Skip to content

GitLab

Unverified Commit 1e8cf276 authored by Dhruv Nair's avatar Dhruv Nair Committed by GitHub
Browse files

[CI] Nightly Test Updates (#9380) 



* update

* update

* update

* update

* update

---------
Co-authored-by: default avatarSayak Paul <spsayakpaul@gmail.com>
Co-authored-by: default avatarYiYi Xu <yixu310@gmail.com>
parent 6cf8d98c
Hide whitespace changes
Inline Side-by-side
Showing with 122 additions and 700 deletions
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
View file @ 1e8cf276
...@@ -76,7 +76,7 @@ def _test_inpaint_compile(in_queue, out_queue, timeout): ...@@ -76,7 +76,7 @@ def _test_inpaint_compile(in_queue, out_queue, timeout):
inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed) inputs["generator"] = torch.Generator(device=torch_device).manual_seed(seed)
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config) pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
...@@ -628,7 +628,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -628,7 +628,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_ddim(self): def test_stable_diffusion_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.to(torch_device) pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None) pipe.set_progress_bar_config(disable=None)
...@@ -645,7 +645,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -645,7 +645,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_fp16(self): def test_stable_diffusion_inpaint_fp16(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None "botp/stable-diffusion-v1-5-inpainting", torch_dtype=torch.float16, safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.to(torch_device) pipe.to(torch_device)
...@@ -662,7 +662,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -662,7 +662,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_pndm(self): def test_stable_diffusion_inpaint_pndm(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config) pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device) pipe.to(torch_device)
...@@ -680,7 +680,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -680,7 +680,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_k_lms(self): def test_stable_diffusion_inpaint_k_lms(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device) pipe.to(torch_device)
...@@ -702,7 +702,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -702,7 +702,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
torch.cuda.reset_peak_memory_stats() torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16 "botp/stable-diffusion-v1-5-inpainting", safety_checker=None, torch_dtype=torch.float16
) )
pipe.set_progress_bar_config(disable=None) pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1) pipe.enable_attention_slicing(1)
...@@ -728,7 +728,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -728,7 +728,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_pil_input_resolution_test(self): def test_stable_diffusion_inpaint_pil_input_resolution_test(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device) pipe.to(torch_device)
...@@ -747,7 +747,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -747,7 +747,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_inpaint_strength_test(self): def test_stable_diffusion_inpaint_strength_test(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
...@@ -767,7 +767,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase): ...@@ -767,7 +767,7 @@ class StableDiffusionInpaintPipelineSlowTests(unittest.TestCase):
assert np.abs(expected_slice - image_slice).max() < 1e-3 assert np.abs(expected_slice - image_slice).max() < 1e-3
def test_stable_diffusion_simple_inpaint_ddim(self): def test_stable_diffusion_simple_inpaint_ddim(self):
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None) pipe = StableDiffusionInpaintPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None)
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.to(torch_device) pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None) pipe.set_progress_bar_config(disable=None)
...@@ -818,7 +818,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -818,7 +818,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
def test_stable_diffusion_inpaint_ddim(self): def test_stable_diffusion_inpaint_ddim(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.vae = vae pipe.vae = vae
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
...@@ -840,7 +840,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -840,7 +840,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16 "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
) )
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None "botp/stable-diffusion-v1-5-inpainting", torch_dtype=torch.float16, safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.vae = vae pipe.vae = vae
...@@ -860,7 +860,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -860,7 +860,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
def test_stable_diffusion_inpaint_pndm(self): def test_stable_diffusion_inpaint_pndm(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.vae = vae pipe.vae = vae
...@@ -881,7 +881,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -881,7 +881,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
def test_stable_diffusion_inpaint_k_lms(self): def test_stable_diffusion_inpaint_k_lms(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.vae = vae pipe.vae = vae
...@@ -906,7 +906,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -906,7 +906,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16 "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
