fix

app.py

@@ -13,6 +13,10 @@ import os
 import time
 from gradio_imageslider import ImageSlider
 
+from diffusers import FlowMatchEulerDiscreteScheduler, AutoencoderKL
+from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
+from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
+
 
 MARKDOWN = """
 # FLUX.1 Inpainting with lora
@@ -25,7 +29,7 @@ HF_TOKEN = os.environ.get("HF_TOKEN")
 
 login(token=HF_TOKEN)
 
-
+bfl_repo="black-forest-labs/FLUX.1-dev"
 
 class calculateDuration:
     def __init__(self, activity_name=""):
@@ -64,8 +68,15 @@ def remove_background(image: Image.Image, threshold: int = 50) -> Image.Image:
     image.putdata(new_data)
     return image
 
-pipe = FluxInpaintPipeline.from_pretrained(
-    "black-forest-labs/FLUX.1-schnell", torch_dtype=torch.bfloat16).to(DEVICE)
+# text_encoder = CLIPTextModel.from_pretrained(os.path.join(os.getcwd(), "flux_text_encoders/clip_l.safetensors"), torch_dtype=dtype)
+# tokenizer = CLIPTokenizer.from_pretrained("openai/clip-vit-large-patch14", torch_dtype=dtype)
+# text_encoder_2 = T5EncoderModel.from_pretrained(os.path.join(os.getcwd(), "flux_text_encoders/t5xxl_fp8_e4m3fn.safetensors"), torch_dtype=dtype)
+# tokenizer_2 = T5TokenizerFast.from_pretrained(bfl_repo, subfolder="tokenizer_2", torch_dtype=dtype)
+# vae = AutoencoderKL.from_pretrained(bfl_repo, subfolder="vae", torch_dtype=dtype)
+# transformer = FluxTransformer2DModel.from_pretrained(bfl_repo, subfolder="transformer", torch_dtype=dtype)
+
+
+pipe = FluxInpaintPipeline.from_pretrained(bfl_repo, torch_dtype=torch.bfloat16).to(DEVICE)
 
 
 def resize_image_dimensions(
@@ -144,6 +155,7 @@ def process(
             strength=strength_slider,
             generator=generator,
             num_inference_steps=num_inference_steps_slider,
+            max_sequence_length=256,
             joint_attention_kwargs={"scale": lora_scale},
         ).images[0]
     
@@ -190,14 +202,14 @@ with gr.Blocks() as demo:
                     max_lines=1,
                     placeholder="Enter your model path",
                     info="Currently, only LoRA hosted on Hugging Face'model can be loaded properly.",
-                    value="XLabs-AI/flux-lora-collection"
+                    value="XLabs-AI/flux-RealismLora"
                 )
                 lora_weights = gr.Textbox(
                     label="Lora weights",
                     show_label=True,
                     max_lines=1,
                     placeholder="Enter your lora weights name",
-                    value="anime_lora.safetensors"
+                    value="lora.safetensors"
                 )
                 lora_scale = gr.Slider(
                     label="Lora scale",
@@ -241,7 +253,7 @@ with gr.Blocks() as demo:
                         minimum=1,
                         maximum=50,
                         step=1,
-                        value=20,
+                        value=28,
                     )
         with gr.Column():
             output_image_component = ImageSlider(label="Generate image", type="pil", slider_color="pink")