sayakpaul/diffusers-qa-chatbot-artifacts · Datasets at Hugging Face

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from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler
from diffusers.utils import load_image, make_image_grid

# Prepare image
# Detect the canny map in low resolution to avoid high-frequency details
image = load_image(
"https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"
).resize((384, 384))

image = np.array(image)

low_threshold = 100
high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image).resize((1024, 1216))

# load adapter
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")

unet = UNet2DConditionModel.from_pretrained(
"latent-consistency/lcm-sdxl",
torch_dtype=torch.float16,
variant="fp16",
)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
unet=unet,
adapter=adapter,
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

prompt = "Mystical fairy in real, magic, 4k picture, high quality"
negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"

generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
image=canny_image,
num_inference_steps=4,
guidance_scale=5,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=1,
generator=generator,
).images[0]
grid = make_image_grid([canny_image, image], rows=1, cols=2)
EulerDiscreteScheduler The Euler scheduler (Algorithm 2) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson. EulerDiscreteScheduler class diffusers.EulerDiscreteScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' interpolation_type: str = 'linear' use_karras_sigmas: Optional = False sigma_min: Optional = None sigma_max: Optional = None timestep_spacing: str = 'linspace' timestep_type: str = 'discrete' steps_offset: int = 0 rescale_betas_zero_snr: bool = False ) Parameters num_train_timesteps (int, defaults to 1000) —
The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) —
The starting beta value of inference. beta_end (float, defaults to 0.02) —
The final beta value. beta_schedule (str, defaults to "linear") —
The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
linear or scaled_linear. trained_betas (np.ndarray, optional) —
Pass an array of betas directly to the constructor to bypass beta_start and beta_end. prediction_type (str, defaults to epsilon, optional) —
Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process),
sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen
Video paper). interpolation_type(str, defaults to "linear", optional) —
The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of
"linear" or "log_linear". use_karras_sigmas (bool, optional, defaults to False) —
Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True,
the sigmas are determined according to a sequence of noise levels {σi}. timestep_spacing (str, defaults to "linspace") —
The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and
Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) —
An offset added to the inference steps. You can use a combination of offset=1 and
set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable
Diffusion. rescale_betas_zero_snr (bool, defaults to False) —
Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and
dark samples instead of limiting it to samples with medium brightness. Loosely related to
--offset_noise. Euler scheduler. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic
methods the library implements for all schedulers such as loading and saving. scale_model_input < source > ( sample: FloatTensor timestep: Union ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —
The input sample. timestep (int, optional) —
The current timestep in the diffusion chain. Returns
torch.FloatTensor

A scaled input sample.
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the
current timestep. Scales the denoising model input by (sigma2 + 1) 0.5 to match the Euler algorithm. set_timesteps < source > ( num_inference_steps: int device: Union = None ) Parameters num_inference_steps (int) —
The number of diffusion steps used when generating samples with a pre-trained model. device (str or torch.device, optional) —
The device to which the timesteps should be moved to. If None, the timesteps are not moved. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: Union sample: FloatTensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: Optional = None return_dict: bool = True ) → EulerDiscreteSchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —
The direct output from learned diffusion model. timestep (float) —
The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) —
A current instance of a sample created by the diffusion process. s_churn (float) — s_tmin (float) — s_tmax (float) — s_noise (float, defaults to 1.0) —
Scaling factor for noise added to the sample. generator (torch.Generator, optional) —
A random number generator. return_dict (bool) —
Whether or not to return a EulerDiscreteSchedulerOutput or
tuple. Returns
EulerDiscreteSchedulerOutput or tuple

If return_dict is True, EulerDiscreteSchedulerOutput is
returned, otherwise a tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion
process from the learned model outputs (most often the predicted noise). EulerDiscreteSchedulerOutput class diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —
Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the
denoising loop. pred_original_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —

from diffusers import StableDiffusionXLAdapterPipeline, UNet2DConditionModel, T2IAdapter, LCMScheduler

from diffusers.utils import load_image, make_image_grid

# Prepare image

# Detect the canny map in low resolution to avoid high-frequency details

image = load_image(

"https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_canny.jpg"

).resize((384, 384))

image = np.array(image)

low_threshold = 100

high_threshold = 200

image = cv2.Canny(image, low_threshold, high_threshold)

image = image[:, :, None]

image = np.concatenate([image, image, image], axis=2)

canny_image = Image.fromarray(image).resize((1024, 1216))

