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Running
on
Zero
File size: 6,296 Bytes
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import gradio as gr
import numpy as np
import random
import spaces
import torch
from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler, AutoencoderTiny, AutoencoderKL
from transformers import CLIPTextModel, CLIPTokenizer,T5EncoderModel, T5TokenizerFast
from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
dtype = torch.bfloat16
device = "cuda" if torch.cuda.is_available() else "cpu"
taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
good_vae = AutoencoderKL.from_pretrained("black-forest-labs/FLUX.1-dev", subfolder="vae", torch_dtype=dtype).to(device)
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=dtype, vae=taef1).to(device)
torch.cuda.empty_cache()
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
@spaces.GPU()
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, guidance_scale=3.5, num_inference_steps=28, lora_id=None, lora_scale=0.95, progress=gr.Progress(track_tqdm=True)):
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator().manual_seed(seed)
# for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
# prompt=prompt,
# guidance_scale=guidance_scale,
# num_inference_steps=num_inference_steps,
# width=width,
# height=height,
# generator=generator,
# output_type="pil",
# good_vae=good_vae,
# ):
# yield img, seed
# Handle LoRA loading
# Load LoRA weights and prepare joint_attention_kwargs
if lora_id and lora_id.strip() != "":
pipe.unload_lora_weights()
pipe.load_lora_weights(lora_id.strip())
joint_attention_kwargs = {"scale": lora_scale}
else:
joint_attention_kwargs = None
try:
# Call the custom pipeline function with the correct keyword argument
for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
prompt=prompt,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps,
width=width,
height=height,
generator=generator,
output_type="pil",
good_vae=good_vae, # Assuming good_vae is defined elsewhere
joint_attention_kwargs=joint_attention_kwargs, # Fixed parameter name
):
yield img, seed
finally:
# Unload LoRA weights if they were loaded
if lora_id:
pipe.unload_lora_weights()
examples = [
"a tiny astronaut hatching from an egg on the moon",
"a cat holding a sign that says hello world",
"an anime illustration of a wiener schnitzel",
]
css = """
#col-container {
margin: 0 auto;
max-width: 960px;
}
.generate-btn {
background: linear-gradient(90deg, #4B79A1 0%, #283E51 100%) !important;
border: none !important;
color: white !important;
}
.generate-btn:hover {
transform: translateY(-2px);
box-shadow: 0 5px 15px rgba(0,0,0,0.2);
}
"""
with gr.Blocks(css=css) as app:
gr.HTML("<center><h1>FLUX.1-Dev with LoRA support</h1></center>")
with gr.Column(elem_id="col-container"):
with gr.Row():
with gr.Column():
with gr.Row():
text_prompt = gr.Textbox(label="Prompt", placeholder="Enter a prompt here", lines=3, elem_id="prompt-text-input")
with gr.Row():
custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path (optional)", placeholder="multimodalart/vintage-ads-flux")
with gr.Row():
with gr.Accordion("Advanced Settings", open=False):
lora_scale = gr.Slider(
label="LoRA Scale",
minimum=0,
maximum=2,
step=0.01,
value=0.95,
)
with gr.Row():
width = gr.Slider(label="Width", value=1024, minimum=64, maximum=2048, step=8)
height = gr.Slider(label="Height", value=1024, minimum=64, maximum=2048, step=8)
seed = gr.Slider(label="Seed", value=-1, minimum=-1, maximum=4294967296, step=1)
randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
with gr.Row():
steps = gr.Slider(label="Inference steps steps", value=28, minimum=1, maximum=100, step=1)
cfg = gr.Slider(label="Guidance Scale", value=3.5, minimum=1, maximum=20, step=0.5)
# method = gr.Radio(label="Sampling method", value="DPM++ 2M Karras", choices=["DPM++ 2M Karras", "DPM++ SDE Karras", "Euler", "Euler a", "Heun", "DDIM"])
with gr.Row():
# text_button = gr.Button("Run", variant='primary', elem_id="gen-button")
text_button = gr.Button("✨ Generate Image", variant='primary', elem_classes=["generate-btn"])
with gr.Column():
with gr.Row():
image_output = gr.Image(type="pil", label="Image Output", elem_id="gallery")
# gr.Markdown(article_text)
with gr.Column():
gr.Examples(
examples = examples,
inputs = [text_prompt],
)
gr.on(
triggers=[text_button.click, text_prompt.submit],
fn = infer,
inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale],
outputs=[image_output, seed]
)
# text_button.click(query, inputs=[custom_lora, text_prompt, steps, cfg, randomize_seed, seed, width, height], outputs=[image_output,seed_output, seed])
# text_button.click(infer, inputs=[text_prompt, seed, randomize_seed, width, height, cfg, steps, custom_lora, lora_scale], outputs=[image_output,seed_output, seed])
app.launch(share=True)
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