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7aa6af1138
* update
* update
* update
* update
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* Revert "merge main"
This reverts commit 65efbcead5.
977 lines
36 KiB
Python
977 lines
36 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 copy
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import gc
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import tempfile
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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, CLIPTextModelWithProjection, CLIPTokenizer
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from diffusers import (
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AutoencoderKL,
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerDiscreteScheduler,
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HeunDiscreteScheduler,
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LCMScheduler,
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StableDiffusionXLImg2ImgPipeline,
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StableDiffusionXLPipeline,
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UNet2DConditionModel,
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UniPCMultistepScheduler,
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)
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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_image,
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numpy_cosine_similarity_distance,
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require_torch_accelerator,
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slow,
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torch_device,
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)
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from ..pipeline_params import (
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TEXT_TO_IMAGE_BATCH_PARAMS,
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TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS,
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TEXT_TO_IMAGE_IMAGE_PARAMS,
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TEXT_TO_IMAGE_PARAMS,
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)
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from ..test_pipelines_common import (
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IPAdapterTesterMixin,
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PipelineLatentTesterMixin,
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PipelineTesterMixin,
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SDFunctionTesterMixin,
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)
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enable_full_determinism()
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class StableDiffusionXLPipelineFastTests(
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SDFunctionTesterMixin,
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IPAdapterTesterMixin,
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PipelineLatentTesterMixin,
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PipelineTesterMixin,
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unittest.TestCase,
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):
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pipeline_class = StableDiffusionXLPipeline
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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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callback_cfg_params = TEXT_TO_IMAGE_CALLBACK_CFG_PARAMS.union({"add_text_embeds", "add_time_ids"})
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test_layerwise_casting = True
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test_group_offloading = True
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def get_dummy_components(self, time_cond_proj_dim=None):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(2, 4),
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layers_per_block=2,
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time_cond_proj_dim=time_cond_proj_dim,
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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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# SD2-specific config below
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attention_head_dim=(2, 4),
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use_linear_projection=True,
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addition_embed_type="text_time",
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addition_time_embed_dim=8,
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transformer_layers_per_block=(1, 2),
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projection_class_embeddings_input_dim=80, # 6 * 8 + 32
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cross_attention_dim=64,
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norm_num_groups=1,
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)
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scheduler = EulerDiscreteScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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steps_offset=1,
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beta_schedule="scaled_linear",
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timestep_spacing="leading",
