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# coding=utf-8
# Copyright 2023 HuggingFace Inc.
#
# 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.
import gc
import os
import shutil
import unittest
from collections import OrderedDict
from pathlib import Path
import torch
from diffusers import (
AutoPipelineForImage2Image,
AutoPipelineForInpainting,
AutoPipelineForText2Image,
ControlNetModel,
DiffusionPipeline,
)
from diffusers.pipelines.auto_pipeline import (
AUTO_IMAGE2IMAGE_PIPELINES_MAPPING,
AUTO_INPAINT_PIPELINES_MAPPING,
AUTO_TEXT2IMAGE_PIPELINES_MAPPING,
)
from diffusers.utils.testing_utils import slow
PRETRAINED_MODEL_REPO_MAPPING = OrderedDict(
[
("stable-diffusion", "runwayml/stable-diffusion-v1-5"),
("if", "DeepFloyd/IF-I-XL-v1.0"),
("kandinsky", "kandinsky-community/kandinsky-2-1"),
("kandinsky22", "kandinsky-community/kandinsky-2-2-decoder"),
]
)
class AutoPipelineFastTest(unittest.TestCase):
def test_from_pipe_consistent(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
original_config = dict(pipe.config)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
pipe = AutoPipelineForText2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
def test_from_pipe_override(self):
pipe = AutoPipelineForText2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-pipe", requires_safety_checker=False
)
pipe = AutoPipelineForImage2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
pipe = AutoPipelineForText2Image.from_pipe(pipe, requires_safety_checker=True)
assert pipe.config.requires_safety_checker is True
def test_from_pipe_consistent_sdxl(self):
pipe = AutoPipelineForImage2Image.from_pretrained(
"hf-internal-testing/tiny-stable-diffusion-xl-pipe",
requires_aesthetics_score=True,
force_zeros_for_empty_prompt=False,
)
original_config = dict(pipe.config)
pipe = AutoPipelineForText2Image.from_pipe(pipe)
pipe = AutoPipelineForImage2Image.from_pipe(pipe)
assert dict(pipe.config) == original_config
def test_kwargs_local_files_only(self):
repo = "hf-internal-testing/tiny-stable-diffusion-torch"
tmpdirname = DiffusionPipeline.download(repo)
tmpdirname = Path(tmpdirname)
# edit commit_id to so that it's not the latest commit
commit_id = tmpdirname.name
new_commit_id = commit_id + "hug"
ref_dir = tmpdirname.parent.parent / "refs/main"
with open(ref_dir, "w") as f:
f.write(new_commit_id)
new_tmpdirname = tmpdirname.parent / new_commit_id
os.rename(tmpdirname, new_tmpdirname)
try:
AutoPipelineForText2Image.from_pretrained(repo, local_files_only=True)
except OSError:
assert False, "not able to load local files"
shutil.rmtree(tmpdirname.parent.parent)
def test_from_pipe_controlnet_text2img(self):
pipe = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForText2Image.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_img2img(self):
pipe = AutoPipelineForImage2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-pipe")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForImage2Image.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionImg2ImgPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_inpaint(self):
pipe = AutoPipelineForInpainting.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=controlnet)
assert pipe.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe.components
pipe = AutoPipelineForInpainting.from_pipe(pipe, controlnet=None)
assert pipe.__class__.__name__ == "StableDiffusionInpaintPipeline"
assert "controlnet" not in pipe.components
def test_from_pipe_controlnet_new_task(self):
pipe_text2img = AutoPipelineForText2Image.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch")
controlnet = ControlNetModel.from_pretrained("hf-internal-testing/tiny-controlnet")
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_text2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
pipe_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=None)
assert pipe_inpaint.__class__.__name__ == "StableDiffusionInpaintPipeline"
assert "controlnet" not in pipe_inpaint.components
# testing `from_pipe` for text2img controlnet
## 1. from a different controlnet pipe, without controlnet argument
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
## 2. from a different controlnet pipe, with controlnet argument
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
## 3. from same controlnet pipeline class, with a different controlnet component
pipe_control_text2img = AutoPipelineForText2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)
