runwayml take-down make change to sd-legacy

Author: AMEERAZAM08

examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py

@@ -160,7 +160,7 @@ def save_model_card(
 from diffusers import AutoPipelineForText2Image
 import torch
 {diffusers_imports_pivotal}
-pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
+pipeline = AutoPipelineForText2Image.from_pretrained('stable-diffusion-v1-5/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
 pipeline.load_lora_weights('{repo_id}', weight_name='pytorch_lora_weights.safetensors')
 {diffusers_example_pivotal}
 image = pipeline('{validation_prompt if validation_prompt else instance_prompt}').images[0]

scripts/convert_blipdiffusion_to_diffusers.py

@@ -303,10 +303,10 @@ def save_blip_diffusion_model(model, args):
     qformer = get_qformer(model)
     qformer.eval()
 
-    text_encoder = ContextCLIPTextModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="text_encoder")
-    vae = AutoencoderKL.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="vae")
+    text_encoder = ContextCLIPTextModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="text_encoder")
+    vae = AutoencoderKL.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="vae")
 
-    unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
+    unet = UNet2DConditionModel.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
     vae.eval()
     text_encoder.eval()
     scheduler = PNDMScheduler(
@@ -316,7 +316,7 @@ def save_blip_diffusion_model(model, args):
         set_alpha_to_one=False,
         skip_prk_steps=True,
     )
-    tokenizer = CLIPTokenizer.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="tokenizer")
+    tokenizer = CLIPTokenizer.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="tokenizer")
     image_processor = BlipImageProcessor()
     blip_diffusion = BlipDiffusionPipeline(
         tokenizer=tokenizer,

src/diffusers/loaders/single_file.py

@@ -329,7 +329,7 @@ def from_single_file(cls, pretrained_model_link_or_path, **kwargs):
 
         >>> # Enable float16 and move to GPU
         >>> pipeline = StableDiffusionPipeline.from_single_file(
-        ...     "https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
+        ...     "https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt",
         ...     torch_dtype=torch.float16,
         ... )
         >>> pipeline.to("cuda")

src/diffusers/loaders/textual_inversion.py

@@ -333,7 +333,7 @@ def load_textual_inversion(
         from diffusers import StableDiffusionPipeline
         import torch
 
-        model_id = "runwayml/stable-diffusion-v1-5"
+        model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
         pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 
         pipe.load_textual_inversion("sd-concepts-library/cat-toy")
@@ -352,7 +352,7 @@ def load_textual_inversion(
         from diffusers import StableDiffusionPipeline
         import torch
 
-        model_id = "runwayml/stable-diffusion-v1-5"
+        model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
         pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16).to("cuda")
 
         pipe.load_textual_inversion("./charturnerv2.pt", token="charturnerv2")
@@ -469,7 +469,7 @@ def unload_textual_inversion(
         from diffusers import AutoPipelineForText2Image
         import torch
 
-        pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
+        pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
 
         # Example 1
         pipeline.load_textual_inversion("sd-concepts-library/gta5-artwork")

src/diffusers/models/autoencoders/consistency_decoder_vae.py

@@ -60,7 +60,7 @@ class ConsistencyDecoderVAE(ModelMixin, ConfigMixin):
 
         >>> vae = ConsistencyDecoderVAE.from_pretrained("openai/consistency-decoder", torch_dtype=torch.float16)
         >>> pipe = StableDiffusionPipeline.from_pretrained(
-        ...     "runwayml/stable-diffusion-v1-5", vae=vae, torch_dtype=torch.float16
+        ...     "stable-diffusion-v1-5/stable-diffusion-v1-5", vae=vae, torch_dtype=torch.float16
         ... ).to("cuda")
 
         >>> image = pipe("horse", generator=torch.manual_seed(0)).images[0]