Improve single loading file

src/diffusers/loaders.py

@@ -1389,7 +1389,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
         use_auth_token = kwargs.pop("use_auth_token", None)
         revision = kwargs.pop("revision", None)
         extract_ema = kwargs.pop("extract_ema", False)
-        image_size = kwargs.pop("image_size", 512)
+        image_size = kwargs.pop("image_size", None)
         scheduler_type = kwargs.pop("scheduler_type", "pndm")
         num_in_channels = kwargs.pop("num_in_channels", None)
         upcast_attention = kwargs.pop("upcast_attention", None)

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -24,6 +24,7 @@
     AutoFeatureExtractor,
     BertTokenizerFast,
     CLIPImageProcessor,
+    CLIPTextConfig,
     CLIPTextModel,
     CLIPTextModelWithProjection,
     CLIPTokenizer,
@@ -48,7 +49,7 @@
     PNDMScheduler,
     UnCLIPScheduler,
 )
-from ...utils import is_omegaconf_available, is_safetensors_available, logging
+from ...utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available, logging
 from ...utils.import_utils import BACKENDS_MAPPING
 from ..latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
 from ..paint_by_example import PaintByExampleImageEncoder
@@ -57,6 +58,10 @@
 from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
 
 
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+    from accelerate.utils import set_module_tensor_to_device
+
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -770,11 +775,12 @@ def _copy_layers(hf_layers, pt_layers):
 
 
 def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
-    text_model = (
-        CLIPTextModel.from_pretrained("openai/clip-vit-large-patch14", local_files_only=local_files_only)
-        if text_encoder is None
-        else text_encoder
-    )
+    if text_encoder is None:
+        config_name = "openai/clip-vit-large-patch14"
+        config = CLIPTextConfig.from_pretrained(config_name)
+
+        with init_empty_weights():
+            text_model = CLIPTextModel(config)
 
     keys = list(checkpoint.keys())
 
@@ -787,7 +793,8 @@ def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder
             if key.startswith(prefix):
                 text_model_dict[key[len(prefix + ".") :]] = checkpoint[key]
 
-    text_model.load_state_dict(text_model_dict)
+    for param_name, param in text_model_dict.items():
+        set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
 
     return text_model
 
@@ -884,14 +891,26 @@ def convert_paint_by_example_checkpoint(checkpoint):
     return model
 
 
-def convert_open_clip_checkpoint(checkpoint, prefix="cond_stage_model.model."):
+def convert_open_clip_checkpoint(
+    checkpoint, config_name, prefix="cond_stage_model.model.", has_projection=False, **config_kwargs
+):
     # text_model = CLIPTextModel.from_pretrained("stabilityai/stable-diffusion-2", subfolder="text_encoder")
-    text_model = CLIPTextModelWithProjection.from_pretrained(
-        "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
-    )
+    # text_model = CLIPTextModelWithProjection.from_pretrained(
+    #    "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", projection_dim=1280
+    # )
+    config = CLIPTextConfig.from_pretrained(config_name, **config_kwargs)
+
+    with init_empty_weights():
+        text_model = CLIPTextModelWithProjection(config) if has_projection else CLIPTextModel(config)
 
     keys = list(checkpoint.keys())
 
+    keys_to_ignore = []
+    if config_name == "stabilityai/stable-diffusion-2" and config.num_hidden_layers == 23:
+        # make sure to remove all keys > 22
+        keys_to_ignore += [k for k in keys if k.startswith("cond_stage_model.model.transformer.resblocks.23")]
+        keys_to_ignore += ["cond_stage_model.model.text_projection"]
+
     text_model_dict = {}
 
     if prefix + "text_projection" in checkpoint:
@@ -902,8 +921,8 @@ def convert_open_clip_checkpoint(checkpoint, prefix="cond_stage_model.model."):
     text_model_dict["text_model.embeddings.position_ids"] = text_model.text_model.embeddings.get_buffer("position_ids")
 
     for key in keys:
-        # if "resblocks.23" in key:  # Diffusers drops the final layer and only uses the penultimate layer
-        #     continue
+        if key in keys_to_ignore:
+            continue
         if key[len(prefix) :] in textenc_conversion_map:
             if key.endswith("text_projection"):
                 value = checkpoint[key].T
@@ -931,7 +950,8 @@ def convert_open_clip_checkpoint(checkpoint, prefix="cond_stage_model.model."):
 
                 text_model_dict[new_key] = checkpoint[key]
 
