text stringlengths 1 1.02k | class_index int64 0 1.38k | source stringclasses 431
values |
|---|---|---|
# Create bitmasks between 0 and 255 (inclusive) indicating the state
# of the eight corners of each cube.
bitmasks = (field > 0).to(torch.uint8)
bitmasks = bitmasks[:-1, :, :] | (bitmasks[1:, :, :] << 1)
bitmasks = bitmasks[:, :-1, :] | (bitmasks[:, 1:, :] << 2)
bitmasks = bitmas... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Compute all vertices across all edges in the grid, even though we will
# throw some out later. We have (X-1)*Y*Z + X*(Y-1)*Z + X*Y*(Z-1) vertices.
# These are all midpoints, and don't account for interpolation (which is
# done later based on the used edge midpoints).
edge_midpoints = t... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Create a flat array of [X, Y, Z] indices for each cube.
cube_indices = torch.zeros(
grid_size[0] - 1, grid_size[1] - 1, grid_size[2] - 1, 3, device=dev, dtype=torch.long
)
cube_indices[range(grid_size[0] - 1), :, :, 0] = torch.arange(grid_size[0] - 1, device=dev)[:, None, None]
... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Apply the LUT to figure out the triangles.
flat_bitmasks = bitmasks.reshape(-1).long() # must cast to long for indexing to believe this not a mask
local_tris = cases[flat_bitmasks]
local_masks = masks[flat_bitmasks]
# Compute the global edge indices for the triangles.
global_t... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Now we have a bunch of indices into the full list of possible vertices,
# but we want to reduce this list to only the used vertices.
used_vertex_indices = torch.unique(selected_tris.view(-1))
used_edge_midpoints = edge_midpoints[used_vertex_indices]
old_index_to_new_index = torch.zeros... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# Compute the actual interpolated coordinates corresponding to edge midpoints.
v1 = torch.floor(used_edge_midpoints).to(torch.long)
v2 = torch.ceil(used_edge_midpoints).to(torch.long)
s1 = field[v1[:, 0], v1[:, 1], v1[:, 2]]
s2 = field[v2[:, 0], v2[:, 1], v2[:, 2]]
p1 = (v1.float... | 196 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class MLPNeRFModelOutput(BaseOutput):
density: torch.Tensor
signed_distance: torch.Tensor
channels: torch.Tensor
ts: torch.Tensor | 197 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class MLPNeRSTFModel(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
d_hidden: int = 256,
n_output: int = 12,
n_hidden_layers: int = 6,
act_fn: str = "swish",
insert_direction_at: int = 4,
):
super().__init__()
# Instantiate ... | 198 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
if act_fn == "swish":
# self.activation = swish
# yiyi testing:
self.activation = lambda x: F.silu(x)
else:
raise ValueError(f"Unsupported activation function {act_fn}")
self.sdf_activation = torch.tanh
self.density_activation = torch.nn.functiona... | 198 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
h_preact = h
h_directionless = None
for i, layer in enumerate(self.mlp):
if i == self.config.insert_direction_at: # 4 in the config
h_directionless = h_preact
h_direction = encode_direction(position, direction=direction)
h = torch.cat([h, h_di... | 198 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
elif rendering_mode == "stf":
h_channels = activation["stf"]
density = self.density_activation(h_density)
signed_distance = self.sdf_activation(activation["sdf"])
channels = self.channel_activation(h_channels)
# yiyi notes: I think signed_distance is not used
return... | 198 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class ChannelsProj(nn.Module):
def __init__(
self,
*,
vectors: int,
channels: int,
d_latent: int,
):
super().__init__()
self.proj = nn.Linear(d_latent, vectors * channels)
self.norm = nn.LayerNorm(channels)
self.d_latent = d_latent
... | 199 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class ShapEParamsProjModel(ModelMixin, ConfigMixin):
"""
project the latent representation of a 3D asset to obtain weights of a multi-layer perceptron (MLP).
