mirror of
https://github.com/huggingface/diffusers.git
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2625fb59dc
* up * convert dual unet * revert dual attn * adapt for vd-official * test the full pipeline * mixed inference * mixed inference for text2img * add image prompting * fix clip norm * split text2img and img2img * fix format * refactor text2img * mega pipeline * add optimus * refactor image var * wip text_unet * text unet end to end * update tests * reshape * fix image to text * add some first docs * dual guided pipeline * fix token ratio * propose change * dual transformer as a native module * DualTransformer(nn.Module) * DualTransformer(nn.Module) * correct unconditional image * save-load with mega pipeline * remove image to text * up * uP * fix * up * final fix * remove_unused_weights * test updates * save progress * uP * fix dual prompts * some fixes * finish * style * finish renaming * up * fix * fix * fix * finish Co-authored-by: anton-l <anton@huggingface.co>
113 lines
3.8 KiB
Python
113 lines
3.8 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 tempfile
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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 diffusers import VersatileDiffusionDualGuidedPipeline
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from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
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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 VersatileDiffusionDualGuidedPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
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pass
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@slow
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@require_torch_gpu
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class VersatileDiffusionDualGuidedPipelineIntegrationTests(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_remove_unused_weights_save_load(self):
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pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
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# remove text_unet
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pipe.remove_unused_weights()
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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second_prompt = load_image(
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"https://raw.githubusercontent.com/SHI-Labs/Versatile-Diffusion/master/assets/benz.jpg"
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)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = pipe(
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prompt="first prompt",
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image=second_prompt,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=2,
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output_type="numpy",
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).images
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with tempfile.TemporaryDirectory() as tmpdirname:
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pipe.save_pretrained(tmpdirname)
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pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained(tmpdirname)
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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generator = generator.manual_seed(0)
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new_image = pipe(
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prompt="first prompt",
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image=second_prompt,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=2,
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output_type="numpy",
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).images
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assert np.abs(image - new_image).sum() < 1e-5, "Models don't have the same forward pass"
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def test_inference_dual_guided(self):
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pipe = VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion")
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pipe.remove_unused_weights()
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pipe.to(torch_device)
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pipe.set_progress_bar_config(disable=None)
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first_prompt = "cyberpunk 2077"
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second_prompt = load_image(
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"https://raw.githubusercontent.com/SHI-Labs/Versatile-Diffusion/master/assets/benz.jpg"
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)
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = pipe(
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prompt=first_prompt,
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image=second_prompt,
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text_to_image_strength=0.75,
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generator=generator,
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guidance_scale=7.5,
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num_inference_steps=50,
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output_type="numpy",
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).images
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image_slice = image[0, 253:256, 253:256, -1]
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assert image.shape == (1, 512, 512, 3)
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expected_slice = np.array([0.014, 0.0112, 0.0136, 0.0145, 0.0107, 0.0113, 0.0272, 0.0215, 0.0216])
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assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
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