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Regarding the RunwayML path for V1.5 did change to stable-diffusion-v1-5/[stable-diffusion-v1-5/ stable-diffusion-inpainting] (#10476)

Browse Source 
* Update pipeline_controlnet.py

* Update pipeline_controlnet_img2img.py

runwayml Take-down so change all from to this
stable-diffusion-v1-5/stable-diffusion-v1-5

* Update pipeline_controlnet_inpaint.py

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* runwayml take-down make change to sd-legacy

* Update convert_blipdiffusion_to_diffusers.py

style change
This commit is contained in:
Ameer Azam
2025-01-07 04:31:52 +05:30
committed by GitHub
parent 8f2253c58c
commit 4f5e3e35d2
37 changed files with 87 additions and 88 deletions
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2
examples/advanced_diffusion_training/train_dreambooth_lora_sd15_advanced.py
View File

@@ -160,7 +160,7 @@ to trigger concept `{key}` → use `{tokens}` in your prompt \n
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]

9
scripts/convert_blipdiffusion_to_diffusers.py
View File

@@ -303,10 +303,9 @@ 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")
unet = UNet2DConditionModel.from_pretrained("runwayml/stable-diffusion-v1-5", subfolder="unet")
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("stable-diffusion-v1-5/stable-diffusion-v1-5", subfolder="unet")
vae.eval()
text_encoder.eval()
scheduler = PNDMScheduler(
@@ -316,7 +315,7 @@ 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,

2
src/diffusers/loaders/single_file.py
View File

@@ -329,7 +329,7 @@ class FromSingleFileMixin:
>>> # 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")

6
src/diffusers/loaders/textual_inversion.py
View File

@@ -333,7 +333,7 @@ class TextualInversionLoaderMixin:
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 @@ class TextualInversionLoaderMixin:
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 @@ class TextualInversionLoaderMixin:
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")

2
src/diffusers/models/autoencoders/consistency_decoder_vae.py
View File

@@ -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]

16
src/diffusers/pipelines/auto_pipeline.py
View File

@@ -293,7 +293,7 @@ class AutoPipelineForText2Image(ConfigMixin):
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
@@ -385,7 +385,7 @@ class AutoPipelineForText2Image(ConfigMixin):
```py
>>> from diffusers import AutoPipelineForText2Image
>>> pipeline = AutoPipelineForText2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipeline = AutoPipelineForText2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> image = pipeline(prompt).images[0]
```
"""
@@ -448,7 +448,7 @@ class AutoPipelineForText2Image(ConfigMixin):
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
>>> pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... "stable-diffusion-v1-5/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_t2i = AutoPipelineForText2Image.from_pipe(pipe_i2i)
@@ -589,7 +589,7 @@ class AutoPipelineForImage2Image(ConfigMixin):
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
@@ -681,7 +681,7 @@ class AutoPipelineForImage2Image(ConfigMixin):
```py
>>> from diffusers import AutoPipelineForImage2Image
>>> pipeline = AutoPipelineForImage2Image.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipeline = AutoPipelineForImage2Image.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image).images[0]
```
"""
@@ -756,7 +756,7 @@ class AutoPipelineForImage2Image(ConfigMixin):
>>> from diffusers import AutoPipelineForText2Image, AutoPipelineForImage2Image
>>> pipe_t2i = AutoPipelineForText2Image.from_pretrained(
... "runwayml/stable-diffusion-v1-5", requires_safety_checker=False
... "stable-diffusion-v1-5/stable-diffusion-v1-5", requires_safety_checker=False
... )
>>> pipe_i2i = AutoPipelineForImage2Image.from_pipe(pipe_t2i)
@@ -900,7 +900,7 @@ class AutoPipelineForInpainting(ConfigMixin):
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
@@ -992,7 +992,7 @@ class AutoPipelineForInpainting(ConfigMixin):
```py
>>> from diffusers import AutoPipelineForInpainting
>>> pipeline = AutoPipelineForInpainting.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipeline = AutoPipelineForInpainting.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> image = pipeline(prompt, image=init_image, mask_image=mask_image).images[0]
```
"""

