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import os
import torch
from diffusers import (
StableDiffusionPipeline,
StableDiffusionXLPipeline,
StableVideoDiffusionPipeline,
DDIMScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionXLImg2ImgPipeline
)
from PIL import Image
import numpy as np
import time
import spaces
# Global pipelines cache
_model_cache = {}
def list_available_image_models():
"""Return list of available image generation models"""
return [
"stabilityai/stable-diffusion-xl-base-1.0",
"stabilityai/sdxl-turbo",
"runwayml/stable-diffusion-v1-5",
"stabilityai/stable-diffusion-2-1"
]
def list_available_video_models():
"""Return list of available video generation models"""
return [
"stabilityai/stable-video-diffusion-img2vid-xt",
"stabilityai/stable-video-diffusion-img2vid"
]
def _get_model_key(model_name, is_img2img=False):
"""Generate a unique key for the model cache"""
return f"{model_name}_{'img2img' if is_img2img else 'txt2img'}"
def _load_image_pipeline(model_name, is_img2img=False):
"""Load image generation pipeline with caching"""
model_key = _get_model_key(model_name, is_img2img)
if model_key not in _model_cache:
print(f"Loading image model: {model_name} ({is_img2img})")
if "xl" in model_name.lower():
# SDXL model
if is_img2img:
pipeline = StableDiffusionXLImg2ImgPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
else:
pipeline = StableDiffusionXLPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16,
variant="fp16",
use_safetensors=True
)
else:
# SD 1.5/2.x model
if is_img2img:
pipeline = StableDiffusionImg2ImgPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16
)
else:
pipeline = StableDiffusionPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16
)
pipeline.enable_model_cpu_offload()
pipeline.safety_checker = None # disable safety checker for performance
_model_cache[model_key] = pipeline
return _model_cache[model_key]
def _load_video_pipeline(model_name):
"""Load video generation pipeline with caching"""
if model_name not in _model_cache:
print(f"Loading video model: {model_name}")
pipeline = StableVideoDiffusionPipeline.from_pretrained(
model_name,
torch_dtype=torch.float16,
variant="fp16"
)
pipeline.enable_model_cpu_offload()
# Enable forward chunking for lower VRAM use
pipeline.unet.enable_forward_chunking(chunk_size=1)
_model_cache[model_name] = pipeline
return _model_cache[model_name]
@spaces.GPU
def preview_image_generation(prompt, image_model="stabilityai/stable-diffusion-xl-base-1.0", width=1024, height=576, seed=None):
"""
Generate a preview image from a prompt
Args:
prompt: Text prompt for image generation
image_model: Model to use
width/height: Image dimensions
seed: Random seed (None for random)
Returns:
PIL Image object
"""
pipeline = _load_image_pipeline(image_model)
generator = None
if seed is not None:
generator = torch.Generator(device="cuda").manual_seed(seed)
with torch.autocast("cuda"):
image = pipeline(
prompt,
width=width,
height=height,
generator=generator,
num_inference_steps=30
).images[0]
return image
@spaces.GPU
def create_video_segments(
segments,
scene_prompts,
image_model="stabilityai/stable-diffusion-xl-base-1.0",
video_model="stabilityai/stable-video-diffusion-img2vid-xt",
width=1024,
height=576,
dynamic_fps=True,
base_fps=None,
seed=None,
work_dir=".",
image_mode="Independent",
strength=0.5,
progress_callback=None
):
"""
Generate an image and a short video clip for each segment.
Args:
segments: List of segment dictionaries with timing info
scene_prompts: List of text prompts for each segment
image_model: Model to use for image generation
video_model: Model to use for video generation
width/height: Video dimensions
dynamic_fps: If True, adjust FPS to match segment duration
base_fps: Base FPS when dynamic_fps is False
seed: Random seed (None or 0 for random)
work_dir: Directory to save intermediate files
image_mode: "Independent" or "Consistent (Img2Img)" for style continuity
strength: Strength parameter for img2img (0-1, lower preserves more reference)
progress_callback: Function to call with progress updates
Returns:
List of file paths to the segment video clips
"""
# Initialize image and video pipelines
txt2img_pipe = _load_image_pipeline(image_model)
video_pipe = _load_video_pipeline(video_model)
# Set manual seed if provided
generator = None
if seed is not None and int(seed) != 0:
generator = torch.Generator(device="cuda").manual_seed(int(seed))
segment_files = []
reference_image = None
for idx, (seg, prompt) in enumerate(zip(segments, scene_prompts)):
if progress_callback:
progress_percent = (idx / len(segments)) * 100
progress_callback(progress_percent, f"Generating scene {idx+1}/{len(segments)}")
seg_start = seg["start"]
seg_end = seg["end"]
seg_dur = max(seg_end - seg_start, 0.001)
# Determine FPS for this segment
if dynamic_fps:
# Use 25 frames spanning the segment duration
fps = 25.0 / seg_dur
# Cap FPS to 30 to avoid too high frame rate for very short segments
if fps > 30.0:
fps = 30.0
else:
fps = base_fps or 10.0 # use given fixed fps, default 10 if not set
# 1. Generate initial frame image with Stable Diffusion
img_filename = os.path.join(work_dir, f"segment{idx:02d}_img.png")
with torch.autocast("cuda"):
if image_mode == "Consistent (Img2Img)" and reference_image is not None:
# Use img2img with reference image for style consistency
img2img_pipe = _load_image_pipeline(image_model, is_img2img=True)
image = img2img_pipe(
prompt=prompt,
image=reference_image,
strength=strength,
generator=generator,
num_inference_steps=30
).images[0]
else:
# Regular text-to-image generation
image = txt2img_pipe(
prompt=prompt,
width=width,
height=height,
generator=generator,
num_inference_steps=30
).images[0]
# Save the image for inspection
image.save(img_filename)
# Update reference image for next segment if using consistent mode
if image_mode == "Consistent (Img2Img)":
reference_image = image
# 2. Generate video frames from the image using stable video diffusion
with torch.autocast("cuda"):
video_frames = video_pipe(
image,
num_frames=25,
fps=fps,
decode_chunk_size=1,
generator=generator
).frames[0]
# Save video frames to a file (mp4)
seg_filename = os.path.join(work_dir, f"segment_{idx:03d}.mp4")
from diffusers.utils import export_to_video
export_to_video(video_frames, seg_filename, fps=fps)
segment_files.append(seg_filename)
# Free memory from frames
del video_frames
torch.cuda.empty_cache()
# Return list of video segment files
return segment_files |