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README.md

@@ -4,7 +4,7 @@ emoji: 🎨➡️🧨
 colorFrom: indigo
 colorTo: purple
 sdk: gradio
-sdk_version: 4.42.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
 license: mit

app.py

@@ -33,18 +33,22 @@ It saves you the trouble of typing them in.<br>
     )
     with gr.Column():
         dl_url = gr.Textbox(label="URL to download", placeholder="https://huggingface.co/marduk191/Flux.1_collection/blob/main/flux.1_dev_fp8_fp16t5-marduk191.safetensors", value="", max_lines=1)
-        hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value="", max_lines=1)
-        hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value="", max_lines=1)
-        hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="", max_lines=1)
-        civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
-        is_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False, visible=False)
-        data_type = gr.Radio(label="Output data type", info="It only affects transformer and text encoder.", choices=["bf16", "fp8", "qfloat8"], value="fp8")
-        model_type = gr.Radio(label="Original model type", choices=["dev", "schnell"], value="dev")
-        is_dequat = gr.Checkbox(label="Dequantization", info="Deadly slow", value=False)
-        use_original = gr.CheckboxGroup(label="Use original version", choices=["vae", "text_encoder", "text_encoder_2"], value=["vae", "text_encoder"])
-        is_fix_only = gr.Checkbox(label="Only fixing", value=False)
-        is_private = gr.Checkbox(label="Create private repo", value=True)
-        is_overwrite = gr.Checkbox(label="Overweite repo", value=True)
+        with gr.Row():
+            hf_user = gr.Textbox(label="Your HF user ID", placeholder="username", value="", max_lines=1)
+            hf_repo = gr.Textbox(label="New repo name", placeholder="reponame", info="If empty, auto-complete", value="", max_lines=1)
+        with gr.Row():
+            hf_token = gr.Textbox(label="Your HF write token", placeholder="hf_...", value="", max_lines=1)
+            civitai_key = gr.Textbox(label="Your Civitai API Key (Optional)", info="If you download model from Civitai...", placeholder="", value="", max_lines=1)
+        with gr.Row():
+            data_type = gr.Radio(label="Output data type", choices=["bf16", "fp8"], value="fp8")
+            model_type = gr.Radio(label="Original model repo", choices=["dev", "schnell", "dev fp8", "schnell fp8"], value="dev")
+            use_original = gr.CheckboxGroup(label="Use original repo version", choices=["vae", "text_encoder", "text_encoder_2"], value=["vae", "text_encoder"])
+        with gr.Row():
+            is_dequat = gr.Checkbox(label="Dequantization", info="Deadly slow", value=False)
+            is_upload_sf = gr.Checkbox(label="Upload single safetensors file into new repo", value=False, visible=False)
+            is_fix_only = gr.Checkbox(label="Only fixing", value=False)
+            is_private = gr.Checkbox(label="Create private repo", value=True)
+            is_overwrite = gr.Checkbox(label="Overweite repo", value=True)
         run_button = gr.Button(value="Submit")
         repo_urls = gr.CheckboxGroup(visible=False, choices=[], value=None)
         output_md = gr.Markdown(label="Output")

convert_url_to_diffusers_flux_gr.py

@@ -1,3 +1,4 @@
+import spaces
 import json
 import torch
 from safetensors.torch import load_file, save_file
@@ -5,6 +6,7 @@ from pathlib import Path
 import gc
 import gguf
 from dequant import dequantize_tensor # https://github.com/city96/ComfyUI-GGUF
+from huggingface_hub import HfFolder
 
 import os
 import argparse
@@ -14,13 +16,16 @@ import gradio as gr
 import subprocess
 subprocess.run('pip cache purge', shell=True)
 
-import spaces
 @spaces.GPU()
 def spaces_dummy():
     pass
 
-flux_dev_repo = "ChuckMcSneed/FLUX.1-dev"
-flux_schnell_repo = "black-forest-labs/FLUX.1-schnell"
+flux_diffusers_repos = {
+    "dev": "ChuckMcSneed/FLUX.1-dev",
+    "schnell": "black-forest-labs/FLUX.1-schnell",
+    "dev fp8": "John6666/flux1-dev-fp8-flux",
+    "schnell fp8": "John6666/flux1-schnell-fp8-flux",
+}
 system_temp_dir = "temp"
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -38,6 +43,13 @@ TORCH_DTYPE = [torch.float32, torch.float, torch.float64, torch.double, torch.fl
 
 TORCH_QUANTIZED_DTYPE = [torch.quint8, torch.qint8, torch.qint32, torch.quint4x2]
 
+def get_token():
+    try:
+        token = HfFolder.get_token()
+    except Exception:
+        token = ""
+    return token
+
 def list_sub(a, b):
     return [e for e in a if e not in b]
 
@@ -47,6 +59,8 @@ def is_repo_name(s):
 
 def clear_cache():
     torch.cuda.empty_cache()
+    #torch.cuda.reset_max_memory_allocated()
+    #torch.cuda.reset_peak_memory_stats()
     gc.collect()
 
 def clear_sd(sd: dict):
@@ -54,6 +68,8 @@ def clear_sd(sd: dict):
         sd.pop(k)
     del sd
     torch.cuda.empty_cache()
+    #torch.cuda.reset_max_memory_allocated()
+    #torch.cuda.reset_peak_memory_stats()
     gc.collect()
 
 def clone_sd(sd: dict):
@@ -181,8 +197,7 @@ def is_repo_exists(repo_id):
 
 def create_diffusers_repo(new_repo_id, diffusers_folder, is_private, is_overwrite, progress=gr.Progress(track_tqdm=True)):
     from huggingface_hub import HfApi
-    import os
-    hf_token = os.environ.get("HF_TOKEN")
+    hf_token = get_token()
     api = HfApi()
     try:
         progress(0, desc="Start uploading...")
@@ -440,12 +455,11 @@ with torch.no_grad():
                 print(f"Saving quantized FLUX.1 {name} to {path}")
             else:
                 progress(0.5, desc=f"Saving FLUX.1 {name} to: {path}")
-                if False and path.endswith("/transformer"):
+                if False and path.endswith("/transformer"): # omitted
                     from diffusers import FluxTransformer2DModel
                     has_guidance = any("guidance" in k for k in sd)
-                    with init_empty_weights():
-                        model = FluxTransformer2DModel(guidance_embeds=has_guidance)
-                    model.to("cpu")
+                    #with init_empty_weights():
+                    model = FluxTransformer2DModel(guidance_embeds=has_guidance).to("cpu")
                     model.load_state_dict(sd, strict=True)
                     print(f"Saving FLUX.1 {name} to: {path} (FluxTransformer2DModel)")
                     if metadata is not None:
@@ -658,49 +672,48 @@ def download_repo(repo_name, path, use_original=["vae", "text_encoder"], progres
     print(f"Downloading {repo_name}.")
     try:
         if "text_encoder_2" in use_original:
-            snapshot_download(repo_id=repo_name, local_dir=path, ignore_patterns=["transformer/diffusion*.*", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.png", "*.webp"])
+            snapshot_download(repo_id=repo_name, local_dir=path, ignore_patterns=["transformer/diffusion*.*", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.jpeg", "*.png", "*.webp"])
         else:
-            snapshot_download(repo_id=repo_name, local_dir=path, ignore_patterns=["transformer/diffusion*.*", "text_encoder_2/model*.*", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.png", "*.webp"])
+            snapshot_download(repo_id=repo_name, local_dir=path, ignore_patterns=["transformer/diffusion*.*", "text_encoder_2/model*.*", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.jpeg", "*.png", "*.webp"])
     except Exception as e:
         print(e)
 
-def copy_nontensor_files(from_path, to_path, use_original=["vae", "text_encoder"]):
+def copy_missing_files(from_path, to_path, use_original=["vae", "text_encoder"]):
     import shutil
     if "text_encoder_2" in use_original:
         te_from = str(Path(from_path, "text_encoder_2"))
         te_to = str(Path(to_path, "text_encoder_2"))
         print(f"Copying Text Encoder 2 files {te_from} to {te_to}")
-        shutil.copytree(te_from, te_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.png", "*.webp"), dirs_exist_ok=True)
+        shutil.copytree(te_from, te_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True)
     if "text_encoder" in use_original:
         te1_from = str(Path(from_path, "text_encoder"))
         te1_to = str(Path(to_path, "text_encoder"))
         print(f"Copying Text Encoder 1 files {te1_from} to {te1_to}")
-        shutil.copytree(te1_from, te1_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.png", "*.webp"), dirs_exist_ok=True)
+        shutil.copytree(te1_from, te1_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True)
     if "vae" in use_original:
         vae_from = str(Path(from_path, "vae"))
         vae_to = str(Path(to_path, "vae"))
         print(f"Copying VAE files {vae_from} to {vae_to}")
-        shutil.copytree(vae_from, vae_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.png", "*.webp"), dirs_exist_ok=True)
+        shutil.copytree(vae_from, vae_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True)
     tn2_from = str(Path(from_path, "tokenizer_2"))
     tn2_to = str(Path(to_path, "tokenizer_2"))
     print(f"Copying Tokenizer 2 files {tn2_from} to {tn2_to}")
-    shutil.copytree(tn2_from, tn2_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.png", "*.webp"), dirs_exist_ok=True)
+    shutil.copytree(tn2_from, tn2_to, ignore=shutil.ignore_patterns(".*", "README*", "*.md", "*.jpg", "*.jpeg", "*.png", "*.webp"), dirs_exist_ok=True)
     print(f"Copying non-tensor files {from_path} to {to_path}")
-    shutil.copytree(from_path, to_path, ignore=shutil.ignore_patterns("*.safetensors", "*.bin", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.png", "*.webp", "*.index.json"), dirs_exist_ok=True)
+    shutil.copytree(from_path, to_path, ignore=shutil.ignore_patterns("*.safetensors", "*.bin", "*.sft", ".*", "README*", "*.md", "*.index", "*.jpg", "*.jpeg", "*.png", "*.webp", "*.index.json"), dirs_exist_ok=True)
     
