Update app_exp.py

one comit before is all loads just fix the fa3 if req

app_exp.py

@@ -6,46 +6,16 @@ import sys
 import subprocess
 import tempfile
 import numpy as np
-import site
-import importlib
+import spaces
 from PIL import Image
-from huggingface_hub import snapshot_download, hf_hub_download
-
-# ============================================================
-# 0️⃣ Install required packages
-# ============================================================
-subprocess.run(["pip3", "install", "-U", "cache-dit"], check=True)
-
-
-
-import cache_dit
-
-enable_fa3 = False  # default if FA3 cannot be loaded
-
-try:
-    print("Installing FlashAttention 3...")
-    flash_attention_wheel = hf_hub_download(
-        repo_id="rahul7star/flash-attn-3",
-        repo_type="model",
-        filename="128/flash_attn_3-3.0.0b1-cp39-abi3-linux_x86_64.whl",
-    )
-    subprocess.run(["pip", "install", flash_attention_wheel], check=True)
-    site.addsitedir(site.getsitepackages()[0])
-    importlib.invalidate_caches()
-    enable_fa3 = True
-    print("✅ FlashAttention 3 installed and enabled")
-except Exception as e:
-    print(f"⚠️ Could not install FlashAttention 3: {e}")
-    # enable_fa3 remains False
 
-
-# ============================================================
-# 1️⃣ Repository & Weights
-# ============================================================
+# Define paths
 REPO_PATH = "LongCat-Video"
 CHECKPOINT_DIR = os.path.join(REPO_PATH, "weights", "LongCat-Video")
 
+# Clone the repository if it doesn't exist
 if not os.path.exists(REPO_PATH):
+    print(f"Cloning LongCat-Video repository to '{REPO_PATH}'...")
     subprocess.run(
         ["git", "clone", "https://github.com/meituan-longcat/LongCat-Video.git", REPO_PATH],
         check=True
@@ -53,6 +23,7 @@ if not os.path.exists(REPO_PATH):
 
 sys.path.insert(0, os.path.abspath(REPO_PATH))
 
+from huggingface_hub import snapshot_download
 from longcat_video.pipeline_longcat_video import LongCatVideoPipeline
 from longcat_video.modules.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
 from longcat_video.modules.autoencoder_kl_wan import AutoencoderKLWan
@@ -60,9 +31,9 @@ from longcat_video.modules.longcat_video_dit import LongCatVideoTransformer3DMod
 from longcat_video.context_parallel import context_parallel_util
 from transformers import AutoTokenizer, UMT5EncoderModel
 from diffusers.utils import export_to_video
-from transformers import BitsAndBytesConfig as TransformersBitsAndBytesConfig
 from diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig
 
+# Download weights if needed
 if not os.path.exists(CHECKPOINT_DIR):
     snapshot_download(
         repo_id="meituan-longcat/LongCat-Video",
@@ -71,60 +42,33 @@ if not os.path.exists(CHECKPOINT_DIR):
         ignore_patterns=["*.md", "*.gitattributes", "assets/*"]
     )
 
-# ============================================================
-# 2️⃣ Device & Models (with cache & quantization)
-# ============================================================
-device = "cuda" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.bfloat16 if device=="cuda" else torch.float32
 pipe = None
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.bfloat16 if device == "cuda" else torch.float32
 
+print("--- Initializing Models ---")
 try:
     cp_split_hw = context_parallel_util.get_optimal_split(1)
     tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT_DIR, subfolder="tokenizer", torch_dtype=torch_dtype)
-
-    # Text encoder with 4-bit quantization
-    text_encoder = UMT5EncoderModel.from_pretrained(
-        CHECKPOINT_DIR,
-        subfolder="text_encoder",
-        torch_dtype=torch_dtype,
-        quantization_config=TransformersBitsAndBytesConfig(
-            load_in_4bit=True,
-            bnb_4bit_quant_type="nf4",
-            bnb_4bit_compute_dtype=torch_dtype
-        )
-    )
-
+    text_encoder = UMT5EncoderModel.from_pretrained(CHECKPOINT_DIR, subfolder="text_encoder", torch_dtype=torch_dtype)
     vae = AutoencoderKLWan.from_pretrained(CHECKPOINT_DIR, subfolder="vae", torch_dtype=torch_dtype)
     scheduler = FlowMatchEulerDiscreteScheduler.from_pretrained(CHECKPOINT_DIR, subfolder="scheduler", torch_dtype=torch_dtype)
 