4
src/diffusers/pipelines/controlnet/pipeline_controlnet.py
View File

@@ -80,7 +80,7 @@ EXAMPLE_DOC_STRING = """
>>> # load control net and stable diffusion v1-5
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
>>> pipe = StableDiffusionControlNetPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> # speed up diffusion process with faster scheduler and memory optimization
@@ -198,7 +198,7 @@ class StableDiffusionControlNetPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

4
src/diffusers/pipelines/controlnet/pipeline_controlnet_img2img.py
View File

@@ -71,7 +71,7 @@ EXAMPLE_DOC_STRING = """
>>> # load control net and stable diffusion v1-5
>>> controlnet = ControlNetModel.from_pretrained("lllyasviel/sd-controlnet-canny", torch_dtype=torch.float16)
>>> pipe = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> # speed up diffusion process with faster scheduler and memory optimization
@@ -168,7 +168,7 @@ class StableDiffusionControlNetImg2ImgPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

8
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint.py
View File

@@ -83,7 +83,7 @@ EXAMPLE_DOC_STRING = """
... "lllyasviel/control_v11p_sd15_inpaint", torch_dtype=torch.float16
... )
>>> pipe = StableDiffusionControlNetInpaintPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, torch_dtype=torch.float16
... )
>>> pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
@@ -141,9 +141,9 @@ class StableDiffusionControlNetInpaintPipeline(
<Tip>
This pipeline can be used with checkpoints that have been specifically fine-tuned for inpainting
([runwayml/stable-diffusion-inpainting](https://huggingface.co/runwayml/stable-diffusion-inpainting)) as well as
([stable-diffusion-v1-5/stable-diffusion-inpainting](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting)) as well as
default text-to-image Stable Diffusion checkpoints
([runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)). Default text-to-image
([stable-diffusion-v1-5/stable-diffusion-v1-5](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5)). Default text-to-image
Stable Diffusion checkpoints might be preferable for ControlNets that have been fine-tuned on those, such as
[lllyasviel/control_v11p_sd15_inpaint](https://huggingface.co/lllyasviel/control_v11p_sd15_inpaint).
@@ -167,7 +167,7 @@ class StableDiffusionControlNetInpaintPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/controlnet/pipeline_controlnet_inpaint_sd_xl.py
View File

@@ -1622,7 +1622,7 @@ class StableDiffusionXLControlNetInpaintPipeline(
# 8. Check that sizes of mask, masked image and latents match
if num_channels_unet == 9:
# default case for runwayml/stable-diffusion-inpainting
# default case for stable-diffusion-v1-5/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:

4
src/diffusers/pipelines/controlnet/pipeline_flax_controlnet.py
View File

@@ -75,7 +75,7 @@ EXAMPLE_DOC_STRING = """
... "lllyasviel/sd-controlnet-canny", from_pt=True, dtype=jnp.float32
... )
>>> pipe, params = FlaxStableDiffusionControlNetPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32
... "stable-diffusion-v1-5/stable-diffusion-v1-5", controlnet=controlnet, revision="flax", dtype=jnp.float32
... )
>>> params["controlnet"] = controlnet_params
@@ -132,7 +132,7 @@ class FlaxStableDiffusionControlNetPipeline(FlaxDiffusionPipeline):
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

