Add controlnet and vae from single file (huggingface#4084)

* Add controlnet from single file

* Updates

* make style

* finish

* Apply suggestions from code review

Co-authored-by: Sayak Paul <[email protected]>

---------

Co-authored-by: Sayak Paul <[email protected]>

Author: 2 people

docs/source/en/api/loaders.mdx

@@ -35,3 +35,11 @@ Adapters (textual inversion, LoRA, hypernetworks) allow you to modify a diffusio
 ## FromSingleFileMixin
 
 [[autodoc]] loaders.FromSingleFileMixin
+
+## FromOriginalControlnetMixin
+
+[[autodoc]] loaders.FromOriginalControlnetMixin
+
+## FromOriginalVAEMixin
+
+[[autodoc]] loaders.FromOriginalVAEMixin

docs/source/en/api/models/autoencoderkl.mdx

@@ -6,6 +6,18 @@ The abstract from the paper is:
 
 *How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Our contributions are two-fold. First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable posterior using the proposed lower bound estimator. Theoretical advantages are reflected in experimental results.*
 
+## Loading from the original format
+
+By default the [`AutoencoderKL`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
+from the original format using [`FromOriginalVAEMixin.from_single_file`] as follows:
+
+```py
+from diffusers import AutoencoderKL
+
+url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"  # can also be local file
+model = AutoencoderKL.from_single_file(url)
+```
+
 ## AutoencoderKL
 
 [[autodoc]] AutoencoderKL
@@ -28,4 +40,4 @@ The abstract from the paper is:
 
 ## FlaxDecoderOutput
 
-[[autodoc]] models.vae_flax.FlaxDecoderOutput
+[[autodoc]] models.vae_flax.FlaxDecoderOutput

docs/source/en/api/models/controlnet.mdx

@@ -6,6 +6,21 @@ The abstract from the paper is:
 
 *We present a neural network structure, ControlNet, to control pretrained large diffusion models to support additional input conditions. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). Moreover, training a ControlNet is as fast as fine-tuning a diffusion model, and the model can be trained on a personal devices. Alternatively, if powerful computation clusters are available, the model can scale to large amounts (millions to billions) of data. We report that large diffusion models like Stable Diffusion can be augmented with ControlNets to enable conditional inputs like edge maps, segmentation maps, keypoints, etc. This may enrich the methods to control large diffusion models and further facilitate related applications.*
 
+## Loading from the original format
+
+By default the [`ControlNetModel`] should be loaded with [`~ModelMixin.from_pretrained`], but it can also be loaded
+from the original format using [`FromOriginalControlnetMixin.from_single_file`] as follows:
+
+```py
+from diffusers import StableDiffusionControlnetPipeline, ControlNetModel
+
+url = "https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"  # can also be a local path
+controlnet = ControlNetModel.from_single_file(url)
+
+url = "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned.safetensors"  # can also be a local path
+pipe = StableDiffusionControlnetPipeline.from_single_file(url, controlnet=controlnet)
+```
+
 ## ControlNetModel
 
 [[autodoc]] ControlNetModel
@@ -20,4 +35,4 @@ The abstract from the paper is:
 
 ## FlaxControlNetOutput
 
-[[autodoc]] models.controlnet_flax.FlaxControlNetOutput
+[[autodoc]] models.controlnet_flax.FlaxControlNetOutput

src/diffusers/loaders.py

@@ -18,6 +18,7 @@
 import torch.nn as nn
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..loaders import FromOriginalVAEMixin
 from ..utils import BaseOutput, apply_forward_hook
 from .attention_processor import AttentionProcessor, AttnProcessor
 from .modeling_utils import ModelMixin
@@ -38,7 +39,7 @@ class AutoencoderKLOutput(BaseOutput):
     latent_dist: "DiagonalGaussianDistribution"
 
 
-class AutoencoderKL(ModelMixin, ConfigMixin):
+class AutoencoderKL(ModelMixin, ConfigMixin, FromOriginalVAEMixin):
     r"""
     A VAE model with KL loss for encoding images into latents and decoding latent representations into images.
 

