Create wan2_fast.py

wan2_fast.py

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+import torch
+from diffusers import AutoencoderKLWan, WanPipeline, UniPCMultistepScheduler
+from diffusers.utils import export_to_video
+import gradio as gr
+import tempfile
+import spaces
+from huggingface_hub import hf_hub_download
+import numpy as np
+import random
+import os
+# LIGHT WEIGHT 1.3b
+# MODEL_ID = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+# LORA_REPO_ID = "Kijai/WanVideo_comfy"
+# LORA_FILENAME = "Wan21_CausVid_bidirect2_T2V_1_3B_lora_rank32.safetensors"
+
+
+MODEL_ID = "Wan-AI/Wan2.1-T2V-14B-Diffusers"
+
+#MODEL_ID ="FastWan/FastWan_T2V_14B_480p_lora_rank_128_bf16.safetensors"
+
+
+LORA_REPO_ID = "Kijai/WanVideo_comfy"
+LORA_FILENAME = "Lightx2v/lightx2v_T2V_14B_cfg_step_distill_v2_lora_rank256_bf16.safetensors"
+#LORA_FILENAME = "Pusa/Wan21_PusaV1_LoRA_14B_rank512_bf16.safetensors"
+
+
+
+
+# LORA_REPO_ID = "RaphaelLiu/PusaV1"
+# LORA_FILENAME="pusa_v1.safetensors"
+#LORA_REPO_ID = "Kijai/WanVideo_comfy"
+#LORA_FILENAME = "Wan21_CausVid_14B_T2V_lora_rank32.safetensors"
+
+
+
+
+
+
+
+
+vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanPipeline.from_pretrained(
+    MODEL_ID, vae=vae, torch_dtype=torch.bfloat16
+)
+pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0)
+pipe.to("cuda")
+
+causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME)
+pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora")
+pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95])
+pipe.fuse_lora()
+
+# MOD_VALUE = 32
+# DEFAULT_H_SLIDER_VALUE =  512
+# DEFAULT_W_SLIDER_VALUE =  896
+
+# # Environment variable check
+# IS_ORIGINAL_SPACE = os.environ.get("IS_ORIGINAL_SPACE", "True") == "True"
+
+# # Original limits
+# ORIGINAL_SLIDER_MIN_H, ORIGINAL_SLIDER_MAX_H = 128, 1280
+# ORIGINAL_SLIDER_MIN_W, ORIGINAL_SLIDER_MAX_W = 128, 1280
+# ORIGINAL_MAX_DURATION = round(81/24, 1)  # MAX_FRAMES_MODEL/FIXED_FPS
+
+# # Limited space constants
+# LIMITED_MAX_RESOLUTION = 640
+# LIMITED_MAX_DURATION = 2.0
+# LIMITED_MAX_STEPS = 4
+
+# # Set limits based on environment variable
+# if IS_ORIGINAL_SPACE:
+#     SLIDER_MIN_H, SLIDER_MAX_H = 128, LIMITED_MAX_RESOLUTION
+#     SLIDER_MIN_W, SLIDER_MAX_W = 128, LIMITED_MAX_RESOLUTION
+#     MAX_DURATION = LIMITED_MAX_DURATION
+#     MAX_STEPS = LIMITED_MAX_STEPS
+# else:
+#     SLIDER_MIN_H, SLIDER_MAX_H = ORIGINAL_SLIDER_MIN_H, ORIGINAL_SLIDER_MAX_H
+#     SLIDER_MIN_W, SLIDER_MAX_W = ORIGINAL_SLIDER_MIN_W, ORIGINAL_SLIDER_MAX_W
+#     MAX_DURATION = ORIGINAL_MAX_DURATION
+#     MAX_STEPS = 8
+
+# MAX_SEED = np.iinfo(np.int32).max
+
+# FIXED_FPS = 24
+# FIXED_OUTPUT_FPS = 18 # we downspeed the output video as a temporary "trick"
+# MIN_FRAMES_MODEL = 8
+# MAX_FRAMES_MODEL = 81 
+
+
+#New math to make it High Res
+
+MOD_VALUE = 32
+
+# Defaults for higher-res generation
+DEFAULT_H_SLIDER_VALUE = 768
+DEFAULT_W_SLIDER_VALUE = 1344  # 16:9 friendly and divisible by MOD_VALUE
+
+# Original Space = Hugging Face space with compute limits
+IS_ORIGINAL_SPACE = os.environ.get("IS_ORIGINAL_SPACE", "True") == "True"
+
+# Conservative limits for low-end environments
+LIMITED_MAX_RESOLUTION = 640
+LIMITED_MAX_DURATION = 2.0
+LIMITED_MAX_STEPS = 4
+
+# Generous limits for local or Pro spaces
+ORIGINAL_SLIDER_MIN_H, ORIGINAL_SLIDER_MAX_H = 128, 1536
+ORIGINAL_SLIDER_MIN_W, ORIGINAL_SLIDER_MAX_W = 128, 1536
+ORIGINAL_MAX_DURATION = round(81 / 24, 1)  # 3.4 seconds
+ORIGINAL_MAX_STEPS = 8
+
+# Use limited or original (generous) settings
+if IS_ORIGINAL_SPACE:
+    SLIDER_MIN_H, SLIDER_MAX_H = 128, LIMITED_MAX_RESOLUTION
+    SLIDER_MIN_W, SLIDER_MAX_W = 128, LIMITED_MAX_RESOLUTION
+    MAX_DURATION = LIMITED_MAX_DURATION
+    MAX_STEPS = LIMITED_MAX_STEPS
+else:
+    SLIDER_MIN_H, SLIDER_MAX_H = ORIGINAL_SLIDER_MIN_H, ORIGINAL_SLIDER_MAX_H