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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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sample_size=128,
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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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# SD2-specific config below
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hidden_act="gelu",
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projection_dim=32,
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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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text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
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tokenizer_2 = 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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"text_encoder_2": text_encoder_2,
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"tokenizer_2": tokenizer_2,
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"image_encoder": 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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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": 5.0,
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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_xl_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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sd_pipe = StableDiffusionXLPipeline(**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([0.5388, 0.5452, 0.4694, 0.4583, 0.5253, 0.4832, 0.5288, 0.5035, 0.47])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_xl_euler_lcm(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components(time_cond_proj_dim=256)
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sd_pipe = StableDiffusionXLPipeline(**components)
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sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
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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([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_xl_euler_lcm_custom_timesteps(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components(time_cond_proj_dim=256)
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sd_pipe = StableDiffusionXLPipeline(**components)
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sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
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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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del inputs["num_inference_steps"]
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inputs["timesteps"] = [999, 499]
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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([0.4917, 0.6555, 0.4348, 0.5219, 0.7324, 0.4855, 0.5168, 0.5447, 0.5156])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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def test_stable_diffusion_ays(self):
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from diffusers.schedulers import AysSchedules
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timestep_schedule = AysSchedules["StableDiffusionXLTimesteps"]
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sigma_schedule = AysSchedules["StableDiffusionXLSigmas"]
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components(time_cond_proj_dim=256)
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sd_pipe = StableDiffusionXLPipeline(**components)
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sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
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sd_pipe = sd_pipe.to(torch_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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inputs["num_inference_steps"] = 10
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output = sd_pipe(**inputs).images
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inputs = self.get_dummy_inputs(device)
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inputs["num_inference_steps"] = None
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inputs["timesteps"] = timestep_schedule
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output_ts = sd_pipe(**inputs).images
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inputs = self.get_dummy_inputs(device)
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inputs["num_inference_steps"] = None
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inputs["sigmas"] = sigma_schedule
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output_sigmas = sd_pipe(**inputs).images
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assert np.abs(output_sigmas.flatten() - output_ts.flatten()).max() < 1e-3, (
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"ays timesteps and ays sigmas should have the same outputs"
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)
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assert np.abs(output.flatten() - output_ts.flatten()).max() > 1e-3, (
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"use ays timesteps should have different outputs"
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)
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assert np.abs(output.flatten() - output_sigmas.flatten()).max() > 1e-3, (
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"use ays sigmas should have different outputs"
