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Running
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Zero
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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:
pipe.unload_lora_weights()
pipe.load_lora_weights(lora_id)
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: 520px;
}
"""
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.Markdown(f"""# FLUX.1 [dev] LoRA
12B param rectified flow transformer guidance-distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/)
[[non-commercial license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)] [[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-dev)]
""")
with gr.Row():
prompt = gr.Text(
label="Prompt",
show_label=False,
max_lines=1,
placeholder="Enter your prompt",
container=False,
)
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=8,
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=8,
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=15,
step=0.1,
value=3.5,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
with gr.Row():
lora_id = gr.Textbox(
label="LoRA Model ID (HuggingFace path)",
placeholder="username/lora-model",
max_lines=1
)
lora_scale = gr.Slider(
label="LoRA Scale",
minimum=0,
maximum=2,
step=0.01,
value=0.95,
)
gr.Examples(
examples = examples,
fn = infer,
inputs = [prompt],
outputs = [result, seed],
cache_examples="lazy"
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn = infer,
inputs = [prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,lora_id,lora_scale],
outputs = [result, seed]
)
demo.launch()
# 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=1216, step=8)
# height = gr.Slider(label="Height", value=1024, minimum=64, maximum=1216, 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")
# with gr.Row():
# seed_output = gr.Textbox(label="Seed Used", show_copy_button = True)
# # 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_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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