-    text_model.load_state_dict(text_model_dict)
+    for param_name, param in text_model_dict.items():
+        set_module_tensor_to_device(text_model, param_name, "cpu", value=param)
 
     return text_model
 
@@ -1061,7 +1081,7 @@ def convert_controlnet_checkpoint(
 def download_from_original_stable_diffusion_ckpt(
     checkpoint_path: str,
     original_config_file: str = None,
-    image_size: int = 512,
+    image_size: Optional[int] = None,
     prediction_type: str = None,
     model_type: str = None,
     extract_ema: bool = False,
@@ -1144,6 +1164,7 @@ def download_from_original_stable_diffusion_ckpt(
         LDMTextToImagePipeline,
         PaintByExamplePipeline,
         StableDiffusionControlNetPipeline,
+        StableDiffusionInpaintPipeline,
         StableDiffusionPipeline,
         StableDiffusionXLImg2ImgPipeline,
         StableDiffusionXLPipeline,
@@ -1166,12 +1187,9 @@ def download_from_original_stable_diffusion_ckpt(
         if not is_safetensors_available():
             raise ValueError(BACKENDS_MAPPING["safetensors"][1])
 
-        from safetensors import safe_open
+        from safetensors.torch import load_file as safe_load
 
-        checkpoint = {}
-        with safe_open(checkpoint_path, framework="pt", device="cpu") as f:
-            for key in f.keys():
-                checkpoint[key] = f.get_tensor(key)
+        checkpoint = safe_load(checkpoint_path, device="cpu")
     else:
         if device is None:
             device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -1183,7 +1201,7 @@ def download_from_original_stable_diffusion_ckpt(
     if "global_step" in checkpoint:
         global_step = checkpoint["global_step"]
     else:
-        logger.warning("global_step key not found in model")
+        logger.debug("global_step key not found in model")
         global_step = None
 
     # NOTE: this while loop isn't great but this controlnet checkpoint has one additional
@@ -1230,9 +1248,15 @@ def download_from_original_stable_diffusion_ckpt(
             model_type = "SDXL"
         else:
             model_type = "SDXL-Refiner"
+        if image_size is None:
+            image_size = 1024
 
-    if num_in_channels is not None:
-        original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
+    if num_in_channels is None and pipeline_class == StableDiffusionInpaintPipeline:
+        num_in_channels = 9
+    elif num_in_channels is None:
+        num_in_channels = 4
+
+    original_config["model"]["params"]["unet_config"]["params"]["in_channels"] = num_in_channels
 
     if (
         "parameterization" in original_config["model"]["params"]
@@ -1263,7 +1287,6 @@ def download_from_original_stable_diffusion_ckpt(
     num_train_timesteps = getattr(original_config.model.params, "timesteps", None) or 1000
 
     if model_type in ["SDXL", "SDXL-Refiner"]:
-        image_size = 1024
         scheduler_dict = {
             "beta_schedule": "scaled_linear",
             "beta_start": 0.00085,
@@ -1279,7 +1302,6 @@ def download_from_original_stable_diffusion_ckpt(
         }
         scheduler = EulerDiscreteScheduler.from_config(scheduler_dict)
         scheduler_type = "euler"
-        vae_path = "stabilityai/sdxl-vae"
     else:
         beta_start = getattr(original_config.model.params, "linear_start", None) or 0.02
         beta_end = getattr(original_config.model.params, "linear_end", None) or 0.085
@@ -1318,25 +1340,45 @@ def download_from_original_stable_diffusion_ckpt(
     # Convert the UNet2DConditionModel model.
     unet_config = create_unet_diffusers_config(original_config, image_size=image_size)
     unet_config["upcast_attention"] = upcast_attention
-    unet = UNet2DConditionModel(**unet_config)
+    with init_empty_weights():
+        unet = UNet2DConditionModel(**unet_config)
 
     converted_unet_checkpoint = convert_ldm_unet_checkpoint(
         checkpoint, unet_config, path=checkpoint_path, extract_ema=extract_ema
     )
-    unet.load_state_dict(converted_unet_checkpoint)
+
+    for param_name, param in converted_unet_checkpoint.items():
+        set_module_tensor_to_device(unet, param_name, "cpu", value=param)
 
     # Convert the VAE model.
     if vae_path is None:
         vae_config = create_vae_diffusers_config(original_config, image_size=image_size)
         converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
 