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)
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def test_ip_adapter(self):
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expected_pipe_slice = None
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if torch_device == "cpu":
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expected_pipe_slice = np.array([0.5388, 0.5452, 0.4694, 0.4583, 0.5253, 0.4832, 0.5288, 0.5035, 0.4766])
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return super().test_ip_adapter(expected_pipe_slice=expected_pipe_slice)
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def test_attention_slicing_forward_pass(self):
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super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)
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def test_inference_batch_single_identical(self):
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super().test_inference_batch_single_identical(expected_max_diff=3e-3)
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@require_torch_accelerator
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def test_stable_diffusion_xl_offloads(self):
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pipes = []
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)
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pipes.append(sd_pipe)
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionXLPipeline(**components)
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sd_pipe.enable_model_cpu_offload(device=torch_device)
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pipes.append(sd_pipe)
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components = self.get_dummy_components()
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sd_pipe = StableDiffusionXLPipeline(**components)
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sd_pipe.enable_sequential_cpu_offload(device=torch_device)
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pipes.append(sd_pipe)
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image_slices = []
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for pipe in pipes:
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pipe.unet.set_default_attn_processor()
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inputs = self.get_dummy_inputs(torch_device)
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image = pipe(**inputs).images
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image_slices.append(image[0, -3:, -3:, -1].flatten())
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assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
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assert np.abs(image_slices[0] - image_slices[2]).max() < 1e-3
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@unittest.skip("We test this functionality elsewhere already.")
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def test_save_load_optional_components(self):
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pass
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def test_stable_diffusion_two_xl_mixture_of_denoiser_fast(self):
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components = self.get_dummy_components()
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pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
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pipe_1.unet.set_default_attn_processor()
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pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
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pipe_2.unet.set_default_attn_processor()
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def assert_run_mixture(
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num_steps,
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split,
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scheduler_cls_orig,
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expected_tss,
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num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
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):
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inputs = self.get_dummy_inputs(torch_device)
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inputs["num_inference_steps"] = num_steps
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class scheduler_cls(scheduler_cls_orig):
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pass
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pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
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pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
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# Let's retrieve the number of timesteps we want to use
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pipe_1.scheduler.set_timesteps(num_steps)
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expected_steps = pipe_1.scheduler.timesteps.tolist()
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if pipe_1.scheduler.order == 2:
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expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
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expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
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expected_steps = expected_steps_1 + expected_steps_2
