Flux-dev-fp8 with Hyper-FLUX.1-dev-8steps-lora

### Describe the bug

It seems that Hyper-FLUX.1-dev-8steps-lora can not support Flux-dev-fp8, the image seems the same when I load or not load Hyper-FLUX.1-dev-8steps-lora.
These are my code, Can any one use Hyper-FLUX.1-dev-8steps-lora on Flux-dev-fp8
self.transformer = FluxTransformer2DModel.from_single_file(os.path.join(self.model_root, self.config["transformer_path"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.transformer, weights=qfloat8)
freeze(self.transformer)

self.text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(self.model_root, self.config["text_encoder_2_repo"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.text_encoder_2, weights=qfloat8)
freeze(self.text_encoder_2)

self.pipe = FluxPipeline.from_pretrained(os.path.join(self.model_root, self.config["flux_repo"]), transformer=None, text_encoder_2=None, torch_dtype=torch.bfloat16).to(self.device)

self.pipe.transformer = self.transformer
self.pipe.text_encoder_2 = self.text_encoder_2

self.pipe.load_lora_weights(load_file(os.path.join(self.model_root, self.config["8steps_lora"]), device=self.device), adapter_name="8steps")
self.pipe.fuse_lora(lora_scale=1.0)

### Reproduction

It seems that Hyper-FLUX.1-dev-8steps-lora can not support Flux-dev-fp8, the image seems the same when I load or not load Hyper-FLUX.1-dev-8steps-lora.
These are my code, Can any one use Hyper-FLUX.1-dev-8steps-lora on Flux-dev-fp8
self.transformer = FluxTransformer2DModel.from_single_file(os.path.join(self.model_root, self.config["transformer_path"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.transformer, weights=qfloat8)
freeze(self.transformer)

self.text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(self.model_root, self.config["text_encoder_2_repo"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.text_encoder_2, weights=qfloat8)
freeze(self.text_encoder_2)

self.pipe = FluxPipeline.from_pretrained(os.path.join(self.model_root, self.config["flux_repo"]), transformer=None, text_encoder_2=None, torch_dtype=torch.bfloat16).to(self.device)

self.pipe.transformer = self.transformer
self.pipe.text_encoder_2 = self.text_encoder_2

self.pipe.load_lora_weights(load_file(os.path.join(self.model_root, self.config["8steps_lora"]), device=self.device), adapter_name="8steps")
self.pipe.fuse_lora(lora_scale=1.0)

### Logs

_No response_

### System Info

It seems that Hyper-FLUX.1-dev-8steps-lora can not support Flux-dev-fp8, the image seems the same when I load or not load Hyper-FLUX.1-dev-8steps-lora.
These are my code, Can any one use Hyper-FLUX.1-dev-8steps-lora on Flux-dev-fp8
self.transformer = FluxTransformer2DModel.from_single_file(os.path.join(self.model_root, self.config["transformer_path"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.transformer, weights=qfloat8)
freeze(self.transformer)

self.text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(self.model_root, self.config["text_encoder_2_repo"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.text_encoder_2, weights=qfloat8)
freeze(self.text_encoder_2)

self.pipe = FluxPipeline.from_pretrained(os.path.join(self.model_root, self.config["flux_repo"]), transformer=None, text_encoder_2=None, torch_dtype=torch.bfloat16).to(self.device)

self.pipe.transformer = self.transformer
self.pipe.text_encoder_2 = self.text_encoder_2

self.pipe.load_lora_weights(load_file(os.path.join(self.model_root, self.config["8steps_lora"]), device=self.device), adapter_name="8steps")
self.pipe.fuse_lora(lora_scale=1.0)

### Who can help?

It seems that Hyper-FLUX.1-dev-8steps-lora can not support Flux-dev-fp8, the image seems the same when I load or not load Hyper-FLUX.1-dev-8steps-lora.
These are my code, Can any one use Hyper-FLUX.1-dev-8steps-lora on Flux-dev-fp8
self.transformer = FluxTransformer2DModel.from_single_file(os.path.join(self.model_root, self.config["transformer_path"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.transformer, weights=qfloat8)
freeze(self.transformer)

self.text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(self.model_root, self.config["text_encoder_2_repo"]), torch_dtype=torch.bfloat16).to(self.device)
quantize(self.text_encoder_2, weights=qfloat8)
freeze(self.text_encoder_2)

self.pipe = FluxPipeline.from_pretrained(os.path.join(self.model_root, self.config["flux_repo"]), transformer=None, text_encoder_2=None, torch_dtype=torch.bfloat16).to(self.device)

self.pipe.transformer = self.transformer
self.pipe.text_encoder_2 = self.text_encoder_2

self.pipe.load_lora_weights(load_file(os.path.join(self.model_root, self.config["8steps_lora"]), device=self.device), adapter_name="8steps")
self.pipe.fuse_lora(lora_scale=1.0)

Start by reproducing the reported comparison using FluxTransformer2DModel, T5EncoderModel, FluxPipeline, quantize, and fuse_lora with the code shown. Compare outputs with and without Hyper-FLUX.1-dev-8steps-lora on the quantized transformer, then check whether the adapter is loaded and fused as expected. Done means identifying the compatibility issue or documenting the required configuration, with reproducible results.

Scritto dal modello di indicizzazione a partire dal testo della issue.