# 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 tempfile import traceback import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer from diffusers import ( AutoencoderKL, ControlNetModel, DDIMScheduler, EulerDiscreteScheduler, StableDiffusionControlNetPipeline, UNet2DConditionModel, ) from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device from diffusers.utils.import_utils import is_xformers_available from diffusers.utils.testing_utils import ( enable_full_determinism, require_torch_2, require_torch_gpu, run_test_in_subprocess, ) from ..pipeline_params import ( IMAGE_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS, ) from ..test_pipelines_common import ( PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin, ) enable_full_determinism() # Will be run via run_test_in_subprocess def _test_stable_diffusion_compile(in_queue, out_queue, timeout): error = None try: _ = in_queue.get(timeout=timeout) controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.to("cuda") pipe.set_progress_bar_config(disable=None) pipe.unet.to(memory_format=torch.channels_last) pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) pipe.controlnet.to(memory_format=torch.channels_last) pipe.controlnet = torch.compile(pipe.controlnet, mode="reduce-overhead", fullgraph=True) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "bird" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) output = pipe(prompt, image, generator=generator, output_type="np") image = output.images[0] assert image.shape == (768, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out_full.npy" ) assert np.abs(expected_image - image).max() < 1.0 except Exception: error = f"{traceback.format_exc()}" results = {"error": error} out_queue.put(results, timeout=timeout) out_queue.join() class ControlNetPipelineFastTests( PipelineLatentTesterMixin, PipelineKarrasSchedulerTesterMixin, PipelineTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = IMAGE_TO_IMAGE_IMAGE_PARAMS image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) torch.manual_seed(0) controlnet = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), cross_attention_dim=32, conditioning_embedding_out_channels=(16, 32), ) torch.manual_seed(0) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = 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, ) torch.manual_seed(0) text_encoder_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, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") components = { "unet": unet, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) controlnet_embedder_scale_factor = 2 image = randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ) inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", "image": image, } return inputs def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-3) class StableDiffusionMultiControlNetPipelineFastTests( PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) torch.manual_seed(0) def init_weights(m): if isinstance(m, torch.nn.Conv2d): torch.nn.init.normal(m.weight) m.bias.data.fill_(1.0) controlnet1 = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), cross_attention_dim=32, conditioning_embedding_out_channels=(16, 32), ) controlnet1.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) controlnet2 = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), cross_attention_dim=32, conditioning_embedding_out_channels=(16, 32), ) controlnet2.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = 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, ) torch.manual_seed(0) text_encoder_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, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") controlnet = MultiControlNetModel([controlnet1, controlnet2]) components = { "unet": unet, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) controlnet_embedder_scale_factor = 2 images = [ randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), ] inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", "image": images, } return inputs def test_control_guidance_switch(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) scale = 10.0 steps = 4 inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_1 = pipe(**inputs)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_3 = pipe(**inputs, control_guidance_start=[0.1, 0.3], control_guidance_end=[0.2, 0.7])[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5, 0.8])[0] # make sure that all outputs are different assert np.sum(np.abs(output_1 - output_2)) > 1e-3 assert np.sum(np.abs(output_1 - output_3)) > 1e-3 assert np.sum(np.abs(output_1 - output_4)) > 1e-3 def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-3) def test_save_pretrained_raise_not_implemented_exception(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) with tempfile.TemporaryDirectory() as tmpdir: try: # save_pretrained is not implemented for Multi-ControlNet pipe.save_pretrained(tmpdir) except NotImplementedError: pass class StableDiffusionMultiControlNetOneModelPipelineFastTests( PipelineTesterMixin, PipelineKarrasSchedulerTesterMixin, unittest.TestCase ): pipeline_class = StableDiffusionControlNetPipeline params = TEXT_TO_IMAGE_PARAMS batch_params = TEXT_TO_IMAGE_BATCH_PARAMS image_params = frozenset([]) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess def get_dummy_components(self): torch.manual_seed(0) unet = UNet2DConditionModel( block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, ) torch.manual_seed(0) def init_weights(m): if isinstance(m, torch.nn.Conv2d): torch.nn.init.normal(m.weight) m.bias.data.fill_(1.0) controlnet = ControlNetModel( block_out_channels=(32, 64), layers_per_block=2, in_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), cross_attention_dim=32, conditioning_embedding_out_channels=(16, 32), ) controlnet.controlnet_down_blocks.apply(init_weights) torch.manual_seed(0) scheduler = DDIMScheduler( beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False, ) torch.manual_seed(0) vae = 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, ) torch.manual_seed(0) text_encoder_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, ) text_encoder = CLIPTextModel(text_encoder_config) tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip") controlnet = MultiControlNetModel([controlnet]) components = { "unet": unet, "controlnet": controlnet, "scheduler": scheduler, "vae": vae, "text_encoder": text_encoder, "tokenizer": tokenizer, "safety_checker": None, "feature_extractor": None, } return components def get_dummy_inputs(self, device, seed=0): if str(device).startswith("mps"): generator = torch.manual_seed(seed) else: generator = torch.Generator(device=device).manual_seed(seed) controlnet_embedder_scale_factor = 2 images = [ randn_tensor( (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor), generator=generator, device=torch.device(device), ), ] inputs = { "prompt": "A painting of a squirrel eating a burger", "generator": generator, "num_inference_steps": 2, "guidance_scale": 6.0, "output_type": "numpy", "image": images, } return inputs def test_control_guidance_switch(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) scale = 10.0 steps = 4 inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_1 = pipe(**inputs)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_2 = pipe(**inputs, control_guidance_start=0.1, control_guidance_end=0.2)[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_3 = pipe( **inputs, control_guidance_start=[0.1], control_guidance_end=[0.2], )[0] inputs = self.get_dummy_inputs(torch_device) inputs["num_inference_steps"] = steps inputs["controlnet_conditioning_scale"] = scale output_4 = pipe(**inputs, control_guidance_start=0.4, control_guidance_end=[0.5])[0] # make sure that all outputs are different assert np.sum(np.abs(output_1 - output_2)) > 1e-3 assert np.sum(np.abs(output_1 - output_3)) > 1e-3 assert np.sum(np.abs(output_1 - output_4)) > 1e-3 def test_attention_slicing_forward_pass(self): return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3) @unittest.skipIf( torch_device != "cuda" or not is_xformers_available(), reason="XFormers attention is only available with CUDA and `xformers` installed", ) def test_xformers_attention_forwardGenerator_pass(self): self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3) def test_inference_batch_single_identical(self): self._test_inference_batch_single_identical(expected_max_diff=2e-3) def test_save_pretrained_raise_not_implemented_exception(self): components = self.get_dummy_components() pipe = self.pipeline_class(**components) pipe.to(torch_device) pipe.set_progress_bar_config(disable=None) with tempfile.TemporaryDirectory() as tmpdir: try: # save_pretrained is not implemented for Multi-ControlNet pipe.save_pretrained(tmpdir) except NotImplementedError: pass @slow @require_torch_gpu class ControlNetPipelineSlowTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def test_canny(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "bird" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (768, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny_out.npy" ) assert np.abs(expected_image - image).max() < 9e-2 def test_depth(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-depth") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "Stormtrooper's lecture" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (512, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth_out.npy" ) assert np.abs(expected_image - image).max() < 8e-1 def test_hed(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-hed") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "oil painting of handsome old man, masterpiece" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (704, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/man_hed_out.npy" ) assert np.abs(expected_image - image).max() < 8e-2 def test_mlsd(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-mlsd") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "room" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (704, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/room_mlsd_out.npy" ) assert np.abs(expected_image - image).max() < 5e-2 def test_normal(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-normal") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "cute toy" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (512, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/cute_toy_normal_out.npy" ) assert