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[feat] IP Adapters (author @okotaku ) (#5713) 



* add ip-adapter


---------
Co-authored-by: default avatarokotaku <to78314910@gmail.com>
Co-authored-by: default avatarsayakpaul <spsayakpaul@gmail.com>
Co-authored-by: default avataryiyixuxu <yixu310@gmail,com>
Co-authored-by: default avatarPatrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: default avatarSteven Liu <59462357+stevhliu@users.noreply.github.com>
parent 6fac1369
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docs/source/en/using-diffusers/loading_adapters.md
View file @ ba352aea
...@@ -307,3 +307,331 @@ prompt = "a house by william eggleston, sunrays, beautiful, sunlight, sunrays, b ...@@ -307,3 +307,331 @@ prompt = "a house by william eggleston, sunrays, beautiful, sunlight, sunrays, b
image = pipeline(prompt=prompt).images[0] image = pipeline(prompt=prompt).images[0]
image image
``` ```
## IP-Adapter
[IP-Adapter](https://ip-adapter.github.io/) is an effective and lightweight adapter that adds image prompting capabilities to a diffusion model. This adapter works by decoupling the cross-attention layers of the image and text features. All the other model components are frozen and only the embedded image features in the UNet are trained. As a result, IP-Adapter files are typically only ~100MBs.
IP-Adapter works with most of our pipelines, including Stable Diffusion, Stable Diffusion XL (SDXL), ControlNet, T2I-Adapter, AnimateDiff. And you can use any custom models finetuned from the same base models. It also works with LCM-Lora out of box.
<Tip>
You can find official IP-Adapter checkpoints in [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter).
IP-Adapter was contributed by [okotaku](https://github.com/okotaku).
</Tip>
Let's first create a Stable Diffusion Pipeline.
```py
from diffusers import AutoPipelineForText2Image
import torch
from diffusers.utils import load_image
pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
```
Now load the [h94/IP-Adapter](https://huggingface.co/h94/IP-Adapter) weights with the [`~loaders.IPAdapterMixin.load_ip_adapter`] method.
```py
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
```
<Tip>
IP-Adapter relies on an image encoder to generate the image features, if your IP-Adapter weights folder contains a "image_encoder" subfolder, the image encoder will be automatically loaded and registered to the pipeline. Otherwise you can so load a [`~transformers.CLIPVisionModelWithProjection`] model and pass it to a Stable Diffusion pipeline when you create it.
```py
from diffusers import AutoPipelineForText2Image, CLIPVisionModelWithProjection
import torch
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
"h94/IP-Adapter",
subfolder="models/image_encoder",
torch_dtype=torch.float16,
).to("cuda")
pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5", image_encoder=image_encoder, torch_dtype=torch.float16).to("cuda")
```
</Tip>
IP-Adapter allows you to use both image and text to condition the image generation process. For example, let's use the bear image from the [Textual Inversion](#textual-inversion) section as the image prompt (`ip_adapter_image`) along with a text prompt to add "sunglasses". 😎
```py
pipeline.set_ip_adapter_scale(0.6)
image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/load_neg_embed.png")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
    prompt='best quality, high quality, wearing sunglasses',
    ip_adapter_image=image,
    negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
    num_inference_steps=50,
    generator=generator,
).images
images[0]
```
<div class="flex justify-center">
    <img src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ip-bear.png" />
</div>
<Tip>
You can use the [`~loaders.IPAdapterMixin.set_ip_adapter_scale`] method to adjust the text prompt and image prompt condition ratio.  If you're only using the image prompt, you should set the scale to `1.0`. You can lower the scale to get more generation diversity, but it'll be less aligned with the prompt.
`scale=0.5` can achieve good results in most cases when you use both text and image prompts.
</Tip>
IP-Adapter also works great with Image-to-Image and Inpainting pipelines. See below examples of how you can use it with Image-to-Image and Inpaint.
<hfoptions id="tasks">
<hfoption id="image-to-image">
```py
from diffusers import AutoPipelineForImage2Image
import torch
from diffusers.utils import load_image
pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/vermeer.jpg")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/river.png")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
    prompt='best quality, high quality',
    image = image,
    ip_adapter_image=ip_image,
    num_inference_steps=50,
    generator=generator,
    strength=0.6,
).images
images[0]
```
</hfoption>
<hfoption id="inpaint">
```py
from diffusers import AutoPipelineForInpaint
import torch
from diffusers.utils import load_image
pipeline = AutoPipelineForInpaint.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float).to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/inpaint_image.png")
mask = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/mask.png")
ip_image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/girl.png")
image = image.resize((512, 768))
mask = mask.resize((512, 768))
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
prompt='best quality, high quality',
image = image,
mask_image = mask,
ip_adapter_image=ip_image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
strength=0.5,
).images
images[0]
```
</hfoption>
</hfoptions>
IP-Adapters can also be used with [SDXL](../api/pipelines/stable_diffusion/stable_diffusion_xl.md)
```python
from diffusers import AutoPipelineForText2Image
from diffusers.utils import load_image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
torch_dtype=torch.float16
).to("cuda")
image = load_image("https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
image = pipeline(
prompt="best quality, high quality",
ip_adapter_image=image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=25,
generator=generator,
).images[0]
image.save("sdxl_t2i.png")
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/watercolor_painting.jpeg"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/sdxl_t2i.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
</div>
</div>
### LCM-Lora
You can use IP-Adapter with LCM-Lora to achieve "instant fine-tune" with custom images. Note that you need to load IP-Adapter weights before loading the LCM-Lora weights.
```py
from diffusers import DiffusionPipeline, LCMScheduler
import torch
from diffusers.utils import load_image
model_id = "sd-dreambooth-library/herge-style"
lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
pipe.load_lora_weights(lcm_lora_id)
pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config)
pipe.enable_model_cpu_offload()
prompt = "best quality, high quality"
image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
images = pipe(
prompt=prompt,
ip_adapter_image=image,
num_inference_steps=4,
guidance_scale=1,
).images[0]
```
### Other pipelines
IP-Adapter is compatible with any pipeline that (1) uses a text prompt and (2) uses Stable Diffusion or Stable Diffusion XL checkpoint. To use IP-Adapter with a different pipeline, all you need to do is to run `load_ip_adapter()` method after you create the pipeline, and then pass your image to the pipeline as `ip_adapter_image`
<Tip>
🤗 Diffusers currently only supports using IP-Adapter with some of the most popular pipelines, feel free to open a [feature request](https://github.com/huggingface/diffusers/issues/new/choose) if you have a cool use-case and require integrating IP-adapters with a pipeline that does not support it yet!
</Tip>
You can find below examples on how to use IP-Adapter with ControlNet and AnimateDiff.