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else:
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expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
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expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))
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# now we monkey patch step `done_steps`
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# list into the step function for testing
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done_steps = []
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old_step = copy.copy(scheduler_cls.step)
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def new_step(self, *args, **kwargs):
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done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
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return old_step(self, *args, **kwargs)
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scheduler_cls.step = new_step
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inputs_1 = {
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**inputs,
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**{
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"denoising_end": 1.0 - (split / num_train_timesteps),
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"output_type": "latent",
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},
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}
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latents = pipe_1(**inputs_1).images[0]
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assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
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inputs_2 = {
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**inputs,
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**{
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"denoising_start": 1.0 - (split / num_train_timesteps),
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"image": latents,
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},
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}
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pipe_2(**inputs_2).images[0]
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assert expected_steps_2 == done_steps[len(expected_steps_1) :]
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assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
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steps = 10
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for split in [300, 700]:
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for scheduler_cls_timesteps in [
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(EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
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(
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HeunDiscreteScheduler,
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[
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901.0,
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801.0,
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801.0,
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701.0,
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701.0,
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601.0,
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601.0,
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501.0,
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501.0,
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401.0,
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401.0,
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301.0,
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301.0,
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201.0,
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201.0,
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101.0,
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101.0,
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1.0,
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1.0,
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],
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),
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]:
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assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
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@slow
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def test_stable_diffusion_two_xl_mixture_of_denoiser(self):
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components = self.get_dummy_components()
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pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
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pipe_1.unet.set_default_attn_processor()
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pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
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pipe_2.unet.set_default_attn_processor()
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def assert_run_mixture(
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num_steps,
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split,
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scheduler_cls_orig,
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expected_tss,
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num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
