Update app.py

app.py

@@ -5,46 +5,20 @@ import time
 from os import path
 from safetensors.torch import load_file
 from huggingface_hub import hf_hub_download
-import imageio
-import numpy as np
-import torch
-import rembg
-from PIL import Image
-from torchvision.transforms import v2
-from pytorch_lightning import seed_everything
-from omegaconf import OmegaConf
-from einops import rearrange, repeat
-from tqdm import tqdm
-from diffusers import DiffusionPipeline, EulerAncestralDiscreteScheduler
-import gradio as gr
-import shutil
-import tempfile
-from functools import partial
-from optimum.quanto import quantize, qfloat8, freeze
-from diffusers import  DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
-
-from src.utils.train_util import instantiate_from_config
-from src.utils.camera_util import (
-    FOV_to_intrinsics, 
-    get_zero123plus_input_cameras,
-    get_circular_camera_poses,
-)
-from src.utils.mesh_util import save_obj, save_glb
-from src.utils.infer_util import remove_background, resize_foreground, images_to_video
 
-# Set up cache path
 cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
 os.environ["TRANSFORMERS_CACHE"] = cache_path
 os.environ["HF_HUB_CACHE"] = cache_path
 os.environ["HF_HOME"] = cache_path
 
-huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
-
-if not path.exists(cache_path):
-    os.makedirs(cache_path, exist_ok=True)
+import gradio as gr
+import torch
+from diffusers import FluxPipeline
 
 torch.backends.cuda.matmul.allow_tf32 = True
 
+huggingface_token = os.getenv("HUGGINGFACE_TOKEN")
+
 class timer:
     def __init__(self, method_name="timed process"):
         self.method = method_name
@@ -55,216 +29,83 @@ class timer:
         end = time.time()
         print(f"{self.method} took {str(round(end - self.start, 2))}s")
 