-    # DiT model with FP8/4-bit quantization + cache
+    bnb_4bit_config = DiffusersBitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16
+    )
+
     dit = LongCatVideoTransformer3DModel.from_pretrained(
         CHECKPOINT_DIR,
-        enable_flashattn3=enable_fa3,
+        enable_flashattn3=False,
+        enable_flashattn2=False,
         enable_xformers=True,
         subfolder="dit",
         cp_split_hw=cp_split_hw,
-        torch_dtype=torch_dtype
-    )
-
-    # Enable Cache-DiT
-    cache_dit.enable_cache(
-        cache_dit.BlockAdapter(
-            transformer=dit,
-            blocks=dit.blocks,
-            forward_pattern=cache_dit.ForwardPattern.Pattern_3,
-            check_forward_pattern=False,
-            has_separate_cfg=False
-        ),
-        cache_config=cache_dit.DBCacheConfig(
-            Fn_compute_blocks=1,
-            Bn_compute_blocks=1,
-            max_warmup_steps=5,
-            max_cached_steps=50,
-            max_continuous_cached_steps=50,
-            residual_diff_threshold=0.01,
-            num_inference_steps=50
-        )
+        torch_dtype=torch_dtype,
+        quantization_config=bnb_4bit_config
     )
 
     pipe = LongCatVideoPipeline(
@@ -135,172 +79,159 @@ try:
         dit=dit,
     )
     pipe.to(device)
-    print("✅ Models loaded with Cache-DiT and quantization")
+
+    pipe.dit.load_lora(os.path.join(CHECKPOINT_DIR, 'lora/cfg_step_lora.safetensors'), 'cfg_step_lora')
+    pipe.dit.load_lora(os.path.join(CHECKPOINT_DIR, 'lora/refinement_lora.safetensors'), 'refinement_lora')
 
 except Exception as e:
-    print(f"❌ Failed to load models: {e}")
+    print("Failed to load models:", e)
     pipe = None
 
-# ============================================================
-# 3️⃣ Generation Helper
-# ============================================================
 def torch_gc():
     if torch.cuda.is_available():
         torch.cuda.empty_cache()
         torch.cuda.ipc_collect()
 
-def check_duration(
+def check_duration(*args, duration_t2v=2, **kwargs):
+    fps = 30
+    return duration_t2v * fps  # total frames
+
+@spaces.GPU(duration=check_duration)
+def generate_video(
     mode,
-    prompt, 
-    neg_prompt, 
+    prompt,
+    neg_prompt,
     image,
     height, width, resolution,
     seed,
     use_distill,
     use_refine,
-    progress
+    duration_t2v=2,
+    progress=gr.Progress(track_tqdm=True)
 ):
-    if use_distill and resolution=="480p":
-        return 180
-    elif resolution=="720p":
-        return 360
-    else:
-        return 900
-
-@spaces.GPU(duration=90)
-def generate_video(mode, prompt, neg_prompt, image, height, width, resolution,
-                   seed, use_distill, use_refine, duration_sec, progress=gr.Progress(track_tqdm=True)):
-
     if pipe is None:
-        raise gr.Error("Models not loaded")
+        raise gr.Error("Models failed to load.")
+
+    fps = 30
+    num_frames = duration_t2v * fps
+    print(prompt)
 
-    fps = 15 if use_distill else 30
-    num_frames = int(duration_sec * fps)
     generator = torch.Generator(device=device).manual_seed(int(seed))
     is_distill = use_distill or use_refine
-    print(prompt)
 
-    progress(0.2, desc="Stage 1: Base Video Generation")
-    pipe.dit.enable_loras(['cfg_step_lora'] if is_distill else [])
-    num_inference_steps = 12 if is_distill else 24
-    guidance_scale = 2.0 if is_distill else 4.0
-    curr_neg_prompt = "" if is_distill else neg_prompt
+    if is_distill:
+        pipe.dit.enable_loras(['cfg_step_lora'])
+        num_inference_steps = 16
+        guidance_scale = 1.0
+        current_neg_prompt = ""
+    else:
+        num_inference_steps = 50
+        guidance_scale = 4.0
+        current_neg_prompt = neg_prompt
 
