trellis first

app.py

@@ -1,9 +1,13 @@
 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
@@ -15,51 +19,28 @@ from trellis.pipelines import TrellisImageTo3DPipeline
 from trellis.representations import Gaussian, MeshExtractResult
 from trellis.utils import render_utils, postprocessing_utils
 
-
+# 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:
-    """
-    Preprocess the input image.
-
-    Args:
-        image (Image.Image): The input image.
-
-    Returns:
-        Image.Image: The preprocessed image.
-    """
-    processed_image = pipeline.preprocess_image(image)
+    processed_image = trellis_pipeline.preprocess_image(image)
     return processed_image
 
-
-def preprocess_images(images: List[Tuple[Image.Image, str]]) -> List[Image.Image]:
-    """
-    Preprocess a list of input images.
-    
-    Args:
-        images (List[Tuple[Image.Image, str]]): The input images.
-        
-    Returns:
-        List[Image.Image]: The preprocessed images.
-    """
-    images = [image[0] for image in images]
-    processed_images = [pipeline.preprocess_image(image) for image in images]
-    return processed_images
-
-
 def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
     return {
         'gaussian': {
@@ -75,9 +56,8 @@ def pack_state(gs: Gaussian, mesh: MeshExtractResult) -> dict:
             'faces': mesh.faces.cpu().numpy(),
         },
     }
-    
-    
-def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
+
+def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
     gs = Gaussian(
         aabb=state['gaussian']['aabb'],
         sh_degree=state['gaussian']['sh_degree'],
@@ -99,77 +79,69 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict, str]:
     
     return gs, mesh
 
-
 def get_seed(randomize_seed: bool, seed: int) -> int:
-    """
-    Get the random seed.
-    """
     return np.random.randint(0, MAX_SEED) if randomize_seed else seed
 
+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,
-    multiimages: List[Tuple[Image.Image, str]],
-    is_multiimage: bool,
     seed: int,
     ss_guidance_strength: float,
     ss_sampling_steps: int,
     slat_guidance_strength: float,
     slat_sampling_steps: int,
-    multiimage_algo: Literal["multidiffusion", "stochastic"],
     req: gr.Request,
 ) -> Tuple[dict, str]:
-    """
-    Convert an image to a 3D model.
-
-    Args:
-        image (Image.Image): The input image.
-        multiimages (List[Tuple[Image.Image, str]]): The input images in multi-image mode.
-        is_multiimage (bool): Whether is in multi-image mode.
-        seed (int): The random seed.
-        ss_guidance_strength (float): The guidance strength for sparse structure generation.
-        ss_sampling_steps (int): The number of sampling steps for sparse structure generation.
-        slat_guidance_strength (float): The guidance strength for structured latent generation.
-        slat_sampling_steps (int): The number of sampling steps for structured latent generation.
-        multiimage_algo (Literal["multidiffusion", "stochastic"]): The algorithm for multi-image generation.
-
-    Returns:
-        dict: The information of the generated 3D model.
-        str: The path to the video of the 3D model.
-    """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
-    if not is_multiimage:
-        outputs = 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,
-            },
-        )
-    else:
-        outputs = pipeline.run_multi_image(
-            [image[0] for image in multiimages],
-            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,
-            },
-            mode=multiimage_algo,
-        )
+    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))]
@@ -179,7 +151,6 @@ def image_to_3d(
     torch.cuda.empty_cache()
     return state, video_path
 
-
 @spaces.GPU(duration=90)
 def extract_glb(
     state: dict,
@@ -187,17 +158,6 @@ def extract_glb(
     texture_size: int,
     req: gr.Request,
 ) -> Tuple[str, str]:
-    """
-    Extract a GLB file from the 3D model.
-
-    Args:
-        state (dict): The state of the generated 3D model.
-        mesh_simplify (float): The mesh simplification factor.
-        texture_size (int): The texture resolution.
-
-    Returns:
-        str: The path to the extracted GLB file.
-    """
     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)
@@ -206,18 +166,8 @@ def extract_glb(
     torch.cuda.empty_cache()
     return glb_path, glb_path
 
-
 @spaces.GPU
 def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
-    """
-    Extract a Gaussian file from the 3D model.
-
-    Args:
-        state (dict): The state of the generated 3D model.
-
-    Returns:
-        str: The path to the extracted Gaussian file.
-    """
     user_dir = os.path.join(TMP_DIR, str(req.session_hash))
     gs, _ = unpack_state(state)
     gaussian_path = os.path.join(user_dir, 'sample.ply')
@@ -225,61 +175,30 @@ def extract_gaussian(state: dict, req: gr.Request) -> Tuple[str, str]:
     torch.cuda.empty_cache()
     return gaussian_path, gaussian_path
 
-
-def prepare_multi_example() -> List[Image.Image]:
-    multi_case = list(set([i.split('_')[0] for i in os.listdir("assets/example_multi_image")]))
-    images = []
-    for case in multi_case:
-        _images = []
-        for i in range(1, 4):
-            img = Image.open(f'assets/example_multi_image/{case}_{i}.png')
-            W, H = img.size
-            img = img.resize((int(W / H * 512), 512))
-            _images.append(np.array(img))
-        images.append(Image.fromarray(np.concatenate(_images, axis=1)))
-    return images
-
-
-def split_image(image: Image.Image) -> List[Image.Image]:
-    """
-    Split an image into multiple views.
-    """
-    image = np.array(image)
-    alpha = image[..., 3]
-    alpha = np.any(alpha>0, axis=0)
-    start_pos = np.where(~alpha[:-1] & alpha[1:])[0].tolist()
-    end_pos = np.where(alpha[:-1] & ~alpha[1:])[0].tolist()
-    images = []
-    for s, e in zip(start_pos, end_pos):
-        images.append(Image.fromarray(image[:, s:e+1]))
-    return [preprocess_image(image) for image in images]
-
-
-with gr.Blocks(delete_cache=(600, 600)) as demo:
+# Gradio Interface
+with gr.Blocks() as demo:
     gr.Markdown("""
-    ## Image to 3D Asset with [TRELLIS](https://trellis3d.github.io/)
-    * Upload an image and click "Generate" to create a 3D asset. If the image has alpha channel, it be used as the mask. Otherwise, we use `rembg` to remove the background.
+    ## 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.
-    
-    ✨New: 1) Experimental multi-image support. 2) Gaussian file extraction.
     """)
     
