Create app.py

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import json
+from deforum_engine import DeforumRunner
+
+runner = DeforumRunner(device="cpu")
+
+def process(prompts_json, neg, frames, w, h, z, a, tx, ty, stre, noi, fps, steps, 
+            cadence, color, border, 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: return None, None, None
+    
+    yield from runner.render(
+        prompts, neg, int(frames), 256, 256, 
+        z, a, tx, ty, stre, noi, int(fps), int(steps),
+        int(cadence), color, border, init_img,
+        model, lora, sched
+    )
+
+def stop_gen():
+    runner.stop()
+    return gr.update(value="Stopping...")
+
+css = "#col-container {max_width: 950px; margin-left: auto; margin-right: auto;}"
+with gr.Blocks() as demo:
+    gr.Markdown("# 🌀 Authentic Deforum (CPU/LCM)")
+    
+    with gr.Row(elem_id="col-container"):
+        with gr.Column(scale=1):
+            with gr.Accordion("⚙️ Model & Init", open=False):
+                model = gr.Dropdown(label="Model", value="AlekseyCalvin/acs_model",
+                                    choices=["AlekseyCalvin/acs_model", "runwayml/stable-diffusion-v1-5"])
+                lora = gr.Dropdown(label="LoRA", value="latent-consistency/lcm-lora-sdv1-5",
+                                   choices=["latent-consistency/lcm-lora-sdv1-5", "None"])
+                sched = gr.Dropdown(label="Scheduler", value="LCM", choices=["LCM", "Euler A"])
+                init_img = gr.Image(label="Init Image (Optional)", type="pil", height=150)
+
+            prompts = gr.Code(label="Prompts (JSON)", language="json", height=150,
+                              value='{\n "0": "a mystical forest, highly detailed, 8k",\n "40": "a forest fire, intricate details"\n}')
+            neg = gr.Textbox(label="Negative", value="blur, low quality, watermark")
+            
+            with gr.Row():
+                frames = gr.Number(label="Frames", value=120, step=10)
+                steps = gr.Slider(2, 12, value=4, step=1, label="Steps (LCM: 4-8)")
+                cadence = gr.Slider(1, 4, value=1, step=1, label="Cadence (Warp-only frames)")
+                fps = gr.Number(label="FPS", value=12)
+
+            with gr.Accordion("🎬 Motion & Coherence", open=True):
+                with gr.Row():
+                    color = gr.Dropdown(label="Color Match", value="LAB", choices=["None", "LAB", "HSV"])
+                    border = gr.Dropdown(label="Border Mode", value="Reflect", choices=["Reflect", "Replicate", "Wrap", "Black"])
+                
+                zoom = gr.Textbox(label="Zoom", value="0:(1.01)")
+                angle = gr.Textbox(label="Angle", value="0:(0.5*sin(t/10))")
+                tx = gr.Textbox(label="TX", value="0:(1*cos(t/15))")
+                ty = gr.Textbox(label="TY", value="0:(0)")
+                stre = gr.Textbox(label="Strength (Decay)", value="0:(0.65)")
+                noi = gr.Textbox(label="Noise (Grain)", value="0:(0.03)")
+                
+            with gr.Row():
+                btn = gr.Button("Generate", variant="primary", scale=3)
+                stop = gr.Button("Stop", variant="stop", scale=1)
+            
+        with gr.Column(scale=1):
+            img = gr.Image(label="Live Preview")
+            vid = gr.Video(label="Final Video")
+            files = gr.File(label="Frames ZIP")
+            stop_msg = gr.Markdown("", visible=True)
+
+    inputs = [prompts, neg, frames, gr.State(256), gr.State(256), zoom, angle, tx, ty, stre, noi, fps, steps, cadence, color, border, init_img, model, lora, sched]
+    btn.click(process, inputs=inputs, outputs=[img, vid, files])
+    stop.click(stop_gen, outputs=stop_msg)
+
+if __name__ == "__main__":
+    demo.queue().launch(css=css, theme=gr.themes.Glass())