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add test for "Adapting SDXL" guide
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@ -58,9 +58,9 @@ Then, define the inference parameters by setting the appropriate prompt / seed /
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(
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enable=True, scale=0.75
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# Enable self-attention guidance to enhance the quality of the generated images
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) # Enable self-attention guidance to enhance the quality of the generated images
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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# ... Inference process
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# ... Inference process
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@ -76,10 +76,10 @@ with no_grad(): # Disable gradient calculation for memory-efficient inference
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)
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)
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time_ids = sdxl.default_time_ids
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time_ids = sdxl.default_time_ids
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manual_seed(seed=seed)
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manual_seed(seed)
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# Using a higher latents inner dim to improve resolution of generated images
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# SDXL typically generates 1024x1024, here we use a higher resolution.
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x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -131,8 +131,8 @@ predicted_image.save("vanilla_sdxl.png")
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manual_seed(seed=seed)
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manual_seed(seed=seed)
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# Using a higher latents inner dim to improve resolution of generated images
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# SDXL typically generates 1024x1024, here we use a higher resolution.
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x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -213,8 +213,8 @@ manager.add_loras("scifi-lora", tensors=scifi_lora_weights)
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manual_seed(seed=seed)
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manual_seed(seed=seed)
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# Using a higher latents inner dim to improve resolution of generated images
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# SDXL typically generates 1024x1024, here we use a higher resolution.
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x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -304,8 +304,8 @@ manager.add_loras("pixel-art-lora", load_from_safetensors("pixel-art-xl-v1.1.saf
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manual_seed(seed=seed)
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manual_seed(seed=seed)
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# Using a higher latents inner dim to improve resolution of generated images
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# SDXL typically generates 1024x1024, here we use a higher resolution.
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x = torch.randn(size=(1, 4, 256, 256), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((2048, 2048)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -440,7 +440,7 @@ with torch.no_grad():
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ip_adapter.set_clip_image_embedding(clip_image_embedding)
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ip_adapter.set_clip_image_embedding(clip_image_embedding)
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manual_seed(seed=seed)
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manual_seed(seed=seed)
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x = torch.randn(size=(1, 4, 128, 128), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -578,7 +578,7 @@ with torch.no_grad():
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t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
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t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition))
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manual_seed(seed=seed)
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manual_seed(seed=seed)
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x = torch.randn(size=(1, 4, 128, 128), device=sdxl.device, dtype=sdxl.dtype)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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# Diffusion process
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# Diffusion process
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for step in sdxl.steps:
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for step in sdxl.steps:
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@ -253,6 +253,20 @@ def download_loras():
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)
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)
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download_file("https://sliders.baulab.info/weights/xl_sliders/eyesize.pt", dest_folder, expected_hash="ee170e4d")
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download_file("https://sliders.baulab.info/weights/xl_sliders/eyesize.pt", dest_folder, expected_hash="ee170e4d")
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dest_folder = os.path.join(test_weights_dir, "loras")
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download_file(
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"https://civitai.com/api/download/models/140624",
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filename="Sci-fi_Environments_sdxl.safetensors",
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dest_folder=dest_folder,
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expected_hash="6a4afda8",
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)
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download_file(
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"https://civitai.com/api/download/models/135931",
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filename="pixel-art-xl-v1.1.safetensors",
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dest_folder=dest_folder,
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expected_hash="71aaa6ca",
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)
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def download_preprocessors():
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def download_preprocessors():
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dest_folder = os.path.join(test_weights_dir, "carolineec", "informativedrawings")
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dest_folder = os.path.join(test_weights_dir, "carolineec", "informativedrawings")
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321
tests/e2e/test_doc_examples.py
Normal file
321
tests/e2e/test_doc_examples.py
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@ -0,0 +1,321 @@
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import gc
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from pathlib import Path