-def find_cuda():
-    cuda_home = os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH')
-    if cuda_home and os.path.exists(cuda_home):
-        return cuda_home
-    nvcc_path = shutil.which('nvcc')
-    if nvcc_path:
-        cuda_path = os.path.dirname(os.path.dirname(nvcc_path))
-        return cuda_path
-    return None
-
-cuda_path = find_cuda()
-if cuda_path:
-    print(f"CUDA installation found at: {cuda_path}")
-else:
-    print("CUDA installation not found")
-
-
-dtype = torch.bfloat16
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to("cuda")
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1, token=huggingface_token).to("cuda")
-torch.cuda.empty_cache()
-
-# Load and fuse LoRA BEFORE quantizing
-print('Loading and fusing lora, please wait...')
-lora_path = hf_hub_download("gokaygokay/Flux-Game-Assets-LoRA-v2", "game_asst.safetensors")
-pipe.load_lora_weights(lora_path)
-pipe.fuse_lora(lora_scale=1.0)
-pipe.unload_lora_weights()
-pipe.enable_model_cpu_offload()
-
-
-# Load 3D generation models
-config_path = 'configs/instant-mesh-large.yaml'
-config = OmegaConf.load(config_path)
-config_name = os.path.basename(config_path).replace('.yaml', '')
-model_config = config.model_config
-infer_config = config.infer_config
-
-IS_FLEXICUBES = True if config_name.startswith('instant-mesh') else False
-
-# Load diffusion model for 3D generation
-print('Loading diffusion model ...')
-pipeline = DiffusionPipeline.from_pretrained(
-    "sudo-ai/zero123plus-v1.2", 
-    custom_pipeline="zero123plus",
-    torch_dtype=torch.float16,
-)
-pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(
-    pipeline.scheduler.config, timestep_spacing='trailing'
-)
-
-# Load custom white-background UNet
-unet_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="diffusion_pytorch_model.bin", repo_type="model")
-state_dict = torch.load(unet_ckpt_path, map_location='cpu')
-pipeline.unet.load_state_dict(state_dict, strict=True)
-
-pipeline = pipeline.to(device)
-
-# Load reconstruction model
-print('Loading reconstruction model ...')
-model_ckpt_path = hf_hub_download(repo_id="TencentARC/InstantMesh", filename="instant_mesh_large.ckpt", repo_type="model")
-model = instantiate_from_config(model_config)
-state_dict = torch.load(model_ckpt_path, map_location='cpu')['state_dict']
-state_dict = {k[14:]: v for k, v in state_dict.items() if k.startswith('lrm_generator.') and 'source_camera' not in k}
-model.load_state_dict(state_dict, strict=True)
-
-model = model.to(device)
-
-print('Loading Finished!')
-
-def get_render_cameras(batch_size=1, M=120, radius=2.5, elevation=10.0, is_flexicubes=False):
-    c2ws = get_circular_camera_poses(M=M, radius=radius, elevation=elevation)
-    if is_flexicubes:
-        cameras = torch.linalg.inv(c2ws)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1, 1)
-    else:
-        extrinsics = c2ws.flatten(-2)
-        intrinsics = FOV_to_intrinsics(50.0).unsqueeze(0).repeat(M, 1, 1).float().flatten(-2)
-        cameras = torch.cat([extrinsics, intrinsics], dim=-1)
-        cameras = cameras.unsqueeze(0).repeat(batch_size, 1, 1)
-    return cameras
-
-def preprocess(input_image, do_remove_background):
-    rembg_session = rembg.new_session() if do_remove_background else None
-    if do_remove_background:
-        input_image = remove_background(input_image, rembg_session)
-        input_image = resize_foreground(input_image, 0.85)
-    return input_image
-
-ts_cutoff = 2
-
-@spaces.GPU
-def generate_flux_image(prompt, height, width, steps, scales, seed):
-    return pipe(
-        prompt=prompt, 
-        width=int(height),
-        height=int(width),
-        num_inference_steps=int(steps), 
-        generator=torch.Generator().manual_seed(int(seed)),
-        guidance_scale=float(scales),
-        timestep_to_start_cfg=ts_cutoff,
-    ).images[0]
-
-
-@spaces.GPU
-def generate_mvs(input_image, sample_steps, sample_seed):
-    seed_everything(sample_seed)
-    z123_image = pipeline(
-        input_image, 
-        num_inference_steps=sample_steps
-    ).images[0]
-    show_image = np.asarray(z123_image, dtype=np.uint8)
-    show_image = torch.from_numpy(show_image)
-    show_image = rearrange(show_image, '(n h) (m w) c -> (n m) h w c', n=3, m=2)
-    show_image = rearrange(show_image, '(n m) h w c -> (n h) (m w) c', n=2, m=3)
-    show_image = Image.fromarray(show_image.numpy())
-    return z123_image, show_image
-
-@spaces.GPU
-def make3d(images):
-    global model
-    if IS_FLEXICUBES:
-        model.init_flexicubes_geometry(device, use_renderer=False)
-    model = model.eval()
-
-    images = np.asarray(images, dtype=np.float32) / 255.0
-    images = torch.from_numpy(images).permute(2, 0, 1).contiguous().float()
-    images = rearrange(images, 'c (n h) (m w) -> (n m) c h w', n=3, m=2)
-
-    input_cameras = get_zero123plus_input_cameras(batch_size=1, radius=4.0).to(device)
-    render_cameras = get_render_cameras(batch_size=1, radius=2.5, is_flexicubes=IS_FLEXICUBES).to(device)
-
-    images = images.unsqueeze(0).to(device)
-    images = v2.functional.resize(images, (320, 320), interpolation=3, antialias=True).clamp(0, 1)
-
-    mesh_fpath = tempfile.NamedTemporaryFile(suffix=f".obj", delete=False).name
-    mesh_basename = os.path.basename(mesh_fpath).split('.')[0]
-    mesh_dirname = os.path.dirname(mesh_fpath)
-    mesh_glb_fpath = os.path.join(mesh_dirname, f"{mesh_basename}.glb")
+if not path.exists(cache_path):
+    os.makedirs(cache_path, exist_ok=True)
 
-    with torch.no_grad():
-        planes = model.forward_planes(images, input_cameras)
-        mesh_out = model.extract_mesh(
-            planes,
-            use_texture_map=False,
-            **infer_config,
-        )
-        vertices, faces, vertex_colors = mesh_out
-        vertices = vertices[:, [1, 2, 0]]
-        save_glb(vertices, faces, vertex_colors, mesh_glb_fpath)
-        save_obj(vertices, faces, vertex_colors, mesh_fpath)
-    
-    return mesh_fpath, mesh_glb_fpath
+pipe = FluxPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16, token=huggingface_token)
+pipe.load_lora_weights(hf_hub_download("gokaygokay/Flux-Game-Assets-LoRA-v2", "game_asst.safetensors"))
+pipe.fuse_lora(lora_scale=1)
+pipe.to(device="cuda", dtype=torch.bfloat16)
 
