Expand Single File support in SD3 Pipeline (#8517)

* update

* update

docs/source/en/api/loaders/single_file.md

@@ -35,6 +35,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`StableDiffusionXLInstructPix2PixPipeline`]
 - [`StableDiffusionXLControlNetPipeline`]
 - [`StableDiffusionXLKDiffusionPipeline`]
+- [`StableDiffusion3Pipeline`]
 - [`LatentConsistencyModelPipeline`]
 - [`LatentConsistencyModelImg2ImgPipeline`]
 - [`StableDiffusionControlNetXSPipeline`]
@@ -49,6 +50,7 @@ The [`~loaders.FromSingleFileMixin.from_single_file`] method allows you to load:
 - [`StableCascadeUNet`]
 - [`AutoencoderKL`]
 - [`ControlNetModel`]
+- [`SD3Transformer2DModel`]
 
 ## FromSingleFileMixin
 

docs/source/en/api/pipelines/stable_diffusion/stable_diffusion_3.md

@@ -21,9 +21,9 @@ The abstract from the paper is:
 
 ## Usage Example
 
-_As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._ 
+_As the model is gated, before using it with diffusers you first need to go to the [Stable Diffusion 3 Medium Hugging Face page](https://huggingface.co/stabilityai/stable-diffusion-3-medium-diffusers), fill in the form and accept the gate. Once you are in, you need to login so that your system knows you’ve accepted the gate._
 
-Use the command below to log in: 
+Use the command below to log in:
 
 ```bash
 huggingface-cli login
@@ -211,17 +211,38 @@ model = SD3Transformer2DModel.from_single_file("https://huggingface.co/stability
 
 ## Loading the single checkpoint for the `StableDiffusion3Pipeline`
 
-```python
-from diffusers import StableDiffusion3Pipeline
-from transformers import T5EncoderModel
+### Loading the single file checkpoint without T5
 
-text_encoder_3 = T5EncoderModel.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", subfolder="text_encoder_3", torch_dtype=torch.float16)
-pipe = StableDiffusion3Pipeline.from_single_file("https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors", torch_dtype=torch.float16, text_encoder_3=text_encoder_3)
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_single_file(
+    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips.safetensors",
+    torch_dtype=torch.float16,
+    text_encoder_3=None
+)
+pipe.enable_model_cpu_offload()
+
+image = pipe("a picture of a cat holding a sign that says hello world").images[0]
+image.save('sd3-single-file.png')
 ```
 
-<Tip>
-`from_single_file` support for the `fp8` version of the checkpoints is coming soon. Watch this space.
-</Tip>
+### Loading the single file checkpoint without T5
+
+```python
+import torch
+from diffusers import StableDiffusion3Pipeline
+
+pipe = StableDiffusion3Pipeline.from_single_file(
+    "https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/sd3_medium_incl_clips_t5xxlfp8.safetensors",
+    torch_dtype=torch.float16,
+)
+pipe.enable_model_cpu_offload()
+
+image = pipe("a picture of a cat holding a sign that says hello world").images[0]
+image.save('sd3-single-file-t5-fp8.png')
+```
 
 ## StableDiffusion3Pipeline
 

src/diffusers/loaders/single_file.py

@@ -28,9 +28,11 @@ from .single_file_utils import (
     _legacy_load_safety_checker,
     _legacy_load_scheduler,
     create_diffusers_clip_model_from_ldm,
+    create_diffusers_t5_model_from_checkpoint,
     fetch_diffusers_config,
     fetch_original_config,
     is_clip_model_in_single_file,
+    is_t5_in_single_file,
     load_single_file_checkpoint,
 )
 
@@ -118,6 +120,16 @@ def load_single_file_sub_model(
             is_legacy_loading=is_legacy_loading,
         )
 