For more details, see the original paper:
"""
@register_to_config
def __init__(
self,
*,
param_names: Tuple[str]... | 200 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# check inputs
if len(param_names) != len(param_shapes):
raise ValueError("Must provide same number of `param_names` as `param_shapes`")
self.projections = nn.ModuleDict({})
for k, (vectors, channels) in zip(param_names, param_shapes):
self.projections[_sanitize_name(k)] ... | 200 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class ShapERenderer(ModelMixin, ConfigMixin):
@register_to_config
def __init__(
self,
*,
param_names: Tuple[str] = (
"nerstf.mlp.0.weight",
"nerstf.mlp.1.weight",
"nerstf.mlp.2.weight",
"nerstf.mlp.3.weight",
),
param_shapes... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
self.params_proj = ShapEParamsProjModel(
param_names=param_names,
param_shapes=param_shapes,
d_latent=d_latent,
)
self.mlp = MLPNeRSTFModel(d_hidden, n_output, n_hidden_layers, act_fn, insert_direction_at)
self.void = VoidNeRFModel(background=background, chann... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
C(r) := sum(
transmittance(t[i]) * integrate(
lambda t: density(t) * channels(t) * transmittance(t), [t[i], t[i + 1]],
) for i in range(len(parts))
) + transmittance(t[-1]) * void_model(t[-1]).channels
where | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
1) transmittance(s) := exp(-integrate(density, [t[0], s])) calculates the probability of light passing through
the volume specified by [t[0], s]. (transmittance of 1 means light can pass freely) 2) density and channels are
obtained by evaluating the appropriate part.model at time t. 3) [t[i], t[i + 1]] ... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
Args:
rays: [batch_size x ... x 2 x 3] origin and direction. sampler: disjoint volume integrals. n_samples:
number of ts to sample. prev_model_outputs: model outputs from the previous rendering step, including
:return: A tuple of
- `channels`
- A importance sampl... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
if prev_model_out is not None:
# Append the previous ts now before fprop because previous
# rendering used a different model and we can't reuse the output.
ts = torch.sort(torch.cat([ts, prev_model_out.ts], dim=-2), dim=-2).values
batch_size, *_shape, _t0_dim = vrange.t0.sha... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# 3. Integrate the model results
channels, weights, transmittance = integrate_samples(
vrange, model_out.ts, model_out.density, model_out.channels
)
# 4. Clean up results that do not intersect with the volume.
transmittance = torch.where(vrange.intersected, transmittance, to... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
@torch.no_grad()
def decode_to_image(
self,
latents,
device,
size: int = 64,
ray_batch_size: int = 4096,
n_coarse_samples=64,
n_fine_samples=128,
):
# project the parameters from the generated latents
projected_params = self.params_proj(lat... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# render rays with coarse, stratified samples.
_, fine_sampler, coarse_model_out = self.render_rays(rays_batch, coarse_sampler, n_coarse_samples)
# Then, render with additional importance-weighted ray samples.
channels, _, _ = self.render_rays(
rays_batch, fine_sample... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
# 2. update the mlp layers of the renderer
for name, param in self.mlp.state_dict().items():
if f"nerstf.{name}" in projected_params.keys():
param.copy_(projected_params[f"nerstf.{name}"].squeeze(0))
# 3. decoding with STF rendering
# 3.1 query the SDF values at vert... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
assert (
len(fields.shape) == 3 and fields.shape[-1] == 1
), f"expected [meta_batch x inner_batch] SDF results, but got {fields.shape}"
fields = fields.reshape(1, *([grid_size] * 3))
# create grid 128 x 128 x 128
# - force a negative border around the SDFs to close off all ... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
mesh_mask = torch.tensor(mesh_mask, device=fields.device)
max_vertices = max(len(m.verts) for m in raw_meshes)
# 3.2. query the texture color head at each vertex of the resulting mesh.
texture_query_positions = torch.stack(
[m.verts[torch.arange(0, max_vertices) % len(m.verts)] for ... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
textures = _convert_srgb_to_linear(textures)
textures = textures.float()
# 3.3 augument the mesh with texture data
assert len(textures.shape) == 3 and textures.shape[-1] == len(
texture_channels
), f"expected [meta_batch x inner_batch x texture_channels] field results, but g... | 201 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/renderer.py |
class ShapEPipelineOutput(BaseOutput):
"""
Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].
