from pathlib import Path from typing import Iterator from warnings import warn import pytest import torch from PIL import Image from refiners.fluxion.utils import image_to_tensor, load_from_safetensors, manual_seed from refiners.foundationals.clip.concepts import ConceptExtender from refiners.foundationals.latent_diffusion import ( SD1ControlnetAdapter, SD1IPAdapter, SD1T2IAdapter, SD1UNet, SDFreeUAdapter, SDXLIPAdapter, SDXLT2IAdapter, StableDiffusion_1, StableDiffusion_1_Inpainting, ) from refiners.foundationals.latent_diffusion.lora import SD1LoraAdapter from refiners.foundationals.latent_diffusion.multi_diffusion import DiffusionTarget from refiners.foundationals.latent_diffusion.reference_only_control import ReferenceOnlyControlAdapter from refiners.foundationals.latent_diffusion.restart import Restart from refiners.foundationals.latent_diffusion.schedulers import DDIM from refiners.foundationals.latent_diffusion.schedulers.scheduler import NoiseSchedule from refiners.foundationals.latent_diffusion.stable_diffusion_1.multi_diffusion import SD1MultiDiffusion from refiners.foundationals.latent_diffusion.stable_diffusion_xl.model import StableDiffusion_XL from tests.utils import ensure_similar_images @pytest.fixture(scope="module") def ref_path(test_e2e_path: Path) -> Path: return test_e2e_path / "test_diffusion_ref" @pytest.fixture(scope="module") def cutecat_init(ref_path: Path) -> Image.Image: return Image.open(ref_path / "cutecat_init.png").convert("RGB") @pytest.fixture(scope="module") def kitchen_dog(ref_path: Path) -> Image.Image: return Image.open(ref_path / "kitchen_dog.png").convert("RGB") @pytest.fixture(scope="module") def kitchen_dog_mask(ref_path: Path) -> Image.Image: return Image.open(ref_path / "kitchen_dog_mask.png").convert("RGB") @pytest.fixture(scope="module") def woman_image(ref_path: Path) -> Image.Image: return Image.open(ref_path / "woman.png").convert("RGB") @pytest.fixture(scope="module") def statue_image(ref_path: Path) -> Image.Image: return Image.open(ref_path / "statue.png").convert("RGB") @pytest.fixture def expected_image_std_random_init(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_random_init.png").convert("RGB") @pytest.fixture def expected_karras_random_init(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_karras_random_init.png").convert("RGB") @pytest.fixture def expected_image_std_random_init_sag(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_random_init_sag.png").convert("RGB") @pytest.fixture def expected_image_std_init_image(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_init_image.png").convert("RGB") @pytest.fixture def expected_image_std_inpainting(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_std_inpainting.png").convert("RGB") @pytest.fixture def expected_image_controlnet_stack(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_controlnet_stack.png").convert("RGB") @pytest.fixture def expected_image_ip_adapter_woman(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_image_ip_adapter_woman.png").convert("RGB") @pytest.fixture def expected_image_ip_adapter_plus_statue(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_image_ip_adapter_plus_statue.png").convert("RGB") @pytest.fixture def expected_image_sdxl_ip_adapter_woman(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_image_sdxl_ip_adapter_woman.png").convert("RGB") @pytest.fixture def expected_image_sdxl_ip_adapter_plus_woman(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_image_sdxl_ip_adapter_plus_woman.png").convert("RGB") @pytest.fixture def expected_image_ip_adapter_controlnet(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_ip_adapter_controlnet.png").convert("RGB") @pytest.fixture def expected_sdxl_ddim_random_init(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init.png").convert(mode="RGB") @pytest.fixture def expected_sdxl_ddim_random_init_sag(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_cutecat_sdxl_ddim_random_init_sag.png").convert(mode="RGB") @pytest.fixture(scope="module", params=["canny", "depth", "lineart", "normals", "sam"]) def controlnet_data( ref_path: Path, test_weights_path: Path, request: pytest.FixtureRequest ) -> Iterator[tuple[str, Image.Image, Image.Image, Path]]: cn_name: str = request.param condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB") expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB") weights_fn = { "depth": "lllyasviel_control_v11f1p_sd15_depth", "canny": "lllyasviel_control_v11p_sd15_canny", "lineart": "lllyasviel_control_v11p_sd15_lineart", "normals": "lllyasviel_control_v11p_sd15_normalbae", "sam": "mfidabel_controlnet-segment-anything", } weights_path = test_weights_path / "controlnet" / f"{weights_fn[cn_name]}.safetensors" yield (cn_name, condition_image, expected_image, weights_path) @pytest.fixture(scope="module") def controlnet_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]: cn_name = "canny" condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB") expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB") weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11p_sd15_canny.safetensors" return