[Revision] Don't recommend using revision

docs/source/api/pipelines/overview.mdx

@@ -139,9 +139,9 @@ from diffusers import StableDiffusionImg2ImgPipeline
 
 # load the pipeline
 device = "cuda"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
-).to(device)
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
+    device
+)
 
 # let's download an initial image
 url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"
@@ -189,7 +189,6 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
     "runwayml/stable-diffusion-inpainting",
-    revision="fp16",
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")

docs/source/api/pipelines/stable_diffusion_2.mdx

@@ -113,7 +113,7 @@ import torch
 
 # load model and scheduler
 model_id = "stabilityai/stable-diffusion-x4-upscaler"
-pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, revision="fp16", torch_dtype=torch.float16)
+pipeline = StableDiffusionUpscalePipeline.from_pretrained(model_id, torch_dtype=torch.float16)
 pipeline = pipeline.to("cuda")
 
 # let's download an  image

docs/source/optimization/fp16.mdx

@@ -79,7 +79,7 @@ To save more GPU memory and get even more speed, you can load and run the model
 ```Python
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
@@ -107,7 +107,7 @@ from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
@@ -134,7 +134,7 @@ from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
@@ -159,7 +159,7 @@ from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
@@ -179,7 +179,7 @@ from diffusers import StableDiffusionPipeline
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")
@@ -234,7 +234,6 @@ def generate_inputs():
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")
 unet = pipe.unet
@@ -298,7 +297,6 @@ class UNet2DConditionOutput:
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")
 
@@ -349,7 +347,6 @@ import torch
 
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16",
     torch_dtype=torch.float16,
 ).to("cuda")
 

docs/source/using-diffusers/custom_pipeline_examples.mdx

@@ -58,7 +58,6 @@ guided_pipeline = DiffusionPipeline.from_pretrained(
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
-    revision="fp16",
     torch_dtype=torch.float16,
 )
 guided_pipeline.enable_attention_slicing()
@@ -113,7 +112,6 @@ import torch
 
 pipe = DiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
-    revision="fp16",
     torch_dtype=torch.float16,
     safety_checker=None,  # Very important for videos...lots of false positives while interpolating
     custom_pipeline="interpolate_stable_diffusion",
@@ -159,7 +157,6 @@ pipe = DiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     custom_pipeline="stable_diffusion_mega",
     torch_dtype=torch.float16,
-    revision="fp16",
 )
 pipe.to("cuda")
 pipe.enable_attention_slicing()
@@ -204,7 +201,7 @@ from diffusers import DiffusionPipeline
 import torch
 
 pipe = DiffusionPipeline.from_pretrained(
-    "hakurei/waifu-diffusion", custom_pipeline="lpw_stable_diffusion", revision="fp16", torch_dtype=torch.float16
+    "hakurei/waifu-diffusion", custom_pipeline="lpw_stable_diffusion", torch_dtype=torch.float16
 )
 pipe = pipe.to("cuda")
 
@@ -268,7 +265,7 @@ diffuser_pipeline = DiffusionPipeline.from_pretrained(
     custom_pipeline="speech_to_image_diffusion",
     speech_model=model,
     speech_processor=processor,
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 

docs/source/using-diffusers/img2img.mdx

@@ -24,9 +24,9 @@ from diffusers import StableDiffusionImg2ImgPipeline
 
 # load the pipeline
 device = "cuda"
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", revision="fp16", torch_dtype=torch.float16
-).to(device)
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16).to(
+    device
+)
 
 # let's download an initial image
 url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

docs/source/using-diffusers/inpaint.mdx

@@ -42,7 +42,6 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
     "runwayml/stable-diffusion-inpainting",
-    revision="fp16",
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")

examples/community/README.md

@@ -57,7 +57,7 @@ guided_pipeline = DiffusionPipeline.from_pretrained(
     custom_pipeline="clip_guided_stable_diffusion",
     clip_model=clip_model,
     feature_extractor=feature_extractor,
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 guided_pipeline.enable_attention_slicing()
@@ -208,7 +208,7 @@ import torch
 pipe = DiffusionPipeline.from_pretrained(
     'hakurei/waifu-diffusion',
     custom_pipeline="lpw_stable_diffusion",
-    revision="fp16",
+
     torch_dtype=torch.float16
 )
 pipe=pipe.to("cuda")
@@ -275,7 +275,7 @@ diffuser_pipeline = DiffusionPipeline.from_pretrained(
     custom_pipeline="speech_to_image_diffusion",
     speech_model=model,
     speech_processor=processor,
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 
@@ -333,7 +333,7 @@ import torch
 pipe = DiffusionPipeline.from_pretrained(
     "CompVis/stable-diffusion-v1-4",
     custom_pipeline="wildcard_stable_diffusion",
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 prompt = "__animal__ sitting on a __object__ wearing a __clothing__"
@@ -567,7 +567,7 @@ diffuser_pipeline = DiffusionPipeline.from_pretrained(
     detection_pipeline=language_detection_pipeline,
     translation_model=trans_model,
     translation_tokenizer=trans_tokenizer,
-    revision="fp16",
+
     torch_dtype=torch.float16,
 )
 
@@ -615,7 +615,7 @@ mask_image = PIL.Image.open(mask_path).convert("RGB").resize((512, 512))
 pipe = DiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-inpainting",
     custom_pipeline="img2img_inpainting",
-    revision="fp16",
+
     torch_dtype=torch.float16
 )
 pipe = pipe.to("cuda")

examples/community/wildcard_stable_diffusion.py

@@ -68,7 +68,7 @@ class WildcardStableDiffusionPipeline(DiffusionPipeline):
     Example Usage:
         pipe = WildcardStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4",
-            revision="fp16",
+
             torch_dtype=torch.float16,
         )
         prompt = "__animal__ sitting on a __object__ wearing a __clothing__"

src/diffusers/pipelines/README.md

@@ -113,7 +113,6 @@ from diffusers import StableDiffusionImg2ImgPipeline
 device = "cuda"
 pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
-    revision="fp16", 
     torch_dtype=torch.float16,
 ).to(device)
 
@@ -161,7 +160,6 @@ mask_image = download_image(mask_url).resize((512, 512))
 
 pipe = StableDiffusionInpaintPipeline.from_pretrained(
     "runwayml/stable-diffusion-inpainting",
-    revision="fp16",
     torch_dtype=torch.float16,
 )
 pipe = pipe.to("cuda")

tests/pipelines/altdiffusion/test_alt_diffusion.py

@@ -248,9 +248,7 @@ def test_alt_diffusion_fast_ddim(self):
     def test_alt_diffusion_text2img_pipeline_fp16(self):
         torch.cuda.reset_peak_memory_stats()
         model_id = "BAAI/AltDiffusion"
-        pipe = AltDiffusionPipeline.from_pretrained(
-            model_id, revision="fp16", torch_dtype=torch.float16, safety_checker=None
-        )
+        pipe = AltDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 

tests/pipelines/stable_diffusion/test_stable_diffusion.py

@@ -527,9 +527,7 @@ def test_stable_diffusion_dpm(self):
 
     def test_stable_diffusion_attention_slicing(self):
         torch.cuda.reset_peak_memory_stats()
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -555,9 +553,7 @@ def test_stable_diffusion_attention_slicing(self):
 
     def test_stable_diffusion_vae_slicing(self):
         torch.cuda.reset_peak_memory_stats()
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         pipe.enable_attention_slicing()
@@ -588,9 +584,7 @@ def test_stable_diffusion_vae_slicing(self):
         assert np.abs(image_sliced - image).max() < 4e-3
 
     def test_stable_diffusion_fp16_vs_autocast(self):
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
 
@@ -629,9 +623,7 @@ def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
 
         callback_fn.has_been_called = False
 
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         pipe.enable_attention_slicing()
@@ -645,16 +637,12 @@ def test_stable_diffusion_low_cpu_mem_usage(self):
         pipeline_id = "CompVis/stable-diffusion-v1-4"
 
         start_time = time.time()
-        pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(
-            pipeline_id, revision="fp16", torch_dtype=torch.float16
-        )
+        pipeline_low_cpu_mem_usage = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16)
         pipeline_low_cpu_mem_usage.to(torch_device)
         low_cpu_mem_usage_time = time.time() - start_time
 
         start_time = time.time()
-        _ = StableDiffusionPipeline.from_pretrained(
-            pipeline_id, revision="fp16", torch_dtype=torch.float16, low_cpu_mem_usage=False
-        )
+        _ = StableDiffusionPipeline.from_pretrained(pipeline_id, torch_dtype=torch.float16, low_cpu_mem_usage=False)
         normal_load_time = time.time() - start_time
 
         assert 2 * low_cpu_mem_usage_time < normal_load_time
@@ -664,9 +652,7 @@ def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()
 
-        pipe = StableDiffusionPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", revision="fp16", torch_dtype=torch.float16
-        )
+        pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", torch_dtype=torch.float16)
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
         pipe.enable_attention_slicing(1)

tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py

@@ -303,7 +303,7 @@ def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
         callback_fn.has_been_called = False
 
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", safety_checker=None, revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -320,7 +320,7 @@ def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
         torch.cuda.reset_peak_memory_stats()
 
         pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", safety_checker=None, revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py

@@ -212,7 +212,7 @@ def test_stable_diffusion_inpaint_ddim(self):
 
     def test_stable_diffusion_inpaint_fp16(self):
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", revision="fp16", torch_dtype=torch.float16, safety_checker=None
+            "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16, safety_checker=None
         )
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -266,7 +266,7 @@ def test_stable_diffusion_inpaint_with_sequential_cpu_offloading(self):
         torch.cuda.reset_peak_memory_stats()
 
         pipe = StableDiffusionInpaintPipeline.from_pretrained(
-            "runwayml/stable-diffusion-inpainting", safety_checker=None, revision="fp16", torch_dtype=torch.float16
+            "runwayml/stable-diffusion-inpainting", safety_checker=None, torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py

@@ -425,7 +425,7 @@ def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
         callback_fn.has_been_called = False
 
         pipe = StableDiffusionInpaintPipelineLegacy.from_pretrained(
-            "CompVis/stable-diffusion-v1-4", safety_checker=None, revision="fp16", torch_dtype=torch.float16
+            "CompVis/stable-diffusion-v1-4", safety_checker=None, torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)

tests/pipelines/stable_diffusion_2/test_stable_diffusion.py

@@ -304,7 +304,7 @@ def test_stable_diffusion_k_lms(self):
     def test_stable_diffusion_attention_slicing(self):
         torch.cuda.reset_peak_memory_stats()
         pipe = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-base", revision="fp16", torch_dtype=torch.float16
+            "stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -352,7 +352,7 @@ def callback_fn(step: int, timestep: int, latents: torch.FloatTensor) -> None:
         callback_fn.has_been_called = False
 
         pipe = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-base", revision="fp16", torch_dtype=torch.float16
+            "stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
@@ -369,7 +369,7 @@ def test_stable_diffusion_pipeline_with_sequential_cpu_offloading(self):
         torch.cuda.reset_peak_memory_stats()
 
         pipe = StableDiffusionPipeline.from_pretrained(
-            "stabilityai/stable-diffusion-2-base", revision="fp16", torch_dtype=torch.float16
+            "stabilityai/stable-diffusion-2-base", torch_dtype=torch.float16
         )
         pipe = pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)