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Remove IP-Adapter and T2I-Adapter support from MultiDiffusionPipeline.

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This commit is contained in:
Ryan Dick 2024-06-17 11:43:48 -04:00
parent 889d13e02a
commit 20322d781e
1 changed files with 9 additions and 12 deletions
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21
invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
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@ -1,7 +1,7 @@
from __future__ import annotations
from contextlib import nullcontext
from typing import Any, Callable, List, Optional
from typing import Any, Callable, Optional
import torch
@ -14,7 +14,7 @@ from invokeai.backend.stable_diffusion.diffusers_pipeline import (
is_inpainting_model,
)
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import IPAdapterData, TextConditioningData
from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher, UNetIPAdapterData
from invokeai.backend.stable_diffusion.diffusion.unet_attention_patcher import UNetAttentionPatcher
class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
@ -63,6 +63,11 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
masked_latents: Optional[torch.Tensor] = None,
is_gradient_mask: bool = False,
) -> torch.Tensor:
if ip_adapter_data is not None:
raise NotImplementedError("ip_adapter_data is not supported in MultiDiffusionPipeline")
if t2i_adapter_data is not None:
raise NotImplementedError("t2i_adapter_data is not supported in MultiDiffusionPipeline")
# TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
# cases where densoisings_start and denoising_end are set such that there are no timesteps.
if init_timestep.shape[0] == 0 or timesteps.shape[0] == 0:
@ -106,20 +111,14 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
is_gradient_mask=is_gradient_mask,
)
use_ip_adapter = ip_adapter_data is not None
use_regional_prompting = (
conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None
)
unet_attention_patcher = None
attn_ctx = nullcontext()
if use_ip_adapter or use_regional_prompting:
ip_adapters: Optional[List[UNetIPAdapterData]] = (
[{"ip_adapter": ipa.ip_adapter_model, "target_blocks": ipa.target_blocks} for ipa in ip_adapter_data]
if use_ip_adapter
else None
)
unet_attention_patcher = UNetAttentionPatcher(ip_adapters)
if use_regional_prompting:
unet_attention_patcher = UNetAttentionPatcher(ip_adapter_data=None)
attn_ctx = unet_attention_patcher.apply_ip_adapter_attention(self.invokeai_diffuser.model)
with attn_ctx:
@ -146,8 +145,6 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
mask=mask,
masked_latents=masked_latents,
control_data=control_data,
ip_adapter_data=ip_adapter_data,
t2i_adapter_data=t2i_adapter_data,
)
latents = step_output.prev_sample
predicted_original = getattr(step_output, "pred_original_sample", None)