cn_name, condition_image, expected_image, weights_path @pytest.fixture(scope="module") def controlnet_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]: cn_name = "depth" condition_image = Image.open(ref_path / f"cutecat_guide_{cn_name}.png").convert("RGB") expected_image = Image.open(ref_path / f"expected_controlnet_{cn_name}.png").convert("RGB") weights_path = test_weights_path / "controlnet" / "lllyasviel_control_v11f1p_sd15_depth.safetensors" return cn_name, condition_image, expected_image, weights_path @pytest.fixture(scope="module") def t2i_adapter_data_depth(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]: name = "depth" condition_image = Image.open(ref_path / f"cutecat_guide_{name}.png").convert("RGB") expected_image = Image.open(ref_path / f"expected_t2i_adapter_{name}.png").convert("RGB") weights_path = test_weights_path / "T2I-Adapter" / "t2iadapter_depth_sd15v2.safetensors" return name, condition_image, expected_image, weights_path @pytest.fixture(scope="module") def t2i_adapter_xl_data_canny(ref_path: Path, test_weights_path: Path) -> tuple[str, Image.Image, Image.Image, Path]: name = "canny" condition_image = Image.open(ref_path / f"fairy_guide_{name}.png").convert("RGB") expected_image = Image.open(ref_path / f"expected_t2i_adapter_xl_{name}.png").convert("RGB") weights_path = test_weights_path / "T2I-Adapter" / "t2i-adapter-canny-sdxl-1.0.safetensors" return name, condition_image, expected_image, weights_path @pytest.fixture(scope="module") def lora_data_pokemon(ref_path: Path, test_weights_path: Path) -> tuple[Image.Image, Path]: expected_image = Image.open(ref_path / "expected_lora_pokemon.png").convert("RGB") weights_path = test_weights_path / "loras" / "pcuenq_pokemon_lora.safetensors" return expected_image, weights_path @pytest.fixture def scene_image_inpainting_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "inpainting-scene.png").convert("RGB") @pytest.fixture def mask_image_inpainting_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "inpainting-mask.png").convert("RGB") @pytest.fixture def target_image_inpainting_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "inpainting-target.png").convert("RGB") @pytest.fixture def expected_image_inpainting_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_inpainting_refonly.png").convert("RGB") @pytest.fixture def expected_image_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_refonly.png").convert("RGB") @pytest.fixture def condition_image_refonly(ref_path: Path) -> Image.Image: return Image.open(ref_path / "cyberpunk_guide.png").convert("RGB") @pytest.fixture def expected_image_textual_inversion_random_init(ref_path: Path) -> Image.Image: return Image.open(ref_path / "expected_textual_inversion_random_init.png").convert("RGB") @pytest.fixture def expected_multi_diffusion(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_multi_diffusion.png").convert(mode="RGB") @pytest.fixture def expected_restart(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_restart.png").convert(mode="RGB") @pytest.fixture def expected_freeu(ref_path: Path) -> Image.Image: return Image.open(fp=ref_path / "expected_freeu.png").convert(mode="RGB") @pytest.fixture def text_embedding_textual_inversion(test_textual_inversion_path: Path) -> torch.Tensor: return torch.load(test_textual_inversion_path / "gta5-artwork" / "learned_embeds.bin")[""] # type: ignore @pytest.fixture(scope="module") def text_encoder_weights(test_weights_path: Path) -> Path: text_encoder_weights = test_weights_path / "CLIPTextEncoderL.safetensors" if not text_encoder_weights.is_file(): warn(f"could not find weights at {text_encoder_weights}, skipping") pytest.skip(allow_module_level=True) return text_encoder_weights @pytest.fixture(scope="module") def lda_weights(test_weights_path: Path) -> Path: lda_weights = test_weights_path / "lda.safetensors" if not lda_weights.is_file(): warn(f"could not find weights at {lda_weights}, skipping") pytest.skip(allow_module_level=True) return lda_weights @pytest.fixture(scope="module") def unet_weights_std(test_weights_path: Path) -> Path: unet_weights_std = test_weights_path / "unet.safetensors" if not unet_weights_std.is_file(): warn(f"could not find weights at {unet_weights_std}, skipping") pytest.skip(allow_module_level=True) return unet_weights_std @pytest.fixture(scope="module") def unet_weights_inpainting(test_weights_path: Path) -> Path: unet_weights_inpainting = test_weights_path / "inpainting" / "unet.safetensors" if not unet_weights_inpainting.is_file(): warn(f"could not find weights at {unet_weights_inpainting}, skipping") pytest.skip(allow_module_level=True) return unet_weights_inpainting @pytest.fixture(scope="module") def lda_ft_mse_weights(test_weights_path: Path) -> Path: lda_weights = test_weights_path / "lda_ft_mse.safetensors" if not lda_weights.is_file(): warn(f"could not find weights at {lda_weights}, skipping") pytest.skip(allow_module_level=True) return lda_weights @pytest.fixture(scope="module") def ip_adapter_weights(test_weights_path: Path) -> Path: ip_adapter_weights = test_weights_path / "ip-adapter_sd15.safetensors" if not ip_adapter_weights.is_file(): warn(f"could not find weights at {ip_adapter_weights}, skipping") pytest.skip(allow_module_level=True) return ip_adapter_weights @pytest.fixture(scope="module") def ip_adapter_plus_weights(test_weights_path: Path) -> Path: ip_adapter_weights = test_weights_path / "ip-adapter-plus_sd15.safetensors" if not ip_adapter_weights.is_file(): warn(f"could not find weights at {ip_adapter_weights}, skipping") pytest.skip(allow_module_level=True) return ip_adapter_weights @pytest.fixture(scope="module") def sdxl_ip_adapter_weights(test_weights_path: Path) -> Path: ip_adapter_weights = test_weights_path / "ip-adapter_sdxl_vit-h.safetensors" if not ip_adapter_weights.is_file(): warn(f"could not find weights at {ip_adapter_weights}, skipping") pytest.skip(allow_module_level=True) return ip_adapter_weights @pytest.fixture(scope="module") def sdxl_ip_adapter_plus_weights(test_weights_path: Path) -> Path: ip_adapter_weights = test_weights_path / "ip-adapter-plus_sdxl_vit-h.safetensors" if not ip_adapter_weights.is_file(): warn(f"could not find weights at {ip_adapter_weights}, skipping") pytest.skip(allow_module_level=True) return ip_adapter_weights @pytest.fixture(scope="module") def image_encoder_weights(test_weights_path: Path) -> Path: image_encoder_weights = test_weights_path / "CLIPImageEncoderH.safetensors" if not image_encoder_weights.is_file(): warn(f"could not find weights at {image_encoder_weights}, skipping") pytest.skip(allow_module_level=True) return image_encoder_weights @pytest.fixture def sd15_std( text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device ) -> StableDiffusion_1: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() sd15 = StableDiffusion_1(device=test_device) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_std) return sd15 @pytest.fixture def sd15_std_float16( text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device ) -> StableDiffusion_1: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() sd15 = StableDiffusion_1(device=test_device, dtype=torch.float16) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_std) return sd15 @pytest.fixture def sd15_inpainting( text_encoder_weights: Path, lda_weights: Path, unet_weights_inpainting: Path, test_device: torch.device ) -> StableDiffusion_1_Inpainting: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() unet = SD1UNet(in_channels=9) sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_inpainting) return sd15 @pytest.fixture def sd15_inpainting_float16( text_encoder_weights: Path, lda_weights: Path, unet_weights_inpainting: Path, test_device: torch.device ) -> StableDiffusion_1_Inpainting: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() unet = SD1UNet(in_channels=9) sd15 = StableDiffusion_1_Inpainting(unet=unet, device=test_device, dtype=torch.float16) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_inpainting) return sd15 @pytest.fixture def sd15_ddim( text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device ) -> StableDiffusion_1: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() ddim_scheduler = DDIM(num_inference_steps=20) sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_std) return sd15 @pytest.fixture def sd15_ddim_karras( text_encoder_weights: Path, lda_weights: Path, unet_weights_std: Path, test_device: torch.device ) -> StableDiffusion_1: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() ddim_scheduler = DDIM(num_inference_steps=20, noise_schedule=NoiseSchedule.KARRAS) sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device) sd15.clip_text_encoder.load_from_safetensors(text_encoder_weights) sd15.lda.load_from_safetensors(lda_weights) sd15.unet.load_from_safetensors(unet_weights_std) return sd15 @pytest.fixture def sd15_ddim_lda_ft_mse( text_encoder_weights: Path, lda_ft_mse_weights: Path, unet_weights_std: Path, test_device: torch.device ) -> StableDiffusion_1: if test_device.type == "cpu": warn("not running on CPU, skipping") pytest.skip() ddim_scheduler = DDIM(num_inference_steps=20) sd15 = StableDiffusion_1(scheduler=ddim_scheduler, device=test_device) sd15.clip_text_encoder.load_state_dict(load_from_safetensors(text_encoder_weights)) sd15.lda.load_state_dict(load_from_safetensors(lda_ft_mse_weights)) sd15.unet.load_state_dict(load_from_safetensors(unet_weights_std)) return sd15 @pytest.fixture def sdxl_lda_weights(test_weights_path: Path) -> Path: sdxl_lda_weights = test_weights_path / "sdxl-lda.safetensors" if not sdxl_lda_weights.is_file(): warn(message=f"could not find weights at {sdxl_lda_weights}, skipping") pytest.skip(allow_module_level=True) return sdxl_lda_weights @pytest.fixture def sdxl_unet_weights(test_weights_path: Path) -> Path: sdxl_unet_weights = test_weights_path / "sdxl-unet.safetensors" if not sdxl_unet_weights.is_file(): warn(message=f"could not find weights at {sdxl_unet_weights}, skipping") pytest.skip(allow_module_level=True) return