@yiyixuxu @asomoza Could you review this new PR? @DannHuang will follow it up.

thanks for your PR!
@asomoza can you give this a first review and test it out?

The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update.

I’m currently testing it and comparing the results. In the meantime, I’m curious as to why you decided to create a new function instead of modifying set_ip_adapter_scale. Do you anticipate a use case where we’ll need to use both functions simultaneously?

I’m currently testing it and comparing the results. In the meantime, I’m curious as to why you decided to create a new function instead of modifying set_ip_adapter_scale. Do you anticipate a use case where we’ll need to use both functions simultaneously?

I think you are right, the two functions essentially do the same thing and we shall merge them. Sorry for confusing.

Hi all, this PR is updated. Specifically:

1. activate_ip_adapter() is now merged to set_ip_adapter_scale() and is fully compatible to the original usage. Now IP-Adapters can be controlled by a float or a list of float (which is the original usage), and also by a scale_config or a list of scale_configs. For example:

# To use style and layout from 2 reference images
scale_configs = [
            {
                "down": {
                    "block_2": [0.0, 1.0]
                }
            },
            {
                "up": {
                    "block_0": [0.0, 1.0, 0.0]
                }
            }
        ]
pipeline.set_ip_adapter_scale(scale_configs)

2. _maybe_expand_lora_scales() now takes an additional default_scale arg which has a default value 1.0. I believe the behavior of existing code is unchanged and the ip_adapter_utils.py is removed.

I've tested it and it works, thank you for you work. I left a couple of questions.

I think pretty much the use case for this is to be able to use one image for style and maybe another one for the composition, right now that's not possible though.

In case of multiple reference images, I believe we can achieve this by load the same IP-Adapter for multiple times, and set the correspond IP-Adapter to style-mode/layout-mode. The following code works for me:

...
pipe.load_ip_adapter(ip_adapter_path, subfolder="sdxl_models", weight_name=["ip-adapter_sdxl.bin", "ip-adapter_sdxl.bin"], image_encoder_path=image_encoder_path)
...
pipe.set_ip_adapter_scale(scale_configs)
images = pipe(
    prompt="a llama, masterpiece, best quality, high quality",
    ip_adapter_image=[style_img, composition_img],
    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
    scale=1.0,
    guidance_scale=5,
    num_inference_steps=30,
    generator=generator,
).images

Please let me know if there is any other issue, thanks all :)

For this:

In case of multiple reference images, I believe we can achieve this by load the same IP-Adapter for multiple times, and set the correspond IP-Adapter to style-mode/layout-mode.

This time I really don't see a solution to prevent loading the same weights multiple times without changing how IP Adapters are loaded in the pipelines.

All the mentioned issues is solved. Please let me known if there is any other.

This time I really don't see a solution to prevent loading the same weights multiple times without changing how IP Adapters are loaded in the pipelines.

IMO we want to load two IP Adapters, one for style control and the other for layout. They happened to have same pre-trained weight in this specific case but they do not need to be the same actually. We believe this implementation have a minimum modification to the original IP-Adapter pipeline, but we have no idea if it is the best solution to @asomoza's case.

@yiyixuxu Should be ready to merge.

cc @sayakpaul can you give a final review?

also, it seems like quality changed files that are not supposed to be changed - I've seen this issue in multiple PRs now, what's going on?

also, it seems like quality changed files that are not supposed to be changed - I've seen this issue in multiple PRs now, what's going on?

Here's my hypothesis.

#7314 made changes to the dependencies included in "quality". Refer to the setup.py:

So with this dependency included, whenever you run make style it will re-format the docstring and documentation pages if needed.

Contributors may not have updated the quality dependencies before running make quality and make style which is why there are likely changes in unexpected files. LMK if this is unclear.

Yup, can confirm my hypothesis with c45b1c7. See the number of file changes dropped to 4 from 17.

Yup, can confirm my hypothesis with c45b1c7. See the number of file changes dropped to 4 from 17.

Thanks for your solution! This really confused us as the we passed the quality check at local version. We will test with the latest dependencies and solve the quality check ASAP.

Looking great! Could we have some documentation with test code on how to use this feature as well?

When testing, prefer non-human objects.

@fabiorigano would be great to have your reviews on this too :-)

Absolutely, will update it soon.

Most of the issues are resolved except those related to the use case of default_scale, I believe the current version is as expected. @sayakpaul Can I mark those comments as resolved? BTW, should I add documentation and tests to docs/source/en/using-diffusers/ip_adapter.md and tests/pipelines/ip_adapters?

Hi all, I've just pushed some updates to this PR. Specifically:

1. multi masked IP inputs is now supported. For the case @yiyixuxu provides:
   
   with InstantStyle that injecting ip_female_style and ip_male_style to only style layers:
   
2. now can set different scales for masked IP in set_ip_adapter_scale. For example, to set scales for 3 masked IP in up.blocks.0 layer, use this scale config:

scale_0 = { "up": { "block_0": [[0.75, 0.75, 0.3]]}}

this will set scale [0.75, 0.75, 0.3] for the corresponding 3 masked IP to both 3 transformer blocks in up.blocks.0. My solution here require a length-1 list over list of scales to avoid the ambiguity against specifying different scales for the transformer blocks. For details please refer to https://github.com/DannHuang/diffusers/blob/6fc9a3af2df947d99e80148cc7c40d4abb0ac86d/src/diffusers/loaders/unet_loader_utils.py#L143-L145
3) a test case from @yiyixuxu is included.
4) conflicts resolved.

it seems like I still have some problems with my quality dependencies, can u help me with it again @sayakpaul, really thanks a lot!

it seems like I still have some problems with my quality dependencies, can u help me with it again @sayakpaul, really thanks a lot!

you have to run make quality how to open a PR

@fabiorigano @sayakpaul Formatted.

very nice work! thank you all!!

run the example:
attention_processor.py", line 1157, in call
hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
AttributeError: 'tuple' object has no attribute 'shape'
@DannHuang

run the example: attention_processor.py", line 1157, in call hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape AttributeError: 'tuple' object has no attribute 'shape' @DannHuang

Hi @wodsoe,
It seems like there is a mistake in the example, really sorry for confusing.
The following code should work:

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")

Please let me know if there is any further issue. Thanks a lot!

