Sayak Paul
a89a14fa7a
* add: first draft for a better LoRA enabler. * make fix-copies. * feat: backward compatibility. * add: entry to the docs. * add: tests. * fix: docs. * fix: norm group test for UNet3D. * feat: add support for flat dicts. * add depcrcation message instead of warning.
214 lines
8.6 KiB
Python
214 lines
8.6 KiB
Python
# coding=utf-8
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# Copyright 2023 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import tempfile
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import unittest
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import torch
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import torch.nn as nn
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNet2DConditionModel
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from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
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from diffusers.models.attention_processor import LoRAAttnProcessor
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from diffusers.utils import TEXT_ENCODER_TARGET_MODULES, floats_tensor, torch_device
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def create_unet_lora_layers(unet: nn.Module):
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lora_attn_procs = {}
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for name in unet.attn_processors.keys():
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cross_attention_dim = None if name.endswith("attn1.processor") else unet.config.cross_attention_dim
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if name.startswith("mid_block"):
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hidden_size = unet.config.block_out_channels[-1]
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elif name.startswith("up_blocks"):
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block_id = int(name[len("up_blocks.")])
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hidden_size = list(reversed(unet.config.block_out_channels))[block_id]
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elif name.startswith("down_blocks"):
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block_id = int(name[len("down_blocks.")])
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hidden_size = unet.config.block_out_channels[block_id]
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lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=hidden_size, cross_attention_dim=cross_attention_dim)
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unet_lora_layers = AttnProcsLayers(lora_attn_procs)
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return lora_attn_procs, unet_lora_layers
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def create_text_encoder_lora_layers(text_encoder: nn.Module):
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text_lora_attn_procs = {}
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for name, module in text_encoder.named_modules():
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if any([x in name for x in TEXT_ENCODER_TARGET_MODULES]):
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text_lora_attn_procs[name] = LoRAAttnProcessor(hidden_size=module.out_features, cross_attention_dim=None)
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text_encoder_lora_layers = AttnProcsLayers(text_lora_attn_procs)
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return text_encoder_lora_layers
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class LoraLoaderMixinTests(unittest.TestCase):
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def get_dummy_components(self):
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torch.manual_seed(0)
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unet = UNet2DConditionModel(
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block_out_channels=(32, 64),
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layers_per_block=2,
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sample_size=32,
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in_channels=4,
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out_channels=4,
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down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
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up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"),
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cross_attention_dim=32,
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)
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scheduler = DDIMScheduler(
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beta_start=0.00085,
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beta_end=0.012,
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beta_schedule="scaled_linear",
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clip_sample=False,
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set_alpha_to_one=False,
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)
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torch.manual_seed(0)
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vae = AutoencoderKL(
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block_out_channels=[32, 64],
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in_channels=3,
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out_channels=3,
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down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"],
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up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"],
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latent_channels=4,
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)
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text_encoder_config = CLIPTextConfig(
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bos_token_id=0,
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eos_token_id=2,
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hidden_size=32,
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intermediate_size=37,
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layer_norm_eps=1e-05,
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num_attention_heads=4,
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num_hidden_layers=5,
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pad_token_id=1,
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vocab_size=1000,
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)
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text_encoder = CLIPTextModel(text_encoder_config)
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tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
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unet_lora_attn_procs, unet_lora_layers = create_unet_lora_layers(unet)
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text_encoder_lora_layers = create_text_encoder_lora_layers(text_encoder)
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pipeline_components = {
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"unet": unet,
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"scheduler": scheduler,
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"vae": vae,
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"text_encoder": text_encoder,
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"tokenizer": tokenizer,
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"safety_checker": None,
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"feature_extractor": None,
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}
