Create app_lora.py

app_lora.py

@@ -0,0 +1,178 @@
+import torch
+from diffusers import AutoencoderKLWan, WanImageToVideoPipeline, UniPCMultistepScheduler
+from diffusers.utils import export_to_video
+from transformers import CLIPVisionModel
+import gradio as gr
+import tempfile
+import spaces
+from huggingface_hub import hf_hub_download
+import numpy as np
+from PIL import Image
+import random
+
+MODEL_ID = "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers"
+
+LORA_REPO_ID = "vrgamedevgirl84/Wan14BT2VFusioniX"
+LORA_FILENAME = "FusionX_LoRa/Wan2.1_T2V_14B_FusionX_LoRA.safetensors"
+
+image_encoder = CLIPVisionModel.from_pretrained(MODEL_ID, subfolder="image_encoder", torch_dtype=torch.float32)
+vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32)
+pipe = WanImageToVideoPipeline.from_pretrained(
+    MODEL_ID, vae=vae, image_encoder=image_encoder, 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=[0.75])
+pipe.fuse_lora()
+
+MOD_VALUE = 32
+DEFAULT_H_SLIDER_VALUE = 640
+DEFAULT_W_SLIDER_VALUE = 1024
+NEW_FORMULA_MAX_AREA = 640.0 * 1024.0 
+
+SLIDER_MIN_H, SLIDER_MAX_H = 128, 1024
+SLIDER_MIN_W, SLIDER_MAX_W = 128, 1024
+MAX_SEED = np.iinfo(np.int32).max
+
+FIXED_FPS = 24
+MIN_FRAMES_MODEL = 8
+MAX_FRAMES_MODEL = 81 
+
+default_prompt_i2v = "make this image come alive, cinematic motion, smooth animation"
+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 _calculate_new_dimensions_wan(pil_image, mod_val, calculation_max_area,
+                                 min_slider_h, max_slider_h,
+                                 min_slider_w, max_slider_w,
+                                 default_h, default_w):
+    orig_w, orig_h = pil_image.size
+    if orig_w <= 0 or orig_h <= 0:
+        return default_h, default_w
+
+    aspect_ratio = orig_h / orig_w
+    
+    calc_h = round(np.sqrt(calculation_max_area * aspect_ratio))
+    calc_w = round(np.sqrt(calculation_max_area / aspect_ratio))
+
+    calc_h = max(mod_val, (calc_h // mod_val) * mod_val)
+    calc_w = max(mod_val, (calc_w // mod_val) * mod_val)
+    
+    new_h = int(np.clip(calc_h, min_slider_h, (max_slider_h // mod_val) * mod_val))
+    new_w = int(np.clip(calc_w, min_slider_w, (max_slider_w // mod_val) * mod_val))
+    
+    return new_h, new_w
+
+def handle_image_upload_for_dims_wan(uploaded_pil_image, current_h_val, current_w_val):
+    if uploaded_pil_image is None:
+        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
+    try:
+        new_h, new_w = _calculate_new_dimensions_wan(
+            uploaded_pil_image, MOD_VALUE, NEW_FORMULA_MAX_AREA,
+            SLIDER_MIN_H, SLIDER_MAX_H, SLIDER_MIN_W, SLIDER_MAX_W,
+            DEFAULT_H_SLIDER_VALUE, DEFAULT_W_SLIDER_VALUE
+        )
+        return gr.update(value=new_h), gr.update(value=new_w)
+    except Exception as e:
+        gr.Warning("Error attempting to calculate new dimensions")
+        return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE)
+
+def get_duration(input_image, 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(input_image, 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)):
+    
+    if input_image is None:
+        raise gr.Error("Please upload an input image.")
+
+    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)
+
+    resized_image = input_image.resize((target_w, target_h))
+
+    with torch.inference_mode():
+        output_frames_list = pipe(
+            image=resized_image, 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_FPS)
+    return video_path, current_seed
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Fast 4 steps Wan 2.1 I2V (14B) with CausVid LoRA")
+    gr.Markdown("[CausVid](https://github.com/tianweiy/CausVid) is a distilled version of Wan 2.1 to run faster in just 4-8 steps, [extracted as LoRA by Kijai](https://huggingface.co/Kijai/WanVideo_comfy/blob/main/Wan21_CausVid_14B_T2V_lora_rank32.safetensors) and is compatible with 🧨 diffusers")
+    with gr.Row():
+        with gr.Column():
+            input_image_component = gr.Image(type="pil", label="Input Image (auto-resized to target H/W)")
+            prompt_input = gr.Textbox(label="Prompt", value=default_prompt_i2v)
+            duration_seconds_input = gr.Slider(minimum=round(MIN_FRAMES_MODEL/FIXED_FPS,1), maximum=round(MAX_FRAMES_MODEL/FIXED_FPS,1), 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.")
+            
+            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=DEFAULT_H_SLIDER_VALUE, label=f"Output Height (multiple of {MOD_VALUE})")
+                    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})")
+                steps_slider = gr.Slider(minimum=1, maximum=30, 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)
+
+    input_image_component.upload(
+        fn=handle_image_upload_for_dims_wan,
+        inputs=[input_image_component, height_input, width_input],
+        outputs=[height_input, width_input]
+    )
+    
+    input_image_component.clear( 
+        fn=handle_image_upload_for_dims_wan,
+        inputs=[input_image_component, height_input, width_input],
+        outputs=[height_input, width_input]
+    )
+    
+    ui_inputs = [
+        input_image_component, 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])
+
+    gr.Examples(
+        examples=[ 
+            ["peng.png", "a penguin playfully dancing in the snow, Antarctica", 896, 512],
+            ["forg.jpg", "the frog jumps around", 448, 832],
+        ],
+        inputs=[input_image_component, prompt_input, height_input, width_input], outputs=[video_output, seed_input], fn=generate_video, cache_examples="lazy"
+    )
+
+if __name__ == "__main__":
+    demo.queue().launch()