Upload 3 files

app.py

@@ -17,7 +17,7 @@ from mod import (models, clear_cache, get_repo_safetensors, is_repo_name, is_rep
                  get_control_union_mode, set_control_union_mode, get_control_params)
 from flux import (search_civitai_lora, select_civitai_lora, search_civitai_lora_json,
                   download_my_lora, get_all_lora_tupled_list, apply_lora_prompt,
-                  update_loras)
+                  update_loras, get_t2i_model_info)
 from tagger.tagger import predict_tags_wd, compose_prompt_to_copy
 from tagger.fl2flux import predict_tags_fl2_flux
 
@@ -296,6 +296,7 @@ css = '''
 .card_internal{display: flex;height: 100px;margin-top: .5em}
 .card_internal img{margin-right: 1em}
 .styler{--form-gap-width: 0px !important}
+#model-info {text-align: center; !important}
 '''
 with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as app:
     with gr.Tab("FLUX LoRA the Explorer"):
@@ -340,7 +341,9 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as app:
                 deselect_lora_button = gr.Button("Deselect LoRA", variant="secondary")
             with gr.Column(scale=4):
                 result = gr.Image(label="Generated Image", format="png", show_share_button=False)
-        model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
+        with gr.Group():
+            model_name = gr.Dropdown(label="Base Model", info="You can enter a huggingface model repo_id to want to use.", choices=models, value=models[0], allow_custom_value=True)
+            model_info = gr.Markdown(elem_id="model-info")
         with gr.Row():
             with gr.Accordion("Advanced Settings", open=False):
                 with gr.Column():
@@ -457,7 +460,7 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as app:
         outputs=[result],
         queue=True,
         show_api=False,
-    )
+    ).success(get_t2i_model_info, [model_name], [model_info], queue=False, show_api=False)
     prompt_enhance.click(enhance_prompt, [prompt], [prompt], queue=False, show_api=False)
 
     gr.on(
@@ -506,18 +509,14 @@ with gr.Blocks(theme='Nymbo/Nymbo_Theme', fill_width=True, css=css) as app:
         ).success(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
         cn_image_ref[i].upload(set_control_union_image, [cn_num[i], cn_mode[i], cn_image_ref[i], height, width, cn_res[i]], [cn_image[i]], queue=False, show_api=False)
 
-    tagger_generate_from_image.click(
-            lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
+    tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
     ).success(
         predict_tags_wd,
         [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
         [v2_series, v2_character, prompt, v2_copy],
         show_api=False,
-    ).success(
-        predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
-    ).success(
-        compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False,
-    )
+    ).success(predict_tags_fl2_flux, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
+    ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)
 
     with gr.Tab("FLUX Prompt Generator"):
         from prompt import (PromptGenerator, HuggingFaceInferenceNode, florence_caption,

flux.py

@@ -11,41 +11,14 @@ warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffuse
 warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
 warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
 from pathlib import Path
-from env import (
-    hf_token,
-    hf_read_token, # to use only for private repos
-    CIVITAI_API_KEY,
-    HF_LORA_PRIVATE_REPOS1,
-    HF_LORA_PRIVATE_REPOS2,
-    HF_LORA_ESSENTIAL_PRIVATE_REPO,
-    HF_VAE_PRIVATE_REPO,
-    directory_models,
-    directory_loras,
-    directory_vaes,
-    download_model_list,
-    download_lora_list,
-    download_vae_list,
-)
-from modutils import (
-    to_list,
-    list_uniq,
-    list_sub,
-    get_lora_model_list,
-    download_private_repo,
-    safe_float,
-    escape_lora_basename,
-    to_lora_key,
-    to_lora_path,
-    get_local_model_list,
-    get_private_lora_model_lists,
-    get_valid_lora_name,
-    get_valid_lora_path,
-    get_valid_lora_wt,
-    get_lora_info,
-    normalize_prompt_list,
-    get_civitai_info,
-    search_lora_on_civitai,
-)
+from env import (hf_token, hf_read_token, # to use only for private repos
+    CIVITAI_API_KEY, HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2, HF_LORA_ESSENTIAL_PRIVATE_REPO,
+    HF_VAE_PRIVATE_REPO, directory_models, directory_loras, directory_vaes,
+    download_model_list, download_lora_list, download_vae_list)
+from modutils import (to_list, list_uniq, list_sub, get_lora_model_list, download_private_repo,
+    safe_float, escape_lora_basename, to_lora_key, to_lora_path, get_local_model_list,
+    get_private_lora_model_lists, get_valid_lora_name, get_valid_lora_path, get_valid_lora_wt,
+    get_lora_info, normalize_prompt_list, get_civitai_info, search_lora_on_civitai)
 
