uP

convert_flax_to_pt.py

@@ -2,109 +2,16 @@ import argparse
 import json
 import os
 import shutil
+import torch
 from tempfile import TemporaryDirectory
 from typing import List, Optional
+from diffusers import StableDiffusionPipeline, ControlNetModel
 
 from huggingface_hub import CommitInfo, CommitOperationAdd, Discussion, HfApi, hf_hub_download
 from huggingface_hub.file_download import repo_folder_name
 
 
-class AlreadyExists(Exception):
-    pass
-
-
-def is_index_stable_diffusion_like(config_dict):
-    if "_class_name" not in config_dict:
-        return False
-
-    compatible_classes = [
-        "AltDiffusionImg2ImgPipeline",
-        "AltDiffusionPipeline",
-        "CycleDiffusionPipeline",
-        "StableDiffusionImageVariationPipeline",
-        "StableDiffusionImg2ImgPipeline",
-        "StableDiffusionInpaintPipeline",
-        "StableDiffusionInpaintPipelineLegacy",
-        "StableDiffusionPipeline",
-        "StableDiffusionPipelineSafe",
-        "StableDiffusionUpscalePipeline",
-        "VersatileDiffusionDualGuidedPipeline",
-        "VersatileDiffusionImageVariationPipeline",
-        "VersatileDiffusionPipeline",
-        "VersatileDiffusionTextToImagePipeline",
-        "OnnxStableDiffusionImg2ImgPipeline",
-        "OnnxStableDiffusionInpaintPipeline",
-        "OnnxStableDiffusionInpaintPipelineLegacy",
-        "OnnxStableDiffusionPipeline",
-        "StableDiffusionOnnxPipeline",
-        "FlaxStableDiffusionPipeline",
-    ]
-    return config_dict["_class_name"] in compatible_classes
-
-
-def convert_single(model_id: str, folder: str) -> List["CommitOperationAdd"]:
-    config_file = "model_index.json"
-    # os.makedirs(os.path.join(folder, "scheduler"), exist_ok=True)
-    model_index_file = hf_hub_download(repo_id=model_id, filename="model_index.json")
-
-    with open(model_index_file, "r") as f:
-        index_dict = json.load(f)
-        if index_dict.get("feature_extractor", None) is None:
-            print(f"{model_id} has no feature extractor")
-            return False, False
-
-        if index_dict["feature_extractor"][-1] != "CLIPFeatureExtractor":
-            print(f"{model_id} is not out of date or is not CLIP")
-            return False, False
-
-    # old_config_file = hf_hub_download(repo_id=model_id, filename=config_file)
-    old_config_file = model_index_file
-
-    new_config_file = os.path.join(folder, config_file)
-    success = convert_file(old_config_file, new_config_file)
-    if success:
-        operations = [CommitOperationAdd(path_in_repo=config_file, path_or_fileobj=new_config_file)]
-        model_type = success
-        return operations, model_type
-    else:
-        return False, False
-
-
-def convert_file(
-    old_config: str,
-    new_config: str,
-):
-    with open(old_config, "r") as f:
-        old_dict = json.load(f)
-
-    old_dict["feature_extractor"][-1] = "CLIPImageProcessor"
-    # if "clip_sample" not in old_dict:
-    #     print("Make scheduler DDIM compatible")
-    #     old_dict["clip_sample"] = False
-    # else:
-    #     print("No matching config")
-    #     return False
-
-    with open(new_config, 'w') as f:
-        json_str = json.dumps(old_dict, indent=2, sort_keys=True) + "\n"
-        f.write(json_str)
-
-    return "Stable Diffusion"
-
-
-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:
-            return discussion
-
-
 def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["CommitInfo"]:
-#    pr_title = "Correct `sample_size` of {}'s unet to have correct width and height default"
-    pr_title = "Fix deprecation warning by changing `CLIPFeatureExtractor` to `CLIPImageProcessor`."
     info = api.model_info(model_id)
     filenames = set(s.rfilename for s in info.siblings)
 
