As a work-around, this would be fine...

convert.py

@@ -1,103 +1,105 @@
-import gradio as gr
-import requests
-import os
-import shutil
-from pathlib import Path
-import tempfile
-from tempfile import TemporaryDirectory
-
-
-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, download_controlnet_from_original_ckpt
-)
-from transformers import CONFIG_MAPPING
-
-
-COMMIT_MESSAGE = " This PR adds fp32 and fp16 weights in PyTorch and safetensors format to {}"
-
-
-def convert_single(model_id: str, token: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, token=token)
-
-    if model_type == "v1":
-        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
-    elif model_type == "v2":
-        if sample_size == 512:
-            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)
-    
-    response = requests.get(config_url)
-    with tempfile.NamedTemporaryFile(delete=False, mode='wb') as tmp_file:
-        tmp_file.write(response.content)
-        temp_config_file_path = tmp_file.name
-        
-    if model_type == "ControlNet":
-        progress(0.2, desc="Converting ControlNet Model")
-        pipeline = download_controlnet_from_original_ckpt(ckpt_file, temp_config_file_path, 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, temp_config_file_path, 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(**to_args)
-    pipeline.save_pretrained(folder, variant="fp16")
-    pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16")
-
-    return folder
-
-
-def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
-    try:
-        discussions = api.get_repo_discussions(repo_id=model_id)
-    except Exception:
-        return None
-    for discussion in discussions:
-        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
-            details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num)
-            if details.target_branch == "refs/heads/main":
-                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, progress=gr.Progress()):
-    api = HfApi()
-
-    pr_title = "Adding `diffusers` weights of this model"
-
-    with TemporaryDirectory() as d:
-        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
-        os.makedirs(folder)
-        new_pr = None
-        try:
-            folder = convert_single(model_id, token, 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)
-
-        return link
+import gradio as gr
+import requests
+import os
+import shutil
+from pathlib import Path
+import tempfile
+from tempfile import TemporaryDirectory
+
+
+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, download_controlnet_from_original_ckpt
+)
+from transformers import CONFIG_MAPPING
+
+
+COMMIT_MESSAGE = " This PR adds fp32 and fp16 weights in PyTorch and safetensors format to {}"
+
+
+def convert_single(model_id: str, token: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, token=token)
+
+    if model_type == "v1":
+        config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
+    elif model_type == "v2":
+        if sample_size == 512:
+            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)
+    
+    response = requests.get(config_url)
+    with tempfile.NamedTemporaryFile(delete=False, mode='wb') as tmp_file:
+        tmp_file.write(response.content)
+        temp_config_file_path = tmp_file.name
+        
+    if model_type == "ControlNet":
+        progress(0.2, desc="Converting ControlNet Model")
+        pipeline = download_controlnet_from_original_ckpt(ckpt_file, temp_config_file_path, 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, temp_config_file_path, 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(**to_args)
+    from diffusers import StableDiffusionPipeline
+    pipeline = StableDiffusionPipeline.from_pretrained(folder, use_safetensors=True, torch_dtype=torch.float16)
+    pipeline.save_pretrained(folder, variant="fp16")
+    pipeline.save_pretrained(folder, safe_serialization=True, variant="fp16")
+
+    return folder
+
+
+def previous_pr(api: "HfApi", model_id: str, pr_title: str) -> Optional["Discussion"]:
+    try:
+        discussions = api.get_repo_discussions(repo_id=model_id)
+    except Exception:
+        return None
+    for discussion in discussions:
+        if discussion.status == "open" and discussion.is_pull_request and discussion.title == pr_title:
+            details = api.get_discussion_details(repo_id=model_id, discussion_num=discussion.num)
+            if details.target_branch == "refs/heads/main":
+                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, progress=gr.Progress()):
+    api = HfApi()
+
+    pr_title = "Adding `diffusers` weights of this model"
+
+    with TemporaryDirectory() as d:
+        folder = os.path.join(d, repo_folder_name(repo_id=model_id, repo_type="models"))
+        os.makedirs(folder)
+        new_pr = None
+        try:
+            folder = convert_single(model_id, token, 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)
+
+        return link