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):
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inputs = self.get_dummy_inputs(torch_device)
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inputs["num_inference_steps"] = num_steps
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class scheduler_cls(scheduler_cls_orig):
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pass
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pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
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pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
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# Let's retrieve the number of timesteps we want to use
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pipe_1.scheduler.set_timesteps(num_steps)
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expected_steps = pipe_1.scheduler.timesteps.tolist()
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if pipe_1.scheduler.order == 2:
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expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
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expected_steps_2 = expected_steps_1[-1:] + list(filter(lambda ts: ts < split, expected_tss))
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expected_steps = expected_steps_1 + expected_steps_2
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else:
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expected_steps_1 = list(filter(lambda ts: ts >= split, expected_tss))
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expected_steps_2 = list(filter(lambda ts: ts < split, expected_tss))
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# now we monkey patch step `done_steps`
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# list into the step function for testing
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done_steps = []
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old_step = copy.copy(scheduler_cls.step)
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def new_step(self, *args, **kwargs):
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done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
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return old_step(self, *args, **kwargs)
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scheduler_cls.step = new_step
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inputs_1 = {
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**inputs,
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**{
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"denoising_end": 1.0 - (split / num_train_timesteps),
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"output_type": "latent",
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},
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}
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latents = pipe_1(**inputs_1).images[0]
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assert expected_steps_1 == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
|
|
|
|
inputs_2 = {
|
|
**inputs,
|
|
**{
|
|
"denoising_start": 1.0 - (split / num_train_timesteps),
|
|
"image": latents,
|
|
},
|
|
}
|
|
pipe_2(**inputs_2).images[0]
|
|
|
|
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
|
|
assert expected_steps == done_steps, f"Failure with {scheduler_cls.__name__} and {num_steps} and {split}"
|
|
|
|
steps = 10
|
|
for split in [300, 500, 700]:
|
|
for scheduler_cls_timesteps in [
|
|
(DDIMScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
|
|
(EulerDiscreteScheduler, [901, 801, 701, 601, 501, 401, 301, 201, 101, 1]),
|
|
(DPMSolverMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),
|
|
(UniPCMultistepScheduler, [901, 811, 721, 631, 541, 451, 361, 271, 181, 91]),
|
|
(
|
|
HeunDiscreteScheduler,
|
|
[
|
|
901.0,
|
|
801.0,
|
|
801.0,
|
|
701.0,
|
|
701.0,
|
|
601.0,
|
|
601.0,
|
|
501.0,
|
|
501.0,
|
|
401.0,
|
|
401.0,
|
|
301.0,
|
|
301.0,
|
|
201.0,
|
|
201.0,
|
|
101.0,
|
|
101.0,
|
|
1.0,
|
|
1.0,
|
|
],
|
|
),
|
|
]:
|
|
assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
|
|
|
|
steps = 25
|
|
for split in [300, 500, 700]:
|
|
for scheduler_cls_timesteps in [
|
|
(
|
|
DDIMScheduler,
|
|
[
|
|
961,
|
|
921,
|
|
881,
|
|
841,
|
|
801,
|
|
761,
|
|
721,
|
|
681,
|
|
641,
|
|
601,
|
|
561,
|
|
521,
|
|
481,
|
|
441,
|
|
401,
|
|
361,
|
|
321,
|
|
281,
|
|
241,
|
|
201,
|
|
161,
|
|
121,
|
|
81,
|
|
41,
|
|
1,
|
|
],
|
|
),
|
|
(
|
|
EulerDiscreteScheduler,
|
|
[
|
|
961.0,
|
|
921.0,
|
|
881.0,
|
|
841.0,
|
|
801.0,
|
|
761.0,
|
|
721.0,
|
|
681.0,
|
|
641.0,
|
|
601.0,
|
|
561.0,
|
|
521.0,
|
|
481.0,
|
|
441.0,
|
|
401.0,
|
|
361.0,
|
|
321.0,
|
|
281.0,
|
|
241.0,
|
|
201.0,
|
|
161.0,
|
|
121.0,
|
|
81.0,
|
|
41.0,
|
|
1.0,
|
|
],
|
|
),
|
|
(
|
|
DPMSolverMultistepScheduler,
|
|
[
|
|
951,
|
|
913,
|
|
875,
|
|
837,
|
|
799,
|
|
761,
|
|
723,
|
|
685,
|
|
647,
|
|
609,
|
|
571,
|
|
533,
|
|
495,
|
|
457,
|
|
419,
|
|
381,
|
|
343,
|
|
305,
|
|
267,
|
|
229,
|
|
191,
|
|
153,
|
|
115,
|
|
77,
|
|
39,
|
|
],
|
|
),
|
|
(
|
|
UniPCMultistepScheduler,
|
|
[
|
|
951,
|
|
913,
|
|
875,
|
|
837,
|
|
799,
|
|
761,
|
|
723,
|
|
685,
|
|
647,
|
|
609,
|
|
571,
|
|
533,
|
|
495,
|
|
457,
|
|
419,
|
|
381,
|
|
343,
|
|
305,
|
|
267,
|
|
229,
|
|
191,
|
|
153,
|
|
115,
|
|
77,
|
|
39,
|
|
],
|
|
),
|
|
(
|
|
HeunDiscreteScheduler,
|
|