Args:
images (`torch.Tensor`)
A list of images for 3D rendering.
"""
images: Union[List[List[PIL.Image.Image]], List[List[np.ndarray]]] | 202 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
class ShapEPipeline(DiffusionPipeline):
"""
Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (downloading, saving, runn... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
text_encoder ([`~transformers.CLIPTextModelWithProjection`]):
Frozen text-encoder.
tokenizer ([`~transformers.CLIPTokenizer`]):
A `CLIPT... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
def __init__(
self,
prior: PriorTransformer,
text_encoder: CLIPTextModelWithProjection,
tokenizer: CLIPTokenizer,
scheduler: HeunDiscreteScheduler,
shap_e_renderer: ShapERenderer,
):
super().__init__()
self.register_modules(
prior=prior,
... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_prompt(
self,
prompt,
device,
num_images_per_prompt,
do_classifier_free_guidance,
):
len(prompt) if isinstance(prompt, list) else 1
# YiYi Notes: set pad_token_id to be 0, ... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(text_input_ids, untruncated_ids):
removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1])
logger.warning(
"The following part of your input was truncated ... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
# Rescale the features to ... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
@torch.no_grad()
@replace_example_docstring(EXAMPLE_DOC_STRING)
def __call__(
self,
prompt: str,
num_images_per_prompt: int = 1,
num_inference_steps: int = 25,
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
latents: Optional[torch.Tenso... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide the image generation.
num_images_per_prompt (`int`, *optional*, defaults to 1):
The number of images to generate per prompt.
num_inference_steps (`int`, *optional*, defaults to 25):
... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
tensor is generated by sampling using the supplied random `generator`.
guidance_scale (`float`, *optional*, defaults to 4.0):
A higher guidance scale value encourages the model to generate images closely linked to the text
`prompt` at the expense of lower image quality. Guida... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
Examples:
Returns:
[`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the gene... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
num_embeddings = self.prior.config.num_embeddings
embedding_dim = self.prior.config.embedding_dim
latents = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim),
prompt_embeds.dtype,
device,
generator,
latents,
self.s... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
noise_pred = self.prior(
scaled_model_input,
timestep=t,
proj_embedding=prompt_embeds,
).predicted_image_embedding
# remove the variance
noise_pred, _ = noise_pred.split(
scaled_model_input.shape[2], dim=2
)... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
if output_type not in ["np", "pil", "latent", "mesh"]:
raise ValueError(
f"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}"
)
if output_type == "latent":
return ShapEPipelineOutput(images=latents)
im... | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
if output_type == "pil":
images = [self.numpy_to_pil(image) for image in images]
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=images) | 203 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e.py |
class DifferentiableProjectiveCamera:
"""
Implements a batch, differentiable, standard pinhole camera
"""
origin: torch.Tensor # [batch_size x 3]
x: torch.Tensor # [batch_size x 3]
y: torch.Tensor # [batch_size x 3]
z: torch.Tensor # [batch_size x 3]
width: int
height: int
x... | 204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/camera.py |
def get_image_coords(self) -> torch.Tensor:
"""
:return: coords of shape (width * height, 2)
"""
pixel_indices = torch.arange(self.height * self.width)
coords = torch.stack(
[
pixel_indices % self.width,
torch.div(pixel_indices, self.wi... | 204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/camera.py |
def get_camera_rays(self, coords: torch.Tensor) -> torch.Tensor:
batch_size, *shape, n_coords = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
flat = coords.view(batch_size, -1, 2)
res = self.resolution()
fov = self.fov()
fracs = (f... | 204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/camera.py |
def resize_image(self, width: int, height: int) -> "DifferentiableProjectiveCamera":
"""
Creates a new camera for the resized view assuming the aspect ratio does not change.