) )
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16 "botp/stable-diffusion-v1-5-inpainting", safety_checker=None, torch_dtype=torch.float16
) )
pipe.vae = vae pipe.vae = vae
pipe.set_progress_bar_config(disable=None) pipe.set_progress_bar_config(disable=None)
...@@ -930,7 +930,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -930,7 +930,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
"cross-attention/asymmetric-autoencoder-kl-x-1-5", "cross-attention/asymmetric-autoencoder-kl-x-1-5",
) )
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.vae = vae pipe.vae = vae
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config) pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
...@@ -951,7 +951,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -951,7 +951,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
def test_stable_diffusion_inpaint_strength_test(self): def test_stable_diffusion_inpaint_strength_test(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained( pipe = StableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", safety_checker=None "botp/stable-diffusion-v1-5-inpainting", safety_checker=None
) )
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.vae = vae pipe.vae = vae
...@@ -973,7 +973,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -973,7 +973,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
def test_stable_diffusion_simple_inpaint_ddim(self): def test_stable_diffusion_simple_inpaint_ddim(self):
vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5") vae = AsymmetricAutoencoderKL.from_pretrained("cross-attention/asymmetric-autoencoder-kl-x-1-5")
pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None) pipe = StableDiffusionInpaintPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None)
pipe.vae = vae pipe.vae = vae
pipe.unet.set_default_attn_processor() pipe.unet.set_default_attn_processor()
pipe.to(torch_device) pipe.to(torch_device)
...@@ -993,7 +993,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te ...@@ -993,7 +993,7 @@ class StableDiffusionInpaintPipelineAsymmetricAutoencoderKLSlowTests(unittest.Te
vae = AsymmetricAutoencoderKL.from_pretrained( vae = AsymmetricAutoencoderKL.from_pretrained(
"cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16 "cross-attention/asymmetric-autoencoder-kl-x-1-5", torch_dtype=torch.float16
) )
filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt") filename = hf_hub_download("botp/stable-diffusion-v1-5-inpainting", filename="sd-v1-5-inpainting.ckpt")
pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16) pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
pipe.vae = vae pipe.vae = vae
...@@ -1042,7 +1042,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): ...@@ -1042,7 +1042,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
return inputs return inputs
def test_inpaint_ddim(self): def test_inpaint_ddim(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("botp/stable-diffusion-v1-5-inpainting")
sd_pipe.to(torch_device) sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
...@@ -1057,7 +1057,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): ...@@ -1057,7 +1057,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
assert max_diff < 1e-3 assert max_diff < 1e-3
def test_inpaint_pndm(self): def test_inpaint_pndm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("botp/stable-diffusion-v1-5-inpainting")
sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.scheduler = PNDMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device) sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
...@@ -1073,7 +1073,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): ...@@ -1073,7 +1073,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
assert max_diff < 1e-3 assert max_diff < 1e-3
def test_inpaint_lms(self): def test_inpaint_lms(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("botp/stable-diffusion-v1-5-inpainting")
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device) sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
...@@ -1089,7 +1089,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase): ...@@ -1089,7 +1089,7 @@ class StableDiffusionInpaintPipelineNightlyTests(unittest.TestCase):
assert max_diff < 1e-3 assert max_diff < 1e-3
def test_inpaint_dpm(self): def test_inpaint_dpm(self):
sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting") sd_pipe = StableDiffusionInpaintPipeline.from_pretrained("botp/stable-diffusion-v1-5-inpainting")
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.to(torch_device) sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
......
tests/pipelines/stable_diffusion_2/test_stable_diffusion.py
View file @ 1e8cf276
...@@ -23,7 +23,6 @@ from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer ...@@ -23,7 +23,6 @@ from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import ( from diffusers import (
AutoencoderKL, AutoencoderKL,
DDIMScheduler, DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler, EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler, EulerDiscreteScheduler,
LMSDiscreteScheduler, LMSDiscreteScheduler,
...@@ -353,34 +352,6 @@ class StableDiffusion2PipelineSlowTests(unittest.TestCase): ...@@ -353,34 +352,6 @@ class StableDiffusion2PipelineSlowTests(unittest.TestCase):
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]) expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
assert np.abs(image_slice - expected_slice).max() < 7e-3 assert np.abs(image_slice - expected_slice).max() < 7e-3
def test_stable_diffusion_pndm(self):