sdxl_unet_weights @pytest.fixture def sdxl_text_encoder_weights(test_weights_path: Path) -> Path: sdxl_double_text_encoder_weights = test_weights_path / "DoubleCLIPTextEncoder.safetensors" if not sdxl_double_text_encoder_weights.is_file(): warn(message=f"could not find weights at {sdxl_double_text_encoder_weights}, skipping") pytest.skip(allow_module_level=True) return sdxl_double_text_encoder_weights @pytest.fixture def sdxl_ddim( sdxl_text_encoder_weights: Path, sdxl_lda_weights: Path, sdxl_unet_weights: Path, test_device: torch.device ) -> StableDiffusion_XL: if test_device.type == "cpu": warn(message="not running on CPU, skipping") pytest.skip() scheduler = DDIM(num_inference_steps=30) sdxl = StableDiffusion_XL(scheduler=scheduler, device=test_device) sdxl.clip_text_encoder.load_from_safetensors(tensors_path=sdxl_text_encoder_weights) sdxl.lda.load_from_safetensors(tensors_path=sdxl_lda_weights) sdxl.unet.load_from_safetensors(tensors_path=sdxl_unet_weights) return sdxl @torch.no_grad() def test_diffusion_std_random_init( sd15_std: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device ): sd15 = sd15_std n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_std_random_init) @torch.no_grad() def test_diffusion_karras_random_init( sd15_ddim_karras: StableDiffusion_1, expected_karras_random_init: Image.Image, test_device: torch.device ): sd15 = sd15_ddim_karras prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_karras_random_init, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_std_random_init_float16( sd15_std_float16: StableDiffusion_1, expected_image_std_random_init: Image.Image, test_device: torch.device ): sd15 = sd15_std_float16 n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) assert clip_text_embedding.dtype == torch.float16 sd15.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_std_random_init, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_std_random_init_sag( sd15_std: StableDiffusion_1, expected_image_std_random_init_sag: Image.Image, test_device: torch.device ): sd15 = sd15_std n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) sd15.set_self_attention_guidance(enable=True, scale=0.75) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_std_random_init_sag) @torch.no_grad() def test_diffusion_std_init_image( sd15_std: StableDiffusion_1, cutecat_init: Image.Image, expected_image_std_init_image: Image.Image, ): sd15 = sd15_std n_steps = 35 first_step = 5 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) manual_seed(2) x = sd15.init_latents((512, 512), cutecat_init, first_step=first_step) for step in sd15.steps[first_step:]: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_std_init_image) @torch.no_grad() def test_rectangular_init_latents( sd15_std: StableDiffusion_1, cutecat_init: Image.Image, ): sd15 = sd15_std # Just check latents initialization with a non-square image (and not the entire diffusion) width, height = 512, 504 rect_init_image = cutecat_init.crop((0, 0, width, height)) x = sd15.init_latents((height, width), rect_init_image) assert sd15.lda.decode_latents(x).size == (width, height) @torch.no_grad() def test_diffusion_inpainting( sd15_inpainting: StableDiffusion_1_Inpainting, kitchen_dog: Image.Image, kitchen_dog_mask: Image.Image, expected_image_std_inpainting: Image.Image, test_device: torch.device, ): sd15 = sd15_inpainting n_steps = 30 prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) # PSNR and SSIM values are large because with float32 we get large differences even v.s. ourselves. ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=25, min_ssim=0.95) @torch.no_grad() def test_diffusion_inpainting_float16( sd15_inpainting_float16: StableDiffusion_1_Inpainting, kitchen_dog: Image.Image, kitchen_dog_mask: Image.Image, expected_image_std_inpainting: Image.Image, test_device: torch.device, ): sd15 = sd15_inpainting_float16 n_steps = 30 prompt = "a large white cat, detailed high-quality professional image, sitting on a chair, in a kitchen" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) assert clip_text_embedding.dtype == torch.float16 sd15.set_num_inference_steps(n_steps) sd15.set_inpainting_conditions(kitchen_dog, kitchen_dog_mask) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) # PSNR and SSIM values are large because float16 is even worse than float32. ensure_similar_images(predicted_image, expected_image_std_inpainting, min_psnr=20, min_ssim=0.92) @torch.no_grad() def test_diffusion_controlnet( sd15_std: StableDiffusion_1, controlnet_data: tuple[str, Image.Image, Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std n_steps = 30 cn_name, condition_image, expected_image, cn_weights_path = controlnet_data if not cn_weights_path.is_file(): warn(f"could not find weights at {cn_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) controlnet = SD1ControlnetAdapter( sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path) ).inject() cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_controlnet_structural_copy( sd15_std: StableDiffusion_1, controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path], test_device: torch.device, ): sd15_base = sd15_std sd15 = sd15_base.structural_copy() n_steps = 30 cn_name, condition_image, expected_image, cn_weights_path = controlnet_data_canny if not cn_weights_path.is_file(): warn(f"could not find weights at {cn_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) controlnet = SD1ControlnetAdapter( sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path) ).inject() cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_controlnet_float16( sd15_std_float16: StableDiffusion_1, controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std_float16 n_steps = 30 cn_name, condition_image, expected_image, cn_weights_path = controlnet_data_canny if not cn_weights_path.is_file(): warn(f"could not find weights at {cn_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) controlnet = SD1ControlnetAdapter( sd15.unet, name=cn_name, scale=0.5, weights=load_from_safetensors(cn_weights_path) ).inject() cn_condition = image_to_tensor(condition_image.convert("RGB"), device=test_device, dtype=torch.float16) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: controlnet.set_controlnet_condition(cn_condition) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_controlnet_stack( sd15_std: StableDiffusion_1, controlnet_data_depth: tuple[str, Image.Image, Image.Image, Path], controlnet_data_canny: tuple[str, Image.Image, Image.Image, Path], expected_image_controlnet_stack: Image.Image, test_device: torch.device, ): sd15 = sd15_std n_steps = 30 _, depth_condition_image, _, depth_cn_weights_path = controlnet_data_depth _, canny_condition_image, _, canny_cn_weights_path = controlnet_data_canny if not canny_cn_weights_path.is_file(): warn(f"could not find weights at {canny_cn_weights_path}, skipping") pytest.skip(allow_module_level=True) if not depth_cn_weights_path.is_file(): warn(f"could not find weights at {depth_cn_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) depth_controlnet = SD1ControlnetAdapter( sd15.unet, name="depth", scale=0.3, weights=load_from_safetensors(depth_cn_weights_path) ).inject() canny_controlnet = SD1ControlnetAdapter( sd15.unet, name="canny", scale=0.7, weights=load_from_safetensors(canny_cn_weights_path) ).inject() depth_cn_condition = image_to_tensor(depth_condition_image.convert("RGB"), device=test_device) canny_cn_condition = image_to_tensor(canny_condition_image.convert("RGB"), device=test_device) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: depth_controlnet.set_controlnet_condition(depth_cn_condition) canny_controlnet.set_controlnet_condition(canny_cn_condition) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_controlnet_stack, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_lora( sd15_std: StableDiffusion_1, lora_data_pokemon: tuple[Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std n_steps = 30 expected_image, lora_weights_path = lora_data_pokemon if not lora_weights_path.is_file(): warn(f"could not find weights at {lora_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat" clip_text_embedding = sd15.compute_clip_text_embedding(prompt) sd15.set_num_inference_steps(n_steps) SD1LoraAdapter.from_safetensors(target=sd15, checkpoint_path=lora_weights_path, scale=1.0).inject() manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_lora_float16( sd15_std_float16: StableDiffusion_1, lora_data_pokemon: tuple[Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std_float16 n_steps = 30 expected_image, lora_weights_path = lora_data_pokemon if not lora_weights_path.is_file(): warn(f"could not find weights at {lora_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat" clip_text_embedding = sd15.compute_clip_text_embedding(prompt) sd15.set_num_inference_steps(n_steps) SD1LoraAdapter.from_safetensors(target=sd15, checkpoint_path=lora_weights_path, scale=1.0).inject() manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=33, min_ssim=0.98) @torch.no_grad() def test_diffusion_lora_twice( sd15_std: StableDiffusion_1, lora_data_pokemon: tuple[Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std n_steps = 30 expected_image, lora_weights_path = lora_data_pokemon if not lora_weights_path.is_file(): warn(f"could not find weights at {lora_weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat" clip_text_embedding = sd15.compute_clip_text_embedding(prompt) sd15.set_num_inference_steps(n_steps) # The same LoRA is used twice which is not a common use case: this is purely for testing purpose SD1LoraAdapter.from_safetensors(target=sd15, checkpoint_path=lora_weights_path, scale=0.4).inject() SD1LoraAdapter.from_safetensors(target=sd15, checkpoint_path=lora_weights_path, scale=0.6).inject() manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_refonly( sd15_ddim: StableDiffusion_1, condition_image_refonly: Image.Image, expected_image_refonly: Image.Image, test_device: torch.device, ): sd15 = sd15_ddim prompt = "Chicken" clip_text_embedding = sd15.compute_clip_text_embedding(prompt) refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject() guide = sd15.lda.encode_image(condition_image_refonly) guide = torch.cat((guide, guide)) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: noise = torch.randn(2, 4, 64, 64, device=test_device) noised_guide = sd15.scheduler.add_noise(guide, noise, step) refonly_adapter.set_controlnet_condition(noised_guide) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) torch.randn(2, 4, 64, 64, device=test_device) # for SD Web UI reproductibility only predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_refonly, min_psnr=35, min_ssim=0.99) @torch.no_grad() def test_diffusion_inpainting_refonly( sd15_inpainting: StableDiffusion_1_Inpainting, scene_image_inpainting_refonly: Image.Image, target_image_inpainting_refonly: Image.Image, mask_image_inpainting_refonly: Image.Image, expected_image_inpainting_refonly: Image.Image, test_device: torch.device, ): sd15 = sd15_inpainting n_steps = 30 prompt = "" # unconditional clip_text_embedding = sd15.compute_clip_text_embedding(prompt) refonly_adapter = ReferenceOnlyControlAdapter(sd15.unet).inject() sd15.set_num_inference_steps(n_steps) sd15.set_inpainting_conditions(target_image_inpainting_refonly, mask_image_inpainting_refonly) guide = sd15.lda.encode_image(scene_image_inpainting_refonly) guide = torch.cat((guide, guide)) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: noise = torch.randn_like(guide) noised_guide = sd15.scheduler.add_noise(guide, noise, step) # See https://github.com/Mikubill/sd-webui-controlnet/pull/1275 ("1.1.170 reference-only begin to support # inpaint variation models") noised_guide = torch.cat([noised_guide, torch.zeros_like(noised_guide)[:, 0:1, :, :], guide], dim=1) refonly_adapter.set_controlnet_condition(noised_guide) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_inpainting_refonly, min_psnr=35, min_ssim=0.99) @torch.no_grad() def test_diffusion_textual_inversion_random_init( sd15_std: StableDiffusion_1, expected_image_textual_inversion_random_init: Image.Image, text_embedding_textual_inversion: torch.Tensor, test_device: torch.device, ): sd15 = sd15_std conceptExtender = ConceptExtender(sd15.clip_text_encoder) conceptExtender.add_concept("", text_embedding_textual_inversion) conceptExtender.inject() n_steps = 30 prompt = "a cute cat on a " clip_text_embedding = sd15.compute_clip_text_embedding(prompt) sd15.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_textual_inversion_random_init, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_ip_adapter( sd15_ddim_lda_ft_mse: StableDiffusion_1, ip_adapter_weights: Path, image_encoder_weights: Path, woman_image: Image.Image, expected_image_ip_adapter_woman: Image.Image, test_device: torch.device, ): sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16) n_steps = 50 # See tencent-ailab/IP-Adapter best practices section: # # If you only use the image prompt, you can set the scale=1.0 and text_prompt="" (or some generic text # prompts, e.g. "best quality", you can also use any negative text prompt). # # The prompts below are the ones used by default by IPAdapter's generate method if none are specified prompt = "best quality, high quality" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights)) ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights) ip_adapter.inject() clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image)) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) clip_text_embedding = torch.cat( ( torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), ) ) sd15.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_ip_adapter_woman) @torch.no_grad() def test_diffusion_sdxl_ip_adapter( sdxl_ddim: StableDiffusion_XL, sdxl_ip_adapter_weights: Path, image_encoder_weights: Path, woman_image: Image.Image, expected_image_sdxl_ip_adapter_woman: Image.Image, test_device: torch.device, ): sdxl = sdxl_ddim.to(dtype=torch.float16) n_steps = 30 prompt = "best quality, high quality" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ip_adapter = SDXLIPAdapter(target=sdxl.unet, weights=load_from_safetensors(sdxl_ip_adapter_weights)) ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights) ip_adapter.inject() with torch.no_grad(): clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( text=prompt, negative_text=negative_prompt ) clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image)) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) clip_text_embedding = torch.cat( ( torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), ) ) time_ids = sdxl.default_time_ids sdxl.