source stringclasses 273
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/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | 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 way, the ControlNet can use the canny image as a control to guide the model to gen... | 64_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | Load an image and use the [opencv-python](https://github.com/opencv/opencv-python) library to extract the canny image:
```py
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import cv2
import numpy as np | 64_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | 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 | 64_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/dif... | 64_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/vermeer_canny_edged.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figc... | 64_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption>
</div>
</div>
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... | 64_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
) | 64_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#text-to-image | .md | pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now pass your prompt and canny image to the pipeline:
```py
output = pipe(
"the mona lisa", image=canny_image
).images[0]
make_image_grid([original_image, canny_image, output], rows=1, cols=3)
```
<div ... | 64_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | 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 depth map, an image which contains spatial information. This way, the ControlNet can use the... | 64_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | You'll use the [`StableDiffusionControlNetImg2ImgPipeline`] for this task, which is different from the [`StableDiffusionControlNetPipeline`] because it allows you to pass an initial image as the starting point for the image generation process.
Load an image and use the `depth-estimation` [`~transformers.Pipeline`] fr... | 64_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | 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"
) | 64_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | 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 | 64_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | 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 offlo... | 64_3_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11f1p_sd15_depth", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
) | 64_3_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | 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:
```py
output = pipe(
"lego batman and robin", image=image, control_image=depth_map,
).images[0]
make_image_grid([image, output], rows=1,... | 64_3_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#image-to-image | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-img2img-2.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">generated image<... | 64_3_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | 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 an inpainting mask. This way, the ControlNet can use the inpainting mask as a control to guid... | 64_4_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | init_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"
)
init_image = init_image.resize((512, 512)) | 64_4_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | mask_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-mask.jpg"
)
mask_image = mask_image.resize((512, 512))
make_image_grid([init_image, mask_image], rows=1, cols=2)
```
Create a function to prepare the control image from the initial and ... | 64_4_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | ```py
import numpy as np
import torch | 64_4_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | def make_inpaint_condition(image, image_mask):
image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
assert image.shape[0:1] == image_mask.shape[0:1]
image[image_mask > 0.5] = -1.0 # set as masked pixel
image = np.expand_dims(image,... | 64_4_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | control_image = make_inpaint_condition(init_image, mask_image)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint.jpg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">original image</fi... | 64_4_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">mask image</figcaption>
</div>
</div>
Load a ControlNet model conditioned on inpainting and pass it to the [`StableDiffusionControlNetInpaintPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to speed up inference and reduce me... | 64_4_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
"stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True
) | 64_4_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now pass your prompt, initial image, mask image, and control image to the pipeline:
```py
output = pipe(
"corgi face with large ears, detailed, pixar, animated, disney",
num_inference_steps=20,
eta=1.0,
i... | 64_4_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#inpainting | .md | ).images[0]
make_image_grid([init_image, mask_image, output], rows=1, cols=3)
```
<div class="flex justify-center">
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/controlnet-inpaint-result.png"/>
</div> | 64_4_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | [Guess mode](https://github.com/lllyasviel/ControlNet/discussions/188) does not require supplying a prompt to a ControlNet at all! This forces the ControlNet encoder to do its best to "guess" the contents of the input control map (depth map, pose estimation, canny edge, etc.). | 64_5_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | Guess mode adjusts the scale of the output residuals from a ControlNet by a fixed ratio depending on the block depth. The shallowest `DownBlock` corresponds to 0.1, and as the blocks get deeper, the scale increases exponentially such that the scale of the `MidBlock` output becomes 1.0.
<Tip>
Guess mode does not hav... | 64_5_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | <Tip>
Guess mode does not have any impact on prompt conditioning and you can still provide a prompt if you want.
</Tip>
Set `guess_mode=True` in the pipeline, and it is [recommended](https://github.com/lllyasviel/ControlNet#guess-mode--non-prompt-mode) to set the `guidance_scale` value between 3.0 and 5.0.