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
         <div style="text-align: center; max-width: 650px; margin: 0 auto;">
-            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem;">Flux Image to 3D Model Generator</h1>
+            <h1 style="font-size: 2.5rem; font-weight: 700; margin-bottom: 1rem; display: contents;">Hyper-FLUX-8steps-LoRA</h1>
+            <p style="font-size: 1rem; margin-bottom: 1.5rem;">AutoML team from ByteDance</p>
         </div>
         """
     )
 
     with gr.Row():
         with gr.Column(scale=3):
-            prompt = gr.Textbox(
-                label="Your Image Description",
-                placeholder="E.g., A serene landscape with mountains and a lake at sunset",
-                lines=3
-            )
-            
-            with gr.Accordion("Advanced Settings", open=False):
-                with gr.Group():
-                    with gr.Row():
-                        height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
-                        width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
-                    
-                    with gr.Row():
-                        steps = gr.Slider(label="Inference Steps", minimum=10, maximum=50, step=1, value=28)
-                        scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
-                    
-                    seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
-            
-            generate_btn = gr.Button("Generate 3D Model", variant="primary")
+            with gr.Group():
+                prompt = gr.Textbox(
+                    label="Your Image Description",
+                    placeholder="E.g., A serene landscape with mountains and a lake at sunset",
+                    lines=3
+                )
+                
+                with gr.Accordion("Advanced Settings", open=False):
+                    with gr.Group():
+                        with gr.Row():
+                            height = gr.Slider(label="Height", minimum=256, maximum=1152, step=64, value=1024)
+                            width = gr.Slider(label="Width", minimum=256, maximum=1152, step=64, value=1024)
+                        
+                        with gr.Row():
+                            steps = gr.Slider(label="Inference Steps", minimum=10, maximum=50, step=1, value=28)
+                            scales = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=5.0, step=0.1, value=3.5)
+                        
+                        seed = gr.Number(label="Seed (for reproducibility)", value=3413, precision=0)
+                
+                generate_btn = gr.Button("Generate Image", variant="primary", scale=1)
 
         with gr.Column(scale=4):
-            flux_output = gr.Image(label="Generated Flux Image")
-            mv_show_images = gr.Image(label="Generated Multi-views")
-            with gr.Row():
-                with gr.Tab("OBJ"):
-                    output_model_obj = gr.Model3D(label="Output Model (OBJ Format)")
-                with gr.Tab("GLB"):
-                    output_model_glb = gr.Model3D(label="Output Model (GLB Format)")
-
-    mv_images = gr.State()
+            output = gr.Image(label="Your Generated Image")
+    
+    gr.Markdown(
+        """
+        <div style="max-width: 650px; margin: 2rem auto; padding: 1rem; border-radius: 10px; background-color: #f0f0f0;">
+            <h2 style="font-size: 1.5rem; margin-bottom: 1rem;">How to Use</h2>
+            <ol style="padding-left: 1.5rem;">
+                <li>Enter a detailed description of the image you want to create.</li>
+                <li>Adjust advanced settings if desired (tap to expand).</li>
+                <li>Tap "Generate Image" and wait for your creation!</li>
+            </ol>
+            <p style="margin-top: 1rem; font-style: italic;">Tip: Be specific in your description for best results!</p>
+        </div>
+        """
+    )
 
-    def process_pipeline(prompt, height, width, steps, scales, seed):
-        flux_image = generate_flux_image(prompt, height, width, steps, scales, seed)
-        processed_image = preprocess(flux_image, do_remove_background=True)
-        mv_images, show_image = generate_mvs(processed_image, steps, seed)
-        obj_path, glb_path = make3d(mv_images)
-        return flux_image, show_image, obj_path, glb_path
+    @spaces.GPU
+    def process_image(height, width, steps, scales, prompt, seed):
+        global pipe
+        with torch.inference_mode(), torch.autocast("cuda", dtype=torch.bfloat16), timer("inference"):
+            return pipe(
+                prompt=[prompt],
+                generator=torch.Generator().manual_seed(int(seed)),
+                num_inference_steps=int(steps),
+                guidance_scale=float(scales),
+                height=int(height),
+                width=int(width),
+                max_sequence_length=256
+            ).images[0]
 
     generate_btn.click(
-        fn=process_pipeline,
-        inputs=[prompt, height, width, steps, scales, seed],
-        outputs=[flux_output, mv_show_images, output_model_obj, output_model_glb]
+        process_image,
+        inputs=[height, width, steps, scales, prompt, seed],
+        outputs=output
     )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()