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

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

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

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

# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")

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=1.5,
adapter_conditioning_scale=0.8,
adapter_conditioning_factor=1,
generator=generator,
).images[0]
make_image_grid([canny_image, image], rows=1, cols=2) Inpainting LCM-LoRA can be used for inpainting as well. Copied import torch
from diffusers import AutoPipelineForInpainting, LCMScheduler
from diffusers.utils import load_image, make_image_grid

pipe = AutoPipelineForInpainting.from_pretrained(
"runwayml/stable-diffusion-inpainting",
torch_dtype=torch.float16,
variant="fp16",
).to("cuda")

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

# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")

# load base and mask image
init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")
mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")

# generator = torch.Generator("cuda").manual_seed(92)
prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"
generator = torch.manual_seed(0)
image = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
generator=generator,
num_inference_steps=4,
guidance_scale=4,
).images[0]
make_image_grid([init_image, mask_image, image], rows=1, cols=3) AnimateDiff AnimateDiff allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow.
LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let’s look at how we can perform animation with LCM-LoRA and AnimateDiff. Copied import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler
from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5")
pipe = AnimateDiffPipeline.from_pretrained(
"frankjoshua/toonyou_beta6",
motion_adapter=adapter,
).to("cuda")

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

# load LCM-LoRA
pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")

pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 1.2])

prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress"
generator = torch.manual_seed(0)
frames = pipe(
prompt=prompt,
num_inference_steps=5,
guidance_scale=1.25,
cross_attention_kwargs={"scale": 1},
num_frames=24,
generator=generator
).frames[0]
export_to_gif(frames, "animation.gif")
UNet3DConditionModel The UNet model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it’s number of dimensions and whether it is a conditional model or not. This is a 3D UNet conditional model. The abstract from the paper is: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net. UNet3DConditionModel class diffusers.UNet3DConditionModel < source > ( sample_size: Optional = None in_channels: int = 4 out_channels: int = 4 down_block_types: Tuple = ('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') up_block_types: Tuple = ('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') block_out_channels: Tuple = (320, 640, 1280, 1280) layers_per_block: int = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 act_fn: str = 'silu' norm_num_groups: Optional = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 1024 attention_head_dim: Union = 64 num_attention_heads: Union = None ) Parameters sample_size (int or Tuple[int, int], optional, defaults to None) —
Height and width of input/output sample. in_channels (int, optional, defaults to 4) — The number of channels in the input sample. out_channels (int, optional, defaults to 4) — The number of channels in the output. down_block_types (Tuple[str], optional, defaults to ("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")) —
The tuple of downsample blocks to use. up_block_types (Tuple[str], optional, defaults to ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")) —
The tuple of upsample blocks to use. block_out_channels (Tuple[int], optional, defaults to (320, 640, 1280, 1280)) —
The tuple of output channels for each block. layers_per_block (int, optional, defaults to 2) — The number of layers per block. downsample_padding (int, optional, defaults to 1) — The padding to use for the downsampling convolution. mid_block_scale_factor (float, optional, defaults to 1.0) — The scale factor to use for the mid block. act_fn (str, optional, defaults to "silu") — The activation function to use. norm_num_groups (int, optional, defaults to 32) — The number of groups to use for the normalization.
If None, normalization and activation layers is skipped in post-processing. norm_eps (float, optional, defaults to 1e-5) — The epsilon to use for the normalization. cross_attention_dim (int, optional, defaults to 1024) — The dimension of the cross attention features. attention_head_dim (int, optional, defaults to 64) — The dimension of the attention heads. num_attention_heads (int, optional) — The number of attention heads. A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample
shaped output. This model inherits from ModelMixin. Check the superclass documentation for it’s generic methods implemented
for all models (such as downloading or saving). disable_freeu < source > ( ) Disables the FreeU mechanism. enable_forward_chunking < source > ( chunk_size: Optional = None dim: int = 0 ) Parameters chunk_size (int, optional) —
The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually

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

pipe = StableDiffusionXLAdapterPipeline.from_pretrained(

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

adapter=adapter,

torch_dtype=torch.float16,

variant="fp16",

).to("cuda")

# set scheduler

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

# load LCM-LoRA

pipe.load_lora_weights("latent-consistency/lcm-lora-sdxl")

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=1.5,

adapter_conditioning_scale=0.8,

adapter_conditioning_factor=1,

generator=generator,

).images[0]

make_image_grid([canny_image, image], rows=1, cols=2) Inpainting LCM-LoRA can be used for inpainting as well. Copied import torch

from diffusers import AutoPipelineForInpainting, LCMScheduler

from diffusers.utils import load_image, make_image_grid

pipe = AutoPipelineForInpainting.from_pretrained(

"runwayml/stable-diffusion-inpainting",

torch_dtype=torch.float16,

variant="fp16",

).to("cuda")

