amused update links to new repo

docs/source/en/api/pipelines/amused.md

@@ -18,8 +18,8 @@ Amused is a vqvae token based transformer that can generate an image in fewer fo
 
 | Model | Params |
 |-------|--------|
-| [amused-256](https://huggingface.co/huggingface/amused-256) | 603M |
-| [amused-512](https://huggingface.co/huggingface/amused-512) | 608M |
+| [amused-256](https://huggingface.co/amused/amused-256) | 603M |
+| [amused-512](https://huggingface.co/amused/amused-512) | 608M |
 
 ## AmusedPipeline
 

examples/amused/README.md

@@ -29,7 +29,7 @@ accelerate launch train_amused.py \
     --train_batch_size <batch size> \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 1e-4 \
-    --pretrained_model_name_or_path huggingface/amused-256 \
+    --pretrained_model_name_or_path amused/amused-256 \
     --instance_data_dataset  'm1guelpf/nouns' \
     --image_key image \
     --prompt_key text \
@@ -70,7 +70,7 @@ accelerate launch train_amused.py \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 2e-5 \
     --use_8bit_adam \
-    --pretrained_model_name_or_path huggingface/amused-256 \
+    --pretrained_model_name_or_path amused/amused-256 \
     --instance_data_dataset  'm1guelpf/nouns' \
     --image_key image \
     --prompt_key text \
@@ -109,7 +109,7 @@ accelerate launch train_amused.py \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 8e-4 \
     --use_lora \
-    --pretrained_model_name_or_path huggingface/amused-256 \
+    --pretrained_model_name_or_path amused/amused-256 \
     --instance_data_dataset  'm1guelpf/nouns' \
     --image_key image \
     --prompt_key text \
@@ -155,7 +155,7 @@ accelerate launch train_amused.py \
     --train_batch_size <batch size> \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 8e-5 \
-    --pretrained_model_name_or_path huggingface/amused-512 \
+    --pretrained_model_name_or_path amused/amused-512 \
     --instance_data_dataset  'monadical-labs/minecraft-preview' \
     --prompt_prefix 'minecraft ' \
     --image_key image \
@@ -191,7 +191,7 @@ accelerate launch train_amused.py \
     --train_batch_size <batch size> \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 5e-6 \
-    --pretrained_model_name_or_path huggingface/amused-512 \
+    --pretrained_model_name_or_path amused/amused-512 \
     --instance_data_dataset  'monadical-labs/minecraft-preview' \
     --prompt_prefix 'minecraft ' \
     --image_key image \
@@ -228,7 +228,7 @@ accelerate launch train_amused.py \
     --gradient_accumulation_steps <gradient accumulation steps> \
     --learning_rate 1e-4 \
     --use_lora \
-    --pretrained_model_name_or_path huggingface/amused-512 \
+    --pretrained_model_name_or_path amused/amused-512 \
     --instance_data_dataset  'monadical-labs/minecraft-preview' \
     --prompt_prefix 'minecraft ' \
     --image_key image \
@@ -276,7 +276,7 @@ accelerate launch train_amused.py \
     --mixed_precision fp16 \
     --report_to wandb \
     --use_lora \
-    --pretrained_model_name_or_path huggingface/amused-256 \
+    --pretrained_model_name_or_path amused/amused-256 \
     --train_batch_size 1 \
     --lr_scheduler constant \
     --learning_rate 4e-4 \
@@ -308,7 +308,7 @@ accelerate launch train_amused.py \
     --mixed_precision fp16 \
     --report_to wandb \
     --use_lora \
-    --pretrained_model_name_or_path huggingface/amused-512 \
+    --pretrained_model_name_or_path amused/amused-512 \
     --train_batch_size 1 \
     --lr_scheduler constant \
     --learning_rate 1e-3 \

src/diffusers/pipelines/amused/pipeline_amused.py

@@ -31,7 +31,7 @@
         >>> from diffusers import AmusedPipeline
 
         >>> pipe = AmusedPipeline.from_pretrained(
-        ...     "huggingface/amused-512", variant="fp16", torch_dtype=torch.float16
+        ...     "amused/amused-512", variant="fp16", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 

src/diffusers/pipelines/amused/pipeline_amused_img2img.py

@@ -32,7 +32,7 @@
         >>> from diffusers.utils import load_image
 
         >>> pipe = AmusedImg2ImgPipeline.from_pretrained(
-        ...     "huggingface/amused-512", variant="fp16", torch_dtype=torch.float16
+        ...     "amused/amused-512", variant="fp16", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 

src/diffusers/pipelines/amused/pipeline_amused_inpaint.py

@@ -33,7 +33,7 @@
         >>> from diffusers.utils import load_image
 
         >>> pipe = AmusedInpaintPipeline.from_pretrained(
-        ...     "huggingface/amused-512", variant="fp16", torch_dtype=torch.float16
+        ...     "amused/amused-512", variant="fp16", torch_dtype=torch.float16
         ... )
         >>> pipe = pipe.to("cuda")
 

tests/pipelines/amused/test_amused.py

@@ -133,7 +133,7 @@ def test_inference_batch_single_identical(self):
 @require_torch_gpu
 class AmusedPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
-        pipe = AmusedPipeline.from_pretrained("huggingface/amused-256")
+        pipe = AmusedPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
 