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
pipe.scheduler = PNDMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506])
assert np.abs(image_slice - expected_slice).max() < 7e-3
def test_stable_diffusion_k_lms(self):
pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images
image_slice = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.10440, 0.13115, 0.11100, 0.10141, 0.11440, 0.07215, 0.11332, 0.09693, 0.10006])
assert np.abs(image_slice - expected_slice).max() < 3e-3
@require_torch_gpu @require_torch_gpu
def test_stable_diffusion_attention_slicing(self): def test_stable_diffusion_attention_slicing(self):
torch.cuda.reset_peak_memory_stats() torch.cuda.reset_peak_memory_stats()
...@@ -413,124 +384,6 @@ class StableDiffusion2PipelineSlowTests(unittest.TestCase): ...@@ -413,124 +384,6 @@ class StableDiffusion2PipelineSlowTests(unittest.TestCase):
max_diff = numpy_cosine_similarity_distance(image.flatten(), image_sliced.flatten()) max_diff = numpy_cosine_similarity_distance(image.flatten(), image_sliced.flatten())
assert max_diff < 5e-3 assert max_diff < 5e-3
def test_stable_diffusion_text2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.3862, -0.4507, -1.1729, 0.0686, -1.1045, 0.7124, -1.8301, 0.1903, 1.2773]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[0.2720, -0.1863, -0.7383, -0.5029, -0.7534, 0.3970, -0.7646, 0.4468, 1.2686]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == inputs["num_inference_steps"]
@require_torch_gpu
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.8 GB is allocated
assert mem_bytes < 2.8 * 10**9
@require_torch_gpu
def test_stable_diffusion_pipeline_with_model_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
inputs = self.get_inputs(torch_device, dtype=torch.float16)
# Normal inference
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
outputs = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# With model offloading
# Reload but don't move to cuda
pipe = StableDiffusionPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-base",
torch_dtype=torch.float16,
)
pipe.unet.set_default_attn_processor()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device, dtype=torch.float16)
outputs_offloaded = pipe(**inputs)
mem_bytes_offloaded = torch.cuda.max_memory_allocated()
images = outputs.images
images_offloaded = outputs_offloaded.images
max_diff = numpy_cosine_similarity_distance(images.flatten(), images_offloaded.flatten())
assert max_diff < 1e-3
assert mem_bytes_offloaded < mem_bytes
assert mem_bytes_offloaded < 3 * 10**9
for module in pipe.text_encoder, pipe.unet, pipe.vae:
assert module.device == torch.device("cpu")
# With attention slicing
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe.enable_attention_slicing()
_ = pipe(**inputs)
mem_bytes_slicing = torch.cuda.max_memory_allocated()
assert mem_bytes_slicing < mem_bytes_offloaded
@nightly @nightly
@require_torch_accelerator @require_torch_accelerator
...@@ -554,99 +407,22 @@ class StableDiffusion2PipelineNightlyTests(unittest.TestCase): ...@@ -554,99 +407,22 @@ class StableDiffusion2PipelineNightlyTests(unittest.TestCase):
"prompt": "a photograph of an astronaut riding a horse", "prompt": "a photograph of an astronaut riding a horse",
"latents": latents, "latents": latents,
"generator": generator, "generator": generator,
"num_inference_steps": 50, "num_inference_steps": 2,
"guidance_scale": 7.5, "guidance_scale": 7.5,
"output_type": "np", "output_type": "np",
} }
return inputs return inputs
def test_stable_diffusion_2_0_default_ddim(self): def test_stable_diffusion_2_1_default(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-base").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_0_base_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_2_1_default_pndm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_pndm.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_ddim(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = DDIMScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_lms(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_euler(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device) sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = EulerDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device)
image = sd_pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_euler.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_stable_diffusion_dpm(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base").to(torch_device)
sd_pipe.scheduler = DPMSolverMultistepScheduler.from_config(
sd_pipe.scheduler.config, final_sigmas_type="sigma_min"
)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs(torch_device) inputs = self.get_inputs(torch_device)
inputs["num_inference_steps"] = 25
image = sd_pipe(**inputs).images[0] image = sd_pipe(**inputs).images[0]
expected_image = load_numpy( expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main" "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_2_text2img/stable_diffusion_2_1_base_dpm_multi.npy" "/stable_diffusion_2_text2img/stable_diffusion_2_0_pndm.npy"
) )
max_diff = np.abs(expected_image - image).max() max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3 assert max_diff < 1e-3
tests/pipelines/stable_diffusion_2/test_stable_diffusion_depth.py
View file @ 1e8cf276
...@@ -32,9 +32,6 @@ from transformers import ( ...@@ -32,9 +32,6 @@ from transformers import (
from diffusers import ( from diffusers import (
AutoencoderKL, AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
LMSDiscreteScheduler,
PNDMScheduler, PNDMScheduler,