"""
assert width * self.height == height * self.width, "The aspect ratio should not change."
return Differ... | 204 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/camera.py |
class ShapEPipelineOutput(BaseOutput):
"""
Output class for [`ShapEPipeline`] and [`ShapEImg2ImgPipeline`].
Args:
images (`torch.Tensor`)
A list of images for 3D rendering.
"""
images: Union[PIL.Image.Image, np.ndarray] | 205 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
class ShapEImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for generating latent representation of a 3D asset and rendering with the NeRF method from an image.
This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
implemented for all pipelines (down... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
Args:
prior ([`PriorTransformer`]):
The canonical unCLIP prior to approximate the image embedding from the text embedding.
image_encoder ([`~transformers.CLIPVisionModel`]):
Frozen image-encoder.
image_processor ([`~transformers.CLIPImageProcessor`]):
A `CLIP... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
def __init__(
self,
prior: PriorTransformer,
image_encoder: CLIPVisionModel,
image_processor: CLIPImageProcessor,
scheduler: HeunDiscreteScheduler,
shap_e_renderer: ShapERenderer,
):
super().__init__()
self.register_modules(
prior=prior,
... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
latents = latents * scheduler.init_noise_sigma
return latents
def _encode_image(
self,
image,
device,
num_images_per_prompt,
do_classifier_free_guidance,
):
if isinstance(image, List) and isinstance(image[0], torch.Tensor):
image = torch.cat(i... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
image_embeds = torch.cat([negative_image_embeds, image_embeds])
return image_embeds
@tor... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
Args:
image (`torch.Tensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.Tensor]`, `List[PIL.Image.Image]`, or `List[np.ndarray]`):
`Image` or tensor representing an image batch to be used as the starting point. Can also accept image
latents as image, but if passing latents ... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
guid... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
(`np.array`), `"latent"` (`torch.Tensor`), or mesh ([`MeshDecoderOutput`]).
return_dict (`bool`, *optional*, defaults to `True`):
Whether or not to return a [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] instead of a plain
tuple. | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
Examples:
Returns:
[`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] or `tuple`:
If `return_dict` is `True`, [`~pipelines.shap_e.pipeline_shap_e.ShapEPipelineOutput`] is returned,
otherwise a `tuple` is returned where the first element is a list with the gene... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
do_classifier_free_guidance = guidance_scale > 1.0
image_embeds = self._encode_image(image, device, num_images_per_prompt, do_classifier_free_guidance)
# prior
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps = self.scheduler.timesteps
num_embeddings ... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
scaled_model_input = self.scheduler.scale_model_input(latent_model_input, t)
... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
latents = self.scheduler.step(
noise_pred,
timestep=t,
sample=latents,
).prev_sample
if XLA_AVAILABLE:
xm.mark_step()
if output_type not in ["np", "pil", "latent", "mesh"]:
raise ValueError(
f"O... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
else:
# np, pil
for i, latent in enumerate(latents):
image = self.shap_e_renderer.decode_to_image(
latent[None, :],
device,
size=frame_size,
)
images.append(image)
images = to... | 206 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py |
class SemanticStableDiffusionPipelineOutput(BaseOutput):
"""
Output class for Stable Diffusion pipelines.
Args:
images (`List[PIL.Image.Image]` or `np.ndarray`)
List of denoised PIL images of length `batch_size` or NumPy array of shape `(batch_size, height, width,
num_channe... | 207 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_output.py |
class SemanticStableDiffusionPipeline(DiffusionPipeline, StableDiffusionMixin):
r"""
Pipeline for text-to-image generation using Stable Diffusion with latent editing.
