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[BUG] fixes in kadinsky pipeline #11080

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Merged
merged 8 commits into from
Apr 21, 2025
Merged

[BUG] fixes in kadinsky pipeline #11080

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4f3bd13
bug fix kadinsky pipeline
ishan-modi Mar 17, 2025
e8e29ac
code quality updates
ishan-modi Mar 17, 2025
b24c6a8
update
ishan-modi Mar 17, 2025
e6fa809
update
ishan-modi Apr 9, 2025
32a5c8a
addressed PR comments
ishan-modi Apr 10, 2025
cdd54f4
addressed PR comments
ishan-modi Apr 10, 2025
410bc5a
update
ishan-modi Apr 10, 2025
237e502
Merge branch 'main' into fixes-issue-11060
ishan-modi Apr 19, 2025
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7 changes: 6 additions & 1 deletion src/diffusers/image_processor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -116,6 +116,7 @@ def __init__(
vae_scale_factor: int = 8,
vae_latent_channels: int = 4,
resample: str = "lanczos",
reducing_gap: int = None,
do_normalize: bool = True,
do_binarize: bool = False,
do_convert_rgb: bool = False,
Expand Down Expand Up @@ -498,7 +499,11 @@ def resize(
raise ValueError(f"Only PIL image input is supported for resize_mode {resize_mode}")
if isinstance(image, PIL.Image.Image):
if resize_mode == "default":
image = image.resize((width, height), resample=PIL_INTERPOLATION[self.config.resample])
image = image.resize(
(width, height),
resample=PIL_INTERPOLATION[self.config.resample],
reducing_gap=self.config.reducing_gap,
)
elif resize_mode == "fill":
image = self._resize_and_fill(image, width, height)
elif resize_mode == "crop":
Expand Down
33 changes: 13 additions & 20 deletions src/diffusers/pipelines/kandinsky/pipeline_kandinsky_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,14 +14,13 @@

from typing import Callable, List, Optional, Union

import numpy as np
import PIL.Image
import torch
from PIL import Image
from transformers import (
XLMRobertaTokenizer,
)

from ...image_processor import VaeImageProcessor
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import (
Expand Down Expand Up @@ -95,15 +94,6 @@ def get_new_h_w(h, w, scale_factor=8):
return new_h * scale_factor, new_w * scale_factor


def prepare_image(pil_image, w=512, h=512):
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image


class KandinskyImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
Expand Down Expand Up @@ -143,7 +133,16 @@ def __init__(
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
self.movq_scale_factor = (
2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
)
movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
self.image_processor = VaeImageProcessor(
vae_scale_factor=self.movq_scale_factor,
vae_latent_channels=movq_latent_channels,
resample="bicubic",
reducing_gap=1,
)

def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
Expand Down Expand Up @@ -417,7 +416,7 @@ def __call__(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)

image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
image = image.to(dtype=prompt_embeds.dtype, device=device)

latents = self.movq.encode(image)["latents"]
Expand Down Expand Up @@ -498,13 +497,7 @@ def __call__(
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()

if output_type == "pil":
image = self.numpy_to_pil(image)
image = self.image_processor.postprocess(image, output_type)

if not return_dict:
return (image,)
Expand Down
44 changes: 11 additions & 33 deletions src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_controlnet_img2img.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,10 @@

from typing import Callable, List, Optional, Union

import numpy as np
import PIL.Image
import torch
from PIL import Image

from ...image_processor import VaeImageProcessor
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDPMScheduler
from ...utils import (
Expand Down Expand Up @@ -105,27 +104,6 @@
"""


# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
def downscale_height_and_width(height, width, scale_factor=8):
new_height = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
new_width = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor


# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
def prepare_image(pil_image, w=512, h=512):
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image


class KandinskyV22ControlnetImg2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
Expand Down Expand Up @@ -157,7 +135,14 @@ def __init__(
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
self.image_processor = VaeImageProcessor(
vae_scale_factor=movq_scale_factor,
vae_latent_channels=movq_latent_channels,
resample="bicubic",
reducing_gap=1,
)

# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
Expand Down Expand Up @@ -316,15 +301,14 @@ def __call__(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)

image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
image = image.to(dtype=image_embeds.dtype, device=device)

latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
latents = self.prepare_latents(
latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
)
Expand Down Expand Up @@ -379,13 +363,7 @@ def __call__(
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}")

if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()

if output_type == "pil":
image = self.numpy_to_pil(image)
image = self.image_processor.postprocess(image, output_type)

if not return_dict:
return (image,)
Expand Down
50 changes: 11 additions & 39 deletions src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_img2img.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -14,11 +14,10 @@

from typing import Callable, Dict, List, Optional, Union

import numpy as np
import PIL.Image
import torch
from PIL import Image

from ...image_processor import VaeImageProcessor
from ...models import UNet2DConditionModel, VQModel
from ...schedulers import DDPMScheduler
from ...utils import deprecate, is_torch_xla_available, logging
Expand Down Expand Up @@ -76,27 +75,6 @@
"""


# Copied from diffusers.pipelines.kandinsky2_2.pipeline_kandinsky2_2.downscale_height_and_width
def downscale_height_and_width(height, width, scale_factor=8):
new_height = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
new_width = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor


# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.prepare_image
def prepare_image(pil_image, w=512, h=512):
pil_image = pil_image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image


class KandinskyV22Img2ImgPipeline(DiffusionPipeline):
"""
Pipeline for image-to-image generation using Kandinsky
Expand Down Expand Up @@ -129,7 +107,14 @@ def __init__(
scheduler=scheduler,
movq=movq,
)
self.movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1)
movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
self.image_processor = VaeImageProcessor(
vae_scale_factor=movq_scale_factor,
vae_latent_channels=movq_latent_channels,
resample="bicubic",
reducing_gap=1,
)

# Copied from diffusers.pipelines.kandinsky.pipeline_kandinsky_img2img.KandinskyImg2ImgPipeline.get_timesteps
def get_timesteps(self, num_inference_steps, strength, device):
Expand Down Expand Up @@ -319,15 +304,14 @@ def __call__(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)

image = torch.cat([prepare_image(i, width, height) for i in image], dim=0)
image = torch.cat([self.image_processor.preprocess(i, width, height) for i in image], dim=0)
image = image.to(dtype=image_embeds.dtype, device=device)

latents = self.movq.encode(image)["latents"]
latents = latents.repeat_interleave(num_images_per_prompt, dim=0)
self.scheduler.set_timesteps(num_inference_steps, device=device)
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)
height, width = downscale_height_and_width(height, width, self.movq_scale_factor)
latents = self.prepare_latents(
latents, latent_timestep, batch_size, num_images_per_prompt, image_embeds.dtype, device, generator
)
Expand Down Expand Up @@ -383,21 +367,9 @@ def __call__(
if XLA_AVAILABLE:
xm.mark_step()

if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `pil` ,`np` and `latent` are supported not output_type={output_type}"
)

if not output_type == "latent":
# post-processing
image = self.movq.decode(latents, force_not_quantize=True)["sample"]
if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()

if output_type == "pil":
image = self.numpy_to_pil(image)
image = self.image_processor.postprocess(image, output_type)
else:
image = latents

Expand Down
43 changes: 11 additions & 32 deletions src/diffusers/pipelines/kandinsky3/pipeline_kandinsky3_img2img.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,12 @@
import inspect
from typing import Callable, Dict, List, Optional, Union

import numpy as np
import PIL
import PIL.Image
import torch
from transformers import T5EncoderModel, T5Tokenizer

from ...image_processor import VaeImageProcessor
from ...loaders import StableDiffusionLoraLoaderMixin
from ...models import Kandinsky3UNet, VQModel
from ...schedulers import DDPMScheduler
Expand Down Expand Up @@ -53,24 +53,6 @@
"""


def downscale_height_and_width(height, width, scale_factor=8):
new_height = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
new_width = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor


def prepare_image(pil_image):
arr = np.array(pil_image.convert("RGB"))
arr = arr.astype(np.float32) / 127.5 - 1
arr = np.transpose(arr, [2, 0, 1])
image = torch.from_numpy(arr).unsqueeze(0)
return image


class Kandinsky3Img2ImgPipeline(DiffusionPipeline, StableDiffusionLoraLoaderMixin):
model_cpu_offload_seq = "text_encoder->movq->unet->movq"
_callback_tensor_inputs = [
Expand All @@ -94,6 +76,14 @@ def __init__(
self.register_modules(
tokenizer=tokenizer, text_encoder=text_encoder, unet=unet, scheduler=scheduler, movq=movq
)
movq_scale_factor = 2 ** (len(self.movq.config.block_out_channels) - 1) if getattr(self, "movq", None) else 8
movq_latent_channels = self.movq.config.latent_channels if getattr(self, "movq", None) else 4
self.image_processor = VaeImageProcessor(
vae_scale_factor=movq_scale_factor,
vae_latent_channels=movq_latent_channels,
resample="bicubic",
reducing_gap=1,
)

def get_timesteps(self, num_inference_steps, strength, device):
# get the original timestep using init_timestep
Expand Down Expand Up @@ -566,7 +556,7 @@ def __call__(
f"Input is in incorrect format: {[type(i) for i in image]}. Currently, we only support PIL image and pytorch tensor"
)

image = torch.cat([prepare_image(i) for i in image], dim=0)
image = torch.cat([self.image_processor.preprocess(i) for i in image], dim=0)
image = image.to(dtype=prompt_embeds.dtype, device=device)
# 4. Prepare timesteps
self.scheduler.set_timesteps(num_inference_steps, device=device)
Expand Down Expand Up @@ -630,20 +620,9 @@ def __call__(
xm.mark_step()

# post-processing
if output_type not in ["pt", "np", "pil", "latent"]:
raise ValueError(
f"Only the output types `pt`, `pil`, `np` and `latent` are supported not output_type={output_type}"
)
if not output_type == "latent":
image = self.movq.decode(latents, force_not_quantize=True)["sample"]

if output_type in ["np", "pil"]:
image = image * 0.5 + 0.5
image = image.clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).float().numpy()

if output_type == "pil":
image = self.numpy_to_pil(image)
image = self.image_processor.postprocess(image, output_type)
else:
image = latents

Expand Down
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