240 lines
8.3 KiB
Python
240 lines
8.3 KiB
Python
# coding=utf-8
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# Copyright 2022 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 gc
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import random
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import unittest
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import numpy as np
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import torch
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from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
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from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNet2DConditionModel
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from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
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from diffusers.utils.testing_utils import require_torch_gpu
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from ...test_pipelines_common import PipelineTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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class CycleDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pipeline_class = CycleDiffusionPipeline
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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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num_train_timesteps=1000,
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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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torch.manual_seed(0)
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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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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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return components
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def get_dummy_inputs(self, device, seed=0):
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image = floats_tensor((1, 3, 32, 32), rng=random.Random(seed)).to(device)
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if str(device).startswith("mps"):
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generator = torch.manual_seed(seed)
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else:
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generator = torch.Generator(device=device).manual_seed(seed)
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inputs = {
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"prompt": "An astronaut riding an elephant",
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"source_prompt": "An astronaut riding a horse",
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"image": image,
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"generator": generator,
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"num_inference_steps": 2,
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"eta": 0.1,
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"strength": 0.8,
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"guidance_scale": 3,
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"source_guidance_scale": 1,
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"output_type": "numpy",
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}
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return inputs
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def test_stable_diffusion_cycle(self):
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device = "cpu" # ensure determinism for the device-dependent torch.Generator
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components = self.get_dummy_components()
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pipe = CycleDiffusionPipeline(**components)
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pipe = pipe.to(device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(device)
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output = pipe(**inputs)
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images = output.images
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image_slice = images[0, -3:, -3:, -1]
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assert images.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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@unittest.skipIf(torch_device != "cuda", "This test requires a GPU")
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def test_stable_diffusion_cycle_fp16(self):
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components = self.get_dummy_components()
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for name, module in components.items():
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if hasattr(module, "half"):
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components[name] = module.half()
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pipe = CycleDiffusionPipeline(**components)
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pipe = pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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inputs = self.get_dummy_inputs(torch_device)
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output = pipe(**inputs)
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images = output.images
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image_slice = images[0, -3:, -3:, -1]
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assert images.shape == (1, 32, 32, 3)
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expected_slice = np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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@slow
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@require_torch_gpu
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class CycleDiffusionPipelineIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def test_cycle_diffusion_pipeline_fp16(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/cycle-diffusion/black_colored_car.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy"
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)
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init_image = init_image.resize((512, 512))
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model_id = "CompVis/stable-diffusion-v1-4"
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scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = CycleDiffusionPipeline.from_pretrained(
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model_id, scheduler=scheduler, safety_checker=None, torch_dtype=torch.float16, revision="fp16"
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)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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source_prompt = "A black colored car"
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prompt = "A blue colored car"
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generator = torch.manual_seed(0)
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output = pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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image=init_image,
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num_inference_steps=100,
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eta=0.1,
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strength=0.85,
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guidance_scale=3,
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source_guidance_scale=1,
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generator=generator,
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output_type="np",
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)
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image = output.images
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# the values aren't exactly equal, but the images look the same visually
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assert np.abs(image - expected_image).max() < 5e-1
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def test_cycle_diffusion_pipeline(self):
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init_image = load_image(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
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"/cycle-diffusion/black_colored_car.png"
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)
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expected_image = load_numpy(
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"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy"
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)
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init_image = init_image.resize((512, 512))
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model_id = "CompVis/stable-diffusion-v1-4"
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scheduler = DDIMScheduler.from_pretrained(model_id, subfolder="scheduler")
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pipe = CycleDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, safety_checker=None)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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pipe.enable_attention_slicing()
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source_prompt = "A black colored car"
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prompt = "A blue colored car"
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generator = torch.manual_seed(0)
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output = pipe(
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prompt=prompt,
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source_prompt=source_prompt,
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image=init_image,
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num_inference_steps=100,
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eta=0.1,
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strength=0.85,
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guidance_scale=3,
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source_guidance_scale=1,
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generator=generator,
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output_type="np",
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)
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image = output.images
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assert np.abs(image - expected_image).max() < 1e-2
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