controlnet

app.py

@@ -24,7 +24,7 @@ demo = gr.Interface(
         gr.Text(max_lines=1, label="your_hf_token"),
         gr.Text(max_lines=1, label="model_id"),
         gr.Text(max_lines=1, label="filename"),
-        gr.Radio(label="Model type", choices=["v1", "v2"]),
+        gr.Radio(label="Model type", choices=["v1", "v2", "ControlNet"]),
         gr.Radio(label="Sample size (px)", choices=[512, 768]),
         gr.Radio(label="Scheduler type", choices=["pndm", "heun", "euler", "dpm", "ddim"], value="dpm"),
         gr.Radio(label="Extract EMA or non-EMA?", choices=["ema", "non-ema"], value="ema"),

convert.py

@@ -1,28 +1,29 @@
-import argparse
+import gradio as gr
 import requests
-import json
 import os
 import shutil
-from collections import defaultdict
-from inspect import signature
+from pathlib import Path
 from tempfile import TemporaryDirectory
-from typing import Dict, List, Optional, Set
+from typing import Optional
 
 import torch
 from io import BytesIO
 
 from huggingface_hub import CommitInfo, Discussion, HfApi, hf_hub_download
 from huggingface_hub.file_download import repo_folder_name
-from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
+    download_from_original_stable_diffusion_ckpt, download_controlnet_from_original_ckpt
+)
 from transformers import CONFIG_MAPPING
 
 
-COMMIT_MESSAGE = " This PR adds the both fp32 and fp16 in PyTorch and safetensors format to {}"
+COMMIT_MESSAGE = " This PR adds fp32 and fp16 weights in PyTorch and safetensors format to {}"
 
 
-def convert_single(model_id: str, filename: str, model_type: str, sample_size: int, scheduler_type: str, extract_ema: bool, folder: str):
+def convert_single(model_id: str, filename: str, model_type: str, sample_size: int, scheduler_type: str, extract_ema: bool, folder: str, progress):
     from_safetensors = filename.endswith(".safetensors")
 
+    progress(0, desc="Downloading model")
     local_file = os.path.join(model_id, filename)
     ckpt_file = local_file if os.path.isfile(local_file) else hf_hub_download(repo_id=model_id, filename=filename)
 
@@ -33,15 +34,25 @@ def convert_single(model_id: str, filename: str, model_type: str, sample_size: i
             config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference.yaml"
         else:
             config_url = "https://raw.githubusercontent.com/Stability-AI/stablediffusion/main/configs/stable-diffusion/v2-inference-v.yaml"
+    elif model_type == "ControlNet":
+        config_url = (Path(model_id)/"resolve/main"/filename).with_suffix(".yaml")
+        config_url = "https://huggingface.co/" + str(config_url)
 
     config_file = BytesIO(requests.get(config_url).content)
 
-    pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, config_file, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema)
+    if model_type == "ControlNet":
+        progress(0.2, desc="Converting ControlNet Model")
+        pipeline = download_controlnet_from_original_ckpt(ckpt_file, config_file, image_size=sample_size, from_safetensors=from_safetensors, extract_ema=extract_ema)
+        to_args = {"dtype": torch.float16}
+    else:
+        progress(0.1, desc="Converting Model")
+        pipeline = download_from_original_stable_diffusion_ckpt(ckpt_file, config_file, image_size=sample_size, scheduler_type=scheduler_type, from_safetensors=from_safetensors, extract_ema=extract_ema)
+        to_args = {"torch_dtype": torch.float16}
 
     pipeline.save_pretrained(folder)
     pipeline.save_pretrained(folder, safe_serialization=True)
 
-    pipeline = pipeline.to(torch_dtype=torch.float16)
+    pipeline = pipeline.to(**to_args)
     pipeline.save_pretrained(folder, variant="fp16")
     pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16")
 
@@ -60,7 +71,7 @@ def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discuss
                 return discussion
 
 
-def convert(token: str, model_id: str, filename: str, model_type: str, sample_size: int = 512, scheduler_type: str = "pndm", extract_ema: bool = True):
+def convert(token: str, model_id: str, filename: str, model_type: str, sample_size: int = 512, scheduler_type: str = "pndm", extract_ema: bool = True, progress=gr.Progress()):
     api = HfApi()
 
     pr_title = "Adding `diffusers` weights of this model"
@@ -70,10 +81,14 @@ def convert(token: str, model_id: str, filename: str, model_type: str, sample_si
         os.makedirs(folder)
         new_pr = None
         try:
-            folder = convert_single(model_id, filename, model_type, sample_size, scheduler_type, extract_ema, folder)
-            new_pr  = api.upload_folder(folder_path=folder, path_in_repo="./", repo_id=model_id, repo_type="model", token=token, commit_description=COMMIT_MESSAGE.format(model_id), create_pr=True)
+            folder = convert_single(model_id, filename, model_type, sample_size, scheduler_type, extract_ema, folder, progress)
+            progress(0.7, desc="Uploading to Hub")
+            new_pr  = api.upload_folder(folder_path=folder, path_in_repo="./", repo_id=model_id, repo_type="model", token=token, commit_message=pr_title, commit_description=COMMIT_MESSAGE.format(model_id), create_pr=True)
             pr_number = new_pr.split("%2F")[-1].split("/")[0]
             link = f"Pr created at: {'https://huggingface.co/' + os.path.join(model_id, 'discussions', pr_number)}"
+            progress(1, desc="Done")
+        except Exception as e:
+            raise gr.exceptions.Error(str(e))
         finally:
             shutil.rmtree(folder)