I am not able to get the InstantStyle implementation working with IP-Adapter. It is not directly related to @wodsoe , but it comes from attention_processors.py too.

Im using the exact example from the Diffusers IP-Adapter documentation.

import os
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'

from diffusers import AutoPipelineForImage2Image
from diffusers.utils import load_image
import torch

pipeline = AutoPipelineForImage2Image.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16).to("cuda")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")

scale = {
    "down": {"block_2": [0.0, 1.0]},
    "up": {"block_0": [0.0, 1.0, 0.0]},
}
pipeline.set_ip_adapter_scale(scale)

style_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg")

generator = torch.Generator(device="cpu").manual_seed(42)
image = pipeline(
    prompt="a cat, masterpiece, best quality, high quality",
    image=style_image,
    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
    guidance_scale=5,
    num_inference_steps=30,
    generator=generator,
).images[0]
image

Using AutoPipelineForImage2Image i got an error because the ip_adapter_image parameter is not defined in the code.

ValueError: <class 'diffusers.models.unets.unet_2d_condition.UNet2DConditionModel'> 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`

Then, if I use AutoPipelineForText2Image giving 'style_image' as the 'ip_adapter_image' i get the next error. The same happens when with AutoPipelineForImage2Image using 'style_image' for both 'image' and 'ip_adapter_image'.

File ~/miniconda3/envs/if/lib/python3.10/site-packages/diffusers/models/attention_processor.py:2417, in IPAdapterAttnProcessor2_0.__call__(self, attn, hidden_states, encoder_hidden_states, attention_mask, temb, scale, ip_adapter_masks)
   2413         mask_downsample = mask_downsample.to(dtype=query.dtype, device=query.device)
   2415         current_ip_hidden_states = current_ip_hidden_states * mask_downsample
-> 2417     hidden_states = hidden_states + scale * current_ip_hidden_states
   2419 # linear proj
   2420 hidden_states = attn.to_out[0](hidden_states)

TypeError: unsupported operand type(s) for *: 'dict' and 'Tensor'

Maybe Im doing something wrong. IP-Adapter works fine when 'scale' is a float number for the traditional implementation, but it doesn't seem to work for this specific case. Thanks and sorry for the long reply :)

I can reproduce this problem. The code snippet is the same as what's provided in https://huggingface.co/docs/diffusers/main/en/using-diffusers/ip_adapter#style--layout-control.

Hi @emberMd, Thanks for trying InstantStyle! It's a little bit weird because replacing AutoPipelineForImage2Image with AutoPipelineForText2Image and passing style_image as ip_adapter_image to the pipe line works fine with me. Here is the complete code for your reference:

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")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")

scale = {
    "down": {"block_2": [0.0, 1.0]},
    "up": {"block_0": [0.0, 1.0, 0.0]},
}
pipeline.set_ip_adapter_scale(scale)

style_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg")
generator = torch.Generator(device="cpu").manual_seed(42)
image = pipeline(
    prompt="a cat, masterpiece, best quality, high quality",
    ip_adapter_image=style_image,
    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
    guidance_scale=5,
    num_inference_steps=30,
    generator=generator,
).images[0]
image

For the second error in your reply, I guess it's due to the version problem as the dictionary type scale was set to each attn_processor. Could you please try clone the latest diffusers repo and directly install from source?

Hi @DannHuang! thanks for the fast reply.

I guess it's weird, the code you passed still doesn't work for me. I imagined that it could be due to a version problem in Diffusers, but since I am already on 0.27.2 I wanted to confirm first that I was not doing anything strange in my code.

I will install from the repo and see if that solves it. Thanks so much for the help!

image=style_image should be ip_adapter_image=style_image

@sayakpaul my bad, thanks a lot! @emberMd the code snippet is updated.

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")
pipeline.load_ip_adapter("h94/IP-Adapter", subfolder="sdxl_models", weight_name="ip-adapter_sdxl.bin")

scale = {
    "down": {"block_2": [0.0, 1.0]},
    "up": {"block_0": [0.0, 1.0, 0.0]},
}
pipeline.set_ip_adapter_scale(scale)

style_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg")
generator = torch.Generator(device="cpu").manual_seed(42)
image = pipeline(
    prompt="a cat, masterpiece, best quality, high quality",
    ip_adapter_image=style_image,
    negative_prompt="text, watermark, lowres, low quality, worst quality, deformed, glitch, low contrast, noisy, saturation, blurry",
    guidance_scale=5,
    num_inference_steps=30,
    generator=generator,
).images[0]
image

Thanks @sayakpaul @DannHuang! Really appreciate it

I was aware of the 'image=style_image' error, but i was using DIffusers 0.27.2, now with 0.28.0.dev0 it works fine. My bad

@emberMd Good to know the errors were solved. Hope you can enjoy InstantStyle :)

@DannHuang , @sayakpaul
I get an error when I add xformers and flash-attention.

AttributeError: 'tuple' object has no attribute 'shape'

@DannHuang can we update the doc with the correct example?

@yiyixuxu Yeah sure, we will update the doc soon.

@yiyixuxu Hi, the doc is updated in #7806. Please have a check!

@DannHuang
merged! thank you:)