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from warnings import warn
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import pytest
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import torch
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from PIL import Image
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from refiners.fluxion.utils import load_from_safetensors, manual_seed, no_grad
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from refiners.foundationals.latent_diffusion import SDXLIPAdapter
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from refiners.foundationals.latent_diffusion.lora import SDLoraManager
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from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL
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from tests.utils import ensure_similar_images
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@pytest.fixture(autouse=True)
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def ensure_gc():
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# Avoid GPU OOMs
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# See https://github.com/pytest-dev/pytest/discussions/8153#discussioncomment-214812
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gc.collect()
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@pytest.fixture(scope="module")
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def ref_path(test_e2e_path: Path) -> Path:
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return test_e2e_path / "test_doc_examples_ref"
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@pytest.fixture(scope="module")
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def sdxl_text_encoder_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "DoubleCLIPTextEncoder.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_lda_fp16_fix_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "sdxl-lda-fp16-fix.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_unet_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "sdxl-unet.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def sdxl_ip_adapter_plus_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "ip-adapter-plus_sdxl_vit-h.safetensors"
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if not path.is_file():
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warn(f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture(scope="module")
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def image_encoder_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "CLIPImageEncoderH.safetensors"
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if not path.is_file():
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warn(f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def scifi_lora_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "loras" / "Sci-fi_Environments_sdxl.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def pixelart_lora_weights(test_weights_path: Path) -> Path:
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path = test_weights_path / "loras" / "pixel-art-xl-v1.1.safetensors"
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if not path.is_file():
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warn(message=f"could not find weights at {path}, skipping")
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pytest.skip(allow_module_level=True)
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return path
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@pytest.fixture
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def sdxl(
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sdxl_text_encoder_weights: Path,
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sdxl_lda_fp16_fix_weights: Path,
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sdxl_unet_weights: Path,
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test_device: torch.device,
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) -> StableDiffusion_XL:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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sdxl = StableDiffusion_XL(device=test_device, dtype=torch.float16)
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sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights)
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sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_fp16_fix_weights)
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sdxl.unet.load_from_safetensors(tensors_path=sdxl_unet_weights)
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return sdxl
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@pytest.fixture
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def image_prompt_german_castle(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "german-castle.jpg").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_vanilla(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_image_guide_adapting_sdxl_vanilla.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_single_lora(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_image_guide_adapting_sdxl_single_lora.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_multiple_loras(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_image_guide_adapting_sdxl_multiple_loras.png").convert("RGB")
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@pytest.fixture
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def expected_image_guide_adapting_sdxl_loras_ip_adapter(ref_path: Path) -> Image.Image:
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return Image.open(ref_path / "expected_image_guide_adapting_sdxl_loras_ip_adapter.png").convert("RGB")
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@no_grad()
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def test_guide_adapting_sdxl_vanilla(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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expected_image_guide_adapting_sdxl_vanilla: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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expected_image = expected_image_guide_adapting_sdxl_vanilla
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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manual_seed(seed)
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# The guide uses 2048x2048 but it is too slow for tests.
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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for step in sdxl.steps:
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x = sdxl(
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x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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pooled_text_embedding=pooled_text_embedding,
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time_ids=time_ids,
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)
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predicted_image = sdxl.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_guide_adapting_sdxl_single_lora(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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scifi_lora_weights: Path,
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expected_image_guide_adapting_sdxl_single_lora: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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expected_image = expected_image_guide_adapting_sdxl_single_lora
