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>>> def download_image(url): |
... response = requests.get(url) |
... return PIL.Image.open(BytesIO(response.content)).convert("RGB") |
>>> img_url = "https://github.com/Xiang-cd/DiffEdit-stable-diffusion/raw/main/assets/origin.png" |
>>> init_image = download_image(img_url).resize((768, 768)) |
>>> pipe = StableDiffusionDiffEditPipeline.from_pretrained( |
... "stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16 |
... ) |
>>> pipe = pipe.to("cuda") |
>>> pipeline.scheduler = DDIMScheduler.from_config(pipeline.scheduler.config) |
>>> pipeline.inverse_scheduler = DDIMInverseScheduler.from_config(pipeline.scheduler.config) |
>>> pipeline.enable_model_cpu_offload() |
>>> mask_prompt = "A bowl of fruits" |
>>> prompt = "A bowl of pears" |
>>> mask_image = pipe.generate_mask(image=init_image, source_prompt=prompt, target_prompt=mask_prompt) |
>>> image_latents = pipe.invert(image=init_image, prompt=mask_prompt).latents |
>>> image = pipe(prompt=prompt, mask_image=mask_image, image_latents=image_latents).images[0] disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously enabled, this method will go back to |
computing decoding in one step. disable_vae_tiling < source > ( ) Disable tiled VAE decoding. If enable_vae_tiling was previously enabled, this method will go back to |
computing decoding in one step. enable_vae_slicing < source > ( ) Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to |
compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. enable_vae_tiling < source > ( ) Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to |
compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow |
processing larger images. encode_prompt < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None ) Parameters prompt (str or List[str], optional... |
prompt to be encoded |
device — (torch.device): |
torch device num_images_per_prompt (int) — |
number of images that should be generated per prompt do_classifier_free_guidance (bool) — |
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) — |
The prompt or prompts not to guide the image generation. If not defined, one has to pass |
negative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is |
less than 1). prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not |
provided, text embeddings will be generated from prompt input argument. negative_prompt_embeds (torch.FloatTensor, optional) — |
Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt |
weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input |
argument. lora_scale (float, optional) — |
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) — |
Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that |
the output of the pre-final layer will be used for computing the prompt embeddings. Encodes the prompt into text encoder hidden states. StableDiffusionPipelineOutput class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters ... |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected (List[bool]) — |
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or |
None if safety checking could not be performed. Output class for Stable Diffusion pipelines. |
Text-to-Video Generation with AnimateDiff Overview AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai. The abstract of the paper is the following: With the advance of text-to-image models (e.g., Stab... |
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter |
from diffusers.utils import export_to_gif |
# Load the motion adapter |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
# load SD 1.5 based finetuned model |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) |
scheduler = DDIMScheduler.from_pretrained( |
model_id, |
subfolder="scheduler", |
clip_sample=False, |
timestep_spacing="linspace", |
beta_schedule="linear", |
steps_offset=1, |
) |
pipe.scheduler = scheduler |
# enable memory savings |
pipe.enable_vae_slicing() |
pipe.enable_model_cpu_offload() |
output = pipe( |
prompt=( |
"masterpiece, bestquality, highlydetailed, ultradetailed, sunset, " |
"orange sky, warm lighting, fishing boats, ocean waves seagulls, " |
"rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, " |
"golden hour, coastal landscape, seaside scenery" |
), |
negative_prompt="bad quality, worse quality", |
num_frames=16, |
guidance_scale=7.5, |
num_inference_steps=25, |
generator=torch.Generator("cpu").manual_seed(42), |
) |
frames = output.frames[0] |
export_to_gif(frames, "animation.gif") |
Here are some sample outputs: masterpiece, bestquality, sunset. |
AnimateDiff tends to work better with finetuned Stable Diffusion models. If you plan on using a scheduler that can clip samples, make sure to disable it by setting clip_sample=False in the scheduler as this can also have an adverse effect on generated samples. Additionally, the AnimateDiff checkpoints can be ... |
from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter |
from diffusers.utils import export_to_gif |
# Load the motion adapter |
adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16) |
# load SD 1.5 based finetuned model |
model_id = "SG161222/Realistic_Vision_V5.1_noVAE" |
pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16) |
pipe.load_lora_weights( |
"guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out" |
) |
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