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77 lines
3.9 KiB
Markdown
77 lines
3.9 KiB
Markdown
<!--Copyright 2025 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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-->
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# Legacy checkpoints
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These settings apply mainly to older Stable Diffusion family checkpoints.
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## Safety checker
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Diffusers provides a [safety checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) for older Stable Diffusion checkpoints to prevent generating harmful content. It screens the generated output against a set of hardcoded harmful concepts. Newer models such as Qwen-Image do not have this checker.
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If you want to disable the safety checker, pass `safety_checker=None` in [`~DiffusionPipeline.from_pretrained`] as shown below.
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```py
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from diffusers import DiffusionPipeline
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pipeline = DiffusionPipeline.from_pretrained(
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"stable-diffusion-v1-5/stable-diffusion-v1-5", safety_checker=None
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)
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"""
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You have disabled the safety checker for <class 'diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline'> by passing `safety_checker=None`. Ensure that you abide by the conditions of the Stable Diffusion license and do not expose unfiltered results in services or applications open to the public. Both the diffusers team and Hugging Face strongly recommend keeping the safety filter enabled in all public-facing circumstances, disabling it only for use cases that involve analyzing network behavior or auditing its results. For more information, please have a look at https://github.com/huggingface/diffusers/pull/254 .
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"""
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```
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## Rescaling schedules
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Denoising begins with pure noise, so the signal-to-noise (SNR) ratio starts at zero. Some models don't actually start from pure noise, which makes bright and dark extremes hard to generate.
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> [!TIP]
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> Train your own model with `v_prediction` by adding the `--prediction_type="v_prediction"` flag to your training script. You can also [search](https://huggingface.co/search/full-text?q=v_prediction&type=model) for existing models trained with `v_prediction`.
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For a `v_prediction` model, enable these scheduler arguments.
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- Set `rescale_betas_zero_snr=True` to rescale the noise schedule to the very last timestep with exactly zero SNR
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- Set `timestep_spacing="trailing"` to force sampling from the last timestep with pure noise
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```py
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from diffusers import DiffusionPipeline, DDIMScheduler
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pipeline = DiffusionPipeline.from_pretrained("ptx0/pseudo-journey-v2", device_map="cuda") # or "mps", "xpu", "cpu"
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pipeline.scheduler = DDIMScheduler.from_config(
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pipeline.scheduler.config, rescale_betas_zero_snr=True, timestep_spacing="trailing"
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)
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```
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Set `guidance_rescale` in the pipeline to avoid overexposed images. A lower value increases brightness, but some details may appear washed out.
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```py
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prompt = """
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cinematic photo of a snowy mountain at night with the northern lights aurora borealis
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overhead, 35mm photograph, film, professional, 4k, highly detailed
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"""
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image = pipeline(prompt, guidance_rescale=0.7).images[0]
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```
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<div class="flex gap-4">
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<div>
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<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/no-zero-snr.png"/>
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<figcaption class="mt-2 text-center text-sm text-gray-500">without zero SNR</figcaption>
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</div>
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<div>
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<img class="rounded-xl" src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/zero-snr.png"/>
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<figcaption class="mt-2 text-center text-sm text-gray-500">with zero SNR and trailing spacing</figcaption>
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</div>
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</div>
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