pix2pix tests no write to fs (#2497)
* attend and excite batch test causing timeouts * pix2pix tests, no write to fs
This commit is contained in:
Will Berman
@@ -209,6 +209,13 @@ def load_numpy(arry: Union[str, np.ndarray], local_path: Optional[str] = None) -
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return arry
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def load_pt(url: str):
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response = requests.get(url)
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response.raise_for_status()
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arry = torch.load(BytesIO(response.content))
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return arry
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def load_image(image: Union[str, PIL.Image.Image]) -> PIL.Image.Image:
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"""
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Args:
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@@ -17,9 +17,7 @@ import gc
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import unittest
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import numpy as np
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import requests
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import torch
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from PIL import Image
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import (
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@@ -33,7 +31,7 @@ from diffusers import (
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UNet2DConditionModel,
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)
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from diffusers.utils import load_numpy, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu, skip_mps
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from diffusers.utils.testing_utils import load_image, load_pt, require_torch_gpu, skip_mps
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from ...test_pipelines_common import PipelineTesterMixin
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@@ -41,16 +39,20 @@ from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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def download_from_url(embedding_url, local_filepath):
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r = requests.get(embedding_url)
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with open(local_filepath, "wb") as f:
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f.write(r.content)
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@skip_mps
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class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = StableDiffusionPix2PixZeroPipeline
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@classmethod
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def setUpClass(cls):
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cls.source_embeds = load_pt(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/src_emb_0.pt"
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)
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cls.target_embeds = load_pt(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/tgt_emb_0.pt"
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)
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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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@@ -103,15 +105,6 @@ class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.
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return components
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def get_dummy_inputs(self, device, seed=0):
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src_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/src_emb_0.pt"
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tgt_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/tgt_emb_0.pt"
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for url in [src_emb_url, tgt_emb_url]:
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download_from_url(url, url.split("/")[-1])
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src_embeds = torch.load(src_emb_url.split("/")[-1])
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target_embeds = torch.load(tgt_emb_url.split("/")[-1])
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generator = torch.manual_seed(seed)
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inputs = {
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@@ -120,8 +113,8 @@ class StableDiffusionPix2PixZeroPipelineFastTests(PipelineTesterMixin, unittest.
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"cross_attention_guidance_amount": 0.15,
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"source_embeds": src_embeds,
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"target_embeds": target_embeds,
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"source_embeds": self.source_embeds,
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"target_embeds": self.target_embeds,
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"output_type": "numpy",
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}
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return inputs
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@@ -237,26 +230,27 @@ class StableDiffusionPix2PixZeroPipelineSlowTests(unittest.TestCase):
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gc.collect()
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torch.cuda.empty_cache()
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@classmethod
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def setUpClass(cls):
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cls.source_embeds = load_pt(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat.pt"
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)
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cls.target_embeds = load_pt(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/dog.pt"
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)
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def get_inputs(self, seed=0):
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generator = torch.manual_seed(seed)
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src_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/cat.pt"
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tgt_emb_url = "https://hf.co/datasets/sayakpaul/sample-datasets/resolve/main/dog.pt"
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for url in [src_emb_url, tgt_emb_url]:
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download_from_url(url, url.split("/")[-1])
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src_embeds = torch.load(src_emb_url.split("/")[-1])
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target_embeds = torch.load(tgt_emb_url.split("/")[-1])
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inputs = {
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"prompt": "turn him into a cyborg",
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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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"cross_attention_guidance_amount": 0.15,
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"source_embeds": src_embeds,
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"target_embeds": target_embeds,
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"source_embeds": self.source_embeds,
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"target_embeds": self.target_embeds,
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"output_type": "numpy",
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}
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return inputs
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@@ -364,10 +358,17 @@ class InversionPipelineSlowTests(unittest.TestCase):
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gc.collect()
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torch.cuda.empty_cache()
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def test_stable_diffusion_pix2pix_inversion(self):
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img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
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@classmethod
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def setUpClass(cls):
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raw_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png"
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)
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raw_image = raw_image.convert("RGB").resize((512, 512))
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cls.raw_image = raw_image
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def test_stable_diffusion_pix2pix_inversion(self):
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pipe = StableDiffusionPix2PixZeroPipeline.from_pretrained(
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"CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
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)
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@@ -380,7 +381,7 @@ class InversionPipelineSlowTests(unittest.TestCase):
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pipe.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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output = pipe.invert(caption, image=raw_image, generator=generator, num_inference_steps=10)
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output = pipe.invert(caption, image=self.raw_image, generator=generator, num_inference_steps=10)
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inv_latents = output[0]
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image_slice = inv_latents[0, -3:, -3:, -1].flatten()
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@@ -391,9 +392,6 @@ class InversionPipelineSlowTests(unittest.TestCase):
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assert np.abs(expected_slice - image_slice.cpu().numpy()).max() < 1e-3
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def test_stable_diffusion_pix2pix_full(self):
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img_url = "https://github.com/pix2pixzero/pix2pix-zero/raw/main/assets/test_images/cats/cat_6.png"
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raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB").resize((512, 512))
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# numpy array of https://huggingface.co/datasets/hf-internal-testing/diffusers-images/blob/main/pix2pix/dog.png
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/dog.npy"
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@@ -411,7 +409,7 @@ class InversionPipelineSlowTests(unittest.TestCase):
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pipe.set_progress_bar_config(disable=None)
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generator = torch.manual_seed(0)
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output = pipe.invert(caption, image=raw_image, generator=generator)
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output = pipe.invert(caption, image=self.raw_image, generator=generator)
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inv_latents = output[0]
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source_prompts = 4 * ["a cat sitting on the street", "a cat playing in the field", "a face of a cat"]
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