# coding=utf-8 # Copyright 2023 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import gc import glob import json import os import random import shutil import sys import tempfile import traceback import unittest import unittest.mock as mock import numpy as np import PIL import requests_mock import safetensors.torch import torch from parameterized import parameterized from PIL import Image from requests.exceptions import HTTPError from transformers import CLIPImageProcessor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, ConfigMixin, DDIMPipeline, DDIMScheduler, DDPMPipeline, DDPMScheduler, DiffusionPipeline, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, ModelMixin, PNDMScheduler, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionPipeline, UNet2DConditionModel, UNet2DModel, UniPCMultistepScheduler, logging, ) from diffusers.pipelines.pipeline_utils import variant_compatible_siblings from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME from diffusers.utils import ( CONFIG_NAME, WEIGHTS_NAME, floats_tensor, is_compiled_module, nightly, require_torch_2, slow, torch_device, ) from diffusers.utils.testing_utils import ( CaptureLogger, enable_full_determinism, get_tests_dir, load_numpy, require_compel, require_flax, require_torch_gpu, run_test_in_subprocess, ) enable_full_determinism() # Will be run via run_test_in_subprocess def _test_from_save_pretrained_dynamo(in_queue, out_queue, timeout): error = None try: # 1. Load models model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) model = torch.compile(model) scheduler = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline(model, scheduler) # previous diffusers versions stripped compilation off # compiled modules assert is_compiled_module(ddpm.unet) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) with tempfile.TemporaryDirectory() as tmpdirname: ddpm.save_pretrained(tmpdirname) new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) new_ddpm.to(torch_device) generator = torch.Generator(device=torch_device).manual_seed(0) image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images generator = torch.Generator(device=torch_device).manual_seed(0) new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() class CustomEncoder(ModelMixin, ConfigMixin): def __init__(self): super().__init__() class CustomPipeline(DiffusionPipeline): def __init__(self, encoder: CustomEncoder, scheduler: DDIMScheduler): super().__init__() self.register_modules(encoder=encoder, scheduler=scheduler) class DownloadTests(unittest.TestCase): def test_one_request_upon_cached(self): # TODO: For some reason this test fails on MPS where no HEAD call is made. if torch_device == "mps": return with tempfile.TemporaryDirectory() as tmpdirname: with requests_mock.mock(real_http=True) as m: DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe", cache_dir=tmpdirname) download_requests = [r.method for r in m.request_history] assert download_requests.count("HEAD") == 15, "15 calls to files" assert download_requests.count("GET") == 17, "15 calls to files + model_info + model_index.json" assert ( len(download_requests) == 32 ), "2 calls per file (15 files) + send_telemetry, model_info and model_index.json" with requests_mock.mock(real_http=True) as m: DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname ) cache_requests = [r.method for r in m.request_history] assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD" assert cache_requests.count("GET") == 1, "model info is only GET" assert ( len(cache_requests) == 2 ), "We should call only `model_info` to check for _commit hash and `send_telemetry`" def test_less_downloads_passed_object(self): with tempfile.TemporaryDirectory() as tmpdirname: cached_folder = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname ) # make sure safety checker is not downloaded assert "safety_checker" not in os.listdir(cached_folder) # make sure rest is downloaded assert "unet" in os.listdir(cached_folder) assert "tokenizer" in os.listdir(cached_folder) assert "vae" in os.listdir(cached_folder) assert "model_index.json" in os.listdir(cached_folder) assert "scheduler" in os.listdir(cached_folder) assert "feature_extractor" in os.listdir(cached_folder) def test_less_downloads_passed_object_calls(self): # TODO: For some reason this test fails on MPS where no HEAD call is made. if torch_device == "mps": return with tempfile.TemporaryDirectory() as tmpdirname: with requests_mock.mock(real_http=True) as m: DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname ) download_requests = [r.method for r in m.request_history] # 15 - 2 because no call to config or model file for `safety_checker` assert download_requests.count("HEAD") == 13, "13 calls to files" # 17 - 2 because no call to config or model file for `safety_checker` assert download_requests.count("GET") == 15, "13 calls to files + model_info + model_index.json" assert ( len(download_requests) == 28 ), "2 calls per file (13 files) + send_telemetry, model_info and model_index.json" with requests_mock.mock(real_http=True) as m: DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname ) cache_requests = [r.method for r in m.request_history] assert cache_requests.count("HEAD") == 1, "model_index.json is only HEAD" assert cache_requests.count("GET") == 1, "model info is only GET" assert ( len(cache_requests) == 2 ), "We should call only `model_info` to check for _commit hash and `send_telemetry`" def test_download_only_pytorch(self): with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe", safety_checker=None, cache_dir=tmpdirname ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a flax file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack assert not any(f.endswith(".msgpack") for f in files) # We need to never convert this tiny model to safetensors for this test to pass assert not any(f.endswith(".safetensors") for f in files) def test_force_safetensors_error(self): with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights with self.assertRaises(EnvironmentError): tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe-no-safetensors", safety_checker=None, cache_dir=tmpdirname, use_safetensors=True, ) def test_download_safetensors(self): with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe-safetensors", safety_checker=None, cache_dir=tmpdirname, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a pytorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack assert not any(f.endswith(".bin") for f in files) def test_download_safetensors_index(self): for variant in ["fp16", None]: with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", cache_dir=tmpdirname, use_safetensors=True, variant=variant, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a safetensors file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder if variant is None: assert not any("fp16" in f for f in files) else: model_files = [f for f in files if "safetensors" in f] assert all("fp16" in f for f in model_files) assert len([f for f in files if ".safetensors" in f]) == 8 assert not any(".bin" in f for f in files) def test_download_bin_index(self): for variant in ["fp16", None]: with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", cache_dir=tmpdirname, use_safetensors=False, variant=variant, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a safetensors file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-indexes/tree/main/text_encoder if variant is None: assert not any("fp16" in f for f in files) else: model_files = [f for f in files if "bin" in f] assert all("fp16" in f for f in model_files) assert len([f for f in files if ".bin" in f]) == 8 assert not any(".safetensors" in f for f in files) def test_download_no_openvino_by_default(self): with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-stable-diffusion-open-vino", cache_dir=tmpdirname, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # make sure that by default no openvino weights are downloaded assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) assert not any("openvino_" in f for f in files) def test_download_no_onnx_by_default(self): with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline", cache_dir=tmpdirname, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # make sure that by default no onnx weights are downloaded assert all((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) assert not any((f.endswith(".onnx") or f.endswith(".pb")) for f in files) with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = DiffusionPipeline.download( "hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline", cache_dir=tmpdirname, use_onnx=True, ) all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # if `use_onnx` is specified make sure weights are downloaded assert any((f.endswith(".json") or f.endswith(".bin") or f.endswith(".txt")) for f in files) assert any((f.endswith(".onnx")) for f in files) assert any((f.endswith(".pb")) for f in files) def test_download_no_safety_checker(self): prompt = "hello" pipe = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) pipe = pipe.to(torch_device) generator = torch.manual_seed(0) out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images pipe_2 = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") pipe_2 = pipe_2.to(torch_device) generator = torch.manual_seed(0) out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images assert np.max(np.abs(out - out_2)) < 1e-3 def test_load_no_safety_checker_explicit_locally(self): prompt = "hello" pipe = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) pipe = pipe.to(torch_device) generator = torch.manual_seed(0) out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None) pipe_2 = pipe_2.to(torch_device) generator = torch.manual_seed(0) out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images assert np.max(np.abs(out - out_2)) < 1e-3 def test_load_no_safety_checker_default_locally(self): prompt = "hello" pipe = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") pipe = pipe.to(torch_device) generator = torch.manual_seed(0) out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe_2 = StableDiffusionPipeline.from_pretrained(tmpdirname) pipe_2 = pipe_2.to(torch_device) generator = torch.manual_seed(0) out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images assert np.max(np.abs(out - out_2)) < 1e-3 def test_cached_files_are_used_when_no_internet(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # Download this model to make sure it's in the cache. orig_pipe = DiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")} # Under the mock environment we get a 500 error when trying to reach the model. with mock.patch("requests.request", return_value=response_mock): # Download this model to make sure it's in the cache. pipe = DiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")} for m1, m2 in zip(orig_comps.values(), comps.values()): for p1, p2 in zip(m1.parameters(), m2.parameters()): if p1.data.ne(p2.data).sum() > 0: assert False, "Parameters not the same!" def test_local_files_only_are_used_when_no_internet(self): # A mock response for an HTTP head request to emulate server down response_mock = mock.Mock() response_mock.status_code = 500 response_mock.headers = {} response_mock.raise_for_status.side_effect = HTTPError response_mock.json.return_value = {} # first check that with local files only the pipeline can only be used if cached with self.assertRaises(FileNotFoundError): with tempfile.TemporaryDirectory() as tmpdirname: orig_pipe = DiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True, cache_dir=tmpdirname ) # now download orig_pipe = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-torch") # make sure it can be loaded with local_files_only orig_pipe = DiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", local_files_only=True ) orig_comps = {k: v for k, v in orig_pipe.components.items() if hasattr(v, "parameters")} # Under the mock environment we get a 500 error when trying to connect to the internet. # Make sure it works local_files_only only works here! with mock.patch("requests.request", return_value=response_mock): # Download this model to make sure it's in the cache. pipe = DiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") comps = {k: v for k, v in pipe.components.items() if hasattr(v, "parameters")} for m1, m2 in zip(orig_comps.values(), comps.values()): for p1, p2 in zip(m1.parameters(), m2.parameters()): if p1.data.ne(p2.data).sum() > 0: assert False, "Parameters not the same!" def test_download_from_variant_folder(self): for safe_avail in [False, True]: import diffusers diffusers.utils.import_utils._safetensors_available = safe_avail other_format = ".bin" if safe_avail else ".safetensors" with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = StableDiffusionPipeline.download( "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname ) all_root_files = [t[-1] for t in os.walk(tmpdirname)] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a variant file even if we have some here: # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet assert len(files) == 15, f"We should only download 15 files, not {len(files)}" assert not any(f.endswith(other_format) for f in files) # no variants assert not any(len(f.split(".")) == 3 for f in files) diffusers.utils.import_utils._safetensors_available = True def test_download_variant_all(self): for safe_avail in [False, True]: import diffusers diffusers.utils.import_utils._safetensors_available = safe_avail other_format = ".bin" if safe_avail else ".safetensors" this_format = ".safetensors" if safe_avail else ".bin" variant = "fp16" with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = StableDiffusionPipeline.download( "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant ) all_root_files = [t[-1] for t in os.walk(tmpdirname)] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a non-variant file even if we have some here: # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet assert len(files) == 15, f"We should only download 15 files, not {len(files)}" # unet, vae, text_encoder, safety_checker assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 4 # all checkpoints should have variant ending assert not any(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) assert not any(f.endswith(other_format) for f in files) diffusers.utils.import_utils._safetensors_available = True def test_download_variant_partly(self): for safe_avail in [False, True]: import diffusers diffusers.utils.import_utils._safetensors_available = safe_avail other_format = ".bin" if safe_avail else ".safetensors" this_format = ".safetensors" if safe_avail else ".bin" variant = "no_ema" with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = StableDiffusionPipeline.download( "hf-internal-testing/stable-diffusion-all-variants", cache_dir=tmpdirname, variant=variant ) all_root_files = [t[-1] for t in os.walk(tmpdirname)] files = [item for sublist in all_root_files for item in sublist] unet_files = os.listdir(os.path.join(tmpdirname, "unet")) # Some of the downloaded files should be a non-variant file, check: # https://huggingface.co/hf-internal-testing/stable-diffusion-all-variants/tree/main/unet assert len(files) == 15, f"We should only download 15 files, not {len(files)}" # only unet has "no_ema" variant assert f"diffusion_pytorch_model.{variant}{this_format}" in unet_files assert len([f for f in files if f.endswith(f"{variant}{this_format}")]) == 1 # vae, safety_checker and text_encoder should have no variant assert sum(f.endswith(this_format) and not f.endswith(f"{variant}{this_format}") for f in files) == 3 assert not any(f.endswith(other_format) for f in files) diffusers.utils.import_utils._safetensors_available = True def test_download_broken_variant(self): for safe_avail in [False, True]: import diffusers diffusers.utils.import_utils._safetensors_available = safe_avail # text encoder is missing no variant and "no_ema" variant weights, so the following can't work for variant in [None, "no_ema"]: with self.assertRaises(OSError) as error_context: with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant=variant, ) assert "Error no file name" in str(error_context.exception) # text encoder has fp16 variants so we can load it with tempfile.TemporaryDirectory() as tmpdirname: tmpdirname = StableDiffusionPipeline.download( "hf-internal-testing/stable-diffusion-broken-variants", cache_dir=tmpdirname, variant="fp16" ) all_root_files = [t[-1] for t in os.walk(tmpdirname)] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a non-variant file even if we have some here: # https://huggingface.co/hf-internal-testing/stable-diffusion-broken-variants/tree/main/unet assert len(files) == 15, f"We should only download 15 files, not {len(files)}" # only unet has "no_ema" variant diffusers.utils.import_utils._safetensors_available = True def test_local_save_load_index(self): prompt = "hello" for variant in [None, "fp16"]: for use_safe in [True, False]: pipe = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-pipe-indexes", variant=variant, use_safetensors=use_safe, safety_checker=None, ) pipe = pipe.to(torch_device) generator = torch.manual_seed(0) out = pipe(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe_2 = StableDiffusionPipeline.from_pretrained( tmpdirname, safe_serialization=use_safe, variant=variant ) pipe_2 = pipe_2.to(torch_device) generator = torch.manual_seed(0) out_2 = pipe_2(prompt, num_inference_steps=2, generator=generator, output_type="numpy").images assert np.max(np.abs(out - out_2)) < 1e-3 def test_text_inversion_download(self): pipe = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) pipe = pipe.to(torch_device) num_tokens = len(pipe.tokenizer) # single token load local with tempfile.TemporaryDirectory() as tmpdirname: ten = {"<*>": torch.ones((32,))} torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) pipe.load_textual_inversion(tmpdirname) token = pipe.tokenizer.convert_tokens_to_ids("<*>") assert token == num_tokens, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32 assert pipe._maybe_convert_prompt("<*>", pipe.tokenizer) == "<*>" prompt = "hey <*>" out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # single token load local with weight name with tempfile.TemporaryDirectory() as tmpdirname: ten = {"<**>": 2 * torch.ones((1, 32))} torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) pipe.load_textual_inversion(tmpdirname, weight_name="learned_embeds.bin") token = pipe.tokenizer.convert_tokens_to_ids("<**>") assert token == num_tokens + 1, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 assert pipe._maybe_convert_prompt("<**>", pipe.tokenizer) == "<**>" prompt = "hey <**>" out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # multi token load with tempfile.TemporaryDirectory() as tmpdirname: ten = {"<***>": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))])} torch.save(ten, os.path.join(tmpdirname, "learned_embeds.bin")) pipe.load_textual_inversion(tmpdirname) token = pipe.tokenizer.convert_tokens_to_ids("<***>") token_1 = pipe.tokenizer.convert_tokens_to_ids("<***>_1") token_2 = pipe.tokenizer.convert_tokens_to_ids("<***>_2") assert token == num_tokens + 2, "Added token must be at spot `num_tokens`" assert token_1 == num_tokens + 3, "Added token must be at spot `num_tokens`" assert token_2 == num_tokens + 4, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 assert pipe._maybe_convert_prompt("<***>", pipe.tokenizer) == "<***> <***>_1 <***>_2" prompt = "hey <***>" out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # multi token load a1111 with tempfile.TemporaryDirectory() as tmpdirname: ten = { "string_to_param": { "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))]) }, "name": "<****>", } torch.save(ten, os.path.join(tmpdirname, "a1111.bin")) pipe.load_textual_inversion(tmpdirname, weight_name="a1111.bin") token = pipe.tokenizer.convert_tokens_to_ids("<****>") token_1 = pipe.tokenizer.convert_tokens_to_ids("<****>_1") token_2 = pipe.tokenizer.convert_tokens_to_ids("<****>_2") assert token == num_tokens + 5, "Added token must be at spot `num_tokens`" assert token_1 == num_tokens + 6, "Added token must be at spot `num_tokens`" assert token_2 == num_tokens + 7, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 assert pipe._maybe_convert_prompt("<****>", pipe.tokenizer) == "<****> <****>_1 <****>_2" prompt = "hey <****>" out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # multi embedding load with tempfile.TemporaryDirectory() as tmpdirname1: with tempfile.TemporaryDirectory() as tmpdirname2: ten = {"<*****>": torch.ones((32,))} torch.save(ten, os.path.join(tmpdirname1, "learned_embeds.bin")) ten = {"<******>": 2 * torch.ones((1, 32))} torch.save(ten, os.path.join(tmpdirname2, "learned_embeds.bin")) pipe.load_textual_inversion([tmpdirname1, tmpdirname2]) token = pipe.tokenizer.convert_tokens_to_ids("<*****>") assert token == num_tokens + 8, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32 assert pipe._maybe_convert_prompt("<*****>", pipe.tokenizer) == "<*****>" token = pipe.tokenizer.convert_tokens_to_ids("<******>") assert token == num_tokens + 9, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 assert pipe._maybe_convert_prompt("<******>", pipe.tokenizer) == "<******>" prompt = "hey <*****> <******>" out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # single token state dict load ten = {"": torch.ones((32,))} pipe.load_textual_inversion(ten) token = pipe.tokenizer.convert_tokens_to_ids("") assert token == num_tokens + 10, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 32 assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" prompt = "hey " out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # multi embedding state dict load ten1 = {"": torch.ones((32,))} ten2 = {"": 2 * torch.ones((1, 32))} pipe.load_textual_inversion([ten1, ten2]) token = pipe.tokenizer.convert_tokens_to_ids("") assert token == num_tokens + 11, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 32 assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" token = pipe.tokenizer.convert_tokens_to_ids("") assert token == num_tokens + 12, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 64 assert pipe._maybe_convert_prompt("", pipe.tokenizer) == "" prompt = "hey " out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # auto1111 multi-token state dict load ten = { "string_to_param": { "*": torch.cat([3 * torch.ones((1, 32)), 4 * torch.ones((1, 32)), 5 * torch.ones((1, 32))]) }, "name": "", } pipe.load_textual_inversion(ten) token = pipe.tokenizer.convert_tokens_to_ids("") token_1 = pipe.tokenizer.convert_tokens_to_ids("_1") token_2 = pipe.tokenizer.convert_tokens_to_ids("_2") assert token == num_tokens + 13, "Added token must be at spot `num_tokens`" assert token_1 == num_tokens + 14, "Added token must be at spot `num_tokens`" assert token_2 == num_tokens + 15, "Added token must be at spot `num_tokens`" assert pipe.text_encoder.get_input_embeddings().weight[-3].sum().item() == 96 assert pipe.text_encoder.get_input_embeddings().weight[-2].sum().item() == 128 assert pipe.text_encoder.get_input_embeddings().weight[-1].sum().item() == 160 assert pipe._maybe_convert_prompt("", pipe.tokenizer) == " _1 _2" prompt = "hey " out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) # multiple references to multi embedding ten = {"": torch.ones(3, 32)} pipe.load_textual_inversion(ten) assert ( pipe._maybe_convert_prompt(" ", pipe.tokenizer) == " _1 _2 _1 _2" ) prompt = "hey " out = pipe(prompt, num_inference_steps=1, output_type="numpy").images assert out.shape == (1, 128, 128, 3) def test_download_ignore_files(self): # Check https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files/blob/72f58636e5508a218c6b3f60550dc96445547817/model_index.json#L4 with tempfile.TemporaryDirectory() as tmpdirname: # pipeline has Flax weights tmpdirname = DiffusionPipeline.download("hf-internal-testing/tiny-stable-diffusion-pipe-ignore-files") all_root_files = [t[-1] for t in os.walk(os.path.join(tmpdirname))] files = [item for sublist in all_root_files for item in sublist] # None of the downloaded files should be a pytorch file even if we have some here: # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_flax_model.msgpack assert not any(f in ["vae/diffusion_pytorch_model.bin", "text_encoder/config.json"] for f in files) assert len(files) == 14 class CustomPipelineTests(unittest.TestCase): def test_load_custom_pipeline(self): pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" ) pipeline = pipeline.to(torch_device) # NOTE that `"CustomPipeline"` is not a class that is defined in this library, but solely on the Hub # under https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L24 assert pipeline.__class__.__name__ == "CustomPipeline" def test_load_custom_github(self): pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="main" ) # make sure that on "main" pipeline gives only ones because of: https://github.com/huggingface/diffusers/pull/1690 with torch.no_grad(): output = pipeline() assert output.numel() == output.sum() # hack since Python doesn't like overwriting modules: https://stackoverflow.com/questions/3105801/unload-a-module-in-python # Could in the future work with hashes instead. del sys.modules["diffusers_modules.git.one_step_unet"] pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="one_step_unet", custom_revision="0.10.2" ) with torch.no_grad(): output = pipeline() assert output.numel() != output.sum() assert pipeline.