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
@@ -145,7 +145,7 @@ def test_amused_256(self):
         assert np.abs(image_slice - expected_slice).max() < 3e-3
 
     def test_amused_256_fp16(self):
-        pipe = AmusedPipeline.from_pretrained("huggingface/amused-256", variant="fp16", torch_dtype=torch.float16)
+        pipe = AmusedPipeline.from_pretrained("amused/amused-256", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
 
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
@@ -157,7 +157,7 @@ def test_amused_256_fp16(self):
         assert np.abs(image_slice - expected_slice).max() < 7e-3
 
     def test_amused_512(self):
-        pipe = AmusedPipeline.from_pretrained("huggingface/amused-512")
+        pipe = AmusedPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
 
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images
@@ -169,7 +169,7 @@ def test_amused_512(self):
         assert np.abs(image_slice - expected_slice).max() < 3e-3
 
     def test_amused_512_fp16(self):
-        pipe = AmusedPipeline.from_pretrained("huggingface/amused-512", variant="fp16", torch_dtype=torch.float16)
+        pipe = AmusedPipeline.from_pretrained("amused/amused-512", variant="fp16", torch_dtype=torch.float16)
         pipe.to(torch_device)
 
         image = pipe("dog", generator=torch.Generator().manual_seed(0), num_inference_steps=2, output_type="np").images

tests/pipelines/amused/test_amused_img2img.py

@@ -137,7 +137,7 @@ def test_inference_batch_single_identical(self):
 @require_torch_gpu
 class AmusedImg2ImgPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
-        pipe = AmusedImg2ImgPipeline.from_pretrained("huggingface/amused-256")
+        pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
 
         image = (
@@ -163,7 +163,7 @@ def test_amused_256(self):
 
     def test_amused_256_fp16(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained(
-            "huggingface/amused-256", torch_dtype=torch.float16, variant="fp16"
+            "amused/amused-256", torch_dtype=torch.float16, variant="fp16"
         )
         pipe.to(torch_device)
 
@@ -189,7 +189,7 @@ def test_amused_256_fp16(self):
         assert np.abs(image_slice - expected_slice).max() < 1e-2
 
     def test_amused_512(self):
-        pipe = AmusedImg2ImgPipeline.from_pretrained("huggingface/amused-512")
+        pipe = AmusedImg2ImgPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
 
         image = (
@@ -214,7 +214,7 @@ def test_amused_512(self):
 
     def test_amused_512_fp16(self):
         pipe = AmusedImg2ImgPipeline.from_pretrained(
-            "huggingface/amused-512", variant="fp16", torch_dtype=torch.float16
+            "amused/amused-512", variant="fp16", torch_dtype=torch.float16
         )
         pipe.to(torch_device)
 

tests/pipelines/amused/test_amused_inpaint.py

@@ -141,7 +141,7 @@ def test_inference_batch_single_identical(self):
 @require_torch_gpu
 class AmusedInpaintPipelineSlowTests(unittest.TestCase):
     def test_amused_256(self):
-        pipe = AmusedInpaintPipeline.from_pretrained("huggingface/amused-256")
+        pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-256")
         pipe.to(torch_device)
 
         image = (
@@ -175,7 +175,7 @@ def test_amused_256(self):
 
     def test_amused_256_fp16(self):
         pipe = AmusedInpaintPipeline.from_pretrained(
-            "huggingface/amused-256", variant="fp16", torch_dtype=torch.float16
+            "amused/amused-256", variant="fp16", torch_dtype=torch.float16
         )
         pipe.to(torch_device)
 
@@ -209,7 +209,7 @@ def test_amused_256_fp16(self):
         assert np.abs(image_slice - expected_slice).max() < 0.1
 
     def test_amused_512(self):
-        pipe = AmusedInpaintPipeline.from_pretrained("huggingface/amused-512")
+        pipe = AmusedInpaintPipeline.from_pretrained("amused/amused-512")
         pipe.to(torch_device)
 
         image = (
@@ -243,7 +243,7 @@ def test_amused_512(self):
 
     def test_amused_512_fp16(self):
         pipe = AmusedInpaintPipeline.from_pretrained(
-            "huggingface/amused-512", variant="fp16", torch_dtype=torch.float16
+            "amused/amused-512", variant="fp16", torch_dtype=torch.float16
         )
         pipe.to(torch_device)