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Fix type mismatch error, add tests for negative prompts #823

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Merged
merged 1 commit into from
Oct 13, 2022
Merged

Fix type mismatch error, add tests for negative prompts #823

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4 changes: 2 additions & 2 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -234,8 +234,8 @@ def __call__(
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
" {type(prompt)}."
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
Expand Down
7 changes: 4 additions & 3 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
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Expand Up @@ -195,7 +195,6 @@ def __call__(
"""
if isinstance(prompt, str):
batch_size = 1
prompt = [prompt]
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the cause of the error in #820

elif isinstance(prompt, list):
batch_size = len(prompt)
else:
Expand Down Expand Up @@ -250,8 +249,8 @@ def __call__(
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
" {type(prompt)}."
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
Expand Down Expand Up @@ -285,6 +284,8 @@ def __call__(
init_latents = init_latent_dist.sample(generator=generator)
init_latents = 0.18215 * init_latents

if isinstance(prompt, str):
prompt = [prompt]
if len(prompt) > init_latents.shape[0] and len(prompt) % init_latents.shape[0] == 0:
# expand init_latents for batch_size
deprecation_message = (
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -266,8 +266,8 @@ def __call__(
uncond_tokens = [""]
elif type(prompt) is not type(negative_prompt):
raise TypeError(
"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
" {type(prompt)}."
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt]
Expand Down
4 changes: 2 additions & 2 deletions src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,8 +108,8 @@ def __call__(
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
" {type(prompt)}."
f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
f" {type(prompt)}."
)
elif isinstance(negative_prompt, str):
uncond_tokens = [negative_prompt] * batch_size
Expand Down
128 changes: 128 additions & 0 deletions tests/test_pipelines.py
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Original file line number Diff line number Diff line change
Expand Up @@ -575,6 +575,46 @@ def test_stable_diffusion_attention_chunk(self):

assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 1e-4

def test_stable_diffusion_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)

prompt = "A painting of a squirrel eating a burger"
negative_prompt = "french fries"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
prompt,
negative_prompt=negative_prompt,
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
)

image = output.images
image_slice = image[0, -3:, -3:, -1]

assert image.shape == (1, 128, 128, 3)
expected_slice = np.array([0.4851, 0.4617, 0.4765, 0.5127, 0.4845, 0.5153, 0.5141, 0.4886, 0.4719])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_score_sde_ve_pipeline(self):
unet = self.dummy_uncond_unet
scheduler = ScoreSdeVeScheduler()
Expand Down Expand Up @@ -704,6 +744,48 @@ def test_stable_diffusion_img2img(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_img2img_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

init_image = self.dummy_image.to(device)

# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionImg2ImgPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)

prompt = "A painting of a squirrel eating a burger"
negative_prompt = "french fries"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
prompt,
negative_prompt=negative_prompt,
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
)
image = output.images
image_slice = image[0, -3:, -3:, -1]

assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4065, 0.3783, 0.4050, 0.5266, 0.4781, 0.4252, 0.4203, 0.4692, 0.4365])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_img2img_multiple_init_images(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
Expand Down Expand Up @@ -861,6 +943,52 @@ def test_stable_diffusion_inpaint(self):
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_inpaint_negative_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
scheduler = PNDMScheduler(skip_prk_steps=True)
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

image = self.dummy_image.cpu().permute(0, 2, 3, 1)[0]
init_image = Image.fromarray(np.uint8(image)).convert("RGB")
mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((128, 128))

# make sure here that pndm scheduler skips prk
sd_pipe = StableDiffusionInpaintPipeline(
unet=unet,
scheduler=scheduler,
vae=vae,
text_encoder=bert,
tokenizer=tokenizer,
safety_checker=self.dummy_safety_checker,
feature_extractor=self.dummy_extractor,
)
sd_pipe = sd_pipe.to(device)
sd_pipe.set_progress_bar_config(disable=None)

prompt = "A painting of a squirrel eating a burger"
negative_prompt = "french fries"
generator = torch.Generator(device=device).manual_seed(0)
output = sd_pipe(
prompt,
negative_prompt=negative_prompt,
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="np",
init_image=init_image,
mask_image=mask_image,
)

image = output.images
image_slice = image[0, -3:, -3:, -1]

assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.4765, 0.5339, 0.4541, 0.6240, 0.5439, 0.4055, 0.5503, 0.5891, 0.5150])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

def test_stable_diffusion_num_images_per_prompt(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
unet = self.dummy_cond_unet
Expand Down