__class__.__name__ == "UnetSchedulerOneForwardPipeline" def test_run_custom_pipeline(self): pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline="hf-internal-testing/diffusers-dummy-pipeline" ) pipeline = pipeline.to(torch_device) images, output_str = pipeline(num_inference_steps=2, output_type="np") assert images[0].shape == (1, 32, 32, 3) # compare output to https://huggingface.co/hf-internal-testing/diffusers-dummy-pipeline/blob/main/pipeline.py#L102 assert output_str == "This is a test" def test_local_custom_pipeline_repo(self): local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline") pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path ) pipeline = pipeline.to(torch_device) images, output_str = pipeline(num_inference_steps=2, output_type="np") assert pipeline.__class__.__name__ == "CustomLocalPipeline" assert images[0].shape == (1, 32, 32, 3) # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102 assert output_str == "This is a local test" def test_local_custom_pipeline_file(self): local_custom_pipeline_path = get_tests_dir("fixtures/custom_pipeline") local_custom_pipeline_path = os.path.join(local_custom_pipeline_path, "what_ever.py") pipeline = DiffusionPipeline.from_pretrained( "google/ddpm-cifar10-32", custom_pipeline=local_custom_pipeline_path ) pipeline = pipeline.to(torch_device) images, output_str = pipeline(num_inference_steps=2, output_type="np") assert pipeline.__class__.__name__ == "CustomLocalPipeline" assert images[0].shape == (1, 32, 32, 3) # compare to https://github.com/huggingface/diffusers/blob/main/tests/fixtures/custom_pipeline/pipeline.py#L102 assert output_str == "This is a local test" def test_custom_model_and_pipeline(self): pipe = CustomPipeline( encoder=CustomEncoder(), scheduler=DDIMScheduler(), ) with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe_new = CustomPipeline.from_pretrained(tmpdirname) pipe_new.save_pretrained(tmpdirname) conf_1 = dict(pipe.config) conf_2 = dict(pipe_new.config) del conf_2["_name_or_path"] assert conf_1 == conf_2 @slow @require_torch_gpu def test_download_from_git(self): # Because adaptive_avg_pool2d_backward_cuda # does not have a deterministic implementation. clip_model_id = "laion/CLIP-ViT-B-32-laion2B-s34B-b79K" feature_extractor = CLIPImageProcessor.from_pretrained(clip_model_id) clip_model = CLIPModel.from_pretrained(clip_model_id, torch_dtype=torch.float16) pipeline = DiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", custom_pipeline="clip_guided_stable_diffusion", clip_model=clip_model, feature_extractor=feature_extractor, torch_dtype=torch.float16, ) pipeline.enable_attention_slicing() pipeline = pipeline.to(torch_device) # NOTE that `"CLIPGuidedStableDiffusion"` is not a class that is defined in the pypi package of th e library, but solely on the community examples folder of GitHub under: # https://github.com/huggingface/diffusers/blob/main/examples/community/clip_guided_stable_diffusion.py assert pipeline.__class__.__name__ == "CLIPGuidedStableDiffusion" image = pipeline("a prompt", num_inference_steps=2, output_type="np").images[0] assert image.shape == (512, 512, 3) def test_save_pipeline_change_config(self): pipe = DiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) with tempfile.TemporaryDirectory() as tmpdirname: pipe.save_pretrained(tmpdirname) pipe = DiffusionPipeline.from_pretrained(tmpdirname) assert pipe.scheduler.__class__.__name__ == "PNDMScheduler" # let's make sure that changing the scheduler is correctly reflected with tempfile.TemporaryDirectory() as tmpdirname: pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) pipe.save_pretrained(tmpdirname) pipe = DiffusionPipeline.from_pretrained(tmpdirname) assert pipe.scheduler.__class__.__name__ == "DPMSolverMultistepScheduler" class PipelineFastTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() import diffusers diffusers.utils.import_utils._safetensors_available = True def dummy_image(self): batch_size = 1 num_channels = 3 sizes = (32, 32) image = floats_tensor((batch_size, num_channels) + sizes, rng=random.Random(0)).to(torch_device) return image def dummy_uncond_unet(self, sample_size=32): torch.manual_seed(0) model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=sample_size, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) return model def dummy_cond_unet(self, sample_size=32): torch.manual_seed(0) model = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=sample_size, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) return model @property def dummy_vae(self): torch.manual_seed(0) model = AutoencoderKL( block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, ) return model @property def dummy_text_encoder(self): torch.manual_seed(0) config = CLIPTextConfig( bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, ) return CLIPTextModel(config) @property def dummy_extractor(self): def extract(*args, **kwargs): class Out: def __init__(self): self.pixel_values = torch.ones([0]) def to(self, device): self.pixel_values.to(device) return self return Out() return extract @parameterized.expand( [ [DDIMScheduler, DDIMPipeline, 32], [DDPMScheduler, DDPMPipeline, 32], [DDIMScheduler, DDIMPipeline, (32, 64)], [DDPMScheduler, DDPMPipeline, (64, 32)], ] ) def test_uncond_unet_components(self, scheduler_fn=DDPMScheduler, pipeline_fn=DDPMPipeline, sample_size=32): unet = self.dummy_uncond_unet(sample_size) scheduler = scheduler_fn() pipeline = pipeline_fn(unet, scheduler).to(torch_device) generator = torch.manual_seed(0) out_image = pipeline( generator=generator, num_inference_steps=2, output_type="np", ).images sample_size = (sample_size, sample_size) if isinstance(sample_size, int) else sample_size assert out_image.shape == (1, *sample_size, 3) def test_stable_diffusion_components(self): """Test that components property works correctly""" unet = self.dummy_cond_unet() scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") image = self.dummy_image().cpu().permute(0, 2, 3, 1)[0] init_image = Image.fromarray(np.uint8(image)).convert("RGB") mask_image = Image.fromarray(np.uint8(image + 4)).convert("RGB").resize((32, 32)) # make sure here that pndm scheduler skips prk inpaint = StableDiffusionInpaintPipelineLegacy( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ).to(torch_device) img2img = StableDiffusionImg2ImgPipeline(**inpaint.components).to(torch_device) text2img = StableDiffusionPipeline(**inpaint.components).to(torch_device) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) image_inpaint = inpaint( [prompt], generator=generator, num_inference_steps=2, output_type="np", image=init_image, mask_image=mask_image, ).images image_img2img = img2img( [prompt], generator=generator, num_inference_steps=2, output_type="np", image=init_image, ).images image_text2img = text2img( [prompt], generator=generator, num_inference_steps=2, output_type="np", ).images assert image_inpaint.shape == (1, 32, 32, 3) assert image_img2img.shape == (1, 32, 32, 3) assert image_text2img.shape == (1, 64, 64, 3) @require_torch_gpu def test_pipe_false_offload_warn(self): unet = self.dummy_cond_unet() scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") sd = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) sd.enable_model_cpu_offload() logger = logging.get_logger("diffusers.pipelines.pipeline_utils") with CaptureLogger(logger) as cap_logger: sd.to("cuda") assert "It is strongly recommended against doing so" in str(cap_logger) sd = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) def test_set_scheduler(self): unet = self.dummy_cond_unet() scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") sd = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, DDIMScheduler) sd.scheduler = DDPMScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, DDPMScheduler) sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, PNDMScheduler) sd.scheduler = LMSDiscreteScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, LMSDiscreteScheduler) sd.scheduler = EulerDiscreteScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, EulerDiscreteScheduler) sd.scheduler = EulerAncestralDiscreteScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, EulerAncestralDiscreteScheduler) sd.scheduler = DPMSolverMultistepScheduler.from_config(sd.scheduler.config) assert isinstance(sd.scheduler, DPMSolverMultistepScheduler) def test_set_component_to_none(self): unet = self.dummy_cond_unet() scheduler = PNDMScheduler(skip_prk_steps=True) vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") pipeline = StableDiffusionPipeline( unet=unet, scheduler=scheduler, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "This is a flower" out_image = pipeline( prompt=prompt, generator=generator, num_inference_steps=1, output_type="np", ).images pipeline.feature_extractor = None generator = torch.Generator(device="cpu").manual_seed(0) out_image_2 = pipeline( prompt=prompt, generator=generator, num_inference_steps=1, output_type="np", ).images assert out_image.shape == (1, 64, 64, 3) assert np.abs(out_image - out_image_2).max() < 1e-3 def test_set_scheduler_consistency(self): unet = self.dummy_cond_unet() pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") ddim = DDIMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") sd = StableDiffusionPipeline( unet=unet, scheduler=pndm, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) pndm_config = sd.scheduler.config sd.scheduler = DDPMScheduler.from_config(pndm_config) sd.scheduler = PNDMScheduler.from_config(sd.scheduler.config) pndm_config_2 = sd.scheduler.config pndm_config_2 = {k: v for k, v in pndm_config_2.items() if k in pndm_config} assert dict(pndm_config) == dict(pndm_config_2) sd = StableDiffusionPipeline( unet=unet, scheduler=ddim, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=None, feature_extractor=self.dummy_extractor, ) ddim_config = sd.scheduler.config sd.scheduler = LMSDiscreteScheduler.from_config(ddim_config) sd.scheduler = DDIMScheduler.from_config(sd.scheduler.config) ddim_config_2 = sd.scheduler.config ddim_config_2 = {k: v for k, v in ddim_config_2.items() if k in ddim_config} assert dict(ddim_config) == dict(ddim_config_2) def test_save_safe_serialization(self): pipeline = StableDiffusionPipeline.from_pretrained("hf-internal-testing/tiny-stable-diffusion-torch") with tempfile.TemporaryDirectory() as tmpdirname: pipeline.save_pretrained(tmpdirname, safe_serialization=True) # Validate that the VAE safetensor exists and are of the correct format vae_path = os.path.join(tmpdirname, "vae", "diffusion_pytorch_model.safetensors") assert os.path.exists(vae_path), f"Could not find {vae_path}" _ = safetensors.torch.load_file(vae_path) # Validate that the UNet safetensor exists and are of the correct format unet_path = os.path.join(tmpdirname, "unet", "diffusion_pytorch_model.safetensors") assert os.path.exists(unet_path), f"Could not find {unet_path}" _ = safetensors.torch.load_file(unet_path) # Validate that the text encoder safetensor exists and are of the correct format text_encoder_path = os.path.join(tmpdirname, "text_encoder", "model.safetensors") assert os.path.exists(text_encoder_path), f"Could not find {text_encoder_path}" _ = safetensors.torch.load_file(text_encoder_path) pipeline = StableDiffusionPipeline.from_pretrained(tmpdirname) assert pipeline.unet is not None assert pipeline.vae is not None assert pipeline.text_encoder is not None assert pipeline.scheduler is not None assert pipeline.feature_extractor is not None def test_no_pytorch_download_when_doing_safetensors(self): # by default we don't download with tempfile.TemporaryDirectory() as tmpdirname: _ = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname ) path = os.path.join( tmpdirname, "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all", "snapshots", "07838d72e12f9bcec1375b0482b80c1d399be843", "unet", ) # safetensors exists assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors")) # pytorch does not assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin")) def test_no_safetensors_download_when_doing_pytorch(self): # mock diffusers safetensors not available import diffusers diffusers.utils.import_utils._safetensors_available = False with tempfile.TemporaryDirectory() as tmpdirname: _ = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", cache_dir=tmpdirname ) path = os.path.join( tmpdirname, "models--hf-internal-testing--diffusers-stable-diffusion-tiny-all", "snapshots", "07838d72e12f9bcec1375b0482b80c1d399be843", "unet", ) # safetensors does not exists assert not os.path.exists(os.path.join(path, "diffusion_pytorch_model.safetensors")) # pytorch does assert