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import gradio as gr
import numpy as np
import spaces
import torch
import random
from peft import PeftModel
from diffusers import FluxControlPipeline, FluxTransformer2DModel
from image_gen_aux import DepthPreprocessor
MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 2048
# Initialize models without moving to CUDA yet
pipe = FluxControlPipeline.from_pretrained(
"black-forest-labs/FLUX.1-Depth-dev",
torch_dtype=torch.bfloat16
)
processor = DepthPreprocessor.from_pretrained("LiheYoung/depth-anything-large-hf")
def cleanup_memory():
"""Clean up GPU memory"""
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
@spaces.GPU
def load_lora(lora_path):
if not lora_path.strip():
return "Please provide a valid LoRA path"
try:
cleanup_memory()
# Move to GPU within the wrapped function
pipe.to("cuda")
pipe.enable_model_cpu_offload()
# Unload any existing LoRA weights first
try:
pipe.unload_lora_weights()
except:
pass
# Load new LoRA weights
pipe.load_lora_weights(lora_path)
return f"Successfully loaded LoRA weights from {lora_path}"
except Exception as e:
cleanup_memory()
return f"Error loading LoRA weights: {str(e)}"
@spaces.GPU
def unload_lora():
try:
cleanup_memory()
pipe.to("cuda")
pipe.unload_lora_weights()
return "Successfully unloaded LoRA weights"
except Exception as e:
cleanup_memory()
return f"Error unloading LoRA weights: {str(e)}"
def round_to_multiple(number, multiple):
"""Round a number to the nearest multiple"""
return multiple * round(number / multiple)
@spaces.GPU
def infer(control_image, prompt, seed=42, randomize_seed=False, width=1024, height=1024,
guidance_scale=3.5, num_inference_steps=28, progress=gr.Progress(track_tqdm=True)):
try:
cleanup_memory()
if randomize_seed:
seed = random.randint(0, MAX_SEED)
# Ensure dimensions are divisible by 16
width = round_to_multiple(width, 16)
height = round_to_multiple(height, 16)
# Move pipeline to GPU within the wrapped function
pipe.to("cuda")
# Process control image
control_image = processor(control_image)[0].convert("RGB")
# Generate image
with torch.inference_mode():
image = pipe(
prompt=prompt,
control_image=control_image,
height=height,
width=width,
num_inference_steps=num_inference_steps,
guidance_scale=guidance_scale,
generator=torch.Generator("cuda").manual_seed(seed),
).images[0]
cleanup_memory()
return image, seed
except Exception as e:
cleanup_memory()
return None, f"Error during inference: {str(e)}"
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 Depth [dev] with LoRA Support
12B param rectified flow transformer structural conditioning tuned, 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)]
""")
# LoRA controls
with gr.Row():
lora_path = gr.Textbox(
label="HuggingFace LoRA Path",
placeholder="e.g., Borcherding/FLUX.1-dev-LoRA-AutumnSpringTrees"
)
load_lora_btn = gr.Button("Load LoRA")
unload_lora_btn = gr.Button("Unload LoRA")
lora_status = gr.Textbox(label="LoRA Status", interactive=False)
control_image = gr.Image(label="Upload the image for control", type="pil")
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)
error_message = gr.Textbox(label="Error", visible=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=16, # Changed to 16 to ensure divisibility
value=1024,
)
height = gr.Slider(
label="Height",
minimum=256,
maximum=MAX_IMAGE_SIZE,
step=16, # Changed to 16 to ensure divisibility
value=1024,
)
with gr.Row():
guidance_scale = gr.Slider(
label="Guidance Scale",
minimum=1,
maximum=30,
step=0.5,
value=10,
)
num_inference_steps = gr.Slider(
label="Number of inference steps",
minimum=1,
maximum=50,
step=1,
value=28,
)
# Event handlers
load_lora_btn.click(
fn=load_lora,
inputs=[lora_path],
outputs=[lora_status]
)
unload_lora_btn.click(
fn=unload_lora,
inputs=[],
outputs=[lora_status]
)
gr.on(
triggers=[run_button.click, prompt.submit],
fn=infer,
inputs=[control_image, prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
outputs=[result, seed]
)
demo.launch() |