterMixin.required_optional_params - {
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"num_images_per_prompt",
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"callback",
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"callback_steps",
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}
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
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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=4,
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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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)
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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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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components = {
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"unet": unet,
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"scheduler": scheduler,
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"vqvae": vae,
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"bert": text_encoder,
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"tokenizer": tokenizer,
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}
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return components
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def get_dummy_inputs(self, device, seed=0):
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "np",
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}
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return inputs
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def test_inference_text2img(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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pipe = LDMTextToImagePipeline(**components)
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pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 16, 16, 3)
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expected_slice = np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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@nightly
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@require_torch_accelerator
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class LDMTextToImagePipelineSlowTests(unittest.TestCase):
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def setUp(self):
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super().setUp()
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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super().tearDown()
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gc.collect()
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backend_empty_cache(torch_device)
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.manual_seed(seed)
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latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
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latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"latents": latents,
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 6.0,
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"output_type": "np",
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}
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return inputs
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def test_ldm_default_ddim(self):
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pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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image = pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1].flatten()
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878])
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max_diff = np.abs(expected_slice - image_slice).max()
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assert max_diff < 1e-3
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@nightly
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@require_torch_accelerator
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class LDMTextToImagePipelineNightlyTests(unittest.TestCase):
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def setUp(self):
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super().setUp()
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gc.collect()
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backend_empty_cache(torch_device)
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def tearDown(self):
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super().tearDown()
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gc.collect()
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backend_empty_cache(torch_device)
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.manual_seed(seed)
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latents = np.random.RandomState(seed).standard_normal((1, 4, 32, 32))
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latents = torch.from_numpy(latents).to(device=device, dtype=dtype)
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inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"latents": latents,
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"generator": generator,
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"num_inference_steps": 50,
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"guidance_scale": 6.0,
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"output_type": "np",
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}
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return inputs
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def test_ldm_default_ddim(self):
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pipe = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_inputs(torch_device)
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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/ldm_text2img/ldm_large_256_ddim.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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