<hfoptions id="model">
<hfoption id="ControlNet">
```
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
import torch
from diffusers.utils import load_image
controlnet_model_path = "lllyasviel/control_v11f1p_sd15_depth"
controlnet = ControlNetModel.from_pretrained(controlnet_model_path, torch_dtype=torch.float16)
pipeline = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16)
pipeline.to("cuda")
image = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png")
depth_map = load_image("https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/depth.png")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
generator = torch.Generator(device="cpu").manual_seed(33)
images = pipeline(
prompt='best quality, high quality',
image=depth_map,
ip_adapter_image=image,
negative_prompt="monochrome, lowres, bad anatomy, worst quality, low quality",
num_inference_steps=50,
generator=generator,
).images
images[0]
```
<div class="flex flex-row gap-4">
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/statue.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">input image</figcaption>
</div>
<div class="flex-1">
<img class="rounded-xl" src="https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/ipa-controlnet-out.png"/>
<figcaption class="mt-2 text-center text-sm text-gray-500">adapted image</figcaption>
</div>
</div>
</hfoption>
<hfoption id="AnimateDiff">
```py
# animate diff + ip adapter
import torch
from diffusers import MotionAdapter, AnimateDiffPipeline, DDIMScheduler
from diffusers.utils import export_to_gif, load_image
# Load the motion adapter
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
# load SD 1.5 based finetuned model
model_id = "Lykon/DreamShaper"
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
# scheduler
scheduler = DDIMScheduler(
clip_sample=False,
beta_start=0.00085,
beta_end=0.012,
beta_schedule="linear",
timestep_spacing="trailing",
steps_offset=1
)
pipe.scheduler = scheduler
# enable memory savings
pipe.enable_vae_slicing()
pipe.enable_model_cpu_offload()
# load ip_adapter
pipe.load_ip_adapter("h94/IP-Adapter", subfolder="models", weight_name="ip-adapter_sd15.bin")
# load motion adapters
pipe.load_lora_weights("guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-tilt-up", adapter_name="tilt-up")
pipe.load_lora_weights("guoyww/animatediff-motion-lora-pan-left", adapter_name="pan-left")
seed = 42
image = load_image("https://user-images.githubusercontent.com/24734142/266492875-2d50d223-8475-44f0-a7c6-08b51cb53572.png")
images = [image] * 3
prompts = ["best quality, high quality"] * 3
negative_prompt = "bad quality, worst quality"
adapter_weights = [[0.75, 0.0, 0.0], [0.0, 0.0, 0.75], [0.0, 0.75, 0.75]]
# generate
output_frames = []
for prompt, image, adapter_weight in zip(prompts, images, adapter_weights):
pipe.set_adapters(["zoom-out", "tilt-up", "pan-left"], adapter_weights=adapter_weight)
output = pipe(
prompt= prompt,
num_frames=16,
guidance_scale=7.5,
num_inference_steps=30,
ip_adapter_image = image,
generator=torch.Generator("cpu").manual_seed(seed),
)
frames = output.frames[0]
output_frames.extend(frames)
export_to_gif(output_frames, "test_out_animation.gif")
```
</hfoption>
</hfoptions>
src/diffusers/loaders/__init__.py
View file @ ba352aea
...@@ -62,6 +62,7 @@ if is_torch_available(): ...@@ -62,6 +62,7 @@ if is_torch_available():
_import_structure["single_file"].extend(["FromSingleFileMixin"]) _import_structure["single_file"].extend(["FromSingleFileMixin"])
_import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin"] _import_structure["lora"] = ["LoraLoaderMixin", "StableDiffusionXLLoraLoaderMixin"]
_import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"] _import_structure["textual_inversion"] = ["TextualInversionLoaderMixin"]
_import_structure["ip_adapter"] = ["IPAdapterMixin"]
if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
...@@ -72,6 +73,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT: ...@@ -72,6 +73,7 @@ if TYPE_CHECKING or DIFFUSERS_SLOW_IMPORT:
from .utils import AttnProcsLayers from .utils import AttnProcsLayers
if is_transformers_available(): if is_transformers_available():
from .ip_adapter import IPAdapterMixin
from .lora import LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin from .lora import LoraLoaderMixin, StableDiffusionXLLoraLoaderMixin
from .single_file import FromSingleFileMixin from .single_file import FromSingleFileMixin
from .textual_inversion import TextualInversionLoaderMixin from .textual_inversion import TextualInversionLoaderMixin
......
src/diffusers/loaders/ip_adapter.py 0 → 100644
View file @ ba352aea
# Copyright 2023 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 agreed to in writing, software
# distributed under the License is distributed on 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.
import os
from typing import Dict, Union
import torch
from safetensors import safe_open
from ..utils import (
DIFFUSERS_CACHE,
HF_HUB_OFFLINE,
_get_model_file,
is_transformers_available,
logging,
)
if is_transformers_available():
from transformers import (
CLIPImageProcessor,
CLIPVisionModelWithProjection,
)
from ..models.attention_processor import (
IPAdapterAttnProcessor,
IPAdapterAttnProcessor2_0,
)
logger = logging.get_logger(__name__)
class IPAdapterMixin:
"""Mixin for handling IP Adapters."""
def load_ip_adapter(
self,
pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],
subfolder: str,
weight_name: str,
**kwargs,
):
"""
Parameters:
pretrained_model_name_or_path_or_dict (`str` or `os.PathLike` or `dict`):
Can be either:
- A string, the *model id* (for example `google/ddpm-celebahq-256`) of a pretrained model hosted on
the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
with [`ModelMixin.save_pretrained`].
- A [torch state
dict](https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict).
cache_dir (`Union[str, os.PathLike]`, *optional*):
Path to a directory where a downloaded pretrained model configuration is cached if the standard cache
is not used.
force_download (`bool`, *optional*, defaults to `False`):
Whether or not to force the (re-)download of the model weights and configuration files, overriding the
cached versions if they exist.
resume_download (`bool`, *optional*, defaults to `False`):
Whether or not to resume downloading the model weights and configuration files. If set to `False`, any
incompletely downloaded files are deleted.
proxies (`Dict[str, str]`, *optional*):
A dictionary of proxy servers to use by protocol or endpoint, for example, `{'http': 'foo.bar:3128',
'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
local_files_only (`bool`, *optional*, defaults to `False`):
Whether to only load local model weights and configuration files or not. If set to `True`, the model
won't be downloaded from the Hub.
use_auth_token (`str` or *bool*, *optional*):
The token to use as HTTP bearer authorization for remote files. If `True`, the token generated from
`diffusers-cli login` (stored in `~/.huggingface`) is used.
revision (`str`, *optional*, defaults to `"main"`):
The specific model version to use. It can be a branch name, a tag name, a commit id, or any identifier
allowed by Git.
subfolder (`str`, *optional*, defaults to `""`):
The subfolder location of a model file within a larger model repository on the Hub or locally.
"""
# Load the main state dict first.
cache_dir = kwargs.pop("cache_dir", DIFFUSERS_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
proxies = kwargs.pop("proxies", None)
local_files_only = kwargs.pop("local_files_only", HF_HUB_OFFLINE)
use_auth_token = kwargs.pop("use_auth_token", None)
revision = kwargs.pop("revision", None)
user_agent = {
"file_type": "attn_procs_weights",
"framework": "pytorch",
}
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
model_file = _get_model_file(
pretrained_model_name_or_path_or_dict,
weights_name=weight_name,
cache_dir=cache_dir,
force_download=force_download,
resume_download=resume_download,
proxies=proxies,
local_files_only=local_files_only,
use_auth_token=use_auth_token,
revision=revision,
subfolder=subfolder,
user_agent=user_agent,
)
if weight_name.endswith(".safetensors"):
state_dict = {"image_proj": {}, "ip_adapter": {}}
with safe_open(model_file, framework="pt", device="cpu") as f:
for key in f.keys():
if key.startswith("image_proj."):
state_dict["image_proj"][key.replace("image_proj.", "")] = f.get_tensor(key)
elif key.startswith("ip_adapter."):
state_dict["ip_adapter"][key.replace("ip_adapter.", "")] = f.get_tensor(key)
else:
state_dict = torch.load(model_file, map_location="cpu")
else:
state_dict = pretrained_model_name_or_path_or_dict
keys = list(state_dict.keys())
if keys != ["image_proj", "ip_adapter"]:
raise ValueError("Required keys are (`image_proj` and `ip_adapter`) missing from the state dict.")
# load CLIP image encoer here if it has not been registered to the pipeline yet
if hasattr(self, "image_encoder") and getattr(self, "image_encoder", None) is None:
if not isinstance(pretrained_model_name_or_path_or_dict, dict):
logger.info(f"loading image_encoder from {pretrained_model_name_or_path_or_dict}")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
pretrained_model_name_or_path_or_dict,
subfolder=os.path.join(subfolder, "image_encoder"),
).to(self.device, dtype=self.dtype)
self.image_encoder = image_encoder
else:
raise ValueError("`image_encoder` cannot be None when using IP Adapters.")