This model inherits from [`DiffusionPipeline`] and builds on the [`StableDiffusionPipeline`]. Check the superclass
documentation for the... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Args:
vae ([`AutoencoderKL`]):
Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
text_encoder ([`~transformers.CLIPTextModel`]):
Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14))... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Please refer to the [model card](https://huggingface.co/runwayml/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 `saf... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
model_cpu_offload_seq = "text_encoder->unet->vae"
_optional_components = ["safety_checker", "feature_extractor"]
def __init__(
self,
vae: AutoencoderKL,
text_encoder: CLIPTextModel,
tokenizer: CLIPTokenizer,
unet: UNet2DConditionModel,
scheduler: KarrasDiffusionS... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if safety_checker is None and requires_safety_checker:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if safety_checker is not None and feature_extractor is None:
raise ValueError(
"Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
" checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` i... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
def run_safety_checker(self, image, device, dtype):
if self.safety_checker is None:
has_nsfw_concept = None
else:
if torch.is_tensor(image):
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
def decode_latents(self, latents):
deprecation_message = "The decode_latents method is deprecated and will be removed in 1.0.0. Please use VaeImageProcessor.postprocess(...) instead"
d... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
def prepare_extra_step_kwargs(self, generator, eta):
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used w... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion_k_diffusion.pipeline_stable_diffusion_k_diffusion.StableDiffusionKDiffusionPipeline.check_inputs
def check_inputs(
self,
prompt,
height,
width,
callback_steps,
negative_prompt=None,
prompt_embeds=None,
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0):
raise ValueError(
f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
f" {type(callback_steps)}."
)
if callback_on_step_end_tensor_... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if prompt is not None and prompt_embeds is not None:
raise ValueError(
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
" only forward one of the two."
)
elif prompt is None and prompt_embeds is None:
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if prompt_embeds is not None and negative_prompt_embeds is not None:
if prompt_embeds.shape != negative_prompt_embeds.shape:
raise ValueError(
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
f" got: `pr... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
def prepare_latents(self, batch_size, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (
batch_size,
num_channels_latents,
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# scale the initial noise by the standard deviation required by the scheduler
latents = latents * self.scheduler.init_noise_sigma
return latents | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
@torch.no_grad()
def __call__(
self,
prompt: Union[str, List[str]],
height: Optional[int] = None,
width: Optional[int] = None,
num_inference_steps: int = 50,
guidance_scale: float = 7.5,
negative_prompt: Optional[Union[str, List[str]]] = None,
num_imag... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
edit_warmup_steps: Optional[Union[int, List[int]]] = 10,
edit_cooldown_steps: Optional[Union[int, List[int]]] = None,
edit_threshold: Optional[Union[float, List[float]]] = 0.9,
edit_momentum_scale: Optional[float] = 0.1,
edit_mom_beta: Optional[float] = 0.4,
edit_weights: Optiona... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Args:
prompt (`str` or `List[str]`):
The prompt or prompts to guide image generation.
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
The height in pixels of the generated image.
width (`int`, *optional*,... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
negative_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to guide what to not include in image generation. If not defined, you need to
pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
num_images_per_prompt (`int`,... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor is generated by sampling using the supplied random `generator`.
outp... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
The frequency at which the `callback` function is called. If not specified, the callback is called at
every step.
editing_prompt (`str` or `List[str]`, *optional*):
The prompt or prompts to use for semantic guidance. Semantic guidance is disabled by setting
`e... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Guidance scale for semantic guidance. If provided as a list, values should correspond to
`editing_prompt`.
edit_warmup_steps (`float` or `List[float]`, *optional*, defaults to 10):
Number of diffusion steps (for each prompt) for which semantic guidance is not applied. Momentu... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
momentum is disabled. Momentum is already built up during warmup (for diffusion steps smaller than
`sld_warmup_steps`). Momentum is only added to latent guidance once all warmup periods are finished.
