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5d54e97 | 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 | import gradio as gr
import numpy as np
import random
import spaces #[uncomment to use ZeroGPU]
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(duration=75) #[uncomment to use ZeroGPU]
def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, lora_model, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device).manual_seed(seed)
# Handle LoRA if needed (ChromaPipeline may not support LoRA by default)
# Uncomment and adapt if LoRA support is available
# if lora_model:
# pipe.load_lora_weights(lora_model)
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 - Anaglyph 3D Image Generation
Generate stereoscopic 3D images optimized for red-cyan anaglyph glasses
""")
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):
lora_model = gr.Textbox(
label="LoRA model id (if supported)",
placeholder="Leave empty if not using LoRA",
visible=False # Hidden by default as ChromaPipeline may not support LoRA
)
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1.0,
maximum=10.0,
step=0.1,
value=3.0, # Default from your example
)
seed = gr.Slider(
label="Seed",
minimum=0,
maximum=MAX_SEED,
step=1,
value=433, # Using the seed from your example
)
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, # Default resolution
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=32,
value=1024, # Default resolution
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=100,
step=1,
value=40, # Default from your example
)
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, lora_model],
outputs=[result, seed]
)
demo.queue().launch() |