     with gr.Row():
         with gr.Column():
-            with gr.Tabs() as input_tabs:
-                with gr.Tab(label="Single Image", id=0) as single_image_input_tab:
-                    image_prompt = gr.Image(label="Image Prompt", format="png", image_mode="RGBA", type="pil", height=300)
-                with gr.Tab(label="Multiple Images", id=1) as multiimage_input_tab:
-                    multiimage_prompt = gr.Gallery(label="Image Prompt", format="png", type="pil", height=300, columns=3)
-                    gr.Markdown("""
-                        Input different views of the object in separate images. 
-                        
-                        *NOTE: this is an experimental algorithm without training a specialized model. It may not produce the best results for all images, especially those having different poses or inconsistent details.*
-                    """)
-            
-            with gr.Accordion(label="Generation Settings", open=False):
-                seed = gr.Slider(0, MAX_SEED, label="Seed", value=0, step=1)
+            # 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)
@@ -288,97 +207,45 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
                 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)
-                multiimage_algo = gr.Radio(["stochastic", "multidiffusion"], label="Multi-image Algorithm", value="stochastic")
 
             generate_btn = gr.Button("Generate")
             
-            with gr.Accordion(label="GLB Extraction Settings", open=False):
+            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)
-            gr.Markdown("""
-                        *NOTE: Gaussian file can be very large (~50MB), it will take a while to display and download.*
-                        """)
 
         with gr.Column():
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True, height=300)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=10.0, height=300)
+            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)  
-    
-    is_multiimage = gr.State(False)
-    output_buf = gr.State()
+                download_gs = gr.DownloadButton(label="Download Gaussian", interactive=False)
 
-    # Example images at the bottom of the page
-    with gr.Row() as single_image_example:
-        examples = gr.Examples(
-            examples=[
-                f'assets/example_image/{image}'
-                for image in os.listdir("assets/example_image")
-            ],
-            inputs=[image_prompt],
-            fn=preprocess_image,
-            outputs=[image_prompt],
-            run_on_click=True,
-            examples_per_page=64,
-        )
-    with gr.Row(visible=False) as multiimage_example:
-        examples_multi = gr.Examples(
-            examples=prepare_multi_example(),
-            inputs=[image_prompt],
-            fn=split_image,
-            outputs=[multiimage_prompt],
-            run_on_click=True,
-            examples_per_page=8,
-        )
+    output_buf = gr.State()
 
-    # Handlers
+    # Event handlers
     demo.load(start_session)
     demo.unload(end_session)
-    
-    single_image_input_tab.select(
-        lambda: tuple([False, gr.Row.update(visible=True), gr.Row.update(visible=False)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
-    )
-    multiimage_input_tab.select(
-        lambda: tuple([True, gr.Row.update(visible=False), gr.Row.update(visible=True)]),
-        outputs=[is_multiimage, single_image_example, multiimage_example]
-    )
-    
-    image_prompt.upload(
-        preprocess_image,
-        inputs=[image_prompt],
-        outputs=[image_prompt],
-    )
-    multiimage_prompt.upload(
-        preprocess_images,
-        inputs=[multiimage_prompt],
-        outputs=[multiimage_prompt],
-    )
 
     generate_btn.click(
-        get_seed,
-        inputs=[randomize_seed, seed],
-        outputs=[seed],
+        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=[image_prompt, multiimage_prompt, is_multiimage, seed, ss_guidance_strength, ss_sampling_steps, slat_guidance_strength, slat_sampling_steps, multiimage_algo],
+        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],
     )
 
-    video_output.clear(
-        lambda: tuple([gr.Button(interactive=False), gr.Button(interactive=False)]),
-        outputs=[extract_glb_btn, extract_gs_btn],
-    )
-
     extract_glb_btn.click(
         extract_glb,
         inputs=[output_buf, mesh_simplify, texture_size],
@@ -401,14 +268,24 @@ with gr.Blocks(delete_cache=(600, 600)) as demo:
         lambda: gr.Button(interactive=False),
         outputs=[download_glb],
     )
-    
 
-# Launch the Gradio app
+# Initialize both pipelines
 if __name__ == "__main__":
-    pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
-    pipeline.cuda()
+    # Initialize Flux pipeline
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    flux_pipeline = DiffusionPipeline.from_pretrained(
+        "black-forest-labs/FLUX.1-dev", 
+        torch_dtype=torch.bfloat16
+    )
+    flux_pipeline.load_lora_weights("gokaygokay/Flux-Game-Assets-LoRA-v2")
+    flux_pipeline = flux_pipeline.to(device)
+
+    # Initialize Trellis pipeline
+    trellis_pipeline = TrellisImageTo3DPipeline.from_pretrained("JeffreyXiang/TRELLIS-image-large")
+    trellis_pipeline.cuda()
     try:
-        pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))    # Preload rembg
+        trellis_pipeline.preprocess_image(Image.fromarray(np.zeros((512, 512, 3), dtype=np.uint8)))
     except:
         pass
-    demo.launch()
+    
+    demo.launch()