```p... | 64_5_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | from diffusers.utils import load_image, make_image_grid
import numpy as np
import torch
from PIL import Image
import cv2 | 64_5_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", use_safetensors=True)
pipe = StableDiffusionControlNetPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, use_safetensors=True).to("cuda")
original_image = load_image("https://huggingface.co/takuma... | 64_5_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | 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) | 64_5_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | image = pipe("", image=canny_image, guess_mode=True, guidance_scale=3.0).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/dif... | 64_5_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#guess-mode | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">regular mode with prompt</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/takuma104/controlnet_dev/resolve/main/gen_compare_guess_mode/output_images/diffusers/output_bird_canny_0_gm.png"/>
<figcaption class="mt-2 text-center text-sm... | 64_5_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | There aren't too many ControlNet models compatible with Stable Diffusion XL (SDXL) at the moment, but we've trained two full-sized ControlNet models for SDXL conditioned on canny edge detection and depth maps. We're also experimenting with creating smaller versions of these SDXL-compatible ControlNet models so it is ea... | 64_6_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | Let's use a SDXL ControlNet conditioned on canny images to generate an image. Start by loading an image and prepare the canny image:
```py
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import cv2
... | 64_6_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | original_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
)
image = np.array(original_image)
low_threshold = 100
high_threshold = 200 | 64_6_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | image = cv2.Canny(image, low_threshold, high_threshold)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
make_image_grid([original_image, canny_image], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingfac... | 64_6_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/hf-logo-canny.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption... | 64_6_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">canny image</figcaption>
</div>
</div>
Load a SDXL ControlNet model conditioned on canny edge detection and pass it to the [`StableDiffusionXLControlNetPipeline`]. You can also enable model offloading to reduce memory usage.
```py
controlnet = ControlNetMod... | 64_6_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | "diffusers/controlnet-canny-sdxl-1.0",
torch_dtype=torch.float16,
use_safetensors=True
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
controlne... | 64_6_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | controlnet=controlnet,
vae=vae,
torch_dtype=torch.float16,
use_safetensors=True
)
pipe.enable_model_cpu_offload()
```
Now pass your prompt (and optionally a negative prompt if you're using one) and canny image to the pipeline:
<Tip> | 64_6_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | ```
Now pass your prompt (and optionally a negative prompt if you're using one) and canny image to the pipeline:
<Tip>
The [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.controlnet_conditioning_scale) pa... | 64_6_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | </Tip>
```py
prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = 'low quality, bad quality, sketches' | 64_6_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | image = pipe(
prompt,
negative_prompt=negative_prompt,
image=canny_image,
controlnet_conditioning_scale=0.5,
).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/diffusers/controlnet-canny-sdxl-1.0/r... | 64_6_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | </div>
You can use [`StableDiffusionXLControlNetPipeline`] in guess mode as well by setting the parameter to `True`:
```py
from diffusers import StableDiffusionXLControlNetPipeline, ControlNetModel, AutoencoderKL
from diffusers.utils import load_image, make_image_grid
import numpy as np
import torch
import cv2
from... | 64_6_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | prompt = "aerial view, a futuristic research complex in a bright foggy jungle, hard lighting"
negative_prompt = "low quality, bad quality, sketches"
original_image = load_image(
"https://hf.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/hf-logo.png"
) | 64_6_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | controlnet = ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
)
vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabil... | 64_6_13 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | image = np.array(original_image)
image = cv2.Canny(image, 100, 200)
image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image) | 64_6_14 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | image = pipe(
prompt, negative_prompt=negative_prompt, controlnet_conditioning_scale=0.5, image=canny_image, guess_mode=True,
).images[0]
make_image_grid([original_image, canny_image, image], rows=1, cols=3)
```
<Tip>
You can use a refiner model with `StableDiffusionXLControlNetPipeline` to improve image quality, j... | 64_6_15 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#controlnet-with-stable-diffusion-xl | .md | See the [Refine image quality](./sdxl#refine-image-quality) section to learn how to use the refiner model.
Make sure to use `StableDiffusionXLControlNetPipeline` and pass `image` and `controlnet_conditioning_scale`.
```py
base = StableDiffusionXLControlNetPipeline(...)
image = base(
prompt=prompt,
controlnet_conditio... | 64_6_16 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | <Tip>
Replace the SDXL model with a model like [stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) to use multiple conditioning inputs with Stable Diffusion models.
</Tip>
You can compose multiple ControlNet conditionings from different image inputs to... | 64_7_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | 1. mask conditionings such that they don't overlap (for example, mask the area of a canny image where the pose conditioning is located)
2. experiment with the [`controlnet_conditioning_scale`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline.__call__.con... | 64_7_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | In this example, you'll combine a canny image and a human pose estimation image to generate a new image.
Prepare the canny image conditioning:
```py
from diffusers.utils import load_image, make_image_grid
from PIL import Image
import numpy as np
import cv2 | 64_7_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | original_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/landscape.png"
)
image = np.array(original_image)
low_threshold = 100
high_threshold = 200
image = cv2.Canny(image, low_threshold, high_threshold)
# zero out middle columns of image where pose will b... | 64_7_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | image = image[:, :, None]
image = np.concatenate([image, image, image], axis=2)
canny_image = Image.fromarray(image)
make_image_grid([original_image, canny_image], rows=1, cols=2)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/m... | 64_7_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/landscape_canny_masked.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">canny im... | 64_7_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | </div>
For human pose estimation, install [controlnet_aux](https://github.com/patrickvonplaten/controlnet_aux):
```py
# uncomment to install the necessary library in Colab
#!pip install -q controlnet-aux
```
Prepare the human pose estimation conditioning:
```py
from controlnet_aux import OpenposeDetector | 64_7_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | openpose = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
original_image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/person.png"
)
openpose_image = openpose(original_image)
make_image_grid([original_image, openpose_image], rows=1, cols=2)
```
<div ... | 64_7_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/controlnet/person_pose.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">human pose image</f... | 64_7_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">human pose image</figcaption>
</div>
</div>
Load a list of ControlNet models that correspond to each conditioning, and pass them to the [`StableDiffusionXLControlNetPipeline`]. Use the faster [`UniPCMultistepScheduler`] and enable model offloading to reduce m... | 64_7_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | controlnets = [
ControlNetModel.from_pretrained(
"thibaud/controlnet-openpose-sdxl-1.0", torch_dtype=torch.float16
),
ControlNetModel.from_pretrained(
"diffusers/controlnet-canny-sdxl-1.0", torch_dtype=torch.float16, use_safetensors=True
),
] | 64_7_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16, use_safetensors=True)
pipe = StableDiffusionXLControlNetPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0", controlnet=controlnets, vae=vae, torch_dtype=torch.float16, use_safetensors=True
)
pipe.scheduler ... | 64_7_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | )
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
```
Now you can pass your prompt (an optional negative prompt if you're using one), canny image, and pose image to the pipeline:
```py
prompt = "a giant standing in a fantasy landscape, best quality"
negati... | 64_7_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | generator = torch.manual_seed(1)
images = [openpose_image.resize((1024, 1024)), canny_image.resize((1024, 1024))] | 64_7_13 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | images = pipe(
prompt,
image=images,
num_inference_steps=25,
generator=generator,
negative_prompt=negative_prompt,
num_images_per_prompt=3,
controlnet_conditioning_scale=[1.0, 0.8],
).images
make_image_grid([original_image, canny_image, openpose_image,
images[0].resize((512, 512)), images[1].resize((512, 512)), images[... | 64_7_14 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/controlnet.md | https://huggingface.co/docs/diffusers/en/using-diffusers/controlnet/#multicontrolnet | .md | ```
<div class="flex justify-center">
<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/multicontrolnet.png"/>
</div> | 64_7_15 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/ | .md | <!--Copyright 2024 The HuggingFace Team. All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
the License. You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agr... | 65_0_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/ | .md | an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
