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[Docs] Update image masking and face id example (#7780)

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* [Docs] Update image masking and face id example

* Update docs

* Fix docs
This commit is contained in:
Fabio Rigano
2024-04-27 00:51:11 +02:00
committed by GitHub
parent 0d2d424fbe
commit 8e4ca1b6b2
1 changed files with 26 additions and 8 deletions
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docs/source/en/using-diffusers/ip_adapter.md
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@@ -277,7 +277,7 @@ images = pipeline(
### IP-Adapter masking
Binary masks specify which portion of the output image should be assigned to an IP-Adapter. This is useful for composing more than one IP-Adapter image. For each input IP-Adapter image, you must provide a binary mask an an IP-Adapter.
Binary masks specify which portion of the output image should be assigned to an IP-Adapter. This is useful for composing more than one IP-Adapter image. For each input IP-Adapter image, you must provide a binary mask.
To start, preprocess the input IP-Adapter images with the [`~image_processor.IPAdapterMaskProcessor.preprocess()`] to generate their masks. For optimal results, provide the output height and width to [`~image_processor.IPAdapterMaskProcessor.preprocess()`]. This ensures masks with different aspect ratios are appropriately stretched. If the input masks already match the aspect ratio of the generated image, you don't have to set the `height` and `width`.
@@ -305,13 +305,18 @@ masks = processor.preprocess([mask1, mask2], height=output_height, width=output_
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When there is more than one input IP-Adapter image, load them as a list to ensure each image is assigned to a different IP-Adapter. Each of the input IP-Adapter images here correspond to the masks generated above.
When there is more than one input IP-Adapter image, load them as a list and provide the IP-Adapter scale list. Each of the input IP-Adapter images here corresponds to one of the masks generated above.
```py
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors"])
pipeline.set_ip_adapter_scale([[0.7, 0.7]]) # one scale for each image-mask pair
face_image1 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl1.png")
face_image2 = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/ip_mask_girl2.png")
ip_images = [[face_image1], [face_image2]]
ip_images = [[face_image1, face_image2]]
masks = [masks.reshape(1, masks.shape[0], masks.shape[2], masks.shape[3])]
```
<div class="flex flex-row gap-4">
@@ -328,8 +333,6 @@ ip_images = [[face_image1], [face_image2]]
Now pass the preprocessed masks to `cross_attention_kwargs` in the pipeline call.
```py
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name=["ip-adapter-plus-face_sdxl_vit-h.safetensors"] * 2)
pipeline.set_ip_adapter_scale([0.7] * 2)
generator = torch.Generator(device="cpu").manual_seed(0)
num_images = 1
@@ -436,7 +439,7 @@ image = torch.from_numpy(faces[0].normed_embedding)
ref_images_embeds.append(image.unsqueeze(0))
ref_images_embeds = torch.stack(ref_images_embeds, dim=0).unsqueeze(0)
neg_ref_images_embeds = torch.zeros_like(ref_images_embeds)
id_embeds = torch.cat([neg_ref_images_embeds, ref_images_embeds]).to(dtype=torch.float16, device="cuda"))
id_embeds = torch.cat([neg_ref_images_embeds, ref_images_embeds]).to(dtype=torch.float16, device="cuda")
generator = torch.Generator(device="cpu").manual_seed(42)
@@ -452,13 +455,28 @@ images = pipeline(
Both IP-Adapter FaceID Plus and Plus v2 models require CLIP image embeddings. You can prepare face embeddings as shown previously, then you can extract and pass CLIP embeddings to the hidden image projection layers.
```py
clip_embeds = pipeline.prepare_ip_adapter_image_embeds([ip_adapter_images], None, torch.device("cuda"), num_images, True)[0]
from insightface.utils import face_align
ref_images_embeds = []
ip_adapter_images = []
app = FaceAnalysis(name="buffalo_l", providers=['CUDAExecutionProvider', 'CPUExecutionProvider'])
app.prepare(ctx_id=0, det_size=(640, 640))
image = cv2.cvtColor(np.asarray(image), cv2.COLOR_BGR2RGB)
faces = app.get(image)
ip_adapter_images.append(face_align.norm_crop(image, landmark=faces[0].kps, image_size=224))
image = torch.from_numpy(faces[0].normed_embedding)
ref_images_embeds.append(image.unsqueeze(0))
ref_images_embeds = torch.stack(ref_images_embeds, dim=0).unsqueeze(0)
neg_ref_images_embeds = torch.zeros_like(ref_images_embeds)
id_embeds = torch.cat([neg_ref_images_embeds, ref_images_embeds]).to(dtype=torch.float16, device="cuda")
clip_embeds = pipeline.prepare_ip_adapter_image_embeds(
[ip_adapter_images], None, torch.device("cuda"), num_images, True)[0]
pipeline.unet.encoder_hid_proj.image_projection_layers[0].clip_embeds = clip_embeds.to(dtype=torch.float16)
pipeline.unet.encoder_hid_proj.image_projection_layers[0].shortcut = False # True if Plus v2
```
### Multi IP-Adapter
More than one IP-Adapter can be used at the same time to generate specific images in more diverse styles. For example, you can use IP-Adapter-Face to generate consistent faces and characters, and IP-Adapter Plus to generate those faces in a specific style.