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 128, 128, device=test_device, dtype=torch.float16) with torch.no_grad(): 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, condition_scale=5, ) # See https://huggingface.co/madebyollin/sdxl-vae-fp16-fix: "SDXL-VAE generates NaNs in fp16 because the # internal activation values are too big" sdxl.lda.to(dtype=torch.float32) predicted_image = sdxl.lda.decode_latents(x.to(dtype=torch.float32)) ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_woman) @torch.no_grad() def test_diffusion_ip_adapter_controlnet( sd15_ddim: StableDiffusion_1, ip_adapter_weights: Path, image_encoder_weights: Path, lora_data_pokemon: tuple[Image.Image, Path], controlnet_data_depth: tuple[str, Image.Image, Image.Image, Path], expected_image_ip_adapter_controlnet: Image.Image, test_device: torch.device, ): sd15 = sd15_ddim.to(dtype=torch.float16) n_steps = 50 input_image, _ = lora_data_pokemon # use the Pokemon LoRA output as input _, depth_condition_image, _, depth_cn_weights_path = controlnet_data_depth prompt = "best quality, high quality" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ip_adapter = SD1IPAdapter(target=sd15.unet, weights=load_from_safetensors(ip_adapter_weights)) ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights) ip_adapter.inject() depth_controlnet = SD1ControlnetAdapter( sd15.unet, name="depth", scale=1.0, weights=load_from_safetensors(depth_cn_weights_path), ).inject() clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(input_image)) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) clip_text_embedding = torch.cat( ( torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), ) ) depth_cn_condition = image_to_tensor( depth_condition_image.convert("RGB"), device=test_device, dtype=torch.float16, ) sd15.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: depth_controlnet.set_controlnet_condition(depth_cn_condition) x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_ip_adapter_controlnet) @torch.no_grad() def test_diffusion_ip_adapter_plus( sd15_ddim_lda_ft_mse: StableDiffusion_1, ip_adapter_plus_weights: Path, image_encoder_weights: Path, statue_image: Image.Image, expected_image_ip_adapter_plus_statue: Image.Image, test_device: torch.device, ): sd15 = sd15_ddim_lda_ft_mse.to(dtype=torch.float16) n_steps = 50 prompt = "best quality, high quality" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ip_adapter = SD1IPAdapter( target=sd15.unet, weights=load_from_safetensors(ip_adapter_plus_weights), fine_grained=True ) ip_adapter.clip_image_encoder.load_from_safetensors(image_encoder_weights) ip_adapter.inject() clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(statue_image)) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) clip_text_embedding = torch.cat( ( torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), ) ) sd15.set_num_inference_steps(n_steps) manual_seed(42) # seed=42 is used in the official IP-Adapter demo x = torch.randn(1, 4, 64, 64, device=test_device, dtype=torch.float16) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image_ip_adapter_plus_statue, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_diffusion_sdxl_ip_adapter_plus( sdxl_ddim: StableDiffusion_XL, sdxl_ip_adapter_plus_weights: Path, image_encoder_weights: Path, woman_image: Image.Image, expected_image_sdxl_ip_adapter_plus_woman: Image.Image, test_device: torch.device, ): sdxl = sdxl_ddim.to(dtype=torch.float16) n_steps = 30 prompt = "best quality, high quality" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" ip_adapter = SDXLIPAdapter( target=sdxl.unet, weights=load_from_safetensors(sdxl_ip_adapter_plus_weights), 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, negative_text=negative_prompt ) clip_image_embedding = ip_adapter.compute_clip_image_embedding(ip_adapter.preprocess_image(woman_image)) negative_text_embedding, conditional_text_embedding = clip_text_embedding.chunk(2) negative_image_embedding, conditional_image_embedding = clip_image_embedding.chunk(2) clip_text_embedding = torch.cat( ( torch.cat([negative_text_embedding, negative_image_embedding], dim=1), torch.cat([conditional_text_embedding, conditional_image_embedding], dim=1), ) ) time_ids = sdxl.default_time_ids sdxl.set_num_inference_steps(n_steps) manual_seed(2) x = torch.randn(1, 4, 128, 128, device=test_device, dtype=torch.float16) 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, condition_scale=5, ) sdxl.lda.to(dtype=torch.float32) predicted_image = sdxl.lda.decode_latents(x.to(dtype=torch.float32)) ensure_similar_images(predicted_image, expected_image_sdxl_ip_adapter_plus_woman) @torch.no_grad() def test_sdxl_random_init( sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init: Image.Image, test_device: torch.device ) -> None: sdxl = sdxl_ddim expected_image = expected_sdxl_ddim_random_init n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( text=prompt, negative_text=negative_prompt ) time_ids = sdxl.default_time_ids sdxl.set_num_inference_steps(num_inference_steps=n_steps) manual_seed(seed=2) x = torch.randn(1, 4, 128, 128, device=test_device) 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, condition_scale=5, ) predicted_image = sdxl.lda.decode_latents(x=x) ensure_similar_images(img_1=predicted_image, img_2=expected_image, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_sdxl_random_init_sag( sdxl_ddim: StableDiffusion_XL, expected_sdxl_ddim_random_init_sag: Image.Image, test_device: torch.device ) -> None: sdxl = sdxl_ddim expected_image = expected_sdxl_ddim_random_init_sag