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1160ead | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | import gradio as gr
import torch
from PIL import Image
from diffusers import StableDiffusionXLPipeline, ControlNetModel, AutoencoderKL
from transformers import CLIPVisionModelWithProjection
import spaces
import os
import numpy as np
# Pre-defined head rotation angles (pitch, yaw in degrees)
VIEW_ANGLES = {
"front": (0, 0),
"3/4-left": (0, -35),
"3/4-right": (0, 35),
"profile-left": (0, -90),
"profile-right": (0, 90),
}
# Global pipeline (loaded once)
pipe = None
ip_adapter = None
def load_pipeline():
"""Load the IP-Adapter FaceID Plus pipeline"""
global pipe
if pipe is not None:
return pipe
# Load IP-Adapter FaceID Plus with SDXL
from diffusers import StableDiffusionXLPipeline
from diffusers.utils import load_image
# Base model - using a good anime-capable SDXL model
model_id = "stabilityai/stable-diffusion-xl-base-1.0"
pipe = StableDiffusionXLPipeline.from_pretrained(
model_id,
torch_dtype=torch.float16,
variant="fp16",
)
# Load IP-Adapter FaceID Plus
pipe.load_ip_adapter(
"h94/IP-Adapter-FaceID",
subfolder=None,
weight_name="ip-adapter-faceid-plusv2_sdxl.bin",
image_encoder_folder="models/image_encoder",
)
pipe.set_ip_adapter_scale(0.7)
pipe.enable_model_cpu_offload()
return pipe
@spaces.GPU
def generate_view(
face_image: Image.Image,
view_angle: str,
prompt: str,
negative_prompt: str = "",
strength: float = 0.8,
guidance_scale: float = 5.0,
num_steps: int = 30,
seed: int = 42,
) -> Image.Image:
"""
Generate a character view from a reference face.
Args:
face_image: Reference face image (front view ideally)
view_angle: One of 'front', '3/4-left', '3/4-right', 'profile-left', 'profile-right'
prompt: Description of the character/style
negative_prompt: What to avoid
strength: IP-Adapter strength (0.0-1.0)
guidance_scale: CFG scale
num_steps: Number of inference steps
seed: Random seed for reproducibility
Returns:
Generated image at the specified angle
"""
pipe = load_pipeline()
# Add view-specific prompt additions
angle_prompts = {
"front": "front view, facing camera, symmetrical",
"3/4-left": "3/4 view, turned slightly left, three quarter view",
"3/4-right": "3/4 view, turned slightly right, three quarter view",
"profile-left": "side profile, facing left, profile view",
"profile-right": "side profile, facing right, profile view",
}
view_prompt = angle_prompts.get(view_angle, "")
full_prompt = f"{prompt}, {view_prompt}, portrait, character art, consistent character"
default_negative = "deformed, ugly, bad anatomy, blurry, low quality, worst quality, text, watermark"
full_negative = f"{negative_prompt}, {default_negative}" if negative_prompt else default_negative
# Set seed for reproducibility
generator = torch.Generator(device="cpu").manual_seed(seed)
# Generate with IP-Adapter face reference
result = pipe(
prompt=full_prompt,
negative_prompt=full_negative,
ip_adapter_image=face_image,
num_inference_steps=num_steps,
guidance_scale=guidance_scale,
generator=generator,
width=1024,
height=1024,
)
return result.images[0]
# Gradio Interface
with gr.Blocks(title="Character View Generator") as demo:
gr.Markdown("""
# Character View Generator
Generate consistent character views from a reference face image.
Designed for character sheet pipelines.
""")
with gr.Row():
with gr.Column():
face_input = gr.Image(label="Reference Face", type="pil")
view_dropdown = gr.Dropdown(
choices=list(VIEW_ANGLES.keys()),
value="front",
label="Target View Angle"
)
prompt_input = gr.Textbox(
label="Prompt",
placeholder="anime character, blue skin, orange eyes...",
lines=2
)
negative_input = gr.Textbox(
label="Negative Prompt (optional)",
placeholder="realistic, photo...",
lines=2
)
with gr.Row():
strength_slider = gr.Slider(0.0, 1.0, value=0.8, label="Identity Strength")
guidance_slider = gr.Slider(1.0, 20.0, value=5.0, label="Guidance Scale")
with gr.Row():
steps_slider = gr.Slider(10, 50, value=30, step=1, label="Steps")
seed_input = gr.Number(value=42, label="Seed", precision=0)
generate_btn = gr.Button("Generate View", variant="primary")
with gr.Column():
output_image = gr.Image(label="Generated View")
generate_btn.click(
fn=generate_view,
inputs=[
face_input,
view_dropdown,
prompt_input,
negative_input,
strength_slider,
guidance_slider,
steps_slider,
seed_input,
],
outputs=output_image,
api_name="generate_view" # Enables API access
)
if __name__ == "__main__":
demo.queue(api_open=True)
demo.launch()
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