StableDiffusionDepth2ImgPipeline, StableDiffusionDepth2ImgPipeline,
UNet2DConditionModel, UNet2DConditionModel,
...@@ -416,102 +413,6 @@ class StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase): ...@@ -416,102 +413,6 @@ class StableDiffusionDepth2ImgPipelineSlowTests(unittest.TestCase):
assert np.abs(expected_slice - image_slice).max() < 6e-1 assert np.abs(expected_slice - image_slice).max() < 6e-1
def test_stable_diffusion_depth2img_pipeline_k_lms(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None
)
pipe.unet.set_default_attn_processor()
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 480, 640, 3)
expected_slice = np.array([0.6363, 0.6274, 0.6309, 0.6370, 0.6226, 0.6286, 0.6213, 0.6453, 0.6306])
assert np.abs(expected_slice - image_slice).max() < 8e-4
def test_stable_diffusion_depth2img_pipeline_ddim(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None
)
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs()
image = pipe(**inputs).images
image_slice = image[0, 253:256, 253:256, -1].flatten()
assert image.shape == (1, 480, 640, 3)
expected_slice = np.array([0.6424, 0.6524, 0.6249, 0.6041, 0.6634, 0.6420, 0.6522, 0.6555, 0.6436])
assert np.abs(expected_slice - image_slice).max() < 5e-4
def test_stable_diffusion_depth2img_intermediate_state(self):
number_of_steps = 0
def callback_fn(step: int, timestep: int, latents: torch.Tensor) -> None:
callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 60, 80)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.7168, -1.5137, -0.1418, -2.9219, -2.7266, -2.4414, -2.1035, -3.0078, -1.7051]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
elif step == 2:
latents = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 60, 80)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.7109, -1.5068, -0.1403, -2.9160, -2.7207, -2.4414, -2.1035, -3.0059, -1.7090]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 5e-2
callback_fn.has_been_called = False
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16
)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
inputs = self.get_inputs(dtype=torch.float16)
pipe(**inputs, callback=callback_fn, callback_steps=1)
assert callback_fn.has_been_called
assert number_of_steps == 2
def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-depth", safety_checker=None, torch_dtype=torch.float16
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
inputs = self.get_inputs(dtype=torch.float16)
_ = pipe(**inputs)
mem_bytes = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9
@nightly @nightly
@require_torch_gpu @require_torch_gpu
...@@ -535,14 +436,14 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase): ...@@ -535,14 +436,14 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
"prompt": "two tigers", "prompt": "two tigers",
"image": init_image, "image": init_image,
"generator": generator, "generator": generator,
"num_inference_steps": 3, "num_inference_steps": 2,
"strength": 0.75, "strength": 0.75,
"guidance_scale": 7.5, "guidance_scale": 7.5,
"output_type": "np", "output_type": "np",
} }
return inputs return inputs
def test_depth2img_pndm(self): def test_depth2img(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth") pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.to(torch_device) pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None) pipe.set_progress_bar_config(disable=None)
...@@ -556,52 +457,3 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase): ...@@ -556,52 +457,3 @@ class StableDiffusionImg2ImgPipelineNightlyTests(unittest.TestCase):
) )
max_diff = np.abs(expected_image - image).max() max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3 assert max_diff < 1e-3
def test_depth2img_ddim(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_ddim.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_lms(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = LMSDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_lms.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
def test_img2img_dpm(self):
pipe = StableDiffusionDepth2ImgPipeline.from_pretrained("stabilityai/stable-diffusion-2-depth")
pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_inputs()
inputs["num_inference_steps"] = 30
image = pipe(**inputs).images[0]
expected_image = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main"
"/stable_diffusion_depth2img/stable_diffusion_2_0_dpm_multi.npy"
)
max_diff = np.abs(expected_image - image).max()
assert max_diff < 1e-3
tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3.py
View file @ 1e8cf276
...@@ -265,18 +265,37 @@ class StableDiffusion3PipelineSlowTests(unittest.TestCase): ...@@ -265,18 +265,37 @@ class StableDiffusion3PipelineSlowTests(unittest.TestCase):
image_slice = image[0, :10, :10] image_slice = image[0, :10, :10]
expected_slice = np.array( expected_slice = np.array(
[ [
[0.36132812, 0.30004883, 0.25830078], 0.4648,
[0.36669922, 0.31103516, 0.23754883], 0.4404,
[0.34814453, 0.29248047, 0.23583984], 0.4177,
[0.35791016, 0.30981445, 0.23999023], 0.5063,
[0.36328125, 0.31274414, 0.2607422], 0.4800,
[0.37304688, 0.32177734, 0.26171875], 0.4287,
[0.3671875, 0.31933594, 0.25756836], 0.5425,
[0.36035156, 0.31103516, 0.2578125], 0.5190,
[0.3857422, 0.33789062, 0.27563477], 0.4717,
[0.3701172, 0.31982422, 0.265625], 0.5430,
], 0.5195,
dtype=np.float32, 0.4766,
0.5361,
0.5122,
0.4612,
0.4871,
0.4749,
0.4058,
0.4756,
0.4678,
0.3804,
0.4832,
0.4822,
0.3799,
0.5103,
0.5034,
0.3953,
0.5073,
0.4839,
0.3884,
]
) )
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten()) max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
......
tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_img2img.py
View file @ 1e8cf276
...@@ -238,20 +238,39 @@ class StableDiffusion3Img2ImgPipelineSlowTests(unittest.TestCase): ...@@ -238,20 +238,39 @@ class StableDiffusion3Img2ImgPipelineSlowTests(unittest.TestCase):
image_slice = image[0, :10, :10] image_slice = image[0, :10, :10]
expected_slice = np.array( expected_slice = np.array(
[ [
[0.50097656, 0.44726562, 0.40429688], 0.5435,
[0.5048828, 0.45703125, 0.38110352], 0.4673,
[0.4987793, 0.45141602, 0.38134766], 0.5732,
[0.49682617, 0.45336914, 0.38354492], 0.4438,
[0.49804688, 0.4555664, 0.39379883], 0.3557,
[0.5083008, 0.4645996, 0.40039062], 0.4912,
[0.50341797, 0.46240234, 0.39770508], 0.4331,
[0.49926758, 0.4572754, 0.39575195], 0.3491,
[0.50634766, 0.46435547, 0.39794922], 0.4915,
[0.50341797, 0.4572754, 0.39746094], 0.4287,
], 0.3477,
dtype=np.float32, 0.4849,
0.4355,
0.3469,
0.4871,
0.4431,
0.3538,
0.4912,
0.4521,
0.3643,
0.5059,
0.4587,
0.3730,
0.5166,
0.4685,
0.3845,
0.5264,
0.4746,
0.3914,
0.5342,
]
) )
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten()) max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
assert max_diff < 1e-4, f"Outputs are not close enough, got {image_slice}" assert max_diff < 1e-4, f"Outputs are not close enough, got {max_diff}"
tests/pipelines/stable_diffusion_adapter/test_stable_diffusion_adapter.py
View file @ 1e8cf276
...@@ -607,175 +607,14 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): ...@@ -607,175 +607,14 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
gc.collect() gc.collect()
torch.cuda.empty_cache() torch.cuda.empty_cache()
def test_stable_diffusion_adapter_color(self):
adapter_model = "TencentARC/t2iadapter_color_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "snail"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth(self):
adapter_model = "TencentARC/t2iadapter_depth_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "snail"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/color.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_color_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_depth_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "desk"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_depth_sd_v15(self): def test_stable_diffusion_adapter_depth_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_depth_sd15v2" adapter_model = "TencentARC/t2iadapter_depth_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5" sd_model = "Jiali/stable-diffusion-1.5"
prompt = "desk" prompt = "desk"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png" image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/desk_depth.png"
input_channels = 3 input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy" out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_depth_sd15v2.npy"
out_url = "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_adapter/sd_adapter_v15_zoe_depth.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_keypose_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_keypose_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "person"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/person_keypose.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_keypose_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_openpose_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_openpose_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "person"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/iron_man_pose.png"
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_openpose_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_seg_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_seg_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "motorcycle"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
)
input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_seg_sd14v1.npy"
image = load_image(image_url) image = load_image(image_url)
expected_out = load_numpy(out_url) expected_out = load_numpy(out_url)
...@@ -797,11 +636,11 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): ...@@ -797,11 +636,11 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_adapter_zoedepth_sd_v15(self): def test_stable_diffusion_adapter_zoedepth_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_zoedepth_sd15v1" adapter_model = "TencentARC/t2iadapter_zoedepth_sd15v1"
sd_model = "runwayml/stable-diffusion-v1-5" sd_model = "Jiali/stable-diffusion-1.5"
prompt = "motorcycle" prompt = "motorcycle"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png" image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motorcycle.png"
input_channels = 3 input_channels = 3
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_zoedepth_sd15v1.npy" out_url = "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_adapter/sd_adapter_v15_zoe_depth.npy"
image = load_image(image_url) image = load_image(image_url)
expected_out = load_numpy(out_url) expected_out = load_numpy(out_url)
