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* Update pipeline_wuerstchen_prior.py * prior_num_inference_steps updated * height, width, num_inference_steps, and guidance_scale synced * parameters synced * latent_mean, latent_std, and resolution_multiple synced * prior_num_inference_steps changed * Formatted pipeline_wuerstchen_prior.py * Update src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_prior.py --------- Co-authored-by: Kashif Rasul <kashif.rasul@gmail.com>
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@@ -213,13 +213,13 @@ class WuerstchenDecoderPipeline(DiffusionPipeline):
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Image Embeddings either extracted from an image or generated by a Prior Model.
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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num_inference_steps (`int`, *optional*, defaults to 30):
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num_inference_steps (`int`, *optional*, defaults to 12):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
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timesteps are used. Must be in descending order.
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guidance_scale (`float`, *optional*, defaults to 4.0):
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guidance_scale (`float`, *optional*, defaults to 0.0):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
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@@ -194,7 +194,7 @@ class WuerstchenCombinedPipeline(DiffusionPipeline):
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
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`prior_guidance_scale > 1`. Higher guidance scale encourages to generate images that are closely linked
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to the text `prompt`, usually at the expense of lower image quality.
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prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 30):
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prior_num_inference_steps (`Union[int, Dict[float, int]]`, *optional*, defaults to 60):
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The number of prior denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference. For more specific timestep spacing, you can pass customized
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`prior_timesteps`
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@@ -82,6 +82,12 @@ class WuerstchenPriorPipeline(DiffusionPipeline):
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[CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
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scheduler ([`DDPMWuerstchenScheduler`]):
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A scheduler to be used in combination with `prior` to generate image embedding.
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latent_mean ('float', *optional*, defaults to 42.0):
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Mean value for latent diffusers.
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latent_std ('float', *optional*, defaults to 1.0):
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Standard value for latent diffusers.
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resolution_multiple ('float', *optional*, defaults to 42.67):
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Default resolution for multiple images generated.
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"""
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model_cpu_offload_seq = "text_encoder->prior"
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@@ -282,17 +288,17 @@ class WuerstchenPriorPipeline(DiffusionPipeline):
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Args:
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prompt (`str` or `List[str]`):
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The prompt or prompts to guide the image generation.
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height (`int`, *optional*, defaults to 512):
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height (`int`, *optional*, defaults to 1024):
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The height in pixels of the generated image.
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width (`int`, *optional*, defaults to 512):
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width (`int`, *optional*, defaults to 1024):
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The width in pixels of the generated image.
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num_inference_steps (`int`, *optional*, defaults to 30):
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num_inference_steps (`int`, *optional*, defaults to 60):
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The number of denoising steps. More denoising steps usually lead to a higher quality image at the
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expense of slower inference.
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timesteps (`List[int]`, *optional*):
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Custom timesteps to use for the denoising process. If not defined, equal spaced `num_inference_steps`
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timesteps are used. Must be in descending order.
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guidance_scale (`float`, *optional*, defaults to 4.0):
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guidance_scale (`float`, *optional*, defaults to 8.0):
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Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
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`decoder_guidance_scale` is defined as `w` of equation 2. of [Imagen
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Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting
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