assert pipe_control_text2img.__class__.__name__ == "StableDiffusionControlNetPipeline"
assert "controlnet" in pipe_control_text2img.components
# testing from_pipe for inpainting
## 1. from a different controlnet pipeline class
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
## from a different controlnet pipe, with a different controlnet
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
## from same controlnet pipe, with a different controlnet
pipe_control_inpaint = AutoPipelineForInpainting.from_pipe(pipe_control_inpaint, controlnet=controlnet)
assert pipe_control_inpaint.__class__.__name__ == "StableDiffusionControlNetInpaintPipeline"
assert "controlnet" in pipe_control_inpaint.components
# testing from_pipe from img2img controlnet
## from a different controlnet pipe, without controlnet argument
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
# from a different controlnet pipe, with a different controlnet component
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_text2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
# from same controlnet pipeline class, with a different controlnet
pipe_control_img2img = AutoPipelineForImage2Image.from_pipe(pipe_control_img2img, controlnet=controlnet)
assert pipe_control_img2img.__class__.__name__ == "StableDiffusionControlNetImg2ImgPipeline"
assert "controlnet" in pipe_control_img2img.components
@slow
class AutoPipelineIntegrationTest(unittest.TestCase):
def test_pipe_auto(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
# test txt2img
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_txt2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_txt2img, pipe_to
gc.collect()
# test img2img
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
if "kandinsky" not in model_name:
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_img2img)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_img2img, pipe_to
gc.collect()
# test inpaint
if "kandinsky" not in model_name:
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, variant="fp16", torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING[model_name])
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_inpaint)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING[model_name])
del pipe_inpaint, pipe_to
gc.collect()
def test_from_pipe_consistent(self):
for model_name, model_repo in PRETRAINED_MODEL_REPO_MAPPING.items():
if model_name in ["kandinsky", "kandinsky22"]:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image]
else:
auto_pipes = [AutoPipelineForText2Image, AutoPipelineForImage2Image, AutoPipelineForInpainting]
# test from_pretrained
for pipe_from_class in auto_pipes:
pipe_from = pipe_from_class.from_pretrained(model_repo, variant="fp16", torch_dtype=torch.float16)
pipe_from_config = dict(pipe_from.config)
for pipe_to_class in auto_pipes:
pipe_to = pipe_to_class.from_pipe(pipe_from)
self.assertEqual(dict(pipe_to.config), pipe_from_config)
del pipe_from, pipe_to
gc.collect()
def test_controlnet(self):
# test from_pretrained
model_repo = "runwayml/stable-diffusion-v1-5"
controlnet_repo = "lllyasviel/sd-controlnet-canny"
controlnet = ControlNetModel.from_pretrained(controlnet_repo, torch_dtype=torch.float16)
pipe_txt2img = AutoPipelineForText2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_txt2img, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_img2img = AutoPipelineForImage2Image.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_img2img, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
pipe_inpaint = AutoPipelineForInpainting.from_pretrained(
model_repo, controlnet=controlnet, torch_dtype=torch.float16
)
self.assertIsInstance(pipe_inpaint, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
# test from_pipe
for pipe_from in [pipe_txt2img, pipe_img2img, pipe_inpaint]:
pipe_to = AutoPipelineForText2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_TEXT2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_txt2img.config))
pipe_to = AutoPipelineForImage2Image.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_IMAGE2IMAGE_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_img2img.config))
pipe_to = AutoPipelineForInpainting.from_pipe(pipe_from)
self.assertIsInstance(pipe_to, AUTO_INPAINT_PIPELINES_MAPPING["stable-diffusion-controlnet"])
self.assertEqual(dict(pipe_to.config), dict(pipe_inpaint.config))
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