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import gradio as gr
import json
from deforum_engine import DeforumRunner
runner = DeforumRunner(device="cpu")
def process(prompts_json, neg, frames, width, height,
z, a, tx, ty, stre, noi,
fps, steps, cadence,
color, border, seed_beh, init_img,
model, lora, sched):
try:
p_dict = json.loads(prompts_json.replace("'", '"'))
prompts = {int(k): v for k, v in p_dict.items()}
except Exception as e:
yield None, None, None, f"JSON Error: {str(e)}"
return
# Pass exactly 20 args + self implicitly
yield from runner.render(
prompts, neg, int(frames), int(width), int(height),
z, a, tx, ty, stre, noi,
int(fps), int(steps), int(cadence),
color, border, seed_beh, init_img,
model, lora, sched
)
def stop_gen():
runner.stop()
return "Stopping..."
css = """
#col-container {max_width: 1000px; margin: 0 auto;}
"""
with gr.Blocks() as demo:
gr.Markdown("# 🌀 Deforum CPU: Full Featured\nAuthentic implementation with Cadence, Seed Control, and proper Color Coherence.")
with gr.Row(elem_id="col-container"):
with gr.Column(scale=1):
with gr.Accordion("⚙️ Engine Settings", open=False):
model = gr.Dropdown(label="Model", value="AlekseyCalvin/acs_model",
choices=["AlekseyCalvin/acs_model", "runwayml/stable-diffusion-v1-5", "IDKiro/sdxs-512-dreamshaper"])
lora = gr.Dropdown(label="LoRA", value="latent-consistency/lcm-lora-sdv1-5",
choices=["latent-consistency/lcm-lora-sdv1-5", "None"])
sched = gr.Dropdown(label="Sampler", value="LCM",
choices=["LCM", "Euler A", "DDIM", "DPM++ 2M"])
seed_beh = gr.Dropdown(label="Seed Behavior", value="iter", choices=["iter", "fixed", "random"])
init_img = gr.Image(label="Init Image", type="pil", height=200)
prompts = gr.Code(label="Prompts (JSON)", language="json",
value='{\n "0": "a beautiful forest, sun rays, 8k",\n "30": "forest fire, smoke, dramatic lighting"\n}')
neg = gr.Textbox(label="Negative Prompt", value="lowres, text, error, cropped, worst quality, low quality")
with gr.Row():
frames = gr.Number(label="Max Frames", value=120)
fps = gr.Number(label="FPS", value=15)
with gr.Row():
width = gr.Slider(256, 512, value=256, step=64, label="Width")
height = gr.Slider(256, 512, value=256, step=64, label="Height")
with gr.Row():
steps = gr.Slider(1, 20, value=4, step=1, label="Steps")
cadence = gr.Slider(1, 8, value=2, step=1, label="Cadence (Speed/Smoothness)")
with gr.Accordion("🎬 Motion & Coherence", open=True):
with gr.Row():
color = gr.Dropdown(label="Color Match", value="LAB", choices=["None", "LAB", "HSV", "RGB"])
border = gr.Dropdown(label="Border Mode", value="Reflect", choices=["Reflect", "Replicate", "Wrap", "Black"])
z = gr.Textbox(label="Zoom", value="0:(1.01)")
a = gr.Textbox(label="Angle", value="0:(0)")
tx = gr.Textbox(label="Translation X", value="0:(0)")
ty = gr.Textbox(label="Translation Y", value="0:(0)")
stre = gr.Textbox(label="Strength (Decay)", value="0:(0.65)")
noi = gr.Textbox(label="Noise (Grain)", value="0:(0.02)")
with gr.Row():
btn = gr.Button("GENERATE", variant="primary", scale=2)
stop = gr.Button("STOP", variant="stop", scale=1)
with gr.Column(scale=1):
status = gr.Markdown("Ready")
preview = gr.Image(label="Last Frame")
video_out = gr.Video(label="Rendered Video")
zip_out = gr.File(label="Frames ZIP")
# Arguments: 20 inputs + self implicitly handled by click
inputs = [
prompts, neg, frames, width, height,
z, a, tx, ty, stre, noi,
fps, steps, cadence,
color, border, seed_beh, init_img,
model, lora, sched
]
btn.click(process, inputs=inputs, outputs=[preview, video_out, zip_out, status])
stop.click(stop_gen, outputs=status)
demo.queue().launch(css=css, theme=gr.themes.Glass()) |