...@@ -819,70 +658,13 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): ...@@ -819,70 +658,13 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten()) max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2 assert max_diff < 1e-2
def test_stable_diffusion_adapter_canny_sd_v14(self):
adapter_model = "TencentARC/t2iadapter_canny_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "toy"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd14v1.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_canny_sd_v15(self): def test_stable_diffusion_adapter_canny_sd_v15(self):
adapter_model = "TencentARC/t2iadapter_canny_sd15v2" adapter_model = "TencentARC/t2iadapter_canny_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5" sd_model = "Jiali/stable-diffusion-1.5"
prompt = "toy" prompt = "toy"
image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png" image_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
input_channels = 1 input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_canny_sd15v2.npy" out_url = "https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_adapter/sd_adapter_v15_zoe_depth.npy"
image = load_image(image_url)
expected_out = load_numpy(out_url)
if input_channels == 1:
image = image.convert("L")
adapter = T2IAdapter.from_pretrained(adapter_model, torch_dtype=torch.float16)
pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing()
generator = torch.Generator(device="cpu").manual_seed(0)
out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2
def test_stable_diffusion_adapter_sketch_sd14(self):
adapter_model = "TencentARC/t2iadapter_sketch_sd14v1"
sd_model = "CompVis/stable-diffusion-v1-4"
prompt = "cat"
image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png"
)
input_channels = 1
out_url = "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/t2iadapter_sketch_sd14v1.npy"
image = load_image(image_url) image = load_image(image_url)
expected_out = load_numpy(out_url) expected_out = load_numpy(out_url)
...@@ -906,7 +688,7 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): ...@@ -906,7 +688,7 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
def test_stable_diffusion_adapter_sketch_sd15(self): def test_stable_diffusion_adapter_sketch_sd15(self):
adapter_model = "TencentARC/t2iadapter_sketch_sd15v2" adapter_model = "TencentARC/t2iadapter_sketch_sd15v2"
sd_model = "runwayml/stable-diffusion-v1-5" sd_model = "Jiali/stable-diffusion-1.5"
prompt = "cat" prompt = "cat"
image_url = ( image_url = (
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png" "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/edge.png"
...@@ -933,25 +715,3 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase): ...@@ -933,25 +715,3 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten()) max_diff = numpy_cosine_similarity_distance(out.flatten(), expected_out.flatten())
assert max_diff < 1e-2 assert max_diff < 1e-2
def test_stable_diffusion_adapter_pipeline_with_sequential_cpu_offloading(self):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
adapter = T2IAdapter.from_pretrained("TencentARC/t2iadapter_seg_sd14v1")
pipe = StableDiffusionAdapterPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", adapter=adapter, safety_checker=None
)
pipe.set_progress_bar_config(disable=None)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/motor.png"
)
pipe(prompt="foo", image=image, num_inference_steps=2)
mem_bytes = torch.cuda.max_memory_allocated()
assert mem_bytes < 5 * 10**9
tests/pipelines/stable_diffusion_safe/test_safe_diffusion.py
View file @ 1e8cf276
...@@ -277,7 +277,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase): ...@@ -277,7 +277,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase):
torch.cuda.empty_cache() torch.cuda.empty_cache()
def test_harm_safe_stable_diffusion(self): def test_harm_safe_stable_diffusion(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None) sd_pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device) sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
...@@ -338,7 +338,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase): ...@@ -338,7 +338,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_nudity_safe_stable_diffusion(self): def test_nudity_safe_stable_diffusion(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None) sd_pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None)
sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config) sd_pipe.scheduler = LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config)
sd_pipe = sd_pipe.to(torch_device) sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
...@@ -392,7 +392,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase): ...@@ -392,7 +392,7 @@ class SafeDiffusionPipelineIntegrationTests(unittest.TestCase):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_nudity_safetychecker_safe_stable_diffusion(self): def test_nudity_safetychecker_safe_stable_diffusion(self):
sd_pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5") sd_pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
sd_pipe = sd_pipe.to(torch_device) sd_pipe = sd_pipe.to(torch_device)
sd_pipe.set_progress_bar_config(disable=None) sd_pipe.set_progress_bar_config(disable=None)
......