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lora_components = {
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"unet_lora_layers": unet_lora_layers,
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"text_encoder_lora_layers": text_encoder_lora_layers,
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"unet_lora_attn_procs": unet_lora_attn_procs,
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}
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return pipeline_components, lora_components
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def get_dummy_inputs(self):
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batch_size = 1
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sequence_length = 10
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num_channels = 4
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sizes = (32, 32)
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generator = torch.manual_seed(0)
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noise = floats_tensor((batch_size, num_channels) + sizes)
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input_ids = torch.randint(1, sequence_length, size=(batch_size, sequence_length), generator=generator)
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pipeline_inputs = {
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"prompt": "A painting of a squirrel eating a burger",
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"generator": generator,
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"num_inference_steps": 2,
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"guidance_scale": 6.0,
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"output_type": "numpy",
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}
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return noise, input_ids, pipeline_inputs
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def test_lora_save_load(self):
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pipeline_components, lora_components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**pipeline_components)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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noise, input_ids, pipeline_inputs = self.get_dummy_inputs()
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original_images = sd_pipe(**pipeline_inputs).images
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orig_image_slice = original_images[0, -3:, -3:, -1]
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with tempfile.TemporaryDirectory() as tmpdirname:
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LoraLoaderMixin.save_lora_weights(
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save_directory=tmpdirname,
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unet_lora_layers=lora_components["unet_lora_layers"],
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text_encoder_lora_layers=lora_components["text_encoder_lora_layers"],
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)
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self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
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sd_pipe.load_lora_weights(tmpdirname)
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lora_images = sd_pipe(**pipeline_inputs).images
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lora_image_slice = lora_images[0, -3:, -3:, -1]
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# Outputs shouldn't match.
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self.assertFalse(torch.allclose(torch.from_numpy(orig_image_slice), torch.from_numpy(lora_image_slice)))
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def test_lora_save_load_safetensors(self):
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pipeline_components, lora_components = self.get_dummy_components()
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sd_pipe = StableDiffusionPipeline(**pipeline_components)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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noise, input_ids, pipeline_inputs = self.get_dummy_inputs()
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original_images = sd_pipe(**pipeline_inputs).images
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orig_image_slice = original_images[0, -3:, -3:, -1]
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with tempfile.TemporaryDirectory() as tmpdirname:
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LoraLoaderMixin.save_lora_weights(
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save_directory=tmpdirname,
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unet_lora_layers=lora_components["unet_lora_layers"],
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text_encoder_lora_layers=lora_components["text_encoder_lora_layers"],
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safe_serialization=True,
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)
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self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
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sd_pipe.load_lora_weights(tmpdirname)
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lora_images = sd_pipe(**pipeline_inputs).images
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lora_image_slice = lora_images[0, -3:, -3:, -1]
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# Outputs shouldn't match.
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self.assertFalse(torch.allclose(torch.from_numpy(orig_image_slice), torch.from_numpy(lora_image_slice)))
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def test_lora_save_load_legacy(self):
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pipeline_components, lora_components = self.get_dummy_components()
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unet_lora_attn_procs = lora_components["unet_lora_attn_procs"]
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sd_pipe = StableDiffusionPipeline(**pipeline_components)
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sd_pipe = sd_pipe.to(torch_device)
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sd_pipe.set_progress_bar_config(disable=None)
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noise, input_ids, pipeline_inputs = self.get_dummy_inputs()
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original_images = sd_pipe(**pipeline_inputs).images
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orig_image_slice = original_images[0, -3:, -3:, -1]
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with tempfile.TemporaryDirectory() as tmpdirname:
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unet = sd_pipe.unet
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unet.set_attn_processor(unet_lora_attn_procs)
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unet.save_attn_procs(tmpdirname)
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self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.bin")))
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sd_pipe.load_lora_weights(tmpdirname)
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lora_images = sd_pipe(**pipeline_inputs).images
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lora_image_slice = lora_images[0, -3:, -3:, -1]
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# Outputs shouldn't match.
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self.assertFalse(torch.allclose(torch.from_numpy(orig_image_slice), torch.from_numpy(lora_image_slice)))
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