# create feature extractor if it has not been registered to the pipeline yet
if hasattr(self, "feature_extractor") and getattr(self, "feature_extractor", None) is None:
self.feature_extractor = CLIPImageProcessor()
# load ip-adapter into unet
self.unet._load_ip_adapter_weights(state_dict)
def set_ip_adapter_scale(self, scale):
for attn_processor in self.unet.attn_processors.values():
if isinstance(attn_processor, (IPAdapterAttnProcessor, IPAdapterAttnProcessor2_0)):
attn_processor.scale = scale
src/diffusers/loaders/unet.py
View file @ ba352aea
...@@ -18,8 +18,10 @@ from typing import Callable, Dict, List, Optional, Union ...@@ -18,8 +18,10 @@ from typing import Callable, Dict, List, Optional, Union
import safetensors import safetensors
import torch import torch
import torch.nn.functional as F
from torch import nn from torch import nn
from ..models.embeddings import ImageProjection
from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta from ..models.modeling_utils import _LOW_CPU_MEM_USAGE_DEFAULT, load_model_dict_into_meta
from ..utils import ( from ..utils import (
DIFFUSERS_CACHE, DIFFUSERS_CACHE,
...@@ -662,4 +664,72 @@ class UNet2DConditionLoadersMixin: ...@@ -662,4 +664,72 @@ class UNet2DConditionLoadersMixin:
if hasattr(self, "peft_config"): if hasattr(self, "peft_config"):
self.peft_config.pop(adapter_name, None) self.peft_config.pop(adapter_name, None)
def _load_ip_adapter_weights(self, state_dict):
from ..models.attention_processor import (
AttnProcessor,
AttnProcessor2_0,
IPAdapterAttnProcessor,
IPAdapterAttnProcessor2_0,
)
# set ip-adapter cross-attention processors & load state_dict
attn_procs = {}
key_id = 1
for name in self.attn_processors.keys():
cross_attention_dim = None if name.endswith("attn1.processor") else self.config.cross_attention_dim
if name.startswith("mid_block"):
hidden_size = self.config.block_out_channels[-1]
elif name.startswith("up_blocks"):
block_id = int(name[len("up_blocks.")])
hidden_size = list(reversed(self.config.block_out_channels))[block_id]
elif name.startswith("down_blocks"):
block_id = int(name[len("down_blocks.")])
hidden_size = self.config.block_out_channels[block_id]
if cross_attention_dim is None or "motion_modules" in name:
attn_processor_class = (
AttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else AttnProcessor
)
attn_procs[name] = attn_processor_class()
else:
attn_processor_class = (
IPAdapterAttnProcessor2_0 if hasattr(F, "scaled_dot_product_attention") else IPAdapterAttnProcessor
)
attn_procs[name] = attn_processor_class(
hidden_size=hidden_size, cross_attention_dim=cross_attention_dim, scale=1.0
).to(dtype=self.dtype, device=self.device)
value_dict = {}
for k, w in attn_procs[name].state_dict().items():
value_dict.update({f"{k}": state_dict["ip_adapter"][f"{key_id}.{k}"]})
attn_procs[name].load_state_dict(value_dict)
key_id += 2
self.set_attn_processor(attn_procs)
# create image projection layers.
clip_embeddings_dim = state_dict["image_proj"]["proj.weight"].shape[-1]
cross_attention_dim = state_dict["image_proj"]["proj.weight"].shape[0] // 4
image_projection = ImageProjection(
cross_attention_dim=cross_attention_dim, image_embed_dim=clip_embeddings_dim, num_image_text_embeds=4
)
image_projection.to(dtype=self.dtype, device=self.device)
# load image projection layer weights
image_proj_state_dict = {}
image_proj_state_dict.update(
{
"image_embeds.weight": state_dict["image_proj"]["proj.weight"],
"image_embeds.bias": state_dict["image_proj"]["proj.bias"],
"norm.weight": state_dict["image_proj"]["norm.weight"],
"norm.bias": state_dict["image_proj"]["norm.bias"],
}
)
image_projection.load_state_dict(image_proj_state_dict)
self.encoder_hid_proj = image_projection.to(device=self.device, dtype=self.dtype)
self.config.encoder_hid_dim_type = "ip_image_proj"
delete_adapter_layers delete_adapter_layers
src/diffusers/models/attention_processor.py
View file @ ba352aea
...@@ -1975,6 +1975,250 @@ class LoRAAttnAddedKVProcessor(nn.Module): ...@@ -1975,6 +1975,250 @@ class LoRAAttnAddedKVProcessor(nn.Module):
return attn.processor(attn, hidden_states, *args, **kwargs) return attn.processor(attn, hidden_states, *args, **kwargs)
class IPAdapterAttnProcessor(nn.Module):
r"""
Attention processor for IP-Adapater.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
num_tokens (`int`, defaults to 4):
The context length of the image features.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
"""
def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
super().__init__()
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.scale = scale
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
scale=1.0,
):
if scale != 1.0:
logger.warning("`scale` of IPAttnProcessor should be set with `set_ip_adapter_scale`.")
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
# split hidden states
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
encoder_hidden_states, ip_hidden_states = (
encoder_hidden_states[:, :end_pos, :],
encoder_hidden_states[:, end_pos:, :],
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
query = attn.head_to_batch_dim(query)
key = attn.head_to_batch_dim(key)
value = attn.head_to_batch_dim(value)
attention_probs = attn.get_attention_scores(query, key, attention_mask)
hidden_states = torch.bmm(attention_probs, value)
hidden_states = attn.batch_to_head_dim(hidden_states)
# for ip-adapter
ip_key = self.to_k_ip(ip_hidden_states)
ip_value = self.to_v_ip(ip_hidden_states)
ip_key = attn.head_to_batch_dim(ip_key)
ip_value = attn.head_to_batch_dim(ip_value)
ip_attention_probs = attn.get_attention_scores(query, ip_key, None)
ip_hidden_states = torch.bmm(ip_attention_probs, ip_value)
ip_hidden_states = attn.batch_to_head_dim(ip_hidden_states)
hidden_states = hidden_states + self.scale * ip_hidden_states
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
class IPAdapterAttnProcessor2_0(torch.nn.Module):
r"""
Attention processor for IP-Adapater for PyTorch 2.0.
Args:
hidden_size (`int`):
The hidden size of the attention layer.
cross_attention_dim (`int`):
The number of channels in the `encoder_hidden_states`.
num_tokens (`int`, defaults to 4):
The context length of the image features.
scale (`float`, defaults to 1.0):
the weight scale of image prompt.
"""
def __init__(self, hidden_size, cross_attention_dim=None, num_tokens=4, scale=1.0):
super().__init__()
if not hasattr(F, "scaled_dot_product_attention"):
raise ImportError(
f"{self.__class__.__name__} requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
)
self.hidden_size = hidden_size
self.cross_attention_dim = cross_attention_dim
self.num_tokens = num_tokens
self.scale = scale
self.to_k_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
self.to_v_ip = nn.Linear(cross_attention_dim or hidden_size, hidden_size, bias=False)
def __call__(
self,
attn,
hidden_states,
encoder_hidden_states=None,
attention_mask=None,
temb=None,
scale=1.0,
):
if scale != 1.0:
logger.warning("`scale` of IPAttnProcessor should be set by `set_ip_adapter_scale`.")
residual = hidden_states
if attn.spatial_norm is not None:
hidden_states = attn.spatial_norm(hidden_states, temb)
input_ndim = hidden_states.ndim
if input_ndim == 4:
batch_size, channel, height, width = hidden_states.shape
hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)
batch_size, sequence_length, _ = (
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
)
if attention_mask is not None:
attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
# scaled_dot_product_attention expects attention_mask shape to be
# (batch, heads, source_length, target_length)
attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])
if attn.group_norm is not None:
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)
query = attn.to_q(hidden_states)
if encoder_hidden_states is None:
encoder_hidden_states = hidden_states
elif attn.norm_cross:
encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)
# split hidden states
end_pos = encoder_hidden_states.shape[1] - self.num_tokens
encoder_hidden_states, ip_hidden_states = (
encoder_hidden_states[:, :end_pos, :],
encoder_hidden_states[:, end_pos:, :],
)
key = attn.to_k(encoder_hidden_states)
value = attn.to_v(encoder_hidden_states)
inner_dim = key.shape[-1]
head_dim = inner_dim // attn.heads
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
hidden_states = F.scaled_dot_product_attention(
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
)
hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
hidden_states = hidden_states.to(query.dtype)
# for ip-adapter
ip_key = self.to_k_ip(ip_hidden_states)
ip_value = self.to_v_ip(ip_hidden_states)
ip_key = ip_key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
ip_value = ip_value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
# the output of sdp = (batch, num_heads, seq_len, head_dim)
# TODO: add support for attn.scale when we move to Torch 2.1
ip_hidden_states = F.scaled_dot_product_attention(
query, ip_key, ip_value, attn_mask=None, dropout_p=0.0, is_causal=False
)
ip_hidden_states = ip_hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
ip_hidden_states = ip_hidden_states.to(query.dtype)
hidden_states = hidden_states + self.scale * ip_hidden_states
# linear proj
hidden_states = attn.to_out[0](hidden_states)
# dropout
hidden_states = attn.to_out[1](hidden_states)
if input_ndim == 4:
hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)
if attn.residual_connection:
hidden_states = hidden_states + residual
hidden_states = hidden_states / attn.rescale_output_factor
return hidden_states
LORA_ATTENTION_PROCESSORS = ( LORA_ATTENTION_PROCESSORS = (
LoRAAttnProcessor, LoRAAttnProcessor,
LoRAAttnProcessor2_0, LoRAAttnProcessor2_0,
...@@ -1998,6 +2242,8 @@ CROSS_ATTENTION_PROCESSORS = ( ...@@ -1998,6 +2242,8 @@ CROSS_ATTENTION_PROCESSORS = (
LoRAAttnProcessor, LoRAAttnProcessor,
LoRAAttnProcessor2_0, LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor, LoRAXFormersAttnProcessor,
IPAdapterAttnProcessor,
IPAdapterAttnProcessor2_0,
) )
AttentionProcessor = Union[ AttentionProcessor = Union[
......