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_k_diffusion.py
View file @ 1e8cf276
...@@ -58,7 +58,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -58,7 +58,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
[prompt], [prompt],
generator=generator, generator=generator,
guidance_scale=9.0, guidance_scale=9.0,
num_inference_steps=20, num_inference_steps=2,
height=512, height=512,
width=512, width=512,
output_type="np", output_type="np",
...@@ -69,9 +69,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -69,9 +69,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
image_slice = image[0, -3:, -3:, -1] image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3) assert image.shape == (1, 512, 512, 3)
expected_slice = np.array( expected_slice = np.array([0.5420, 0.5038, 0.2439, 0.5371, 0.4660, 0.1906, 0.5221, 0.4290, 0.2566])
[0.79600024, 0.796546, 0.80682373, 0.79428387, 0.7905743, 0.8008807, 0.786183, 0.7835959, 0.797892]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
...@@ -90,7 +88,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -90,7 +88,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
[prompt], [prompt],
generator=generator, generator=generator,
guidance_scale=7.5, guidance_scale=7.5,
num_inference_steps=15, num_inference_steps=2,
output_type="np", output_type="np",
use_karras_sigmas=True, use_karras_sigmas=True,
height=512, height=512,
...@@ -102,9 +100,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -102,9 +100,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
image_slice = image[0, -3:, -3:, -1] image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3) assert image.shape == (1, 512, 512, 3)
expected_slice = np.array( expected_slice = np.array([0.6418, 0.6424, 0.6462, 0.6271, 0.6314, 0.6295, 0.6249, 0.6339, 0.6335])
[0.9506951, 0.9527786, 0.95309967, 0.9511477, 0.952523, 0.9515326, 0.9511933, 0.9480397, 0.94930184]
)
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
...@@ -124,7 +120,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -124,7 +120,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
generator=torch.manual_seed(seed), generator=torch.manual_seed(seed),
noise_sampler_seed=seed, noise_sampler_seed=seed,
guidance_scale=9.0, guidance_scale=9.0,
num_inference_steps=20, num_inference_steps=2,
output_type="np", output_type="np",
height=512, height=512,
width=512, width=512,
...@@ -134,7 +130,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase): ...@@ -134,7 +130,7 @@ class StableDiffusionXLKPipelineIntegrationTests(unittest.TestCase):
generator=torch.manual_seed(seed), generator=torch.manual_seed(seed),
noise_sampler_seed=seed, noise_sampler_seed=seed,
guidance_scale=9.0, guidance_scale=9.0,
num_inference_steps=20, num_inference_steps=2,
output_type="np", output_type="np",
height=512, height=512,
width=512, width=512,
......
tests/pipelines/test_pipelines_auto.py
View file @ 1e8cf276
...@@ -40,7 +40,7 @@ from diffusers.utils.testing_utils import slow ...@@ -40,7 +40,7 @@ from diffusers.utils.testing_utils import slow
PRETRAINED_MODEL_REPO_MAPPING = OrderedDict( PRETRAINED_MODEL_REPO_MAPPING = OrderedDict(
[ [
("stable-diffusion", "runwayml/stable-diffusion-v1-5"), ("stable-diffusion", "Jiali/stable-diffusion-1.5"),
("if", "DeepFloyd/IF-I-XL-v1.0"), ("if", "DeepFloyd/IF-I-XL-v1.0"),
("kandinsky", "kandinsky-community/kandinsky-2-1"), ("kandinsky", "kandinsky-community/kandinsky-2-1"),
("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"), ("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"),
...@@ -539,7 +539,7 @@ class AutoPipelineIntegrationTest(unittest.TestCase): ...@@ -539,7 +539,7 @@ class AutoPipelineIntegrationTest(unittest.TestCase):
def test_controlnet(self): def test_controlnet(self):
# test from_pretrained # test from_pretrained
model_repo = "runwayml/stable-diffusion-v1-5" model_repo = "Jiali/stable-diffusion-1.5"
controlnet_repo = "lllyasviel/sd-controlnet-canny" controlnet_repo = "lllyasviel/sd-controlnet-canny"
controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16) controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)
......
tests/pipelines/text_to_video_synthesis/test_text_to_video_zero.py
View file @ 1e8cf276
...@@ -40,7 +40,7 @@ class TextToVideoZeroPipelineSlowTests(unittest.TestCase): ...@@ -40,7 +40,7 @@ class TextToVideoZeroPipelineSlowTests(unittest.TestCase):
torch.cuda.empty_cache() torch.cuda.empty_cache()
def test_full_model(self): def test_full_model(self):
model_id = "runwayml/stable-diffusion-v1-5" model_id = "Jiali/stable-diffusion-1.5"
pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda") pipe = TextToVideoZeroPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
generator = torch.Generator(device="cuda").manual_seed(0) generator = torch.Generator(device="cuda").manual_seed(0)
......