n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( text=prompt, negative_text=negative_prompt ) time_ids = sdxl.default_time_ids sdxl.set_num_inference_steps(num_inference_steps=n_steps) sdxl.set_self_attention_guidance(enable=True, scale=0.75) manual_seed(seed=2) x = torch.randn(1, 4, 128, 128, device=test_device) 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, condition_scale=5, ) predicted_image = sdxl.lda.decode_latents(x=x) ensure_similar_images(img_1=predicted_image, img_2=expected_image) @torch.no_grad() def test_multi_diffusion(sd15_ddim: StableDiffusion_1, expected_multi_diffusion: Image.Image) -> None: manual_seed(seed=2) sd = sd15_ddim multi_diffusion = SD1MultiDiffusion(sd) clip_text_embedding = sd.compute_clip_text_embedding(text="a panorama of a mountain") target_1 = DiffusionTarget( size=(64, 64), offset=(0, 0), clip_text_embedding=clip_text_embedding, start_step=0, ) target_2 = DiffusionTarget( size=(64, 64), offset=(0, 16), clip_text_embedding=clip_text_embedding, start_step=0, ) noise = torch.randn(1, 4, 64, 80, device=sd.device, dtype=sd.dtype) x = noise for step in sd.steps: x = multi_diffusion( x, noise=noise, step=step, targets=[target_1, target_2], ) result = sd.lda.decode_latents(x=x) ensure_similar_images(img_1=result, img_2=expected_multi_diffusion, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_t2i_adapter_depth( sd15_std: StableDiffusion_1, t2i_adapter_data_depth: tuple[str, Image.Image, Image.Image, Path], test_device: torch.device, ): sd15 = sd15_std n_steps = 30 name, condition_image, expected_image, weights_path = t2i_adapter_data_depth if not weights_path.is_file(): warn(f"could not find weights at {weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) t2i_adapter = SD1T2IAdapter(target=sd15.unet, name=name, weights=load_from_safetensors(weights_path)).inject() condition = image_to_tensor(condition_image.convert("RGB"), device=test_device) t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition)) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image) @torch.no_grad() def test_t2i_adapter_xl_canny( sdxl_ddim: StableDiffusion_XL, t2i_adapter_xl_data_canny: tuple[str, Image.Image, Image.Image, Path], test_device: torch.device, ): sdxl = sdxl_ddim n_steps = 30 name, condition_image, expected_image, weights_path = t2i_adapter_xl_data_canny if not weights_path.is_file(): warn(f"could not find weights at {weights_path}, skipping") pytest.skip(allow_module_level=True) prompt = "Mystical fairy in real, magic, 4k picture, high quality" negative_prompt = ( "extra digit, fewer digits, cropped, worst quality, low quality, glitch, deformed, mutated, ugly, disfigured" ) clip_text_embedding, pooled_text_embedding = sdxl.compute_clip_text_embedding( text=prompt, negative_text=negative_prompt ) time_ids = sdxl.default_time_ids sdxl.set_num_inference_steps(n_steps) t2i_adapter = SDXLT2IAdapter(target=sdxl.unet, name=name, weights=load_from_safetensors(weights_path)).inject() t2i_adapter.set_scale(0.8) condition = image_to_tensor(condition_image.convert("RGB"), device=test_device) t2i_adapter.set_condition_features(features=t2i_adapter.compute_condition_features(condition)) manual_seed(2) x = torch.randn(1, 4, condition_image.height // 8, condition_image.width // 8, device=test_device) 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, condition_scale=7.5, ) predicted_image = sdxl.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_image) @torch.no_grad() def test_restart( sd15_ddim: StableDiffusion_1, expected_restart: Image.Image, test_device: torch.device, ): sd15 = sd15_ddim n_steps = 30 prompt = "a cute cat, detailed high-quality professional image" negative_prompt = "lowres, bad anatomy, bad hands, cropped, worst quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) restart = Restart(ldm=sd15) manual_seed(2) x = torch.randn(1, 4, 64, 64, device=test_device) for step in sd15.steps: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=8, ) if step == restart.start_step: x = restart( x, clip_text_embedding=clip_text_embedding, condition_scale=8, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_restart, min_psnr=35, min_ssim=0.98) @torch.no_grad() def test_freeu( sd15_std: StableDiffusion_1, expected_freeu: Image.Image, ): sd15 = sd15_std n_steps = 50 first_step = 1 prompt = "best quality, high quality cute cat" negative_prompt = "monochrome, lowres, bad anatomy, worst quality, low quality" clip_text_embedding = sd15.compute_clip_text_embedding(text=prompt, negative_text=negative_prompt) sd15.set_num_inference_steps(n_steps) SDFreeUAdapter( sd15.unet, backbone_scales=[1.2, 1.2, 1.2, 1.4, 1.4, 1.4], skip_scales=[0.9, 0.9, 0.9, 0.2, 0.2, 0.2] ).inject() manual_seed(9752) x = sd15.init_latents(size=(512, 512), first_step=first_step).to(device=sd15.device, dtype=sd15.dtype) for step in sd15.steps[first_step:]: x = sd15( x, step=step, clip_text_embedding=clip_text_embedding, condition_scale=7.5, ) predicted_image = sd15.lda.decode_latents(x) ensure_similar_images(predicted_image, expected_freeu)