src/diffusers/models/unet_2d_condition.py
View file @ ba352aea
...@@ -1022,6 +1022,15 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin) ...@@ -1022,6 +1022,15 @@ class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin)
) )
image_embeds = added_cond_kwargs.get("image_embeds") image_embeds = added_cond_kwargs.get("image_embeds")
encoder_hidden_states = self.encoder_hid_proj(image_embeds) encoder_hidden_states = self.encoder_hid_proj(image_embeds)
elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
# 2. pre-process # 2. pre-process
sample = self.conv_in(sample) sample = self.conv_in(sample)
......
src/diffusers/models/unet_motion_model.py
View file @ ba352aea
...@@ -208,6 +208,8 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): ...@@ -208,6 +208,8 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
motion_max_seq_length: int = 32, motion_max_seq_length: int = 32,
motion_num_attention_heads: int = 8, motion_num_attention_heads: int = 8,
use_motion_mid_block: int = True, use_motion_mid_block: int = True,
encoder_hid_dim: Optional[int] = None,
encoder_hid_dim_type: Optional[str] = None,
): ):
super().__init__() super().__init__()
...@@ -248,6 +250,9 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): ...@@ -248,6 +250,9 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
act_fn=act_fn, act_fn=act_fn,
) )
if encoder_hid_dim_type is None:
self.encoder_hid_proj = None
# class embedding # class embedding
self.down_blocks = nn.ModuleList([]) self.down_blocks = nn.ModuleList([])
self.up_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([])
...@@ -684,6 +689,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): ...@@ -684,6 +689,7 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
timestep_cond: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None,
attention_mask: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None,
down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None,
mid_block_additional_residual: Optional[torch.Tensor] = None, mid_block_additional_residual: Optional[torch.Tensor] = None,
return_dict: bool = True, return_dict: bool = True,
...@@ -767,6 +773,16 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin): ...@@ -767,6 +773,16 @@ class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin):
emb = self.time_embedding(t_emb, timestep_cond) emb = self.time_embedding(t_emb, timestep_cond)
emb = emb.repeat_interleave(repeats=num_frames, dim=0) emb = emb.repeat_interleave(repeats=num_frames, dim=0)
if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj":
if "image_embeds" not in added_cond_kwargs:
raise ValueError(
f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`"
)
image_embeds = added_cond_kwargs.get("image_embeds")
image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype)
encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1)
encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0) encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0)
# 2. pre-process # 2. pre-process
......
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion.py
View file @ ba352aea
...@@ -17,11 +17,11 @@ from typing import Any, Callable, Dict, List, Optional, Union ...@@ -17,11 +17,11 @@ from typing import Any, Callable, Dict, List, Optional, Union
import torch import torch
from packaging import version from packaging import version
from transformers import CLIPImageProcessor, XLMRobertaTokenizer from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
from ...configuration_utils import FrozenDict from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -74,7 +74,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): ...@@ -74,7 +74,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): class AltDiffusionPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
r""" r"""
Pipeline for text-to-image generation using Alt Diffusion. Pipeline for text-to-image generation using Alt Diffusion.
...@@ -86,6 +88,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -86,6 +88,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
...@@ -108,7 +111,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -108,7 +111,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
...@@ -121,6 +124,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -121,6 +124,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -197,6 +201,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -197,6 +201,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -444,6 +449,19 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -444,6 +449,19 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
has_nsfw_concept = None has_nsfw_concept = None
...@@ -652,6 +670,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -652,6 +670,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
latents: Optional[torch.FloatTensor] = None, latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -698,6 +717,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -698,6 +717,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -797,12 +817,18 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -797,12 +817,18 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
lora_scale=lora_scale, lora_scale=lora_scale,
clip_skip=self.clip_skip, clip_skip=self.clip_skip,
) )
# For classifier free guidance, we need to do two forward passes. # For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch # Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes # to avoid doing two forward passes
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Prepare timesteps # 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device) self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps timesteps = self.scheduler.timesteps
...@@ -823,7 +849,10 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -823,7 +849,10 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.5 Optionally get Guidance Scale Embedding # 6.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 6.2 Optionally get Guidance Scale Embedding
timestep_cond = None timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None: if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
...@@ -847,6 +876,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL ...@@ -847,6 +876,7 @@ class AltDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraL
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond, timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/alt_diffusion/pipeline_alt_diffusion_img2img.py
View file @ ba352aea
...@@ -19,11 +19,11 @@ import numpy as np ...@@ -19,11 +19,11 @@ import numpy as np
import PIL.Image import PIL.Image
import torch import torch
from packaging import version from packaging import version
from transformers import CLIPImageProcessor, XLMRobertaTokenizer from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection, XLMRobertaTokenizer
from ...configuration_utils import FrozenDict from ...configuration_utils import FrozenDict
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -111,7 +111,7 @@ def preprocess(image): ...@@ -111,7 +111,7 @@ def preprocess(image):
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline with Stable->Alt, CLIPTextModel->RobertaSeriesModelWithTransformation, CLIPTokenizer->XLMRobertaTokenizer, AltDiffusionSafetyChecker->StableDiffusionSafetyChecker
class AltDiffusionImg2ImgPipeline( class AltDiffusionImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-guided image-to-image generation using Alt Diffusion. Pipeline for text-guided image-to-image generation using Alt Diffusion.
...@@ -124,6 +124,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -124,6 +124,7 @@ class AltDiffusionImg2ImgPipeline(
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
...@@ -146,7 +147,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -146,7 +147,7 @@ class AltDiffusionImg2ImgPipeline(
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
...@@ -159,6 +160,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -159,6 +160,7 @@ class AltDiffusionImg2ImgPipeline(
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -235,6 +237,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -235,6 +237,7 @@ class AltDiffusionImg2ImgPipeline(
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -453,6 +456,19 @@ class AltDiffusionImg2ImgPipeline( ...@@ -453,6 +456,19 @@ class AltDiffusionImg2ImgPipeline(
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
has_nsfw_concept = None has_nsfw_concept = None
...@@ -705,6 +721,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -705,6 +721,7 @@ class AltDiffusionImg2ImgPipeline(
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -754,6 +771,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -754,6 +771,7 @@ class AltDiffusionImg2ImgPipeline(
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -846,6 +864,11 @@ class AltDiffusionImg2ImgPipeline( ...@@ -846,6 +864,11 @@ class AltDiffusionImg2ImgPipeline(
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Preprocess image # 4. Preprocess image
image = self.image_processor.preprocess(image) image = self.image_processor.preprocess(image)
...@@ -868,7 +891,10 @@ class AltDiffusionImg2ImgPipeline( ...@@ -868,7 +891,10 @@ class AltDiffusionImg2ImgPipeline(
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.5 Optionally get Guidance Scale Embedding # 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None: if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
...@@ -892,6 +918,7 @@ class AltDiffusionImg2ImgPipeline( ...@@ -892,6 +918,7 @@ class AltDiffusionImg2ImgPipeline(
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond, timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/animatediff/pipeline_animatediff.py
View file @ ba352aea
...@@ -18,10 +18,10 @@ from typing import Any, Callable, Dict, List, Optional, Union ...@@ -18,10 +18,10 @@ from typing import Any, Callable, Dict, List, Optional, Union
import numpy as np import numpy as np
import torch import torch
from transformers import CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...image_processor import VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel, UNetMotionModel from ...models import AutoencoderKL, UNet2DConditionModel, UNetMotionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...models.unet_motion_model import MotionAdapter from ...models.unet_motion_model import MotionAdapter
...@@ -77,7 +77,7 @@ class AnimateDiffPipelineOutput(BaseOutput): ...@@ -77,7 +77,7 @@ class AnimateDiffPipelineOutput(BaseOutput):
frames: Union[torch.Tensor, np.ndarray] frames: Union[torch.Tensor, np.ndarray]
class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin): class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin):
r""" r"""
Pipeline for text-to-video generation. Pipeline for text-to-video generation.