os.path.exists(os.path.join(path, "diffusion_pytorch_model.bin")) diffusers.utils.import_utils._safetensors_available = True def test_optional_components(self): unet = self.dummy_cond_unet() pndm = PNDMScheduler.from_config("hf-internal-testing/tiny-stable-diffusion-torch", subfolder="scheduler") vae = self.dummy_vae bert = self.dummy_text_encoder tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") orig_sd = StableDiffusionPipeline( unet=unet, scheduler=pndm, vae=vae, text_encoder=bert, tokenizer=tokenizer, safety_checker=unet, feature_extractor=self.dummy_extractor, ) sd = orig_sd assert sd.config.requires_safety_checker is True with tempfile.TemporaryDirectory() as tmpdirname: sd.save_pretrained(tmpdirname) # Test that passing None works sd = StableDiffusionPipeline.from_pretrained( tmpdirname, feature_extractor=None, safety_checker=None, requires_safety_checker=False ) assert sd.config.requires_safety_checker is False assert sd.config.safety_checker == (None, None) assert sd.config.feature_extractor == (None, None) with tempfile.TemporaryDirectory() as tmpdirname: sd.save_pretrained(tmpdirname) # Test that loading previous None works sd = StableDiffusionPipeline.from_pretrained(tmpdirname) assert sd.config.requires_safety_checker is False assert sd.config.safety_checker == (None, None) assert sd.config.feature_extractor == (None, None) orig_sd.save_pretrained(tmpdirname) # Test that loading without any directory works shutil.rmtree(os.path.join(tmpdirname, "safety_checker")) with open(os.path.join(tmpdirname, sd.config_name)) as f: config = json.load(f) config["safety_checker"] = [None, None] with open(os.path.join(tmpdirname, sd.config_name), "w") as f: json.dump(config, f) sd = StableDiffusionPipeline.from_pretrained(tmpdirname, requires_safety_checker=False) sd.save_pretrained(tmpdirname) sd = StableDiffusionPipeline.from_pretrained(tmpdirname) assert sd.config.requires_safety_checker is False assert sd.config.safety_checker == (None, None) assert sd.config.feature_extractor == (None, None) # Test that loading from deleted model index works with open(os.path.join(tmpdirname, sd.config_name)) as f: config = json.load(f) del config["safety_checker"] del config["feature_extractor"] with open(os.path.join(tmpdirname, sd.config_name), "w") as f: json.dump(config, f) sd = StableDiffusionPipeline.from_pretrained(tmpdirname) assert sd.config.requires_safety_checker is False assert sd.config.safety_checker == (None, None) assert sd.config.feature_extractor == (None, None) with tempfile.TemporaryDirectory() as tmpdirname: sd.save_pretrained(tmpdirname) # Test that partially loading works sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor) assert sd.config.requires_safety_checker is False assert sd.config.safety_checker == (None, None) assert sd.config.feature_extractor != (None, None) # Test that partially loading works sd = StableDiffusionPipeline.from_pretrained( tmpdirname, feature_extractor=self.dummy_extractor, safety_checker=unet, requires_safety_checker=[True, True], ) assert sd.config.requires_safety_checker == [True, True] assert sd.config.safety_checker != (None, None) assert sd.config.feature_extractor != (None, None) with tempfile.TemporaryDirectory() as tmpdirname: sd.save_pretrained(tmpdirname) sd = StableDiffusionPipeline.from_pretrained(tmpdirname, feature_extractor=self.dummy_extractor) assert sd.config.requires_safety_checker == [True, True] assert sd.config.safety_checker != (None, None) assert sd.config.feature_extractor != (None, None) def test_name_or_path(self): model_path = "hf-internal-testing/tiny-stable-diffusion-torch" sd = DiffusionPipeline.from_pretrained(model_path) assert sd.name_or_path == model_path with tempfile.TemporaryDirectory() as tmpdirname: sd.save_pretrained(tmpdirname) sd = DiffusionPipeline.from_pretrained(tmpdirname) assert sd.name_or_path == tmpdirname def test_warning_no_variant_available(self): variant = "fp16" with self.assertWarns(FutureWarning) as warning_context: cached_folder = StableDiffusionPipeline.download( "hf-internal-testing/diffusers-stable-diffusion-tiny-all", variant=variant ) assert "but no such modeling files are available" in str(warning_context.warning) assert variant in str(warning_context.warning) def get_all_filenames(directory): filenames = glob.glob(directory + "/**", recursive=True) filenames = [f for f in filenames if os.path.isfile(f)] return filenames filenames = get_all_filenames(str(cached_folder)) all_model_files, variant_model_files = variant_compatible_siblings(filenames, variant=variant) # make sure that none of the model names are variant model names assert len(variant_model_files) == 0 assert len(all_model_files) > 0 @slow @require_torch_gpu class PipelineSlowTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_smart_download(self): model_id = "hf-internal-testing/unet-pipeline-dummy" with tempfile.TemporaryDirectory() as tmpdirname: _ = DiffusionPipeline.from_pretrained(model_id, cache_dir=tmpdirname, force_download=True) local_repo_name = "--".join(["models"] + model_id.split("/")) snapshot_dir = os.path.join(tmpdirname, local_repo_name, "snapshots") snapshot_dir = os.path.join(snapshot_dir, os.listdir(snapshot_dir)[0]) # inspect all downloaded files to make sure that everything is included assert os.path.isfile(os.path.join(snapshot_dir, DiffusionPipeline.config_name)) assert os.path.isfile(os.path.join(snapshot_dir, CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, SCHEDULER_CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, WEIGHTS_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "scheduler", SCHEDULER_CONFIG_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) assert os.path.isfile(os.path.join(snapshot_dir, "unet", WEIGHTS_NAME)) # let's make sure the super large numpy file: # https://huggingface.co/hf-internal-testing/unet-pipeline-dummy/blob/main/big_array.npy # is not downloaded, but all the expected ones assert not os.path.isfile(os.path.join(snapshot_dir, "big_array.npy")) def test_warning_unused_kwargs(self): model_id = "hf-internal-testing/unet-pipeline-dummy" logger = logging.get_logger("diffusers.pipelines") with tempfile.TemporaryDirectory() as tmpdirname: with CaptureLogger(logger) as cap_logger: DiffusionPipeline.from_pretrained( model_id, not_used=True, cache_dir=tmpdirname, force_download=True, ) assert ( cap_logger.out.strip().split("\n")[-1] == "Keyword arguments {'not_used': True} are not expected by DDPMPipeline and will be ignored." ) def test_from_save_pretrained(self): # 1. Load models model = UNet2DModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=3, out_channels=3, down_block_types=("DownBlock2D", "AttnDownBlock2D"), up_block_types=("AttnUpBlock2D", "UpBlock2D"), ) scheduler = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline(model, scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) with tempfile.TemporaryDirectory() as tmpdirname: ddpm.save_pretrained(tmpdirname) new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) new_ddpm.to(torch_device) generator = torch.Generator(device=torch_device).manual_seed(0) image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images generator = torch.Generator(device=torch_device).manual_seed(0) new_image = new_ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" @require_torch_2 def test_from_save_pretrained_dynamo(self): run_test_in_subprocess(test_case=self, target_func=_test_from_save_pretrained_dynamo, inputs=None) def test_from_pretrained_hub(self): model_path = "google/ddpm-cifar10-32" scheduler = DDPMScheduler(num_train_timesteps=10) ddpm = DDPMPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm = ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub = ddpm_from_hub.to(torch_device) ddpm_from_hub.set_progress_bar_config(disable=None) generator = torch.Generator(device=torch_device).manual_seed(0) image = ddpm(generator=generator, num_inference_steps=5, output_type="numpy").images generator = torch.Generator(device=torch_device).manual_seed(0) new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" def test_from_pretrained_hub_pass_model(self): model_path = "google/ddpm-cifar10-32" scheduler = DDPMScheduler(num_train_timesteps=10) # pass unet into DiffusionPipeline unet = UNet2DModel.from_pretrained(model_path) ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet, scheduler=scheduler) ddpm_from_hub_custom_model = ddpm_from_hub_custom_model.to(torch_device) ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path, scheduler=scheduler) ddpm_from_hub = ddpm_from_hub.to(torch_device) ddpm_from_hub_custom_model.set_progress_bar_config(disable=None) generator = torch.Generator(device=torch_device).manual_seed(0) image = ddpm_from_hub_custom_model(generator=generator, num_inference_steps=5, output_type="numpy").images generator = torch.Generator(device=torch_device).manual_seed(0) new_image = ddpm_from_hub(generator=generator, num_inference_steps=5, output_type="numpy").images assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass" def test_output_format(self): model_path = "google/ddpm-cifar10-32" scheduler = DDIMScheduler.from_pretrained(model_path) pipe = DDIMPipeline.from_pretrained(model_path, scheduler=scheduler) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) images = pipe(output_type="numpy").images assert images.shape == (1, 32, 32, 3) assert isinstance(images, np.ndarray) images = pipe(output_type="pil", num_inference_steps=4).images assert isinstance(images, list) assert len(images) == 1 assert isinstance(images[0], PIL.Image.Image) # use PIL by default images = pipe(num_inference_steps=4).images assert isinstance(images, list) assert isinstance(images[0], PIL.Image.Image) @require_flax def test_from_flax_from_pt(self): pipe_pt = StableDiffusionPipeline.from_pretrained( "hf-internal-testing/tiny-stable-diffusion-torch", safety_checker=None ) pipe_pt.to(torch_device) from diffusers import FlaxStableDiffusionPipeline with tempfile.TemporaryDirectory() as tmpdirname: pipe_pt.save_pretrained(tmpdirname) pipe_flax, params = FlaxStableDiffusionPipeline.from_pretrained( tmpdirname, safety_checker=None, from_pt=True ) with tempfile.TemporaryDirectory() as tmpdirname: pipe_flax.save_pretrained(tmpdirname, params=params) pipe_pt_2 = StableDiffusionPipeline.from_pretrained(tmpdirname, safety_checker=None, from_flax=True) pipe_pt_2.to(torch_device) prompt = "Hello" generator = torch.manual_seed(0) image_0 = pipe_pt( [prompt], generator=generator, num_inference_steps=2, output_type="np", ).images[0] generator = torch.manual_seed(0) image_1 = pipe_pt_2( [prompt], generator=generator, num_inference_steps=2, output_type="np", ).images[0] assert np.abs(image_0 - image_1).sum() < 1e-5, "Models don't give the same forward pass" @require_compel def test_weighted_prompts_compel(self): from compel import Compel pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4") pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() pipe.enable_attention_slicing() compel = Compel(tokenizer=pipe.tokenizer, text_encoder=pipe.text_encoder) prompt = "a red cat playing with a ball{}" prompts = [prompt.format(s) for s in ["", "++", "--"]] prompt_embeds = compel(prompts) generator = [torch.Generator(device="cpu").manual_seed(33) for _ in range(prompt_embeds.shape[0])] images = pipe( prompt_embeds=prompt_embeds, generator=generator, num_inference_steps=20, output_type="numpy" ).images for i, image in enumerate(images): expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" f"/compel/forest_{i}.npy" ) assert np.abs(image - expected_image).max() < 3e-1 @nightly @require_torch_gpu class PipelineNightlyTests(unittest.TestCase): def tearDown(self): # clean up the VRAM after each test super().tearDown() gc.collect() torch.cuda.empty_cache() def test_ddpm_ddim_equality_batched(self): seed = 0 model_id = "google/ddpm-cifar10-32" unet = UNet2DModel.from_pretrained(model_id) ddpm_scheduler = DDPMScheduler() ddim_scheduler = DDIMScheduler() ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler) ddpm.to(torch_device) ddpm.set_progress_bar_config(disable=None) ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler) ddim.to(torch_device) ddim.set_progress_bar_config(disable=None) generator = torch.Generator(device=torch_device).manual_seed(seed) ddpm_images = ddpm(batch_size=2, generator=generator, output_type="numpy").images generator = torch.Generator(device=torch_device).manual_seed(seed) ddim_images = ddim( batch_size=2, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy", use_clipped_model_output=True, # Need this to make DDIM match DDPM ).images # the values aren't exactly equal, but the images look the same visually assert np.abs(ddpm_images - ddim_images).max() < 1e-1