10
src/diffusers/pipelines/pipeline_flax_utils.py
View File

@@ -237,14 +237,14 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of FlaxUNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
Some weights of FlaxUNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
```
Parameters:
pretrained_model_name_or_path (`str` or `os.PathLike`, *optional*):
Can be either:
- A string, the *repo id* (for example `runwayml/stable-diffusion-v1-5`) of a pretrained pipeline
- A string, the *repo id* (for example `stable-diffusion-v1-5/stable-diffusion-v1-5`) of a pretrained pipeline
hosted on the Hub.
- A path to a *directory* (for example `./my_model_directory`) containing the model weights saved
using [`~FlaxDiffusionPipeline.save_pretrained`].
@@ -293,7 +293,7 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
>>> # Requires to be logged in to Hugging Face hub,
>>> # see more in [the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline, params = FlaxDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5",
... "stable-diffusion-v1-5/stable-diffusion-v1-5",
... variant="bf16",
... dtype=jnp.bfloat16,
... )
@@ -301,7 +301,7 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
>>> # Download pipeline, but use a different scheduler
>>> from diffusers import FlaxDPMSolverMultistepScheduler
>>> model_id = "runwayml/stable-diffusion-v1-5"
>>> model_id = "stable-diffusion-v1-5/stable-diffusion-v1-5"
>>> dpmpp, dpmpp_state = FlaxDPMSolverMultistepScheduler.from_pretrained(
... model_id,
... subfolder="scheduler",
@@ -559,7 +559,7 @@ class FlaxDiffusionPipeline(ConfigMixin, PushToHubMixin):
... )
>>> text2img = FlaxStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", variant="bf16", dtype=jnp.bfloat16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="bf16", dtype=jnp.bfloat16
... )
>>> img2img = FlaxStableDiffusionImg2ImgPipeline(**text2img.components)
```

4
src/diffusers/pipelines/pipeline_loading_utils.py
View File

@@ -813,9 +813,9 @@ def _maybe_raise_warning_for_inpainting(pipeline_class, pretrained_model_name_or
"You are using a legacy checkpoint for inpainting with Stable Diffusion, therefore we are loading the"
f" {StableDiffusionInpaintPipelineLegacy} class instead of {StableDiffusionInpaintPipeline}. For"
" better inpainting results, we strongly suggest using Stable Diffusion's official inpainting"
" checkpoint: https://huggingface.co/runwayml/stable-diffusion-inpainting instead or adapting your"
" checkpoint: https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting instead or adapting your"
f" checkpoint {pretrained_model_name_or_path} to the format of"
" https://huggingface.co/runwayml/stable-diffusion-inpainting. Note that we do not actively maintain"
" https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-inpainting. Note that we do not actively maintain"
" the {StableDiffusionInpaintPipelineLegacy} class and will likely remove it in version 1.0.0."
)
deprecate("StableDiffusionInpaintPipelineLegacy", "1.0.0", deprecation_message, standard_warn=False)

