Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -9,6 +9,7 @@ from huggingface_hub import hf_hub_download
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import logging
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import numpy as np
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from PIL import Image
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# --- Global Model Loading & LoRA Handling ---
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MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
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@@ -71,6 +72,9 @@ SLIDER_MAX_H = 896
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SLIDER_MIN_W = 128
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SLIDER_MAX_W = 896
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def _calculate_new_dimensions_wan(pil_image: Image.Image, mod_val: int, calculation_max_area: float,
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min_slider_h: int, max_slider_h: int,
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min_slider_w: int, max_slider_w: int,
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@@ -130,16 +134,17 @@ def handle_image_upload_for_dims_wan(uploaded_pil_image: Image.Image | None, cur
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# --- Gradio Interface Function ---
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@spaces.GPU
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def generate_video(input_image: Image.Image, prompt: str, negative_prompt: str,
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height: int, width: int, duration_seconds: float,
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guidance_scale: float, steps: int,
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-
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if input_image is None:
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raise gr.Error("Please upload an input image.")
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# Constants for frame calculation
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FIXED_FPS = 24
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MIN_FRAMES_MODEL = 8
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MAX_FRAMES_MODEL = 81
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logger.info("Starting video generation...")
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logger.info(f" Input Image: Uploaded (Original size: {input_image.size if input_image else 'N/A'})")
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@@ -149,24 +154,25 @@ def generate_video(input_image: Image.Image, prompt: str, negative_prompt: str,
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target_height = int(height)
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target_width = int(width)
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# duration_seconds is already float
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guidance_scale_val = float(guidance_scale)
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steps_val = int(steps)
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# Calculate number of frames based on duration and fixed FPS
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num_frames_for_pipeline = int(round(duration_seconds * FIXED_FPS))
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# Clamp num_frames to be within model's supported range
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num_frames_for_pipeline = max(MIN_FRAMES_MODEL, min(MAX_FRAMES_MODEL, num_frames_for_pipeline))
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# Ensure at least MIN_FRAMES_MODEL if rounding leads to a very small number (or zero)
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if num_frames_for_pipeline < MIN_FRAMES_MODEL:
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num_frames_for_pipeline = MIN_FRAMES_MODEL
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logger.info(f" Duration: {duration_seconds:.1f}s, Fixed FPS (conditioning & export): {FIXED_FPS}")
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logger.info(f" Calculated Num Frames: {num_frames_for_pipeline} (clamped to [{MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL}])")
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logger.info(f" Guidance Scale: {guidance_scale_val}, Steps: {steps_val}")
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# Ensure dimensions are compatible.
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if target_height % MOD_VALUE_H != 0:
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logger.warning(f"Height {target_height} is not a multiple of {MOD_VALUE_H}. Adjusting...")
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target_height = (target_height // MOD_VALUE_H) * MOD_VALUE_H
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@@ -188,16 +194,16 @@ def generate_video(input_image: Image.Image, prompt: str, negative_prompt: str,
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negative_prompt=negative_prompt,
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height=target_height,
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width=target_width,
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num_frames=num_frames_for_pipeline,
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guidance_scale=guidance_scale_val,
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num_inference_steps=steps_val,
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generator=torch.Generator(device="cuda").manual_seed(
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).frames[0]
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with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
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video_path = tmpfile.name
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export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
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logger.info(f"Video successfully generated and saved to {video_path}")
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return video_path
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@@ -222,6 +228,21 @@ with gr.Blocks() as demo:
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value=default_negative_prompt,
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lines=3
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)
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with gr.Row():
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height_input = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})")
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width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width (multiple of {MOD_VALUE})")
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@@ -253,7 +274,9 @@ with gr.Blocks() as demo:
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width_input,
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duration_seconds_input,
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guidance_scale_input,
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steps_slider
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]
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generate_button.click(
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@@ -264,8 +287,9 @@ with gr.Blocks() as demo:
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gr.Examples(
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examples=[
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["
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],
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inputs=inputs_for_click_and_examples,
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outputs=video_output,
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import logging
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import numpy as np
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from PIL import Image
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import random # Added for random seed generation
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# --- Global Model Loading & LoRA Handling ---
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MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
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SLIDER_MIN_W = 128
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SLIDER_MAX_W = 896
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# --- Constant for Seed ---
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MAX_SEED = np.iinfo(np.int32).max
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def _calculate_new_dimensions_wan(pil_image: Image.Image, mod_val: int, calculation_max_area: float,
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min_slider_h: int, max_slider_h: int,
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min_slider_w: int, max_slider_w: int,
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# --- Gradio Interface Function ---
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@spaces.GPU
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def generate_video(input_image: Image.Image, prompt: str, negative_prompt: str,
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height: int, width: int, duration_seconds: float,
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guidance_scale: float, steps: int,
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seed: int, randomize_seed: bool,
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progress=gr.Progress(track_tqdm=True)):
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if input_image is None:
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raise gr.Error("Please upload an input image.")
