Update app.py

app.py

@@ -1,20 +1,18 @@
-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 *
+from typing import Tuple
 import torch
 import numpy as np
 import imageio
 from easydict import EasyDict as edict
 from PIL import Image
+from fastapi import FastAPI
+from fastapi.responses import FileResponse
+from pydantic import BaseModel
+from diffusers import DiffusionPipeline
 from trellis.pipelines import TrellisImageTo3DPipeline
 from trellis.representations import Gaussian, MeshExtractResult
 from trellis.utils import render_utils, postprocessing_utils
@@ -22,24 +20,14 @@ from trellis.utils import render_utils, postprocessing_utils
 NUM_INFERENCE_STEPS = 8
 
 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
@@ -85,224 +73,163 @@ def unpack_state(state: dict) -> Tuple[Gaussian, edict]:
 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,
-    progress: gr.Progress = gr.Progress(track_tqdm=True),
-) -> Image.Image:
-    """Generate image using Flux pipeline"""
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+# Initialize both pipelines at startup
+from diffusers import FluxTransformer2DModel, FluxPipeline, BitsAndBytesConfig, GGUFQuantizationConfig
+from transformers import T5EncoderModel, BitsAndBytesConfig as BitsAndBytesConfigTF
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.bfloat16 if device == "cuda" else torch.float32
+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 = "dkatz2391/Flux1Dev"
+
+if device == "cuda":
+    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
+    )
+    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
+        )
+        transformer = FluxTransformer2DModel.from_single_file(
+            file_url,
+            subfolder="transformer",
+            torch_dtype=dtype,
+            config=single_file_base_model,
+            quantization_config=quantization_config
+        )
+else:
+    # CPU fallback: no quantization
+    text_encoder_2 = T5EncoderModel.from_pretrained(
+        single_file_base_model,
+        subfolder="text_encoder_2",
+        torch_dtype=dtype,
+        config=single_file_base_model
+    )
+    transformer = FluxTransformer2DModel.from_single_file(
+        file_url,
+        subfolder="transformer",
+        torch_dtype=dtype,
+        config=single_file_base_model
+    )
+
+flux_pipeline = FluxPipeline.from_pretrained(
+    single_file_base_model,
+    transformer=transformer,
+    text_encoder_2=text_encoder_2,
+    torch_dtype=dtype
+)
+flux_pipeline.to(device)
+
+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
+
+# FastAPI app
+app = FastAPI()
+
+class TextToImageRequest(BaseModel):
+    prompt: str
+    seed: int = 42
+    randomize_seed: bool = True
+    width: int = 1024
+    height: int = 1024
+    guidance_scale: float = 3.5
+
+@app.post("/text-to-image")
+def text_to_image_api(req: TextToImageRequest):
+    # Generate image using Flux pipeline
+    seed = get_seed(req.randomize_seed, req.seed)
     generator = torch.Generator(device=device).manual_seed(seed)
-    prompt = "wbgmsst, " + prompt + ", 3D isometric, white background"
+    prompt = "wbgmsst, " + req.prompt + ", 3D isometric, white background"
     image = flux_pipeline(
         prompt=prompt,
-        guidance_scale=guidance_scale,
+        guidance_scale=req.guidance_scale,
         num_inference_steps=NUM_INFERENCE_STEPS,
-        width=width,
-        height=height,
+        width=req.width,
+        height=req.height,
         generator=generator,
     ).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))
+    return {"image_path": filepath}
+
+class ImageTo3DRequest(BaseModel):
+    image_path: str
+    seed: int = 42
+    ss_guidance_strength: float = 7.5
+    ss_sampling_steps: int = 12
+    slat_guidance_strength: float = 3.0
+    slat_sampling_steps: int = 12
+
+@app.post("/image-to-3d")
+def image_to_3d_api(req: ImageTo3DRequest):
+    # Load image
+    image = Image.open(req.image_path)
     outputs = trellis_pipeline.run(
         image,
-        seed=seed,
+        seed=req.seed,
         formats=["gaussian", "mesh"],
         preprocess_image=False,
         sparse_structure_sampler_params={
-            "steps": ss_sampling_steps,
-            "cfg_strength": ss_guidance_strength,
+            "steps": req.ss_sampling_steps,
+            "cfg_strength": req.ss_guidance_strength,
         },
         slat_sampler_params={
-            "steps": slat_sampling_steps,
-            "cfg_strength": slat_guidance_strength,
+            "steps": req.slat_sampling_steps,
+            "cfg_strength": req.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))]
+    user_dir = TMP_DIR  # You can customize this per request if needed
     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.
-    * [TRELLIS Model](https://huggingface.co/JeffreyXiang/TRELLIS-image-large) [Trellis Github](https://github.com/microsoft/TRELLIS) [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) [safetensors to GGUF for Flux](https://github.com/ruSauron/to-gguf-bat) [Thanks to John6666](https://huggingface.co/John6666)
-    """)
-    
-    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(512, 1024, label="Width", value=1024, step=16)
-                    height = gr.Slider(512, 1024, label="Height", value=1024, step=16)
-                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=8, step=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")
-            
-        with gr.Column():
-            
-            video_output = gr.Video(label="Generated 3D Asset", autoplay=True, loop=True)
-            model_output = LitModel3D(label="Extracted GLB/Gaussian", exposure=8.0, height=400)
-            
-            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()
+    # Save GLB (placeholder: you must implement actual GLB saving logic)
+    glb_path = os.path.join(user_dir, 'output.glb')
+    # TODO: Save the actual GLB to glb_path
+    return {
+        "state": state,
+        "video_path": video_path,
+        "glb_path": glb_path
+    }
 
-    # Event handlers
-    demo.load(start_session)
-    demo.unload(end_session)
+@app.get("/image/{filename}")
+def get_image(filename: str):
+    file_path = os.path.join(SAVE_DIR, filename)
+    return FileResponse(file_path, media_type="image/png")
 
-    generate_btn.click(
-        generate_flux_image,
-        inputs=[prompt, seed, randomize_seed, width, height, guidance_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],
-    )
+@app.get("/mp4/{filename}")
+def get_mp4(filename: str):
+    file_path = os.path.join(TMP_DIR, filename)
+    return FileResponse(file_path, media_type="video/mp4")
 
-    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, 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")
-    # 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(share=True)
+@app.get("/glb/{filename}")
+def get_glb(filename: str):
+    file_path = os.path.join(TMP_DIR, filename)
+    return FileResponse(file_path, media_type="model/gltf-binary")