# load adapter

adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-canny-sdxl-1.0", torch_dtype=torch.float16, varient="fp16").to("cuda")

unet = UNet2DConditionModel.from_pretrained(

"latent-consistency/lcm-sdxl",

torch_dtype=torch.float16,

variant="fp16",

)

pipe = StableDiffusionXLAdapterPipeline.from_pretrained(

"stabilityai/stable-diffusion-xl-base-1.0",

unet=unet,

adapter=adapter,

torch_dtype=torch.float16,

variant="fp16",

).to("cuda")

pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)

prompt = "Mystical fairy in real, magic, 4k picture, high quality"

negative_prompt = "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured"

generator = torch.manual_seed(0)

image = pipe(

prompt=prompt,

negative_prompt=negative_prompt,

image=canny_image,

num_inference_steps=4,

guidance_scale=5,

adapter_conditioning_scale=0.8,

adapter_conditioning_factor=1,

generator=generator,

).images[0]

grid = make_image_grid([canny_image, image], rows=1, cols=2)

EulerDiscreteScheduler The Euler scheduler (Algorithm 2) is from the Elucidating the Design Space of Diffusion-Based Generative Models paper by Karras et al. This is a fast scheduler which can often generate good outputs in 20-30 steps. The scheduler is based on the original k-diffusion implementation by Katherine Crowson. EulerDiscreteScheduler class diffusers.EulerDiscreteScheduler < source > ( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None prediction_type: str = 'epsilon' interpolation_type: str = 'linear' use_karras_sigmas: Optional = False sigma_min: Optional = None sigma_max: Optional = None timestep_spacing: str = 'linspace' timestep_type: str = 'discrete' steps_offset: int = 0 rescale_betas_zero_snr: bool = False ) Parameters num_train_timesteps (int, defaults to 1000) —

The number of diffusion steps to train the model. beta_start (float, defaults to 0.0001) —

The starting beta value of inference. beta_end (float, defaults to 0.02) —

The final beta value. beta_schedule (str, defaults to "linear") —

The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from

linear or scaled_linear. trained_betas (np.ndarray, optional) —

Pass an array of betas directly to the constructor to bypass beta_start and beta_end. prediction_type (str, defaults to epsilon, optional) —

Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process),

sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen

Video paper). interpolation_type(str, defaults to "linear", optional) —

The interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be on of

"linear" or "log_linear". use_karras_sigmas (bool, optional, defaults to False) —

Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True,

the sigmas are determined according to a sequence of noise levels {σi}. timestep_spacing (str, defaults to "linspace") —

The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and

Sample Steps are Flawed for more information. steps_offset (int, defaults to 0) —

An offset added to the inference steps. You can use a combination of offset=1 and

set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable

Diffusion. rescale_betas_zero_snr (bool, defaults to False) —

Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and

dark samples instead of limiting it to samples with medium brightness. Loosely related to

--offset_noise. Euler scheduler. This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic

methods the library implements for all schedulers such as loading and saving. scale_model_input < source > ( sample: FloatTensor timestep: Union ) → torch.FloatTensor Parameters sample (torch.FloatTensor) —

The input sample. timestep (int, optional) —

The current timestep in the diffusion chain. Returns

torch.FloatTensor

A scaled input sample.

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the

current timestep. Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the Euler algorithm. set_timesteps < source > ( num_inference_steps: int device: Union = None ) Parameters num_inference_steps (int) —

The number of diffusion steps used when generating samples with a pre-trained model. device (str or torch.device, optional) —

The device to which the timesteps should be moved to. If None, the timesteps are not moved. Sets the discrete timesteps used for the diffusion chain (to be run before inference). step < source > ( model_output: FloatTensor timestep: Union sample: FloatTensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: Optional = None return_dict: bool = True ) → EulerDiscreteSchedulerOutput or tuple Parameters model_output (torch.FloatTensor) —

The direct output from learned diffusion model. timestep (float) —

The current discrete timestep in the diffusion chain. sample (torch.FloatTensor) —

A current instance of a sample created by the diffusion process. s_churn (float) — s_tmin (float) — s_tmax (float) — s_noise (float, defaults to 1.0) —

Scaling factor for noise added to the sample. generator (torch.Generator, optional) —

A random number generator. return_dict (bool) —

Whether or not to return a EulerDiscreteSchedulerOutput or

tuple. Returns

EulerDiscreteSchedulerOutput or tuple

If return_dict is True, EulerDiscreteSchedulerOutput is

returned, otherwise a tuple is returned where the first element is the sample tensor.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion

process from the learned model outputs (most often the predicted noise). EulerDiscreteSchedulerOutput class diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput < source > ( prev_sample: FloatTensor pred_original_sample: Optional = None ) Parameters prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —

Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the

denoising loop. pred_original_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) —