-        vae = AutoencoderKL(**vae_config)
-        vae.load_state_dict(converted_vae_checkpoint)
+        if (
+            "model" in original_config
+            and "params" in original_config.model
+            and "scale_factor" in original_config.model.params
+        ):
+            vae_scaling_factor = original_config.model.params.scale_factor
+        else:
+            vae_scaling_factor = 0.18215  # default SD scaling factor
+
+        vae_config["scaling_factor"] = vae_scaling_factor
+
+        with init_empty_weights():
+            vae = AutoencoderKL(**vae_config)
+
+        for param_name, param in converted_vae_checkpoint.items():
+            set_module_tensor_to_device(vae, param_name, "cpu", value=param)
     else:
         vae = AutoencoderKL.from_pretrained(vae_path)
 
     if model_type == "FrozenOpenCLIPEmbedder":
-        text_model = convert_open_clip_checkpoint(checkpoint)
+        config_name = "stabilityai/stable-diffusion-2"
+        config_kwargs = {"subfolder": "text_encoder"}
+
+        text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
         tokenizer = CLIPTokenizer.from_pretrained("stabilityai/stable-diffusion-2", subfolder="tokenizer")
 
         if stable_unclip is None:
@@ -1469,7 +1511,12 @@ def download_from_original_stable_diffusion_ckpt(
             tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14")
             text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
             tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
-            text_encoder_2 = convert_open_clip_checkpoint(checkpoint, prefix="conditioner.embedders.1.model.")
+
+            config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            config_kwargs = {"projection_dim": 1280}
+            text_encoder_2 = convert_open_clip_checkpoint(
+                checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
+            )
 
             pipe = StableDiffusionXLPipeline(
                 vae=vae,
@@ -1485,7 +1532,12 @@ def download_from_original_stable_diffusion_ckpt(
             tokenizer = None
             text_encoder = None
             tokenizer_2 = CLIPTokenizer.from_pretrained("laion/CLIP-ViT-bigG-14-laion2B-39B-b160k", pad_token="!")
-            text_encoder_2 = convert_open_clip_checkpoint(checkpoint, prefix="conditioner.embedders.0.model.")
+
+            config_name = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
+            config_kwargs = {"projection_dim": 1280}
+            text_encoder_2 = convert_open_clip_checkpoint(
+                checkpoint, config_name, prefix="conditioner.embedders.0.model.", has_projection=True, **config_kwargs
+            )
 
             pipe = StableDiffusionXLImg2ImgPipeline(
                 vae=vae,

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -24,7 +24,7 @@
 
 from ...configuration_utils import FrozenDict
 from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import deprecate, is_accelerate_available, is_accelerate_version, logging, randn_tensor
@@ -153,7 +153,9 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image: bool
     return mask, masked_image
 
 
-class StableDiffusionInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionInpaintPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-guided image inpainting using Stable Diffusion.
 

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -20,17 +20,20 @@
 
 import numpy as np
 import torch
+from huggingface_hub import hf_hub_download
 from PIL import Image
 from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
 
 from diffusers import (
     AutoencoderKL,
+    DDIMScheduler,
     DPMSolverMultistepScheduler,
     LMSDiscreteScheduler,
     PNDMScheduler,
     StableDiffusionInpaintPipeline,
     UNet2DConditionModel,
 )
+from diffusers.models.attention_processor import AttnProcessor
 from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint import prepare_mask_and_masked_image
 from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, slow, torch_device
 from diffusers.utils.testing_utils import (
@@ -512,6 +515,42 @@ def test_stable_diffusion_simple_inpaint_ddim(self):
 
         assert np.abs(expected_slice - image_slice).max() < 6e-4
 
+    def test_download_local(self):
+        filename = hf_hub_download("runwayml/stable-diffusion-inpainting", filename="sd-v1-5-inpainting.ckpt")
+
+        pipe = StableDiffusionInpaintPipeline.from_single_file(filename, torch_dtype=torch.float16)
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.to("cuda")
+
+        inputs = self.get_inputs(torch_device)
+        inputs["num_inference_steps"] = 1
+        image_out = pipe(**inputs).images[0]
+
+        assert image_out.shape == (512, 512, 3)
+
+    def test_download_ckpt_diff_format_is_same(self):
+        ckpt_path = "https://huggingface.co/runwayml/stable-diffusion-inpainting/blob/main/sd-v1-5-inpainting.ckpt"
+
+        pipe = StableDiffusionInpaintPipeline.from_single_file(ckpt_path)
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.unet.set_attn_processor(AttnProcessor())
+        pipe.to("cuda")
+
+        inputs = self.get_inputs(torch_device)
+        inputs["num_inference_steps"] = 5
+        image_ckpt = pipe(**inputs).images[0]
+
+        pipe = StableDiffusionInpaintPipeline.from_pretrained("runwayml/stable-diffusion-inpainting")
+        pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        pipe.unet.set_attn_processor(AttnProcessor())
+        pipe.to("cuda")
+
+        inputs = self.get_inputs(torch_device)
+        inputs["num_inference_steps"] = 5
+        image = pipe(**inputs).images[0]
+
+        assert np.max(np.abs(image - image_ckpt)) < 1e-4
+
 
 @nightly
 @require_torch_gpu