 def save_flux_other_diffusers(path: str, model_type: str = "dev", use_original: list = ["vae", "text_encoder"], progress=gr.Progress(track_tqdm=True)):
     import shutil
     progress(0, desc="Loading FLUX.1 Components.")
     print("Loading FLUX.1 Components.")
     temppath = system_temp_dir
-    if model_type == "schnell": repo = flux_schnell_repo
-    else: repo = flux_dev_repo
+    repo = flux_diffusers_repos.get(model_type, None) if model_type in flux_diffusers_repos else flux_diffusers_repos.get("dev", None)
     os.makedirs(temppath, exist_ok=True)
     os.makedirs(path, exist_ok=True)
     download_repo(repo, temppath, use_original)
     progress(0.5, desc="Saving FLUX.1 Components.")
     print("Saving FLUX.1 Components.")
-    copy_nontensor_files(temppath, path, use_original)
+    copy_missing_files(temppath, path, use_original)
     shutil.rmtree(temppath)
 
 with torch.no_grad():
@@ -855,10 +868,10 @@ def convert_url_to_diffusers_flux(url, civitai_key="", is_upload_sf=False, data_
     if hf_repo != "": new_repo_id = f"{hf_user}/{hf_repo}"
     flux_to_diffusers_lowmem(new_file, new_repo_name, dtype, quantization, model_type, dequant, use_original, new_repo_id)
 
-    """if is_upload_sf:
+    if is_upload_sf:
         import shutil
         shutil.move(str(Path(new_file).resolve()), str(Path(new_repo_name, Path(new_file).name).resolve()))
-    else: os.remove(new_file)"""
+    else: os.remove(new_file)
 
     progress(1, desc="Converted.")
     q.put(new_repo_name)
@@ -902,7 +915,8 @@ def convert_url_to_diffusers_repo_flux(dl_url, hf_user, hf_repo, hf_token, civit
         print(f"Invalid user name: {hf_user}")
         progress(1, desc=f"Invalid user name: {hf_user}")
         return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
-    if hf_token and not os.environ.get("HF_TOKEN"): os.environ['HF_TOKEN'] = hf_token
+    if not hf_token and os.environ.get("HF_TOKEN"): HfFolder.save_token(os.environ.get("HF_TOKEN"))
+    else: HfFolder.save_token(hf_token)
     if not civitai_key and os.environ.get("CIVITAI_API_KEY"): civitai_key = os.environ.get("CIVITAI_API_KEY")
     q = mp.Queue()
     if fix_only:
@@ -927,7 +941,7 @@ def convert_url_to_diffusers_repo_flux(dl_url, hf_user, hf_repo, hf_token, civit
         print(f"Repo already exists: {new_repo_id}")
         progress(1, desc=f"Repo already exists: {new_repo_id}")
         return gr.update(value=repo_urls, choices=repo_urls), gr.update(value="")
-    #save_readme_md(new_path, dl_url)
+    save_readme_md(new_path, dl_url)
     repo_url = create_diffusers_repo(new_repo_id, new_path, is_private, is_overwrite)
     shutil.rmtree(new_path)
     if not repo_urls: repo_urls = []
@@ -953,12 +967,12 @@ if __name__ == "__main__":
     quantization = False
     if args.dtype == "fp8": dtype = torch.float8_e4m3fn
     elif args.dtype == "fp16": dtype = torch.float16
-    elif args.dtype == "qfloat8":
-        dtype = torch.bfloat16
-        quantization = True
+    #elif args.dtype == "qfloat8":
+    #    dtype = torch.bfloat16
+    #    quantization = True
     else: dtype = torch.bfloat16
 
-    use_original = ["vae", "text_encoder"]
+    use_original = ["vae", "text_encoder", "text_encoder_2"]
     new_repo_id = ""
     use_local = True