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README.md

@@ -31,6 +31,36 @@ The FLUX.1 models are also available via API from the following sources
 ## ComfyUI
 `FLUX.1 [schnell]` is also available in [Comfy UI](https://github.com/comfyanonymous/ComfyUI) for local inference with a node-based workflow.
 
+## Diffusers
+To use `FLUX.1 [schnell]` with the 🧨 diffusers python library, first install or upgrade diffusers
+
+```shell
+pip install git+https://github.com/huggingface/diffusers.git
+```
+
+Then you can use `FluxPipeline` to run the model
+
+```python
+import torch
+from diffusers import FluxPipeline
+
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16)
+pipe.enable_model_cpu_offload() #save some VRAM by offloading the model to CPU. Remove this if you have enough GPU power
+
+prompt = "A cat holding a sign that says hello world"
+image = pipe(
+    prompt,
+    guidance_scale=0.0,
+    output_type="pil",
+    num_inference_steps=4,
+    max_sequence_length=256,
+    generator=torch.Generator("cpu").manual_seed(0)
+).images[0]
+image.save("flux-schnell.png")
+```
+
+To learn more check out the [diffusers](https://huggingface.co/docs/diffusers/main/en/api/pipelines/flux) documentation
+
 ---
 # Limitations
 - This model is not intended or able to provide factual information.

model_index.json

@@ -0,0 +1,32 @@
+{
+  "_class_name": "FluxPipeline",
+  "_diffusers_version": "0.30.0.dev0",
+  "scheduler": [
+    "diffusers",
+    "FlowMatchEulerDiscreteScheduler"
+  ],
+  "text_encoder": [
+    "transformers",
+    "CLIPTextModel"
+  ],
+  "text_encoder_2": [
+    "transformers",
+    "T5EncoderModel"
+  ],
+  "tokenizer": [
+    "transformers",
+    "CLIPTokenizer"
+  ],
+  "tokenizer_2": [
+    "transformers",
+    "T5TokenizerFast"
+  ],
+  "transformer": [
+    "diffusers",
+    "FluxTransformer2DModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}