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sayakpaul
/
diffusers-qa-chatbot-artifacts

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... guidance_scale=7,
... editing_prompt=[
... "smiling, smile", # Concepts to apply
... "glasses, wearing glasses",
... "curls, wavy hair, curly hair",
... "beard, full beard, mustache",
... ],
... reverse_editing_direction=[
... False,
... False,
... False,
... False,
... ], # Direction of guidance i.e. increase all concepts
... edit_warmup_steps=[10, 10, 10, 10], # Warmup period for each concept
... edit_guidance_scale=[4, 5, 5, 5.4], # Guidance scale for each concept
... edit_threshold=[
... 0.99,
... 0.975,
... 0.925,
... 0.96,
... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
... edit_mom_beta=0.6, # Momentum beta
... edit_weights=[1, 1, 1, 1, 1], # Weights of the individual concepts against each other
... )
>>> image = out.images[0] StableDiffusionSafePipelineOutput class diffusers.pipelines.semantic_stable_diffusion.pipeline_output.SemanticStableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Image] or np.ndarray) —
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) —
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or
None if safety checking could not be performed. Output class for Stable Diffusion pipelines.
ControlNet ControlNet is a type of model for controlling image diffusion models by conditioning the model with an additional input image. There are many types of conditioning inputs (canny edge, user sketching, human pose, depth, and more) you can use to control a diffusion model. This is hugely useful because it affor...
#!pip install -q diffusers transformers accelerate opencv-python Text-to-image For text-to-image, you normally pass a text prompt to the model. But with ControlNet, you can specify an additional conditioning input. Let’s condition the model with a canny image, a white outline of an image on a black background. This wa...
from PIL import Image
import cv2
import numpy as np
original_image = load_image(
"https://hf.co/datasets/huggingface/documentation-images/resolve/main/diffusers/input_image_vermeer.png"
)
image = np.array(original_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) original image canny image Next, load a ControlNet model conditioned on canny edge detection and pass it to the StableDiffusionControlNetPipeline. Use the faster UniPCMultistepScheduler and enable model offloading to speed up inference and reduce memory usage. Copied from diffus...
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload() Now pass your prompt and canny image to the pipeline: Copied output = pipe(
"the mona lisa", image=canny_image
).images[0]
make_image_grid([original_image, canny_image, output], rows=1, cols=3) Image-to-image For image-to-image, you’d typically pass an initial image and a prompt to the pipeline to generate a new image. With ControlNet, you can pass an additional conditioning input to guide the model. Let’s condition the model with a dept...
import numpy as np
from transformers import pipeline
from diffusers.utils import load_image, make_image_grid
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img.jpg"
)
def get_depth_map(image, depth_estimator):
image = depth_estimator(image)["depth"]
image = np.array(image)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
detected_map = torch.from_numpy(image).float() / 255.0
depth_map = detected_map.permute(2, 0, 1)
return depth_map
depth_estimator = pipeline("depth-estimation")
depth_map = get_depth_map(image, depth_estimator).unsqueeze(0).half().to("cuda") Next, load a ControlNet model conditioned on depth maps and pass it to the StableDiffusionControlNetImg2ImgPipeline. Use the faster UniPCMultistepScheduler and enable model offloading to speed up inference and reduce memory usage. Copied...
import torch
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload() Now pass your prompt, initial image, and depth map to the pipeline: Copied output = pipe(
"lego batman and robin", image=image, control_image=depth_map,
).images[0]
make_image_grid([image, output], rows=1, cols=2) original image generated image Inpainting For inpainting, you need an initial image, a mask image, and a prompt describing what to replace the mask with. ControlNet models allow you to add another control image to condition a model with. Let’s condition the model with...
init_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"
)
init_image = init_image.resize((512, 512))
mask_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg"