File size: 4,549 Bytes
ac2243f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | import gc
import pytest
import torch
from diffusers import (
StableDiffusionInpaintPipeline,
)
from diffusers.utils import load_image
from ..testing_utils import (
backend_empty_cache,
enable_full_determinism,
require_torch_accelerator,
slow,
torch_device,
)
from .single_file_testing_utils import SDSingleFileTesterMixin
enable_full_determinism()
@slow
@require_torch_accelerator
class TestStableDiffusionInpaintPipelineSingleFileSlow(SDSingleFileTesterMixin):
pipeline_class = StableDiffusionInpaintPipeline
ckpt_path = "https://huggingface.co/botp/stable-diffusion-v1-5-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml"
repo_id = "botp/stable-diffusion-v1-5-inpainting"
def setup_method(self):
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)
def test_single_file_loading_4_channel_unet(self):
# Test loading single file inpaint with a 4 channel UNet
ckpt_path = "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors"
pipe = self.pipeline_class.from_single_file(ckpt_path)
assert pipe.unet.config.in_channels == 4
@pytest.mark.skip(reason="runwayml original config has been removed")
def test_single_file_components_with_original_config(self):
return
@pytest.mark.skip(reason="runwayml original config has been removed")
def test_single_file_components_with_original_config_local_files_only(self):
return
@slow
@require_torch_accelerator
class TestStableDiffusion21InpaintPipelineSingleFileSlow(SDSingleFileTesterMixin):
pipeline_class = StableDiffusionInpaintPipeline
ckpt_path = (
"https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/blob/main/512-inpainting-ema.safetensors"
)
original_config = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inpainting-inference.yaml"
repo_id = "stabilityai/stable-diffusion-2-inpainting"
def setup_method(self):
gc.collect()
backend_empty_cache(torch_device)
def teardown_method(self):
gc.collect()
backend_empty_cache(torch_device)
def get_inputs(self, device, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)
init_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_image.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_inpaint/input_bench_mask.png"
)
inputs = {
"prompt": "Face of a yellow cat, high resolution, sitting on a park bench",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 7.5,
"output_type": "np",
}
return inputs
def test_single_file_format_inference_is_same_as_pretrained(self):
super().test_single_file_format_inference_is_same_as_pretrained(expected_max_diff=1e-3)
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