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Fix/update the LDM pipeline and tests (#1743)
* Fix/update LDM tests * batched generators
This commit is contained in:
Anton Lozhkov
GitHub
@@ -128,29 +128,42 @@ class LDMTextToImagePipeline(DiffusionPipeline):
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# get unconditional embeddings for classifier free guidance
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if guidance_scale != 1.0:
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uncond_input = self.tokenizer([""] * batch_size, padding="max_length", max_length=77, return_tensors="pt")
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uncond_input = self.tokenizer(
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[""] * batch_size, padding="max_length", max_length=77, truncation=True, return_tensors="pt"
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)
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uncond_embeddings = self.bert(uncond_input.input_ids.to(self.device))[0]
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# get prompt text embeddings
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text_input = self.tokenizer(prompt, padding="max_length", max_length=77, return_tensors="pt")
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text_input = self.tokenizer(prompt, padding="max_length", max_length=77, truncation=True, return_tensors="pt")
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text_embeddings = self.bert(text_input.input_ids.to(self.device))[0]
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# get the initial random noise unless the user supplied it
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latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
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if isinstance(generator, list) and len(generator) != batch_size:
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raise ValueError(
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f"You have passed a list of generators of length {len(generator)}, but requested an effective batch"
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f" size of {batch_size}. Make sure the batch size matches the length of the generators."
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)
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if latents is None:
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if self.device.type == "mps":
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# randn does not work reproducibly on mps
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latents = torch.randn(latents_shape, generator=generator, device="cpu").to(self.device)
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rand_device = "cpu" if self.device.type == "mps" else self.device
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if isinstance(generator, list):
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latents_shape = (1,) + latents_shape[1:]
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latents = [
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torch.randn(latents_shape, generator=generator[i], device=rand_device, dtype=text_embeddings.dtype)
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for i in range(batch_size)
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]
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latents = torch.cat(latents, dim=0)
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else:
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latents = torch.randn(
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latents_shape,
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generator=generator,
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device=self.device,
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latents_shape, generator=generator, device=rand_device, dtype=text_embeddings.dtype
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)
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latents = latents.to(self.device)
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else:
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if latents.shape != latents_shape:
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raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
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latents = latents.to(self.device)
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latents = latents.to(self.device)
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self.scheduler.set_timesteps(num_inference_steps)
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@@ -13,24 +13,29 @@
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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 diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
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from diffusers.utils.testing_utils import require_torch, slow, torch_device
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from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, slow, torch_device
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class LDMTextToImagePipelineFastTests(unittest.TestCase):
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@property
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def dummy_cond_unet(self):
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class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = LDMTextToImagePipeline
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test_cpu_offload = False
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def get_dummy_components(self):
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torch.manual_seed(0)
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model = UNet2DConditionModel(
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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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@@ -40,25 +45,24 @@ class LDMTextToImagePipelineFastTests(unittest.TestCase):
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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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return model
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@property
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def dummy_vae(self):
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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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model = AutoencoderKL(
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block_out_channels=[32, 64],
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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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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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return model
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@property
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def dummy_text_encoder(self):
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torch.manual_seed(0)
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config = CLIPTextConfig(
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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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@@ -69,96 +73,117 @@ class LDMTextToImagePipelineFastTests(unittest.TestCase):
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pad_token_id=1,
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vocab_size=1000,
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)
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return CLIPTextModel(config)
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def test_inference_text2img(self):
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if torch_device != "cpu":
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return
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unet = self.dummy_cond_unet
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scheduler = DDIMScheduler()
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vae = self.dummy_vae
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bert = self.dummy_text_encoder
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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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ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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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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prompt = "A painting of a squirrel eating a burger"
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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": "numpy",
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}
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return inputs
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# Warmup pass when using mps (see #372)
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if torch_device == "mps":
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generator = torch.manual_seed(0)
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_ = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
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).images
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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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device = torch_device if torch_device != "mps" else "cpu"
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generator = torch.Generator(device=device).manual_seed(0)
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image = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
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).images
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device = torch_device if torch_device != "mps" else "cpu"
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generator = torch.Generator(device=device).manual_seed(0)
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image_from_tuple = ldm(
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[prompt],
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generator=generator,
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guidance_scale=6.0,
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num_inference_steps=2,
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output_type="numpy",
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return_dict=False,
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)[0]
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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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image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
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assert image.shape == (1, 16, 16, 3)
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expected_slice = np.array([0.6806, 0.5454, 0.5638, 0.4893, 0.4656, 0.4257, 0.6248, 0.5217, 0.5498])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
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expected_slice = np.array([0.59450, 0.64078, 0.55509, 0.51229, 0.69640, 0.36960, 0.59296, 0.60801, 0.49332])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
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@slow
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@require_torch
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class LDMTextToImagePipelineIntegrationTests(unittest.TestCase):
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def test_inference_text2img(self):
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ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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@require_torch_gpu
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class LDMTextToImagePipelineSlowTests(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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prompt = "A painting of a squirrel eating a burger"
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).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": "numpy",
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}
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return inputs
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device = torch_device if torch_device != "mps" else "cpu"
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generator = torch.Generator(device=device).manual_seed(0)
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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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image = ldm(
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[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
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).images
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image_slice = image[0, -3:, -3:, -1]
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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([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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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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def test_inference_text2img_fast(self):
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ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
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ldm.to(torch_device)
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ldm.set_progress_bar_config(disable=None)
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prompt = "A painting of a squirrel eating a burger"
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@nightly
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@require_torch_gpu
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class LDMTextToImagePipelineNightlyTests(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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device = torch_device if torch_device != "mps" else "cpu"
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generator = torch.Generator(device=device).manual_seed(0)
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def get_inputs(self, device, dtype=torch.float32, seed=0):
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generator = torch.Generator(device=device).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": "numpy",
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}
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return inputs
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image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
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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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image_slice = image[0, -3:, -3:, -1]
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inputs = self.get_inputs(torch_device)
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image = pipe(**inputs).images[0]
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assert image.shape == (1, 256, 256, 3)
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expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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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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