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Allow SD attend and excite pipeline to work with any size output images (#2835)

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Allow stable diffusion attend and excite pipeline to work with any size output image. Re: #2476, #2603
This commit is contained in:
Joseph Coffland
2023-04-13 08:54:16 -07:00
committed by GitHub
parent 3bf5ce21ad
commit 3eaead0c4a
1 changed files with 10 additions and 8 deletions
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18
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_attend_and_excite.py
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@@ -14,7 +14,7 @@
import inspect
import math
from typing import Any, Callable, Dict, List, Optional, Union
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
@@ -76,7 +76,7 @@ class AttentionStore:
def __call__(self, attn, is_cross: bool, place_in_unet: str):
if self.cur_att_layer >= 0 and is_cross:
if attn.shape[1] == self.attn_res**2:
if attn.shape[1] == np.prod(self.attn_res):
self.step_store[place_in_unet].append(attn)
self.cur_att_layer += 1
@@ -98,7 +98,7 @@ class AttentionStore:
attention_maps = self.get_average_attention()
for location in from_where:
for item in attention_maps[location]:
cross_maps = item.reshape(-1, self.attn_res, self.attn_res, item.shape[-1])
cross_maps = item.reshape(-1, self.attn_res[0], self.attn_res[1], item.shape[-1])
out.append(cross_maps)
out = torch.cat(out, dim=0)
out = out.sum(0) / out.shape[0]
@@ -109,7 +109,7 @@ class AttentionStore:
self.step_store = self.get_empty_store()
self.attention_store = {}
def __init__(self, attn_res=16):
def __init__(self, attn_res):
"""
Initialize an empty AttentionStore :param step_index: used to visualize only a specific step in the diffusion
process
@@ -724,7 +724,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversion
max_iter_to_alter: int = 25,
thresholds: dict = {0: 0.05, 10: 0.5, 20: 0.8},
scale_factor: int = 20,
attn_res: int = 16,
attn_res: Optional[Tuple[int]] = None,
):
r"""
Function invoked when calling the pipeline for generation.
@@ -796,8 +796,8 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversion
Dictionary defining the iterations and desired thresholds to apply iterative latent refinement in.
scale_factor (`int`, *optional*, default to 20):
Scale factor that controls the step size of each Attend and Excite update.
attn_res (`int`, *optional*, default to 16):
The resolution of most semantic attention map.
attn_res (`tuple`, *optional*, default computed from width and height):
The 2D resolution of the semantic attention map.
Examples:
@@ -870,7 +870,9 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, TextualInversion
# 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)
self.attention_store = AttentionStore(attn_res=attn_res)
if attn_res is None:
attn_res = int(np.ceil(width / 32)), int(np.ceil(height / 32))
self.attention_store = AttentionStore(attn_res)
self.register_attention_control()
# default config for step size from original repo