[docs] diffusers gguf checkpoints

docs/source/en/quantization/gguf.md

@@ -77,3 +77,40 @@ Once installed, set `DIFFUSERS_GGUF_CUDA_KERNELS=true`  to use optimized kernels
 - Q5_K
 - Q6_K
 
+## Using Diffusers checkpoints
+
+You can convert a Diffusers checkpoint to GGUF and use it to perform inference. Use the Space below to
+run conversion:
+
+<script
+	type="module"
+	src="https://gradio.s3-us-west-2.amazonaws.com/5.41.1/gradio.js"
+></script>
+
+<gradio-app src="https://diffusers-internal-dev-diffusers-to-gguf.hf.space"></gradio-app>
+
+Once it is obtained, you can run inference:
+
+```py
+import torch
+
+from diffusers import FluxPipeline, FluxTransformer2DModel, GGUFQuantizationConfig
+
+ckpt_path = (
+    "https://huggingface.co/sayakpaul/different-lora-from-civitai/blob/main/flux_dev_diffusers-q4_0.gguf"
+)
+transformer = FluxTransformer2DModel.from_single_file(
+    ckpt_path,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+    torch_dtype=torch.bfloat16,
+)
+pipe = FluxPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-dev",
+    transformer=transformer,
+    torch_dtype=torch.bfloat16,
+)
+pipe.enable_model_cpu_offload()
+prompt = "A cat holding a sign that says hello world"
+image = pipe(prompt, generator=torch.manual_seed(0)).images[0]
+image.save("flux-gguf.png")
+```

src/diffusers/loaders/single_file_model.py

@@ -153,9 +153,17 @@
         "checkpoint_mapping_fn": convert_cosmos_transformer_checkpoint_to_diffusers,
         "default_subfolder": "transformer",
     },
+    "QwenImageTransformer2DModel": {
+        "checkpoint_mapping_fn": lambda x: x,
+        "default_subfolder": "transformer",
+    },
 }
 
 
+def _should_convert_state_dict_to_diffusers(model_state_dict, checkpoint_state_dict):
+    return not set(model_state_dict.keys()).issubset(set(checkpoint_state_dict.keys()))
+
+
 def _get_single_file_loadable_mapping_class(cls):
     diffusers_module = importlib.import_module(__name__.split(".")[0])
     for loadable_class_str in SINGLE_FILE_LOADABLE_CLASSES:
@@ -381,19 +389,23 @@ def from_single_file(cls, pretrained_model_link_or_path_or_dict: Optional[str] =
             model_kwargs = {k: kwargs.get(k) for k in kwargs if k in expected_kwargs or k in optional_kwargs}
             diffusers_model_config.update(model_kwargs)
 
+        ctx = init_empty_weights if is_accelerate_available() else nullcontext
+        with ctx():
+            model = cls.from_config(diffusers_model_config)
+
         checkpoint_mapping_kwargs = _get_mapping_function_kwargs(checkpoint_mapping_fn, **kwargs)
-        diffusers_format_checkpoint = checkpoint_mapping_fn(
-            config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
-        )
+
+        if _should_convert_state_dict_to_diffusers(model.state_dict(), checkpoint):
+            diffusers_format_checkpoint = checkpoint_mapping_fn(
+                config=diffusers_model_config, checkpoint=checkpoint, **checkpoint_mapping_kwargs
+            )
+        else:
+            diffusers_format_checkpoint = checkpoint
+
         if not diffusers_format_checkpoint:
             raise SingleFileComponentError(
                 f"Failed to load {mapping_class_name}. Weights for this component appear to be missing in the checkpoint."
             )
-
-        ctx = init_empty_weights if is_accelerate_available() else nullcontext
-        with ctx():
-            model = cls.from_config(diffusers_model_config)
-
         # Check if `_keep_in_fp32_modules` is not None
         use_keep_in_fp32_modules = (cls._keep_in_fp32_modules is not None) and (
             (torch_dtype == torch.float16) or hasattr(hf_quantizer, "use_keep_in_fp32_modules")

src/diffusers/loaders/single_file_utils.py

@@ -60,6 +60,7 @@
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 CHECKPOINT_KEY_NAMES = {
+    "v1": "model.diffusion_model.output_blocks.11.0.skip_connection.weight",
     "v2": "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
     "xl_base": "conditioner.embedders.1.model.transformer.resblocks.9.mlp.c_proj.bias",
     "xl_refiner": "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias",

tests/quantization/gguf/test_gguf.py

@@ -212,6 +212,7 @@ def _check_for_gguf_linear(model):
 
 class FluxGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
     ckpt_path = "https://huggingface.co/city96/FLUX.1-dev-gguf/blob/main/flux1-dev-Q2_K.gguf"
+    diffusers_ckpt_path = "https://huggingface.co/sayakpaul/flux-diffusers-gguf/blob/main/model-Q4_0.gguf"
     torch_dtype = torch.bfloat16
     model_cls = FluxTransformer2DModel
     expected_memory_use_in_gb = 5
@@ -296,6 +297,16 @@ def test_pipeline_inference(self):
         max_diff = numpy_cosine_similarity_distance(expected_slice, output_slice)
         assert max_diff < 1e-4
 
+    def test_loading_gguf_diffusers_format(self):
+        model = self.model_cls.from_single_file(
+            self.diffusers_ckpt_path,
+            subfolder="transformer",
+            quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),
+            config="black-forest-labs/FLUX.1-dev",
+        )
+        model.to("cuda")
+        model(**self.get_dummy_inputs())
+
 
 class SD35LargeGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase):
     ckpt_path = "https://huggingface.co/city96/stable-diffusion-3.5-large-gguf/blob/main/sd3.5_large-Q4_0.gguf"