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
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manager = SDLoraManager(sdxl)
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manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
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clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
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text=prompt + ", best quality, high quality",
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negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
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)
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time_ids = sdxl.default_time_ids
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manual_seed(seed)
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x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
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for step in sdxl.steps:
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x = sdxl(
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x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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pooled_text_embedding=pooled_text_embedding,
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time_ids=time_ids,
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)
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predicted_image = sdxl.lda.decode_latents(x)
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ensure_similar_images(predicted_image, expected_image)
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@no_grad()
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def test_guide_adapting_sdxl_multiple_loras(
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test_device: torch.device,
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sdxl: StableDiffusion_XL,
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scifi_lora_weights: Path,
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pixelart_lora_weights: Path,
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expected_image_guide_adapting_sdxl_multiple_loras: Image.Image,
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) -> None:
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if test_device.type == "cpu":
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warn(message="not running on CPU, skipping")
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pytest.skip()
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expected_image = expected_image_guide_adapting_sdxl_multiple_loras
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prompt = "a futuristic castle surrounded by a forest, mountains in the background"
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seed = 42
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sdxl.set_inference_steps(50, first_step=0)
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sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
||||||
|
|
||||||
|
manager = SDLoraManager(sdxl)
|
||||||
|
manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights))
|
||||||
|
manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.4)
|
||||||
|
|
||||||
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
||||||
|
text=prompt + ", best quality, high quality",
|
||||||
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
||||||
|
)
|
||||||
|
time_ids = sdxl.default_time_ids
|
||||||
|
|
||||||
|
manual_seed(seed)
|
||||||
|
x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
|
||||||
|
for step in sdxl.steps:
|
||||||
|
x = sdxl(
|
||||||
|
x,
|
||||||
|
step=step,
|
||||||
|
clip_text_embedding=clip_text_embedding,
|
||||||
|
pooled_text_embedding=pooled_text_embedding,
|
||||||
|
time_ids=time_ids,
|
||||||
|
)
|
||||||
|
|
||||||
|
predicted_image = sdxl.lda.decode_latents(x)
|
||||||
|
ensure_similar_images(predicted_image, expected_image)
|
||||||
|
|
||||||
|
|
||||||
|
@no_grad()
|
||||||
|
def test_guide_adapting_sdxl_loras_ip_adapter(
|
||||||
|
test_device: torch.device,
|
||||||
|
sdxl: StableDiffusion_XL,
|
||||||
|
sdxl_ip_adapter_plus_weights: Path,
|
||||||
|
image_encoder_weights: Path,
|
||||||
|
scifi_lora_weights: Path,
|
||||||
|
pixelart_lora_weights: Path,
|
||||||
|
image_prompt_german_castle: Image.Image,
|
||||||
|
expected_image_guide_adapting_sdxl_loras_ip_adapter: Image.Image,
|
||||||
|
) -> None:
|
||||||
|
if test_device.type == "cpu":
|
||||||
|
warn(message="not running on CPU, skipping")
|
||||||
|
pytest.skip()
|
||||||
|
|
||||||
|
expected_image = expected_image_guide_adapting_sdxl_loras_ip_adapter
|
||||||
|
|
||||||
|
prompt = "a futuristic castle surrounded by a forest, mountains in the background"
|
||||||
|
seed = 42
|
||||||
|
sdxl.set_inference_steps(50, first_step=0)
|
||||||
|
sdxl.set_self_attention_guidance(enable=True, scale=0.75)
|
||||||
|
|
||||||
|
manager = SDLoraManager(sdxl)
|
||||||
|
manager.add_loras("scifi-lora", load_from_safetensors(scifi_lora_weights), scale=1.5)
|
||||||
|
manager.add_loras("pixel-art-lora", load_from_safetensors(pixelart_lora_weights), scale=1.55)
|
||||||
|
|
||||||
|
ip_adapter = SDXLIPAdapter(
|
||||||
|
target=sdxl.unet,
|
||||||
|
weights=load_from_safetensors(sdxl_ip_adapter_plus_weights),
|
||||||
|
scale=1.0,
|
||||||
|
fine_grained=True,
|
||||||
|
)
|
||||||
|
ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights)
|
||||||
|
ip_adapter.inject()
|
||||||
|
|
||||||
|
clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding(
|
||||||
|
text=prompt + ", best quality, high quality",
|
||||||
|
negative_text="monochrome, lowres, bad anatomy, worst quality, low quality",
|
||||||
|
)
|
||||||
|
time_ids = sdxl.default_time_ids
|
||||||
|
|
||||||
|
image_prompt_preprocessed = ip_adapter.preprocess_image(image_prompt_german_castle)
|
||||||
|
clip_image_embedding = ip_adapter.compute_clip_image_embedding(image_prompt_preprocessed)
|
||||||
|
ip_adapter.set_clip_image_embedding(clip_image_embedding)
|
||||||
|
|
||||||
|
manual_seed(seed)
|
||||||
|
x = sdxl.init_latents((1024, 1024)).to(sdxl.device, sdxl.dtype)
|
||||||
|
for step in sdxl.steps:
|
||||||
|
x = sdxl(
|
||||||
|
x,
|
||||||
|
step=step,
|
||||||
|
clip_text_embedding=clip_text_embedding,
|
||||||
|
pooled_text_embedding=pooled_text_embedding,
|
||||||
|
time_ids=time_ids,
|
||||||
|
)
|
||||||
|
|
||||||
|
predicted_image = sdxl.lda.decode_latents(x)
|
||||||
|
ensure_similar_images(predicted_image, expected_image)
|
||||||
|
|
||||||
|
|
||||||
|
# We do not (yet) test the last example using T2i-Adapter with Zoe Depth.
|
5
tests/e2e/test_doc_examples_ref/README.md
Normal file
5
tests/e2e/test_doc_examples_ref/README.md
Normal file
|
@ -0,0 +1,5 @@
|
||||||
|
# Note about this data
|
||||||
|
|
||||||
|
Everything in this directory comes from Refiners' documentation.
|
||||||
|
|
||||||
|
Some outputs are different because we perform inference in 1024x1024 and not 2048x2048.
|
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After Width: | Height: | Size: 1.8 MiB |
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After Width: | Height: | Size: 1.2 MiB |
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After Width: | Height: | Size: 1.7 MiB |
BIN
tests/e2e/test_doc_examples_ref/german-castle.jpg
Normal file
BIN
tests/e2e/test_doc_examples_ref/german-castle.jpg
Normal file
Binary file not shown.
After Width: | Height: | Size: 2.7 MiB |
Loading…
Reference in a new issue