Skip to content

Update IP Adapter implementation to latest #6115

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 1 commit into from
Closed

Update IP Adapter implementation to latest #6115

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
28 changes: 21 additions & 7 deletions examples/community/llm_grounded_diffusion.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,7 @@
from diffusers.configuration_utils import FrozenDict
from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.models import AutoencoderKL, ImageProjection, UNet2DConditionModel
from diffusers.models.attention import Attention, GatedSelfAttentionDense
from diffusers.models.attention_processor import AttnProcessor2_0
from diffusers.models.lora import adjust_lora_scale_text_encoder
Expand Down Expand Up @@ -911,7 +911,10 @@ def __call__(
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)
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_images_per_prompt, output_hidden_state
)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])

Expand Down Expand Up @@ -1431,18 +1434,29 @@ def encode_prompt(
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):
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
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)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
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)

uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
return image_embeds, uncond_image_embeds

# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
Expand Down
28 changes: 21 additions & 7 deletions examples/community/pipeline_animatediff_controlnet.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -24,7 +24,7 @@

from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
from diffusers.loaders import IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin
from diffusers.models import AutoencoderKL, ControlNetModel, UNet2DConditionModel, UNetMotionModel
from diffusers.models import AutoencoderKL, ControlNetModel, ImageProjection, UNet2DConditionModel, UNetMotionModel
from diffusers.models.lora import adjust_lora_scale_text_encoder
from diffusers.models.unet_motion_model import MotionAdapter
from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
Expand Down Expand Up @@ -366,18 +366,29 @@ def encode_prompt(
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):
def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
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)
if output_hidden_states:
image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
uncond_image_enc_hidden_states = self.image_encoder(
torch.zeros_like(image), output_hidden_states=True
).hidden_states[-2]
uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
num_images_per_prompt, dim=0
)
return image_enc_hidden_states, uncond_image_enc_hidden_states
else:
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)

uncond_image_embeds = torch.zeros_like(image_embeds)
return image_embeds, uncond_image_embeds
return image_embeds, uncond_image_embeds

# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
def decode_latents(self, latents):
Expand Down Expand Up @@ -970,7 +981,10 @@ def __call__(
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)
output_hidden_state = False if isinstance(self.unet.encoder_hid_proj, ImageProjection) else True
image_embeds, negative_image_embeds = self.encode_image(
ip_adapter_image, device, num_videos_per_prompt, output_hidden_state
)
if self.do_classifier_free_guidance:
image_embeds = torch.cat([negative_image_embeds, image_embeds])

Expand Down