import os import spaces import torch from diffusers import AutoencoderKLWan, WanPipeline, WanImageToVideoPipeline, UniPCMultistepScheduler from diffusers.utils import export_to_video import gradio as gr import tempfile from huggingface_hub import hf_hub_download import numpy as np from PIL import Image import random MODEL_ID = "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers" HF_MODEL = os.environ.get("HF_UPLOAD_REPO", "rahul7star/wan22TITV5B-image-analysis") # --- CPU-only upload function --- def upload_image_and_prompt_cpu(input_image, prompt_text) -> str: from datetime import datetime import tempfile, os, uuid, shutil from huggingface_hub import HfApi # Instantiate the HfApi class api = HfApi() print(prompt_text) today_str = datetime.now().strftime("%Y-%m-%d") unique_subfolder = f"Upload-Image-{uuid.uuid4().hex[:8]}" hf_folder = f"{today_str}/{unique_subfolder}" # Save image temporarily with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as tmp_img: if isinstance(input_image, str): shutil.copy(input_image, tmp_img.name) else: input_image.save(tmp_img.name, format="PNG") tmp_img_path = tmp_img.name # Upload image using HfApi instance api.upload_file( path_or_fileobj=tmp_img_path, path_in_repo=f"{hf_folder}/input_image.png", repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN") ) # Save prompt as summary.txt summary_file = tempfile.NamedTemporaryFile(delete=False, suffix=".txt").name with open(summary_file, "w", encoding="utf-8") as f: f.write(prompt_text) api.upload_file( path_or_fileobj=summary_file, path_in_repo=f"{hf_folder}/summary.txt", repo_id=HF_MODEL, repo_type="model", token=os.environ.get("HUGGINGFACE_HUB_TOKEN") ) # Cleanup os.remove(tmp_img_path) os.remove(summary_file) return hf_folder # --- Load pipelines --- vae = AutoencoderKLWan.from_pretrained(MODEL_ID, subfolder="vae", torch_dtype=torch.float32) text_to_video_pipe = WanPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16) image_to_video_pipe = WanImageToVideoPipeline.from_pretrained(MODEL_ID, vae=vae, torch_dtype=torch.bfloat16) for pipe in [text_to_video_pipe, image_to_video_pipe]: pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=8.0) pipe.to("cuda") ### very good LORA_REPO_ID = "UnifiedHorusRA/Missionary_POV_Wan_2.2_5B_LoRA" LORA_FILENAME = "missionary-pov-wan2.2_5b-v1.0-vfxai.safetensors" causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME) pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora") # LORA_REPO_ID = "rahul7star/wan2.2Lora" # LORA_FILENAME1 = "missionary-pov-wan2.2_5b-v1.0-vfxai.safetensors" # LORA_FILENAME = "wan2.2_5b_c0wg1rl_72_000002500.safetensors" # causvid_path = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME) # pipe.load_lora_weights(causvid_path, adapter_name="causvid_lora") # ## anotheer exp # LORA_REPO_ID1 = "hjhfgfxj/wan_2.2_5B_lora_lab" # LORA_FILENAME1 = "wan_2.2_5B_realistic_000310500.safetensors" # causvid_path1 = hf_hub_download(repo_id=LORA_REPO_ID, filename=LORA_FILENAME) # pipe.load_lora_weights(causvid_path1, adapter_name="causvid_lora1") # LORA_REPO_ID1 = "hjhfgfxj/wan_2.2_5B_lora_lab" # LORA_FILENAME1 = "wan_2.2_5B_realistic_000310500.safetensors" # causvid_path1 = hf_hub_download(repo_id=LORA_REPO_ID1, filename=LORA_FILENAME1) # pipe.load_lora_weights(causvid_path1, adapter_name="causvid_lora1") # LORA_REPO_ID1 = "UnifiedHorusRA/Cowgirl_WAN2.2_5B" # LORA_FILENAME1 = "wan2.2_5b_c0wg1rl_72_000002500.safetensors" # causvid_path1 = hf_hub_download(repo_id=LORA_REPO_ID1, filename=LORA_FILENAME1) # pipe.load_lora_weights(causvid_path1, adapter_name="causvid_lora1") # LORA_REPO_ID1 = "UnifiedHorusRA/Lora_Anal_WAN2.2_5B_TI2V" # LORA_FILENAME1 = "wan2.2_5B_it2v_greek.safetensors" # causvid_path1 = hf_hub_download(repo_id=LORA_REPO_ID1, filename=LORA_FILENAME1) # pipe.load_lora_weights(causvid_path1, adapter_name="causvid_lora1") #pipe.set_adapters(["causvid_lora","causvid_lora1"], adapter_weights=[0.95,0.95]) pipe.set_adapters(["causvid_lora"], adapter_weights=[0.95]) pipe.fuse_lora() # --- Constants --- MOD_VALUE = 32 DEFAULT_H_SLIDER_VALUE = 896 DEFAULT_W_SLIDER_VALUE = 896 NEW_FORMULA_MAX_AREA = 720 * 1024 SLIDER_MIN_H, SLIDER_MAX_H = 256, 1024 SLIDER_MIN_W, SLIDER_MAX_W = 256, 1024 MAX_SEED = np.iinfo(np.int32).max FIXED_FPS = 24 MIN_FRAMES_MODEL = 25 MAX_FRAMES_MODEL = 193 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" # --- Utility functions --- 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: return gr.update(value=DEFAULT_H_SLIDER_VALUE), gr.update(value=DEFAULT_W_SLIDER_VALUE) def get_duration(*args, **kwargs): return 60 # simplified for example # --- GPU video generation --- @spaces.GPU(duration=get_duration) def generate_video(input_image, prompt, height, width, negative_prompt=default_negative_prompt, duration_seconds=2, guidance_scale=0, steps=4, seed=44, randomize_seed=False, progress=gr.Progress(track_tqdm=True)): 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) if "child" in prompt.lower(): print("Found 'child' in prompt. Exiting loop.") return if input_image is not None: resized_image = input_image.resize((target_w, target_h)) with torch.inference_mode(): output_frames_list = image_to_video_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] else: with torch.inference_mode(): output_frames_list = text_to_video_pipe( 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 # --- Wrapper to upload image/prompt on CPU before GPU generation --- def generate_video_with_upload(input_image, prompt, height, width, negative_prompt=default_negative_prompt, duration_seconds=2, guidance_scale=0, steps=4, seed=44, randomize_seed=False): # Upload on CPU (hidden, no UI) try: upload_image_and_prompt_cpu(input_image, prompt) except Exception as e: print("Upload failed:", e) # Proceed with GPU video generation return generate_video(input_image, prompt, height, width, negative_prompt, duration_seconds, guidance_scale, steps, seed, randomize_seed) # --- Gradio UI --- with gr.Blocks() as demo: gr.Markdown("# Fast Wan 2.2 TI2V 5B Demo") gr.Markdown("""This Demo is using [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers) fine-tuned with Sparse-distill for fast high-quality video generation.""") with gr.Row(): with gr.Column(): input_image_component = gr.Image(type="pil", label="Input Image (optional, 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)") 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) randomize_seed_checkbox = gr.Checkbox(label="Randomize seed", value=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") width_input = gr.Slider(minimum=SLIDER_MIN_W, maximum=SLIDER_MAX_W, step=MOD_VALUE, value=DEFAULT_W_SLIDER_VALUE, label=f"Output Width") steps_slider = gr.Slider(minimum=1, maximum=8, step=1, value=4, label="Inference Steps") guidance_scale_input = gr.Slider(minimum=0.0, maximum=5.0, step=0.01, value=0.0, label="Guidance Scale") 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_with_upload, inputs=ui_inputs, outputs=[video_output, seed_input]) gr.Examples( examples=[ [None, "A person eating spaghetti", 1024, 720], ["cat.png", "The cat removes the glasses from its eyes.", 1088, 800], [None, "A penguin playfully dancing in the snow, Antarctica", 1024, 720], ["peng.png", "A penguin running towards camera joyfully, Antarctica", 896, 512], ], inputs=[input_image_component, prompt_input, height_input, width_input], outputs=[video_output, seed_input], fn=generate_video_with_upload, cache_examples="lazy" ) if __name__ == "__main__": demo.queue().launch()