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# Constants for frame calculation
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FIXED_FPS = 24
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MIN_FRAMES_MODEL = 8
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MAX_FRAMES_MODEL = 81
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logger.info("Starting video generation...")
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logger.info(f" Input Image: Uploaded (Original size: {input_image.size if input_image else 'N/A'})")
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target_height = int(height)
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target_width = int(width)
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guidance_scale_val = float(guidance_scale)
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steps_val = int(steps)
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num_frames_for_pipeline = int(round(duration_seconds * FIXED_FPS))
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num_frames_for_pipeline = max(MIN_FRAMES_MODEL, min(MAX_FRAMES_MODEL, num_frames_for_pipeline))
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if num_frames_for_pipeline < MIN_FRAMES_MODEL:
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num_frames_for_pipeline = MIN_FRAMES_MODEL
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logger.info(f" Duration: {duration_seconds:.1f}s, Fixed FPS (conditioning & export): {FIXED_FPS}")
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logger.info(f" Calculated Num Frames: {num_frames_for_pipeline} (clamped to [{MIN_FRAMES_MODEL}-{MAX_FRAMES_MODEL}])")
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logger.info(f" Guidance Scale: {guidance_scale_val}, Steps: {steps_val}")
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# Seed logic
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current_seed = int(seed)
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if randomize_seed:
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current_seed = random.randint(0, MAX_SEED)
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logger.info(f" Initial Seed: {seed}, Randomize: {randomize_seed}, Using Seed: {current_seed}")
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if target_height % MOD_VALUE_H != 0:
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logger.warning(f"Height {target_height} is not a multiple of {MOD_VALUE_H}. Adjusting...")
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target_height = (target_height // MOD_VALUE_H) * MOD_VALUE_H
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negative_prompt=negative_prompt,
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height=target_height,
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width=target_width,
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num_frames=num_frames_for_pipeline,
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guidance_scale=guidance_scale_val,
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num_inference_steps=steps_val,
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generator=torch.Generator(device="cuda").manual_seed(current_seed) # Use current_seed
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).frames[0]
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with tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) as tmpfile:
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video_path = tmpfile.name
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export_to_video(output_frames_list, video_path, fps=FIXED_FPS)
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logger.info(f"Video successfully generated and saved to {video_path}")
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return video_path
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value=default_negative_prompt,
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lines=3
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)
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# --- Added Seed Controls ---
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seed_input = gr.Slider(
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label="Seed",
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minimum=0,
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maximum=MAX_SEED,
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step=1,
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value=42, # Default seed value
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interactive=True
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)
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randomize_seed_checkbox = gr.Checkbox(
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label="Randomize seed",
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value=True, # Default to randomize
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interactive=True
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)
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# --- End of Added Seed Controls ---
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with gr.Row():
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height_input = gr.Slider(minimum=SLIDER_MIN_H, maximum=SLIDER_MAX_H, step=MOD_VALUE, value=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})")
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width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width (multiple of {MOD_VALUE})")
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width_input,
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duration_seconds_input,
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guidance_scale_input,
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steps_slider,
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seed_input, # Added seed_input
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randomize_seed_checkbox # Added randomize_seed_checkbox
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]
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generate_button.click(
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gr.Examples(
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examples=[
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# Added seed (e.g., 42) and randomize_seed (e.g., True) to examples
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["peng.png", "a penguin playfully dancing in the snow, Antarctica", default_negative_prompt, 896, 512, 2, 1.0, 4, 42, False],
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["forg.jpg", "the frog jumps around", default_negative_prompt, 448, 832, 2, 1.0, 4, 123, False],
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],
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inputs=inputs_for_click_and_examples,
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outputs=video_output,
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