np.abs(expected_image - image).max() < 5e-2 def test_openpose(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "Chef in the kitchen" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (768, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/chef_pose_out.npy" ) assert np.abs(expected_image - image).max() < 8e-2 def test_scribble(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-scribble") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(5) prompt = "bag" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (640, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bag_scribble_out.npy" ) assert np.abs(expected_image - image).max() < 8e-2 def test_seg(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(5) prompt = "house" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (512, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg_out.npy" ) assert np.abs(expected_image - image).max() < 8e-2 def test_sequential_cpu_offloading(self): torch.cuda.empty_cache() torch.cuda.reset_max_memory_allocated() torch.cuda.reset_peak_memory_stats() controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-seg") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.set_progress_bar_config(disable=None) pipe.enable_attention_slicing() pipe.enable_sequential_cpu_offload() prompt = "house" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/house_seg.png" ) _ = pipe( prompt, image, num_inference_steps=2, output_type="np", ) mem_bytes = torch.cuda.max_memory_allocated() # make sure that less than 7 GB is allocated assert mem_bytes < 4 * 10**9 def test_canny_guess_mode(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) output = pipe( prompt, image, generator=generator, output_type="np", num_inference_steps=3, guidance_scale=3.0, guess_mode=True, ) image = output.images[0] assert image.shape == (768, 512, 3) image_slice = image[-3:, -3:, -1] expected_slice = np.array([0.2724, 0.2846, 0.2724, 0.3843, 0.3682, 0.2736, 0.4675, 0.3862, 0.2887]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_canny_guess_mode_euler(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) output = pipe( prompt, image, generator=generator, output_type="np", num_inference_steps=3, guidance_scale=3.0, guess_mode=True, ) image = output.images[0] assert image.shape == (768, 512, 3) image_slice = image[-3:, -3:, -1] expected_slice = np.array([0.1655, 0.1721, 0.1623, 0.1685, 0.1711, 0.1646, 0.1651, 0.1631, 0.1494]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 @require_torch_2 def test_stable_diffusion_compile(self): run_test_in_subprocess(test_case=self, target_func=_test_stable_diffusion_compile, inputs=None) def test_v11_shuffle_global_pool_conditions(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11e_sd15_shuffle") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "New York" image = load_image( "https://huggingface.co/lllyasviel/control_v11e_sd15_shuffle/resolve/main/images/control.png" ) output = pipe( prompt, image, generator=generator, output_type="np", num_inference_steps=3, guidance_scale=7.0, ) image = output.images[0] assert image.shape == (512, 640, 3) image_slice = image[-3:, -3:, -1] expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348]) assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2 def test_load_local(self): controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny") pipe_1 = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet ) controlnet = ControlNetModel.from_single_file( "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth" ) pipe_2 = StableDiffusionControlNetPipeline.from_single_file( "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors", safety_checker=None, controlnet=controlnet, ) pipes = [pipe_1, pipe_2] images = [] for pipe in pipes: pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "bird" image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3) images.append(output.images[0]) del pipe gc.collect() torch.cuda.empty_cache() assert np.abs(images[0] - images[1]).sum() < 1e-3 @slow @require_torch_gpu class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase): def tearDown(self): super().tearDown() gc.collect() torch.cuda.empty_cache() def test_pose_and_canny(self): controlnet_canny = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny") controlnet_pose = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-openpose") pipe = StableDiffusionControlNetPipeline.from_pretrained( "runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=[controlnet_pose, controlnet_canny] ) pipe.enable_model_cpu_offload() pipe.set_progress_bar_config(disable=None) generator = torch.Generator(device="cpu").manual_seed(0) prompt = "bird and Chef" image_canny = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" ) image_pose = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" ) output = pipe(prompt, [image_pose, image_canny], generator=generator, output_type="np", num_inference_steps=3) image = output.images[0] assert image.shape == (768, 512, 3) expected_image = load_numpy( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose_canny_out.npy" ) assert np.abs(expected_image - image).max() < 5e-2