 
 def download_things(directory, url, hf_token="", civitai_api_key=""):
@@ -136,7 +109,9 @@ def get_t2i_model_info(repo_id: str):
     info = []
     url = f"https://huggingface.co/{repo_id}/"
     if not 'diffusers' in tags: return ""
-    if 'diffusers:StableDiffusionXLPipeline' in tags:
+    if 'diffusers:FluxPipeline' in tags:
+        info.append("FLUX.1")
+    elif 'diffusers:StableDiffusionXLPipeline' in tags:
         info.append("SDXL")
     elif 'diffusers:StableDiffusionPipeline' in tags:
         info.append("SD1.5")
@@ -265,25 +240,7 @@ def apply_lora_prompt(lora_info: str):
 
 
 def update_loras(prompt, lora, lora_wt):
-    import re
     on, label, tag, md = get_lora_info(lora)
-    """prompts = prompt.split(",") if prompt else []
-    output_prompts = []
-    for p in prompts:
-        p = str(p).strip()
-        if "<lora" in p:
-            result = re.findall(r'<lora:(.+?):(.+?)>', p)
-            if not result: continue
-            key = result[0][0]
-            wt = result[0][1]
-            path = to_lora_path(key)
-            if not key in loras_dict.keys() or not path: continue
-            if Path(path).exists(): output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
-        elif p:
-            output_prompts.append(p)
-    lora_prompts = []
-    if on: lora_prompts.append(f"<lora:{to_lora_key(lora)}:{lora_wt:.2f}>")
-    output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts))"""
     choices = get_all_lora_tupled_list()
     return gr.update(value=prompt), gr.update(value=lora, choices=choices), gr.update(value=lora_wt),\
      gr.update(value=tag, label=label, visible=on), gr.update(value=md, visible=on)

modutils.py

@@ -1,19 +1,14 @@
+import spaces
 import json
 import gradio as gr
 from huggingface_hub import HfApi
 import os
 from pathlib import Path
 
-from env import (
-    HF_LORA_PRIVATE_REPOS1,
-    HF_LORA_PRIVATE_REPOS2,
-    HF_MODEL_USER_EX,
-    HF_MODEL_USER_LIKES,
-    directory_loras,
-    hf_read_token,
-    hf_token,
-    CIVITAI_API_KEY,
-)
+
+from env import (HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
+    HF_MODEL_USER_EX, HF_MODEL_USER_LIKES,
+    directory_loras, hf_read_token, hf_token, CIVITAI_API_KEY)
 
 
 def get_user_agent():
@@ -112,8 +107,8 @@ def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
         try:
             if oldpath.exists():
                 newpath = oldpath.resolve().rename(Path(filename).resolve())
-        except Exception:
-           pass
+        except Exception as e:
+           print(e)
         finally: 
             output_paths.append(str(newpath))
             output_images.append((str(newpath), str(filename)))
@@ -127,7 +122,8 @@ def download_private_repo(repo_id, dir_path, is_replace):
     try:
         snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token)
     except Exception as e:
-        print(f"Error: Failed to download {repo_id}. ")
+        print(f"Error: Failed to download {repo_id}.")
+        print(e)
         return
     if is_replace:
         for file in Path(dir_path).glob("*"):
@@ -146,7 +142,8 @@ def get_private_model_list(repo_id, dir_path):
     try:
         files = api.list_repo_files(repo_id, token=hf_read_token)
     except Exception as e:
-        print(f"Error: Failed to list {repo_id}. ")
+        print(f"Error: Failed to list {repo_id}.")
+        print(e)
         return []
     model_list = []
     for file in files:
@@ -168,7 +165,8 @@ def download_private_file(repo_id, path, is_replace):
     try:
         hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token)
     except Exception as e:
-        print(f"Error: Failed to download {filename}. ")
+        print(f"Error: Failed to download {filename}.")
+        print(e)
         return
     if is_replace:
         file.resolve().rename(newpath.resolve())
@@ -194,7 +192,8 @@ def get_model_id_list():
         for author in HF_MODEL_USER_EX:
             models_ex = api.list_models(author=author, cardData=True, sort="last_modified")
     except Exception as e:
-        print(f"Error: Failed to list {author}'s models. ")
+        print(f"Error: Failed to list {author}'s models.")
+        print(e)
         return model_ids
     for model in models_likes:
         model_ids.append(model.id) if not model.private else ""
@@ -218,17 +217,17 @@ def get_t2i_model_info(repo_id: str):
         if " " in repo_id or not api.repo_exists(repo_id): return ""
         model = api.model_info(repo_id=repo_id)
     except Exception as e:
-        print(f"Error: Failed to get {repo_id}'s info. ")
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
         return ""
     if model.private or model.gated: return ""
     tags = model.tags
     info = []
     url = f"https://huggingface.co/{repo_id}/"
     if not 'diffusers' in tags: return ""
-    if 'diffusers:StableDiffusionXLPipeline' in tags:
-        info.append("SDXL")
-    elif 'diffusers:StableDiffusionPipeline' in tags:
-        info.append("SD1.5")
+    if 'diffusers:FluxPipeline' in tags: info.append("FLUX.1")
+    elif 'diffusers:StableDiffusionXLPipeline' in tags: info.append("SDXL")
+    elif 'diffusers:StableDiffusionPipeline' in tags: info.append("SD1.5")
     if model.card_data and model.card_data.tags:
         info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
     info.append(f"DLs: {model.downloads}")
@@ -247,6 +246,7 @@ def get_tupled_model_list(model_list):
             if not api.repo_exists(repo_id): continue
             model = api.model_info(repo_id=repo_id)
         except Exception as e:
+            print(e)
             continue
         if model.private or model.gated: continue
         tags = model.tags
@@ -273,8 +273,8 @@ try:
         d = json.load(f)
         for k, v in d.items():
             private_lora_dict[escape_lora_basename(k)] = v
-except Exception:
-    pass
+except Exception as e:
+    print(e)
 loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
 civitai_not_exists_list = []
 loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
@@ -322,6 +322,7 @@ def get_civitai_info(path):
     try:
         r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
     except Exception as e:
+        print(e)
         return ["", "", "", "", ""]
     if not r.ok: return None
     json = r.json()
@@ -420,7 +421,8 @@ def copy_lora(path: str, new_path: str):
     if cpath.exists():
         try:
             shutil.copy(str(cpath.resolve()), str(npath.resolve()))
-        except Exception:
+        except Exception as e:
+            print(e)
             return None
         update_lora_dict(str(npath))
         return new_path
@@ -1215,6 +1217,8 @@ def get_model_pipeline(repo_id: str):
     if model.private or model.gated: return default
     tags = model.tags
     if not 'diffusers' in tags: return default
+    if 'diffusers:FluxPipeline' in tags:
+        return "FluxPipeline"
     if 'diffusers:StableDiffusionXLPipeline' in tags:
         return "StableDiffusionXLPipeline"
     elif 'diffusers:StableDiffusionPipeline' in tags: