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import numpy as np
import random
import spaces
from diffusers import ChromaPipeline
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_repo_id = "lodestones/Chroma1-HD"
if torch.cuda.is_available():
torch_dtype = torch.bfloat16
else:
torch_dtype = torch.float32
pipe = ChromaPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
pipe = pipe.to(device)
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 1024
@spaces.GPU()
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device).manual_seed(seed)
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
num_images_per_prompt=1
).images[0]
return image, seed
examples = [
"A high-fashion close-up portrait of a blonde woman in clear sunglasses. The image uses a bold teal and red color split for dramatic lighting. The background is a simple teal-green. The photo is sharp and well-composed, and is designed for viewing with anaglyph 3D glasses for optimal effect. It looks professionally done.",
"A surreal landscape with vibrant red and cyan color separation, featuring mountains and a lake, optimized for anaglyph 3D viewing",
"A futuristic cityscape at night with neon lights, rendered in stereoscopic 3D style with red-cyan color separation",
]
css="""
#col-container {
margin: 0 auto;
max-width: 760px;
}
#button{
align-self: stretch;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""
# Chroma1-HD
[Chroma1-HD](https://huggingface.co/lodestones/Chroma1-HD) is an 8.9B parameter text-to-image foundational model based on FLUX.1-schnell
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
max_lines=1,
placeholder="Enter your prompt",
)
negative_prompt = gr.Text(
label="Negative prompt",
max_lines=1,
placeholder="Enter a negative prompt",
value="low quality, ugly, unfinished, out of focus, deformed, disfigure, blurry, smudged, restricted palette, flat colors"
)
with gr.Row():
run_button = gr.Button("Run", scale=1, elem_id="button")
result = gr.Image(label="Result", show_label=False)
with gr.Accordion("Advanced Settings", open=False):
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1.0,
maximum=10.0,
step=0.1,
value=3.0,
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=433,
)
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,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=40,
)
gr.Examples(
examples=examples,
inputs=[prompt]
)
gr.on(
triggers=[run_button.click, prompt.submit, negative_prompt.submit],
fn=infer,
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
demo.queue().launch() |