diff --git a/docs/source/en/using-diffusers/loading_adapters.md b/docs/source/en/using-diffusers/loading_adapters.md index c14b38a9dd89..d9d4a675dd37 100644 --- a/docs/source/en/using-diffusers/loading_adapters.md +++ b/docs/source/en/using-diffusers/loading_adapters.md @@ -485,6 +485,69 @@ image.save("sdxl_t2i.png") +You can use the IP-Adapter face model to apply specific faces to your images. It is an effective way to maintain consistent characters in your image generations. +Weights are loaded with the same method used for the other IP-Adapters. + +```python +# Load ip-adapter-full-face_sd15.bin +pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin") +``` + + + +It is recommended to use `DDIMScheduler` and `EulerDiscreteScheduler` for face model. + + + + +```python +import torch +from diffusers import StableDiffusionPipeline, DDIMScheduler +from diffusers.utils import load_image + +noise_scheduler = DDIMScheduler( + num_train_timesteps=1000, + beta_start=0.00085, + beta_end=0.012, + beta_schedule="scaled_linear", + clip_sample=False, + set_alpha_to_one=False, + steps_offset=1 +) + +pipeline = StableDiffusionPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", + torch_dtype=torch.float16, + scheduler=noise_scheduler, +).to("cuda") + +pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin") + +pipeline.set_ip_adapter_scale(0.7) + +image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ai_face2.png") + +generator = torch.Generator(device="cpu").manual_seed(33) + +image = pipeline( + prompt="A photo of a girl wearing a black dress, holding red roses in hand, upper body, behind is the Eiffel Tower", + ip_adapter_image=image, + negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality", + num_inference_steps=50, num_images_per_prompt=1, width=512, height=704, + generator=generator, +).images[0] +``` + +
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+ +
input image
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output image
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### LCM-Lora diff --git a/src/diffusers/loaders/unet.py b/src/diffusers/loaders/unet.py index b155f595e740..7309c3fc709c 100644 --- a/src/diffusers/loaders/unet.py +++ b/src/diffusers/loaders/unet.py @@ -22,7 +22,7 @@ from huggingface_hub.utils import validate_hf_hub_args from torch import nn -from ..models.embeddings import ImageProjection, Resampler +from ..models.embeddings import ImageProjection, MLPProjection, Resampler from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta from ..utils import ( USE_PEFT_BACKEND, @@ -675,6 +675,9 @@ def _load_ip_adapter_weights(self, state_dict): if "proj.weight" in state_dict["image_proj"]: # IP-Adapter num_image_text_embeds = 4 + elif "proj.3.weight" in state_dict["image_proj"]: + # IP-Adapter Full Face + num_image_text_embeds = 257 # 256 CLIP tokens + 1 CLS token else: # IP-Adapter Plus num_image_text_embeds = state_dict["image_proj"]["latents"].shape[1] @@ -744,8 +747,32 @@ def _load_ip_adapter_weights(self, state_dict): "norm.bias": state_dict["image_proj"]["norm.bias"], } ) + image_projection.load_state_dict(image_proj_state_dict) + del image_proj_state_dict + elif "proj.3.weight" in state_dict["image_proj"]: + clip_embeddings_dim = state_dict["image_proj"]["proj.0.weight"].shape[0] + cross_attention_dim = state_dict["image_proj"]["proj.3.weight"].shape[0] + + image_projection = MLPProjection( + cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim + ) + image_projection.to(dtype=self.dtype, device=self.device) + + # load image projection layer weights + image_proj_state_dict = {} + image_proj_state_dict.update( + { + "ff.net.0.proj.weight": state_dict["image_proj"]["proj.0.weight"], + "ff.net.0.proj.bias": state_dict["image_proj"]["proj.0.bias"], + "ff.net.2.weight": state_dict["image_proj"]["proj.2.weight"], + "ff.net.2.bias": state_dict["image_proj"]["proj.2.bias"], + "norm.weight": state_dict["image_proj"]["proj.3.weight"], + "norm.bias": state_dict["image_proj"]["proj.3.bias"], + } + ) image_projection.load_state_dict(image_proj_state_dict) + del image_proj_state_dict else: # IP-Adapter Plus diff --git a/src/diffusers/models/embeddings.py b/src/diffusers/models/embeddings.py index bdd2930d20f9..73abc9869230 100644 --- a/src/diffusers/models/embeddings.py +++ b/src/diffusers/models/embeddings.py @@ -461,6 +461,18 @@ def forward(self, image_embeds: torch.FloatTensor): return image_embeds +class MLPProjection(nn.Module): + def __init__(self, image_embed_dim=1024, cross_attention_dim=1024): + super().__init__() + from .attention import FeedForward + + self.ff = FeedForward(image_embed_dim, cross_attention_dim, mult=1, activation_fn="gelu") + self.norm = nn.LayerNorm(cross_attention_dim) + + def forward(self, image_embeds: torch.FloatTensor): + return self.norm(self.ff(image_embeds)) + + class CombinedTimestepLabelEmbeddings(nn.Module): def __init__(self, num_classes, embedding_dim, class_dropout_prob=0.1): super().__init__() diff --git a/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py b/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py index 7c6349ce2600..ff93ecaf003b 100644 --- a/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py +++ b/tests/pipelines/ip_adapters/test_ip_adapter_stable_diffusion.py @@ -182,6 +182,25 @@ def test_inpainting(self): assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4) + def test_text_to_image_full_face(self): + image_encoder = self.get_image_encoder(repo_id="h94/IP-Adapter", subfolder="models/image_encoder") + pipeline = StableDiffusionPipeline.from_pretrained( + "runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, safety_checker=None, torch_dtype=self.dtype + ) + pipeline.to(torch_device) + pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter-full-face_sd15.bin") + pipeline.set_ip_adapter_scale(0.7) + + inputs = self.get_dummy_inputs() + images = pipeline(**inputs).images + image_slice = images[0, :3, :3, -1].flatten() + + expected_slice = np.array( + [0.1706543, 0.1303711, 0.12573242, 0.21777344, 0.14550781, 0.14038086, 0.40820312, 0.41455078, 0.42529297] + ) + + assert np.allclose(image_slice, expected_slice, atol=1e-4, rtol=1e-4) + @slow @require_torch_gpu