import gradio as gr
import spaces
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
import threading
from PIL import Image
MODEL_ID = "cagliostrolab/animagine-xl-3.1"
device = "cuda" if torch.cuda.is_available() else "cpu"
if torch.cuda.is_available():
torch.cuda.max_memory_allocated(device=device)
pipe = DiffusionPipeline.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16,
use_safetensors=True,
)
else:
pipe = DiffusionPipeline.from_pretrained(MODEL_ID, use_safetensors=True)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1536
def latents_to_rgb(latents):
weights = (
(60, -60, 25, -70),
(60, -5, 15, -50),
(60, 10, -5, -35)
)
weights_tensor = torch.tensor(weights, dtype=latents.dtype, device=latents.device).T
biases_tensor = torch.tensor((150, 140, 130), dtype=latents.dtype, device=latents.device)
rgb_tensor = torch.einsum("...lxy,lr -> ...rxy", latents, weights_tensor) + biases_tensor.view(-1, 1, 1)
image_array = rgb_tensor.clamp(0, 255)[0].byte().cpu().numpy()
image_array = image_array.transpose(1, 2, 0) # Change the order of dimensions
pil_image = Image.fromarray(image_array)
resized_image = pil_image.resize((pil_image.size[0] * 2, pil_image.size[1] * 2), Image.LANCZOS) # Resize 128x128 * ...
return resized_image
class BaseGenerator:
def __init__(self, pipe):
self.pipe = pipe
self.image = None
self.new_image_event = threading.Event()
self.generation_finished = threading.Event()
self.intermediate_image_concurrency(3)
def intermediate_image_concurrency(self, concurrency):
self.concurrency = concurrency
def decode_tensors(self, pipe, step, timestep, callback_kwargs):
latents = callback_kwargs["latents"]
if step % self.concurrency == 0: # every how many steps
print(step)
self.image = latents_to_rgb(latents)
self.new_image_event.set() # Signal that a new image is available
return callback_kwargs
def show_images(self):
while not self.generation_finished.is_set() or self.new_image_event.is_set():
self.new_image_event.wait() # Wait for a new image
self.new_image_event.clear() # Clear the event flag
if self.image:
yield self.image # Yield the new image
def generate_images(self, **kwargs):
if kwargs.get('randomize_seed', False):
kwargs['seed'] = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(kwargs['seed'])
self.image = None
self.image = self.pipe(
height=kwargs['height'],
width=kwargs['width'],
prompt=kwargs['prompt'],
negative_prompt=kwargs['negative_prompt'],
guidance_scale=kwargs['guidance_scale'],
num_inference_steps=kwargs['num_inference_steps'],
generator=generator,
callback_on_step_end=self.decode_tensors,
callback_on_step_end_tensor_inputs=["latents"],
).images[0]
print("finish")
self.new_image_event.set() # Result image
self.generation_finished.set() # Signal that generation is finished
def stream(self, prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
self.generation_finished.clear()
threading.Thread(target=self.generate_images, args=(), kwargs=dict(
prompt=prompt,
negative_prompt=negative_prompt,
seed=seed,
randomize_seed=randomize_seed,
width=width,
height=height,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps
)).start()
return self.show_images()
image_generator = BaseGenerator(pipe)
@spaces.GPU
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, concurrency):
image_generator.intermediate_image_concurrency(concurrency)
stream = image_generator.stream(
prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps
)
yield None
for image in stream:
yield image
css="""
#col-container {
margin: 0 auto;
max-width: 520px;
}
"""
if torch.cuda.is_available():
power_device = "GPU"
else:
power_device = "CPU"
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Text-to-Image: Display each generation step
Gradio template for displaying preview images during generation steps
Currently running on {power_device}.
""")
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
value="1girl, souryuu asuka langley, neon genesis evangelion, solo, upper body, v, smile, looking at viewer, outdoors, night",
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
visible=True,
value="nsfw, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
)
with gr.Row():
run_button = gr.Button("Run", scale=0)
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
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=832,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1216,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance scale",
minimum=0.0,
maximum=30.0,
step=0.1,
value=7.0,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=76,
)
concurrency_gui = gr.Slider(
label="Number of steps to show the next preview image",
minimum=1,
maximum=20,
step=1,
value=3,
)
run_button.click(
fn = infer,
inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, concurrency_gui],
outputs = [result],
show_progress="minimal",
)
demo.queue().launch()