[
|
|
961.0,
|
|
921.0,
|
|
921.0,
|
|
881.0,
|
|
881.0,
|
|
841.0,
|
|
841.0,
|
|
801.0,
|
|
801.0,
|
|
761.0,
|
|
761.0,
|
|
721.0,
|
|
721.0,
|
|
681.0,
|
|
681.0,
|
|
641.0,
|
|
641.0,
|
|
601.0,
|
|
601.0,
|
|
561.0,
|
|
561.0,
|
|
521.0,
|
|
521.0,
|
|
481.0,
|
|
481.0,
|
|
441.0,
|
|
441.0,
|
|
401.0,
|
|
401.0,
|
|
361.0,
|
|
361.0,
|
|
321.0,
|
|
321.0,
|
|
281.0,
|
|
281.0,
|
|
241.0,
|
|
241.0,
|
|
201.0,
|
|
201.0,
|
|
161.0,
|
|
161.0,
|
|
121.0,
|
|
121.0,
|
|
81.0,
|
|
81.0,
|
|
41.0,
|
|
41.0,
|
|
1.0,
|
|
1.0,
|
|
],
|
|
),
|
|
]:
|
|
assert_run_mixture(steps, split, scheduler_cls_timesteps[0], scheduler_cls_timesteps[1])
|
|
|
|
@slow
|
|
def test_stable_diffusion_three_xl_mixture_of_denoiser(self):
|
|
components = self.get_dummy_components()
|
|
pipe_1 = StableDiffusionXLPipeline(**components).to(torch_device)
|
|
pipe_1.unet.set_default_attn_processor()
|
|
pipe_2 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
|
|
pipe_2.unet.set_default_attn_processor()
|
|
pipe_3 = StableDiffusionXLImg2ImgPipeline(**components).to(torch_device)
|
|
pipe_3.unet.set_default_attn_processor()
|
|
|
|
def assert_run_mixture(
|
|
num_steps,
|
|
split_1,
|
|
split_2,
|
|
scheduler_cls_orig,
|
|
num_train_timesteps=pipe_1.scheduler.config.num_train_timesteps,
|
|
):
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["num_inference_steps"] = num_steps
|
|
|
|
class scheduler_cls(scheduler_cls_orig):
|
|
pass
|
|
|
|
pipe_1.scheduler = scheduler_cls.from_config(pipe_1.scheduler.config)
|
|
pipe_2.scheduler = scheduler_cls.from_config(pipe_2.scheduler.config)
|
|
pipe_3.scheduler = scheduler_cls.from_config(pipe_3.scheduler.config)
|
|
|
|
# Let's retrieve the number of timesteps we want to use
|
|
pipe_1.scheduler.set_timesteps(num_steps)
|
|
expected_steps = pipe_1.scheduler.timesteps.tolist()
|
|
|
|
split_1_ts = num_train_timesteps - int(round(num_train_timesteps * split_1))
|
|
split_2_ts = num_train_timesteps - int(round(num_train_timesteps * split_2))
|
|
|
|
if pipe_1.scheduler.order == 2:
|
|
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
|
|
expected_steps_2 = expected_steps_1[-1:] + list(
|
|
filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps)
|
|
)
|
|
expected_steps_3 = expected_steps_2[-1:] + list(filter(lambda ts: ts < split_2_ts, expected_steps))
|
|
expected_steps = expected_steps_1 + expected_steps_2 + expected_steps_3
|
|
else:
|
|
expected_steps_1 = list(filter(lambda ts: ts >= split_1_ts, expected_steps))
|
|
expected_steps_2 = list(filter(lambda ts: ts >= split_2_ts and ts < split_1_ts, expected_steps))
|
|
expected_steps_3 = list(filter(lambda ts: ts < split_2_ts, expected_steps))
|
|
|
|
# now we monkey patch step `done_steps`
|
|
# list into the step function for testing
|
|
done_steps = []
|
|
old_step = copy.copy(scheduler_cls.step)
|
|
|
|
def new_step(self, *args, **kwargs):
|
|
done_steps.append(args[1].cpu().item()) # args[1] is always the passed `t`
|
|
return old_step(self, *args, **kwargs)
|
|
|
|
scheduler_cls.step = new_step
|
|
|
|
inputs_1 = {**inputs, **{"denoising_end": split_1, "output_type": "latent"}}
|
|
latents = pipe_1(**inputs_1).images[0]
|
|
|
|
assert expected_steps_1 == done_steps, (
|
|
f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
|
|
)
|
|
|
|
with self.assertRaises(ValueError) as cm:
|
|
inputs_2 = {
|
|
**inputs,
|
|
**{
|
|
"denoising_start": split_2,
|
|
"denoising_end": split_1,
|
|
"image": latents,
|
|
"output_type": "latent",
|
|
},
|
|
}
|
|
pipe_2(**inputs_2).images[0]
|
|
assert "cannot be larger than or equal to `denoising_end`" in str(cm.exception)
|
|
|
|
inputs_2 = {
|
|
**inputs,
|
|
**{"denoising_start": split_1, "denoising_end": split_2, "image": latents, "output_type": "latent"},
|
|
}
|
|
pipe_2(**inputs_2).images[0]
|
|
|
|
assert expected_steps_2 == done_steps[len(expected_steps_1) :]
|
|
|
|
inputs_3 = {**inputs, **{"denoising_start": split_2, "image": latents}}
|
|
pipe_3(**inputs_3).images[0]
|
|
|
|
assert expected_steps_3 == done_steps[len(expected_steps_1) + len(expected_steps_2) :]
|
|
assert expected_steps == done_steps, (
|
|
f"Failure with {scheduler_cls.__name__} and {num_steps} and {split_1} and {split_2}"
|
|
)
|
|
|
|
for steps in [7, 11, 20]:
|
|
for split_1, split_2 in zip([0.19, 0.32], [0.81, 0.68]):
|
|
for scheduler_cls in [
|
|
DDIMScheduler,
|
|
EulerDiscreteScheduler,
|
|
DPMSolverMultistepScheduler,
|
|
UniPCMultistepScheduler,
|
|
HeunDiscreteScheduler,
|
|
]:
|
|
assert_run_mixture(steps, split_1, split_2, scheduler_cls)
|
|
|
|
def test_stable_diffusion_xl_multi_prompts(self):
|
|
components = self.get_dummy_components()
|
|
sd_pipe = self.pipeline_class(**components).to(torch_device)
|
|
|
|
# forward with single prompt
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
output = sd_pipe(**inputs)
|
|
image_slice_1 = output.images[0, -3:, -3:, -1]
|
|
|
|
# forward with same prompt duplicated
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["prompt_2"] = inputs["prompt"]
|
|
output = sd_pipe(**inputs)
|
|
image_slice_2 = output.images[0, -3:, -3:, -1]
|
|
|
|
# ensure the results are equal
|
|
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
|
|
|
|
# forward with different prompt
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["prompt_2"] = "different prompt"
|
|
output = sd_pipe(**inputs)
|
|
image_slice_3 = output.images[0, -3:, -3:, -1]
|
|
|
|
# ensure the results are not equal
|
|
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
|
