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