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b6e0b016ce
* initial commit * move modules to import struct * add dummy objects and _LazyModule * add lazy import to schedulers * clean up unused imports * lazy import on models module * lazy import for schedulers module * add lazy import to pipelines module * lazy import altdiffusion * lazy import audio diffusion * lazy import audioldm * lazy import consistency model * lazy import controlnet * lazy import dance diffusion ddim ddpm * lazy import deepfloyd * lazy import kandinksy * lazy imports * lazy import semantic diffusion * lazy imports * lazy import stable diffusion * move sd output to its own module * clean up * lazy import t2iadapter * lazy import unclip * lazy import versatile and vq diffsuion * lazy import vq diffusion * helper to fetch objects from modules * lazy import sdxl * lazy import txt2vid * lazy import stochastic karras * fix model imports * fix bug * lazy import * clean up * clean up * fixes for tests * fixes for tests * clean up * remove import of torch_utils from utils module * clean up * clean up * fix mistake import statement * dedicated modules for exporting and loading * remove testing utils from utils module * fixes from merge conflicts * Update src/diffusers/pipelines/kandinsky2_2/__init__.py * fix docs * fix alt diffusion copied from * fix check dummies * fix more docs * remove accelerate import from utils module * add type checking * make style * fix check dummies * remove torch import from xformers check * clean up error message * fixes after upstream merges * dummy objects fix * fix tests * remove unused module import --------- Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
256 lines
9.3 KiB
Python
256 lines
9.3 KiB
Python
# coding=utf-8
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# Copyright 2023 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 (
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AutoencoderKL,
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DDIMScheduler,
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EulerAncestralDiscreteScheduler,
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PNDMScheduler,
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StableDiffusionModelEditingPipeline,
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UNet2DConditionModel,
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)
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from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps, slow, torch_device
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from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
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from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
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enable_full_determinism()
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@skip_mps
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class StableDiffusionModelEditingPipelineFastTests(
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PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase
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):
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pipeline_class = StableDiffusionModelEditingPipeline
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params = TEXT_TO_IMAGE_PARAMS
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batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
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image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
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image_latents_params = TEXT_TO_IMAGE_IMAGE_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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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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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": None,
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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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generator = torch.manual_seed(seed)
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inputs = {
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"prompt": "A field of roses",
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"generator": generator,
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# Setting height and width to None to prevent OOMs on CPU.
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"height": None,
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"width": None,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_model_editing_default_case(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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sd_pipe = StableDiffusionModelEditingPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_model_editing_negative_prompt(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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sd_pipe = StableDiffusionModelEditingPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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negative_prompt = "french fries"
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output = sd_pipe(**inputs, negative_prompt=negative_prompt)
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image = output.images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_model_editing_euler(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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components["scheduler"] = EulerAncestralDiscreteScheduler(
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beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear"
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)
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sd_pipe = StableDiffusionModelEditingPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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image = sd_pipe(**inputs).images
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image_slice = image[0, -3:, -3:, -1]
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assert image.shape == (1, 64, 64, 3)
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expected_slice = np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_model_editing_pndm(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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components["scheduler"] = PNDMScheduler()
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sd_pipe = StableDiffusionModelEditingPipeline(**components)
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sd_pipe = sd_pipe.to(device)
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sd_pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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# the pipeline does not expect pndm so test if it raises error.
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with self.assertRaises(ValueError):
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_ = sd_pipe(**inputs).images
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def test_inference_batch_single_identical(self):
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super().test_inference_batch_single_identical(expected_max_diff=5e-3)
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def test_attention_slicing_forward_pass(self):
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super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)
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@slow
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@require_torch_gpu
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class StableDiffusionModelEditingSlowTests(unittest.TestCase):
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def tearDown(self):
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_inputs(self, seed=0):
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generator = torch.manual_seed(seed)
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inputs = {
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"prompt": "A field of roses",
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"generator": generator,
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"num_inference_steps": 3,
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"guidance_scale": 7.5,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_model_editing_default(self):
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model_ckpt = "CompVis/stable-diffusion-v1-4"
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pipe = StableDiffusionModelEditingPipeline.from_pretrained(model_ckpt, safety_checker=None)
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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, -3:, -3:, -1].flatten()
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array(
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[0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658]
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)
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assert np.abs(expected_slice - image_slice).max() < 1e-2
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# make sure image changes after editing
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pipe.edit_model("A pack of roses", "A pack of blue roses")
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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, 512, 512, 3)
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assert np.abs(expected_slice - image_slice).max() > 1e-1
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def test_stable_diffusion_model_editing_pipeline_with_sequential_cpu_offloading(self):
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torch.cuda.empty_cache()
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torch.cuda.reset_max_memory_allocated()
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torch.cuda.reset_peak_memory_stats()
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model_ckpt = "CompVis/stable-diffusion-v1-4"
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scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler")
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pipe = StableDiffusionModelEditingPipeline.from_pretrained(
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model_ckpt, scheduler=scheduler, safety_checker=None
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)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing(1)
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pipe.enable_sequential_cpu_offload()
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inputs = self.get_inputs()
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_ = pipe(**inputs)
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mem_bytes = torch.cuda.max_memory_allocated()
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# make sure that less than 4.4 GB is allocated
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assert mem_bytes < 4.4 * 10**9
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