Update app.py

app.py

@@ -1,300 +1,300 @@
-import gradio as gr
-import spaces
-from gradio_litmodel3d import LitModel3D
-import os
-import shutil
-import random
-import uuid
-from datetime import datetime
-from diffusers import DiffusionPipeline
-
-os.environ['SPCONV_ALGO'] = 'native'
-from typing import *
-import torch
-import numpy as np
-import imageio
-from easydict import EasyDict as edict
-from PIL import Image
-from trellis.pipelines import TrellisImageTo3DPipeline
-from trellis.representations import Gaussian, MeshExtractResult
-from trellis.utils import render_utils, postprocessing_utils
-
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-# Constants
-MAX_SEED = np.iinfo(np.int32).max
-TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
-os.makedirs(TMP_DIR, exist_ok=True)
-
-# 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)
-
-def start_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    os.makedirs(user_dir, exist_ok=True)
-
-def end_session(req: gr.Request):
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    shutil.rmtree(user_dir)
-
-def preprocess_image(image: Image.Image) -> Image.Image:
-    processed_image = trellis_pipeline.preprocess_image(image)
-    return processed_image
-
-def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
-    return {
-        'gaussian': {
-            **gs.init_params,
-            '_xyz': gs._xyz.cpu().numpy(),
-            '_features_dc': gs._features_dc.cpu().numpy(),
-            '_scaling': gs._scaling.cpu().numpy(),
-            '_rotation': gs._rotation.cpu().numpy(),
-            '_opacity': gs._opacity.cpu().numpy(),
-        },
-        'mesh': {
-            'vertices': mesh.vertices.cpu().numpy(),
-            'faces': mesh.faces.cpu().numpy(),
-        },
-    }
-
-def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
-    gs = Gaussian(
-        aabb=state['gaussian']['aabb'],
-        sh_degree=state['gaussian']['sh_degree'],
-        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
-        scaling_bias=state['gaussian']['scaling_bias'],
-        opacity_bias=state['gaussian']['opacity_bias'],
-        scaling_activation=state['gaussian']['scaling_activation'],
-    )
-    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
-    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
-    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
-    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
-    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
-    
-    mesh = edict(
-        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
-        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
-    )
-    
-    return gs, mesh
-
-def get_seed(randomize_seed: bool, seed: int) -> int:
-    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
-
-@spaces.GPU
-def generate_flux_image(
-    prompt: str,
-    seed: int,
-    randomize_seed: bool,
-    width: int,
-    height: int,
-    guidance_scale: float,
-    num_inference_steps: int,
-    lora_scale: float,
-    progress: gr.Progress = gr.Progress(track_tqdm=True),
-) -> Image.Image:
-    """Generate image using Flux pipeline"""
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    generator = torch.Generator(device=device).manual_seed(seed)
-    
-    image = flux_pipeline(
-        prompt=prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": lora_scale},
-    ).images[0]
-    
-    # Save the generated image
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
-    unique_id = str(uuid.uuid4())[:8]
-    filename = f"{timestamp}_{unique_id}.png"
-    filepath = os.path.join(SAVE_DIR, filename)
-    image.save(filepath)
-    
-    return image
-
-@spaces.GPU
-def image_to_3d(
-    image: Image.Image,
-    seed: int,
-    ss_guidance_strength: float,
-    ss_sampling_steps: int,
-    slat_guidance_strength: float,
-    slat_sampling_steps: int,
-    req: gr.Request,
-) -> Tuple[dict, str]:
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    outputs = trellis_pipeline.run(
-        image,
-        seed=seed,
-        formats=["gaussian", "mesh"],
-        preprocess_image=False,
-        sparse_structure_sampler_params={
-            "steps": ss_sampling_steps,
-            "cfg_strength": ss_guidance_strength,
-        },
-        slat_sampler_params={
-            "steps": slat_sampling_steps,
-            "cfg_strength": slat_guidance_strength,
-        },
-    )
-    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
-    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
-    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
-    video_path = os.path.join(user_dir, 'sample.mp4')
-    imageio.mimsave(video_path, video, fps=15)
-    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
-    torch.cuda.empty_cache()
-    return state, video_path
-
-@spaces.GPU(duration=90)
-def extract_glb(
-    state: dict,
-    mesh_simplify: float,
-    texture_size: int,
-    req: gr.Request,
-) -> Tuple[str, str]:
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    gs, mesh = unpack_state(state)
-    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
-    glb_path = os.path.join(user_dir, 'sample.glb')
-    glb.export(glb_path)
-    torch.cuda.empty_cache()
-    return glb_path, glb_path
-
-@spaces.GPU
-def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    gs, _ = unpack_state(state)
-    gaussian_path = os.path.join(user_dir, 'sample.ply')
-    gs.save_ply(gaussian_path)
-    torch.cuda.empty_cache()