10
src/diffusers/pipelines/pipeline_utils.py
View File

@@ -516,7 +516,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
If you get the error message below, you need to finetune the weights for your downstream task:
```
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at runwayml/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
Some weights of UNet2DConditionModel were not initialized from the model checkpoint at stable-diffusion-v1-5/stable-diffusion-v1-5 and are newly initialized because the shapes did not match:
- conv_in.weight: found shape torch.Size([320, 4, 3, 3]) in the checkpoint and torch.Size([320, 9, 3, 3]) in the model instantiated
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
```
@@ -643,7 +643,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
>>> # Download pipeline that requires an authorization token
>>> # For more information on access tokens, please refer to this section
>>> # of the documentation](https://huggingface.co/docs/hub/security-tokens)
>>> pipeline = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipeline = DiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> # Use a different scheduler
>>> from diffusers import LMSDiscreteScheduler
@@ -1555,7 +1555,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
... StableDiffusionInpaintPipeline,
... )
>>> text2img = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> text2img = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> img2img = StableDiffusionImg2ImgPipeline(**text2img.components)
>>> inpaint = StableDiffusionInpaintPipeline(**text2img.components)
```
@@ -1688,7 +1688,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
>>> from diffusers import StableDiffusionPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5",
... "stable-diffusion-v1-5/stable-diffusion-v1-5",
... torch_dtype=torch.float16,
... use_safetensors=True,
... )
@@ -1735,7 +1735,7 @@ class DiffusionPipeline(ConfigMixin, PushToHubMixin):
```py
>>> from diffusers import StableDiffusionPipeline, StableDiffusionSAGPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5")
>>> new_pipe = StableDiffusionSAGPipeline.from_pipe(pipe)
```
"""

8
src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion.py
View File

@@ -55,7 +55,7 @@ EXAMPLE_DOC_STRING = """
>>> from diffusers import FlaxStableDiffusionPipeline
>>> pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", variant="bf16", dtype=jax.numpy.bfloat16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", variant="bf16", dtype=jax.numpy.bfloat16
... )
>>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -100,7 +100,7 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -141,8 +141,8 @@ class FlaxStableDiffusionPipeline(FlaxDiffusionPipeline):
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

2
src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_img2img.py
View File

@@ -124,7 +124,7 @@ class FlaxStableDiffusionImg2ImgPipeline(FlaxDiffusionPipeline):
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

6
src/diffusers/pipelines/stable_diffusion/pipeline_flax_stable_diffusion_inpaint.py
View File

@@ -127,7 +127,7 @@ class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
[`FlaxDPMSolverMultistepScheduler`].
safety_checker ([`FlaxStableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -168,8 +168,8 @@ class FlaxStableDiffusionInpaintPipeline(FlaxDiffusionPipeline):
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

2
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_img2img.py
View File

@@ -78,7 +78,7 @@ class OnnxStableDiffusionImg2ImgPipeline(DiffusionPipeline):
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""

2
src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion_inpaint.py
View File

@@ -76,7 +76,7 @@ class OnnxStableDiffusionInpaintPipeline(DiffusionPipeline):
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""

8
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py
View File

@@ -55,7 +55,7 @@ EXAMPLE_DOC_STRING = """
>>> import torch
>>> from diffusers import StableDiffusionPipeline
>>> pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
>>> pipe = StableDiffusionPipeline.from_pretrained("stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16)
>>> pipe = pipe.to("cuda")
>>> prompt = "a photo of an astronaut riding a horse on mars"
@@ -184,7 +184,7 @@ class StableDiffusionPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -266,8 +266,8 @@ class StableDiffusionPipeline(
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

4
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_depth2img.py
View File

@@ -124,8 +124,8 @@ class StableDiffusionDepth2ImgPipeline(DiffusionPipeline, TextualInversionLoader
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

6
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_image_variation.py
View File

@@ -57,7 +57,7 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMi
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -106,8 +106,8 @@ class StableDiffusionImageVariationPipeline(DiffusionPipeline, StableDiffusionMi
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

8
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py
View File

@@ -56,7 +56,7 @@ EXAMPLE_DOC_STRING = """
>>> from diffusers import StableDiffusionImg2ImgPipeline
>>> device = "cuda"
>>> model_id_or_path = "runwayml/stable-diffusion-v1-5"
>>> model_id_or_path = "stable-diffusion-v1-5/stable-diffusion-v1-5"
>>> pipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)
>>> pipe = pipe.to(device)
@@ -205,7 +205,7 @@ class StableDiffusionImg2ImgPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -282,8 +282,8 @@ class StableDiffusionImg2ImgPipeline(
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

10
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py
View File

