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import gradio as gr
import torch
from diffusers import StableDiffusionPipeline, DPMSolverSinglestepScheduler
from PIL import Image
# Load a memory-efficient SD variant (under 12GB)
model_id = "runwayml/stable-diffusion-v1-5"
@gr.cache()
def load_model():
pipe = StableDiffusionPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
safety_checker=None,
use_safetensors=True
)
pipe.scheduler = DPMSolverSinglestepScheduler.from_config(pipe.scheduler.config)
pipe = pipe.to("cpu")
pipe.enable_attention_slicing() # Reduces memory by 30%
pipe.enable_model_cpu_offload() # Only loads needed components
return pipe
def generate_character(prompt, seed=42):
try:
pipe = load_model()
generator = torch.Generator(device="cpu").manual_seed(seed)
with torch.inference_mode():
image = pipe(
prompt=f"cartoon character {prompt}, vibrant colors, clean lines",
negative_prompt="blurry, deformed, ugly",
num_inference_steps=20,
guidance_scale=7.5,
width=512,
height=512,
generator=generator
).images[0]
return image
except Exception as e:
return f"Error: {str(e)}\nTry simplifying your prompt."
# Animation through img2img
def generate_animation(prompt, frames=3):
base_image = generate_character(prompt)
if isinstance(base_image, str): # If error
return base_image
images = [base_image]
pipe = load_model()
for i in range(1, frames):
result = pipe(
prompt=prompt,
image=images[-1],
strength=0.3, # Small changes per frame
generator=torch.Generator().manual_seed(i)
)
images.append(result.images[0])
images[0].save(
"animation.gif",
save_all=True,
append_images=images[1:],
duration=500,
loop=0
)
return "animation.gif"
with gr.Blocks(theme=gr.themes.Base()) as demo:
gr.Markdown("# 🎬 Character Animator (12GB Optimized)")
with gr.Row():
prompt = gr.Textbox(
label="Character Description",
placeholder="e.g. 'cyberpunk fox wearing sunglasses'"
)
with gr.Tab("Single Image"):
img_out = gr.Image(label="Generated Character", type="pil")
gen_btn = gr.Button("Generate")
with gr.Tab("Animation"):
anim_out = gr.Image(label="Animation", format="gif")
anim_btn = gr.Button("Create Animation (3 frames)")
gen_btn.click(generate_character, inputs=prompt, outputs=img_out)
anim_btn.click(generate_animation, inputs=prompt, outputs=anim_out)
demo.launch() |