-    return gaussian_path, gaussian_path
-
-# Gradio Interface
-with gr.Blocks() as demo:
-    gr.Markdown("""
-    ## Game Asset Generation to 3D with FLUX and TRELLIS
-    * Enter a prompt to generate a game asset image, then convert it to 3D
-    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
-    """)
-    
-    with gr.Row():
-        with gr.Column():
-            # Flux image generation inputs
-            prompt = gr.Text(label="Prompt", placeholder="Enter your game asset description")
-            with gr.Accordion("Generation Settings", open=False):
-                seed = gr.Slider(0, MAX_SEED, label="Seed", value=42, step=1)
-                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                with gr.Row():
-                    width = gr.Slider(256, 1024, label="Width", value=768, step=32)
-                    height = gr.Slider(256, 1024, label="Height", value=768, step=32)
-                with gr.Row():
-                    guidance_scale = gr.Slider(0.0, 10.0, label="Guidance Scale", value=3.5, step=0.1)
-                    num_inference_steps = gr.Slider(1, 50, label="Steps", value=30, step=1)
-                    lora_scale = gr.Slider(0.0, 1.0, label="LoRA Scale", value=1.0, step=0.1)
-
-            with gr.Accordion("3D Generation Settings", open=False):
-                gr.Markdown("Stage 1: Sparse Structure Generation")
-                with gr.Row():
-                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
-                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
-                gr.Markdown("Stage 2: Structured Latent Generation")
-                with gr.Row():
-                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
-                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
-
-            generate_btn = gr.Button("Generate")
-            
-            with gr.Accordion("GLB Extraction Settings", open=False):
-                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
-                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
-            
-            with gr.Row():
-                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
-                extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
-
-        with gr.Column():
-            generated_image = gr.Image(label="Generated Asset", type="pil")
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian")
-            
-            with gr.Row():
-                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
-                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
-
-    output_buf = gr.State()
-
-    # Event handlers
-    demo.load(start_session)
-    demo.unload(end_session)
-
-    generate_btn.click(
-        generate_flux_image,
-        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_scale],
-        outputs=[generated_image],
-    ).then(
-        image_to_3d,
-        inputs=[generated_image, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
-        outputs=[output_buf, video_output],
-    ).then(
-        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
-    )
-
-    extract_glb_btn.click(
-        extract_glb,
-        inputs=[output_buf, mesh_simplify, texture_size],
-        outputs=[model_output, download_glb],
-    ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_glb],
-    )
-    
-    extract_gs_btn.click(
-        extract_gaussian,
-        inputs=[output_buf],
-        outputs=[model_output, download_gs],
-    ).then(
-        lambda: gr.Button(interactive=True),
-        outputs=[download_gs],
-    )
-
-    model_output.clear(
-        lambda: gr.Button(interactive=False),
-        outputs=[download_glb],
-    )
-
-# Initialize both pipelines
-if __name__ == "__main__":
-    from diffusers import FluxTransformer2DModel, FluxPipeline, BitsAndBytesConfig, BitsAndBytesConfigTF
-    from transformers import T5EncoderModel
-    
-    # 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/gokaygokay_00001_.safetensors"
-    single_file_base_model = "camenduru/FLUX.1-dev-diffusers"
-    quantization_config_tf = BitsAndBytesConfigTF(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_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)
-    quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
-    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, quantization_config=quantization_config, token=huggingface_token)
-
-    # Initialize Trellis pipeline
-    trellis_pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    trellis_pipeline.cuda()
-    try:
-        trellis_pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
-    except:
-        pass
-    
+import gradio as gr
+import spaces
+from gradio_litmodel3d import LitModel3D
+import os
+import shutil
+import random
+import uuid
+from datetime import datetime
+from diffusers import DiffusionPipeline
+
+os.environ['SPCONV_ALGO'] = 'native'
+from typing import *
+import torch
+import numpy as np
+import imageio
+from easydict import EasyDict as edict
+from PIL import Image
+from trellis.pipelines import TrellisImageTo3DPipeline
+from trellis.representations import Gaussian, MeshExtractResult
+from trellis.utils import render_utils, postprocessing_utils
+
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+# Constants
+MAX_SEED = np.iinfo(np.int32).max
+TMP_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'tmp')
+os.makedirs(TMP_DIR, exist_ok=True)
+