edit_mom_beta (`float`, *optional*, defaults to 0.4):
Defines how semantic g... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
correspond to `num_inference_steps`. | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Examples:
```py
>>> import torch
>>> from diffusers import SemanticStableDiffusionPipeline
>>> pipe = SemanticStableDiffusionPipeline.from_pretrained(
... "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda") | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
>>> out = pipe(
... prompt="a photo of the face of a woman",
... num_images_per_prompt=1,
... guidance_scale=7,
... editing_prompt=[
... "smiling, smile", # Concepts to apply
... "glasses, wearing glasses",
... "curls, wavy... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
... ], # Threshold for each concept. Threshold equals the percentile of the latent space that will be discarded. I.e. threshold=0.99 uses 1% of the latent dimensions
... edit_momentum_scale=0.3, # Momentum scale that will be added to the latent guidance
... edit_mom_beta=0.6, # Momentum b... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Returns:
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] or `tuple`:
If `return_dict` is `True`,
[`~pipelines.semantic_stable_diffusion.SemanticStableDiffusionPipelineOutput`] is returned, otherwise a
`tuple` is returned where th... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# 2. Define call parameters
batch_size = 1 if isinstance(prompt, str) else len(prompt)
device = self._execution_device
if editing_prompt:
enable_edit_guidance = True
if isinstance(editing_prompt, str):
editing_prompt = [editing_prompt]
enabled... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
logger.warning(
"The following part of your input was truncated because CLIP can only handle sequences up to"
... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if enable_edit_guidance:
# get safety text embeddings
if editing_prompt_embeddings is None:
edit_concepts_input = self.tokenizer(
[x for item in editing_prompt for x in repeat(item, batch_size)],
padding="max_length",
ma... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if edit_concepts_input_ids.shape[-1] > self.tokenizer.model_max_length:
removed_text = self.tokenizer.batch_decode(
edit_concepts_input_ids[:, self.tokenizer.model_max_length :]
)
logger.warning(
"The following p... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# duplicate text embeddings for each generation per prompt, using mps friendly method
bs_embed_edit, seq_len_edit, _ = edit_concepts.shape
edit_concepts = edit_concepts.repeat(1, num_images_per_prompt, 1)
edit_concepts = edit_concepts.view(bs_embed_edit * num_images_per_prompt, seq_l... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
if do_classifier_free_guidance:
uncond_tokens: List[str]
if negative_prompt is None:
uncond_tokens = [""] * batch_size
elif type(prompt) is not type(negative_prompt):
raise TypeError(
f"`negative_prompt` should be the same type to `... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
max_length = text_input_ids.shape[-1]
uncond_input = self.tokenizer(
uncond_tokens,
padding="max_length",
max_length=max_length,
truncation=True,
return_tensors="pt",
)
uncond_embeddings = self.text_encod... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
if enable_edit_guidance:
text_embeddings = torch.cat([uncond_embeddings, text_embeddin... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# 5. Prepare latent variables
num_channels_latents = self.unet.config.in_channels
latents = self.prepare_latents(
batch_size * num_images_per_prompt,
num_channels_latents,
height,
width,
text_embeddings.dtype,
device,
ge... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
for i, t in enumerate(self.progress_bar(timesteps)):
# expand the latents if we are doing classifier free guidance
latent_model_input = (
torch.cat([latents] * (2 + enabled_editing_prompts)) if do_classifier_free_guidance else latents
)
latent_model_input ... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
# default text guidance
noise_guidance = guidance_scale * (noise_pred_text - noise_pred_uncond)
# noise_guidance = (noise_pred_text - noise_pred_edit_concepts[0])
if self.uncond_estimates is None:
self.uncond_estimates = torch.zeros((num_inference_ste... | 208 | /Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/pipelines/semantic_stable_diffusion/pipeline_semantic_stable_diffusion.py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.