...@@ -101,6 +101,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -101,6 +101,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["feature_extractor", "image_encoder"]
def __init__( def __init__(
self, self,
...@@ -117,6 +118,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -117,6 +118,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
EulerAncestralDiscreteScheduler, EulerAncestralDiscreteScheduler,
DPMSolverMultistepScheduler, DPMSolverMultistepScheduler,
], ],
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
): ):
super().__init__() super().__init__()
unet = UNetMotionModel.from_unet2d(unet, motion_adapter) unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
...@@ -128,6 +131,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -128,6 +131,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
unet=unet, unet=unet,
motion_adapter=motion_adapter, motion_adapter=motion_adapter,
scheduler=scheduler, scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -314,6 +319,20 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -314,6 +319,20 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents): def decode_latents(self, latents):
latents = 1 / self.vae.config.scaling_factor * latents latents = 1 / self.vae.config.scaling_factor * latents
...@@ -512,6 +531,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -512,6 +531,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
latents: Optional[torch.FloatTensor] = None, latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
...@@ -558,6 +578,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -558,6 +578,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or
`np.array`. `np.array`.
...@@ -629,6 +650,11 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -629,6 +650,11 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
if do_classifier_free_guidance: if do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_videos_per_prompt)
if do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Prepare timesteps # 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device) self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps timesteps = self.scheduler.timesteps
...@@ -649,6 +675,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -649,6 +675,8 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# Denoising loop # Denoising loop
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
...@@ -664,6 +692,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo ...@@ -664,6 +692,7 @@ class AnimateDiffPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLo
t, t,
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
cross_attention_kwargs=cross_attention_kwargs, cross_attention_kwargs=cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
).sample ).sample
# perform guidance # perform guidance
......
src/diffusers/pipelines/controlnet/pipeline_controlnet.py
View file @ ba352aea
...@@ -20,10 +20,10 @@ import numpy as np ...@@ -20,10 +20,10 @@ import numpy as np
import PIL.Image import PIL.Image
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -92,7 +92,7 @@ EXAMPLE_DOC_STRING = """ ...@@ -92,7 +92,7 @@ EXAMPLE_DOC_STRING = """
class StableDiffusionControlNetPipeline( class StableDiffusionControlNetPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance. Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
...@@ -102,6 +102,7 @@ class StableDiffusionControlNetPipeline( ...@@ -102,6 +102,7 @@ class StableDiffusionControlNetPipeline(
The pipeline also inherits the following loading methods: The pipeline also inherits the following loading methods:
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
...@@ -128,7 +129,7 @@ class StableDiffusionControlNetPipeline( ...@@ -128,7 +129,7 @@ class StableDiffusionControlNetPipeline(
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
...@@ -142,6 +143,7 @@ class StableDiffusionControlNetPipeline( ...@@ -142,6 +143,7 @@ class StableDiffusionControlNetPipeline(
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -174,6 +176,7 @@ class StableDiffusionControlNetPipeline( ...@@ -174,6 +176,7 @@ class StableDiffusionControlNetPipeline(
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
...@@ -430,6 +433,20 @@ class StableDiffusionControlNetPipeline( ...@@ -430,6 +433,20 @@ class StableDiffusionControlNetPipeline(
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
...@@ -803,6 +820,7 @@ class StableDiffusionControlNetPipeline( ...@@ -803,6 +820,7 @@ class StableDiffusionControlNetPipeline(
latents: Optional[torch.FloatTensor] = None, latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -860,6 +878,7 @@ class StableDiffusionControlNetPipeline( ...@@ -860,6 +878,7 @@ class StableDiffusionControlNetPipeline(
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -997,6 +1016,11 @@ class StableDiffusionControlNetPipeline( ...@@ -997,6 +1016,11 @@ class StableDiffusionControlNetPipeline(
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Prepare image # 4. Prepare image
if isinstance(controlnet, ControlNetModel): if isinstance(controlnet, ControlNetModel):
image = self.prepare_image( image = self.prepare_image(
...@@ -1063,7 +1087,10 @@ class StableDiffusionControlNetPipeline( ...@@ -1063,7 +1087,10 @@ class StableDiffusionControlNetPipeline(
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.1 Create tensor stating which controlnets to keep # 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 7.2 Create tensor stating which controlnets to keep
controlnet_keep = [] controlnet_keep = []
for i in range(len(timesteps)): for i in range(len(timesteps)):
keeps = [ keeps = [
...@@ -1131,6 +1158,7 @@ class StableDiffusionControlNetPipeline( ...@@ -1131,6 +1158,7 @@ class StableDiffusionControlNetPipeline(
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
down_block_additional_residuals=down_block_res_samples, down_block_additional_residuals=down_block_res_samples,
mid_block_additional_residual=mid_block_res_sample, mid_block_additional_residual=mid_block_res_sample,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/controlnet/pipeline_controlnet_sd_xl.py
View file @ ba352aea
...@@ -20,12 +20,23 @@ import numpy as np ...@@ -20,12 +20,23 @@ import numpy as np
import PIL.Image import PIL.Image
import torch import torch
import torch.nn.functional as F import torch.nn.functional as F
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from diffusers.utils.import_utils import is_invisible_watermark_available from diffusers.utils.import_utils import is_invisible_watermark_available
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import (
FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
)
from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
from ...models.attention_processor import ( from ...models.attention_processor import (
AttnProcessor2_0, AttnProcessor2_0,
...@@ -104,7 +115,11 @@ EXAMPLE_DOC_STRING = """ ...@@ -104,7 +115,11 @@ EXAMPLE_DOC_STRING = """
class StableDiffusionXLControlNetPipeline( class StableDiffusionXLControlNetPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin DiffusionPipeline,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
IPAdapterMixin,
FromSingleFileMixin,
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance. Pipeline for text-to-image generation using Stable Diffusion XL with ControlNet guidance.
...@@ -149,7 +164,14 @@ class StableDiffusionXLControlNetPipeline( ...@@ -149,7 +164,14 @@ class StableDiffusionXLControlNetPipeline(
# leave controlnet out on purpose because it iterates with unet # leave controlnet out on purpose because it iterates with unet
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] _optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"feature_extractor",
"image_encoder",
]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
def __init__( def __init__(
...@@ -164,6 +186,8 @@ class StableDiffusionXLControlNetPipeline( ...@@ -164,6 +186,8 @@ class StableDiffusionXLControlNetPipeline(
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
force_zeros_for_empty_prompt: bool = True, force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None, add_watermarker: Optional[bool] = None,
feature_extractor: CLIPImageProcessor = None,
image_encoder: CLIPVisionModelWithProjection = None,
): ):
super().__init__() super().__init__()
...@@ -179,6 +203,8 @@ class StableDiffusionXLControlNetPipeline( ...@@ -179,6 +203,8 @@ class StableDiffusionXLControlNetPipeline(
unet=unet, unet=unet,
controlnet=controlnet, controlnet=controlnet,
scheduler=scheduler, scheduler=scheduler,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True)
...@@ -462,6 +488,20 @@ class StableDiffusionXLControlNetPipeline( ...@@ -462,6 +488,20 @@ class StableDiffusionXLControlNetPipeline(
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta): def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
...@@ -879,6 +919,7 @@ class StableDiffusionXLControlNetPipeline( ...@@ -879,6 +919,7 @@ class StableDiffusionXLControlNetPipeline(
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -959,6 +1000,7 @@ class StableDiffusionXLControlNetPipeline( ...@@ -959,6 +1000,7 @@ class StableDiffusionXLControlNetPipeline(
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs (prompt
weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input weighting). If not provided, pooled `negative_prompt_embeds` are generated from `negative_prompt` input
argument. argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -1100,7 +1142,7 @@ class StableDiffusionXLControlNetPipeline( ...@@ -1100,7 +1142,7 @@ class StableDiffusionXLControlNetPipeline(
) )
guess_mode = guess_mode or global_pool_conditions guess_mode = guess_mode or global_pool_conditions
# 3. Encode input prompt # 3.1 Encode input prompt
text_encoder_lora_scale = ( text_encoder_lora_scale = (
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
) )
...@@ -1125,6 +1167,12 @@ class StableDiffusionXLControlNetPipeline( ...@@ -1125,6 +1167,12 @@ class StableDiffusionXLControlNetPipeline(
clip_skip=self.clip_skip, clip_skip=self.clip_skip,
) )
# 3.2 Encode ip_adapter_image
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Prepare image # 4. Prepare image
if isinstance(controlnet, ControlNetModel): if isinstance(controlnet, ControlNetModel):
image = self.prepare_image( image = self.prepare_image(
...@@ -1299,6 +1347,9 @@ class StableDiffusionXLControlNetPipeline( ...@@ -1299,6 +1347,9 @@ class StableDiffusionXLControlNetPipeline(
down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples]
mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample])
if ip_adapter_image is not None:
added_cond_kwargs["image_embeds"] = image_embeds
# predict the noise residual # predict the noise residual
noise_pred = self.unet( noise_pred = self.unet(
latent_model_input, latent_model_input,
......