-    if mode=="t2v":
+    if mode == "t2v":
         output = pipe.generate_t2v(
             prompt=prompt,
-            negative_prompt=curr_neg_prompt,
+            negative_prompt=current_neg_prompt,
             height=height,
             width=width,
             num_frames=num_frames,
             num_inference_steps=num_inference_steps,
             use_distill=is_distill,
             guidance_scale=guidance_scale,
-            generator=generator
+            generator=generator,
         )[0]
     else:
-        pil_img = Image.fromarray(image)
+        pil_image = Image.fromarray(image)
         output = pipe.generate_i2v(
-            image=pil_img,
+            image=pil_image,
             prompt=prompt,
-            negative_prompt=curr_neg_prompt,
+            negative_prompt=current_neg_prompt,
             resolution=resolution,
             num_frames=num_frames,
             num_inference_steps=num_inference_steps,
             use_distill=is_distill,
             guidance_scale=guidance_scale,
-            generator=generator
+            generator=generator,
         )[0]
 
-    pipe.dit.disable_all_loras()
+    if is_distill:
+        pipe.dit.disable_all_loras()
     torch_gc()
 
     if use_refine:
-        progress(0.5, desc="Stage 2: Refinement")
+        progress(0.5, desc="Refinement")
         pipe.dit.enable_loras(['refinement_lora'])
         pipe.dit.enable_bsa()
-        stage1_video_pil = [(frame*255).astype(np.uint8) for frame in output]
+        stage1_video_pil = [(frame * 255).astype(np.uint8) for frame in output]
         stage1_video_pil = [Image.fromarray(img) for img in stage1_video_pil]
-        refine_image = Image.fromarray(image) if mode=='i2v' else None
+        refine_image = Image.fromarray(image) if mode == 'i2v' else None
         output = pipe.generate_refine(
             image=refine_image,
             prompt=prompt,
             stage1_video=stage1_video_pil,
-            num_cond_frames=1 if mode=='i2v' else 0,
+            num_cond_frames=1 if mode == 'i2v' else 0,
             num_inference_steps=50,
-            generator=generator
+            generator=generator,
         )[0]
         pipe.dit.disable_all_loras()
         pipe.dit.disable_bsa()
         torch_gc()
 
     progress(1.0, desc="Exporting video")
-    print('video generated')
-    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as f:
-        export_to_video(output, f.name, fps=fps)
-        return f.name
-
-# ============================================================
-# 4️⃣ Gradio UI
-# ============================================================
-css=".fillable{max-width:960px !important}"
+    print("video generated")
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as temp_video_file:
+        export_to_video(output, temp_video_file.name, fps=fps)
+        return temp_video_file.name
 
+# --- Gradio UI ---
+css = '.fillable{max-width: 960px !important}'
 with gr.Blocks(css=css) as demo:
-    gr.Markdown("# 🎬 LongCat-Video with Cache-DiT & Quantization")
-    gr.Markdown("13.6B parameter dense video-generation model by Meituan — [[Model](https://huggingface.co/meituan-longcat/LongCat-Video)]")
+    gr.Markdown("# 🎬 LongCat-Video")
+    gr.Markdown("13.6B parameter dense video-generation model — [HuggingFace](https://huggingface.co/meituan-longcat/LongCat-Video)")
 