@@ -146,7 +146,7 @@ class StableDiffusionInpaintPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -224,8 +224,8 @@ class StableDiffusionInpaintPipeline(
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"
@@ -1014,7 +1014,7 @@ class StableDiffusionInpaintPipeline(
>>> mask_image = download_image(mask_url).resize((512, 512))
>>> pipe = StableDiffusionInpaintPipeline.from_pretrained(
... "runwayml/stable-diffusion-inpainting", torch_dtype=torch.float16
... "stable-diffusion-v1-5/stable-diffusion-inpainting", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
@@ -1200,7 +1200,7 @@ class StableDiffusionInpaintPipeline(
# 8. Check that sizes of mask, masked image and latents match
if num_channels_unet == 9:
# default case for runwayml/stable-diffusion-inpainting
# default case for stable-diffusion-v1-5/stable-diffusion-inpainting
num_channels_mask = mask.shape[1]
num_channels_masked_image = masked_image_latents.shape[1]
if num_channels_latents + num_channels_mask + num_channels_masked_image != self.unet.config.in_channels:

2
src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_instruct_pix2pix.py
View File

@@ -114,7 +114,7 @@ class StableDiffusionInstructPix2PixPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/stable_diffusion_attend_and_excite/pipeline_stable_diffusion_attend_and_excite.py
View File

@@ -194,7 +194,7 @@ class StableDiffusionAttendAndExcitePipeline(DiffusionPipeline, StableDiffusionM
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

6
src/diffusers/pipelines/stable_diffusion_diffedit/pipeline_stable_diffusion_diffedit.py
View File

@@ -268,7 +268,7 @@ class StableDiffusionDiffEditPipeline(
A scheduler to be used in combination with `unet` to fill in the unmasked part of the input latents.
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -345,8 +345,8 @@ class StableDiffusionDiffEditPipeline(
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

2
src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen.py
View File

@@ -120,7 +120,7 @@ class StableDiffusionGLIGENPipeline(DiffusionPipeline, StableDiffusionMixin):
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/stable_diffusion_gligen/pipeline_stable_diffusion_gligen_text_image.py
View File

@@ -172,7 +172,7 @@ class StableDiffusionGLIGENTextImagePipeline(DiffusionPipeline, StableDiffusionM
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_k_diffusion.py
View File

@@ -83,7 +83,7 @@ class StableDiffusionKDiffusionPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""

2
src/diffusers/pipelines/stable_diffusion_ldm3d/pipeline_stable_diffusion_ldm3d.py
View File

@@ -203,7 +203,7 @@ class StableDiffusionLDM3DPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/stable_diffusion_panorama/pipeline_stable_diffusion_panorama.py
View File

@@ -179,7 +179,7 @@ class StableDiffusionPanoramaPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

6
src/diffusers/pipelines/stable_diffusion_safe/pipeline_stable_diffusion_safe.py
View File

@@ -46,7 +46,7 @@ class StableDiffusionPipelineSafe(DiffusionPipeline, StableDiffusionMixin, IPAda
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.
@@ -126,8 +126,8 @@ class StableDiffusionPipelineSafe(DiffusionPipeline, StableDiffusionMixin, IPAda
"The configuration file of the unet has set the default `sample_size` to smaller than"
" 64 which seems highly unlikely .If you're checkpoint is a fine-tuned version of any of the"
" following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5"
" \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- stable-diffusion-v1-5/stable-diffusion-v1-5"
" \n- stable-diffusion-v1-5/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the"
" configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`"
" in the config might lead to incorrect results in future versions. If you have downloaded this"
" checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for"

4
src/diffusers/pipelines/stable_diffusion_sag/pipeline_stable_diffusion_sag.py
View File

@@ -47,7 +47,7 @@ EXAMPLE_DOC_STRING = """
>>> from diffusers import StableDiffusionSAGPipeline
>>> pipe = StableDiffusionSAGPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
... "stable-diffusion-v1-5/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
@@ -123,7 +123,7 @@ class StableDiffusionSAGPipeline(DiffusionPipeline, StableDiffusionMixin, Textua
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`~transformers.CLIPImageProcessor`]):
A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`.

2
src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_adapter.py
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@@ -208,7 +208,7 @@ class StableDiffusionAdapterPipeline(DiffusionPipeline, StableDiffusionMixin):
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""

2
src/diffusers/pipelines/t2i_adapter/pipeline_stable_diffusion_xl_adapter.py
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@@ -248,7 +248,7 @@ class StableDiffusionXLAdapterPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
Please, refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for details.
feature_extractor ([`CLIPImageProcessor`]):
Model that extracts features from generated images to be used as inputs for the `safety_checker`.
"""

2
src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero.py
View File

@@ -322,7 +322,7 @@ class TextToVideoZeroPipeline(
[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
safety_checker ([`StableDiffusionSafetyChecker`]):
Classification module that estimates whether generated images could be considered offensive or harmful.
Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details
Please refer to the [model card](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5) for more details
about a model's potential harms.
feature_extractor ([`CLIPImageProcessor`]):
A [`CLIPImageProcessor`] to extract features from generated images; used as inputs to the `safety_checker`.