src/diffusers/pipelines/pipeline_flax_utils.py
View file @ ba352aea
...@@ -538,12 +538,13 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin): ...@@ -538,12 +538,13 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
model = pipeline_class(**init_kwargs, dtype=dtype) model = pipeline_class(**init_kwargs, dtype=dtype)
return model, params return model, params
@staticmethod @classmethod
def _get_signature_keys(obj): def _get_signature_keys(cls, obj):
parameters = inspect.signature(obj.__init__).parameters parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - {"self"} expected_modules = set(required_parameters.keys()) - {"self"}
return expected_modules, optional_parameters return expected_modules, optional_parameters
@property @property
......
src/diffusers/pipelines/pipeline_utils.py
View file @ ba352aea
...@@ -557,7 +557,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin): ...@@ -557,7 +557,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
for name, module in kwargs.items(): for name, module in kwargs.items():
# retrieve library # retrieve library
if module is None: if module is None or isinstance(module, (tuple, list)) and module[0] is None:
register_dict = {name: (None, None)} register_dict = {name: (None, None)}
else: else:
# register the config from the original module, not the dynamo compiled one # register the config from the original module, not the dynamo compiled one
...@@ -1906,12 +1906,19 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin): ...@@ -1906,12 +1906,19 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
" above." " above."
) from model_info_call_error ) from model_info_call_error
@staticmethod @classmethod
def _get_signature_keys(obj): def _get_signature_keys(cls, obj):
parameters = inspect.signature(obj.__init__).parameters parameters = inspect.signature(obj.__init__).parameters
required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty} required_parameters = {k: v for k, v in parameters.items() if v.default == inspect._empty}
optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty}) optional_parameters = set({k for k, v in parameters.items() if v.default != inspect._empty})
expected_modules = set(required_parameters.keys()) - {"self"} expected_modules = set(required_parameters.keys()) - {"self"}
optional_names = list(optional_parameters)
for name in optional_names:
if name in cls._optional_components:
expected_modules.add(name)
optional_parameters.remove(name)
return expected_modules, optional_parameters return expected_modules, optional_parameters
@property @property
......
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
View file @ ba352aea
...@@ -17,11 +17,11 @@ from typing import Any, Callable, Dict, List, Optional, Union ...@@ -17,11 +17,11 @@ from typing import Any, Callable, Dict, List, Optional, Union
import torch import torch
from packaging import version from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict from ...configuration_utils import FrozenDict
from ...image_processor import VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -70,7 +70,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): ...@@ -70,7 +70,9 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
return noise_cfg return noise_cfg
class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin): class StableDiffusionPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin
):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion. Pipeline for text-to-image generation using Stable Diffusion.
...@@ -82,6 +84,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -82,6 +84,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
...@@ -104,7 +107,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -104,7 +107,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
...@@ -117,6 +120,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -117,6 +120,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -193,6 +197,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -193,6 +197,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -440,6 +445,19 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -440,6 +445,19 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
has_nsfw_concept = None has_nsfw_concept = None
...@@ -649,6 +667,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -649,6 +667,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
latents: Optional[torch.FloatTensor] = None, latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -695,6 +714,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -695,6 +714,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -794,12 +814,18 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -794,12 +814,18 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
lora_scale=lora_scale, lora_scale=lora_scale,
clip_skip=self.clip_skip, clip_skip=self.clip_skip,
) )
# For classifier free guidance, we need to do two forward passes. # For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch # Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes # to avoid doing two forward passes
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Prepare timesteps # 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device) self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps timesteps = self.scheduler.timesteps
...@@ -820,7 +846,10 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -820,7 +846,10 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
# 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 6.5 Optionally get Guidance Scale Embedding # 6.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 6.2 Optionally get Guidance Scale Embedding
timestep_cond = None timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None: if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
...@@ -844,6 +873,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo ...@@ -844,6 +873,7 @@ class StableDiffusionPipeline(DiffusionPipeline, TextualInversionLoaderMixin, Lo
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond, timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
View file @ ba352aea
...@@ -19,11 +19,11 @@ import numpy as np ...@@ -19,11 +19,11 @@ import numpy as np
import PIL.Image import PIL.Image
import torch import torch
from packaging import version from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict from ...configuration_utils import FrozenDict
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -106,7 +106,7 @@ def preprocess(image): ...@@ -106,7 +106,7 @@ def preprocess(image):
class StableDiffusionImg2ImgPipeline( class StableDiffusionImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-guided image-to-image generation using Stable Diffusion. Pipeline for text-guided image-to-image generation using Stable Diffusion.
...@@ -119,6 +119,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -119,6 +119,7 @@ class StableDiffusionImg2ImgPipeline(
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`]): vae ([`AutoencoderKL`]):
...@@ -141,7 +142,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -141,7 +142,7 @@ class StableDiffusionImg2ImgPipeline(
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
...@@ -154,6 +155,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -154,6 +155,7 @@ class StableDiffusionImg2ImgPipeline(
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -230,6 +232,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -230,6 +232,7 @@ class StableDiffusionImg2ImgPipeline(
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -450,6 +453,20 @@ class StableDiffusionImg2ImgPipeline( ...@@ -450,6 +453,20 @@ class StableDiffusionImg2ImgPipeline(
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
...@@ -708,6 +725,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -708,6 +725,7 @@ class StableDiffusionImg2ImgPipeline(
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -757,6 +775,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -757,6 +775,7 @@ class StableDiffusionImg2ImgPipeline(
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -849,6 +868,11 @@ class StableDiffusionImg2ImgPipeline( ...@@ -849,6 +868,11 @@ class StableDiffusionImg2ImgPipeline(
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. Preprocess image # 4. Preprocess image
image = self.image_processor.preprocess(image) image = self.image_processor.preprocess(image)
...@@ -871,7 +895,10 @@ class StableDiffusionImg2ImgPipeline( ...@@ -871,7 +895,10 @@ class StableDiffusionImg2ImgPipeline(
# 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 7.5 Optionally get Guidance Scale Embedding # 7.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 7.2 Optionally get Guidance Scale Embedding
timestep_cond = None timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None: if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
...@@ -895,6 +922,7 @@ class StableDiffusionImg2ImgPipeline( ...@@ -895,6 +922,7 @@ class StableDiffusionImg2ImgPipeline(
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond, timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
View file @ ba352aea
...@@ -19,11 +19,11 @@ import numpy as np ...@@ -19,11 +19,11 @@ import numpy as np
import PIL.Image import PIL.Image
import torch import torch
from packaging import version from packaging import version
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
from ...configuration_utils import FrozenDict from ...configuration_utils import FrozenDict
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from ...models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel from ...models import AsymmetricAutoencoderKL, AutoencoderKL, UNet2DConditionModel
from ...models.lora import adjust_lora_scale_text_encoder from ...models.lora import adjust_lora_scale_text_encoder
from ...schedulers import KarrasDiffusionSchedulers from ...schedulers import KarrasDiffusionSchedulers
...@@ -170,7 +170,7 @@ def retrieve_latents(encoder_output, generator): ...@@ -170,7 +170,7 @@ def retrieve_latents(encoder_output, generator):
class StableDiffusionInpaintPipeline( class StableDiffusionInpaintPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin DiffusionPipeline, TextualInversionLoaderMixin, IPAdapterMixin, LoraLoaderMixin, FromSingleFileMixin
): ):
r""" r"""
Pipeline for text-guided image inpainting using Stable Diffusion. Pipeline for text-guided image inpainting using Stable Diffusion.
