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"""
Wrapper pipeline for concept attention.
"""
from dataclasses import dataclass
import PIL
import numpy as np
import matplotlib.pyplot as plt
import torch
import einops
from concept_attention.binary_segmentation_baselines.raw_cross_attention import RawCrossAttentionBaseline, RawCrossAttentionSegmentationModel
from concept_attention.binary_segmentation_baselines.raw_output_space import RawOutputSpaceBaseline, RawOutputSpaceSegmentationModel
from concept_attention.image_generator import FluxGenerator
@dataclass
class ConceptAttentionPipelineOutput():
image: PIL.Image.Image | np.ndarray
concept_heatmaps: list[PIL.Image.Image]
cross_attention_maps: list[PIL.Image.Image]
class ConceptAttentionFluxPipeline():
"""
This is an object that allows you to generate images with flux, and
'encode' images with flux.
"""
def __init__(
self,
model_name: str = "flux-schnell",
offload_model=False,
device="cuda:0"
):
self.model_name = model_name
self.offload_model = offload_model
# Load the generator
self.flux_generator = FluxGenerator(
model_name=model_name,
offload=offload_model,
device=device
)
@torch.no_grad()
def generate_image(
self,
prompt: str,
concepts: list[str],
width: int = 1024,
height: int = 1024,
return_cross_attention = False,
layer_indices = list(range(15, 19)),
return_pil_heatmaps = True,
seed: int = 0,
num_inference_steps: int = 4,
guidance: float = 0.0,
timesteps=None,
softmax: bool = True,
cmap="plasma"
) -> ConceptAttentionPipelineOutput:
"""
Generate an image with flux, given a list of concepts.
"""
assert return_cross_attention is False, "Not supported yet"
assert all([layer_index >= 0 and layer_index < 19 for layer_index in layer_indices]), "Invalid layer index"
assert height == width, "Height and width must be the same for now"
if timesteps is None:
timesteps = list(range(num_inference_steps))
# Run the raw output space object
image, cross_attention_maps, concept_heatmaps = self.flux_generator.generate_image(
width=width,
height=height,
prompt=prompt,
num_steps=num_inference_steps,
concepts=concepts,
seed=seed,
guidance=guidance,
)
# Concept heamaps extraction
if softmax:
concept_heatmaps = torch.nn.functional.softmax(concept_heatmaps, dim=-2)
concept_heatmaps = concept_heatmaps[:, layer_indices]
concept_heatmaps = einops.reduce(
concept_heatmaps,
"time layers batch concepts patches -> batch concepts patches",
reduction="mean"
)
concept_heatmaps = einops.rearrange(
concept_heatmaps,
"batch concepts (h w) -> batch concepts h w",
h=64,
w=64
)
# Cross attention maps
if softmax:
cross_attention_maps = torch.nn.functional.softmax(cross_attention_maps, dim=-2)
cross_attention_maps = cross_attention_maps[:, layer_indices]
cross_attention_maps = einops.reduce(
cross_attention_maps,
"time layers batch concepts patches -> batch concepts patches",
reduction="mean"
)
cross_attention_maps = einops.rearrange(
cross_attention_maps,
"batch concepts (h w) -> batch concepts h w",
h=64,
w=64
)
concept_heatmaps = concept_heatmaps.to(torch.float32).detach().cpu().numpy()[0]
cross_attention_maps = cross_attention_maps.to(torch.float32).detach().cpu().numpy()[0]
# Convert the torch heatmaps to PIL images.
if return_pil_heatmaps:
# Convert to a matplotlib color scheme
colored_heatmaps = []
for concept_heatmap in concept_heatmaps:
concept_heatmap = (concept_heatmap - concept_heatmap.min()) / (concept_heatmap.max() - concept_heatmap.min())
colored_heatmap = plt.get_cmap(cmap)(concept_heatmap)
rgb_image = (colored_heatmap[:, :, :3] * 255).astype(np.uint8)
colored_heatmaps.append(rgb_image)
concept_heatmaps = [PIL.Image.fromarray(concept_heatmap) for concept_heatmap in colored_heatmaps]
colored_cross_attention_maps = []
for cross_attention_map in cross_attention_maps:
cross_attention_map = (cross_attention_map - cross_attention_map.min()) / (cross_attention_map.max() - cross_attention_map.min())
colored_cross_attention_map = plt.get_cmap(cmap)(cross_attention_map)
rgb_image = (colored_cross_attention_map[:, :, :3] * 255).astype(np.uint8)
colored_cross_attention_maps.append(rgb_image)
cross_attention_maps = [PIL.Image.fromarray(cross_attention_map) for cross_attention_map in colored_cross_attention_maps]
return ConceptAttentionPipelineOutput(
image=image,
concept_heatmaps=concept_heatmaps,
cross_attention_maps=cross_attention_maps
)
# def encode_image(
# self,
# image: PIL.Image.Image,
# concepts: list[str],
# prompt: str = "", # Optional
# width: int = 1024,
# height: int = 1024,
# return_cross_attention = False,
# layer_indices = list(range(15, 19)),
# num_samples: int = 1,
# device: str = "cuda:0",
# return_pil_heatmaps: bool = True,
# seed: int = 0,
# cmap="plasma"
# ) -> ConceptAttentionPipelineOutput:
# """
# Encode an image with flux, given a list of concepts.
# """
# assert return_cross_attention is False, "Not supported yet"
# assert all([layer_index >= 0 and layer_index < 19 for layer_index in layer_indices]), "Invalid layer index"
# assert height == width, "Height and width must be the same for now"
# # Run the raw output space object
# concept_heatmaps, _ = self.output_space_segmentation_model.segment_individual_image(
# image=image,
# concepts=concepts,
# caption=prompt,
# device=device,
# softmax=True,
# layers=layer_indices,
# num_samples=num_samples,
# height=height,
# width=width
# )
# concept_heatmaps = concept_heatmaps.detach().cpu().numpy().squeeze()
# # Convert the torch heatmaps to PIL images.
# if return_pil_heatmaps:
# min_val = concept_heatmaps.min()
# max_val = concept_heatmaps.max()
# # Convert to a matplotlib color scheme
# colored_heatmaps = []
# for concept_heatmap in concept_heatmaps:
# # concept_heatmap = (concept_heatmap - concept_heatmap.min()) / (concept_heatmap.max() - concept_heatmap.min())
# concept_heatmap = (concept_heatmap - min_val) / (max_val - min_val)
# colored_heatmap = plt.get_cmap(cmap)(concept_heatmap)
# rgb_image = (colored_heatmap[:, :, :3] * 255).astype(np.uint8)
# colored_heatmaps.append(rgb_image)
# concept_heatmaps = [PIL.Image.fromarray(concept_heatmap) for concept_heatmap in colored_heatmaps]
# return ConceptAttentionPipelineOutput(
# image=image,
# concept_heatmaps=concept_heatmaps
# )
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