src/diffusers/models/autoencoder_kl.py

@@ -19,6 +19,7 @@
 from torch.nn import functional as F
 
 from ..configuration_utils import ConfigMixin, register_to_config
+from ..loaders import FromOriginalControlnetMixin
 from ..utils import BaseOutput, logging
 from .attention_processor import AttentionProcessor, AttnProcessor
 from .embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps
@@ -100,7 +101,7 @@ def forward(self, conditioning):
         return embedding
 
 
-class ControlNetModel(ModelMixin, ConfigMixin):
+class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
     """
     A ControlNet model.
 

src/diffusers/models/controlnet.py

@@ -24,7 +24,7 @@
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -90,7 +90,9 @@
 """
 
 
-class StableDiffusionControlNetPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionControlNetPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
 

src/diffusers/pipelines/controlnet/pipeline_controlnet.py

@@ -24,7 +24,7 @@
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -116,7 +116,9 @@ def prepare_image(image):
     return image
 
 
-class StableDiffusionControlNetImg2ImgPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionControlNetImg2ImgPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py

@@ -25,7 +25,7 @@
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from ...image_processor import VaeImageProcessor
-from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
+from ...loaders import FromSingleFileMixin, LoraLoaderMixin, TextualInversionLoaderMixin
 from ...models import AutoencoderKL, ControlNetModel, UNet2DConditionModel
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -222,7 +222,9 @@ def prepare_mask_and_masked_image(image, mask, height, width, return_image=False
     return mask, masked_image
 
 
-class StableDiffusionControlNetInpaintPipeline(DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin):
+class StableDiffusionControlNetInpaintPipeline(
+    DiffusionPipeline, TextualInversionLoaderMixin, LoraLoaderMixin, FromSingleFileMixin
+):
     r"""
     Pipeline for text-to-image generation using Stable Diffusion with ControlNet guidance.
 

src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py

@@ -621,8 +621,8 @@ def convert_ldm_unet_checkpoint(
 def convert_ldm_vae_checkpoint(checkpoint, config):
     # extract state dict for VAE
     vae_state_dict = {}
-    vae_key = "first_stage_model."
     keys = list(checkpoint.keys())
+    vae_key = "first_stage_model." if any(k.startswith("first_stage_model.") for k in keys) else ""
     for key in keys:
         if key.startswith(vae_key):
             vae_state_dict[key.replace(vae_key, "")] = checkpoint.get(key)
@@ -1064,7 +1064,7 @@ def convert_controlnet_checkpoint(
     if cross_attention_dim is not None:
         ctrlnet_config["cross_attention_dim"] = cross_attention_dim
 
-    controlnet_model = ControlNetModel(**ctrlnet_config)
+    controlnet = ControlNetModel(**ctrlnet_config)
 
     # Some controlnet ckpt files are distributed independently from the rest of the
     # model components i.e. https://huggingface.co/thibaud/controlnet-sd21/
@@ -1082,9 +1082,9 @@ def convert_controlnet_checkpoint(
         skip_extract_state_dict=skip_extract_state_dict,
     )
 
-    controlnet_model.load_state_dict(converted_ctrl_checkpoint)
+    controlnet.load_state_dict(converted_ctrl_checkpoint)
 
-    return controlnet_model
+    return controlnet
 
 
 def download_from_original_stable_diffusion_ckpt(
@@ -1181,7 +1181,7 @@ def download_from_original_stable_diffusion_ckpt(
     )
 
     if pipeline_class is None:
-        pipeline_class = StableDiffusionPipeline
+        pipeline_class = StableDiffusionPipeline if not controlnet else StableDiffusionControlNetPipeline
 
     if prediction_type == "v-prediction":
         prediction_type = "v_prediction"
@@ -1288,8 +1288,7 @@ def download_from_original_stable_diffusion_ckpt(
     if controlnet is None:
         controlnet = "control_stage_config" in original_config.model.params
 
