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Sep 12, 2024
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[CI] Nightly Test Updates #9380

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456eac5
update
DN6 Sep 3, 2024
261e19b
update
DN6 Sep 6, 2024
95d93e2
update
DN6 Sep 6, 2024
c1602e0
update
DN6 Sep 6, 2024
8588abe
Merge branch 'main' into nightly-precision
DN6 Sep 6, 2024
f2c049b
update
DN6 Sep 6, 2024
ff39dbb
Merge branch 'main' into nightly-precision
sayakpaul Sep 11, 2024
e1afd48
Merge branch 'main' into nightly-precision
yiyixuxu Sep 12, 2024
2a037e2
Merge branch 'main' into nightly-precision
yiyixuxu Sep 12, 2024
de4a878
Merge branch 'main' into nightly-precision
sayakpaul Sep 12, 2024
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45 changes: 22 additions & 23 deletions tests/lora/test_lora_layers_sd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -20,7 +20,6 @@
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from huggingface_hub.repocard import RepoCard
from safetensors.torch import load_file
from transformers import CLIPTextModel, CLIPTokenizer

Expand Down Expand Up @@ -103,7 +102,7 @@ def tearDown(self):
@slow
@require_torch_gpu
def test_integration_move_lora_cpu(self):
path = "runwayml/stable-diffusion-v1-5"
path = "Jiali/stable-diffusion-1.5"
lora_id = "takuma104/lora-test-text-encoder-lora-target"

pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
Expand Down Expand Up @@ -162,7 +161,7 @@ def test_integration_move_lora_cpu(self):
def test_integration_move_lora_dora_cpu(self):
from peft import LoraConfig

path = "Lykon/dreamshaper-8"
path = "Jiali/stable-diffusion-1.5"
unet_lora_config = LoraConfig(
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
Expand Down Expand Up @@ -222,7 +221,7 @@ def tearDown(self):
torch.cuda.empty_cache()

def test_integration_logits_with_scale(self):
path = "runwayml/stable-diffusion-v1-5"
path = "Jiali/stable-diffusion-1.5"
lora_id = "takuma104/lora-test-text-encoder-lora-target"

pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
Expand Down Expand Up @@ -254,7 +253,7 @@ def test_integration_logits_with_scale(self):
release_memory(pipe)

def test_integration_logits_no_scale(self):
path = "runwayml/stable-diffusion-v1-5"
path = "Jiali/stable-diffusion-1.5"
lora_id = "takuma104/lora-test-text-encoder-lora-target"

pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float32)
Expand Down Expand Up @@ -284,8 +283,8 @@ def test_dreambooth_old_format(self):
generator = torch.Generator("cpu").manual_seed(0)

lora_model_id = "hf-internal-testing/lora_dreambooth_dog_example"
card = RepoCard.load(lora_model_id)
base_model_id = card.data.to_dict()["base_model"]

base_model_id = "Jiali/stable-diffusion-1.5"

pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
pipe = pipe.to(torch_device)
Expand All @@ -308,8 +307,8 @@ def test_dreambooth_text_encoder_new_format(self):
generator = torch.Generator().manual_seed(0)

lora_model_id = "hf-internal-testing/lora-trained"
card = RepoCard.load(lora_model_id)
base_model_id = card.data.to_dict()["base_model"]

base_model_id = "Jiali/stable-diffusion-1.5"

pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
pipe = pipe.to(torch_device)
Expand Down Expand Up @@ -420,7 +419,7 @@ def test_a1111_with_sequential_cpu_offload(self):
def test_kohya_sd_v15_with_higher_dimensions(self):
generator = torch.Generator().manual_seed(0)

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
torch_device
)
lora_model_id = "hf-internal-testing/urushisato-lora"
Expand All @@ -444,8 +443,8 @@ def test_vanilla_funetuning(self):
generator = torch.Generator().manual_seed(0)

lora_model_id = "hf-internal-testing/sd-model-finetuned-lora-t4"
card = RepoCard.load(lora_model_id)
base_model_id = card.data.to_dict()["base_model"]

base_model_id = "Jiali/stable-diffusion-1.5"

pipe = StableDiffusionPipeline.from_pretrained(base_model_id, safety_checker=None)
pipe = pipe.to(torch_device)
Expand All @@ -468,7 +467,7 @@ def test_unload_kohya_lora(self):
prompt = "masterpiece, best quality, mountain"
num_inference_steps = 2