--> | 65_0_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | [[open-in-colab]]
Image editing typically requires providing a mask of the area to be edited. DiffEdit automatically generates the mask for you based on a text query, making it easier overall to create a mask without image editing software. The DiffEdit algorithm works in three steps: | 65_1_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | 1. the diffusion model denoises an image conditioned on some query text and reference text which produces different noise estimates for different areas of the image; the difference is used to infer a mask to identify which area of the image needs to be changed to match the query text
2. the input image is encoded into ... | 65_1_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | 2. the input image is encoded into latent space with DDIM
3. the latents are decoded with the diffusion model conditioned on the text query, using the mask as a guide such that pixels outside the mask remain the same as in the input image
This guide will show you how to use DiffEdit to edit images without manually cr... | 65_1_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | ```py
# uncomment to install the necessary libraries in Colab
#!pip install -q diffusers transformers accelerate
``` | 65_1_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | #!pip install -q diffusers transformers accelerate
```
The [`StableDiffusionDiffEditPipeline`] requires an image mask and a set of partially inverted latents. The image mask is generated from the [`~StableDiffusionDiffEditPipeline.generate_mask`] function, and includes two parameters, `source_prompt` and `target_prom... | 65_1_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | ```py
source_prompt = "a bowl of fruits"
target_prompt = "a bowl of pears"
```
The partially inverted latents are generated from the [`~StableDiffusionDiffEditPipeline.invert`] function, and it is generally a good idea to include a `prompt` or *caption* describing the image to help guide the inverse latent sampling p... | 65_1_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | Let's load the pipeline, scheduler, inverse scheduler, and enable some optimizations to reduce memory usage:
```py
import torch
from diffusers import DDIMScheduler, DDIMInverseScheduler, StableDiffusionDiffEditPipeline | 65_1_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1",
torch_dtype=torch.float16,
safety_checker=None,
use_safetensors=True,
)
pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.schedul... | 65_1_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
raw_image
```
Use the [`~StableDiffusionDiffEditPipeline.generate_mask`] function to generate the image mask. You'll need to pass it the `source_prompt` and `target_prompt` t... | 65_1_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | source_prompt = "a bowl of fruits"
target_prompt = "a basket of pears"
mask_image = pipeline.generate_mask(
image=raw_image,
source_prompt=source_prompt,
target_prompt=target_prompt,
)
Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))
```
Next, create the inverted latents and pass it... | 65_1_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | ```py
inv_latents = pipeline.invert(prompt=source_prompt, image=raw_image).latents
```
Finally, pass the image mask and inverted latents to the pipeline. The `target_prompt` becomes the `prompt` now, and the `source_prompt` is used as the `negative_prompt`:
```py
output_image = pipeline(
prompt=target_prompt,
mask_... | 65_1_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | ).images[0]
mask_image = Image.fromarray((mask_image.squeeze()*255).astype("uint8"), "L").resize((768, 768))
make_image_grid([raw_image, mask_image, output_image], rows=1, cols=3)
```
<div class="flex gap-4">
<div>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/orig... | 65_1_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#diffedit | .md | <figcaption class="mt-2 text-center text-sm text-gray-500">original image</figcaption>
</div>
<div>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stable-diffusion/blob/main/assets/target.png?raw=true"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">edited image</figcaption>
</div>
</div... | 65_1_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | The source and target embeddings can be automatically generated with the [Flan-T5](https://huggingface.co/docs/transformers/model_doc/flan-t5) model instead of creating them manually.
Load the Flan-T5 model and tokenizer from the 🤗 Transformers library:
```py
import torch
from transformers import AutoTokenizer, T5... | 65_2_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-large")
model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-large", device_map="auto", torch_dtype=torch.float16)
```
Provide some initial text to prompt the model to generate the source and target prompts.
```py
source_concept = "bowl"
target... | 65_2_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | target_text = f"Provide a caption for images containing a {target_concept}. "
"The captions should be in English and should be no longer than 150 characters."