+    elif is_transformers_model and is_t5_in_single_file(checkpoint):
+        loaded_sub_model = create_diffusers_t5_model_from_checkpoint(
+            class_obj,
+            checkpoint=checkpoint,
+            config=cached_model_config_path,
+            subfolder=name,
+            torch_dtype=torch_dtype,
+            local_files_only=local_files_only,
+        )
+
     elif is_tokenizer and is_legacy_loading:
         loaded_sub_model = _legacy_load_clip_tokenizer(
             class_obj, checkpoint=checkpoint, config=cached_model_config_path, local_files_only=local_files_only

src/diffusers/loaders/single_file_utils.py

@@ -252,7 +252,6 @@ LDM_CONTROLNET_KEY = "control_model."
 LDM_CLIP_PREFIX_TO_REMOVE = [
     "cond_stage_model.transformer.",
     "conditioner.embedders.0.transformer.",
-    "text_encoders.clip_l.transformer.",
 ]
 OPEN_CLIP_PREFIX = "conditioner.embedders.0.model."
 LDM_OPEN_CLIP_TEXT_PROJECTION_DIM = 1024
@@ -399,11 +398,14 @@ def is_open_clip_sdxl_model(checkpoint):
 
 
 def is_open_clip_sd3_model(checkpoint):
-    is_open_clip_sdxl_refiner_model(checkpoint)
+    if CHECKPOINT_KEY_NAMES["open_clip_sd3"] in checkpoint:
+        return True
+
+    return False
 
 
 def is_open_clip_sdxl_refiner_model(checkpoint):
-    if CHECKPOINT_KEY_NAMES["open_clip_sd3"] in checkpoint:
+    if CHECKPOINT_KEY_NAMES["open_clip_sdxl_refiner"] in checkpoint:
         return True
 
     return False
@@ -1233,11 +1235,14 @@ def convert_ldm_vae_checkpoint(checkpoint, config):
     return new_checkpoint
 
 
-def convert_ldm_clip_checkpoint(checkpoint):
+def convert_ldm_clip_checkpoint(checkpoint, remove_prefix=None):
     keys = list(checkpoint.keys())
     text_model_dict = {}
 
-    remove_prefixes = LDM_CLIP_PREFIX_TO_REMOVE
+    remove_prefixes = []
+    remove_prefixes.extend(LDM_CLIP_PREFIX_TO_REMOVE)
+    if remove_prefix:
+        remove_prefixes.append(remove_prefix)
 
     for key in keys:
         for prefix in remove_prefixes:
@@ -1376,6 +1381,13 @@ def create_diffusers_clip_model_from_ldm(
     ):
         diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint)
 
+    elif (
+        is_clip_sd3_model(checkpoint)
+        and checkpoint[CHECKPOINT_KEY_NAMES["clip_sd3"]].shape[-1] == position_embedding_dim
+    ):
+        diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint, "text_encoders.clip_l.transformer.")
+        diffusers_format_checkpoint["text_projection.weight"] = torch.eye(position_embedding_dim)
+
     elif is_open_clip_model(checkpoint):
         prefix = "cond_stage_model.model."
         diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)
@@ -1391,9 +1403,11 @@ def create_diffusers_clip_model_from_ldm(
         prefix = "conditioner.embedders.0.model."
         diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)
 
-    elif is_open_clip_sd3_model(checkpoint):
-        prefix = "text_encoders.clip_g.transformer."
-        diffusers_format_checkpoint = convert_open_clip_checkpoint(model, checkpoint, prefix=prefix)
+    elif (
+        is_open_clip_sd3_model(checkpoint)
+        and checkpoint[CHECKPOINT_KEY_NAMES["open_clip_sd3"]].shape[-1] == position_embedding_dim
+    ):
+        diffusers_format_checkpoint = convert_ldm_clip_checkpoint(checkpoint, "text_encoders.clip_g.transformer.")
 
     else:
         raise ValueError("The provided checkpoint does not seem to contain a valid CLIP model.")
@@ -1755,7 +1769,7 @@ def convert_sd3_t5_checkpoint_to_diffusers(checkpoint):
     keys = list(checkpoint.keys())
     text_model_dict = {}
 
-    remove_prefixes = ["text_encoders.t5xxl.transformer.encoder."]
+    remove_prefixes = ["text_encoders.t5xxl.transformer."]
 
     for key in keys:
         for prefix in remove_prefixes: