Create app.py

app.py

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+import gradio as gr
+import torch
+from diffusers import StableDiffusionPipeline
+import os
+import uuid
+import animation_logic as anim
+import video_utils as vid
+
+# --- Model Config (SDXS Optimized) ---
+device = "cpu"
+model_id = "IDKiro/sdxs-512-dreamshaper"
+# Using float32 for CPU stability
+pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32)
+pipe.to(device)
+
+def run_deforum(
+    prompt_list_str, neg_prompt, max_frames, 
+    zoom_str, angle_str, tx_str, ty_str,
+    cadence, fps, color_match
+):
+    width, height = 256, 256
+    try:
+        # Expected format: {0: "prompt", 10: "prompt"}
+        prompts = eval(prompt_list_str)
+    except Exception as e:
+        return None, None, f"Error parsing prompts: {str(e)}"
+
+    # Parse Schedules
+    zoom_s = anim.parse_keyframe_string(zoom_str, int(max_frames))
+    angle_s = anim.parse_keyframe_string(angle_str, int(max_frames))
+    tx_s = anim.parse_keyframe_string(tx_str, int(max_frames))
+    ty_s = anim.parse_keyframe_string(ty_str, int(max_frames))
+    
+    all_frames = []
+    prev_gen_frame = None
+    first_frame = None
+    
+    for f in range(int(max_frames)):
+        if f % cadence == 0:
+            current_prompt = prompts[max(k for k in prompts.keys() if k <= f)]
+            
+            if prev_gen_frame is not None:
+                # Warp
+                init_image = anim.anim_frame_warp(prev_gen_frame, angle_s[f], zoom_s[f], tx_s[f], ty_s[f])
+                # SDXS 1-step Inference
+                new_frame = pipe(
+                    current_prompt, 
+                    image=init_image, 
+                    negative_prompt=neg_prompt,
+                    num_inference_steps=1,
+                    guidance_scale=0.0,
+                    width=width, height=height
+                ).images[0]
+                
+                if color_match and first_frame is not None:
+                    new_frame = anim.maintain_colors(first_frame, new_frame)
+            else:
+                # First frame base generation
+                new_frame = pipe(
+                    current_prompt, 
+                    negative_prompt=neg_prompt,
+                    num_inference_steps=1,
+                    guidance_scale=0.0,
+                    width=width, height=height
+                ).images[0]
+                first_frame = new_frame
+
+            # Cadence Interpolation
+            if cadence > 1 and prev_gen_frame is not None:
+                for i in range(1, cadence):
+                    alpha = i / cadence
+                    interp_frame = anim.lerp_frames(prev_gen_frame, new_frame, alpha)
+                    all_frames.append(interp_frame)
+            
+            all_frames.append(new_frame)
+            prev_gen_frame = new_frame
+            yield new_frame, None, None
+            
+    # Post-Processing
+    unique_id = uuid.uuid4().hex[:6]
+    video_file = vid.frames_to_video(all_frames, f"deforum_{unique_id}.mp4", fps)
+    zip_file = vid.export_to_zip(all_frames, f"frames_{unique_id}.zip")
+    
+    yield all_frames[-1], video_file, zip_file
+
+# --- UI Setup ---
+with gr.Blocks() as demo:
+    gr.Markdown("# 🚀 Deforum SDXS CPU\nModular & Optimized for Free Tiers.")
+    
+    with gr.Row():
+        with gr.Column():
+            p_input = gr.Textbox(label="Prompt Map", value='{0: "hyperrealistic forest", 10: "burning forest"}')
+            n_input = gr.Textbox(label="Negative Prompt", value="blurry, text, watermark")
+            
+            with gr.Row():
+                f_count = gr.Slider(5, 100, value=20, step=1, label="Frames")
+                c_val = gr.Slider(1, 5, value=1, step=1, label="Cadence")
+                fps_val = gr.Number(label="FPS", value=10)
+                color_check = gr.Checkbox(label="Color Match", value=True)
+
+            with gr.Accordion("2D Motion (Keyframes)", open=False):
+                z_in = gr.Textbox(label="Zoom", value="0:(1.03)")
+                a_in = gr.Textbox(label="Angle", value="0:(0)")
+                tx_in = gr.Textbox(label="TX", value="0:(0)")
+                ty_in = gr.Textbox(label="TY", value="0:(0)")
+            
+            run_btn = gr.Button("Generate", variant="primary")
+            
+        with gr.Column():
+            preview = gr.Image(label="Live Stream")
+            video_out = gr.Video(label="Final Render")
+            file_out = gr.File(label="Batch PNGs")
+
+    run_btn.click(
+        fn=run_deforum,
+        inputs=[p_input, n_input, f_count, z_in, a_in, tx_in, ty_in, c_val, fps_val, color_check],
+        outputs=[preview, video_out, file_out]
+    )
+
+demo.launch()