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
torch_device
)
initial_images = pipe(
Expand Down Expand Up @@ -506,7 +505,7 @@ def test_load_unload_load_kohya_lora(self):
prompt = "masterpiece, best quality, mountain"
num_inference_steps = 2

pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", safety_checker=None).to(
pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", safety_checker=None).to(
torch_device
)
initial_images = pipe(
Expand Down Expand Up @@ -548,9 +547,9 @@ def test_load_unload_load_kohya_lora(self):

def test_not_empty_state_dict(self):
# Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
pipe = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
).to(torch_device)
pipe = AutoPipelineForText2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16).to(
torch_device
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
Expand All @@ -562,9 +561,9 @@ def test_not_empty_state_dict(self):

def test_load_unload_load_state_dict(self):
# Makes sure https://github.com/huggingface/diffusers/issues/7054 does not happen again
pipe = AutoPipelineForText2Image.from_pretrained(
"runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
).to(torch_device)
pipe = AutoPipelineForText2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16).to(
torch_device
)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

cached_file = hf_hub_download("hf-internal-testing/lcm-lora-test-sd-v1-5", "test_lora.safetensors")
Expand All @@ -581,7 +580,7 @@ def test_load_unload_load_state_dict(self):
release_memory(pipe)

def test_sdv1_5_lcm_lora(self):
pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe = DiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

Expand Down Expand Up @@ -609,7 +608,7 @@ def test_sdv1_5_lcm_lora(self):
release_memory(pipe)

def test_sdv1_5_lcm_lora_img2img(self):
pipe = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
pipe = AutoPipelineForImage2Image.from_pretrained("Jiali/stable-diffusion-1.5", torch_dtype=torch.float16)
pipe.to(torch_device)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

Expand Down Expand Up @@ -650,7 +649,7 @@ def test_sd_load_civitai_empty_network_alpha(self):
This test simply checks that loading a LoRA with an empty network alpha works fine
See: https://github.com/huggingface/diffusers/issues/5606
"""
pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
pipeline = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5")
pipeline.enable_sequential_cpu_offload()
civitai_path = hf_hub_download("ybelkada/test-ahi-civitai", "ahi_lora_weights.safetensors")
pipeline.load_lora_weights(civitai_path, adapter_name="ahri")
Expand Down
6 changes: 3 additions & 3 deletions tests/models/autoencoders/test_models_vae.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1051,7 +1051,7 @@ def test_encode_decode(self):

def test_sd(self):
vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder") # TODO - update
pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None)
pipe = StableDiffusionPipeline.from_pretrained("Jiali/stable-diffusion-1.5", vae=vae, safety_checker=None)
pipe.to(torch_device)

out = pipe(
Expand Down Expand Up @@ -1099,7 +1099,7 @@ def test_sd_f16(self):
"openai/consistency-decoder", torch_dtype=torch.float16
) # TODO - update
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
"Jiali/stable-diffusion-1.5",
torch_dtype=torch.float16,
vae=vae,
safety_checker=None,
Expand All @@ -1124,7 +1124,7 @@ def test_sd_f16(self):
def test_vae_tiling(self):
vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", vae=vae, safety_checker=None, torch_dtype=torch.float16
"Jiali/stable-diffusion-1.5", vae=vae, safety_checker=None, torch_dtype=torch.float16
)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
Expand Down
8 changes: 4 additions & 4 deletions tests/models/unets/test_models_unet_2d_condition.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1376,7 +1376,7 @@ def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
@require_torch_accelerator
@skip_mps
def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5")
latents = self.get_latents(seed)
encoder_hidden_states = self.get_encoder_hidden_states(seed)

Expand Down Expand Up @@ -1404,7 +1404,7 @@ def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
)
@require_torch_accelerator_with_fp16
def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
model = self.get_unet_model(model_id="Jiali/stable-diffusion-1.5", fp16=True)
latents = self.get_latents(seed, fp16=True)
encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)

Expand Down Expand Up @@ -1433,7 +1433,7 @@ def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
@require_torch_accelerator
@skip_mps
def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting")
latents = self.get_latents(seed, shape=(4, 9, 64, 64))
encoder_hidden_states = self.get_encoder_hidden_states(seed)

Expand Down Expand Up @@ -1461,7 +1461,7 @@ def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
)
@require_torch_accelerator_with_fp16
def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
model = self.get_unet_model(model_id="botp/stable-diffusion-v1-5-inpainting", fp16=True)
latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)

Expand Down
41 changes: 15 additions & 26 deletions tests/pipelines/amused/test_amused.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,15 +13,19 @@
# See the License for the specific language governing permissions and
# limitations under the License.


import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import AmusedPipeline, AmusedScheduler, UVit2DModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from diffusers.utils.testing_utils import (
enable_full_determinism,
require_torch_gpu,
slow,
torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
Expand Down Expand Up @@ -65,19 +69,15 @@ def get_dummy_components(self):
vqvae = VQModel(
act_fn="silu",
block_out_channels=[8],
down_block_types=[
"DownEncoderBlock2D",
],
down_block_types=["DownEncoderBlock2D"],
in_channels=3,
latent_channels=8,
layers_per_block=1,
norm_num_groups=8,
num_vq_embeddings=8,
out_channels=3,
sample_size=8,
up_block_types=[
"UpDecoderBlock2D",
],
up_block_types=["UpDecoderBlock2D"],
mid_block_add_attention=False,
lookup_from_codebook=True,
)
Expand All @@ -96,7 +96,6 @@ def get_dummy_components(self):
)
text_encoder = CLIPTextModelWithProjection(text_encoder_config)
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

components = {
"transformer": transformer,
"scheduler": scheduler,
Expand Down Expand Up @@ -135,47 +134,37 @@ class AmusedPipelineSlowTests(unittest.TestCase):
def test_amused_256(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-256")
pipe.to(torch_device)

image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images

image_slice = image[0, -3:, -3:, -1].flatten()

assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.4011, 0.3992, 0.3790, 0.3856, 0.3772, 0.3711, 0.3919, 0.3850, 0.3625])
assert np.abs(image_slice - expected_slice).max() < 3e-3
expected_slice = np.array([0.4011, 0.3992, 0.379, 0.3856, 0.3772, 0.3711, 0.3919, 0.385, 0.3625])
assert np.abs(image_slice - expected_slice).max() < 0.003

def test_amused_256_fp16(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16)
pipe.to(torch_device)

image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images

image_slice = image[0, -3:, -3:, -1].flatten()

assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.0554, 0.05129, 0.0344, 0.0452, 0.0476, 0.0271, 0.0495, 0.0527, 0.0158])
assert np.abs(image_slice - expected_slice).max() < 7e-3
assert np.abs(image_slice - expected_slice).max() < 0.007

def test_amused_512(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-512")
pipe.to(torch_device)

image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images

image_slice = image[0, -3:, -3:, -1].flatten()

assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9960, 0.9960, 0.9946, 0.9980, 0.9947, 0.9932, 0.9960, 0.9961, 0.9947])
assert np.abs(image_slice - expected_slice).max() < 3e-3
expected_slice = np.array([0.1199, 0.1171, 0.1229, 0.1188, 0.1210, 0.1147, 0.1260, 0.1346, 0.1152])
assert np.abs(image_slice - expected_slice).max() < 0.003
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Probably because of the runner change?


def test_amused_512_fp16(self):
pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
pipe.to(torch_device)

image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images

image_slice = image[0, -3:, -3:, -1].flatten()

assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.9983, 1.0, 1.0, 1.0, 1.0, 0.9989, 0.9994, 0.9976, 0.9977])
assert np.abs(image_slice - expected_slice).max() < 3e-3
expected_slice = np.array([0.1509, 0.1492, 0.1531, 0.1485, 0.1501, 0.1465, 0.1581, 0.1690, 0.1499])
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Same (runner change effects)?

assert np.abs(image_slice - expected_slice).max() < 0.003
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