+# 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)
+
+def start_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    os.makedirs(user_dir, exist_ok=True)
+
+def end_session(req: gr.Request):
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    shutil.rmtree(user_dir)
+
+def preprocess_image(image: Image.Image) -> Image.Image:
+    processed_image = trellis_pipeline.preprocess_image(image)
+    return processed_image
+
+def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
+    return {
+        'gaussian': {
+            **gs.init_params,
+            '_xyz': gs._xyz.cpu().numpy(),
+            '_features_dc': gs._features_dc.cpu().numpy(),
+            '_scaling': gs._scaling.cpu().numpy(),
+            '_rotation': gs._rotation.cpu().numpy(),
+            '_opacity': gs._opacity.cpu().numpy(),
+        },
+        'mesh': {
+            'vertices': mesh.vertices.cpu().numpy(),
+            'faces': mesh.faces.cpu().numpy(),
+        },
+    }
+
+def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
+    gs = Gaussian(
+        aabb=state['gaussian']['aabb'],
+        sh_degree=state['gaussian']['sh_degree'],
+        mininum_kernel_size=state['gaussian']['mininum_kernel_size'],
+        scaling_bias=state['gaussian']['scaling_bias'],
+        opacity_bias=state['gaussian']['opacity_bias'],
+        scaling_activation=state['gaussian']['scaling_activation'],
+    )
+    gs._xyz = torch.tensor(state['gaussian']['_xyz'], device='cuda')
+    gs._features_dc = torch.tensor(state['gaussian']['_features_dc'], device='cuda')
+    gs._scaling = torch.tensor(state['gaussian']['_scaling'], device='cuda')
+    gs._rotation = torch.tensor(state['gaussian']['_rotation'], device='cuda')
+    gs._opacity = torch.tensor(state['gaussian']['_opacity'], device='cuda')
+    
+    mesh = edict(
+        vertices=torch.tensor(state['mesh']['vertices'], device='cuda'),
+        faces=torch.tensor(state['mesh']['faces'], device='cuda'),
+    )
+    
+    return gs, mesh
+
+def get_seed(randomize_seed: bool, seed: int) -> int:
+    return np.random.randint(0, MAX_SEED) if randomize_seed else seed
+
+@spaces.GPU
+def generate_flux_image(
+    prompt: str,
+    seed: int,
+    randomize_seed: bool,
+    width: int,
+    height: int,
+    guidance_scale: float,
+    num_inference_steps: int,
+    lora_scale: float,
+    progress: gr.Progress = gr.Progress(track_tqdm=True),
+) -> Image.Image:
+    """Generate image using Flux pipeline"""
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
+    
+    image = flux_pipeline(
+        prompt=prompt,
+        guidance_scale=guidance_scale,
+        num_inference_steps=num_inference_steps,
+        width=width,
+        height=height,
+        generator=generator,
+        joint_attention_kwargs={"scale": lora_scale},
+    ).images[0]
+    
+    # Save the generated image
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    unique_id = str(uuid.uuid4())[:8]
+    filename = f"{timestamp}_{unique_id}.png"
+    filepath = os.path.join(SAVE_DIR, filename)
+    image.save(filepath)
+    
+    return image
+
+@spaces.GPU
+def image_to_3d(
+    image: Image.Image,
+    seed: int,
+    ss_guidance_strength: float,
+    ss_sampling_steps: int,
+    slat_guidance_strength: float,
+    slat_sampling_steps: int,
+    req: gr.Request,
+) -> Tuple[dict, str]:
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    outputs = trellis_pipeline.run(
+        image,
+        seed=seed,
+        formats=["gaussian", "mesh"],
+        preprocess_image=False,
+        sparse_structure_sampler_params={
+            "steps": ss_sampling_steps,
+            "cfg_strength": ss_guidance_strength,
+        },
+        slat_sampler_params={
+            "steps": slat_sampling_steps,
+            "cfg_strength": slat_guidance_strength,
+        },
+    )
+    video = render_utils.render_video(outputs['gaussian'][0], num_frames=120)['color']
+    video_geo = render_utils.render_video(outputs['mesh'][0], num_frames=120)['normal']
+    video = [np.concatenate([video[i], video_geo[i]], axis=1) for i in range(len(video))]
+    video_path = os.path.join(user_dir, 'sample.mp4')
+    imageio.mimsave(video_path, video, fps=15)
+    state = pack_state(outputs['gaussian'][0], outputs['mesh'][0])
+    torch.cuda.empty_cache()
+    return state, video_path
+
+@spaces.GPU(duration=90)
+def extract_glb(
+    state: dict,
+    mesh_simplify: float,
+    texture_size: int,
+    req: gr.Request,
+) -> Tuple[str, str]:
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    gs, mesh = unpack_state(state)
+    glb = postprocessing_utils.to_glb(gs, mesh, simplify=mesh_simplify, texture_size=texture_size, verbose=False)
+    glb_path = os.path.join(user_dir, 'sample.glb')
+    glb.export(glb_path)
+    torch.cuda.empty_cache()
+    return glb_path, glb_path
+
+@spaces.GPU
+def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
+    user_dir = os.path.join(TMP_DIR, str(req.session_hash))
+    gs, _ = unpack_state(state)
+    gaussian_path = os.path.join(user_dir, 'sample.ply')
+    gs.save_ply(gaussian_path)
+    torch.cuda.empty_cache()
+    return gaussian_path, gaussian_path
+
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("""
+    ## Game Asset Generation to 3D with FLUX and TRELLIS
+    * Enter a prompt to generate a game asset image, then convert it to 3D
+    * If you find the generated 3D asset satisfactory, click "Extract GLB" to extract the GLB file and download it.
