Update README.md

README.md

@@ -1,3 +1,127 @@
 ---
 license: apache-2.0
+pipeline_tag: text-to-image
+tags:
+- flux
+base_model:
+- black-forest-labs/FLUX.1-dev
 ---
+
+# Flux Dev F8 Diffusers
+
+Transformer support in float8_e4m3fn precision.
+
+Requires RTX 3000 or newer card. ***2-3x speedup*** in inference time.
+
+Unlike other implementations, this is compatible with the native FluxPipeline.
+
+You will need the full weight Flux model, but the transformer directory is in float8_e4m3fn.
+
+Also compatible with:
+* [T5 Encoder](https://huggingface.co/twodgirl/Flux-dev-optimum-quant-qfloat8/tree/main/flux-t5) quanto model
+* PuLID
+
+*Make sure your torch version is 2.4 or newer.*
+
+## Inference
+
+Replace and transform the linear layers of a float8 model to bfloat16 on the fly, using 2x less VRAM.
+
+```python
+from diffusers import AutoencoderKL, FluxTransformer2DModel, FluxPipeline
+from linear_8 import replace_regular_linears
+import torch
+
+transformer = FluxTransformer2DModel.from_pretrained('John6666/raemu-flux-v10-fp8-flux',
+                                                     subfolder='transformer',
+                                                     torch_dtype=torch.float8_e4m3fn)
+replace_regular_linears(transformer)
+vae = AutoencoderKL.from_pretrained('black-forest-labs/FLUX.1-dev', subfolder='vae').to(torch.bfloat16)
+pipe = FluxPipeline.from_pretrained('black-forest-labs/FLUX.1-dev',
+                                    transformer=transformer,
+                                    vae=vae)
+pipe.enable_model_cpu_offload()
+```
+
+
+## Inference with PuLID
+
+*Tested with v0.30.2 diffusers library, it's likely to need small modifications in the future version.*
+
+```
+pip install -r requirements.txt
+```
+
+Download [T5 Encoder](https://huggingface.co/twodgirl/Flux-dev-optimum-quant-qfloat8/tree/main/flux-t5) and the content of [PuLID](https://github.com/ToTheBeginning/PuLID) (without the requirements file).
+
+You should have:
+
+```
+eva_clip
+pulid
+flux-t5
+flux_model.py
+the-file-below.py
+```
+
+```python
+from diffusers import AutoencoderKL, FluxPipeline
+from flux_model import FluxTransformer2DModel
+from linear_8 import replace_regular_linears
+import torch
+
+from optimum.quanto.models import QuantizedTransformersModel
+import numpy as np
+from PIL import Image
+from pulid.pipeline_flux import PuLIDPipeline
+from transformers import T5EncoderModel
+from torchvision import transforms
+
+class T5Model(QuantizedTransformersModel):
+    auto_class = T5EncoderModel
+
+class FluxGenerator:
+    def __init__(self, pipe):
+        self.pipe = pipe
+        self.pulid_model = PuLIDPipeline(pipe.transformer, 'cuda', weight_dtype=torch.bfloat16)
+        self.pulid_model.load_pretrain()
+
+    def clear_id(self):
+        self.pipe.transformer.pul_id = None
+        self.pipe.transformer.pul_weight = 1.0
+
+    def set_id(self, id_image, id_weight=1.0, true_cfg=1.0):
+        # Variable use_true_cfg is False by default.
+        use_true_cfg = abs(true_cfg - 1.0) > 1e-2
+        if id_image is not None:
+            id_embeddings, uncond_id_embeddings = self.pulid_model.get_id_embedding(id_image, cal_uncond=use_true_cfg)
+        else:
+            id_embeddings = None
+            uncond_id_embeddings = None
+        # The pipe cannot accept its module's parameters,
+        # change the module's state instead.
+        self.pipe.transformer.pul_id = uncond_id_embeddings if use_true_cfg else id_embeddings
+        self.pipe.transformer.pul_id_weight = id_weight
+
+T5EncoderModel.from_config = lambda c: T5EncoderModel(c).to(dtype=torch.bfloat16)
+t5 = T5Model.from_pretrained('./flux-t5')._wrapped
+transformer = FluxTransformer2DModel.from_pretrained('flux-fp8-e4m3fn',
+                                                     subfolder='transformer',
+                                                     torch_dtype=torch.float8_e4m3fn)
+replace_regular_linears(transformer)
+vae = AutoencoderKL.from_pretrained('flux', subfolder='vae').to(torch.bfloat16)
+pipe = FluxPipeline.from_pretrained('flux',
+                                    text_encoder_2=t5,
+                                    transformer=transformer,
+                                    vae=vae)
+pipe.enable_model_cpu_offload()
+face = transforms.Resize(1024)(Image.open('reference.png').convert('RGB'))
+gen = FluxGenerator(pipe)
+gen.set_id(np.array(face))
+image = pipe('portrait, color, cinematic', num_inference_steps=10).images[0]
+image.save('portrait.png')
+```
+
+## Disclaimer
+
+Use of this code and the model requires citation and attribution to the author via a link to their Hugging Face profile in all resulting work.