|
|
|
# manually set a negative_prompt
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["negative_prompt"] = "negative prompt"
|
|
output = sd_pipe(**inputs)
|
|
image_slice_1 = output.images[0, -3:, -3:, -1]
|
|
|
|
# forward with same negative_prompt duplicated
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["negative_prompt"] = "negative prompt"
|
|
inputs["negative_prompt_2"] = inputs["negative_prompt"]
|
|
output = sd_pipe(**inputs)
|
|
image_slice_2 = output.images[0, -3:, -3:, -1]
|
|
|
|
# ensure the results are equal
|
|
assert np.abs(image_slice_1.flatten() - image_slice_2.flatten()).max() < 1e-4
|
|
|
|
# forward with different negative_prompt
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
inputs["negative_prompt"] = "negative prompt"
|
|
inputs["negative_prompt_2"] = "different negative prompt"
|
|
output = sd_pipe(**inputs)
|
|
image_slice_3 = output.images[0, -3:, -3:, -1]
|
|
|
|
# ensure the results are not equal
|
|
assert np.abs(image_slice_1.flatten() - image_slice_3.flatten()).max() > 1e-4
|
|
|
|
def test_stable_diffusion_xl_negative_conditions(self):
|
|
device = "cpu" # ensure determinism for the device-dependent torch.Generator
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionXLPipeline(**components)
|
|
sd_pipe = sd_pipe.to(device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
inputs = self.get_dummy_inputs(device)
|
|
image = sd_pipe(**inputs).images
|
|
image_slice_with_no_neg_cond = image[0, -3:, -3:, -1]
|
|
|
|
image = sd_pipe(
|
|
**inputs,
|
|
negative_original_size=(512, 512),
|
|
negative_crops_coords_top_left=(0, 0),
|
|
negative_target_size=(1024, 1024),
|
|
).images
|
|
image_slice_with_neg_cond = image[0, -3:, -3:, -1]
|
|
|
|
self.assertTrue(np.abs(image_slice_with_no_neg_cond - image_slice_with_neg_cond).max() > 1e-2)
|
|
|
|
def test_stable_diffusion_xl_save_from_pretrained(self):
|
|
pipes = []
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionXLPipeline(**components).to(torch_device)
|
|
pipes.append(sd_pipe)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
sd_pipe.save_pretrained(tmpdirname)
|
|
sd_pipe = StableDiffusionXLPipeline.from_pretrained(tmpdirname).to(torch_device)
|
|
pipes.append(sd_pipe)
|
|
|
|
image_slices = []
|
|
for pipe in pipes:
|
|
pipe.unet.set_default_attn_processor()
|
|
|
|
inputs = self.get_dummy_inputs(torch_device)
|
|
image = pipe(**inputs).images
|
|
|
|
image_slices.append(image[0, -3:, -3:, -1].flatten())
|
|
|
|
assert np.abs(image_slices[0] - image_slices[1]).max() < 1e-3
|
|
|
|
def test_pipeline_interrupt(self):
|
|
components = self.get_dummy_components()
|
|
sd_pipe = StableDiffusionXLPipeline(**components)
|
|
sd_pipe = sd_pipe.to(torch_device)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "hey"
|
|
num_inference_steps = 3
|
|
|
|
# store intermediate latents from the generation process
|
|
class PipelineState:
|
|
def __init__(self):
|
|
self.state = []
|
|
|
|
def apply(self, pipe, i, t, callback_kwargs):
|
|
self.state.append(callback_kwargs["latents"])
|
|
return callback_kwargs
|
|
|
|
pipe_state = PipelineState()
|
|
sd_pipe(
|
|
prompt,
|
|
num_inference_steps=num_inference_steps,
|
|
output_type="np",
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
callback_on_step_end=pipe_state.apply,
|
|
).images
|
|
|
|
# interrupt generation at step index
|
|
interrupt_step_idx = 1
|
|
|
|
def callback_on_step_end(pipe, i, t, callback_kwargs):
|
|
if i == interrupt_step_idx:
|
|
pipe._interrupt = True
|
|
|
|
return callback_kwargs
|
|
|
|
output_interrupted = sd_pipe(
|
|
prompt,
|
|
num_inference_steps=num_inference_steps,
|
|
output_type="latent",
|
|
generator=torch.Generator("cpu").manual_seed(0),
|
|
callback_on_step_end=callback_on_step_end,
|
|
).images
|
|
|
|
# fetch intermediate latents at the interrupted step
|
|
# from the completed generation process
|
|
intermediate_latent = pipe_state.state[interrupt_step_idx]
|
|
|
|
# compare the intermediate latent to the output of the interrupted process
|
|
# they should be the same
|
|
assert torch.allclose(intermediate_latent, output_interrupted, atol=1e-4)
|
|
|
|
|
|
@slow
|
|
class StableDiffusionXLPipelineIntegrationTests(unittest.TestCase):
|
|
def setUp(self):
|
|
super().setUp()
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def tearDown(self):
|
|
super().tearDown()
|
|
gc.collect()
|
|
backend_empty_cache(torch_device)
|
|
|
|
def test_stable_diffusion_lcm(self):
|
|
torch.manual_seed(0)
|
|
unet = UNet2DConditionModel.from_pretrained(
|
|
"latent-consistency/lcm-ssd-1b", torch_dtype=torch.float16, variant="fp16"
|
|
)
|
|
sd_pipe = StableDiffusionXLPipeline.from_pretrained(
|
|
"segmind/SSD-1B", unet=unet, torch_dtype=torch.float16, variant="fp16"
|
|
).to(torch_device)
|
|
sd_pipe.scheduler = LCMScheduler.from_config(sd_pipe.scheduler.config)
|
|
sd_pipe.set_progress_bar_config(disable=None)
|
|
|
|
prompt = "a red car standing on the side of the street"
|
|
|
|
image = sd_pipe(
|
|
prompt, num_inference_steps=4, guidance_scale=8.0, generator=torch.Generator("cpu").manual_seed(0)
|
|
).images[0]
|
|
expected_image = load_image(
|
|
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/lcm_full/stable_diffusion_ssd_1b_lcm.png"
|
|
)
|
|
|
|
image = sd_pipe.image_processor.pil_to_numpy(image)
|
|
expected_image = sd_pipe.image_processor.pil_to_numpy(expected_image)
|
|
|
|
max_diff = numpy_cosine_similarity_distance(image.flatten(), expected_image.flatten())
|
|
|
|
assert max_diff < 1e-2
|