```
Next, create a utility function to generate the prompts:
```py
@torch.no_grad()
def generate_prompts(input_prompt):
input_ids = tokenizer(input_prompt, r... | 65_2_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | outputs = model.generate(
input_ids, temperature=0.8, num_return_sequences=16, do_sample=True, max_new_tokens=128, top_k=10
)
return tokenizer.batch_decode(outputs, skip_special_tokens=True) | 65_2_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | source_prompts = generate_prompts(source_text)
target_prompts = generate_prompts(target_text)
print(source_prompts)
print(target_prompts)
```
<Tip>
Check out the [generation strategy](https://huggingface.co/docs/transformers/main/en/generation_strategies) guide if you're interested in learning more about strategies... | 65_2_4 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | </Tip>
Load the text encoder model used by the [`StableDiffusionDiffEditPipeline`] to encode the text. You'll use the text encoder to compute the text embeddings:
```py
import torch
from diffusers import StableDiffusionDiffEditPipeline | 65_2_5 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | pipeline = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16, use_safetensors=True
)
pipeline.enable_model_cpu_offload()
pipeline.enable_vae_slicing() | 65_2_6 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | @torch.no_grad()
def embed_prompts(sentences, tokenizer, text_encoder, device="cuda"):
embeddings = []
for sent in sentences:
text_inputs = tokenizer(
sent,
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
return_tensors="pt",
)
text_input_ids = text_inputs.input_ids
prompt_embeds = text_en... | 65_2_7 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | source_embeds = embed_prompts(source_prompts, pipeline.tokenizer, pipeline.text_encoder)
target_embeds = embed_prompts(target_prompts, pipeline.tokenizer, pipeline.text_encoder)
```
Finally, pass the embeddings to the [`~StableDiffusionDiffEditPipeline.generate_mask`] and [`~StableDiffusionDiffEditPipeline.invert`] f... | 65_2_8 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | ```diff
from diffusers import DDIMInverseScheduler, DDIMScheduler
from diffusers.utils import load_image, make_image_grid
from PIL import Image | 65_2_9 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config)
pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config)
img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768)) | 65_2_10 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | mask_image = pipeline.generate_mask(
image=raw_image,
- source_prompt=source_prompt,
- target_prompt=target_prompt,
+ source_prompt_embeds=source_embeds,
+ target_prompt_embeds=target_embeds,
)
inv_latents = pipeline.invert(
- prompt=source_prompt,
+ prompt_embeds=source_embeds,
image=raw_image... | 65_2_11 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-source-and-target-embeddings | .md | inv_latents = pipeline.invert(
- prompt=source_prompt,
+ prompt_embeds=source_embeds,
image=raw_image,
).latents
output_image = pipeline(
mask_image=mask_image,
image_latents=inv_latents,
- prompt=target_prompt,
- negative_prompt=source_prompt,
+ prompt_embeds=target_embeds,
+ negative_prompt_e... | 65_2_12 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | .md | While you can use the `source_prompt` as a caption to help generate the partially inverted latents, you can also use the [BLIP](https://huggingface.co/docs/transformers/model_doc/blip) model to automatically generate a caption.
Load the BLIP model and processor from the 🤗 Transformers library:
```py
import torch
f... | 65_3_0 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | .md | processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base", torch_dtype=torch.float16, low_cpu_mem_usage=True)
```
Create a utility function to generate a caption from the input image:
```py
@torch.n... | 65_3_1 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | .md | inputs = caption_processor(images, text, return_tensors="pt").to(device="cuda", dtype=caption_generator.dtype)
caption_generator.to("cuda")
outputs = caption_generator.generate(**inputs, max_new_tokens=128)
# offload caption generator
caption_generator.to("cpu")
caption = caption_processor.batch_decode(outputs, skip_... | 65_3_2 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | .md | img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png"
raw_image = load_image(img_url).resize((768, 768))
caption = generate_caption(raw_image, model, processor)
```
<div class="flex justify-center">
<figure>
<img class="rounded-xl" src="https://github.com/Xiang-cd/DiffEdit-stabl... | 65_3_3 |
/Users/nielsrogge/Documents/python_projecten/diffusers/docs/source/en/using-diffusers/diffedit.md | https://huggingface.co/docs/diffusers/en/using-diffusers/diffedit/#generate-a-caption-for-inversion | .md | <figcaption class="text-center">generated caption: "a photograph of a bowl of fruit on a table"</figcaption>
</figure>
</div>
Now you can drop the caption into the [`~StableDiffusionDiffEditPipeline.invert`] function to generate the partially inverted latents! | 65_3_4 |
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