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import gradio as gr
import torch
import spaces
from diffusers import DiffusionPipeline
from pathlib import Path
import gc
import subprocess
subprocess.run('pip cache purge', shell=True)
device = "cuda" if torch.cuda.is_available() else "cpu"
torch.set_grad_enabled(False)
models = [
"camenduru/FLUX.1-dev-diffusers",
"black-forest-labs/FLUX.1-schnell",
"sayakpaul/FLUX.1-merged",
"John6666/blue-pencil-flux1-v001-fp8-flux",
"John6666/copycat-flux-test-fp8-v11-fp8-flux",
"John6666/nepotism-fuxdevschnell-v3aio-flux",
"John6666/niji-style-flux-devfp8-fp8-flux",
"John6666/fluxunchained-artfulnsfw-fut516xfp8e4m3fnv11-fp8-flux",
"John6666/fastflux-unchained-t5f16-fp8-flux",
"John6666/the-araminta-flux1a1-fp8-flux",
"John6666/acorn-is-spinning-flux-v11-fp8-flux",
"John6666/fluxescore-dev-v10fp16-fp8-flux",
# "",
]
num_loras = 3
def is_repo_name(s):
import re
return re.fullmatch(r'^[^/,\s]+?/[^/,\s]+?$', s)
def is_repo_exists(repo_id):
from huggingface_hub import HfApi
api = HfApi()
try:
if api.repo_exists(repo_id=repo_id): return True
else: return False
except Exception as e:
print(f"Error: Failed to connect {repo_id}. ")
return True # for safe
def clear_cache():
torch.cuda.empty_cache()
gc.collect()
def get_repo_safetensors(repo_id: str):
from huggingface_hub import HfApi
api = HfApi()
try:
if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(value="", choices=[])
files = api.list_repo_files(repo_id=repo_id)
except Exception as e:
print(f"Error: Failed to get {repo_id}'s info. ")
print(e)
return gr.update(choices=[])
files = [f for f in files if f.endswith(".safetensors")]
if len(files) == 0: return gr.update(value="", choices=[])
else: return gr.update(value=files[0], choices=files)
# Initialize the base model
base_model = models[0]
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
def change_base_model(repo_id: str):
global pipe
try:
if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return
clear_cache()
pipe = DiffusionPipeline.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
except Exception as e:
print(e)
def compose_lora_json(lorajson: list[dict], i: int, name: str, scale: float, filename: str, trigger: str):
lorajson[i]["name"] = str(name) if name != "None" else ""
lorajson[i]["scale"] = float(scale)
lorajson[i]["filename"] = str(filename)
lorajson[i]["trigger"] = str(trigger)
return lorajson
def is_valid_lora(lorajson: list[dict]):
valid = False
for d in lorajson:
if "name" in d.keys() and d["name"] and d["name"] != "None": valid = True
return valid
def get_trigger_word(lorajson: list[dict]):
trigger = ""
for d in lorajson:
if "name" in d.keys() and d["name"] and d["name"] != "None" and d["trigger"]:
trigger += ", " + d["trigger"]
return trigger
# https://github.com/huggingface/diffusers/issues/4919
def fuse_loras(pipe, lorajson: list[dict]):
if not lorajson or not isinstance(lorajson, list): return
a_list = []
w_list = []
for d in lorajson:
if not d or not isinstance(d, dict) or not d["name"] or d["name"] == "None": continue
k = d["name"]
if is_repo_name(k) and is_repo_exists(k):
a_name = Path(k).stem
pipe.load_lora_weights(k, weight_name=d["filename"], adapter_name = a_name)
elif not Path(k).exists():
print(f"LoRA not found: {k}")
continue
else:
w_name = Path(k).name
a_name = Path(k).stem
pipe.load_lora_weights(k, weight_name = w_name, adapter_name = a_name)
a_list.append(a_name)
w_list.append(d["scale"])
if not a_list: return
pipe.set_adapters(a_list, adapter_weights=w_list)
pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
#pipe.unload_lora_weights()
fuse_loras.zerogpu = True
def description_ui():
gr.Markdown(
"""
- Mod of [multimodalart/flux-lora-the-explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer),
[gokaygokay/FLUX-Prompt-Generator](https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator).
"""
) |