tests/single_file/test_stable_diffusion_controlnet_img2img_single_file.py
View file @ 1e8cf276
...@@ -30,11 +30,11 @@ enable_full_determinism() ...@@ -30,11 +30,11 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionControlNetPipeline pipeline_class = StableDiffusionControlNetPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_pruned.safetensors" ckpt_path = "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors"
original_config = ( original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
) )
repo_id = "Lykon/dreamshaper-8" repo_id = "Jiali/stable-diffusion-1.5"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
......
tests/single_file/test_stable_diffusion_controlnet_inpaint_single_file.py
View file @ 1e8cf276
...@@ -29,9 +29,9 @@ enable_full_determinism() ...@@ -29,9 +29,9 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionControlNetInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionControlNetInpaintPipeline pipeline_class = StableDiffusionControlNetInpaintPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_INPAINTING.inpainting.safetensors" 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" original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml"
repo_id = "Lykon/dreamshaper-8-inpainting" repo_id = "botp/stable-diffusion-v1-5-inpainting"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
......
tests/single_file/test_stable_diffusion_controlnet_single_file.py
View file @ 1e8cf276
...@@ -29,11 +29,11 @@ enable_full_determinism() ...@@ -29,11 +29,11 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionControlNetPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionControlNetPipeline pipeline_class = StableDiffusionControlNetPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_pruned.safetensors" ckpt_path = "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors"
original_config = ( original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
) )
repo_id = "Lykon/dreamshaper-8" repo_id = "Jiali/stable-diffusion-1.5"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
......
tests/single_file/test_stable_diffusion_img2img_single_file.py
View file @ 1e8cf276
...@@ -23,11 +23,11 @@ enable_full_determinism() ...@@ -23,11 +23,11 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionImg2ImgPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionImg2ImgPipeline pipeline_class = StableDiffusionImg2ImgPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_pruned.safetensors" ckpt_path = "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors"
original_config = ( original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
) )
repo_id = "Lykon/dreamshaper-8" repo_id = "Jiali/stable-diffusion-1.5"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
......
tests/single_file/test_stable_diffusion_inpaint_single_file.py
View file @ 1e8cf276
...@@ -23,9 +23,9 @@ enable_full_determinism() ...@@ -23,9 +23,9 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionInpaintPipeline pipeline_class = StableDiffusionInpaintPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_INPAINTING.inpainting.safetensors" 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" original_config = "https://raw.githubusercontent.com/runwayml/stable-diffusion/main/configs/stable-diffusion/v1-inpainting-inference.yaml"
repo_id = "Lykon/dreamshaper-8-inpainting" repo_id = "botp/stable-diffusion-v1-5-inpainting"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
...@@ -63,7 +63,7 @@ class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSin ...@@ -63,7 +63,7 @@ class StableDiffusionInpaintPipelineSingleFileSlowTests(unittest.TestCase, SDSin
def test_single_file_loading_4_channel_unet(self): def test_single_file_loading_4_channel_unet(self):
# Test loading single file inpaint with a 4 channel UNet # Test loading single file inpaint with a 4 channel UNet
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_pruned.safetensors" ckpt_path = "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors"
pipe = self.pipeline_class.from_single_file(ckpt_path) pipe = self.pipeline_class.from_single_file(ckpt_path)
assert pipe.unet.config.in_channels == 4 assert pipe.unet.config.in_channels == 4
......
tests/single_file/test_stable_diffusion_single_file.py
View file @ 1e8cf276
...@@ -26,11 +26,11 @@ enable_full_determinism() ...@@ -26,11 +26,11 @@ enable_full_determinism()
@require_torch_gpu @require_torch_gpu
class StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin): class StableDiffusionPipelineSingleFileSlowTests(unittest.TestCase, SDSingleFileTesterMixin):
pipeline_class = StableDiffusionPipeline pipeline_class = StableDiffusionPipeline
ckpt_path = "https://huggingface.co/Lykon/DreamShaper/blob/main/DreamShaper_8_pruned.safetensors" ckpt_path = "https://huggingface.co/Jiali/stable-diffusion-1.5/blob/main/v1-5-pruned-emaonly.safetensors"
original_config = ( original_config = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
) )
repo_id = "Lykon/dreamshaper-8" repo_id = "Jiali/stable-diffusion-1.5"
def setUp(self): def setUp(self):
super().setUp() super().setUp()
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment