bug fix

.gitignore

@@ -1,4 +1,5 @@
 results/*
 pretrained_models/*
 gradio_cached_examples/*
-generated/*
+generated/*
+CIVIT_AI/diffusers_models/*

CIVIT_AI/convert.py

@@ -0,0 +1,182 @@
+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" Conversion script for the LDM checkpoints. """
+
+import argparse
+import importlib
+
+import torch
+
+from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument(
+        "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
+    )
+    # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
+    parser.add_argument(
+        "--original_config_file",
+        default=None,
+        type=str,
+        help="The YAML config file corresponding to the original architecture.",
+    )
+    parser.add_argument(
+        "--num_in_channels",
+        default=None,
+        type=int,
+        help="The number of input channels. If `None` number of input channels will be automatically inferred.",
+    )
+    parser.add_argument(
+        "--scheduler_type",
+        default="pndm",
+        type=str,
+        help="Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']",
+    )
+    parser.add_argument(
+        "--pipeline_type",
+        default=None,
+        type=str,
+        help=(
+            "The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"
+            ". If `None` pipeline will be automatically inferred."
+        ),
+    )
+    parser.add_argument(
+        "--image_size",
+        default=None,
+        type=int,
+        help=(
+            "The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"
+            " Base. Use 768 for Stable Diffusion v2."
+        ),
+    )
+    parser.add_argument(
+        "--prediction_type",
+        default=None,
+        type=str,
+        help=(
+            "The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"
+            " Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."
+        ),
+    )
+    parser.add_argument(
+        "--extract_ema",
+        action="store_true",
+        help=(
+            "Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"
+            " or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"
+            " higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."
+        ),
+    )
+    parser.add_argument(
+        "--upcast_attention",
+        action="store_true",
+        help=(
+            "Whether the attention computation should always be upcasted. This is necessary when running stable"
+            " diffusion 2.1."
+        ),
+    )
+    parser.add_argument(
+        "--from_safetensors",
+        action="store_true",
+        help="If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.",
+    )
+    parser.add_argument(
+        "--to_safetensors",
+        action="store_true",
+        help="Whether to store pipeline in safetensors format or not.",
+    )
+    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
+    parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
+    parser.add_argument(
+        "--stable_unclip",
+        type=str,
+        default=None,
+        required=False,
+        help="Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.",
+    )
+    parser.add_argument(
+        "--stable_unclip_prior",
+        type=str,
+        default=None,
+        required=False,
+        help="Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.",
+    )
+    parser.add_argument(
+        "--clip_stats_path",
+        type=str,
+        help="Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.",
+        required=False,
+    )
+    parser.add_argument(
+        "--controlnet", action="store_true", default=None, help="Set flag if this is a controlnet checkpoint."
+    )
+    parser.add_argument("--half", action="store_true", help="Save weights in half precision.")
+    parser.add_argument(
+        "--vae_path",
+        type=str,
+        default=None,
+        required=False,
+        help="Set to a path, hub id to an already converted vae to not convert it again.",
+    )
+    parser.add_argument(
+        "--pipeline_class_name",
+        type=str,
+        default=None,
+        required=False,
+        help="Specify the pipeline class name",
+    )
+
+    args = parser.parse_args()
+
+    if args.pipeline_class_name is not None:
+        library = importlib.import_module("diffusers")
+        class_obj = getattr(library, args.pipeline_class_name)
+        pipeline_class = class_obj
+    else:
+        pipeline_class = None
+
+    pipe = download_from_original_stable_diffusion_ckpt(
+        checkpoint_path=args.checkpoint_path,
+        original_config_file=args.original_config_file,
+        # config_files=args.config_files,
+        image_size=args.image_size,
+        prediction_type=args.prediction_type,
+        model_type=args.pipeline_type,
+        extract_ema=args.extract_ema,
+        scheduler_type=args.scheduler_type,
+        num_in_channels=args.num_in_channels,
+        upcast_attention=args.upcast_attention,
+        from_safetensors=args.from_safetensors,
+        device=args.device,
+        stable_unclip=args.stable_unclip,
+        stable_unclip_prior=args.stable_unclip_prior,
+        clip_stats_path=args.clip_stats_path,
+        controlnet=args.controlnet,
+        vae_path=args.vae_path,
+        pipeline_class=pipeline_class,
+    )
+
+    if args.half:
+        pipe.to(torch_dtype=torch.float16)
+
+    if args.controlnet:
+        # only save the controlnet model
+        pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
+    else:
+        pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

app.py

@@ -76,7 +76,6 @@ def run(*args):
     num_inversion_step = 20
     cond_step_start = 0.0
     give_control_inversion = True
-    model_id = 'SD 1.5'
     inversion_prompt = ''
     save_folder = ''
     list_of_inputs = [x for x in args]
@@ -111,7 +110,7 @@ def run(*args):
     input_ns.save_folder = save_folder
 
     input_ns.seed = list_of_inputs[11]  
-    input_ns.model_id = const.MODEL_IDS[model_id] 
+    input_ns.model_id = const.MODEL_IDS[list_of_inputs[12]] 
     # input_ns.width = list_of_inputs[23] 
     # input_ns.height = list_of_inputs[24] 
     # input_ns.original_size = list_of_inputs[25]
@@ -123,7 +122,6 @@ def run(*args):
     if str(input_ns.model_id) != 'None':
         input_ns.model_id = install_civitai_model(input_ns.model_id)
 
-    
     device = init_device()
     input_ns = init_paths(input_ns)
 
@@ -195,8 +193,6 @@ with block:
             </h2>
             </div>
             """)
-    with gr.Row():
-        gr.Markdown('## RAVE: Randomized Noise Shuffling for Fast and Consistent Video Editing with Diffusion Models')
     with gr.Row():
         with gr.Column():
             with gr.Row():
@@ -254,6 +250,9 @@ with block:
             with gr.Row():
                 positive_prompts = gr.Textbox(label='Positive prompts')
                 negative_prompts = gr.Textbox(label='Negative prompts')
+                model_id = gr.Dropdown(const.MODEL_IDS,
+                                    label='Model id',
+                                    value='SD 1.5')
             with gr.Row():  
                 preprocess_list = ['depth_zoe', 'lineart_realistic', 'lineart_standard', 'softedge_hed']
                 preprocess_name = gr.Dropdown(preprocess_list,
@@ -309,7 +308,7 @@ with block:
                                 step=1)
 
 
-    inputs = [input_path, preprocess_name, controlnet_conditioning_scale, controlnet_guidance_end, controlnet_guidance_start, grid_size, sample_size, pad, guidance_scale, negative_prompts, positive_prompts, seed]
+    inputs = [input_path, preprocess_name, controlnet_conditioning_scale, controlnet_guidance_end, controlnet_guidance_start, grid_size, sample_size, pad, guidance_scale, negative_prompts, positive_prompts, seed, model_id]
     
     run_button.click(fn=run,
                      inputs=inputs,

utils/constants.py

@@ -27,15 +27,15 @@ PREPROCESSOR_DICT = {
 }
 
 MODEL_IDS = {
-    'Realistic Vision V5.1': '130072',
+    # 'Realistic Vision V5.1': '130072',
     'Realistic Vision V6.0' : '245598',
     'MajicMIXRealisticV7' : '176425',
     'DreamShaper' : '128713',
     'EpicPhotoGasm' : '223670',
-    'DivineEleganceMix (Anime)': '238656',
+    # 'DivineEleganceMix (Anime)': '238656',
     'GhostMix (Anime)': '76907',
-    'CetusMix (Anime)': '105924',
-    'Counterfeit (Anime)': '57618',
+    # 'CetusMix (Anime)': '105924',
+    # 'Counterfeit (Anime)': '57618',
     'SD 1.5': 'None'
 }