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            # Flux image generation inputs
+            prompt = gr.Text(label="Prompt", placeholder="Enter your game asset description")
+            with gr.Accordion("Generation Settings", open=False):
+                seed = gr.Slider(0, MAX_SEED, label="Seed", value=42, step=1)
+                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
+                with gr.Row():
+                    width = gr.Slider(256, 1024, label="Width", value=768, step=32)
+                    height = gr.Slider(256, 1024, label="Height", value=768, step=32)
+                with gr.Row():
+                    guidance_scale = gr.Slider(0.0, 10.0, label="Guidance Scale", value=3.5, step=0.1)
+                    num_inference_steps = gr.Slider(1, 50, label="Steps", value=30, step=1)
+                    lora_scale = gr.Slider(0.0, 1.0, label="LoRA Scale", value=1.0, step=0.1)
+
+            with gr.Accordion("3D Generation Settings", open=False):
+                gr.Markdown("Stage 1: Sparse Structure Generation")
+                with gr.Row():
+                    ss_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=7.5, step=0.1)
+                    ss_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+                gr.Markdown("Stage 2: Structured Latent Generation")
+                with gr.Row():
+                    slat_guidance_strength = gr.Slider(0.0, 10.0, label="Guidance Strength", value=3.0, step=0.1)
+                    slat_sampling_steps = gr.Slider(1, 50, label="Sampling Steps", value=12, step=1)
+
+            generate_btn = gr.Button("Generate")
+            
+            with gr.Accordion("GLB Extraction Settings", open=False):
+                mesh_simplify = gr.Slider(0.9, 0.98, label="Simplify", value=0.95, step=0.01)
+                texture_size = gr.Slider(512, 2048, label="Texture Size", value=1024, step=512)
+            
+            with gr.Row():
+                extract_glb_btn = gr.Button("Extract GLB", interactive=False)
+                extract_gs_btn = gr.Button("Extract Gaussian", interactive=False)
+
+        with gr.Column():
+            generated_image = gr.Image(label="Generated Asset", type="pil")
+            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True)
+            model_output = LitModel3D(label="Extracted GLB/Gaussian")
+            
+            with gr.Row():
+                download_glb = gr.DownloadButton(label="Download GLB", interactive=False)
+                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
+
+    output_buf = gr.State()
+
+    # Event handlers
+    demo.load(start_session)
+    demo.unload(end_session)
+
+    generate_btn.click(
+        generate_flux_image,
+        inputs=[prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_scale],
+        outputs=[generated_image],
+    ).then(
+        image_to_3d,
+        inputs=[generated_image, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps],
+        outputs=[output_buf, video_output],
+    ).then(
+        lambda: tuple([gr.Button(interactive=True), gr.Button(interactive=True)]),
+        outputs=[extract_glb_btn, extract_gs_btn],
+    )
+
+    extract_glb_btn.click(
+        extract_glb,
+        inputs=[output_buf, mesh_simplify, texture_size],
+        outputs=[model_output, download_glb],
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_glb],
+    )
+    
+    extract_gs_btn.click(
+        extract_gaussian,
+        inputs=[output_buf],
+        outputs=[model_output, download_gs],
+    ).then(
+        lambda: gr.Button(interactive=True),
+        outputs=[download_gs],
+    )
+
+    model_output.clear(
+        lambda: gr.Button(interactive=False),
+        outputs=[download_glb],
+    )
+
+# Initialize both pipelines
+if __name__ == "__main__":
+    from diffusers import FluxTransformer2DModel, FluxPipeline, BitsAndBytesConfig
+    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/gokaygokay_00001_.safetensors"
+    single_file_base_model = "camenduru/FLUX.1-dev-diffusers"
+    quantization_config_tf = BitsAndBytesConfigTF(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_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)
+    quantization_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True, bnb_4bit_compute_dtype=torch.bfloat16)
+    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, quantization_config=quantization_config, token=huggingface_token)
+
+    # Initialize Trellis pipeline
+    trellis_pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    trellis_pipeline.cuda()
+    try:
+        trellis_pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
+    except:
+        pass
+    
     demo.launch()