...@@ -182,6 +182,7 @@ class StableDiffusionInpaintPipeline( ...@@ -182,6 +182,7 @@ class StableDiffusionInpaintPipeline(
- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
- [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights
- [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights
- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
Args: Args:
vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]): vae ([`AutoencoderKL`, `AsymmetricAutoencoderKL`]):
...@@ -204,7 +205,7 @@ class StableDiffusionInpaintPipeline( ...@@ -204,7 +205,7 @@ class StableDiffusionInpaintPipeline(
""" """
model_cpu_offload_seq = "text_encoder->unet->vae" model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"] _optional_components = ["safety_checker", "feature_extractor", "image_encoder"]
_exclude_from_cpu_offload = ["safety_checker"] _exclude_from_cpu_offload = ["safety_checker"]
_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "mask", "masked_image_latents"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds", "mask", "masked_image_latents"]
...@@ -217,6 +218,7 @@ class StableDiffusionInpaintPipeline( ...@@ -217,6 +218,7 @@ class StableDiffusionInpaintPipeline(
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
safety_checker: StableDiffusionSafetyChecker, safety_checker: StableDiffusionSafetyChecker,
feature_extractor: CLIPImageProcessor, feature_extractor: CLIPImageProcessor,
image_encoder: CLIPVisionModelWithProjection = None,
requires_safety_checker: bool = True, requires_safety_checker: bool = True,
): ):
super().__init__() super().__init__()
...@@ -298,6 +300,7 @@ class StableDiffusionInpaintPipeline( ...@@ -298,6 +300,7 @@ class StableDiffusionInpaintPipeline(
scheduler=scheduler, scheduler=scheduler,
safety_checker=safety_checker, safety_checker=safety_checker,
feature_extractor=feature_extractor, feature_extractor=feature_extractor,
image_encoder=image_encoder,
) )
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor)
...@@ -521,6 +524,20 @@ class StableDiffusionInpaintPipeline( ...@@ -521,6 +524,20 @@ class StableDiffusionInpaintPipeline(
return prompt_embeds, negative_prompt_embeds return prompt_embeds, negative_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype): def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None: if self.safety_checker is None:
...@@ -837,6 +854,7 @@ class StableDiffusionInpaintPipeline( ...@@ -837,6 +854,7 @@ class StableDiffusionInpaintPipeline(
latents: Optional[torch.FloatTensor] = None, latents: Optional[torch.FloatTensor] = None,
prompt_embeds: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None,
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -902,6 +920,7 @@ class StableDiffusionInpaintPipeline( ...@@ -902,6 +920,7 @@ class StableDiffusionInpaintPipeline(
negative_prompt_embeds (`torch.FloatTensor`, *optional*): negative_prompt_embeds (`torch.FloatTensor`, *optional*):
Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If
not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generated image. Choose between `PIL.Image` or `np.array`. The output format of the generated image. Choose between `PIL.Image` or `np.array`.
return_dict (`bool`, *optional*, defaults to `True`): return_dict (`bool`, *optional*, defaults to `True`):
...@@ -1029,6 +1048,11 @@ class StableDiffusionInpaintPipeline( ...@@ -1029,6 +1048,11 @@ class StableDiffusionInpaintPipeline(
if self.do_classifier_free_guidance: if self.do_classifier_free_guidance:
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
# 4. set timesteps # 4. set timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device) self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps( timesteps, num_inference_steps = self.get_timesteps(
...@@ -1117,7 +1141,10 @@ class StableDiffusionInpaintPipeline( ...@@ -1117,7 +1141,10 @@ class StableDiffusionInpaintPipeline(
# 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline # 9. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)
# 9.5 Optionally get Guidance Scale Embedding # 9.1 Add image embeds for IP-Adapter
added_cond_kwargs = {"image_embeds": image_embeds} if ip_adapter_image is not None else None
# 9.2 Optionally get Guidance Scale Embedding
timestep_cond = None timestep_cond = None
if self.unet.config.time_cond_proj_dim is not None: if self.unet.config.time_cond_proj_dim is not None:
guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt)
...@@ -1146,6 +1173,7 @@ class StableDiffusionInpaintPipeline( ...@@ -1146,6 +1173,7 @@ class StableDiffusionInpaintPipeline(
encoder_hidden_states=prompt_embeds, encoder_hidden_states=prompt_embeds,
timestep_cond=timestep_cond, timestep_cond=timestep_cond,
cross_attention_kwargs=self.cross_attention_kwargs, cross_attention_kwargs=self.cross_attention_kwargs,
added_cond_kwargs=added_cond_kwargs,
return_dict=False, return_dict=False,
)[0] )[0]
......
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
View file @ ba352aea
...@@ -16,11 +16,18 @@ import inspect ...@@ -16,11 +16,18 @@ import inspect
from typing import Any, Callable, Dict, List, Optional, Tuple, Union from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...image_processor import VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import ( from ...loaders import (
FromSingleFileMixin, FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionXLLoraLoaderMixin, StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin, TextualInversionLoaderMixin,
) )
...@@ -94,7 +101,11 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): ...@@ -94,7 +101,11 @@ def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
class StableDiffusionXLPipeline( class StableDiffusionXLPipeline(
DiffusionPipeline, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin DiffusionPipeline,
FromSingleFileMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
IPAdapterMixin,
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion XL. Pipeline for text-to-image generation using Stable Diffusion XL.
...@@ -142,7 +153,14 @@ class StableDiffusionXLPipeline( ...@@ -142,7 +153,14 @@ class StableDiffusionXLPipeline(
""" """
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] _optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [ _callback_tensor_inputs = [
"latents", "latents",
"prompt_embeds", "prompt_embeds",
...@@ -162,6 +180,8 @@ class StableDiffusionXLPipeline( ...@@ -162,6 +180,8 @@ class StableDiffusionXLPipeline(
tokenizer_2: CLIPTokenizer, tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel, unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
force_zeros_for_empty_prompt: bool = True, force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None, add_watermarker: Optional[bool] = None,
): ):
...@@ -175,6 +195,8 @@ class StableDiffusionXLPipeline( ...@@ -175,6 +195,8 @@ class StableDiffusionXLPipeline(
tokenizer_2=tokenizer_2, tokenizer_2=tokenizer_2,
unet=unet, unet=unet,
scheduler=scheduler, scheduler=scheduler,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
) )
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
...@@ -456,6 +478,20 @@ class StableDiffusionXLPipeline( ...@@ -456,6 +478,20 @@ class StableDiffusionXLPipeline(
return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta): def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
...@@ -718,6 +754,7 @@ class StableDiffusionXLPipeline( ...@@ -718,6 +754,7 @@ class StableDiffusionXLPipeline(
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -802,6 +839,7 @@ class StableDiffusionXLPipeline( ...@@ -802,6 +839,7 @@ class StableDiffusionXLPipeline(
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument. input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
...@@ -1000,6 +1038,12 @@ class StableDiffusionXLPipeline( ...@@ -1000,6 +1038,12 @@ class StableDiffusionXLPipeline(
add_text_embeds = add_text_embeds.to(device) add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1) add_time_ids = add_time_ids.to(device).repeat(batch_size * num_images_per_prompt, 1)
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
# 8. Denoising loop # 8. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
...@@ -1037,6 +1081,8 @@ class StableDiffusionXLPipeline( ...@@ -1037,6 +1081,8 @@ class StableDiffusionXLPipeline(
# predict the noise residual # predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet( noise_pred = self.unet(
latent_model_input, latent_model_input,
t, t,
......
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
View file @ ba352aea
...@@ -17,10 +17,21 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union ...@@ -17,10 +17,21 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import PIL.Image import PIL.Image
import torch import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import (
FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
)
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import ( from ...models.attention_processor import (
AttnProcessor2_0, AttnProcessor2_0,
...@@ -104,7 +115,11 @@ def retrieve_latents(encoder_output, generator): ...@@ -104,7 +115,11 @@ def retrieve_latents(encoder_output, generator):
class StableDiffusionXLImg2ImgPipeline( class StableDiffusionXLImg2ImgPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin DiffusionPipeline,
TextualInversionLoaderMixin,
FromSingleFileMixin,
StableDiffusionXLLoraLoaderMixin,
IPAdapterMixin,
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion XL. Pipeline for text-to-image generation using Stable Diffusion XL.
...@@ -155,7 +170,14 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -155,7 +170,14 @@ class StableDiffusionXLImg2ImgPipeline(
""" """
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] _optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [ _callback_tensor_inputs = [
"latents", "latents",
"prompt_embeds", "prompt_embeds",
...@@ -175,6 +197,8 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -175,6 +197,8 @@ class StableDiffusionXLImg2ImgPipeline(
tokenizer_2: CLIPTokenizer, tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel, unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
requires_aesthetics_score: bool = False, requires_aesthetics_score: bool = False,
force_zeros_for_empty_prompt: bool = True, force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None, add_watermarker: Optional[bool] = None,
...@@ -188,6 +212,8 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -188,6 +212,8 @@ class StableDiffusionXLImg2ImgPipeline(
tokenizer=tokenizer, tokenizer=tokenizer,
tokenizer_2=tokenizer_2, tokenizer_2=tokenizer_2,
unet=unet, unet=unet,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
scheduler=scheduler, scheduler=scheduler,
) )
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
...@@ -665,6 +691,20 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -665,6 +691,20 @@ class StableDiffusionXLImg2ImgPipeline(
return latents return latents
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
def _get_add_time_ids( def _get_add_time_ids(
self, self,
original_size, original_size,
...@@ -850,6 +890,7 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -850,6 +890,7 @@ class StableDiffusionXLImg2ImgPipeline(
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -943,6 +984,7 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -943,6 +984,7 @@ class StableDiffusionXLImg2ImgPipeline(
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument. input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
output_type (`str`, *optional*, defaults to `"pil"`): output_type (`str`, *optional*, defaults to `"pil"`):
The output format of the generate image. Choose between The output format of the generate image. Choose between
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
...@@ -1162,6 +1204,12 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -1162,6 +1204,12 @@ class StableDiffusionXLImg2ImgPipeline(
add_text_embeds = add_text_embeds.to(device) add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device) add_time_ids = add_time_ids.to(device)
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
# 9. Denoising loop # 9. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
...@@ -1205,6 +1253,8 @@ class StableDiffusionXLImg2ImgPipeline( ...@@ -1205,6 +1253,8 @@ class StableDiffusionXLImg2ImgPipeline(
# predict the noise residual # predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet( noise_pred = self.unet(
latent_model_input, latent_model_input,
t, t,
......
src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_inpaint.py
View file @ ba352aea
...@@ -18,10 +18,21 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union ...@@ -18,10 +18,21 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np import numpy as np
import PIL.Image import PIL.Image
import torch import torch
from transformers import CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer from transformers import (
CLIPImageProcessor,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionModelWithProjection,
)
from ...image_processor import PipelineImageInput, VaeImageProcessor from ...image_processor import PipelineImageInput, VaeImageProcessor
from ...loaders import FromSingleFileMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin from ...loaders import (
FromSingleFileMixin,
IPAdapterMixin,
StableDiffusionXLLoraLoaderMixin,
TextualInversionLoaderMixin,
)
from ...models import AutoencoderKL, UNet2DConditionModel from ...models import AutoencoderKL, UNet2DConditionModel
from ...models.attention_processor import ( from ...models.attention_processor import (
AttnProcessor2_0, AttnProcessor2_0,
...@@ -249,7 +260,11 @@ def retrieve_latents(encoder_output, generator): ...@@ -249,7 +260,11 @@ def retrieve_latents(encoder_output, generator):
class StableDiffusionXLInpaintPipeline( class StableDiffusionXLInpaintPipeline(
DiffusionPipeline, TextualInversionLoaderMixin, StableDiffusionXLLoraLoaderMixin, FromSingleFileMixin DiffusionPipeline,
TextualInversionLoaderMixin,
StableDiffusionXLLoraLoaderMixin,
FromSingleFileMixin,
IPAdapterMixin,
): ):
r""" r"""
Pipeline for text-to-image generation using Stable Diffusion XL. Pipeline for text-to-image generation using Stable Diffusion XL.
...@@ -301,7 +316,14 @@ class StableDiffusionXLInpaintPipeline( ...@@ -301,7 +316,14 @@ class StableDiffusionXLInpaintPipeline(
model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae"
_optional_components = ["tokenizer", "tokenizer_2", "text_encoder", "text_encoder_2"] _optional_components = [
"tokenizer",
"tokenizer_2",
"text_encoder",
"text_encoder_2",
"image_encoder",
"feature_extractor",
]
_callback_tensor_inputs = [ _callback_tensor_inputs = [
"latents", "latents",
"prompt_embeds", "prompt_embeds",
...@@ -323,6 +345,8 @@ class StableDiffusionXLInpaintPipeline( ...@@ -323,6 +345,8 @@ class StableDiffusionXLInpaintPipeline(
tokenizer_2: CLIPTokenizer, tokenizer_2: CLIPTokenizer,
unet: UNet2DConditionModel, unet: UNet2DConditionModel,
scheduler: KarrasDiffusionSchedulers, scheduler: KarrasDiffusionSchedulers,
image_encoder: CLIPVisionModelWithProjection = None,
feature_extractor: CLIPImageProcessor = None,
requires_aesthetics_score: bool = False, requires_aesthetics_score: bool = False,
force_zeros_for_empty_prompt: bool = True, force_zeros_for_empty_prompt: bool = True,
add_watermarker: Optional[bool] = None, add_watermarker: Optional[bool] = None,
...@@ -336,6 +360,8 @@ class StableDiffusionXLInpaintPipeline( ...@@ -336,6 +360,8 @@ class StableDiffusionXLInpaintPipeline(
tokenizer=tokenizer, tokenizer=tokenizer,
tokenizer_2=tokenizer_2, tokenizer_2=tokenizer_2,
unet=unet, unet=unet,
image_encoder=image_encoder,
feature_extractor=feature_extractor,
scheduler=scheduler, scheduler=scheduler,
) )
self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) self.register_to_config(force_zeros_for_empty_prompt=force_zeros_for_empty_prompt)
...@@ -386,6 +412,20 @@ class StableDiffusionXLInpaintPipeline( ...@@ -386,6 +412,20 @@ class StableDiffusionXLInpaintPipeline(
""" """
self.vae.disable_tiling() self.vae.disable_tiling()
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
def encode_image(self, image, device, num_images_per_prompt):
dtype = next(self.image_encoder.parameters()).dtype
if not isinstance(image, torch.Tensor):
image = self.feature_extractor(image, return_tensors="pt").pixel_values
image = image.to(device=device, dtype=dtype)
image_embeds = self.image_encoder(image).image_embeds
image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
# Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
def encode_prompt( def encode_prompt(
self, self,
...@@ -1074,6 +1114,7 @@ class StableDiffusionXLInpaintPipeline( ...@@ -1074,6 +1114,7 @@ class StableDiffusionXLInpaintPipeline(
negative_prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None,
pooled_prompt_embeds: Optional[torch.FloatTensor] = None, pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None,
ip_adapter_image: Optional[PipelineImageInput] = None,
output_type: Optional[str] = "pil", output_type: Optional[str] = "pil",
return_dict: bool = True, return_dict: bool = True,
cross_attention_kwargs: Optional[Dict[str, Any]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None,
...@@ -1172,6 +1213,7 @@ class StableDiffusionXLInpaintPipeline( ...@@ -1172,6 +1213,7 @@ class StableDiffusionXLInpaintPipeline(
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
input argument. input argument.
ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters.
num_images_per_prompt (`int`, *optional*, defaults to 1): num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt. The number of images to generate per prompt.
eta (`float`, *optional*, defaults to 0.0): eta (`float`, *optional*, defaults to 0.0):
...@@ -1471,6 +1513,12 @@ class StableDiffusionXLInpaintPipeline( ...@@ -1471,6 +1513,12 @@ class StableDiffusionXLInpaintPipeline(
add_text_embeds = add_text_embeds.to(device) add_text_embeds = add_text_embeds.to(device)
add_time_ids = add_time_ids.to(device) add_time_ids = add_time_ids.to(device)
if ip_adapter_image is not None:
image_embeds, negative_image_embeds = self.encode_image(ip_adapter_image, device, num_images_per_prompt)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])
image_embeds = image_embeds.to(device)
# 11. Denoising loop # 11. Denoising loop
num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0) num_warmup_steps = max(len(timesteps) - num_inference_steps * self.scheduler.order, 0)
...@@ -1517,6 +1565,8 @@ class StableDiffusionXLInpaintPipeline( ...@@ -1517,6 +1565,8 @@ class StableDiffusionXLInpaintPipeline(
# predict the noise residual # predict the noise residual
added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids} added_cond_kwargs = {"text_embeds": add_text_embeds, "time_ids": add_time_ids}
if ip_adapter_image is not None:
added_cond_kwargs["image_embeds"] = image_embeds
noise_pred = self.unet( noise_pred = self.unet(
latent_model_input, latent_model_input,
t, t,
......
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