@@ -134,54 +41,9 @@ def convert(api: "HfApi", model_id: str, force: bool = False) -> Optional["Commi
             folder_path=folder,
             repo_id=model_id,
             repo_type="model",
+            create_pr=True,
         )
-)
-
-        new_pr = None
-        try:
-            operations = None
-            pr = previous_pr(api, model_id, pr_title)
-            if pr is not None and not force:
-                url = f"https://huggingface.co/{model_id}/discussions/{pr.num}"
-                new_pr = pr
-                raise AlreadyExists(f"Model {model_id} already has an open PR check out {url}")
-            else:
-                operations, model_type = convert_single(model_id, folder)
-
-            if operations:
-                pr_title = pr_title.format(model_type)
-#                if model_type == "Stable Diffusion 1":
-#                    sample_size = 64
-#                    image_size = 512
-#                elif model_type == "Stable Diffusion 2":
-#                    sample_size = 96
-#                    image_size = 768
-
-#                pr_description = (
-#                        f"Since `diffusers==0.9.0` the width and height is automatically inferred from the `sample_size` attribute of your unet's config. It seems like your diffusion model has the same architecture as {model_type} which means that when using this model, by default an image size of {image_size}x{image_size} should be generated. This in turn means the unet's sample size should be **{sample_size}**. \n\n In order to suppress to update your configuration on the fly and to suppress the deprecation warning added in this PR: https://github.com/huggingface/diffusers/pull/1406/files#r1035703505 it is strongly recommended to merge this PR."
-#                )
-                contributor = model_id.split("/")[0]
-                pr_description = (
-                        f"Hey {contributor} 👋, \n\n Your model repository seems to contain logic to load a feature extractor that is deprecated, which you should notice by seeing the warning: "
-                        "\n\n ```\ntransformers/models/clip/feature_extraction_clip.py:28: FutureWarning: The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. "
-                        f"Please use CLIPImageProcessor instead. warnings.warn(\n``` \n\n when running `pipe = DiffusionPipeline.from_pretrained({model_id})`."
-                        "This PR makes sure that the warning does not show anymore by replacing `CLIPFeatureExtractor` with `CLIPImageProcessor`. This will certainly not change or break your checkpoint, but only" 
-                        "make sure that everything is up to date. \n\n Best, the 🧨 Diffusers team."
-                )
-                new_pr = api.create_commit(
-                    repo_id=model_id,
-                    operations=operations,
-                    commit_message=pr_title,
-                    commit_description=pr_description,
-                    create_pr=True,
-                )
-                print(f"Pr created at {new_pr.pr_url}")
-            else:
-                print(f"No files to convert for {model_id}")
-        finally:
-            shutil.rmtree(folder)
-        return new_pr
-
+        print(model_id)
 
 if __name__ == "__main__":
     DESCRIPTION = """
@@ -196,12 +58,7 @@ if __name__ == "__main__":
         type=str,
         help="The name of the model on the hub to convert. E.g. `gpt2` or `facebook/wav2vec2-base-960h`",
     )
-    parser.add_argument(
-        "--force",
-        action="store_true",
-        help="Create the PR even if it already exists of if the model was already converted.",
-    )
     args = parser.parse_args()
     model_id = args.model_id
     api = HfApi()
-    convert(api, model_id, force=args.force)
+    convert(api, model_id)

parti_prompts.py

@@ -28,8 +28,8 @@ def get_karlo_eval(ckpt):
     pipe = DiffusionPipeline.from_pretrained(ckpt, torch_dtype=torch.float16)
     pipe.to("cuda")
 
-    def karlo_eval(prompt):
-        images = pipe(prompt, prior_num_inference_steps=50, decoder_num_inference_steps=NUM_INFERENCE_STEPS).images
+    def karlo_eval(prompt, generator=None):
+        images = pipe(prompt, prior_num_inference_steps=50, generator=generator, decoder_num_inference_steps=NUM_INFERENCE_STEPS).images
         return images
 
     return karlo_eval