Update app.py

app.py

@@ -1,27 +1,51 @@
 import gradio as gr
 import spaces
 import os
+import torch
 import random
 import uuid
 from datetime import datetime
-from diffusers import DiffusionPipeline
-import torch
-import numpy as np
+from diffusers import FluxTransformer2DModel, FluxPipeline, GGUFQuantizationConfig
+from transformers import T5EncoderModel
 from PIL import Image
+import numpy as np
 
+# Constants
 NUM_INFERENCE_STEPS = 8
+MAX_SEED = np.iinfo(np.int32).max
+SAVE_DIR = "saved_images"
+os.makedirs(SAVE_DIR, exist_ok=True)
 
+# Initialize device
+device = "cuda" if torch.cuda.is_available() else "cpu"
 huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-# Constants
-MAX_SEED = np.iinfo(np.int32).max
 
-# Create permanent storage directory for Flux generated images
-SAVE_DIR = "saved_images"
-if not os.path.exists(SAVE_DIR):
-    os.makedirs(SAVE_DIR, exist_ok=True)
+# Load model
+dtype = torch.bfloat16
+gguf_file_url = "https://huggingface.co/gokaygokay/flux-game/resolve/main/hyperflux_00001_.q8_0.gguf"
+base_model = "black-forest-labs/FLUX.1-dev"
 
-def get_seed(randomize_seed: bool, seed: int) -> int:
-    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
+text_encoder_2 = T5EncoderModel.from_pretrained(
+    base_model,
+    subfolder="text_encoder_2",
+    torch_dtype=dtype,
+    token=huggingface_token
+)
+
+transformer = FluxTransformer2DModel.from_single_file(
+    gguf_file_url,
+    quantization_config=GGUFQuantizationConfig(compute_dtype=dtype),
+    torch_dtype=dtype,
+    token=huggingface_token
+)
+
+flux_pipeline = FluxPipeline.from_pretrained(
+    base_model,
+    transformer=transformer,
+    text_encoder_2=text_encoder_2,
+    torch_dtype=dtype,
+    token=huggingface_token
+).to(device)
 
 @spaces.GPU
 def generate_flux_image(
@@ -54,128 +78,23 @@ def generate_flux_image(
     filepath = os.path.join(SAVE_DIR, filename)
     image.save(filepath)
     
-    return image, seed, filepath
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    ## Game Asset Generation with FLUX
-    * Enter a prompt to generate a game asset image
-    * Images are automatically saved to the 'saved_images' directory
-    * [Flux-Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) 
-    * [Flux Game Assets LoRA](https://huggingface.co/gokaygokay/Flux-Game-Assets-LoRA-v2) 
-    * [Hyper FLUX 8Steps LoRA](https://huggingface.co/ByteDance/Hyper-SD)
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            # Flux image generation inputs
-            prompt = gr.Text(
-                label="Prompt", 
-                placeholder="Enter your game asset description",
-                lines=3
-            )
-            
-            with gr.Accordion("Generation Settings", open=True):
-                seed = gr.Slider(
-                    minimum=0, 
-                    maximum=MAX_SEED, 
-                    label="Seed", 
-                    value=42, 
-                    step=1
-                )
-                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                
-                with gr.Row():
-                    width = gr.Slider(
-                        minimum=512, 
-                        maximum=1024, 
-                        label="Width", 
-                        value=1024, 
-                        step=16
-                    )
-                    height = gr.Slider(
-                        minimum=512, 
-                        maximum=1024, 
-                        label="Height", 
-                        value=1024, 
-                        step=16
-                    )
-                
-                guidance_scale = gr.Slider(
-                    minimum=0.0, 
-                    maximum=10.0, 
-                    label="Guidance Scale", 
-                    value=3.5, 
-                    step=0.1
-                )
-
-            generate_btn = gr.Button("Generate", variant="primary")
-            
-        with gr.Column():
-            generated_image = gr.Image(
-                label="Generated Asset", 
-                type="pil",
-                interactive=False
-            )
-            
-            with gr.Row():
-                seed_output = gr.Number(label="Seed Used", interactive=False)
-                file_path = gr.Text(label="Saved To", interactive=False)
-            
-            download_btn = gr.DownloadButton(
-                label="Download Image",
-                visible=False
-            )
+    return image
 
-    # Event handlers
-    generate_btn.click(
-        generate_flux_image,
-        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale],
-        outputs=[generated_image, seed_output, file_path],
-    ).then(
-        lambda filepath: gr.DownloadButton(visible=True, value=filepath),
-        inputs=[file_path],
-        outputs=[download_btn]
-    )
+# Simple Gradio interface
+demo = gr.Interface(
+    fn=generate_flux_image,
+    inputs=[
+        gr.Textbox(label="Prompt", placeholder="Enter your game asset description"),
+        gr.Slider(0, MAX_SEED, label="Seed", value=42, step=1),
+        gr.Checkbox(label="Randomize Seed", value=True),
+        gr.Slider(512, 1024, label="Width", value=1024, step=16),
+        gr.Slider(512, 1024, label="Height", value=1024, step=16),
+        gr.Slider(0.0, 10.0, label="Guidance Scale", value=3.5, step=0.1),
+    ],
+    outputs=gr.Image(label="Generated Asset", type="pil"),
+    title="Game Asset Generator",
+    description="Generate game assets with FLUX"
+)
 
-    # Examples
-    gr.Examples(
-        examples=[
-            ["medieval sword with glowing runes"],
-            ["wooden treasure chest with gold coins"],
-            ["health potion bottle with red liquid"],
-            ["stone castle tower"],
-            ["pixel art coin sprite"],
-            ["low poly tree"],
-            ["fantasy spell book"],
-            ["iron shield with dragon emblem"],
-        ],
-        inputs=prompt,
-        label="Example Prompts"
-    )
-
-# Initialize Flux pipeline
 if __name__ == "__main__":
-    from diffusers import FluxTransformer2DModel, FluxPipeline, BitsAndBytesConfig, GGUFQuantizationConfig
-    from transformers import T5EncoderModel, BitsAndBytesConfig as BitsAndBytesConfigTF
-    
-    # Initialize Flux pipeline
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-    
-    dtype = torch.bfloat16
-    file_url = "https://huggingface.co/gokaygokay/flux-game/blob/main/hyperflux_00001_.q8_0.gguf"
-    file_url = file_url.replace("/resolve/main/", "/blob/main/").replace("?download=true", "")
-    single_file_base_model = "camenduru/FLUX.1-dev-diffusers"
-    quantization_config_tf = BitsAndBytesConfigTF(load_in_8bit=True, bnb_8bit_compute_dtype=torch.bfloat16)
-    text_encoder_2 = T5EncoderModel.from_pretrained(single_file_base_model, subfolder="text_encoder_2", torch_dtype=dtype, config=single_file_base_model, quantization_config=quantization_config_tf, token=huggingface_token)
-    if ".gguf" in file_url:
-        transformer = FluxTransformer2DModel.from_single_file(file_url, subfolder="transformer", quantization_config=GGUFQuantizationConfig(compute_dtype=dtype), torch_dtype=dtype, config=single_file_base_model)
-    else:
-        quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16, token=huggingface_token)
-        transformer = FluxTransformer2DModel.from_single_file(file_url, subfolder="transformer", torch_dtype=dtype, config=single_file_base_model, quantization_config=quantization_config, token=huggingface_token)
-    flux_pipeline = FluxPipeline.from_pretrained(single_file_base_model, transformer=transformer, text_encoder_2=text_encoder_2, torch_dtype=dtype, token=huggingface_token)
-    flux_pipeline.to("cuda")
-    
     demo.launch()