-    if controlnet:
-        controlnet_model = convert_controlnet_checkpoint(
+        controlnet = convert_controlnet_checkpoint(
             checkpoint, original_config, checkpoint_path, image_size, upcast_attention, extract_ema
         )
 
@@ -1400,13 +1399,13 @@ def download_from_original_stable_diffusion_ckpt(
 
         if stable_unclip is None:
             if controlnet:
-                pipe = StableDiffusionControlNetPipeline(
+                pipe = pipeline_class(
                     vae=vae,
                     text_encoder=text_model,
                     tokenizer=tokenizer,
                     unet=unet,
                     scheduler=scheduler,
-                    controlnet=controlnet_model,
+                    controlnet=controlnet,
                     safety_checker=None,
                     feature_extractor=None,
                     requires_safety_checker=False,
@@ -1503,12 +1502,12 @@ def download_from_original_stable_diffusion_ckpt(
             feature_extractor = None
 
         if controlnet:
-            pipe = StableDiffusionControlNetPipeline(
+            pipe = pipeline_class(
                 vae=vae,
                 text_encoder=text_model,
                 tokenizer=tokenizer,
                 unet=unet,
-                controlnet=controlnet_model,
+                controlnet=controlnet,
                 scheduler=scheduler,
                 safety_checker=safety_checker,
                 feature_extractor=feature_extractor,
@@ -1623,7 +1622,7 @@ def download_controlnet_from_original_ckpt(
     if "control_stage_config" not in original_config.model.params:
         raise ValueError("`control_stage_config` not present in original config")
 
-    controlnet_model = convert_controlnet_checkpoint(
+    controlnet = convert_controlnet_checkpoint(
         checkpoint,
         original_config,
         checkpoint_path,
@@ -1634,4 +1633,4 @@ def download_controlnet_from_original_ckpt(
         cross_attention_dim=cross_attention_dim,
     )
 
-    return controlnet_model
+    return controlnet

src/diffusers/pipelines/stable_diffusion/convert_from_ckpt.py

@@ -199,7 +199,7 @@ def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False)
             torch_dtype=torch_dtype,
             revision=revision,
         )
-        model.to(torch_device).eval()
+        model.to(torch_device)
 
         return model
 
@@ -383,3 +383,22 @@ def test_stable_diffusion_encode_sample(self, seed, expected_slice):
 
         tolerance = 3e-3 if torch_device != "mps" else 1e-2
         assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)
+
+    def test_stable_diffusion_model_local(self):
+        model_id = "stabilityai/sd-vae-ft-mse"
+        model_1 = AutoencoderKL.from_pretrained(model_id).to(torch_device)
+
+        url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"
+        model_2 = AutoencoderKL.from_single_file(url).to(torch_device)
+        image = self.get_sd_image(33)
+
+        with torch.no_grad():
+            sample_1 = model_1(image).sample
+            sample_2 = model_2(image).sample
+
+        assert sample_1.shape == sample_2.shape
+
+        output_slice_1 = sample_1[-1, -2:, -2:, :2].flatten().float().cpu()
+        output_slice_2 = sample_2[-1, -2:, -2:, :2].flatten().float().cpu()
+
+        assert torch_all_close(output_slice_1, output_slice_2, atol=3e-3)

tests/models/test_models_vae.py

@@ -752,6 +752,42 @@ def test_v11_shuffle_global_pool_conditions(self):
         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

tests/pipelines/controlnet/test_controlnet.py

@@ -401,3 +401,49 @@ def test_canny(self):
         )
 
         assert np.abs(expected_image - image).max() < 9e-2
+
+    def test_load_local(self):
+        controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
+        pipe_1 = StableDiffusionControlNetImg2ImgPipeline.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 = StableDiffusionControlNetImg2ImgPipeline.from_single_file(
+            "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
+            safety_checker=None,
+            controlnet=controlnet,
+        )
+        control_image = load_image(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
+        ).resize((512, 512))
+        image = load_image(
+            "https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"
+        ).resize((512, 512))
+
+        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"
+            output = pipe(
+                prompt,
+                image=image,
+                control_image=control_image,
+                strength=0.9,
+                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