+    SLIDER_MIN_W, SLIDER_MAX_W = ORIGINAL_SLIDER_MIN_W, ORIGINAL_SLIDER_MAX_W
+    MAX_DURATION = ORIGINAL_MAX_DURATION
+    MAX_STEPS = ORIGINAL_MAX_STEPS
+
+MAX_SEED = np.iinfo(np.int32).max
+
+FIXED_FPS = 24
+FIXED_OUTPUT_FPS = 18  # reduce final video FPS to save space
+MIN_FRAMES_MODEL = 8
+MAX_FRAMES_MODEL = 81
+
+
+default_prompt_t2v = "cinematic footage, group of pedestrians dancing in the streets of NYC, high quality breakdance, 4K, tiktok video, intricate details, instagram feel, dynamic camera, smooth dance motion, dimly lit, stylish, beautiful faces, smiling, music video"
+default_negative_prompt = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards, watermark, text, signature"
+
+def get_duration(prompt, height, width, 
+                   negative_prompt, duration_seconds,
+                   guidance_scale, steps,
+                   seed, randomize_seed, 
+                   progress):
+    if steps > 4 and duration_seconds > 2:
+        return 90
+    elif steps > 4 or duration_seconds > 2:
+        return 75
+    else:
+        return 60
+
+@spaces.GPU(duration=get_duration)
+def generate_video(prompt, height, width, 
+                   negative_prompt=default_negative_prompt, duration_seconds = 2,
+                   guidance_scale = 1, steps = 4,
+                   seed = 42, randomize_seed = False, 
+                   progress=gr.Progress(track_tqdm=True)):
+    """
+    Generate a video from a text prompt using the Wan 2.1 T2V model with CausVid LoRA.
+    
+    This function takes a text prompt and generates a video based on the provided
+    prompt and parameters. It uses the Wan 2.1 1.3B Text-to-Video model with CausVid LoRA
+    for fast generation in 3-8 steps.
+    
+    Args:
+        prompt (str): Text prompt describing the desired video content.
+        height (int): Target height for the output video. Will be adjusted to multiple of MOD_VALUE (32).
+        width (int): Target width for the output video. Will be adjusted to multiple of MOD_VALUE (32).
+        negative_prompt (str, optional): Negative prompt to avoid unwanted elements. 
+            Defaults to default_negative_prompt (contains unwanted visual artifacts).
+        duration_seconds (float, optional): Duration of the generated video in seconds.
+            Defaults to 2. Clamped between MIN_FRAMES_MODEL/FIXED_FPS and MAX_FRAMES_MODEL/FIXED_FPS.
+        guidance_scale (float, optional): Controls adherence to the prompt. Higher values = more adherence.
+            Defaults to 1.0. Range: 0.0-20.0.
+        steps (int, optional): Number of inference steps. More steps = higher quality but slower.
+            Defaults to 4. Range: 1-30.
+        seed (int, optional): Random seed for reproducible results. Defaults to 42.
+            Range: 0 to MAX_SEED (2147483647).
+        randomize_seed (bool, optional): Whether to use a random seed instead of the provided seed.
+            Defaults to False.
+        progress (gr.Progress, optional): Gradio progress tracker. Defaults to gr.Progress(track_tqdm=True).
+    
+    Returns:
+        tuple: A tuple containing:
+            - video_path (str): Path to the generated video file (.mp4)
+            - current_seed (int): The seed used for generation (useful when randomize_seed=True)
+    
+    Raises:
+        gr.Error: If prompt is empty or None.
+    
+    Note:
+        - Frame count is calculated as duration_seconds * FIXED_FPS (24)
+        - Output dimensions are adjusted to be multiples of MOD_VALUE (32)
+        - The function uses GPU acceleration via the @spaces.GPU decorator
+        - Generation time varies based on steps and duration (see get_duration function)
+    """
+    if not prompt or prompt.strip() == "":
+        raise gr.Error("Please enter a text prompt. Try to use long and precise descriptions.")
+
+    # Apply limits based on environment variable
+    if IS_ORIGINAL_SPACE:
+        height = min(height, LIMITED_MAX_RESOLUTION)
+        width = min(width, LIMITED_MAX_RESOLUTION)
+        duration_seconds = min(duration_seconds, LIMITED_MAX_DURATION)
+        steps = min(steps, LIMITED_MAX_STEPS)
+
+    target_h = max(MOD_VALUE, (int(height) // MOD_VALUE) * MOD_VALUE)
+    target_w = max(MOD_VALUE, (int(width) // MOD_VALUE) * MOD_VALUE)
+    
+    num_frames = np.clip(int(round(duration_seconds * FIXED_FPS)), MIN_FRAMES_MODEL, MAX_FRAMES_MODEL)
+    
+    current_seed = random.randint(0, MAX_SEED) if randomize_seed else int(seed)
+
+    with torch.inference_mode():
+        output_frames_list = pipe(
+            prompt=prompt, negative_prompt=negative_prompt,
+            height=target_h, width=target_w, num_frames=num_frames,
+            guidance_scale=float(guidance_scale), num_inference_steps=int(steps),
+            generator=torch.Generator(device="cuda").manual_seed(current_seed)
+        ).frames[0]
+
+    with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
+        video_path = tmpfile.name
+    export_to_video(output_frames_list, video_path, fps=FIXED_OUTPUT_FPS)
+    return video_path, current_seed
+    
+    
+
+with gr.Blocks(css="body { max-width: 100vw; overflow-x: hidden; }") as demo:
+    gr.HTML('<meta name="viewport" content="width=device-width, initial-scale=1">')
+    # ... your other components here ...
+    gr.Markdown("# ⚡ InstaVideo")
+    gr.Markdown("This Gradio space is a fork of [wan2-1-fast from multimodalart](https://huggingface.co/spaces/multimodalart/wan2-1-fast), and is powered by the Wan CausVid LoRA [from Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_bidirect2_T2V_1_3B_lora_rank32.safetensors).")
+
+    # Add notice for limited spaces
+    if IS_ORIGINAL_SPACE:
+        gr.Markdown("⚠️ **This free public demo limits the resolution to 640px, duration to 2s, and inference steps to 4. For full capabilities please duplicate this space.**")
+    
+    with gr.Row():
+        with gr.Column():
+            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_t2v, placeholder="Describe the video you want to generate...")
+            
+            with gr.Accordion("Advanced Settings", open=False):
+                negative_prompt_input = gr.Textbox(label="Negative Prompt", value=default_negative_prompt, lines=3)
+                seed_input = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=42, interactive=True)
+                randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=True, interactive=True)
+                with gr.Row():
+                    height_input = gr.Slider(
+                        minimum=SLIDER_MIN_H, 
+                        maximum=SLIDER_MAX_H, 
+                        step=MOD_VALUE, 
+                        value=min(DEFAULT_H_SLIDER_VALUE, SLIDER_MAX_H), 
+                        label=f"Output Height (multiple of {MOD_VALUE})"
+                    )
+                    width_input = gr.Slider(
+                        minimum=SLIDER_MIN_W, 
+                        maximum=SLIDER_MAX_W, 
+                        step=MOD_VALUE, 
+                        value=min(DEFAULT_W_SLIDER_VALUE, SLIDER_MAX_W), 
+                        label=f"Output Width (multiple of {MOD_VALUE})"
+                    )
+                duration_seconds_input = gr.Slider(
+                    minimum=round(MIN_FRAMES_MODEL/FIXED_FPS,1), 
+                    maximum=MAX_DURATION, 
+                    step=0.1, 
+                    value=2, 
+                    label="Duration (seconds)", 
+                    info=f"Clamped to model's {MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL} frames at {FIXED_FPS}fps."
+                )
+                steps_slider = gr.Slider(minimum=1, maximum=MAX_STEPS, step=1, value=4, label="Inference Steps") 
+                guidance_scale_input = gr.Slider(minimum=0.0, maximum=20.0, step=0.5, value=1.0, label="Guidance Scale", visible=False)
+
+            generate_button = gr.Button("Generate Video", variant="primary")
+        with gr.Column():
+            video_output = gr.Video(label="Generated Video", autoplay=True, interactive=False)
+    
+    ui_inputs = [
+        prompt_input, height_input, width_input,
+        negative_prompt_input, duration_seconds_input,
+        guidance_scale_input, steps_slider, seed_input, randomize_seed_checkbox
+    ]
+    generate_button.click(fn=generate_video, inputs=ui_inputs, outputs=[video_output, seed_input])
+
+    # Adjust examples based on space limits
+    example_configs = [
+        ["a majestic eagle soaring through mountain peaks, cinematic aerial view", 896, 512],
+        ["a serene ocean wave crashing on a sandy beach at sunset", 448, 832],
+        ["a field of flowers swaying in the wind, spring morning light", 512, 896],
+    ]
+    
+    if IS_ORIGINAL_SPACE:
+        # Limit example resolutions for limited spaces
+        example_configs = [
+            [example[0], min(example[1], LIMITED_MAX_RESOLUTION), min(example[2], LIMITED_MAX_RESOLUTION)]
+            for example in example_configs
+        ]
+    
+    gr.Examples(
+        examples=example_configs,
+        inputs=[prompt_input, height_input, width_input], 
+        outputs=[video_output, seed_input], 
+        fn=generate_video, 
+        cache_examples="lazy"
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()