-    with gr.Tabs():
-        # Text-to-Video
+    with gr.Tabs() as tabs:
         with gr.TabItem("Text-to-Video"):
             mode_t2v = gr.State("t2v")
-            with gr.Row():
-                with gr.Column(scale=2):
-                    prompt_t2v = gr.Textbox(label="Prompt", lines=4)
-                    neg_prompt_t2v = gr.Textbox(label="Negative Prompt", lines=2, value="blurry, low quality")
-                    height_t2v = gr.Slider(256,1024,step=64,value=480,label="Height")
-                    width_t2v = gr.Slider(256,1024,step=64,value=832,label="Width")
-                    seed_t2v = gr.Number(value=42,label="Seed")
-                    distill_t2v = gr.Checkbox(value=True,label="Use Distill Mode")
-                    refine_t2v = gr.Checkbox(value=False,label="Use Refine Mode")
-                    duration_t2v = gr.Slider(1,20,step=1,value=2,label="Video Duration (seconds)")
-                    t2v_button = gr.Button("Generate Video")
-                with gr.Column(scale=3):
-                    video_output_t2v = gr.Video(label="Generated Video")
+            prompt_t2v = gr.Textbox(label="Prompt", lines=4)
+            neg_prompt_t2v = gr.Textbox(label="Negative Prompt", lines=2, value="ugly, blurry, low quality, static, subtitles")
+            height_t2v = gr.Slider(256, 1024, step=64, value=480, label="Height")
+            width_t2v = gr.Slider(256, 1024, step=64, value=832, label="Width")
+            seed_t2v = gr.Number(label="Seed", value=42, precision=0)
+            distill_t2v = gr.Checkbox(label="Use Distill Mode", value=True)
+            refine_t2v = gr.Checkbox(label="Use Refine Mode", value=False)
+            duration_t2v = gr.Slider(1, 20, step=1, value=2, label="Video Duration (seconds)")
+            t2v_button = gr.Button("Generate Video")
+            video_output_t2v = gr.Video(label="Generated Video", interactive=False)
+
+            t2v_button.click(
+                fn=generate_video,
+                inputs=[mode_t2v, prompt_t2v, neg_prompt_t2v, gr.State(None),
+                        height_t2v, width_t2v, gr.State(None),
+                        seed_t2v, distill_t2v, refine_t2v, duration_t2v],
+                outputs=video_output_t2v
+            )
 
-        # Image-to-Video
         with gr.TabItem("Image-to-Video"):
             mode_i2v = gr.State("i2v")
-            with gr.Row():
-                with gr.Column(scale=2):
-                    image_i2v = gr.Image(type="numpy", label="Input Image")
-                    prompt_i2v = gr.Textbox(label="Prompt", lines=4)
-                    neg_prompt_i2v = gr.Textbox(label="Negative Prompt", lines=2, value="blurry, low quality")
-                    resolution_i2v = gr.Dropdown(["480p","720p"], value="480p", label="Resolution")
-                    seed_i2v = gr.Number(value=42,label="Seed")
-                    distill_i2v = gr.Checkbox(value=True,label="Use Distill Mode")
-                    refine_i2v = gr.Checkbox(value=False,label="Use Refine Mode")
-                    duration_i2v = gr.Slider(1,20,step=1,value=2,label="Video Duration (seconds)")
-                    i2v_button = gr.Button("Generate Video")
-                with gr.Column(scale=3):
-                    video_output_i2v = gr.Video(label="Generated Video")
-
-    # Bind events
-    t2v_button.click(
-        generate_video,
-        inputs=[mode_t2v, prompt_t2v, neg_prompt_t2v, gr.State(None),
-                height_t2v, width_t2v, gr.State("480p"),
-                seed_t2v, distill_t2v, refine_t2v, duration_t2v],
-        outputs=video_output_t2v
-    )
-
-    i2v_button.click(
-        generate_video,
-        inputs=[mode_i2v, prompt_i2v, neg_prompt_i2v, image_i2v,
-                gr.State(None), gr.State(None), resolution_i2v,
-                seed_i2v, distill_i2v, refine_i2v, duration_i2v],
-        outputs=video_output_i2v
-    )
-
-# Launch
-if __name__=="__main__":
-    demo.launch()
+            image_i2v = gr.Image(type="numpy", label="Input Image")
+            prompt_i2v = gr.Textbox(label="Prompt", lines=4)
+            neg_prompt_i2v = gr.Textbox(label="Negative Prompt", lines=2, value="ugly, blurry, low quality, static, subtitles, watermark")
+            resolution_i2v = gr.Dropdown(["480p", "720p"], value="480p", label="Resolution")
+            seed_i2v = gr.Number(label="Seed", value=42, precision=0)
+            distill_i2v = gr.Checkbox(label="Use Distill Mode", value=True)
+            refine_i2v = gr.Checkbox(label="Use Refine Mode", value=False)
+            duration_i2v = gr.Slider(1, 20, step=1, value=2, label="Video Duration (seconds)")
+            i2v_button = gr.Button("Generate Video")
+            video_output_i2v = gr.Video(label="Generated Video", interactive=False)
+
+            i2v_button.click(
+                fn=generate_video,
+                inputs=[mode_i2v, prompt_i2v, neg_prompt_i2v, image_i2v,
+                        gr.State(None), gr.State(None), resolution_i2v,
+                        seed_i2v, distill_i2v, refine_i2v, duration_i2v],
+                outputs=video_output_i2v
+            )
+
+if __name__ == "__main__":
+    demo.launch()