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import spaces
import gradio as gr
import numpy as np
import PIL.Image
from PIL import Image
import random
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
import gradio_client.utils
# もっと徹底的なモンキーパッチ
old_get_type = gradio_client.utils.get_type
def new_get_type(schema):
if isinstance(schema, bool):
return "bool"
return old_get_type(schema)
gradio_client.utils.get_type = new_get_type
# _json_schema_to_python_typeの修正も追加
old_json_schema_to_python_type = gradio_client.utils._json_schema_to_python_type
def new_json_schema_to_python_type(schema, defs=None):
if isinstance(schema, bool):
return "bool"
try:
return old_json_schema_to_python_type(schema, defs)
except Exception as e:
# エラーが発生した場合は汎用的な型を返す
return "any"
gradio_client.utils._json_schema_to_python_type = new_json_schema_to_python_type
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Make sure to use torch.float16 consistently throughout the pipeline
pipe = StableDiffusionXLPipeline.from_pretrained(
"votepurchase/novaAnimeXL_ilV60",
torch_dtype=torch.float16,
variant="fp16", # Explicitly use fp16 variant
use_safetensors=True # Use safetensors if available
)
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
pipe.to(device)
# Force all components to use the same dtype
pipe.text_encoder.to(torch.float16)
pipe.text_encoder_2.to(torch.float16)
pipe.vae.to(torch.float16)
pipe.unet.to(torch.float16)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1216
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
# Check and truncate prompt if too long (CLIP can only handle 77 tokens)
if len(prompt.split()) > 60: # Rough estimate to avoid exceeding token limit
print("Warning: Prompt may be too long and will be truncated by the model")
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(seed)
try:
output_image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator
).images[0]
return output_image
except RuntimeError as e:
print(f"Error during generation: {e}")
# Return a blank image with error message
error_img = Image.new('RGB', (width, height), color=(0, 0, 0))
return error_img
css = """
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt (keep it under 60 words for best results)",
container=False,
)
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
value="nsfw, (low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn"
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=0,
)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
width = gr.Slider(
label="Width",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=20.0,
step=0.1,
value=7,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=28,
step=1,
value=28,
)
run_button.click(
fn=infer,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result]
)
demo.queue().launch() |