# set scheduler

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

# load LCM-LoRA

pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")

# load base and mask image

init_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint.png")

mask_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/inpaint_mask.png")

# generator = torch.Generator("cuda").manual_seed(92)

prompt = "concept art digital painting of an elven castle, inspired by lord of the rings, highly detailed, 8k"

generator = torch.manual_seed(0)

image = pipe(

prompt=prompt,

image=init_image,

mask_image=mask_image,

generator=generator,

num_inference_steps=4,

guidance_scale=4,

).images[0]

make_image_grid([init_image, mask_image, image], rows=1, cols=3) AnimateDiff AnimateDiff allows you to animate images using Stable Diffusion models. To get good results, we need to generate multiple frames (16-24), and doing this with standard SD models can be very slow.

LCM-LoRA can be used to speed up the process significantly, as you just need to do 4-8 steps for each frame. Let’s look at how we can perform animation with LCM-LoRA and AnimateDiff. Copied import torch

from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler, LCMScheduler

from diffusers.utils import export_to_gif

adapter = MotionAdapter.from_pretrained("diffusers/animatediff-motion-adapter-v1-5")

pipe = AnimateDiffPipeline.from_pretrained(

"frankjoshua/toonyou_beta6",

motion_adapter=adapter,

).to("cuda")

# set scheduler

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

# load LCM-LoRA

pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5", adapter_name="lcm")

pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-in", weight_name="diffusion_pytorch_model.safetensors", adapter_name="motion-lora")

pipe.set_adapters(["lcm", "motion-lora"], adapter_weights=[0.55, 1.2])

prompt = "best quality, masterpiece, 1girl, looking at viewer, blurry background, upper body, contemporary, dress"

generator = torch.manual_seed(0)

frames = pipe(

prompt=prompt,

num_inference_steps=5,

guidance_scale=1.25,

cross_attention_kwargs={"scale": 1},

num_frames=24,

generator=generator

).frames[0]

export_to_gif(frames, "animation.gif")

UNet3DConditionModel The UNet model was originally introduced by Ronneberger et al. for biomedical image segmentation, but it is also commonly used in 🤗 Diffusers because it outputs images that are the same size as the input. It is one of the most important components of a diffusion system because it facilitates the actual diffusion process. There are several variants of the UNet model in 🤗 Diffusers, depending on it’s number of dimensions and whether it is a conditional model or not. This is a 3D UNet conditional model. The abstract from the paper is: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net. UNet3DConditionModel class diffusers.UNet3DConditionModel < source > ( sample_size: Optional = None in_channels: int = 4 out_channels: int = 4 down_block_types: Tuple = ('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') up_block_types: Tuple = ('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') block_out_channels: Tuple = (320, 640, 1280, 1280) layers_per_block: int = 2 downsample_padding: int = 1 mid_block_scale_factor: float = 1 act_fn: str = 'silu' norm_num_groups: Optional = 32 norm_eps: float = 1e-05 cross_attention_dim: int = 1024 attention_head_dim: Union = 64 num_attention_heads: Union = None ) Parameters sample_size (int or Tuple[int, int], optional, defaults to None) —

Height and width of input/output sample. in_channels (int, optional, defaults to 4) — The number of channels in the input sample. out_channels (int, optional, defaults to 4) — The number of channels in the output. down_block_types (Tuple[str], optional, defaults to ("CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "CrossAttnDownBlock3D", "DownBlock3D")) —

The tuple of downsample blocks to use. up_block_types (Tuple[str], optional, defaults to ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D")) —

The tuple of upsample blocks to use. block_out_channels (Tuple[int], optional, defaults to (320, 640, 1280, 1280)) —

The tuple of output channels for each block. layers_per_block (int, optional, defaults to 2) — The number of layers per block. downsample_padding (int, optional, defaults to 1) — The padding to use for the downsampling convolution. mid_block_scale_factor (float, optional, defaults to 1.0) — The scale factor to use for the mid block. act_fn (str, optional, defaults to "silu") — The activation function to use. norm_num_groups (int, optional, defaults to 32) — The number of groups to use for the normalization.

If None, normalization and activation layers is skipped in post-processing. norm_eps (float, optional, defaults to 1e-5) — The epsilon to use for the normalization. cross_attention_dim (int, optional, defaults to 1024) — The dimension of the cross attention features. attention_head_dim (int, optional, defaults to 64) — The dimension of the attention heads. num_attention_heads (int, optional) — The number of attention heads. A conditional 3D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample

shaped output. This model inherits from ModelMixin. Check the superclass documentation for it’s generic methods implemented

for all models (such as downloading or saving). disable_freeu < source > ( ) Disables the FreeU mechanism. enable_forward_chunking < source > ( chunk_size: Optional = None dim: int = 0 ) Parameters chunk_size (int, optional) —

The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually