# Copyright 2026 Krea AI and The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from ...loaders import Krea2LoraLoaderMixin from ..modular_pipeline import ModularPipeline class Krea2ModularPipeline(ModularPipeline, Krea2LoraLoaderMixin): """ A ModularPipeline for Krea 2. > [!WARNING] > This is an experimental feature! """ default_blocks_name = "Krea2AutoBlocks" @property def patch_size(self): return 2 @property def default_height(self): return 1024 @property def default_width(self): return 1024 @property def vae_scale_factor(self): vae_scale_factor = 8 if getattr(self, "vae", None) is not None: vae_scale_factor = 2 ** len(self.vae.temperal_downsample) return vae_scale_factor @property def requires_unconditional_embeds(self): requires_unconditional_embeds = False if hasattr(self, "guider") and self.guider is not None: requires_unconditional_embeds = self.guider._enabled and self.guider.num_conditions > 1 return requires_unconditional_embeds class Krea2TurboModularPipeline(Krea2ModularPipeline): """ A ModularPipeline for the distilled Krea 2 turbo (TDM) checkpoint. It runs without classifier-free guidance, so it takes no negative prompt and has no guider. > [!WARNING] > This is an experimental feature! """ default_blocks_name = "Krea2TurboAutoBlocks" @property def requires_unconditional_embeds(self): return False